Stem issue 9 vol2

ISSN (PRINT): 2328-3491 ISSN (ONLINE): 2328-3580 ISSN (CD-ROM): 2328-3629

Issue 9, Volume 1, 2 & 3 December-2014 to February-2015

American International Journal of Research in Science, Technology, Engineering & Mathematics

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States.

Offices Overseas: Germany, Australia, India, Netherlands, Canada.

Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrstem@gmail.com

PREFACE We are delighted to welcome you to the ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM). In recent years, advances in science, technology, engineering, and mathematics have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. AIJRSTEM is publishing high-quality, peer-reviewed papers covering topics such as Computer and computational sciences, Physics, Chemistry, Mathematics, Applied

mathematics,

Biochemistry,

Robotics,

Statistics,

Electrical

&

Electronics

engineering, Mechanical & Industrial engineering, Civil Engineering, Aerospace engineering, Chemical engineering, Astrophysics, Nanotechnology, Acoustical engineering, Atmospheric sciences, Biological sciences, Education and Human Resources, Environmental research and education, Geosciences, Social, Behavioral and Economic sciences, Geospatial technology, Cyber security, Transportation, Energy and Power, Healthcare, Hospitality, Medical and dental sciences, Marine sciences, Renewable sources of energy, Green technologies, Theory and models and other closely related fields in the discipline of Science, Technology, Engineering & Mathematics. The editorial board of AIJRSTEM is composed of members of the Teachers & Researchers community who have expertise in the fields of Science,

Technology,

Engineering

&

Mathematics

in

order

to

develop

and

implement widespread expansion of high�quality common standards and assessments.

These fields are the pillars of growth in our modern society and have a wider impact on our daily lives with infinite opportunities in a global marketplace. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to science, technology, engineering & mathematics. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the AIJRSTEM for entrusting us with the important job. We are thankful to the members of the AIJRSTEM editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions.

We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 149 research papers and out of which only 57 research papers are published in three volumes as per the reviewers’ recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the field of science, technology, engineering & mathematics.

This issue of the AIJRSTEM has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in science, technology, engineering & mathematics and may open new area for research and development. We hope you will enjoy this ninth issue of the American International Journal of Research in Science, Technology, Engineering & Mathematics and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The American International Journal of Research in Science, Technology, Engineering & Mathematics (AIJRSTEM), ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3).

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BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand.

Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan.

Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India.

CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands.

Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia.

Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States.

CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand.

Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States.

Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany.

Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States.

Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy.

Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan.

Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain.

Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece.

Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia.

Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic.

Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia.

Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji.

Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain.

Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia.

Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom.

Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic.

Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India.

Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia.

Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil.

Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India.

Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India.

Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India.

Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India.

REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States.

Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany.

Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States.

Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan.

Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan.

Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain.

Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States.

Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece.

Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia.

Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia.

Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain.

Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain.

Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia.

Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil.

Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka.

Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India.

Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco.

Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India.

Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India.

Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India.

Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India.

Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq.

Prof. (Dr.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, India.

Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India.

Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India.

Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune- 411 041, India.

Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106.

Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India.

Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India.

Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India.

Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia.

Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg, R.K.University,Kast urbhadham, Tramba, Rajkot-360020, India.

Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India.

Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran.

Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India.

Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India.

Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana, India.

Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar, Punjab(India) Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA).

Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India.

Prof. (Dr.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India.

Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India.

Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India.

Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India.

Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India.

Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur-313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India.

Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor.

Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women,s College, Gardanibagh, Patna, Bihar, India.

Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India.

Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore, India.

Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India.

Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India.

Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India.

Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India.

Prof. (Dr.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India.

Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India.

Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt.

Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines.

Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India.

Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011, India.

Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India.

Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India.

Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India.

Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India.

Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India.

Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India.

Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India.

Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India.

Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt.

Arash Shaghaghi, University College London, University of London, Great Britain.

Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India.

Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia.

Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India.

Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001.

Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India.

Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India.

Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor.

Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009, India.

Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University, Coimbatore-641003,Tamil Nadu, India.

Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India.

Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066 Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India.

Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057 Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India.

Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India.

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Prof. (Dr.)B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India.

Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India.

Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India.

Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India.

Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India.

Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India.

Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India.

Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India .

Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India.

Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India.

Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India.

Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India.

Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India.

Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Engg., Vizianagaram5 35003, Andhra Pradesh, India.

Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India.

Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India.

Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa.

Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India.

Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty, Ernakulam, Kerala 683544, India.

Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India.

Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India.

Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India.

Prof. Veera Jyothi.B, CBIT ,Hyderabad, Andhra Pradesh, India.

Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India.

Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India.

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Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India.

Prof. (Dr.) Shivanand S.Gornale, Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India.

Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India.

Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman, Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala.

Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi-835215, Jharkhand, India.

Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India.

Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India.

Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India.

Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS-38655, USA Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India.

Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145.

Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal-India Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu-India Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada.

Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India.

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Prof. (Dr.) Meghshyam K. Patil, Assistant Professor & Head, Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India.

Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India.

Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India.

Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics , Bhilai Institute Of Technology, Durg (C.G.) 491001 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India.

Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036.

Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura.

Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India.

Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria.

NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur.

Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India.

Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India.

Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India.

Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India.

Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh.

Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India.

Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India.

Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India.

Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India.

Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212.

Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India.

Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic.

Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan.

Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014.

Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal.

Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India.

Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India.

Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria.

Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran.

Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India.

Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India.

Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India.

Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria.

Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, India.

Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’S SCHOOL, ATHANI, India Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering , Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab,India

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Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology, Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India.

Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India.

Prof. Sunny Narayan, university of Roma Tre, Italy.

Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India.

Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura-India Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran.

Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India.

Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai, 400103, India, Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, India Prof. (Dr.) Har Mohan Rai, Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot.

Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura.

Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036, India.

Prof. (Dr.) Aparna Sarkar, PH.D. Physiology, AIPT, Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP, India.

Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India.

Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher, Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. .

Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. .

Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. .

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Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, India.

Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University, Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN. Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV),Banaras Hindu University Varanasi-221005, India.

Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India.

Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar, PhD(CS), M.Phil(CS), MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India.

Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana), India.

Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India.

Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College, Govind Nagar,Kanpur208006, India.

Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India.

Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura, India.

Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India.

Prof. (Dr.) T Venkat Narayana Rao, C.S.E, Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India.

Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India.

Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India.

Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India.

Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India.

Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India.

Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India.

Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India.

Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.

Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India.

Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India.

Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich – 271801 UP, India.

Satya Rishi Takyar, Senior ISO Consultant, New Delhi, India.

Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh (PB) Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India.

Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

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Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India.

Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India.

Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India.

Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India.

Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India.

Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India.

Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India.

Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India.

Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India.

Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India.

Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India.

Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India.

Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India.

Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana.

Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany.

Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany.

Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India.

Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra.

Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India.

Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India.

Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India.

Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India.

Sumit Sharma, Team Lead, American Express, New Delhi, India.

Vivek Gautam, Senior Software Engineer, Wipro, Noida, India.

Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India.

Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India.

Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India.

Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India.

Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India.

Naresh Setia, Senior Software Engineer, Infogain, Noida, India.

Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India.

Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India.

Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India.

Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India.

Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India.

Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India.

Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Computer and computational sciences  Physics  Chemistry  Mathematics  Actuarial sciences  Applied mathematics  Biochemistry, Bioinformatics  Robotics  Computer engineering  Statistics  Electrical engineering & Electronics  Mechanical engineering  Industrial engineering  Information sciences  Civil Engineering  Aerospace engineering  Chemical engineering  Sports sciences  Military sciences  Astrophysics & Astronomy  Optics  Nanotechnology  Nuclear physics  Operations research  Neurobiology & Biomechanics  Acoustical engineering  Geographic information systems  Atmospheric sciences  Educational/Instructional technology  Biological sciences  Education and Human resource  Extreme engineering applications  Environmental research and education  Geosciences  Social, Behavioral and Economic sciences  Advanced manufacturing technology  Automotive & Construction  Geospatial technology  Cyber security  Transportation  Energy and Power  Healthcare & Hospitality  Medical and dental sciences  Pesticides  Marine and thermal sciences  Pollution  Renewable sources of energy  Industrial pollution control  Hazardous and e-waste management  Green technologies  Artificial/computational intelligence  Theory and models

TABLE OF CONTENTS American International Journal of Research in Science, Technology, Engineering & Mathematics ISSN(Print): 2328-3491, ISSN(Online): 2328-3580, ISSN(CD-ROM): 2328-3629 (December-2014 to February-2015, Issue 9, Volume 1, 2 & 3) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

AIJRSTEM 15-106

TBSC Compensator: Application and Simulation Results for Starting and Voltage Sag Mitigation of Induction Motor Swapnil Dadaso Patil, Dr. Anwar Mubarak Mulla, Dr. Dadgonda Rajgonda Patil

01-11

AIJRSTEM 15-109

On Volterra Type Integral Inequalities in Metric Spaces Hristo Kiskinov, Stepan Kostadinov, Lozanka Trenkova, Andrey Zahariev

12-19

AIJRSTEM 15-113

Review of Public Safety in Some Non Governmental Medical Radiation Facilities in Bangladesh M.Haider, S. Shill and QMR Nizam

20-24

AIJRSTEM 15-115

Reactions of MoOCl4 & MoO2Cl2 with Imidazole, Pyrazole, Acetamide, Succinimide, Benzamide & 2-Thiazoline-2-thiol Gursharan Singh, Seema Sharma, Vikas Mangla, Mamta Goyal, Kavita Singla, Deepika Rani

25-33

AIJRSTEM 15-118

Experimental Investigation on Plain Circular and 2:1 Rectangular Jets at Low Speed Dr. A. Arokkiaswamy

34-38

AIJRSTEM 15-126

Fingerprint Based Gender Classification for Biometric Security: A State-Of-The-Art Technique S. S. Gornale

39-49

AIJRSTEM 15-133

Energy Consumption in Wireless Network Control System Data Transmission K.Sanakkiyan, R.Kannan

50-55

AIJRSTEM 15-134

IMAGE RECONSTRUCTION USING SPARSE BASED MODEL Reetumala Thakre, Prof. Rajendra Singh, Dr. Soni Changlani

56-61

AIJRSTEM 15-135

Automatic Bangladeshi Vehicle Number Plate Recognition System using Neural Network Mohammad Badrul Alam Miah, Sharmin Akter and Chitra Bonik

62-66

AIJRSTEM 15-136

Navigation System for Multi-Floor Indoor Positioning during Urban Combat Operations using Geo-Magnetic Module N.Kanagapriya, N.Solaiyammal

67-70

AIJRSTEM 15-137

Survey on cloud computing technology, application, usage and its stack terminology Santvana Singh, Sarla Singh, Sumit Dubey

71-73

AIJRSTEM 15-139

Future monthly, seasonal and annual rainfall trend prediction for Tarai region of Uttarakhand Arvind Singh Tomar, Praveen Vikram Singh and Om Prakash Kumar

74-76

AIJRSTEM 15-140

Synthesis and Structural Studies of some complexes of Ni(II) and Co(III) with Hexamethylenedibiguanide Bina Rani, R.K. Prasad, and Divya Singh

77-80

AIJRSTEM 15-142

Experimental/Computational Results for Shielding Neutron & Photon Radiations C .E. Okon, I. P. Etim

81-89

AIJRSTEM 15-149

Intense Geomagnetic Storms and their Association with Solar Wind Plasma and Interplanetary Parameters Mukesh Kanwal Tripathi, R.S. Gupta, M.P. Yadav

90-94

AIJRSTEM 15-150

Analysis of modulation technique in free space optics system Vasundhara Shukla, Durgesh Shukla, Jayant Shukla, Richi Nigam

95-98

AIJRSTEM 15-152

Comparison: Introduction to Mobile Ad-Hoc Network and Routing Implementation Meenakshi Mishra, Niketan Mishra, Soni Changlani

99-101

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

AIJRSTEM 15-153

Building scalable geo-replicated storage back ends for web application Rajkumar M. Chauhan

102-106

AIJRSTEM 15-156

Short term Size Deviation of Multivoltine, Multi x Bi and Bivoltine Raw Silk Niharendu Bikash Kar, Mrinal Kanti Majumdar, S. Nirmalkumar

107-112

AIJRSTEM 15-157

Synthesis of Well-defined Dihydroxyl End-Functional Polystyrene using Trifunctional Initiator via Atom Transfer Radical Polymerization Samiul Islam Chowdhury, Tariqul Hasan

113-118

AIJRSTEM 15-158

Identification of Data Quality Issues in Homogeneous Environment Sapna Pujara, Dr. Kanwal Garg

119-123

AIJRSTEM 15-160

Variablity of Rainfall in Bangalore city - Mapping of hourly variation of heavy rainfall event Roopa Bhandiwad

124-139

AIJRSTEM 15-161

Intuitionistic Z-Numbers G Velammal, M Shahila Bhanu

140-142

AIJRSTEM 15-162

Interchangeability of Motor-Cycle Parts for Maintenance Purposes Joseph W. and Chindapi N.

143-149

AIJRSTEM 15-163

Signal Processing for Music Analysis Poonam Priyadarshini, Soubhik Chakraborty

150-153

AIJRSTEM 15-164

REVIEW ON MODELING OF RESISTANCE SPOT WELDING PROCESS Boriwal Lokesh, Sarviya R. M. & Mahapatra M. M

154-159

AIJRSTEM 15-165

Dynamic Query Search Over Graph Stream Ms.Vaishali G. Powar and Mr.Pradeep C. Bhaskar

160-164

AIJRSTEM 15-171

A review on Optimization of material removal rate in micro-drilling process Ranadhir R Landge, Dr. Atul B Borade

165-169

AIJRSTEM 15-178

Application of Various Multi Criteria Decision Making Methods for the Assessment of Different Waste Water Treatment Systems Case Study: JSPM Hadapsar Campus, Pune Poonam K. Wakchaure, Dr. Sameer S. Shastri

170-175

AIJRSTEM 15-179

An Experimental Study of Thermal Performance of Concentric Annular Heat Pipe Nishtha Vijra, Tejinder Paul Singh

176-182

AIJRSTEM 15-183

Stability: Abstract Roadmap of Software Security Kavita Sahu, Raj Shree

183-186

AIJRSTEM 15-185

Image Filtering and Registration by using Hybrid Area and Feature Based Method Kiran P. Dange, Neha P. Athavale

187-193

AIJRSTEM 15-186

An Investigation on the Potential of Biogas Production from Elephant Grass and Guinea Grass Sindra L Summoogum-Utchanah and Doolar Poorneema

194-197

AIJRSTEM 15-187

Discursive Institutionalism: Analysis of the Forest Policy Review Process of Ghana Yaw Amo Sarpong, Daniel K. B. Inkoom, Ibrahim Latif Apaassongo

198-205

Issue 9, Volume 3 Paper Code

Paper Title

Page No.

AIJRSTEM 15-188

Survey on Clustering Techniques of Data Mining Namrata S Gupta, Bijendra S.Agrawal, Rajkumar M. Chauhan

206-211

AIJRSTEM 15-189

Effect of Spacer Length from Vinyl Group of Vinyl-bromoester Initiator on Atom Transfer Radical Polymerization of Styrene Samiul Islam Chowdhury and Tariqul Hasan

212-215

AIJRSTEM 15-190

The Influences of Surface Area on the Efficiency of a New Design of Solar Collector Suitable for Basrah City 30.50 N

216-220

Kawther K. Mankhi; Noori H.N. Al-Hashimi; Jassim M. Al-Asadi AIJRSTEM 15-191

Inertial effects on hydrodynamic convection in a passive mushy layer Prof. Dr.P.K.Srimani, Mr. R.Parthasarathi

221-229

AIJRSTEM 15-192

Initial Thermal Hydraulic Design of a 1000MWe Water Reactor with an Improved Thermal Efficiency C .E. Okon, D. E. Oku

230-238

AIJRSTEM 15-195

A hybrid approach to measure design improvement factor of website Prafulla Bafna, Hema Gaikwad

239-241

AIJRSTEM 15-197

EXCITATION OF DOUBLY-CHARGED YTTRIUM IONS IN ELECTRON-ATOM COLLISIONS Yu. M. Smirnov

242-245

AIJRSTEM 15-199

Simulated Results for Neutron Radiations Shielding Using Monte Carlo C .E. Okon, I. O. Akpan

246-251

AIJRSTEM 15-200

A Review on Various Error Detection and Correction Methods Used in Communication Varinder Singh, Narinder Sharma

252-257

AIJRSTEM 15-205

Dual Layer Image Scrambling Method Using Improved Arnold Transform Gyan Vardhan Artist, Dr. Mahesh Kumar Porwal

258-264

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An Open type Mixed Quadrature Rule using Fejer and Gaussian Quadrature Rules Dwiti Krushna Behera, Ajit Kumar Sethi & Rajani Ballav Dash

265-268

AIJRSTEM 15-215

Land holding effect on energy inputs for soybean production in Malwa plateau of Madhya Pradesh Dilip Jat, R.K. Naik, N.K.Khandelwal, Bharat Patel and Prateek Shrivastava

269-274

AIJRSTEM 15-219

Comparative Study of Dynamic Performance of Multi-Area Interconnected Power Systems with EHVAC/HVDC Links Ram Naresh Mishra, Dr. Prabhat Kumar

275-283

AIJRSTEM 15-223

Behavior of a Discrete SIR Epidemic Model A. George Maria Selvam, R. Janagaraj and D. Jerald Praveen

284-287

AIJRSTEM 15-227

Effect of additives on the structural and magnetic properties of electrodeposited NiMn thin film M.Rajeswari, S.Ganesan

288-292

AIJRSTEM 15-232

Does the knowledge and skill acquired during simulator training gets applied on the job by the seafarers- An empirical study Surender Kumar, Dr. Neeraj Anand, Dr. DK Punia, Dr. BK Saxena

293-297

AIJRSTEM 15-235

An Investigation on Causes and Preventive Measures of Trespass Accidents and Suicides in Katpadi Railway Jurisdiction of Tamil Nadu, India Dr. N. Sundaram, Mr. M. Sriram

298-301

AIJRSTEM 15-239

ACTIVE LEARNING METHODS IN HIGHER EDUCATION Badri Meparishvili, Lela Turmanidze, Gulnara Janelidze

302-305

AIJRSTEM 15-240

Appari’s Design – Get Extra Energy From Existing Resources Sidramappa Shivashankar Dharane, Archita Vijaykumar Malge, Savita Gururaj Malage

306-308

AIJRSTEM 15-242

Heat Transfer and Turbulent Nanofluid Flow over a Double Forward-Facing Step Mohammed Saad Kamel

309-316

AIJRSTEM 15-243

Experimental Simulation Analysis of Current Density Distribution by Kirchhoff’s laws in a resistor network cell model S. R. Rajkumar, Dr.M. Alagar, Dr.S. Somasekaran, S.R. Ravisankar

317-322

AIJRSTEM 15-245

Measuring infrastructure sustainability with the use of eco efficient performance criteria Saroop s and Allopi d

323-327

AIJRSTEM 15-246

Glauber Modeling in Heavy Ion Collisions Abhilasha Saini, Dr. Sudhir Bhardwaj

328-331

American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Building scalable geo-replicated storage back ends for web application 1

Rajkumar M. Chauhan (M.Tech. C.S.E.) 1 Foreman Instructor ITI Amirgadh Ex Asst. Professor BCA College Palanpur, Gujarat, INDIA

Abstract: The goal of this paper is to develop a scalable geo replicated storage back ends for web applications. Web application mostly requires a storage system replicate across different sites also scalable. Generally we use traditionally database having strict consistency guarantee and programming ease, but in concern in geo replicated no SQL are scalable and efficient. For scalable and geo replicated storage for web application we have two system Walter and Lynx. Walter supports transactions and replication of data across sites. Walter have new isolation property called PSI(Parallel snapshot isolation) that allows asynchronously data replication. For implementing PSI Walter uses two techniques one is preferred sites and counting sets. Lynx is database backend for scaling web application. It support optimizing query via denormalization, secondary indexes and join view. A Distributed Transaction Chain (DTC) used to sequence transaction on different node with guarantee that all transactions execute exactly once despite failure. So many application will be developed like micro blogging, social networking. Using these and methods 6pt down.applications are capable for providing scalability across geo replicated site.

Keywords: Walter, Lynx, PSI, DTC, micro blogging, social networking I.

Introduction

There are two major concerns when building a popular web application. First, it must be able to scale up quickly to handle a rapidly growing user base. For example, the popular photo sharing application Instagram saw a nearly exponential growth to over 30 million users in less than two years. Achieving scalability and geo-replication is not an easy task. Web applications are commonly constructed using a multi-tiered design, where application servers use a storage tier to store and share data. Scaling the application tier is easily achieved by running application severs on any machines across multiple data centers, but it is much harder to scale the storage tier and have it support geo-replication. To be scalable, a storage system must divide data into a large number of partitions spread across many machines. As applications often need to access data belonging to multiple partitions, the storage system must coordinate access across different partitions, which comes at a performance cost. The cost of such coordination increases substantially when data is replicated across geographically distant data centers with tens or hundreds of milliseconds of communication delays.

Walter: A geo-replicated transactional key-value store Existing geo-distributed key-value stores provide no transactions or only restricted transactions. Without transactions, an application must carefully coordinate access to data to avoid race conditions, partial writes, overwrites, and other hard problems that cause erratic behavior. Developers must address these same problems for many applications. With transactions, developers are relieved from concerns of atomicity, consistency, isolation, durability, and coordination. For example, in a social networking application, one may want to remove user A from B’s friends list and vice versa. Without transactions, developers must write code carefully to prevent one removal from happening without the other. With transactions, developers simply bundle those updates in a transaction.

With PSI, hosts within a site observe transactions according to a consistent snapshot and a common ordering of transactions. Across sites, PSI enforces only causal ordering, not a global ordering of transactions, allowing the system to replicate transactions asynchronously across sites. With causal ordering, if Alice posts a message that is seen by Bob, and Bob posts a response, no user can see Bob’s response without also seeing Alice’s original post. Besides providing causal ordering, PSI precludes write-write conflicts (two transactions concurrently writing to the same object) so that developers need not write conflict resolution logic.

To prevent write-write conflicts and implement PSI, Walter relies on two techniques: preferred sites and counting sets. In web applications, writes to an object are often made by the user who owns the object, at the site where this user logs into. Therefore, we assign each object to a preferred site, where objects can be written more efficiently. For example, the preferred site for the wall posts of a user is the site closest to the user. Preferred

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sites are less restrictive than primary sites. Walter uses multi-version concurrency control within each site, and it can quickly commit transactions that write objects at their preferred sites or that use csets. For other transactions, Walter resorts to two-phase commit to check for conflicts. We found that the latter type of transaction can be avoided in the applications we built. Using Walter as the storage system, we build WaltSocial, a Facebook-like social networking application.

II. Lynx: A scalable, eventually consistent database In Lynx, each table is split into partitions which are spread across many machines. For better fault-tolerance, Lynx can also optionally replicate data across several geographically distributed data centers. As web applications demand low latency operation, Lynx allows for three common query optimization patterns: denormalization, distributed secondary indexes, and materialized join views. These optimizations essentially pre-compute results so that a query can be satisfied by contacting just one machine, minimizing the latency and overhead for common read operations. Lynx uses a new primitive, called the distributed transaction chain (DTC), to update secondary indexes and join tables. It also exposes the DTC primitive to the application programmer to update denormalized data in related tables.

Denormalization and index/view generation impose consistency constraints among data partitions managed by different machines. One way of enforcing these constraints is to use distributed ACID transactions. But this is a heavy hammer, and comes at a high price. Distributed ACID transactions require tight coordination among machines that manage different data partitions, lengthening the tail latency of operations. When data is replicated across data centers, such coordination requires communication between data centers, further increasing operation latency.

DTC is more powerful than a persistent message queue, a popular technique for modifying data on different machines—examples of this include Amazon’s Simple Queue Service and eBay’s message queue among others. Compared to DTC, the queue interface is low-level and transfers the burden of handling the difficult cases onto the programmers: programmers must not only design applications to explicitly enqueue and dequeue transactions, they must also ensure that no transaction is executed more than once after failure recovery and that the arbitrary interleaving of transactions of different logical operations does not lead to inconsistencies.

III. Design and algorithms Basic architecture There are multiple sites numbered 1, 2, . . . Each site contains a local Walter server and a set of clients. A client communicates with the server via remote procedure calls implemented by the API library. The server executes the actual operations to start and commit transactions, and to access objects.

Walter employs a separate configuration service to keep track of the currently active sites, and the preferred site and replica set for each object container. The configuration service tolerates failures by running as a Paxosbased state machine replicated across multiple sites. A Walter server confirms its role in the system by obtaining lease from the configuration service. The lease assigns a set of containers to a referred site, and it is held by the Walter server at that site. A Walter server caches the mapping from a container to its replica sites to avoid contacting the configuration service at each access. Incorrect cache entries do not affect correctness because a server rejects requests for which it does not hold the corresponding preferred site lease.

At Serveri: // i denotes the site number CurrSeqNoi : integer with last assigned local sequence number CommittedVTSi :vector indicating for each site how many transactions of that site have been committed at site i Historyi [oid]: a sequence of updates of the form 〈data, version〉 to oid, where version = 〈j:n〉 for some j, GotVTSi :vector indicating for each site how many transactions of that site have been received by site i Figure 1: Variables at server on each site.

Figure 1 shows the variables at the server at site i. Variable CurrSeqNoi has the last sequence number assigned by the server, and CommittedVTSi[j] has the sequence number of the last transaction from each site j that was committed at site i. // i denotes the site number At Serveri: operation startTx(x) x.tid ← unique transaction id x.startVTS ← CommittedVTSi return OK operation write(x, oid, data): add 〈oid, DATA(data)〉 to x.updates; return OK

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operation setAdd(x, setid, id): add 〈setid, ADD(id)〉 to x.updates; return OK operation setDel(x, setid, id): add 〈setid, DEL(id)〉 to x.updates; return OK operation read(x, oid) // if oid is locally replicated then return state of oid reflecting x.updates and all versions in Historyi [oid] visible to x.startVTS else return state of oid reflecting x.updates, the versions in Historysite(oid)[oid] visible to x.startVTS, and the versions in Historyi [oid] visible to x.startVTS operation setRead(x, setid): same as read(x, oid) Figure 2: Executing transactions.

To execute transactions, the server at each site i maintains a history denoted Historyi[oid] with a sequence of writes/updates for each object oid, where each update is tagged with the version of the responsible transaction.

This history variable is similar to variable Log in the PSI specification, except that it keeps a list per object, and it has versions not timestamps. When a transaction x starts, Walter obtains a new start vector timestamp startVTS containing the sequence number of the latest transactions from each site that were committed at the local site. To write an object, add to a cset, or remove from a cset, Walter stores this update in a temporary buffer x.updates. To read an object, Walter retrieves its state from the snapshot determined by startVTS and any updates in x.updates. Specifically, for a regular object, Walter returns the last update in x.updates or, if none, the last update in the history visible to startVTS. For a cset object, Walter computes its state by applying the updates in the history visible to startVTS and the updates in x.updates.

Scalability Walter relies on a single server per site to execute and commit transactions, which can become a scalability bottleneck. A simple way to scale the system is to divide a data center into several "local sites", each with its own server, and then partition the objects across the local sites in the data center. This is possible because Walter supports partial replication and allows transactions to operate on an object not replicated at the site—in which case, the transaction accesses the object at another site within the same data center. We should note that PSI allows sites to diverge; to avoid exposing this divergence to users, applications can be designed so that a user always log into the same local site in the data center. Another approach to scalability, which we do not explore paper, is to employ several servers per site and replace the fast commit protocol with distributed commit.

Method void start() int commit() int abort() int read(Oid o, char **buf) int write(Oid o, char *buf, int len) Oid newid(ContainerId cid, OType otype) int setAdd(Oid cset, Id id) int setDel(Oid cset, Id id) int setRead(Oid cset, IdSetIterator **iter) int setReadId(Oid cset, Id id, int *answer)

Description start transaction try to commit abort read object write object get new oid add id to cset delete id from cset read cset read id in cset

C++ Example: PHP Example: Tx x; $x = waStartTx(); x.start(); $buf = waRead($x, $o1); len = x.read(o1, &buf); $err = waWrite($x, $o2, $buf); err = x.write(o2, buf, len); $res = waCommit($x); res = x.commit(); Figure 3: Basic C++ API for Walter and C++ and PHP examples.

IV.

Implementation

The Walter implementation has a client-side library and a server, written in C++, with a total of 30K lines of code. There is also a PHP interface for web development with 600 lines of code. The implementation differs from the design as follows. First, each Walter server uses direct-attached storage devices, instead of a cluster storage system. Second, we have not implemented the scheme to reintegrate a failed site currently, the

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administrator must invoke a script manually to do that. Third, the client interface differs cosmetically due to the pecifics of C++ and PHP. In C++, there is a Transaction class and operations are methods of this class.

Functions read, setRead, and setReadId return the data via a parameter (the C++ return value is a success indication). setRead provides an iterator for the ids in a cset. setReadId indicates the count of an identifier in a cset. commit can optionally inform the client via supplied callbacks—not shown—when the transaction is disaster-safe durable and globally visible (i.e., committed at all sites). There is a function newid to return a fresh oid, explained below.

There are no specialized functions to create or destroy objects. Conceptually, all objects always exist and are initialized to nil, without any space allocated to them. If a client reads a never-written object, it obtains nil.

Function newid returns a unique oid of a never-written object of a chosen type (regular or cset) in a chosen container. Destroying a regular object corresponds to writing nil to it, while destroying a cset object corresponds to updating its elements so that they have zero count. There are some additional functions (not shown), including (a) management functions for initialization, shutdown, creating containers, and destroying containers; and (b) functions that combine multiple operations in a single RPC to the server, to gain performance; these include functions for reading or writing many objects, and for reading all objects whose ids are in a cset. The functions to create and destroy containers run outside a transaction; we expect them to be used relatively rarely.

Identifiers for containers and objects are currently restricted to a fixed length, but it would be easy to make them variable-length.

The server stores object histories in a persistent log and maintains an in-memory cache of recently-used objects.

The persistent log is periodically garbage collected to remove old entries. The entries in the in-memory cache are evicted on an LRU basis. Since it is expensive to reconstruct csets from the log, the eviction policy prefers to evict regular objects rather than csets. There is an in-memory index that keeps, for each object, a list of updates to the object, ordered from most to least recent, where each update includes a pointer to the data in the persistent log and a flag of whether the data is in the cache. To speed up system startup and recovery, Walter periodically checkpoints the index to persistent storage; the checkpoint also describes transactions that are being replicated. Checkpointing is done in the background, so it does not block transaction processing. When the server starts, it reconstructs the index from the checkpointed state and the data in the log after the checkpoint.

To improve disk efficiency, Walter employs group commit to flush many commit records to disk at the same time. To reduce the number of threads, the implementation makes extensive use of asynchronous calls and callbacks when it invokes blocking and slow operations. To enhance network efficiency, Walter propagates transactions in periodic batches, where each batch remotely copies all transactions that committed since the last batch.

The protocol for slow commit may starve because of repeated conflicting instances of fast commit. A simple solution to this problem is to mark objects that caused the abort of slow commit and briefly delay access to them in subsequent fast commits: this delay would allow the next attempt of slow commit to succeed. We have not implemented this mechanism since none of our applications use slow commit.

V. Conclusion This paper presented two storage systems that were specifically designed and built to address the requirements of large-scale web applications. Both systems, Walter and Lynx are georeplicated and employ a relaxed, yet relatively strong consistency model. The chosen consistency guarantees of both systems allow the implementations to achieve high performance at large scale, and make the development of web application on top the systems simple and rapid.

Walter is a transactional geo-replicated key-value store. A key feature behind Walter is Parallel Snapshot Isolation (PSI), a precisely-stated isolation property that permits asynchronous replication across sites without the need for conflict resolution. Walter relies on techniques to avoid conflicts across sites, thereby allowing transactions to commit locally in a site. PSI thus permits an efficient implementation, while also providing strong guarantees to applications. We have demon-strated the usefulness of Walter by building a Facebook-like social networking application and porting a third-party Twitter clone. Both applications were simple to implement and achieved reasonable performance.

Lynx targets intra-site scalability, in addition to geo-replication. It is a distributed database for building scalable web applications. Lynx supports distributed secondary indexes and materialized joins which help programmers optimize queries for low latency operation. Lynx maintains its derived tables using DTC, which executes a series of transactions at different nodes while guaranteeing fault-tolerance and correct interleaving. Lynx also exposes the DTC primitive to programmers for maintaining other types of denormalized data in the application. Lynx provides the consistency guarantee that denormalized data and derived tables are eventually consistent with each other. We have demonstrated the usefulness of Lynx by building an auction service, a microblogging and a social networking website.

References [1] [2]

Private data on database access patterns from a popular dating website.

Amazon simple queue service. http://aws.amazon.com/sqs.

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Rajkumar M. Chauhan, American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(2), December 2014-February 2015, pp. 102-106 [3] [4] [5] [6] [7] [8]

A Twitter clone for the Redis key-value database. http://retwis.antirez.com.

Twitter turns six, 2012. http://blog.twitter.com/2012/03/twitter-turns-six.html.

Daniel Abadi. Replication and the latency-consistency tradeoff. DBMusings. http://dbmsmusings.blogspot.com/.

Amazon. DynamoDB: a fully managed NoSQL database service.http://aws.amazon.com/dynamodb/.

Wikipedia.org Google.com

Acknowledgments I thankful to all person who help me in this paper, special thanks to Mr. Jayesh N. Modi (Assistant Professor, MCA Dept, H.N.G. University Patan), who help me for research paper writing.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Short term Size Deviation of Multivoltine, Multi x Bi and Bivoltine Raw Silk Niharendu Bikash Kar*, Mrinal Kanti Majumdar, S. Nirmalkumar Central Sericulture Research & Training Institute, Ministry of Textiles, Berhampore, West Bengal, INDIA Abstract: Short term deviation of raw silk refers to the variation over a shorter magnitude of length. There are different kinds of raw silk viz., Multivoltine, Multi x Bi and Bivoltine. The present study aims at determining the short term size deviation over a shorter length of 20 metres. Unlike other textiles, the degree of non-uniformity or unevenness of raw silk is determined by some visual and visuo-mechanical tests which do not justly reflect the physical shape and size of the constituent fibre. The evenness variation in raw silk is greatly influenced by the type of cooking, type of reeling contrivances etc. and more importantly by the degree of variation in the size and shape of the constituent filament along the length being unravelled from the silk cocoon. The process parameters, type of machine are generally responsible for addition of irregularities in the yarn while the type of cocoon and type of silkworm varieties etc. are responsible for inherent irregularities. Though addition of irregularities can be controlled by using improved machineries and adopting proper process control measures, but the inherent irregularities in the yarn are genetically controlled..

Key words: Denier, Denier Deviation, Evenness, Co-efficient of Variation I. Introduction In sericultural advanced countries, raw silk is tested and graded according to specified and accepted standards before marketing [1].This has been considered so important for the welfare of the silk industry that special institutions viz., silk conditioning and testing Houses (SCTH) have been established at a considerable expenditure. These are named so because they not only conduct quality tests but also find out the actual mercantile weight of raw silk. Desiccation or conditioning is necessary to prevent fraudulent transaction because silk is highly hygroscopic in nature. In order to assist the weaving industry, raw silk reeling industry requires well-defined standards, which can only be achieved by quality testing. As the demand for silk is global and a number of countries compete in the trade of raw silk, it is necessary to have some internationally accepted norms to enable a fair and equitable transaction of raw silk with transparency. This is the reason why all raw silk produced should be classified through testing [2]. According to Mahadevan et.al, 2011 [3], the important factor which have a direct bearing on productivity and quality of raw silk are the quality of raw material, process parameter, human skill, quality of water and reeling contrivance. He also observed that if the reeling speed is more than the optimum level, then the raw silk characteristics get affected. So for better and uniform quality production of raw silk, care should be taken right from the silkworm rearing till final product of raw silk.

Unlike other textiles, testing of raw silk is based on the some conservative procedures. The tests are being carried out according to the procedure laid down by the International Silk Association (ISA). The equipments used for testing of raw silk are of conventional type but earned a wide preference amongst silk producing and consuming countries in the world. The mechanical testing procedures of raw silk are almost similar everywhere, but the compilation of test results and standards for various grades differs slightly from country to country. In India, Bureau of Indian Standard’s (BIS) norms are followed which is basically based upon the overall standards laid down by ISA. A proper assessment of the quality of silk assists the raw silk producer for decision making in regard to quality improvement by suitable manipulation in the related factors. Cocoon, the raw material for raw silk, being a bio-material, the occurrence of some natural variations is inevitable. Normally these variations in the silk cocoons are not visible in the raw silk filament by bare eye but whenever it is subjected for black board inspection, the size variation become prominent. This variation in the raw silk becomes more prominent at the fabric stage particularly with woven design and causes uneven dyeing particularly with lighter shade. So size deviation is a very important component for determining the ultimate quality of raw silk / fabric. There may be two types of size deviation, one as long term variation which may be the deviation over a comparatively longer length e.g., 100 metres or above and another is short term variation that refers to a shorter length e.g., 20 metres.

The evenness variation test using seriplane winder generally describes the method of determining short term variation over a length of 20 metres or so. The present study is pointed towards finding out the size deviation of

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raw silk over a length of 20 metres length and compares that with the evenness change degree measured through visuo-mechanical method. The evenness variation in raw silk is greatly influenced by the type of cooking, type of reeling contrivances etc. and more importantly by the degree of variation in the size and shape of the constituent filament along the length being unravelled from the cocoon surface. The process parameters, type of machine are generally responsible for addition of irregularities in the yarn while the type of cocoon and type of silkworm varieties etc. are responsible for inherent irregularities. Though addition of irregularities can be controlled by using improved machineries and by adopting proper process control measures, but the inherent irregularities in the yarn are genetically controlled. The study is focussed and restricted on the variation of linear density over the aforesaid sample length which is greatly influenced by the available filament length from a single cocoon and the skill of the operators as well.

II. Materials & Methods In the present experimentation, raw silk of different voltinism viz., Multivoltine, Multi x Bi and Bivoltine were taken. Since there are huge varietal variation in terms of filament length and denier, all the three varieties have been taken to minimise the experimental error. Unlike other textiles, the degree of non-uniformity or unevenness of raw silk is determined by some visual and visuo-mechanical tests which do not reflect the objective analysis of physical shape, size and linear density of the resultant fibre in true sense. Irrespective of voltinism, silkworm Bombyx mori L produces raw silk bave of conical shape pointing towards pelade layer possibly due to gradual loss of concentration of aqueous protein solution resulting in decreasing shear rate at and above which silk fibroin crystallises to form fibre [4], which in turn, suggests that filament become more crystalline or ordered along its length with the advancement of spinning. According to Iizuka, 1983[5], some physical properties of Bombyx mori L are linearly related to the Size of thread. E. Iizuka et.al, 1993 [6] also studied that dynamic elastic modulus and tenacity show a negative correlation and the elongation, a positive correlation. So, the dry strength in terms of Tenacity (gms/denier) naturally increases as the fibre become more crystalline and similarly, elongation varies inversely with crystallinity. Variations in thickness of the individual filament influence the size or thickness of the resultant raw silk yarn. Hence, it is imperative to maintain the size deviation of individual filament within an acceptable range throughout its length. More the variation in individual filament more will be the non-uniformity in the resultant yarn. The decreased denier value may suggest the increased fineness of the silk filament [7]. The quality characteristics of raw silk viz., size deviation, winding break, tenacity and elongation % are strongly influenced by the variation of cocoon size in a given lot.

Significant seasonal variation is also observed in reeling performance and yarn characteristics [8]. The skill of the reeler and other factors like quality of cocoons also happen to be the varying factor [9]. The variations in the yarn have direct bearing on mechanical properties of the resultant yarn, weaving performance and chemical response during wet processing. Therefore, size deviation is an important trait to decide the ultimate quality of raw silk. In the present study, the short term variation in the yarn has been assessed and compared with the evenness variation done separately by the visuo-mechanical evenness test which may throw some light on the evenness change degree along the length of the yarn.

The abbreviated codes of the cocoon varieties selected for the present study are: Multivoltine X Multivoltine (MxM), the cocoon with lower filament length and lower denier Multivoltine X Bivoltine (MxBi), the cocoon with intermittent filament length and denier Bivoltine X Bivoltine (Bi xBi), the cocoon with higher filament length and higher denier The experiment has been designed to conduct the test with Raw silk produced from three breeds/hybrids of silkworm Bombyx mori L of different voltinism as mentioned earlier. Three hanks each of all the varieties of raw silk were taken and a total no. of 9 hanks have been prepared. Ten samples of equal length of 20 metres from each hank were prepared. 90 no. of such test samples @ 30 no. for each variety are made available for conducting the study. Thus total available length for testing was 1800 metres @ 600 metres for each variety.

Standard Deviation of Size and Co-efficient of Variation was determined. Evenness variations by visuomechanical test were also carried out and compared. The equipments / apparatus used for the experimentation were Winding Machine, Epprouvete, Precision Electronic Balance, Seriplane Winder and Evenness Testing Apparatus. The raw silk samples were first unraveled in a winding machine to a 2-flanged bobbin at a winding speed of 120 metres per minute. The bobbins thus prepared were subjected to the metallic epprouvete having circumference of 1.25 metres for preparation of skein (hank) each having 20 metres length. From each hank 10 no. of samples were prepared and a total of 30 samples (from 3 hanks) were made ready from each variety of raw silk. From three varieties of raw silk a total no. of 90 samples were prepared. Size (denier) deviation test determine the degree of size variation within the test pieces of sizing skeins of definite length of raw silk. Size is the linear density i.e., the weight of 9000 meters filament in gram. Mathematically it is represented as: Denier = [WL x 9000] Where, W = weight of filament (gms) and L=length of the filament (meters). Size Deviation can be determined by (SD) ( - x) 2} n] Where, x= Individual value of size, x = Average size n = No. of observation and Co-efficient of Variation (CV %) = [SD÷Mean] x 100

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III. Results and Discussions The size or the thickness for filament yarn is expressed in ‘denier’ unit in direct system of yarn numbering. As raw silk consists of many numbers of individual filaments, the size variation occurs due to variation in thickness of individual filaments of the cocoon and variation in maintaining the equal no. of cocoons during reeling. So the linear density for each 20 metres fragment was calculated and the variations amongst the fragments were recorded. These variations are the size deviation from one fragment to others and have significant importance in deciding the quality of ultimate resultant raw silk / fabric. Minimum the variations minimum will be the size deviations and maximum will be the quality assurance. Table I, II & III show the denier deviations of multivoltine. multi x bi and bivoltine raw silk. To maintain reeling thread of the required size, the average cocoon no. per thread must be maintained to produce resultant silk thread of uniform size in all the ends during reeling. In advanced reeling machine, if the size of the yarn produced found different from the required size it is readjusted by the group size controller. This will ensure the uniformity by improving the accuracy of cocoon supply to each thread during reeling and thus minimise size deviation and evenness of raw silk greatly. But in traditional contrivances, the evenness or size deviation largely depends upon the human skill. Since traditional machines are generally high speed machine where the control mechanisms are also missing, the size variation naturally increases and the quality of the ultimate output deteriorates. Evenness variations are those defective portion of raw silk threads which show stripes caused by variation in the size of raw silk to such degree as is easily noticeable by visual inspection on an inspection board. The degree of such size variation of the raw silk thread is called intensity of variation. Standard are established for the purpose of determining the intensity of variation and are shown by the standard variation photographs. The evenness test gives an indication of sharp and erratic changes of size within a short length of raw silk filament by grasping the degree of these changes, which is very important. The weavers encounter processing problems when filament shows erratic changes in size along its length. The present study recorded an average short term size deviation of multivoltine raw silk as 1.66 and Co-Efficient of Variation as 6.80%. While comparing with the evenness variation, the same was recorded as 90.67 % i.e., both the observations are at par and has resemblance. Similar was the case for multi x bi and bi x bi raw silk where SD & CV % was recorded as 1.97 and 9.18% & 1.40 and 6.05 % respectively. The similar resemblances were found while comparing with the evenness degree recorded as 92.33 and 94.03% respectively for different varieties of raw silk. While analyzing the data it has been observed that lowest size deviation was found in Bivoltine raw silk. Naturally the coefficient of variation was lowest and evenness was highest in case of bivoltine raw silk. Other observations were made that multivoltine raw silk showed lower size deviation and lower coefficient of variation than that of multi x bi raw silk. But the evenness was found to be lower (90.67%) than multi x bi raw silk (92.33%). This observation appears to be little ambiguous and may be either due to poor quality raw material of multi x bi component or due to experimental error. However by and large, the findings of the experiment corroborate the findings of other works done in the same line previously.

The tabular presentations of data for multivoltine, multi x bi and bivoltine raw silk are furnished below: Sl. No. 1 2 3 4 5 6 7 8 9 10

Bobbin-1 Length (m) 20 20 20 20 20 20 20 20 20 20

Bobbin-3 Sl. Length No. (m) 1 20 2 20 3 20 4 20 5 20 6 20 7 20 8 20 9 20 20 10

Race : M x M

Table-IA

Weight(g)

denier

SD

CV%

0.050 0.053 0.054 0.051 0.056 0.056 0.055 0.056 0.060 0.058 Average

22.50 26.85 24.30 22.95 25.20 25.20 24.75 25.20 27.00 26.10 25.01

1.48

5.93

Race : M x M Weight(g) denier 0.048 0.047 0.053 0.051 0.057 0.057 0.056 0.053 0.054 0.058 Average

21.60 21.15 23.85 22.95 25.65 25.65 25.20 23.85 24.30 26.10 24.03

Table-IC SD CV%

1.71

7.13

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Sl. No. 1 2 3 4 5 6 7 8 9 10

Bobbin-2 Length (m) 20 20 20 20 20 20 20 20 20 20

Race : M x M Weight denier (g) 0.054 24.30 0.055 24.75 0.055 24.75 0.056 25.20 0.052 23.40 0.058 26.10 0.058 26.10 0.059 26.55 0.046 20.25 0.054 24.30 24.57 Average

Sl. No. 1 2 3 4 5 6 7 8 9 10

Bobbin-1 Length (m) 20 20 20 20 20 20 20 20 20 20

Race : M x Bi Weight (g) denier 0.043 0.042 0.041 0.042 0.041 0.043 0.047 0.054 0.053 0.048 Average

19.31 18.90 18.45 18.90 18.45 19.31 20.65 24.30 23.85 21.60 20.37

Table-IB SD

CV%

1.80

7.34

Table-IIA SD CV%

2.19

10.74

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Race : M x Bi Weight(g denier ) 0.043 19.31 0.040 18.00 0.039 17.55 0.046 20.70 0.048 21.60 0.041 18.45 0.047 21.15 0.047 21.15 0.045 20.50 0.046 20.70 19.91 Average Bobbin-1 Race : Bi x Bi Sl. Length Weight(g denier No. (m) ) 1 20 0.046 20.70 2 20 0.045 20.25 3 20 0.051 22.95 4 20 0.048 21.60 5 20 0.054 24.30 6 20 0.051 22.95 7 20 0.052 23.40 8 20 0.053 23.85 9 20 0.064 24.30 20 0.053 23.85 10 Average 22.82 Sl. No. 1 2 3 4 5 6 7 8 9 10

Sl. No. 1 2 3 4 5 6 7 8 9 10

Sl. no 1 2 3 4 5 6 7 8 9 10

Bobbin-2 Length (m) 20 20 20 20 20 20 20 20 20 20

Bobbin-3 Race : Bi x Bi Length Weight(g denier (m) ) 20 0.048 21.60 20 0.053 23.85 20 0.051 22.95 20 0.055 24.75 20 0.045 20.25 20 0.044 19.80 20 0.054 24.30 20 0.050 22.50 20 0.055 24.95 20 0.054 24.30 Average 22.93 Bobbin-2 Intensity of variation V3/2 V2 V3/2 V1 V1 V3/2 V1 V1 V3/2 V2

Multi x Multi Width of No. of strip strip (mm) ≤4 mm 1 -do1 -do2 -do2 -do1 -do1 -do1 ≤12 mm 1 -do1 -do1 Average

Table-IIB SD

CV%

1.46

7.34

Table-IIIA SD CV%

1.47

6.45

Table-IIIC SD

CV%

1.85

8.09

Table-IVB Evenness (%) 93 90 86 90 95 93 95 90 93 90 91.5%

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Bobbin-3 Sl. Length No. (m) 1 20 2 20 3 20 4 20 5 20 6 20 20 7 8 20 9 20 10 20

Sl. No. 1 2 3 4 5 6 7 8 9 10

Bobbin-2 Length (m) 20 20 20 20 20 20 20 20 20 20 Average

Bobbin-1 Sl. Intensity of no variation 1 2 3 4 5 6 7 8 9 10

V3/2 V2 V2 V5/2 V3/2 V5/2 V3/2 V3/2 V3/2 V2

Bobbin-3 Sl. Intensity of no variation 1 2 3 4 5 6 7 8 9 10

V3/2 V2 V2 V1 V3/2 V1 V3/2 V3/2 V3/2 V2

Race : Mi x Bi Weight (g) denier 0.051 0.053 0.056 0.055 0.056 0.060 0.046 0.045 0.051 0.039 Average

22.95 23.85 25.20 24.75 25.20 27.00 20.70 20.25 22.95 26.55 23.94

Race : Bi x Bi Weight denier (g) 0.053 23.85 0.045 22.25 0.054 24.30 0.051 23.85 0.052 23.40 0.055 24.95 0.055 24.75 0.054 24.30 0.065 24.75 0.056 25.20 23.96 Multi x Multi Width of No. of strip strip (mm) ≤4 mm 1 -do1 -do1 -do1 -do1 -do1 -do1 -do1 -do1 -do1 Average Multi x Multi Width of No. of strip strip (mm) ≤4 mm 1 -do1 12mm 1 ≤4 mm 1 ≤12 mm 1 ≤4 mm 2 -do1 ≤12 mm 1 ≤4 mm 1 ≤12 mm 1 Average

Table-IIC SD CV%

2.26

9.45

Table-IIIB SD CV%

0.87

3.61

Table-IVA Evenness (%)

93% 90% 90% 85% 93% 85% 93% 93% 93% 90% 90.5% Table-IVC Evenness (%)

93% 90% 85% 95% 88% 90% 93% 88% 93% 85% 90%

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Bobbin-1 Sl. Intensity no of variation 1 V1 2 V3/2 3 V1/2 4 V0 5 V2 6 V0 7 V1 8 V1 9 V2 10 V1 Bobbin-3 Sl. Intensity no of variation 1 V3/2 2 V1 3 V3/2 4 V2 5 V3/2 6 V1 7 V3/2 8 V3/2 9 V1 10 V3/2

Bobbin-2 Sl. no

Intensity of variation V3/2 V1 V3/2 V3/2 V1 V1/2 V1 V1 V1 V1/2

Multi x Bi Width No. of strip of (mm) strip 4 mm 1 12 mm 1 4 mm 1 -do0 -do1 -do0 -do1 -do1 -do1 -do1 Average Multi x Bi Width No. of strip of (mm) strip ≤4 mm 1 ≤4 mm 1 ≤12 mm 1 ≤4 mm 1 -do1 -do2 -do1 ≤12 mm 1 ≤4 mm 1 -do1 Average

Table-VA Evenness (%)

Bi x Bi

TableVIB Evenness (%)

Width of strip (mm) ≤4 mm -do≤12 mm ≤4 mm -do-do-do-do-do-do-

95% 88% 97% 100% 90% 100% 95% 95% 90% 95% 94% Table-VC Evenness (%) 93% 95% 88% 90% 93% 90% 93% 88% 95% 93% 91.8%

Bobbin-2 Sl. Intensity no of variation 1 V1 2 V2 3 V1 4 V3/2 5 V1 6 V3/2 7 V3/2 8 V3/2 9 V1 10 V3/2 Bobbin-1 Sl. Intensit no y of variati on 1 V2 2 V0 3 V0 4 V1 5 V2 6 V0 7 V0 8 V0 9 V1 10 V0 Bobbin-3 Sl. Intensity no of variation 1 V1 2 V3/2 3 V1 4 V1 5 V1 6 V1/2 7 V1 8 V1 9 V1/2 10 V1

Multi x Bi Width No. of strip of (mm) strip ≤4 mm 1 ≤12 mm 1 ≤4 mm 1 -do1 ≤12 mm 1 ≤4 mm 2 -do1 -do1 -do1 -do1 Average Bi x Bi Width No. of strip of (mm) strip ≤4 mm ≤4 mm ≤12 mm ≤4 mm -do-

2 1 1 1 Average Bi x Bi Width No. of strip of (mm) strip ≤12 mm 1 ≤4 mm 1 -do1 -do2 -do1 -do1 -do1 ≤12 mm 1 -do1 -do1 Average

Table-VB Evenness (%) 95% 90% 95% 93% 88% 90% 93% 93% 95% 93% 92% Table-VIA Evenness (%)

80% 100% 100% 95% 85% 100% 100% 100% 95% 100% 95.5% Table-VIC Evenness (%)

No. of strip 88% 1 1 93% 93% 2 2 90% 95% 3 1 88% 90% 4 1 93% 95% 5 1 95% 97% 6 1 97% 95% 7 2 90% 90% 8 1 95% 97% 9 1 95% 95% 10 1 95% 95.5% Average 93.1% Note: SD = Size Deviation; CV = Co-efficient of Variation Av. Size deviation for multi X multi raw silk = 1.66; Av. Size deviation for multi X bi raw silk = 1.97 Av. Size deviation for bi X bi raw silk = 1.40; Av. Co-efficient variation multi X multi raw silk = 6.80% IV. Conclusion

The important factors which have a direct bearing on productivity and quality of raw silk are the quality of raw material, process parameter, human skill, quality of water and reeling contrivances. Particularly uniformity characteristic like size deviation, maximum size deviation, evenness variation of raw silk are significantly influenced by raw material quality and reeling speed. Filament Length (FL) and Non-Breakable Filament Length (NBFL) seem to have wider role in deciding the uniformity of the yarn. Due to inferiority in raw

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material quality, FL and NBFL are forced to be affected negatively which in turn play significant role in maintaining uniformity in the yarn. Because lower FL and NBFL means higher number of casting, but the casting efficiency of the reeler is limited. So when the raw material quality is inferior or reeling speed is higher the reeler has to work going beyond his or her optimum capacity which is impossible. This resulted in the production of a yarn with huge size deviation and higher co-efficient of variation. Since silk cocoon is a biomaterial, there will be some inherent irregularities which cannot be removed. Unlike other textiles, silk therefore cannot be produced with almost zero-defect. However, a reeler should have that much of endeavour to produce yarn with a coefficient of variation below 5% that would help bringing Indian raw silk to the international standard.

References [1] [2] [3] [4] [5] [6] [7] [8] [9]

FAO, Sericulture Manual vol. 3 – Silk Reeling “ Raw silk testing and classification”.

R.K. Datta, Global silk industry: A Complete Source Book on Silk testing and Grading and Modern sSilk Processing Technology, 2005, Universal Publisher USA(E).

B.M. Mahadevan, S.A Hiremath, B.G.Patil, Subrata Das and Subrata Roy. “Studies on factors influencing quality of fine denier silk” 2011. Sericologia vol.51(4) pp. 565-578.

Iizuka, E., Biorheology 3 (1965).

Iizuka, E. (1983) The physico chemical properties of silk fibre forming process. Experientia, 39: 449 – 454.

E. Iizuka, R. Kawano, Y. Kitani, Y. Okachi, M. Shimuzu and A. Fukuda (1993) Studies on the physical properties of Indian nonmulberry silks I. Antheraea Proylei J. Indian J. Seric., 1993, Vol. 32, No. 1, 27 – 36.

N. Sureshkumar. Cocoon filament size deviation in bivoltine silkworm Bombyx Mori L. Indian J. Seric., 199 Vol. 43, No. 1, 95– 98.

M.A. Qader. Influence of cocoon characteristics on silk reeling and quality raw silk production. Bulletin of Sericulture Research, 1993, Vol. II: 2000 – 2004.

B.V. Vasumati and S.L. Chilakwad. Studies on the effect of sample size on raw silk test. Indian J. Seric., 1994, Vol. 33, No. 2, 135 – 136.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Synthesis of Well-defined Dihydroxyl End-Functional Polystyrene using Trifunctional Initiator via Atom Transfer Radical Polymerization 1

Samiul Islam Chowdhury1, Tariqul Hasan2 Department of Chemistry, Bangladesh University of Textiles, Dhaka-1208, BANGLADESH 2 Department of Chemistry, University of Rajshahi, Rajshahi-6205, BANGLADESH

Abstract: 2-bromopropionic [2,2-dimethyl-1,3-dioxolane-4-ylmethyl] ester (BPDME) was synthesized by two step reaction which involved on blocking of two hydroxyl group and esterification of the starting material. 1H NMR of both the intermediate and final products has confirmed the structure of the BPDME. The well-defined dioxolane end-functional polystyrene was synthesized by using BPDME as initiator in ATRP method. The dioxolane end-functional polystyrene obtained was characterized by Gel Permiation Chromatography (GPC) and NMR analysis. Finally the dioxolane end group of polymer was deblocked to free hydroxyl groups by hydrolysis to produce dihydroxy end-functional polystyrene.

Key Words: Atom transfer radical polymerization (ATRP), polystyrene, trifunctional initiator I. Introduction Controlled radical polymerization, atom transfer radical polymerization (ATRP) [1] is a technique whereby a catalytic amount of a Cu(I) coordination complex reversibly abstracts a halogen atom (Cl or Br) from the polymer chain ends, switching them from a dormant to an active propagating state.

The ATRP process exhibits all of the experimental characteristics of a living polymerization, can be used for a large variety of monomers [1–9], allows facile control of chain topology, composition and functionality [10,11] and can employ a wide range of halogenated initiators and macroinitiators [12]. Since it is a catalytic process, the proportion of radicals and consequently the polymerization rate can be easily controlled, based on the amount and activity of the transition metal compounds used as redox catalyst. Moreover, experimental conditions are rather simple and Matyjaszewski and coworkers have showed that polymerization can be carried out in water [13], in a particular case in the presence of oxygen [14] and even at room temperature [15].

A further feature of polymers obtained by ATRP is that they possess an ω-halogen end-group that can be converted to other functional groups. Formation of azides, amines, double bonds and even sulfide have been shown to be feasible [16], however, Among the various functional group dioxolane end group in the polymer chain is a very interesting one because dioxolane end-functional group containing polymer can be converted to dihydroxy endfunctional polymer. Which could be used as “microinitiator” for synthesis of various graft, block, two or three armed copolymer [17 -20]. Therefore a dioxolane group containing initiator 2-bromopropionic [2,2-dimethyl-1,3dioxolane-4-ylmethyl] ester (BPDME) was synthesized and the ATRP of styrene was investigated using BPDME as initiator[20-22]. The initiator efficiency and polymer properties will also be studied. Finally the dioxolane ring was opened by the chemical reaction [20, 23-24].

II. Experimental Materials: Glycerol (99 % Puriss Riedel-de Haen), Acetone (Normapur analytical reagent), Tetrahydofuran (99 %, extra pure, stabilized with BHT, Acros Organics), molecular sieves 5A (Sigma-Aldrich). Dichloromethane (GPR Rectapur stabilized with 0.1 % of ethanol VWR Prolabo), Triethylamine (Reagent Plus 99.5 % Aldrich), Pyridine (ACS For Analysis CARLO ERBA), Methanol (Chromasolv Sigma Aldrich), 2,2’-bipyridyl (bipy, 98 %, Fluka), 2bromopropionylbromide (98 %, Aldrich), p-toluenesulfonic acid (98 %, Aldrich) and styrene (99.5 %, Fluka) were dried over CaH2 and distilled under vacuum just before use. Copper (I) bromide (98 %, Aldrich) was purified according to a published procedure [25].

Analytical Methods: Molecular weight (Mn) and molecular weight distribution (Mw/Mn) of polymer were measured

by Toyo soda HLC-802; Column, GMH6 2 + G4000H8; eluent, CDCl3 as solvent and calibrated by polystyrene standards. 1H and 13C NMR spectra were recorded at room temperature on a JEOL GX 500 spectrometer operated at

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400 MHz in pulse Fourier transform mode with chloroform-d as solvent. The peak of chloroform in chloroform-d (7.26 ppm for 1H) was used as internal reference.

Synthesis: (A) Synthesis of 2,2-dimethyl-1,3-dioxolane-4-methanol. The starting material was synthesized by the following reference procedure [21].

(B)Synthesis of Initiator 2-bromopropionyl[2,2-dimethyl-1,3-dioxolane-4-ylmethyl]ester (BPDME): To a 100 mL

R.B. flask, 5.3 mL (40 mmol) of 2,2-dimethyl-1, 3-dioxolane-4-methanol (1), 11.1 mL (40 mmol) of triethylamine and 40 mL of THF were taken. The reaction mixture was cooled in an ice-water bath. To this solution, 4.65 ml (40 mmol) of 2-bromopropionyl bromide (2) in 20 mL of THF was added drop-wise. The mixture was stirred for another 2 hrs at room temperature (1 reacted with 2 to yield BPDME and HBr was absorbed by triethylamine).

Triethylamine hydrogenbromide salt was filtered out. THF in the filtrate was removed under vacuum at room temperature. The residual was dissolve in diethyl ether and washed with 50 mL of cold water. The organic layer was separated, washed with a saturated solution of sodium carbonate, acidified with HCl (pH = 4.5) and a second aliquot was washed with sodium carbonate. The organic layer was dried over anhydrous sodium sulphate, filtered and solvent was removed under vacuum. The product was finally isolated by vacuum distillation as slightly yellowish oil. The product yield was 79 %.

1

H NMR (CDCl3) of (BPDME): 4.5 ppm (p, 1H, -CH2-CH-O-); 4.4 ppm (q, 1H, Br-CH(CH3)-); 4.2 ppm (d, 2H, O-CH2-CH-); 4.0 and 3.8 ppm (dd, 2H, -CH2-O-); 1.8 ppm (d, 3H, Br-CH(CH3)-);1.35 and 1,40 ppm (s, 6H, C(CH3)2); Polymerization method: Polymerization was carried out in a 25 mL Schelnk tube with magnetic stirrer in nitrogen atmosphere. The reactor was charged with prescribed amount of CuBr, bipyridine and a tiny magnetic capsule.

Three cycles of vacuum-evacuation of reactor and fill-up with nitrogen gas were performed, and the reactor then sealed with rubber septum. A required amount of degassed styrene and initiator BPDME were added with syringe.

The reactor was placed in an oil bath to keep desired temperature by tuning thermostat and the reaction mixture was stirred for certain time using magnetic stirrer. At certain interval, the polymerization was stopped by added methanol followed by cooling the tube into ice-water and the polymer was precipitated in methanol by stirring for overnight.

The polymers obtained were filtered, adequately washed with methanol and dried under vacuum at 60 0C for 6 hrs.

III. Results and Discussion The initiator (BPDME) 2-bromopropionyl[2,2-dimethyl-1,3-dioxolane-4-ylmethyl] ester was synthesized from the reaction between 2,2-dimethyl-1,3-dioxolane-4-methanol and 2-bromopropinyl bromide in presence of triethylamine. The trifunctional hydroxyl compounds in this work are shown in Scheme 1.

O

OH +

Br O

O 1

O

THF Et3N

Br

O

Br O

O

(BPDME)

2

Scheme 1. Preparation of functional initiator BPDME.

The structure of the initiator BPDME obtained was characterized by 1H NMR analysis. In the 1H NMR spectrum of the BPDME, several signals including the signals of 2-bromopropionyl bromide were observed. All signals correspond to the different protons of BPDME were assigned clearly and labeled in Figure 1. g h

O O

de a

f c

O

g h b Br

O

f

c

d ab

e

Figure 1. 1H NMR spectrum of BPDME

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Styrene was then polymerized by ATRP at 115 ºC initiated with BPDME at different styrene/initiator ratio in conjunction with cupper (I) bromide and bipyridine as a catalyst under nitrogen atmosphere. The ratio of styrene and BDPME (St/BPDME) significantly affect on the polymerization. The results obtained were listed in Table 1. The yield of polymers was increased with increasing the ratio of styrene and BDPME. The molecular weight and molecular weight distribution of the polymers obtained were measured with gel permeation chromatography (GPC).

The polymers obtained with high molecular weight (Mn). The Mn value of the polymers was increased with the increasing St/BPDME ratio. The molecular weight distribution of polymer was narrow (M w/Mn ~1.2). The GPC curves of the polymers obtained from the feed ration St/BPDME = 1000, St/BPDME = 500 and St/BPDME = 250 are displayed in Figure 2.

Table 1. Effect of the ratio of styrene/initiator on polymerization of styrene with CuBr/BiPy. Entry

St/BDPME (mmol)

Yield (g)

Mn (103)

Mw (103)

Mw/Mn

1

250

0.51

3.667

4.389

1.19

2

500

0.75

15.42

20.20

1.31

3

1000

1.36

38.20

52.81

1.38

Polymerization conditions [CuBr : Bipy = 1:2, Styrene = 3.1 mL (27mmol), temperature = 115 ºC, time = 5 hrs] The GPC curve of the polymer obtained from higher monomer concentration (St/BPDME = 1000) shifted to the higher molecular weight region than that of the polymer obtained from lower monomer concentration (St/ BPDME = 500). The Mn value (38.2 x 103) of the polymer obtained from the higher feed ration (St/ BPDME = 1000) was more than double than the Mn value (15.4 x 103) of the polymer obtained from the lower feed ration (St/ BPDME = 500). These results suggest that the higher propagation rate occurred at higher monomer concentration. The linear relationship of molecular weight of the polymers with ratio (Figure 3a) also supports the above results. a

40

1

0.5

0

20

0

0

200 400 600 800 1000 1200

Ratio (monomer / Initiator)

Log Mn Figure 2. GPC curves of entry 1, entry 2 and entry 3

b

60

Mn x 103

Yield (g)

1.5

0

200

400

600

800 1000 1200

Ratio (monomer / Initiator)

Figure 3. (a) The plot of Yield Vs Ratio, (b) The plot of Mn Vs Ratio

To produce polymer with narrow polydispersity is one of the main criteria for a living polymerization system. The initiator BPDME combined with CuBr/bipyridene catalyst system gave the polymers with narrow polydispersity (Mw/Mn = 1.14) at 115 ºC (entry 6). This result therefore promotes us to make sure the living nature of BDPME/CuBr/bipyridene catalyst system at 115 ºC. The dependence of polymer yield and M n on polymerization time was investigated by batch method to check the living nature of this catalyst system. The molecular weight and molecular weight distribution of the polymers obtained by different polymerization time were measured by GPC and the results are summarized in Table 2.

Table 2. Time dependence polymerization of styrene with CuBr/BiPy/BDPME using batch method.

Entry

Time (hours)

Yield (g)

Mn (103)

Mw (103)

Mw/Mn

4

5

0.51

3.66

4.38

1.19

5

6.5

0.62

5.56

6.46

1.16

6

8.5

0.92

10.58

12.06

1.14

Polymerization conditions [CuBr = 0.108 mmol, Bipy = 0.216 mmol, Styrene = 3.1 mL, temperature = 115 ºC]

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The GPC curves of polymers obtained from different period of time (5, 6.5 and 8.5 hours) were compared in Figure 4. The GPC curves were shifted to the higher molecular weight region with increasing polymerization time with keeping narrow Mw/Mn.. The plot of Mn vs time showed linear relationship with a slop at initial period in Figure 5(b). The plot of yield vs time displayed in Figure 5(a) also showed linear relationship. These results suggest that the living polymerization of styrene proceeded at 115 ºC by BPDME/CuBr/BiPy catalyst system with slow initiation. a

1 0.75

8

Mn x 103

Yield (g)

b

12

0.5 0.25

4

0

0 0

2

4

6

8

0

10

2

Time (h)

Log Mn Figure 4. GPC curves of entry 4, entry 5 and entry 6.

4

6

8

10

Time (h)

Figure 5. (a) The plot of Yield Vs Time, (b) The plot of Mn Vs Time

The dependence of polymer yield and Mn on polymerization temperature was investigated by batch method to check the living nature of this catalyst system. The yield of the polymers was increased with the increasing temperature.

The GPC curves of the polymers obtained from different temperature are displayed in Figure 6.

Table 3. Effect of the temperature on polymerization of styrene with CuBr/BiPy. Entry

St/

Yield

Mn

Mw

(º C)

(g)

(103)

(103)

Temp

BDPME

Mw/Mnd

(mmol) 7

250

75

trace

-

-

-

8

250

100

0.277

6.043

9.078

1.50

9

250

115

0.511

3.667

4.389

1.19

polymerization conditions; CuBr = 0.108 mmol, Bipy = 0.216 mmol, Styrene = 3.1 mL, Entry 9

time = 5 hrs

Entry 8

20

25

30

Log Mn Figure 6. GPC curves of entry 8 and entry 9.

Styrene was then polymerized by ATRP with BPDME at different condition in conjunction with cupper (I) bromide and bipyridine as a catalyst under nitrogen atmosphere. The hydroxylation of the dioxolane group of polystyrene (PS) was carried out using glacial acetic acid, water and anisole at 80 ºC in THF for 1hour (Scheme 2).The quantitative conversion of dioxolane group to dihydroxyl group was confirmed by the 1H NMR analysis.

O O

i

O OH

BPDME

Br

O O

ii O O

n

Br

HO HO

PS-DX

O n

O

Br

PS-OH

i = CuBr, Bipridine, 115 ºC; ii = glacial aceticacid, water, anisole, 80 ºC Scheme 2. The hydroxylation of the dioxolane group of PS-DX

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The structure of polystyrene obtained using BPDME initiator was investigated by 1H NMR analysis of the polymer and comparison with the spectrum of BPDME initiator. The 1H NMR spectrum of poly styrene obtained with BPDME was displayed in Figure 7. In the 1H NMR spectrum of polystyrene, the signals at 4.6 ppm and 3.7 ppm assignable to (-O-CH-CH2- and –CH-CH2-O-) dioxolane protons and a signal at 4.4 ppm assignable to CH proton α to Br indicates that the polymerization was initiated with BPDME. A broad signal at 7.2 ppm assignable to aromatic protons (meta- and para-position) of styrene unit and at 6.5 ppm for ortho-proton. The signals observed at 1.35 and 1.77 ppm assignable to CH2 and CH protons of main chain of polystyrene, respectively. These results clearly indicate the presence of dioxolane group in the ω-end of polystyrene obtained with BPDME initiator.

J

k

O

O

H2C d, d/

c H2 C

C

e H O

H a

m, n

h H2 C

C

C

O

CH3

g

f H C

H2 C n-1

b H C

Br

o n

i

m

g-k

o

c a b

d

/

d, e

f

Figure 7. 1H NMR spectrum of dioxolane end-functional PS 1

H NMR spectrum of PS-OH was displayed in Figure 8. The peaks at 5.66 and 5.19 ppm assigned for two -OH groups and the signals assignable for main polystyrene chain were also appeared. This observation indicated the dihydroxy end-functional structure of PS-OH. e H b HO

O

C CH2 O f

a HO

m, n

d H2 C

g H C k

j H2 C

i h H2 H C n-1C

c H C

Br

o n m

o a

b

c

d

i, j, k

e f, g h

Figure 8. 1H NMR spectrum of dihydroxy end-functional PS

IV. Conclusion The initiator 2-bromopropionyl[2,2-dimethyl-1,3-dioxolane-4-ylmethyl]ester (BPDME) was synthesized in a good yield. The structure of BPDME was characterized by 1H NMR analysis. The BPDME as initiator was successfully applied for Cu(I)-bipyridine mediated ATRP polymerization of styrene. The polymerization of styrene was investigated at various reaction conditions. The polymerization of styrene with BDPME/CuBr/BiPy (1:1:2) was investigated at various monomer feed ratio at 115 ºC. The yield and molecular weight of polymers were increased linearly with increasing the ratio of styrene and BDPME. These results suggest that the propagation rate was increased with increasing of monomer concentration. In time dependence polymerization, yield vs time plot showed linear relationship whereas the Mn values of the polymer linearly increased against time after a certain period. The molecular weight distribution was very narrow (Mw/Mn = 1.14). These results suggest that the living polymerization of styrene proceeded at 115 ºC by BPDME/CuBr/BiPy catalyst system with slow initiation. The presence of dioxolane group in PS-DX was confirmed by 1H NMR spectrum which indicated the dioxolane chainend structure of the polystyrene. Finally, the dioxolane end group of the polystyrene was converted to dihydroxy

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group by treatment of acetic acid, water and anisole at 80 0 C. The structure of dihydroxy end-group was confirmed by 1H NMR spectrum of the PD-OH. Dihydroxy group containing polystyrene can be use as functional macroinitiator or as coating material for the preparation of nanopartical which are under investigation.

V. References [1] [2]

[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

[18] [19] [20]

[21] [22] [23] [24] [25]

J.S. Wang, K. Matyjaszewski, Controlled/"Living" Radical Polymerization. Halogen Atom Transfer Radical Polymerization Promoted by a Cu(I)/Cu( 11) Redox Process , Macromolecules 28 (1995) 7901.

M. Kato, M. Kamigaito, M. Sawamoto, T. Higashimura, Polymerization of Methyl Methacrylate with the Carbon Tetrachloride/Dichlorotris- (triphenylphosphine)ruthenium(II)/Methylaluminum Bis(2,6-di-tert-butylphenoxide) Initiating System: Possibility of Living Radical Polymerization, Macromolecules 28 (1995) 1721.

Y. Kotani, M. Kato, M. Kamigaito, M. Sawamoto, Living Radical Polymerization of Alkyl Methacrylates with Ruthenium Complex and Synthesis of Their Block Copolymers, Macromolecules 29 (1996) 6979.

K. Matyjaszewski, T.E. Patten, J. Xia, Controlled/“Living” Radical Polymerization. Kinetics of the Homogeneous Atom Transfer Radical Polymerization of Styrene, J. Am. Chem. Soc. 119 (1997) 674.

T.E. Patten, J. Xia, T. Abernathy, K. Matyjaszewski, Polymers with Very Low Polydispersities from Atom Transfer Radical Polymerization, Science 272 (1996) 866.

K. Matyjaszewski, S. Jo, H.-J. Paik, S.G. Gaynor, Synthesis of Well-Defined Polyacrylonitrile by Atom Transfer Radical Polymerization, Macromolecules 30 (1997) 6398.

S. Coca, C.B. Jasieczek, K.L. Beers, K. Matyjaszewski, Polymerization of acrylates by atom transfer radical polymerization.

Homopolymerization of 2-hydroxyethyl acrylate, J. Polym. Sci. Part A Polym. Chem. 36 (1998) 1417.

V. Percec, H.-J. Kim, B. Barboiu, Disulfonyl Chlorides: A Universal Class of Initiators for Metal-Catalyzed “Living” Diradical Polymerization of Styrene(s), Methacrylates, and Acrylates, Macromolecules 30 (1997) 6702.

V. Percec, B. Barboiu, H.-J. Kim, Arenesulfonyl Halides: A Universal Class of Functional Initiators for Metal-Catalyzed “Living” Radical Polymerization of Styrene(s), Methacrylates, and Acrylates, J. Am. Chem. Soc. 120 (1998) 305.

S.G. Gaynor, K. Matyjaszewski, Polym. Prep. 38 (1) (1997) 758.

S.G. Gaynor, K. Matyjaszewski, PMSE 79 (1998) 128.

K. Matyjaszewski, S. Coca, Y. Nakagawa, J. Xia, PMSE 76 (1997) 147.

K. Matyjaszewski, K.L. Beers, A. Muehlebach, S. Coca, X. Zuang, S.G. Gaynor, PMSE 79 (1998) 429.

K. Matyjaszewski, S. Coca, S.G. Gaynor, M. Wei, B.E. Woodworth, Controlled Radical Polymerization in the Presence of Oxyge, Macromolecules 31 (1998) 5967.

J. Xia, S.G. Gaynor, K. Matyjaszewski, Controlled/“Living” Radical Polymerization. Atom Transfer Radical Polymerization of Acrylates at Ambient Temperature, Macromolecules 31 (1998) 5958.

K. Matyjaszewski, J. Xia, Atom Transfer Radical Polymerization, Chem. Rev. 101 (2001) 2921.

Nicholas A. A. Rossi, Yuquan Zou, Mark D. Scott and Jayachandran N. Kizhakkedathu; RAFT Synthesis of Acrylic Copolymers Containing Poly(ethylene glycol) and Dioxolane Functional Groups: Toward Well-Defined Aldehyde Containing Copolymers for Bioconjugation, Macromolecules, 41 (14), (2008), pp 5272–5282.

Glaied O, Delaite C, Dumas P; Synthesis of A2B star block copolymers from a heterotrifunctional initiator. J Polym Sci Part A Polym Chem, 44(6), (2006), 1796–1806.

Jean-d’Amour K, Twibanire and T. Bruce Grindley, Polyester Dendrimers, Polymers, 4, (2012), 794-879.

Claudia Gordin, Christelle Delaite, Hussein Medlej, Delphine Josien-Lefebvre, Kamal Hariri, Mihai Rusu; Synthesis of ABC miktoarm star block copolymers from a new heterotrifunctional initiator by combination of ATRP and ROP, Polym. Bull. 63, (2009):789–801.

Mary Renoll and Melvin S. Newman, dl ISOPROPYLIDENEGLYCEROL, Organic Syntheses, (2003), 73.

Haruna Musa, SYNTHESIS AND CHARACTERISATION OF A WATER SOLUBLE INITIATOR FOR ATOM TRANSFER RADICAL POLYMERISATION, Bajopas Vol 3, (2010) No 2.

Paweł G. Parzuchowski, Martyna Grabowska, Mariusz Tryznowski, and Gabriel Rokicki, Synthesis of Glycerol Based Hyperbranched Polyesters with Primary Hydroxyl Group, Macromolecules, (2006), http://www.ch.pw.edu.pl/~pparzuch/publikac/publ/publ23.pdf Michael A. Carnahan and Mark W. Grinstaff, Synthesis and Characterization of Polyether-Ester Dendrimers from Glycerol and Lactic Acid, J. Am. Chem. Soc. 123, ( 2001), 2905-2906 polymerization of (meth)acrylates.

Miller PJ, Matyjaszewski K; Atom transfer radical poly(dimethylsiloxane) macroinitiators. Macromolecules 32(26), (1999), 8760– 8767.

VI. Acknowledgements I am especially grateful to Dr. Jun-ichi Mamiya, Assistant Professor, Chemical Resources Laboratory, Tokyo Institute of technology, Japan for GPC analysis of polymer samples. I am also grateful to Dr. Suman Chandra Mohanta, Research Fellow, the Institute of Scientific and Industrial Research (ISIR), Osaka University, Japan for NMR analysis of our samples.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Identification of Data Quality Issues in Homogeneous Environment Sapna Pujara1, Dr. Kanwal Garg2 1

1,2

Research Scholar, 2Assistant Professor Department of Computer Science & Applications, Kurukshetra University, Kurukshetra, Haryana, INDIA

Abstract: Maintaining the data quality of the database simply means passing the data through a standard set of test and correct procedures before it may be utilized for any information retrieval. The database may be piled up with the data, collected from different kind of sources or it may be collected from geographically- distributed sites but homogenous in respect of DBMS, OS etc.. All the methods of maintaining the quality of data-repository are composed up of some well defined methods and processes and these steps while deployed over homogenous environment are critically examined in the analysis presented here. The major issues or challenges to be dealt at every stage are highlighted.

Keywords: Data Quality; Data-Repository; Homogenous Environment I. DATA QUALITY ISSUES An ultimate objective of having a repository of the data is to provide qualitative data in the hands of its users.

Here maintaining, improving the quality of data means modeling and structuring of data in such a way which specify and ensures the freshness, completeness, consistent and noise-free data. The versatile and widely accepted meaning of quality is to have and maintain the data as fit for use and as conformance to requirements [Wang, R. Y. and Strong, D. M. (1996)] [Crosby, P. B. (1984)]. Quality decisions must be based on high quality data otherwise it generates maintenance and repair costs. The cost of deprived data quality must be calculated in terms of user requirements. Even accurate data is of modest value if not interpretable and reachable to the user [Ishikawa, K. (1985)]. Even though, the progress in the context of quality improvement is substantial but the real-world data is usually imperfect. To be interpretable and functional in a proficient and valuable manner, data have to satisfy a set of quality criteria. Data satisfying those quality criteria is said to be of high quality. In general, data quality is defined as an aggregated value over a set of quality criteria [F. Naumann, 2002].

II. HOMOGENOUS ENVIRONMENT The model of database being homogenous or heterogeneous is determined according to uniformity and compatibility of underlying DBMS. The similarity of operating system (s/w and h/w) is next decisive factor.

Within a Homogeneous Environment, data structures of the schema over 'n' sites are at least compatible (if not similar), which also leads in establishing similar query structure for every site. Despite of geographical scattered database the semantics of DBMS is uniform in homogeneous database. Application of various method or techniques at every stage of preprocessing over different sites require the deployment of same mechanisms for cleaning etc. hence leads to similar type of resultant data at each site which actually turns the process of integration, reduction and transformation quite easy to perform. The data cleaning stage gives the data passed through same cleaning procedures at each site and hence same methods would have been used to fill in the missing values, to remove the noise, replacement/insertion of default values etc. Hence, the integration stage get the similar type of data (same default value signifies same data item) from each site for integration making the job quite easier which otherwise would have been different computational techniques for different site consuming more man/machine power. Reduction and transformation becomes less clumsy since the common statistical method may be chosen for each location. Towards the goal of assimilating data quality features into the repository, we illustrate a case in which Table T1 and Table T2 (flat files) have been assumed to be sourced from different but homogenous environment.

Table T1 EMP ID

FNAME

LNAME

SEX

DEPTT

PAY

AGE

D.O.B

PAY_BAND

RS1001 JK1002 SA1003 KK1004

RAM JOLLY SNEHA KARAN

SHARMA KUMAR NULL KAPOOR

M M F M

SALES MARKETING MARKETING PRODUCTION

20000 25000 25000 18000

24 203 24 26

01.04.1990 22.11.1991 15.06.1990 17.09.1988

7200-400-9600 7800-500-10300 7800-500-10300 5500-300-8500

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MARITAL STATUS MARRIED SINGLE SINGLE MARRIED

Page 119

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HS1005 MN1006 NM1007 BM1008 KT1009 RL1010

HARSHA MOHAN NEHA BHEEM KIRTI RAHUL

SINGLA NULL MEHTA NULL THAKUR NULL

F M F M F M

FINANCE PRODUCTN SALES MARKETING FINANCE SALES

28000 15000 20000 26000 28000 20000

27 22 28 25 -2 26

10.10.1987 06.05.1992 18.07.1986 12.07.1989 19.12.1987 28.02.1988

8000-400-12000 5500-300-8500 7200-400-9600 7800-500-10300 8000-400-12000 7200-400-9600

SEPERATED SINGLE DIVORCED SEPERATED MARRIED MARRIED

Table T2 SSN

NAME

SEX

P.NO.

D.O.B

QUALIFICATION

1001

R.SHARMA

1

+9023-456122

01/04/1990

GRADUATE

1002

J.KUMAR

1

+8763-207630

22/11/1991

SECONDARY

1003

S.KUMARI

0

+7024-619325

NULL

GRADUATE

1004

K.KAPOOR

1

+9182-745014

17/09/1988

MATRIC

1005

H.SINGLA

0

+8132-465726

10/10/1987

POSTGRADUATE

1006

MOHAN

1

+2748-521731

06/05/1992

MATRIC

1007

N.MEHTA

0

+9245-631278

18/07/1988

SECONDARY

1008

BHEEM

1

+8334-598751

12/07/1989

GRADUATE

1009

K.THAKUR

0

+9126-098327

19/12/1987

POSTGRADUATE

1010

RAHUL

1

+8435-619045

28/02/1988

SECONDARY

III. PRE-PROCESSING PROBLEMS OF HOMOGENEOUS DATABASES Although, the preprocessing part of homogenous databases is quite symmetric in the sense that each stage of preprocessing requires uniform efforts for the cleaning and integrating the data located at each site, yet it is not out of the woods. The preprocessing of homogenous databases is actually surrounded with a small but delicate set of problems. The foremost of them are mentioned here: A. PROBLEMS FACED WHILE DATA CLEANING Manually feeding/inputting the data into Database is error prone. Hence, such Databases must be filtered with some proven mechanisms before it is used for inferring any information. [Maletic, J. I., & Marcus, A, 2000] This is also termed as enhancing the quality of data and this aspect is well taken care by Data Cleaning which includes removing the noise from Database & filling in the missing/omitted values.

A.1 Missing values may happen due to non availability of the data-item at the time of entry or availability of illegal/invalid value at the time of entry or some inadvertent change in data afterwards. As evident in table T1, value for LNAME (Last Name) is missing for tuple no. 3,6,8 & 10. Here, missing values are represented with NULL which is not an acceptable standard for a good DBMS. To treat the presence of missing values, if it is opted to ignore the tuple then the information of particular employee is omitted from the database. In case, it is opted to fill this missing value with some arbitrary value may actually escort the noise.

A.2 Noise can occur in our database in different forms like in-consistency or duplicity of data. This noise actually creeps into our database due to insufficient validation procedures or lack of standard check at data entry or due to mishandling of missing data values [Ciszak, L., 2008]. Here, data values for AGE attribute in table T1 for record numbers two and nine are not valid age values (203, -2 respectively). There are a number of methods to choose from, for clearing away the noise. The process of noise removal is time-consuming too.

A.3 To automate the statistically rich methods of data cleaning is in itself a great challenge for every programmer as it requires high programming skills with proper statistical knowledge since the process of filing in the missing values is accomplished with the outcomes (data values), which are usually different when calculated by using different statistical functions. Hence, the use of one method may produce different value as compared to another method. Hence, this again leads to higher level of noise. The success of correctly filling in the missing values largely depends on the abilities of personnel to find the most relevant & most probable data value.

A.4 Expensive resources like memory & CPI: The programmatically or automatic removal of noisy data from Database requires the selection & usage of a statistical method which filters the data effectively [Maletic, J. I., & Marcus, A, 2010]. The chosen method should not only fulfill the purpose of noise removal but should also take the least amount of time since each statistical method for noise removal is very complex in itself & hence requires many expensive resources like memory & CPU time (Cycle Per Instruction), which should be optimized.

A.5 Conflict resolution: The process being manual or automatic, both systems must have to deal with the problem of conflict resolution while filling missing values at different places. The selection of different method

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for noise removal may lead to different value to rectify, which needs to be resolved. In many cases, the relevant and available information is actually scarce to remove these conflicts. As shown in following instance (table T1.1 and T2.1), extracted from the given case study, table instances T1.1 and T2.1 do not contain enough information to resolve that whether LNAME of the record may be filled with ‘KUMARI’ or not since the E_ID are in different formats and the information for field D.O.B is also not available to match the complete record of the employee.

Table T1. 1 EMP ID

FNAME

SA1003

SNEHA

LNAME SEX NULL

F

DEPTT

PAY

AGE

D.O.B

PAY_BAND

MARITAL STATUS

MARKETING

25000

24

15.06.1990

7800-500-10300

SINGLE

Table T2. 1 SSN

NAME

SEX

P.NO.

D.O.B

QUALIFICATION

1003

S.KUMARI

0

+7024-619325

NULL

GRADUATE

A.6 Maintenance of cleansed data: Besides above stated issues, maintenance of cleansed data, management of multiple alternate values & cleaning lineage are also constitutes the list of issues of data cleaning. Cleansing lineage is generally kept for the records that have been modified and for those that have been verified within the cleansing process as being correct as well. As soon as, any of the values in the data collection is changed, the cleansing workflow has to be repeated for all those records that include the changed value as part of their cleansing lineage. It is obvious from the above mentioned difficulties of data cleaning that the manual cleaning of database is suitable only for organizations with small databases. For bigger databases or warehouses, the automation of data cleaning is inevitable which in turn has its own problems, visualizing availability of computation resources at minimum cost (in terms of memory & time) still yielding accurate results.

B. PROBLEMS FACED WHILE DATA INTEGRATION The process of integration of data/instances collected from homogeneous resources involves a sequence of activities starting from acquisition of data across departments/sites (of an organization), finding common keys followed by synchronizing the data in the context of time or alike scales. Although, the process of Integration is well mature yet it is not smooth enough due to below mentioned obstacles. In fact, the each stage of Data Integration is well equipped with its own limitations.[ Halevy, A., Rajaraman, A., & Ordille, J., 2006] B.1 Schema Matching Problems: Schema Matching is a challenging job while integration. Even though the data is collected from homogeneous sources, the type or constraints etc. of data may be heterogeneous [Almarimi, A., & Pokorný, J., 2005].

Finding common key: This may entail to the problem of finding a common key from two or more instances. The problem of finding common key also exists in given case study. The table T1 contains E_ID as its unique field to identify each employee and table T2 contains SSN as its unique key but the data-format and data-type in both of these tables are different hence it can’t be deduced with surety that these data-values of such keys refer to same records.

Type heterogeneity: The data types of the fields may also be different including width specifications or predefined input pattern etc.[Lim, E. P., Srivastava, J., & Shekhar, S., 1994] e.g. in table T1 & T2 there is a common attribute 'SEX' but one contains value in the form of M/F & other one contains 1/0 for Male/Female respectively. Hence, the problem of data-type heterogeneity arises while integrating data, even in Homogeneous environment. Same problem occurs due to value heterogeneity. As evident from the tables T1 and T2, that T2 contains different value for D.O.B. for record number seven as contained in table T1 for corresponding record; which of these should be assumed to be correct is deceivable.

Semantic heterogeneity: The definition of alike sub-schemas collected from different but similar sources may be different, such as one relation may point to the details of an individual whereas other may specify a family or generic class. In the above case, table T1 has E_ID column as its primary key whereas table T2 has SSN as its unique key to identify its records separately, but actually both of these columns are referring to same employee but with different conventions. Such heterogeneity is difficult to resolve unless the all available information of each record is matched.

B.2 Time Synchronization: This also appears to be a problem (especially in case of temporal Database). Here, lies the problem of finding the compatibility between two different time-periods (such as workingdays & workinghours). This is necessary to figure out the required level of synchronization between two different but alike fields.

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B.3 Socio-logical factors: This plays an important role in successful integration of data since personnel working at different levels have the reluctance to share their data due to various fears such as: loss of power, loss of false fame, non-availability of complete data or meta-data etc.

B.4 Data Integration is AI-complete: The process of Data Integration is actually a good mix of human and machine efforts. In fact, neither only human endeavor is not enough for its implementation, nor only automated solution is likely.[Halevy, A., Rajaraman, A., & Ordille, J., 2006] Even though any such automated application for Data Integration would be only deployer specific. Moreover such system will always give threshold levels of results is also uncertain.

B.5 Miscellaneous technical reasons: Besides all above mentioned problems there are some other miscellaneous hurdles, some of these are only case-specific and rest are general. The job of integration requires proper understanding of sources of data in terms of machine code ASCII or EBCDIC. Semantic meaning of NULL values, need of derived data-fields, need of exact type of join (inner vs. outer). Above all factors the automation of Data Integration has to be concerned for expensive computational resources besides well-defined APIs, flexible list of options to be used with the process of Data Integration C. PROBLEMS FACED WHILE DATA REDUCTION Data in Databases or Data Warehouses accumulates at an unprecedented speed. This accumulation further enhances as the size and reach of organization picks up. To effectively utilize the data for mining purposes need arises to for downsizing the data since the accuracy & efficiency of queries degrades rapidly with the size of data. The various techniques of Data Reduction such as aggregation, numerosity reduction or dimensionality reduction are available [Vora, P., & Oza, B., 2013].

C.1 Reduction ability: Data Aggregation contains highlighting & combining various views of data sets in three dimensional representations. And while Attribute Subset Selection (which implements forward selection/backward elimination or their combination), the irrelevant or redundant attributes are removed and hence the size of data is actually reduced. This process of attribute subset selection is required to be applied on table T1, since it contains two attributes AGE & D.O.B., which in fact provides same information i.e age can be calculated from D.O.B. so, reduction is sometimes necessary to improve the quality of data. Dimensionality Reduction focuses on lossless (compression/conversion) reduction of data.[ Lei Yu, Jieping Ye, Huan Liu] Here in table T1, the salary is expressed in terms of exact multiples of thousand, so a conversion may be applied over this column where all the values are stored after dividing it with 1000 and hence column name itself may be captioned like SALARY(in thousands). Such a conversion will reduce the size of data being transferred to and fro over disk. In all, there are many methods to choose from. The reduction ability of each method is the important criteria to decide on.

C.2 Loss of interpretability: Dimensionality reduction technique of Data reduction, when applied over datasets may lead to loss of interpretability. For example, in table T1, the attribute PAY_BAND field values represent the actual grade pay which certain employee is drawing and this column values if converted to Pay-band I, II, III or IV etc. as per the grade they fall in, then it will be easier to enter and avoid the possible noise. But this conversion may actually lead to loss of interpretability since it will not be directly meaningful and understandable to each user then. Same happens while interpretation of Principal Components, which may be difficult at times since they do not necessarily correspond to meaningful physical quantities.

C.3 Noise Tolerance: Numerosity Reduction method of Data Reduction, also suffers from the problem of estimating/selecting the random variable which acts as a seed for Regression and log-linear methods of Numerosity reduction. The different selection leads to different level of noise tolerance.

D. PROBLEMS FACED WHILE DATA TRANSFORMATION Data Transformation is the process of quantitative/mathematical change in variables by applying some very common (variance-stabilizing) statistical functions such as Square Root/Log/Inverse.

D.1 Difficult to validate: Since the mis-formatted data is rampant, the major limitation with the application of Data Transformation using various methods is, one has to be very careful while interpreting the results of transformed data. Extreme, acute, replica or invalid values anomalies arising due to inconsistencies between integrated data sets, constitute the most tedious component of the analytic process, which may actually inflate the cost. Moreover, reformatting and validating data requires alterations that can be difficult to specify and evaluate.

D.2 High skilled labour: The process of transformation requires writing of distinctive scripts in programming languages like Python or R and exhaustive editing is also required using some interactive tool like spreadsheet etc. The usage of these statistical functions requires (automatic/not-by-hand) skilled knowledge of statistics in order to implement these tools in an informed manner.

D.3. Only for anomalous data: one must understand the data prior to performing analysis & must also know the need of exact data analysis technique to be used. Even though there are valid reasons to implement Data

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Transformation, still it is not advisable to deploy Data Transformation unless there is actually a valid, clear & legitimate reason [Osborne, J. (2005)]. The impact of particular type of transformation being used such as variations in spelling or formatting, measurement units, or coding schemes (e.g., names vs. abbreviations) should also be understood well. Such as in given tables, the FNAME and LNAME columns of table T1 are to be transformed (actually abbreviated or little variation in presentation) to match the NAME attribute of table T2.

IV. CONCLUSION AND FUTURE DIRECTIONS All of the data pre-processing techniques which may be applied on homogenously sourced database are listed in this paper. The application of these scientific and well proved techniques actually raises the quality of data, thus assisting to improve the efficiency and accuracy of text mining, web mining, ubiquitous mining and other data mining activities. Even though these techniques are well established, there are some technological limitations or personnel limitations which urge the usage of these techniques under keen surveillance. The problems which need the attention during implementation of each technique like data-cleaning, data-integration, datatransformation etc. has been presented in this article and these problems also constitute the list of key research issues to be brainstormed on. The inadequate data-quality checks on the process itself, insufficient testing on post-integration datasets, too loose definitions of fields or records, inconsistent interpretation or usage of formats, symbols or codes and finally lack of management attention towards effective pre-processing of data, always keeps the personnel in the vicious cycle of pre-processing.

Although various methods and techniques of data pre-processing have been urbanized, data pre-processing remains an energetic area of research. We believe that outcomes of this research revelation, if properly attended, will give significant strength and ease to the process of pre-processing the homogenously sourced database.

In this research, we have assumed that the database is piled up from the data collected from similar sources. The next step of our further efforts constitutes the investigation of huge semi-structured or unstructured data creeping in from the web sources which actually escorts the inconsistent or dirty data and also uplifts the complexity of the problem.

V. [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]

REFERENCES

Almarimi, A., & Pokorný, J., 2005, "Schema Management for Data Integration: A Short Survey". Acta Polytechnica, 45(1).

Ansari, A., Shafie, A. B., & Md Said, A. B., 2012, "Independent Component Analysis Using Random Projection For Data PreProcessing", International Journal of Computer Science Issues (IJCSI), 9(3).

Berka, P., & Bruha, I., 1998, "Discretization and grouping: Preprocessing steps for data mining". In Principles of Data Mining and Knowledge Discovery, Springer Berlin Heidelberg, pp. 239-245.

Ciszak, L., 2008, October, "Application of clustering and association methods in data cleaning". In Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on IEEE, pp. 97-103.

Gayathri, R., & Malathi, A., 2013, "Exploration of Data Mining Techniques in Record Deduplication". International Journal.

Halevy, A., Rajaraman, A., & Ordille, J., 2006, September, "Data integration: the teenage years", In Proceedings of the 32nd international conference on Very large databases, VLDB Endowment, pp. 9-16.

Kaloyanova, K. M., 2005, "Improving data integration for data warehouse: a data mining approach". In Proceedings of the International Workshop: COMPUTER SCIENCE AND EDICATION, Bulgaria, pp. 39-44.

Lenzerini, M., 2002, June, "Data integration: A theoretical perspective". In Proceedings of the twenty-first ACM SIGMODSIGACT-SIGART symposium on Principles of database systems, ACM, pp. 233-246.

Lim, E. P., Srivastava, J., & Shekhar, S., 1994, February, "Resolving attribute incompatibility in database integration: An evidential reasoning approach", In Data Engineering, 1994, Proceedings, 10th International Conference, IEEE, pp. 154-163.

Lup Low, W., Li Lee, M., & Wang Ling, T., 2001," A knowledge-based approach for duplicate elimination in data cleaning", Information Systems, 26(8), pp. 585-606.

Maletic, J. I., & Marcus, A, 2000, October, "Data Cleansing: Beyond Integrity Analysis", In IQ ,pp. 200-209.

Maletic, J. I., & Marcus, A, 2010, "Data cleansing: A prelude to knowledge discovery", In Data Mining and Knowledge Discovery Handbook, Springer US, pp. 19-32.

Prajapati, D. J., & Prajapati, J. H., "Handling Missing Values: Application to University Data Set", International Journal of emerging trends in engineering and development, 1.

Teng, C. M., 2001, May, "A Comparison of Noise Handling Techniques", In FLAIRS Conference, pp. 269-273.

Vora, P., & Oza, B., 2013, "Improved Data Reduction Technique in Data Mining".

Wang, K., Yu, P. S., & Chakraborty, S., 2004, November, "Bottom-up generalization: A data mining solution to privacy protection". In Data Mining, 2004. ICDM'04, Fourth IEEE International Conference on IEEE, pp. 249-256.

Witten, I. H., & Frank, E, 2005, "Data Mining: Practical machine learning tools and techniques", Morgan Kaufmann.

Zhu, B., He, C., & Liatsis, P., 2012, "A robust missing value imputation method for noisy data", Applied Intelligence, 36(1), 6174.

Ishikawa, K. (1985). What is Total Quality Control?-the Japanese Way. Englewood Cliffs, NJ. Prentice-Hall Wang, R. Y. and Strong, D. M. (1996). “Beyond accuracy: what data quality means to data consumers”. Journal of Management Information Systems, Vol.12, No.4, pp5-34.

Crosby, P. B. (1984). Quality Without Tears. New York: McGraw-Hill Book Company.

F. Naumann, Quality-Driven Query Answering for Integrated Information Systems Lecture Notes in Computer Science, LNCS 2261, Springer, 2002 Osborne, J. (2005). Notes on the use of data transformations. Practical Assessment, Research and Evaluation, 9, 42-50.

Dimensionality Reduction for Data Mining-Techniques, Applications and Trends by Lei Yu, Binghamton University Jieping Ye, Huan Liu, Arizona State University.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Variability of Rainfall in Bangalore city-Mapping of hourly variation of heavy rainfall event Roopa Bhandiwad Karnataka State Natural Disaster Monitoring centre Major Sandeep Unni Krishnan Road, Yelhanka, Bengaluru, Karnataka, INDIA Abstract: This paper deals with study of Rainfall characteristics of Bangalore city, the capital of Karnataka state. Taluk wise rainfall data for 50 years is analyzed for various statistical analysis. The paper also highlights the variability of rainfall in Bangalore city. Bangalore city has suffered during the 2014 rains.

The city witnessed a record breaking rainfall of 140mm /day during the month of October.. Variability of rainfall is better appreciated if the resolution of the rainfall measurement is enhanced. . Bangalore has been identified as one of its kind in the country to have established a dense network of 100 Telemetric Rain Gauge stations within 800 sq km city area to Monitor Rainfall. The Daily and hourly variation of one particular heavy rain event is displayed in this paper to appreciate the resolution of the rainfall data.

Key words: Rainfall, variability, Monitor rainfall, High rain event, Bangalore I. Introduction Variability in the rainfall pattern needs to be understood,as it it the key input for flood forecast and hydrological modelling . It is an important issue in the hydrological science (Atmospheric Research 137 (2014) 176–182). Bangalore was once called the gardenn city .Due to rapid urbanisation the city has transformed to a concrete bunch. Due to global warming and increased human activities ,the climate of the city is varying. The city used to experience a temperature from min 15 deg in winter to max of 33deg in summer.

The annual rainfall is about 920mm. The climatic condition now has gone beyond extremes, the city has suffered an extreme heat of 40deg during the April May in 2014 to a coldest of 10deg during october and November. Bangalore receives rainfall from both the northeast and the southwest monsoons and the wettest months are September, October and August.. The city received a record breaking rainfall of 140mm/ day during 2014 october which is more than 20% of the annual rainfall in a single day. Such a variability needs to be understood. Hence the present paper brings about the summary of the satistical variation and indicates the value of high resolution sensors to study the rainfall variability.

II. Materials and Methods II.1.Study Area The study area is the Bangalore urban district. the municipal corporation is called the BBMP. The statistical analysis is done for the Bangalore Urban district and the high resolution variability shall be displayed for the BBMP(city area) area. Bangalore is the capaital of Karnataka state in India.The Bangalore city has an area of 800 sq km.

Fig. 1: Map showing the taluks of Bangalore Urban and Bangalore BBMP (city)

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II.2.

Rainfall datra and Statistical Analysis for Historical data Daily Rainfall data is collected from 1963-2013 .For statistical charecteristics the taluk wise data for 50 years for Banglore North and Bangalore south is considered . In order to understand the daily and hourly variation of heavy rain events hourly data for the 2014 is considered.The entire data set is carefully checked and missing data is filled from the nearby stations.

Mean Vs Normals The mean monthly rainfall is calculated and compared with the normals.The following table gives the comaprision.

Taluk

Bangalore North

Month

Mean(mm)

Normal(mm)

Jan

1.213725

Feb

Taluk

Month

Mean (mm)

Normal(mm)

2

Jan

1.364

2

5.662549

7

Feb

6.172

8

Mar

10.84294

8

Mar

14.23167

10

Apr

50.07353

37.2

Apr

42.80505

39.6

May

109.7623

98.1

May

102.5917

110.3

Jun

85.46961

71

Jun

84.06147

72.9

Jul

115.4322

92

Jul

92.8494

99

Aug

134.8437

129.3

Aug

118.0824

117.3

Sep

177.808

170

Sep

173.5902

179.1

Oct

161.6351

152

Oct

147.3396

163.3

Nov

63.47078

50.8

Nov

60.0346

60.3

Dec

20.59957

15.1

Dec

14.67079

17.2

Bangalore South

Table 1: Table showing comparison of Mean and Normal rainfall Most of the rain water goes as surface runoff .Precipitation here is usually short duration , high intensity rainfall The rainest months are during september and october.The average rainfall recorded in a month is as given in the table :1. The maximum rainfall recorded during the month of october is 178.9mm and minimum is 9.6 mm as shown in tabe:2. Hence the rainiesat month is october.

Highest and Lowest Rainfall Month

Highest Rainfall(mm)

Lowest Rainfall (mm)

Jan

24.2

0

Feb

42.5

0

March

94.5

0

April

108.6

0

May

116.9

0.8

June

103.76

0

July

123.5

3.5

Aug

103

3.6

Sep

108.4

9.6

Oct

178.9

2.9

Nov

105.6

0

Dec

71.3

0

Table 2: Table showing highest and lowest rainfall recorded in particular months

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Annual Daily Maximum Rainfall (mm) Fig. 2: Histogram of Annual daily maximum Rainfall data Rainfall characteristics The annual rainfall varies from 1500mm to 473mm.The annual distribution of the rainfall is as shown in the histogram (figure no2). The mean value is 902 the standard deviation is 224 the distribution is positive skewed 0.16 and the Kurtosis is negative mean compared to the normal distribution the central peak is lower and a flat distribution. The number of annual rainy days of Bangalore district varies from 34 to 165 days as shown in Table3. Trend analysis (simple regression method) for fifty years (1963 to 2013) rainfall data shows that there is increasing trend of 2.98 i.e. 3 mm per year; However considering the last 13 years (2000-2013) the trend shows there is an increase 16mm per year. Figure 3 shows the Trend Analysis of Annual rainfall of Bangalore District.

The extreme rainfall events for the past 50 years is as shown in the table: no 4

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Fig. 3: Trend of Annual Rainfall Bangalore Urban District Highest daily Rain fall events

Bangalore North

11th Jan

1969

13.5

11th jan

1967

24.2

17th feb

2013

153

22-Feb

1986

42.5

19th April

2001

108

16th march

2008

94.5

26th May

2002

95

17th april

1971

64.4

1st june

2009

84.5

26th May

2002

116.9

18th july

1998

123.5

28th june

2013

103

Bangalore South 4th August

1998

92

22-Jul

1979

67.5

30-Sep

1963

91.9

12-Aug

1996

103

230ct

2005

120.7

14th sep

2007

108.4

16th nov

1991

105.6

1st oct

1997

178.9

5th Dec

1993

66.4

15th Nov

1991

103.6

5th Dec

1993

71.3

Table: 4 Table showing highest daily rainfall events

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Taluk bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north bangalore north

year 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Jan 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 1 3 0 0 0 1 0 0 0 0 3 0 1 0 0 0 1 0 0 2 1 1 0 0 0 0 0 1 0 19

Feb 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 3 5 0 0 0 0 6 0 3 0 1 0 0 0 0 0 1 0 0 0 0 1 3 0 0 1 1 1 0 0 3 0 0 2 0 6 40

March 0 0 2 0 2 1 1 0 0 0 0 1 1 0 2 0 0 2 1 0 0 4 2 0 1 3 3 1 0 0 5 0 1 0 6 1 0 0 1 0 4 2 1 6 0 11 4 1 1 2 4 77

April 6 1 3 1 3 2 2 3 1 2 3 6 2 6 3 6 2 3 2 0 2 4 6 4 6 16 4 4 10 4 5 2 5 9 6 7 7 9 12 1 9 12 12 5 6 4 7 7 10 6 9 267

May 5 5 3 5 3 2 3 8 10 5 9 12 12 3 12 5 10 9 7 6 9 9 7 11 8 10 9 12 12 8 10 12 13 10 4 6 18 12 6 10 2 14 12 16 9 14 10 13 13 12 12 457

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June 4 7 4 7 4 3 7 5 5 5 5 1 7 0 8 4 14 2 1 5 14 13 12 14 12 7 10 12 18 19 11 15 13 14 13 13 10 20 7 13 10 13 11 17 11 7 8 10 15 11 18 489

July 8 10 7 10 7 10 12 8 9 6 13 14 13 7 12 8 10 11 6 5 15 22 16 11 7 22 19 12 16 14 13 19 20 13 13 15 22 13 16 11 16 24 24 11 16 18 10 17 21 17 24 693

august 15 8 13 8 13 5 6 3 10 12 21 11 13 12 12 11 6 2 14 5 26 9 17 17 21 24 19 22 17 19 18 18 22 19 13 19 11 23 19 17 22 19 19 17 18 26 11 18 28 20 27 795

Sep 11 7 6 7 6 11 3 15 6 13 13 12 13 6 11 14 10 10 13 6 21 17 20 17 18 21 15 10 16 15 14 9 18 20 13 20 15 16 17 9 12 20 17 11 16 9 20 14 15 15 25 688

oct 11 7 1 7 1 8 7 6 10 11 17 11 13 5 18 3 8 8 13 4 9 12 8 8 15 6 9 12 12 17 19 22 19 12 10 17 21 15 15 13 16 20 24 9 15 20 6 11 17 17 20 615

Nov 1 8 2 8 2 8 2 3 5 5 4 3 4 9 13 8 6 6 2 3 4 7 9 8 13 2 3 3 5 10 7 8 2 2 18 10 6 3 6 4 3 8 14 7 6 8 13 12 10 9 15 337

Dec 2 1 4 1 4 3 5 1 3 5 6 0 1 1 0 6 0 0 3 0 8 2 3 2 8 3 1 2 0 0 4 3 0 11 7 7 4 3 4 1 1 0 7 1 6 1 3 3 7 6 5 159

Tota; Rainy days 63 54 45 54 45 53 49 52 59 64 91 71 80 50 93 68 71 53 62 34 108 107 101 98 109 115 92 91 106 106 106 109 116 110 104 115 115 117 104 79 96 135 143 101 103 121 92 106 139 116 165

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1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 2 1 3 0 0 0 1 0 0 0 0 3 0 1 0 0 0 1 0 0 2 1 1 0 0 0 0 0 1 0 19

0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 3 5 0 0 0 0 6 0 3 0 1 0 0 0 0 0 1 0 0 0 0 1 3 0 0 1 1 1 0 0 3 0 0 2 0 6 40

0 0 2 0 2 1 1 0 0 0 0 1 1 0 2 0 0 2 1 0 0 4 2 0 1 3 3 1 0 0 5 0 1 0 6 1 0 0 1 0 4 2 1 6 0 11 4 1 1 2 4 77

6 1 3 1 3 2 2 3 1 2 3 6 2 6 3 6 2 3 2 0 2 4 6 4 6 16 4 4 10 4 5 2 5 9 6 7 7 9 12 1 9 12 12 5 6 4 7 7 10 6 9 267

6 10 2 6 6 14 12 14 14 9 3 13 15 4 18 8 8 13 10 7 7 5 8 10 5 7 7 9 13 11 8 9 11 8 7 6 14 13 4 12 2 16 12 10 10 14 16 18 11 10 10 495

11 11 8 12 10 15 7 11 2 12 15 13 15 6 13 14 18 10 4 11 15 13 13 13 11 3 11 16 18 19 12 10 12 14 18 10 11 16 10 9 9 14 11 15 11 7 13 10 15 7 16 600

14 17 8 17 17 22 18 15 15 10 14 18 20 18 17 18 11 14 11 12 14 21 13 11 5 19 16 14 14 8 15 19 17 10 16 14 13 11 12 9 14 19 22 9 14 19 11 19 21 12 22 759

22 14 19 12 13 15 15 17 15 13 18 16 20 20 13 19 11 15 20 12 24 9 12 18 18 23 18 17 18 20 17 15 20 15 18 20 11 25 16 11 20 17 15 12 18 19 12 17 26 19 25 864

10 17 5 20 8 22 10 11 11 15 15 17 16 9 13 13 23 13 17 8 19 14 19 20 12 17 15 8 17 12 10 7 14 22 15 17 13 15 16 9 9 19 15 9 14 8 22 16 13 10 22 721

11 8 2 15 7 12 14 12 5 12 14 12 21 4 19 13 7 9 15 5 10 11 8 6 15 6 7 11 10 14 22 21 15 14 11 14 20 13 13 12 12 20 20 8 15 20 8 14 16 15 15 633

7 11 10 10 5 6 10 6 2 1 3 5 8 14 14 6 19 8 7 8 1 5 7 0 11 2 3 8 8 9 10 7 1 4 16 10 6 3 4 5 4 6 15 9 4 6 17 11 9 12 15 388

7 2 7 0 5 6 7 1 2 5 5 0 1 1 0 9 0 1 5 0 6 2 1 0 13 3 1 2 0 2 4 3 0 10 5 6 3 4 3 1 1 0 7 0 6 0 4 3 1 4 7 166

94 91 66 93 76 115 97 90 67 79 90 101 120 83 114 109 104 88 92 63 98 96 90 88 97 100 85 91 108 99 108 94 99 106 119 105 99 112 92 69 85 128 132 84 98 111 114 116 125 98 151

Table 3: Table showing number of rainy days From the above table it is evident that there is increase in the rainfall during the recent years and due to climate change there is a large variability in the rainfall pattern.

III. High rain event a case study_Bngalore city (BBMP) From 2009 onwards Rainfall data is available for every 15min interval for more number of stations. The increase in the variability of rainfall calls for a demand for high resolution data to better understand the phenomenon, as rainfall variability has key applications.

Network of Rain gauge stations in Bangalore BBMP We have initiated a project in association with civic authorities to monitor, model and forecast urban flooding for Bangalore. Rainfall is being monitored through 100 GPRS enabled Telemetric Rain Gauges at a density 1 to 8 sq km area in the city which is a unique model in the country. Meso-scale Rainfall forecast and high intensity and heavy rainfall alerts are being generated and sent to the concerned authorities at near-real time.

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Fig. 3: Map of BBMP (Bangalore city) with network of 100 Telemetric rain gauges) The recent climatic changes has led to catastrophic rains in Bangalore city. During the year 2013 &2014 the city rainfall has exceeded the records of the history. The sudden flash floods and iadequate drianage capacity has inundated the city completely. This has caused loss of life and property.Fig no:4

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Fig. 4: Newspaper clipping of recent rains during 2014 October IV. High Rain event _23rd sep 2014 for Bangalore BBMP A heavy rain event on September23rd is considered and the daily rainfall (8.30a.m to 8.30am) on that day is as shown in fig: 5 the map indicates the variation of the daily rainfall throught the city. A table of Rainfall values in the same fig is indicating the values crossing 35.5mm of rain on that day. Any rainfall value crossing 35.5mm is considered as rather heavy rain, indicated in light blue colour.Any rainfall value exceeding 64.5mm is a heavy rain.

On 23rd sept more than 30% of city experienced heavy rains. The hourly variations of the same rain is displayed below.

Fig. 5: Daily rainfall map from 8.30a.m of 22/9/2014-8.30a.m of 23/9/2014

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8.30-9.30 am of 23rd Sep

9.30-10.30 am of 23rd Sep

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10.30-11.30 am of 23rd Sep

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11.30-12.30 am of 23rd Sep

12.30-1.30 pm of 23rd Sep

1.30-2.30 pm of 23rd Sep

Fig. 5: Hourly rainfall maps from 8.30a.m of 23/09/2014 to 2:30 p.m of 23/09/2014

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2.30-3.30 pm of 23rd Sep

3.30-4.30 pm of 23rd Sep

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4.30-5.30 pm of 23rd Sep

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5.30-6.30 pm of 23rd Sep

6.30-7.30 pm of 23rd Sep

7.30-8.30 pm of 23rd Sep

Fig. 5: Hourly rainfall maps from 2.30p.m of 23/09/2014 to 8.30p.m of 23/09/2014

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8.30-9.30 pm of 23rd Sep

9.30-10.30 pm of 23rd Sep

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11.30-12.30 pm of 23rd Sep

12.30-1.30 am of 24th Sep

1.30-2.30 am of 24th Sep

Fig. 6: Hourly rainfall maps from 8.30 p.m of 23/09/2014 to 2:30 a.m of 24/09/2014

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3.30-4.30 am of 24th Sep

2.30-3.30am of 24th Sep

5.30-6.30 am of 24th Sep

6.30-7.30 am of 24th Sep

4.30-5.30 am of 24th Sep

7.30-8.30 am of 24th Sep

Fig. 7: Hourly rainfall maps from 2.30p.m of 24/9/2014 to 8.30a.m of 24/09/2014

Observations It is to be noted that many such rain events are studied during the monsoon, the studies mainly reveal that the variability does exists during the rains, rainfall in the city occurs mainly during the evening to late night hours.

The core built up and the law laying areas are inundated quickly.the rainfall intensity is more in the core areas than compared to the outskirts.The reason for such high intensity short duration rains are being studied.some of the heavy rain event maps are displayed below

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Conclusion The statistical analysis was carried out with fewer raingauge data for the Bangalore district , highlighted tthe rainfall variability . Further the higher resolution sensor data and a larger network of the data prooved that the city is highly suceptlable to rainfall variability.

References [1] [2] [3] [4] [5]

Wikipedia (Atmospheric Research 137 (2014) 176–182) Master plan BBMP Ahasan, M.N., Chowdhary, M.A.M., Quadir, D.A., 2010. Variability and trends of summer monsoon rainfall over Bangladesh. J. Hydrol. Meteorol. 7 (1), 1–17.

Ahasan, M.N., Chowdhary, M.A.M., Quadir, D.A., 2011. Simulation of a heavy rainfall event on 14 September 2004 over Dhaka, Bangladesh using MM5 Model. J. Sci. Res. 3 (2), 261–270.

Acknowledgements KSNDMC: Karnataka State Natural Disaster Monitoring Centre BBMP: Bruhat Bengaluru Mahanagara Palika

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Intuitionistic Z-Numbers G Velammal1, M Shahila Bhanu2 Associate Professor, Department of Mathematics, Sri Meenakshi Government Arts College for women Madurai, Tamil Nadu, INDIA

(1&2)

Abstract: In 2011 Zadeh introduced the concept of Z-number. Z-numbers look deceptively simple but they embody a subtle and a very useful technique in fuzzy logic. In this paper we introduce a type of Z-numberthe intuitionistic Z-number and describe the operations on intuitionistic Z-numbers .

Keywords: Z-number, Z-valuation, Fuzzy event probability, Restriction, Intuitionistic number Fuzzy set, Intuitionistic fuzzy set, .

I. Introduction In 2011 Zadeh introduced the concept of Z-number. Z-numbers look deceptively simple but they embody a subtle and a very useful technique in fuzzy logic. In particular they play a significant role in the field of “computing with words”. Ronald R Yager [5] has made a study of Z-numbers and Z-valuations. Shankar K.Pal et al. have provided insight about how Z-numbers can be applied in the field of "computing with words"[3].

In his 2011 paper Zadeh had mentioned that Z-numbers could be generalized in many ways. In this paper we introduce a generalization of Z-number-the intuitionistic Z-number.

II. Preliminary Definitions Definition 1: Fuzzy Set A fuzzy set A defined on a universe X may be given as: A= where : X [0,1] is the membership function of A. The membership value describes the degree of belonging of in A [8]..

Definition 2: Intuitionistic Fuzzy Set (IFS) An intuitionistic fuzzy set A, on a universe X is defined as[2] A = {( (x)) / }, where (x) 1 For each , and (x) are called the degree of membership and the degree of non-membership, respectively of x to A. Definition 3: Restrictions A restriction may be viewed as generalized constraint. Suppose A is a fuzzy set. The statement R(X) : X is A is referred to as a possibilistic restriction ([11],[9]).Here A plays the role of possibility distribution of X. The statement "R(X): X is A" is to be understood as" The Possibility (X=u) is "where is the membership function of A and u is a generic value of X. When X is a random variable, the probability distribution of X plays the role of probabilistic restriction on X ([9],[4]).

A probabilistic restriction is expressed as: R(X) : X isp f, where f is the p.d.f. of X. That statement "R(X) : X isp f " is to be understood as" Prob(u x u+du) = f(u)du" Definition 4 : Fuzzy event probability[10] Let X be a random variable taking real values and A a fuzzy set defined on the real line. The fuzzy event probability of " X is A" is FEP(x A) = p(u)du Definition 5: Z-number Zadeh defines Z-number as follows[6] A Z-number is an ordered pair of fuzzy numbers Z = (A,B), associated with a real-valued uncertain variable X, with the first component A, a restriction on the values which X can take and the second component B, a measure of reliability of the first component.

Definition 6: Z-valuation A Z-valuation is an ordered triple (X,A,B) where A and B are fuzzy numbers[6]

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A Z-valuation is equivalent to an assignment statement "X is (A,B)", where X is an uncertain variable' A is a restriction on the values which X can take and B is referred to as certainty.

Z-valuation (X,A,B) may be viewed as a restriction on X defined by FEP(X is A) is B More explicitly Possibility( FEP(xÎľA) = u ) = Definition7: Z-restriction A Z-restriction is expressed as R(X) : X isz Z where Z is a combination of possibilistic and probabilistic restriction ([11],[9]) defined as Z : EEP(X is A) is B in which A and B are fuzzy numbers.

Equivalently, a Z-valuation (X,A,B) is a Z-reatriction on X. (X,A,B) X is (A,B) Extension Principle A more general version was described in[12]. In this version, we have R(X): g(X) is A (constraint on u is R(Y): (g(u)) subject to

(g(u)))

For a function with two arguments, the extension principle reads: R(X): g(X) is A (constraint on is R(Y): h(Y) is B (constraint on is R(Z): (g(u)) subject to

(h(u))) = min

III. Intuitionistic Z-number Definition An ordered pair (A,B) is said to be a intuitionistic Z-number (IFS Z-number) if A is a fuzzy set defined on the real line and B is a intuitionistic fuzzy set defined on the the interval [0,1].

Z-valuation of IFS Z-number Let X be a real random variable and (A,B) be an IFS Z-number. Let A(x) be the membership function of A. Let B(x) be the membership function of B, and b(x) be the non-membership function of B .Then (the ordered triple (X,A,B) is a Z-valuation which is equivalent to the following statement: FEP(X is A) is B More explicitly we may write: Possibility(FEP(X is A) =u) = B(u) Impossibility(FEP(X is A) = u) =b(u) IV. Operations on IFS Z-numbers Let X,Y be random variables.Given the IFS valued Z-valuations (X,A,B) and (Y,E,F) and an operation the problem is to determine the Z-valuation of X Y is as follows: Let p(x) and q(x) be the underlying probability density functions of X and Y respectively. The known information may be summarized as ( ( ( ( Notice that p,q are not known. Also and .

Let us denote the IFS valued Z-valuation of W by (W,G,H), and the underlying probability density function of W by . The membership function of G is determined by application of Extension Principle: where the supremum is taken over all u,v such that The next step is to determine the possibility distribution of

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. .Note that

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We shall denote the above equation by

. By applying the extension principle we have

Also we have It is clear that .

Let and By the definition of an IFS we have

Without loss of generality we may assume Also we have So for all

.

Therefore So ) That is This inequality proves that and satisfy the requirement to be the membership and non-membership functions of an IFS. H-the membership function and h-the non-membership function of the intuitionistic fuzzy set H are determined to be where the supremum is taken over the set where the supremum is taken over the set Again it is clear that Using we get

for all

That is

for all

So That is Hence the pair of functions H(t), h(t) define an IFS. This concludes the theoretical procedure for computing the IFS valued Z-valuation of .

V. Conclusion In this paper we have introduced a novel type of Zadeh’s Z-number. The procedure for computation with IFS Znumbers has also been given.

VI. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

K.Atanasov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1) (1986) 87-96.

K.Atanassov and S.Stoeva, Intuitionistic fuzzy sets, Proc. Of Polish Symp. On Interval and Fuzzy Mathematics, Ponzan(Au.1983) 23-26 S.K.Pa;eta;. An Insight Into The Z-number Approach To CWW,Fundamental Informatica 124 (2013) 197-229.

Populis.A: Probability, Random variables and Stochastic Processes. McGraw-Hill, New York (1965).

Ronald R. Yager, On a View of Zadeh Z-numbers, pp. 90-101,2012.

L.A.Zadeh, A note on Z-numbers, Information Science 181, 2923-2932 (2011).

L.A.Zadeh : Computing with words – Principal concepts and Idea- Springer,Berlin(to appear).

L.A. Zadeh. Fuzzy sets.Information control and Control 8 (1965) 338-353.

L.A.Zadeh, 1978. Fuzzy sets as a basis for a theory of possibility.Fuzzy Sets and Systems.13-28.

L.A.Zadeh,Probability measures of fuzzy events, Journal of Mathematical Analysis and applications,10,421-427(1968).

L.A.Zadeh , Toward a restriction-centred theory of truth and meaning, Information Science 248 (2013) 1-14.

L.A.Zadeh.The concept of a linguistic variable and its application to approximation reasoning, Part I: Informatiom Science 8 (1975).

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Interchangeability of Motor-Cycle Parts for Maintenance Purposes 1

1

Joseph W. and 2Chindapi N. Department of Mechanical Engineering, Technical Skill Acquisition Center Mubi, Adamawa State, Nigeria.

2 Department of Mechanical Engineering, Federal Polytechnic Mubi, Adamawa State, Nigeria

Abstract: Manufacturers of motor cycle parts, usually supply the motor cycle assemblers with motor cycle parts of same specification. Thus these parts can be interchanged between different makes, series, and brands of motor cycle during maintenance. This study is being prompted by the wide spread counterfeit, high cost and scarcity of motor cycle parts. The study covers motor cycle families such as: HONDA; SUZUKI; KAWASAKI; and YAMAHA which are commonly used in the North-Eastern Nigeria. Honda CG and CGL (125) shows high degree of parts interchangeability, while Yamaha YB50, 80,100 and 125(single and double exhaust) has limited parts interchangeability. Suzuki has parts interchangeability with exception of A100; and Kawasaki also shows high degree of parts interchangeability with exception of GTO-125.

Significance: The study is intended to reveal and address some of the major problems faced by motor cycle mechanics and motor cycle users; were spare parts demands amidst acute scarcity or obsolesce is glaring.

Parts interchangeability is therefore the necessary solution if such a motor cycle must be road worthy all the time.

Key words: interchangeability, motor cycle, Spare parts scarcity, & maintenance.

I. INTRODUCTION Most motor cycle companies are by virtue of practice, source their parts or components from general manufacturer. The manufacturer may design the components and market them, or manufacture based on design provided by the motor cycle assemblers. For instance, Tiger motor cycle parts Ltd supplies Piston and Connecting rod to China motor cycle manufacturers (Tiger Ltd, 2000).

Hence, there is possibility of using a component, or some on different makes of motor cycle. A typical single cylinder or double cylinder four-stroke cycle motor cycle is assembled from about 15,000 different parts (including bolts and nuts ). Some of these components are universal parts, which readily fit into different makes and brands of motor cycle. Some components will fit other makes with some little modification (Michael, 2010).

This work is therefore targeted at identifying parts of same Makes and Brand of motor cycle that are common to Series, brands, or models. These arise from the need to keep some models serviceable when acute shortage, high cost or counterfeiting of parts occur.

II. THE SCOPE OF THE STUDY The scope covers most popularly used motor cycle brands in North eastern states of Nigeria. These include:-Honda Family: (a). CB50, C50, C70, C75, JH50, CD100, CD110, CD125. (b). CG/CGL125, CGL150 -Suzuki Family: A50, A100, K50, K90, AX100, K125, B120, B200 (super double cylinder).

-Kawasaki Family: KH100, ELX100, KC100, GTO 125 -Yamaha Family: YB50, YB80, YB100, YB125 (single and double exhaust), M50, M80, M90.

Some major components in the engine, power train, suspension and break system were covered; but since it could not be possible to report all these findings in a single paper, the cylinder bock, cylinder head, valve springs, piston and piston rings, connecting rod, crank shaft, disc clutches, and gear trains have been selected, and covered as an example. Some motor cycle parts despite their differences in materials used for manufacturing their parts (e.g., carbon content, plastics in place of metal, etc.) may be similar in terms of dimension. Though might affect the durability, performance and fuel economy of the engine, interchangeability may be vital advantage during acute parts scarcity.

III. MATERIALS AND METHOD A. Data collection method Being a new study area, relevant information is scarce. A search through some existing data from motor cycle manufacturer’s manual, maintenance manual, automotive council of Nigeria, and assemble components yielded little result. Thus, decision to search through service manuals, parts manuals, and trade literatures were made.

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Questionnaires were also administered to spare parts dealers, motor cycle assembly companies, and motor cycle mechanics. Some standard measuring instruments such as micrometer screw gauge and vernier caliper were also used to validate the information obtained from the questionnaire. 200 questionnaires were randomly administered as shown in table (1) below.

Table (1): population and sample size Population Sample size -Spare parts dealers 50 -Motor cycle dealers 40 -Motor cycle assembly companies 30 -Motor cycle mechanics 80 Total 200 The recipients all responded, and questions asked include: -Respondent background, whether technical or not technical.

-Types and models of motor cycle spare parts sold.

-Types and model of motor cycle repaired.

-List of spare parts which can fits different makes, and models of motor cycle sold or repaired.

-Is there need for modification, or requires any adjustment for parts to be interchanged.

-What are the effects of parts interchangeability on the performance, durability, and fuel economy of motor cycle(s)?

IV. RESULTS AND DISCUSSION For the purpose of this paper, the following components which frequently fail have been selected to illustrate the result. These are: The Engine block, cylinder heads, valves springs, piston and piston rings, connecting rods, crank shaft, gear trains and disc clutches.

A. ENGINE BLOCK

This is the most complex castings in motor cycle parts which consist of the cylinder barrel, oil galleries, the aircooled fins (Wikipedia, 1959; Michael, 2010). This category of Engine block is made from Aluminium which for easy dissipation of heat. Failure in the engine block occurs due to excessive wear of the cylinder bore which mostly, is reground to lager diameter (bore). Differences are noticed in cylinder bores, attachment point of gear systems, and engine mounting for different models, makes and series.

A.1.

Honda Family Irrespective of their makes or model, perfect interchangeability exist between CG125, CGL125 and CG150; as well as CB50 and C50; and C70, C75 & JH70 respectively. While the block engines of CD100, CD110, & CD125 cannot be interchanged.

A.2.

Suzuki Family The engine block for A100, & AX100; K50 & K90 are un-interchangeable. But AX100 & MAX100; K50, & K90 are perfectly interchangeable irrespective of make or model. K125, B120, & B200 super (double cylinder) has great differences in their engine blocks.

A.3.

Kawasaki Family KH100, ELX100, & KC100 block engines are perfectly interchangeable; But GTO125 engine block can’t fit any of its family members.

A.4.

Yamaha Family YB50, YB80, YB100 & YB125 (single or double exhaust) engine blocks are un-interchangeable.

Yamaha mate: M80 & M90 engine blocks are interchangeable with little modification; while M50 cannot be interchanged with any of its family members.

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B. CYLINDER HEAD Depending on the model/make, this bears the valves, valve mechanisms, spark plugs, cooling fins, and oil lubrication galleries. It is normally bolted to engine block with gasket between. The bolt positions differ from one model or make to others (Pag, 2005).

(a) Honda motor cycle cylinder head

(b) Suzuki motor cycle cylinder head

B.1.

Honda Family Perfect interchangeability of the cylinder head exists between CG125, CGL125 and CG150; as well as CB50 and C50; and C70, C75 & JH70 respectively. While the cylinder heads of CD100, CD110, & CD125 cannot be interchanged.

B.2.

Suzuki Family A100 & AX100; K50 & K90 cylinder heads are un-interchangeable. But AX100 & MAX100; K50, & K90 are interchangeable. K125, B120, & B200 super (double cylinder) cylinder heads are not interchangeable.

B.3.

Kawasaki Family The cylinder head of KH100 & ELX100 are perfectly interchangeable; While KC100 and GTO125 are uninterchangeable with any of its family members.

B.4.

Yamaha Family YB50, YB80, YB100 & YB125 (single or double exhaust) cylinder heads are un-interchangeable. Yamaha mate: The cylinder heads of M80 & M90 are interchangeable; while M50 cylinder head is not interchanged with any of Yamaha mate brands.

C. VALVE SPRINGS The valve springs are compression springs meant for controlling the opening, and closing of the inlet and outlet valves. Made from spring steel wire of 4mm diameter, 20mm spring diameter, with up to 6 coils per spring and stiffness of up to 6KN/m. Depending on the type, model, and make of motor cycle, the springs are used as single or double spring. If two springs are used, springs of opposite helix direction are used per valve to avoid bouncing and jamming of the spring during operation (RCAR, 2009).

C.1.

Honda Family CG125, CGL125 and CG150 have two springs per valve and are interchangeable; CB50 and C50; and C70, C75 & JH70 valve springs are interchangeable. The valve spring of CD100, CD110, & CD125 are also interchangeable.

C.2.

Suzuki, Kawasaki, and Yamaha Families These motor cycle families does not have valves and valve springs; instead, utilizes ports (inlet and outlet) for administering charged fuel into the combustion chamber and expelling exhaust gas out of the chamber.

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D. CAMSHAFT This is a mechanism which is used in controlling the opening and closing of the valve at a specific time. It is mostly situated in the top cylinder having two lobs per cylinder; and driven by timing chain at half the speed of the crankshaft. Irrespective of its profile, all motor cycle camshaft are situated in the top cylinder; mostly acting on the rocker arm without push rod (Michael, 2010).

D.1.

Honda Family CG125, and CGL125; C70, C75 and JH70 have mostly interchangeable cam shaft; While CGL150; CD100, CD110, and CD125 have un-interchangeable camshaft with two above.

D.2.

Suzuki, Kawasaki, and Yamaha Families These families of motor cycles does not have camshaft since there is no valve to operate.

E.

CRANKSHAFT

The crankshaft through the connecting rod converts the reciprocating motion of the piston into rotary motion.

The motor cycle crankshaft is forged from grey cast Iron or forged steel. Mounted on the engine block at its main journals and to the connecting rod at the crankpin. Mostly, the motor cycle crankshaft has one crankpin.

The rotary motion is transmitted to gear system through the clutch system. If fitted accurately, does not fail easily; but if it does, there is no provision for re-grounding as in the case of automobile. Its crankshaft weight balancing can be separated from the crankpin. Differences occur in the crankpin, basic configuration and diameter of journals (Vadervel ltd., 1988).

D.1.

Honda Family The crankshafts of: CG125, CGL125 and CGL150 (30mm diameter) are perfectly interchangeable as well as CB50, and C50; C70, C75, and JH70. CD100, CD110, and CD125 have un-interchangeable crankshafts.

D.2.

Suzuki Family The crank shafts of: A100, AX100, and MAX100; as well as A50, K50 and K90 perfectly interchangeable. The crank shafts of: K125, B120 and B200 (super) are un-interchangeable.

D.3.

Kawasaki Family KH100, ELX100 and KC100 have perfectly interchangeable crankshafts. But crank shaft of GTO125 is an integrated type, which is un-interchangeable.

D.4.

Yamaha Family The crank shafts of Yamaha motor cycles are un-interchangeable, except for Yamaha mate: M80, and M90 brands.

E.

PISTON

This is the device which by its reciprocating motion (up and down), compresses the air fuel mixture thereby converting the chemical energy of the expanding gases to mechanical energy (Power). It is made from

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aluminium for easy heat dissipation in the cylinder walls. Cylindrical in shape and prevent the lubricating oils from entering the combustion chamber and minimizes blow-by of the combustion gases. The differences usually occur in the bore diameter and the height; Standard and re-bored sizes are available (A.E. Engine parts ltd, 1989).

E.1.

Honda Family CG125, CGL125, and CGL150 have a standard size of 56.5mm, and the re-bored size of 58mm diameter respectively; the pistons are perfectly interchangeable. C70, C75, and JH70 have an average piston size of 47mm diameter and are interchangeable. The pistons bore of CD100 (50mm); CD110 (52mm); and CD125 (54mm) and are not interchangeable.

E.2.

Suzuki Family The Piston bore for A100, AX100, and MAX100 is averagely 50mm (Standard) and 51mm (0.50 re-bored) are interchangeable; K50, K90, and A50 have piston bore of 52mm and are interchangeable as well. Great variation of piston sizes exist in K125 (62mm), B120 (60mm), and B200 super (65mm); and are un-interchangeable.

E.3.

Kawasaki Family KH100 and ELX100 piston are perfectly interchangeable, but varies greatly with KC100.

E.4.

Yamaha Family Interchangeability of piston is perfectly available only between Yamaha mates: M50, M80, and M90 respectively.

F. PISTON RINGS The piston rings are mounted on the piston grooves just as an internal circlip arrangement. The ring arrangement comprises of two compression rings on top and oil rings at the bottom. It allows sliding of the piston up and down with little friction; preventing the lubricating oil from getting to the piston crown. The compression rings are made from cast iron, and chrome plated for wear resistance. This one of motor cycle parts with high degree of interchangeability with little modification by packing with shims.

F.1.

Honda Family CG125, CGL125, and CGL150 are the same; CD100, CD110, and CD125 are perfectly interchangeable; CB50, and C50 are also interchangeable.C70, and C75 has same piston rings, but has variation with JH70.

F.2.

Suzuki Family The piston rings of A100, AX100, and MAX100 are the same; K90, K50 and A50 piston rings are interchangeable. K125 and B120 has interchangeable piston rings; but the piston ring of B200 varies from its Brands, and interchangeable with Kawasaki family.

F.3.

Kawasaki Family The piston rings of KH100, ELX100, KC100 and GTO125 are interchangeable with little modification.

F.4.

Yamaha Family The piston rings of: YB50 and M50; YB80, M80, and M90; as well as YB100, YB125 are interchangeable respectively.

G.

CONNECTING ROD

Motor cycle connecting rods are mostly made of alminium alloys, with two ends for attachments. During assembly, the big end is connected to Crankshaft using plain bearings; while the small end with help of gudgeon pin is connected to the piston. This permits easy reciprocation of the piston which is converted to rotary motion.

The length and size of the big and small ends of connecting rods accounts for its differences.

G.1.

Honda Family The connecting rods of CG125, CGL125 and CGL150 are highly interchangeable. CD100, CD110, and CD125 connecting rods also exhibit interchangeability; as well as CB50, and C50. The connecting rods of C70, C75, and JH70 are un-interchangeable.

G.2.

Suzuki Family The connecting rods for: A100, AX100, and MAX100 are the same; Likewise K50, and A50.

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G.3.

Kawasaki Family KH100, ELX100, and KC100 have same connecting rod; but GTO125 has an integrated piston and connecting rod assembly, hence un-interchangeable with any brand of motor cycle.

G.4.

Yamaha Family The connecting rods of YB50, and M70; YB80, M80 and M90 have interchangeability connecting rods; but YB100, and YB125 connecting rods are not interchangeable with any model or brand.

H.

CLUTCH PLATE and DISC

H.1.

Honda Family The clutch plates and clutch disc of CG125, and CGL125 are perfectly interchangeable; but varies with those above. CD100, and CD110 has perfectly interchangeable clutch plates/disc; as well as CB50, and C50. The clutch plates and clutch discs of C70, C75 and JH70 are also perfectly interchangeable.

H.2.

Suzuki Family The clutch plates, and discs of A100, AX100, and MAX100 are perfectly interchangeable. A50 and K90 have interchangeable clutch plates and disc; as well as K125, B200.

H.3.

Kawasaki Family The clutch plates and disc of KH100, ELX100, KC100 and GTO125 are perfectly interchangeable.

H.4.

Yamaha Family The clutch plates and clutch disc of YB80, and YB100 are interchangeable. Mate: M50, M80 and M90 have perfectly interchangeable clutch plates and clutch disc.

I. GEAR TRAIN I.1.

Honda Family CG125 and CGL125 have perfectly interchangeable gear system, but vary to great extent with CGL150. The gear trains of CD100, and CD110; CB50, and C50; and C70, C75, and JH70 are perfectly interchangeable.

I.2.

Suzuki Family The gear trains of A100 despite its selection methods are interchangeable with AX100, and MAX100. A50, and K90 gear trains are interchangeable; as well as K125, and B200.

I.3.

Kawasaki Family KH100, ELX100, and KC100 have perfectly interchangeable gear trains.

I.4.

Yamaha Family There is perfect interchangeability of the gear trains of YB80, and YB100; Mate: M50, M80, and M90 are also interchangeable.

V. CONCLUSION Models and wide range of spare parts to be stocked is experienced from different product of Yamaha motor cycle because of the un-interchangeability of parts/ components of different models. Suzuki and Honda motor cycle families are regarded as the mechanic’s favorite because of their wide range of parts interchangeability and parts simplicity. The possibility of interchangeability between models of motor cycles is a strong factor that has helped the success of Suzuki and Honda in Nigeria. Thus any motor cycle manufacturer wishing to make strong hold of market in Nigeria must first consider wide range of parts interchangeability to compete with the existing brands, the cost of spare parts of new model in terms of maintenance and part procurement. It also aid high commercial use of these motor cycles with high interchangeabilities for fleet operations (within local areas). Hence decision on which model, brand or type of motor cycle to purchase is also made simple. Therefore this result tends to suggest for universal motor cycles with un-restricted part interchangeability.

REFERENCES [1] [2] [3]

Tiger Ltd., 2001: Professional Supplier/Manufacturers of motor cycle part, China.

Michael Tomas, 2010: Big Chief Indian Motor cycle engine.

Wikipedia, 1959: Free encyclopedia.

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Joseph W. et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 9(2), December 2014February 2015, pp. 143-149 [4] [5] [6] [7] [8] [9] [10] [11]

Johnny Pag motor cycles Ltd., 2005: Service Schedule, part list and owner’s Manual.

Research Council for Automobile Repairs, 2009: Motor cycle Design guide to minimize damage and facilitates Repair in low speed or stationary falls.

Vadervel Ltd., 1988: Engine bearings catalogue, Maidenhead England.

A.E.Engine parts Ltd., 1989: Cylinder components catalogue, England.

CGL 125, 2004: Motor cycle owner’s manual.

Jincheng group, 2003: Operation manual AX100 series; China.

Suzuki, 2010: Motor cycle owner’s manual (100-B, 100, 125-3, 125-8A, 150).

JINAN QINQI motor cycle Ltd., 2010: Model QS 90, part catalogue.

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Signal Processing for Music Analysis Poonam Priyadarshini Department of Electronics and Communication Birla Institute of Technology Mesra, B.I.T. Patna, Bihar, INDIA Soubhik Chakraborty Department of Applied Mathematics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, INDIA Abstract: Music information retrieval (MIR) is an emerging research area for both the research community and music industry. It retrieves music by querying or humming from large music database. People searched music of their choice from database by song title, composer and performer. Features of interest may include melody, harmony, rhythm, and instrumentation. Classification of music is done as mood classification, artist recognition, instrument recognition, etc. This paper discuss thedifferent methods of features extraction of music signal .In this paper we are using Indian music Raga as a database .

Keywords: Music information retrieval (MIR), Linear Predicative Coding coefficients ( LPC), Mel frequency cepstral coefficient (MFCC).

I. INTRODUCTION WITH the development of information and multimedia technologies, digital music are available from different media, like radio broadcasting, digital storage such as compact discs (CDs), the Internet, etc. Music information retrieval (MIR) is an emerging research area in multimedia to cope with such necessity. A key problem in MIR is classification, which differentiate each song based on genre, mood, artists, etc. Music classification is an interesting topic because most end users may only be interested in certain types of music. Music listeners often wish to access and use their music collections according to salient features of music in the audio recording.

Features of interest of listeners may include melody, harmony, rhythm, and instrumentation in music. The user can search for the music they are interested from the classification system. On the other hand, different category of music have different properties. We can manage them more effectively and efficiently once they are categorized into different groups.

Most existing work in music information retrieval analyzes music via low-level features such as Mel frequency cepstral coefficient (MFCC) and other spectral coefficients. However, low-level features are insufficient for many applications since they are related to the signal characteristics rather than the semantic content of music.

On the contrary, mid-level features such as chord, rhythm, and instrumentation represent musical attributes and contain rich information for music analysis.

One of the most important mid-level features of music.is chord sequence, which describes harmonic progression and tonal structure of music. Since harmonic progression is strongly related to the perceived emotion, similar chord sequences can be observed in songs that are close in genre, emotion, etc. With chord sequence, songs that are similar in various aspects can be identified and retrieved more effectively.

At the same time, content-based retrieval technology is developing as hot topics, such as content-based image retrieval, content-based audio/video retrieval. Compared with traditional keyword-based music retrieval, content-based music retrieval provides more flexibility and expressiveness. Users may express queries by humming, singing and whistling song.

Low-level features are the important building block for audio classification systems. They are easy to extract and have demonstrated good performance in virtually all music classification tasks. Low-level features can be further divided into two classes of timbre and temporal features.

Timbre describe the quality of a sound, different timbres are produced by different types of sound sources, like different voices and musical instruments. The extraction of timbre features is closely related to spectral analysis of the audio signal. A song is usually split into many local frames in the first step to facilitate subsequent frame level timbre feature extraction. It has two advantages. Firstly, it leads to a more efficient feature extraction process, since we only need to apply spectral analysis to short-time signals. Secondly, it is more effective to model the timbre features in frames with 10–100 ms duration. Due to the non-stationary nature of music, the statistical properties of audio signal of a song depend on time.

By applying frame-level feature analysis, we can assume that the audio signal within each frame is stationary.

After framing, spectral analysis techniques such as fast Fourier transform (FFT) are then applied to the

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windowed signal in each local frame.From the output magnitude spectra, we find features such as spectral centroid (SC), spectral roll off (SR) and spectral bandwidth (SB) capturing statistics of the spectra. For MFCC, the sub bands are linearly spaced in lower frequencies(less than 1KHZ) and logarithmically spaced in higher frequencies. The reason for using log-scale at the higher frequency range is to mirror the human auditory system, whereby finer spectral resolution is achieved at lower frequencies. The local windows used in timbre feature extraction are usually taken from fixed-size intervals regardless of the underlying musical events. It would be desirable to split the signal into unequal intervals corresponding to separate notes where the boundaries are aligned with the onset and offset of the notes [8]. Secondly, the bank of band-pass filters is usually organized to cover octave-based logarithmic spectral range.

Temporal features form another important class of low-level features that capture the temporal evolution of the signal. It is worth noticing that temporal features are usually constructed on top of timbre features or the spectrogram obtained during timbre feature extraction. Timbre features extracted from several frames are further integrated to create the temporal feature. In this way, we can create a richer set of features for classification with different combination of timbre and temporal features. The simplest type of temporal features is statistical moments such as mean, variance, covariance, and kurtosis of timbre features collected from a larger local window called texture window [9].

Mid-Level Features Despite the good performance of low-level features for music classification, they do not capture the intrinsic properties of music that humans perceive and appreciate. The commonly used mid-level features in music analysis include • Rhythm—recurring pattern of tension and release in music; • Pitch—perceived fundamental frequency of the sound; • Harmony—combination of notes simultaneously, to produce chords, and successively, to produce chord progressions.

Rhythm describes how certain patterns occur and recur in the music. Beat and tempo (beat-per-minute, BPM) are two important cues that describe rhythmic content of the music which have been utilized in music classification.

Features Extraction Input query to any information retrieval system consists of relevant and irrelevant information. Features Extraction is the process of discarding as much irrelevant information as possible and representing relevant information in a compact and meaningful form. During the process of proposing an MIR system we investigated a variety of features including some statistical features such as statistical mean, standard deviation and some basic signals properties such as zero-crossing rate and energy. Moreover, we applied some of the digital signal processing algorithms to musical data to extract features such as Fast Fourier Transform FFT, Linear Predicative Coding coefficients LPCC and MFCC. The algorithm for getting MFCC feature is given below.

1.pre-emphasis 2. Framing 3. Hamming windowing 3.Fast fourier Transform to obtain power spectrum 4. Log of FFT 5.Mel filter bank 6.DCT for decorrelation 7.delta delta MFCC coefficients.

II. TECHNIQUES OF FEATURE EXTRACTION The methodology of audio classification involves extracting discriminanatory features from the audio data and feeding them to a pattern classifier. Few techniques generate a pattern form the features and use it for Classification by the degree of correlation. Few other techniques use the numerical values of the features coupled to statistical classification method. In general, methods for detecting pitches can be divided roughly into two categories: time-domain based and frequency-domain based.

In the time-domain ,Zero Crossing Rate (ZCR) is a popular method The basic idea is that ZCR gives information about the spectral content waveform cross zero per unit time . Recently, ZCR appeared in a different form such as VZCR (variance of ZCR) or SZCR (smoothing ZCR) (Huang and Hansen, 2006). They are more suitable for dealing with audio files than ZCR. In the frequency-domain, Fast Fourier Transformation (FFT) is one of the most popular methods. This method is based on the property that every waveform can be divided into simple sine waves. But, a low spectrum rate for longer window may increase the frequency resolution while decreasing the time resolution. Another problem is that the frequency bins of the standard FFT are linearly spaced, while musical pitches are better mapped on a logarithmic scale. The other methods are MFCC (Mel frequency cepstral co-efficient) LPC (Linear predicative coding) LDB (local discriminant bases).

Classifiers for Music Classification: In standard classification, we are presented with a training data set where each example comes with a label. The purpose is to design a classification rule that can best predict the labels for unseen data.

Classifier design is a standard topic in pattern classification. The common choices of classifiers are K-nearest neighbour (K-NN) support vector machine (SVM) and GMM classifier Various other classifiers have also been used for different music classification tasks, including logistic regression artificial neural networks (ANN) [7],[

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8] decision trees[8] linear discriminant analysis (LDA)[6] ,nearest centroid (NC and sparse representationbased classifier (SRC) K-NN and SVM are the two most popular classifiers used for both general classification problems and in music classification as well. K-NN uses training data directly for the classification of testing data. The label of the testing instance is predicted by majority voting on the labels of the nearest instances in the training set. SVM is the state-of-the-art binary classifier based on the large margin principle. Given labeled instances from two classes, Therefore, the SVM has good classification performance since it focuses on the difficult instances. Both K-NN and SVM are applicable to single feature vector representations and pairwise similarity values as well. In the latter case, a kernel matrix is built from pairwise similarity values that can be used directly by the SVM .

Another classifier that can directly handle feature set classification is convolutional neural network (CNN) which is a generalization of the standard neural network model by taking convolutions over the segments of the input signal. Hence, such model can be used for audio classification based on sequence of timbre features like raw MFCC features. MFCC is inarguably the single most important feature type that was widely used for genre classification. It alone can produce quite good classification result compared to many other methods that use more sophisticated collections of features.

B. Statistical Features 1. Skewness : Skewness is the measure of the symmetry or asymmetry of a signal.

Skewness= Y is the mean, s is the standard deviation, and N is the number of data points.

2. Kurtosis: Kurtosis is a measure of the noisiness of a signal. It shows whether the data is peaked or flat relative to a normal distribution.

Kurtosis= Experiment: We are choosing Indian raga for e.g. Todi Raga Bihag Raga for doing analysis of this music signal.

Result: The results of the experiment are shown below.

REFERENCES [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10] [11] [12] [13]

R. Typke, F. Wiering and R.C. Veltkamp “A survey of Music Information Retrieval Systems,” in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), 2005, pp. 153-160.

ISMIR: The international conferences on music information retrieval and related activities [Online].

Available:http://www.ismir.net/ J. S. Downie, “Music information retrieval,” in Annual Review of Information Science and Technology. Medford, NJ: Information Today, 2003, vol. 37, ch. 7, pp. 295–340.

P. Herrera, X. Amatriain, E. Battle, and X. Serra. Toward instrument segmentation for music content description: a critical review of instrument classification techniques. presented at Int. Symp. Music Information Retrieval (ISMIR 2000). [Online].

Available: http://ismir2000.indiana.edu/ A.P.Klapuri, A. J.Eronen, J.T.Astola, “Analysis of the Meter of Acoustic Musical Signals,” IEEE Trans. Audio, Speech, and Language Processing, 2006, 342-355.

T. Li, M. Ogihara, and Q. Li, “A comparative study of content-based music genre classification,” in Proc. SIGIR, 2003 A. Meng and J. Shawe-Taylor, “An investigation of feature models for music genre classification using the support vector classifier,” in Proc. Int. Conf. Music Information Retrieval, 2005.

I. Mierswa and K. Morik, “Automatic feature extraction for classifying audio data,” Mach. Learn., vol. 58, pp. 127–149, 2005..

G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech Audio Process., vol. 10, no. 5, pp. 293–302, K.West and S. Cox, “Finding an optimal segmentation for audio genre classification,” in Proc. Int. Conf. Music Information Retrieval, 2005.2002 Y. E. Kim and B. Whitman, “Singer identification in popular music recordings using voice coding features,” in Proc. Int. Conf.

Music Information Retrieval, 2002.

F. Morchen, A. Ultsch, M. Thies, and I. Lohken, “Modeling timbre distance with temporal statistics from polyphonic music,” IEEE Trans.Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 81–90, 2006.

M. Mandel and D. Ellis, “Song-level features and SVMs for music classification,” in Proc. Int. Conf. Music Information Retrieval, 2005.

Skewness TODI RAAG 1st segment .The Spectral skewness related = 4.9419 TODI RAAG 2nd segment The Spectral skewness related = 5.9917 TODI RAAG 3rd segment.The Spectral skewness related = 5.0561

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Kutosis TODI RAAG 1st segment.The Spectral kurtosis = 31.833 TODI RAAG 2nd segment.The Spectral kurtosis = 56.2213 TODI RAAG 3rd segment.The Spectral kurtosis = 32.0919

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Figure 1: Reading of audio sample

Figure 2: Framing of segment

Figure 5: mfcc plots of todi segment 1

Figure 7: mfcc plots of Bihag segment 1

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Figure 3: Arrangement of frequencies into Mel bands

Figure 4: Conversion from frequency domain to Mel spectrum domain

Figure 6: mfcc plots of todi segment 2

Figure 8: mfcc plots of Bihag segment 2

Page 153

American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

REVIEW ON MODELING OF RESISTANCE SPOT WELDING PROCESS Boriwal Lokesh1, Sarviya R. M.2 & Mahapatra M. M.3 Research scholar, Mechanical Engineering Department, MANIT, Bhopal, INDIA. 2 Professor, Mechanical Engineering Department, MANIT, Bhopal, INDIA. 3 Associate professor, Mechanical & Industrial Engineering Department, I. I. T, Roorkee, INDIA. 1

Abstract: The paper critically studies the available methods of coupled models of resistance spot welding (RSW). Further critical analysis of the processes has been done and some recommendations also given according to the application. The quality of the weld depends upon the optimum process variables such as welding current, weld time and electrode pressure. Different developed models to simulate the three ways coupling of thermal, electrical and mechanical analysis. The coupling procedures can provide a more realistic and efficient computational approach for contact size and for electrode face. Models describing these phenomena should be interconnected with each other to simulate heat generation and growth in the nugget formation. The fuzzy logic modeling method is one of the artificial intelligence techniques which have been in use for the modeling of manufacturing processes such as machining and welding.

Keywords: Resistance spot welding (RSW); Failure mode: Numerical method; Fuzzy logic; Nuero fuzzy I. Introduction The RSW process has been widely used in sheet metal manufacturing operation. RSW widely used in major industries such as automotive and aerospace. RSW have many advantages over the other one such as a no filler material is demanded, a low heat input, a small heat affected zone, low welding distortion and takes comparatively low points of skill from an operator. Resistance spot welding is that area of welding where heat is generated by the flow of current with the help of electrode in the work piece and weld nugget is produced at the faying surface of the work piece. Electrode pressure applied to ensure adequate contact between the work piece being the welded. In resistance spot welding process to produce good weld quality depend upon welding parameter such as welding current, weld time and electrode face. In the resistance welding process it is very different to find out optimal resistance spot welding process parameter. These parameters need to control to produce a good weld quality. Schematic of the process shown in fig. 1. The metal sheet to be joined is brought together under pressure by a pair of electrodes and high welding current. Weld nugget is formed on the faying surface of sheet metal due to contact resistance and joule heating. Due to the interaction between thermal, electrical, mechanical, metallurgical phenomena during the RSW process, deformation, stress and strain will develop in the weld metal. Over the last few years numerical methods are a power full tool for simulating the interaction between thermal, electrical, mechanical, metallurgical phenomena and structural fields during the resistance spot welding by one, two and three dimensional finite element models, Richard, D. et. al.[1].

Figure 1: Schematic view of the process

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Recently, a lot of research work has been carried out on the numerical simulation of the RSW. The finite element method (FEM) is capable of considering the effects of process parameters such as welding current, welding time and electrode pressure on residual stress distribution weld distortion and weld crack in the weld metal. It is a complex process in which coupled interactions exist between electrical, thermal, mechanical, metallurgical phenomena, and even surface behaviors. The coupling procedures can provide a more realistic and efficient computational approach accounts for the variation of contact size; particularly in the application of curved-face electrodes producing a spot weld. The concept of fuzzy sets as a generalization of crisp sets was first introduced by Zadeh, L. A. et. al. and Yager R. P. et. al.[3-4]. The fuzzy logic modeling method is one of the computational intelligence (Cl) techniques which have been in use for the modeling of manufacturing processes such as machining and welding. Recently, CI has provided a number of successful solutions for various industrial problems with similar characteristics, Ali Y.M. [5]. The purpose of this review paper is critically studied the available methods of coupled models and future critically analysis the process and got to some recommendation according to the application.

II. Review on Experimental Work The weld produced by RSW process is commonly known as the nugget and defines the overall integrity of the spot welded structure. The effect of electrical resistance on nugget formation was studied in detail with respect to the process variables by Kaiser et.al.[6]. The electrode deformation during RSW of coated steels was investigated by William et.al.[7]. The thermal cycle of RSW is marked by the very rapid rise of temperature beyond the melting point of the material and due to the resistance heating. The thermal cycle also defines the nugget size, residual stress and deformation of RSW joints. A theoretical study of the thermal response of the work sheet material with respect to the input variables was suggested by Na and Park.[8]. In RSW, the contact resistance between the faying surfaces of the sheets significantly affects the power drawn by the electrical circuit and subsequent resistance heating. The contact rĂŠsistance is dependent on the surface nature and the applied electrode pressure. Often, too high electrode pressure and very smooth faying surface leads to less resistance heating and smaller weld nugget formation. This phenomenon was studied in detail by Davies et.al.[09]. The mechanism of electrode degradation during the RSW of coated steels was investigated by Parker et. al.[10]. A two stage experimental designing methodology was used by Li et.al.[11]. to predict the quality characteristics of resistance spot welds. The step by step formation of RSW nugget was investigated by Cho and Rhee[12] using special sensors and experimental procedure.

III. Faiure Mode Spots weld failures are characterized by complex crack paths from the notch tip in the weld nugget, that can either propagate on the faying surface (Full Interfacial Failure, FIF) or deviate through the sheet thickness (Partial Interfacial Failure, PIF). In the case of mild steel and HSLA ductile failure were observed at the weld nugget boundary under normal loads. Two type of failure mode were occurs during the tensile shear strength and peel shear strength tests: full interfacial failure and nugget pull out failure. Full interfacial failure was accompanies by little plastic deformation investigated by Pouranvari M. et. al.[13]. Fusion size (FZ) of the weld controlled the failure mode and mechanical properties of RSW process. High welding current and FZ were changed the failure mode FIF to nugget pull out. In the full interfacial failure and nugget pullout failure mode experience driving forces are shear stress and tensile stress respectively. Each driving force has a critical value, when this force reached its value failure occurred.

Zuniga S. et. al., Chao Y. et. al, Dancette, S. et. al. and Anastassiou et al.[14-16] Studied the residual stress distribution and microstructural changes in a steel sheet and also investigated the relationship between welding parameters and fatigue behavior. Long .X. et. al., Khanna S. K. et.al. and Nodeh I. R. et. al. [17-18] Developed a thermo-electric - mechanical model to analyze the effects of welding parameters such as welding current and welding time on residual stress distribution. The model also obtained the nugget size in resistance spot weld.

Model result is then compared with the experimental data. Huh H. and Kang W. J.[19] Introduced the thermal, electrical analytical model for predicting the contact resistance between the electrodes and sheets. The contact resistance between electrodes and sheets is specially treated with artificial interface elements in which the material properties of electric and thermal analysis are artificially imposed for physical reasonable simulation.

IV. Fuzzy Logic Narang H. K. et. al. [20]created a fuzzy logic model for predict the weldment macrostructure zones' shape profile characteristics of tungsten inert gas welding. This technique also employed for online prediction of the TIG weldment shape profiles. Naso D. et. al.[21] Developed a fuzzy logic model to implement an intelligent algorithm for online weld quality monitoring of GMAW processes with optical sensors. The model also predicts the weld quality and weld defect in real time. Ambelia et. At.[22] Developed rule base fuzzy logic model using Mamdani approach to identify the carnal knowledge between each input variable (percentage of green fruits, stealing time and steam pressure). Three main levels were applied to build up the theoretical account is;

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fuzzification, inference engine and defuzzification. Lin et. al.[23]. Applied grey fuzzy logic to optimize the electric discharge machining process parameters with multiple responses such as an electrode wear ratio, material removal rate and surface roughness. An orthogonal array, gray relational generating, gray relational coefficient, gray-fuzzy reasoning grade and analysis of variance (ANOVA) are employed to study the performance characteristics in the electrical discharging machine process. Ali Y. M. et. al. [5] Have used threelayer fuzzy model to analyze the effect of input process parameters on surface roughness of the grinding operation. Fuzzy rule for model, generated based on experimental observation.

Often numerical deterministic modeling is time consuming and requires much simplification of the physics, A model developed by Boriwal L. et. al. [24]. Investigated the choice of input process parameter that create acceptable welds in the resistance spot welding of galvanized steel sheets. The process variables were also studied for the interaction effects on weld nugget, tensile-shear and peel strengths. The multiple regression equations of the model were tested for a range of test cases to predict the responses like nugget size, tensile and peel strength. The designed data were further used for response optimization. The predicted optimized input process variables were experimentally verified by comparing the measured and predicted optimized target responses. The methodology adopted in the present investigation indicated adequacy to model, predict and response, optimize the input process variables of resistance spot welding process for welding galvanized steel sheets. These concluded that Selection of the range of RSW input variables that define the acceptable weld characteristics like nugget size, tensile-shear strength and peel strength was emphasized in designing the full factorial experiment matrix. The result of the multiple regression analysis and response surface indicated the suitability of the data for predicting the output responses for a set of test cases. The design of the data was further used for response optimization and the target optimized values of the responses were compared with the experimentally observed ones.

V. Neuro Fuzzy The neuro-fuzzy systems are hybrid intelligent systems that combine features of artificial neural networks and those of fuzzy logic systems by Kandel A.[25] .They are powerful tools which also considers the uncertainties of pattern of relations that exist between the input and output relations of process variables. The neuro-fuzzy models have been successfully applied to modeling and control of various systems Arafeh at al.; Duraisamy et al.; Alimardani and Toyserkani)[26-28]. Successfully applied nero-fuzzy system for laser solid free form fabrication. The neuro fuzzy systems can be broadly classified as cooperative nero-fuzzy systems, neural network driven fuzzy system and hybrid neural network-based systems. The hybrid neuro-fuzzy system imbibes both the quality of neural networks and fuzzy logic systems and hence seems more promising [29-30].

VI. Modeling Process For Resistance Spot Welding Process A) Mesh Generation The RSW process model has three surface contact areas. Contact area 1 and 2 represents the electrode work piece interference and 3 represented the faying surface between the work pieces. Fine meshes were generated between the contact surfaces for obtaining reliable results. Two dimensional axisymmetric schematic model is shown in fig.2. The developed model was meshed using three types of elements, as shown in Fig. 3. Three types of meshing element were used for model. Solid element meshing used for simulated the thermo elastic plastic behavior of the electrode and sheet metals [31-32].

Figure 2: Schematic diagram for the model of RSW,Wang et al.

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Figure 3: mesh generation of developers model.

B) Governing Equation The governing equation for transient conduction in a two dimensional cylindrical coordinate system with internal heat generation can be stated as (1) (2) Where [Dep]=[De]+[Dp] in which, [De] is the elastic stiffness matrix, and [Dp] is the plasticst iffness matrix, [dε] is the infinitesimal strain tensor, [do] is the infinitesimal stress tensor, and [Cth] denotes the thermal stiffnessmatrix.

The electric potential can be expressed by Quasi-Laplace equation

Where ɛ is the electrical conductivity of material, ϕ is electrical potential [33]. The relation between electrical conductivity and electrical resistivity is given by the R ɛ = 1/R (3) C) Boundary Condition A 60 Hz sine wave electrical current flow was assumed to be uniformly distributed at the top surface of the upper electrode and was permitted to flow across the contact areas at both the electrode–workpiece interfaces and faying surface between the sheets , and eventually reaching the bottom surface of the lower electrode. The simulation prodcudure for resistance spot welding process shown in fig. 4.

Figure 4: Simulation flow for resistance spot welding process There are three types of thermal boundaries, temperature boundary (BC1), heat flux boundary (BC2) and heat convection boundary (BC3) defined by Eqs. (4, 5 and 6), respectively:

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T = TBC1 at BC1 (4) q˙ = q˙A [J/(mm2 s)] at BC2 (5) q˙ = ˇβ (TBC3 − T0) at BC3 (6) where TBC1, q˙ A,ˇ, TBC3, T0 are the given temperature at BC1, heat flux at BC2, heat transfer coefficient for both convection and radiation boundary, the temperature at BC3 and the temperature of the surrounding air, respectively [33]. Temperature-dependent physical and mechanical properties of materials were used for both thermo- electrical and thermo- mechanical analysis, [34]. The electrical, thermal and mechanical boundary conditions used in the model shown in fig.5.

Figure 5: Schematic of the thermal and electrical boundary conditions, Esiazadeh, H.,et. al. [35] VII. Conclusion The different models used to illustrate RSW process in the last 30 years, on Finite element method and artificial intelligence technique for resistance spot welding process are presented & discussed in this paper. In most of the analysis of residual plastic strain distribution, deformation of the sheet only 2D axissymetric electrical – thermal and thermo elastic plastic model and for nugget formation, effect of process parameters on the weld nugget size and temperature of faying surface electro-thermo-mechanical finite element (FE) models are considered. Some researchers have developed more sophisticated FEM models which considered temperaturedependent material properties, contact status, phase changing, and coupled field effects in the simulation of the RSW. Further, it was found that, the temperature distribution in the sheet metal was obtained through the electrical, thermal analysis and distribution of residual plastic strain, welding deformation after RSW was obtained through thermo-elastic-plastic analysis. There is further scope of research in three dimensional modeling and artificial intelligence technique.

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Dynamic Query Search Over Graph Stream 1

Ms.Vaishali G. Powar1 and Mr.Pradeep C. Bhaskar2 Department of Computer Science and Technology, Shivaji University, Kolhapur, Maharashtra, India 2 Department of Electronics and Technology, Shivaji University, Kolhapur, Maharashtra, India

Abstract: Graph is widely used in various real applications such as social network modeling and chemical compound analysis. In many applications, graphs are often evolving along the time in a stream fashion instead of remaining static. These evolving graphs can be modeled as graph stream. The available work is proposed on static subgraph search. We proposed method to find match between query stream and graph stream. Stream comprises sequence of graph at continuous timestamps. Graphs are represented in the form of numerical vectors. Using these numerical vectors, dominance is checked between query stream and graph stream. These methods make use of dominant vector and skyline to reduces number of comparisons made to find match between query stream and graph stream.

Keywords: Query Stream; Graph Stream; Numerical Vector; Dominant Vector; Skyline.

I. Introduction Many data domains such as chemical data, biological data, social networks and the web are naturally structured as graphs. Graphs may be evolving along the time in a stream fashion instead of remaining static in many applications like social communities, traffic networks. For example, in a traffic network, the links between nodes are changing over the time. These changing graphs can be modeled as graph stream. Subgraph search is an effective method to find out substructures present in the graph. Graphs grow over a time in graph stream, so it is important to get real time response while finding match between graph streams.

Existing work done for subgraph search include closure tree[1], graphgrep[2],TALE[3], Gcoding[4] and is meant for static graph data. Closure tree uses maximum match between vertices and needs more iterations.

Graphgrep uses maximum path length to filter the graphs as candidate pair, and results in many false positive pairs. TALE uses hashing method to store node neighborhood information. Gcoding assigns a signature to each vertex based on its local structures. A spectral graph code is generated by combining all vertex signatures in a graph. A necessary condition for subgraph isomorphism was derived based on spectral graph codes. In [5], algorithms are proposed to find match between static query pattern and a graph stream, using numerical vectors derived for the graph stream and query pattern. In some applications query pattern may also be dynamic in nature, for example friend list in social networks if assumed as query pattern.

So to deal with dynamic query and graph stream, we proposed dominant vector set and skyline stop join algorithms. It uses node neighbor tree [5] for preprocessing of the graph, where NNT represents edge structure for every node in the graph. NNTs are converted to numerical vectors [5] using dimensions defined for the graph; dimensions show list of edges at each level of the graph. Using numerical vectors dominant relationship is checked to find out the matching candidate pair as a result.

II. Proposed Algorithm In this section data pre-processing and actual algorithms for dynamic query search over graph stream are described.

Graph conversion to NNT

Projection of NNT to Numerical vector

Dominant vector set algorithm

Mapping of Numerical vectors to 2-D space

Skyline stop join algorithm

Matched pairs

Figure 1: System Architecture

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Graph stream and query streams are preprocessed to convert them into nearest neighbor tree (NNT), this NNTs are then converted to the numerical vectors. Finally numerical vectors are used to find out dominant vector and skyline, in dominant vector set algorithm and skyline stop join algorithm.

A Graph preprocessing To minimize number of computations graphs in stream are continually converted to NNTs. Given a graph G of level l, NNT(u) represents tree rooted at node u with all the simple paths in NNT(u) upto level l.

1.

Graph Conversion To NNT Given a graph G(V,E), NNT(u) shows local structure of vertex u to its direct and indirect neighbors in G up to level l. Fig. 2 shows example graph and its respective NNTs. NNTs are shown in the Fig.3. While building the NNT all simple paths up to depth of the graph from the node are traversed and edges are added to it. For example node A in G has path to A and B at level 1. So these edges are added in NNT T1 of A as shown in Fig.3a. At level 2 node B is having path to C, so this edge is added to T1. NNTs are build for all vertex in the graph.

A

B

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Figure 2: Example Graph A

A

C

(a)

A

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(b) (c) (d) Figure 3: (a)NNT T1 (A) (b) NNT T2(A) (c) NNT T3(B) (d) NNT T4(C)

2.

Projection of NNT to Numerical Vectors Before projecting NNT to numerical vector dimension for graph G is derived. Dimension is a triple <l, lab1, lab2>, where it denotes distinct edge lab1 to lab2 at level l in G. After finding dimensions for G, NNTs are projected to numerical vectors by counting no of occurrences of each dimension in the respective NNT. For example dimensions for the NNTs in Fig.3 are as shown in Fig. 4a. Foe every NNT occurrences of the each dimension are counted and count is added to numerical vector. Numerical vectors for NNTs in Fig.3 are shown in Fig.4b. <1,A,A><1A,C><1,A,B><1,B,A><1,B,C><1,C,A><1,C,B> <2,B,C><2,A,B><2,A,C><2,A,A><2,C,A><2,B,A> Figure 4 (a): Dimensions of graph in fig.2 A

A

C

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(1,1,1,0,0,0,0,1,0,0,0,0,0)

(1,0,0,0,0,0,0,0,1,1,0,0,0) (0,0,0,1,1,0,0,0,0,0,1,1,0) (0,0,0,0,0,1,1,0,0,0,1,0,1) Figure 4 (b): Numerical vectors for NNTs in fig.3.

3.

Mapping numerical vectors to 2-dimentional space To minimize the search space, numerical vectors are mapped to zero and non-zero dimensions. All the nodes are arranged by descending order on their 2 dimensions. After arranging in descending order nodes are mapped to

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2-dimentional space. For every node of graph stream 2 counters are observed, a position counter and dominant counter.

B Dynamic Subgraph search over graph stream Preprocessed data is stored in the form of position counters and dominant counter, and updated continuously for respective updates in the graph stream. Dominant vector cover join algorithms and skyline stop join algorithms are then used to find actual subgraph match.

1.

Dominant vector cover join Dominant vector cover join algorithm find out positions for both query and graph stream from the position counter. Position counter denotes position of the node in 2-D space. It then finds out dominant counter of every node in the graph stream with respect to query nodes. Dominant counter of node u in graph stream is n length vector where, every ith element denotes the number of dimensions dominated by u of node i. Here n is no of nodes in the query graph. If graph dominant vector dominates all query nodes then respective query and graph pair is reported as positive candidate pair. Pseudo code for the algorithm is given below.

Procedure Dominant _vector_cover_Join Input: query stream {Q1,Q2,……Qk1}, graph stream {G1,G2,……Gk2} Output: Positive candidate pairs Begin: for i ← 1 to k1 and j ← 1 to k2 do in parallel for u∈ Qi and v∈ Gj do in parallel update position counter for u and v update dominant counter for v mark query vectors dominated by graph vectors according to dominant counter value for i ← 1 to k1 if Gj dominates all vectors of Qi result= result U (i ,j) return result End 2.

Skyline stop join Skyline stop join algorithm first finds out monochromatic skyline [6] for query stream, which is node in the query stream in the 2-D space dominating all the query nodes on one of the dimension. Algorithm then find out bichromatic skyline [6] of query stream across the graph stream. Bichromatic skyline is vector set from graph stream which dominates nodes in monochromatic skyline of query stream. Algorithm when find a bichromatic skyline point with respect to graph it reject the respective pair. Otherwise this pair is included in result as a query graph is subgraph of graph stream. Pseudo code for the algorithm is given below.

Procedure skyline stop join Input: query stream {Q1,Q2,……Qk1}, graph stream {G1,G2,……Gk2} Output: Positive candidate pairs Begin: for i ← 1 to k1 and j ← 1 to k2 do in parallel for u∈ Qi and v∈ Gj do in parallel update position counter for u and v find monotonic skyline S for Qi. find Max node D1 and Max node D2 for Gj if S is bichromatic skyline across D1 and D2 break else result= result U (i ,j) return result End III. Experiments And Results The test dataset for evaluation of the algorithms is selected from [7]. Dataset contains friend list on social networks. Visual studio 2010 software platform is used to perform the experiment. SQL Server 2008 is used as database server.

The experiments are executed on query and graph with average number of 35 nodes. Stream for both query and graph is formed by inserting timer and implementing autodelete_nodes function in the module. Fig.5a shows dominant counter for the stream graph instant. As it is not having non zero count in entire dominated counter set it results in non matching pair of query and graph. Fig.5b shows bichromatic skyline of query with respect to graph and results in non matching pair.

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Figure 5 (a): Progressive Dominated counter

Figure 5 (b): Bichromatic Skyline point Figure 6 shows experimental analysis of the two algorithms in terms of stream size and time required for subgraph search. Y-axis shows time in minutes and X-axis shows average stream size.

10 8 6

DSC

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SKYLINE

2 0 35

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Figure 6: Comparison of time efficiency

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IV. Conclusion Node neighbor tree is used for storing graphs. NNTs are converted to numerical vectors. These numerical vectors are mapped to 2-D space to minimize the search space. Proposed algorithms work with dynamic queries over the graph stream. On graph update, algorithm only updates the position counters reducing pre processing cost. Dominant vector and skylines are used for filtering the subgraph search pairs, which minimizes total number of comparisons. As seen in the time analysis, skyline stop takes time less than dominant vector cover algorithm.

V. References [1] [2] [3] [4] [5 ] [6] [7]

H. He and A.K. Singh, “Closure-Tree: An Index Structure for Graph Queries,” Proc. 22nd Int’l Conf. Data Eng. (ICDE), 2006 D. Shasha, J.T.L. Wang, and R. Giugno, “Algorithmics and Applications of Tree and Graph Searching,” Proc. ACM SIGACT SIGMOD Symp. Principles of Database Systems, 2002.

Y. Tian and J.M. Patel, “Tale: A Tool for Approximate Large Graph Matching,” Proc. 24th Int’l Conf. Data Eng. (ICDE), 2008.

L. Zou, L. Chen, J.X. Yu, and Y. Lu, “A Novel Spectral Coding in a Large Graph Database,” Proc. Int’l Conf. Extending Database Technology (EDBT), 2008.

Lei Chen, Changliang Wang, “Continuous Subgraph Pattern Search over Certain and Uncertain Graph Streams”, IEEE transactions on knowledge and data engineering, vol. 22, no. 8, august 2010 X. Lian and L. Chen, “Monochromatic and Bichromatic Reverse Skyline Search over Uncertain Databases,” Proc. ACM SIGMOD,2008.

Friendster Data Set, https://archive.org/details/friendster-dataset-201107

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A review on Optimization of material removal rate in micro-drilling process Ranadhir R Landge1, Dr. Atul B Borade 2 Ph.D.Research Scholar and Workshop Superintendent (Asstt. Professor), Government College of Engineering, Jalgaon, Maharashtra, India 2 Professor and Head of Mechanical Engineering Department, Jawaharlal Darda Institute of Engineering and Technology, Yavatmal, Maharashtra, India 1

Abstract: Most of automotive components are manufactured using a conventional machining process, such as turning, drilling, milling, shaping and planning, etc. Micro-hole drilling is the precision hole drilling technology to use in manufacturing and workshop. Micro drilling is one of the most fundamental machines technologies and it is moving high precision and high spindle speed application in manufacturing field and increasing productivity and quality. It is an important machining process having attractive applications like Printed circuit boards, Fuel injection nozzles, Watch parts, Camera parts, Medical needles, Aeronautics., Mobile phones, Computers etc. This will be benefited by a comprehensive literature review of drilling processes and optimization. This paper presents a literature review on micro-drilling processes, optimization of MRR depending upon various parameters for other machining processes through taguchi method and fuzzy optimization.

Keywords: Micro-drilling, Cutting tool, Material removal rate, Taguchi, Fuzzy I. Introduction Drilling is one of the basic technology.Micro Drilling is high precision process for smaller holes below 1mm.It is used for the purpose increasing quality of special parts and items. The price of a micro-drill cutting tool itself is relatively low, the costs caused by tool failures are considerably higher.Material removal rate is one of the major manufacturing goal in machining operations. Taguchi method is a well known experiment design method applied in many industries to optimize quality characteristics through the setting of design parameters with orthogonal array, followed by optimization of the Fuzzy parametric deduction to improve MRR. II. Micro Drilling Micro drilling small diameter presents greater problems since coolant fed drills cannot be used and high spindle speeds are required. High spindle speeds also require the use of balanced tool holders Micro drilling is characterized not just by small drills but also a method for precise rotation of the micro drill and a special drilling cycle. In addition, the walls of a micro drilled hole are among the smoothest surfaces produced by conventional processes. This is largely due to the special drilling cycle called a peck cycle. The smallest micro drills are of the spade type. The drills do not have helical flutes as do conventional drills and this makes chip removal from the hole more difficult. Drills with a diameter of 50 micrometers and larger can be made as twist drills. Drills smaller than this are exclusively of the spade type because of the difficulty in fabricating a twist drill of this size.[2] Drilling may affect the mechanical properties of the work piece by creating low residual stresses around the hole opening and a very thin layer of highly stressed and disturbed material on the newly formed surface. This causes the work piece to become more susceptible to corrosion at the stressed surface. A finish operation may be done to avoid the corrosion. Zinc plating or any other standard finish operation of 14 to 20 Âľm can be done which helps to avoid any sort of corrosion.

For fluted drill bits, any chips are removed via the flutes. Chips may be long spirals or small flakes, depending on the material, and process parameters. The type of chips formed can be an indicator of the mach inability of the material, with long gummy chips reducing mach inability.[8] The recommended speeds and feeds for micro drilling are as varied as the materials which can be drilled. Micro drilling is not generally a high speed process since dwelling of the drill at the bottom of the hole can cause hardening of the work piece leading to increased drilling forces. For most metals, typical spindle speeds are in the 2000 to 4000 rpm range and feeds are in the range of a micrometer per revolution, or so. Care must be taken when drilling plastics to avoid melting of the material which can lead to adhesion of the plastic to the drill. This can cause drill breakage or poor sidewall smoothness.[7]

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Azlan Abdul Rahman, Azuddin Mamat (2009) A study on optimum drilling parameter for HSS drilling tool in micro-drilling (0.5 mm to 1.0) processes in order to find the best drilling parameter for brass as a work piece material. This work had given fol1owing outputs: 1) The value of surface roughness are mostly influenced by spindle speed and feed rate. If the value of these spindle and feed rate increases, the value of surface roughness will decrease. The tool diameter gives less influence on the value of surface roughness.2) The value of MRR is decreases when the tool diameter, spindle speed and feed rate are decreases.3) For the accuracy of the holes drills, as drill diameter, feedrate and spindle speed increase the dimensional accuracy of drilled hole will decrease[1] M.M. Okasha, P.T. Mativenga, N. Driver, L. Li (2010) This paper presents the feasibility and basic characteristics of a new approach for micro-drilling In718 alloy sheets at an acute angle, using sequential laser and mechanical drilling Experiment analysis. Sequential laser and mechanical drilling of laser-blind holesJungSoo Nam, Pil-Ho Lee, Sang Won Lee (2011) the experimental characterization of the micro-drilling process with the nanofluid minimum quantity lubrication (MQL Experimental analysis. The experimental results show that the nano fluid MQL significantly increases the number of drilled holes and reduces the drilling torques and thrust forces. In addition, the nano fluid MQL effectively eliminates remaining chips and burrs to enhance the quality of drilled holes.[19] B.K Hinds, G.M Treanor (2011) Analysis of stresses in micro-drills. The finite element method Threedimensional models of drills are defined by setting node points of the drill geometry. Measured forces from experimental tests are used to determine drill loading.[5] Hongyan Shi, Fumin Song, Lianyu Fu (2011). To present a system for accurately measuring drilling force in the printed circuit board micro drilling process and to characterize the drilling force to provide a better understanding of the micro drilling process. Experimental analysis paper presents a system for accurately measuring the drilling force. Drilling force provides fundamental information for the optimal design of micro drill bits. Drilling force can also characterize the micro drilling process, especially the ultra small hole micro drilling process.[9] Dr. K.K.Gupta, Prof.Tapan Jain, Manish Deshmukh (2013)Conventional micro Conventional micro-drilling processes in order to find the best quality of drilling for different types of work piece material, experiment is carried out using calculation drill bit diameter, feed and RPM is increases the MRR is also increase.[14] Rajyalakshmi.G, VenkataRamaiah. P (2013) This paper discusses the application of the Taguchi method with fuzzy logic to optimize the machining parameters for Wire electrical discharge machining with multiple characteristics.[21] III. Material Removal Rate The material removal rate (MRR) in drilling is the volume of material removed by the drill per unit time. For a drill with a diameter D, the cross-sectional area of the drilled hole is πD2/4. The velocity of the drill perpendicular to the work piece f is the product of the feed and the rotational speed N where N = V/π D. Thus, MRR = (πD2 / 4) (f) mm3 /min. Conversion of feed rate fr (mm/rev) to feed rate f (mm/min) f = N fr (mm/min).

Material removal rate is commonly considered as a major manufacturing goal in machining operations in many of the existing research. Taguchi method, an experiment design method, has been widely applied to many industries. It cannot only optimize quality characteristics through the setting of design parameters, but also reduce the sensitivity of the system performance to sources of variation. To achieve the deduction in optimization, it is necessary to first describe the dynamic behavior of the system to be controlled. Because of the number, complexity and unclear, vague nature of the variables of the dynamic systems that may influence the decision maker’s decision, fuzzy set theory is the most suitable solution .Fuzzy linguistic models permit the translation of verbal expressions into numerical ones. Therefore, the input output relationship of the process can be described by the collection of fuzzy control rules involving linguistic variables rather than a complicated dynamic mathematical model.[29] J.Pradeep Kumar, P.Packiaraj (2012) To investigate the effects of drilling parameters such as cutting speed (5,6.5, 8 m/min), feed (0.15, 0.20, 0.25mm/rev) and drill tool diameter (10, 12, 15mm) on surface roughness, tool wear by weight, material removal rate using HSS spiral drill. Taguchi method on Oil hardened/Non Shrinking (OHNS) steel.The feed and speed are used to control surface roughness, tool wear, material removal ratKamalHassana, Anish Kumar, M.P.Garg, (2012) Investigates the effects of process parameters on Material Removal Rate (MRR) in turning. Taguchi method and analysis of variance (ANOVA)/BrassThe design of experiment and analysis to study effect of cutting speed and feed on MRR. drilling machine with H.S.S drill bit. ,Taguchi method on EN8 (harden mild steel) taken as work-piece material. The optimum settings of feed rate and spindle speed so that the thrust force, torque, local circularity error, and machining time can be minimized.[12] Dr. K.K.Gupta,Prof. TapanJain,ManishDeshmukh (2013 )Conventional micro Conventional microdrilling processes in order to find the best quality of drilling for different types of work piece material Experimental analysisThe experiment using calculation drill bit diameter, feed and RPM is increases the MRR is also increase. drilling processes in order to find the best quality of drilling for different types of work piece material

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Experimental analysis The experiment using calculation drill bit diameter, feed and RPM is increases the MRR is also increase.[14].

IV. Taguchi Method The Taguchi method is a robust design method technique, which will provides a simple way to design an efficient and effective experiment. In order to efficiently reduce the conventional experimental tasks, the orthogonal array by using design parameters will be proposed and adopted. The performance measure, signal-tonoise ratio(S/N) can be used to obtain the optimal parameter combinations. In the Taguchi method, a loss function will be defined to calculate the deviation between the experimental value and the desired value.

Usually, there are three categories of the performance characteristics in the analysis of the signal-to- noise ratio, i.e., the lower the-better, the higher the- better, and the nominal-the better .To obtain optimal machining performance, the minimum SR and the maximum MRR are desired. Therefore, the lower the-better SR and the higher-the better MRR should be selected. This method, the S/N ratio is used to determine the deviation of the performance characteristic from the desired value. [22] Orthogonal array is a testing a black box testing technique which is a systematic, statistical way of software testing It is used when the number of inputs to the system is relatively small, but too large to allow for exhaustive testing of every possible input to the systems. It is particularly effective in finding errors associated with faulty logic within computer software systems. Orthogonal arrays can be applied in user interface testing, system testing, regression testing, configuration testing and performance testing. The permutations of factor levels comprising a single treatment are so chosen that their responses are uncorrelated & hence each treatment gives unique piece of information. The net effect of organizing the experiment in such treatments is that the same piece of information is gathered in the minimum number of experiments.[24] Hari Singh ,Pradeep Kumar (2004),(2006),(2008) To obtain an optimal setting of turning process parameters – cutting speed, feed and depth of cut, which may result in optimizing tool wear Taguchi’s Design of experiments/En24 steel Optimizing Tool wear ,feed force and tool life of Carbide Inserts for Turned Parts by setting of process parameters.[25] Dong-Woo Kim1, Myeong-Woo Cho , Tae-Il Seo and Eung-Sug Lee (2008) Application of Design of experiment Method for Thrust Force Minimization in Step-feed Micro Drilling.DOE (Design of Experiment), ANOVA (Analysis of Variance). In this study, only the drilling thrust is taken into account as the most significant factor in order to optimize the step-feed micro drilling processes.[7] Ashish Bharti, S.K.Moulick (2013) To find the optimized multi response factors for micro drilling operation .Drilling operation is influenced by spindle speed, feed rate, tool point angle, presence of coolant & lubricating agent ,vibration ,tool material ,clearance and chip length. In this case study the Machining parameters spindle speed ,feed rate and tool point is analyzed for their effect on the hole diameter produced and the material removal rate Taguchi based method along with ANOVA (Analysis of Variance) and DOE (Design of Experiments) Output found as spindle speed and feed rate increases material removal rate increases along with nominal diameter but tool point angle has not significant effect and remains constant in this process.[2] V. Fuzzy Logic Fuzzy sets are sets whose elements have degrees of membership. Fuzzy sets were introduced by Lotfi A. Zadeh and Dieter Klaua in 1965 as an extension of the classical notion of set. At the same time, defined a more general kind of structures called L-relations, which were studied by him in an abstract algebraic context. Fuzzy relations, which are used now in different areas, such as linguistics decision-making (Kuzmin, 1982) and clustering, are special cases of L-relations when L is the unit interval [0, 1].

In classical set theory, the membership of elements in a set is assessed in binary terms according to a bivalent condition — an element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in a set; this is described with the aid of a membership function valued in the real unit interval [0, 1]. Fuzzy sets generalize classical sets, since the indicator functions of classical sets are special cases of the membership functions of fuzzy sets, if the latter only take values 0 or 1.

In fuzzy set theory, classical bivalent sets are usually called crisp sets. The fuzzy set theory can be used in a wide range of domains in which information is incomplete or imprecise, such as bioinformatics. [15] It has been suggested by Thayer Watkins that Zadeh's ethnicity is an example of a fuzzy set. A fuzzy set is a pair where is a set and For each the value is called the grade of membership of in For a finite set the fuzzy set is often denoted by Let Then is called not included in the fuzzy set if , is called fully included if , and is called a fuzzy member if . The set is called the support of and the set is called its kernel. The function is called the membership function of the fuzzy set Sometimes, more general variants of the notion of fuzzy set are used, with membership functions taking values in a (fixed or variable) algebra or structure of a given kind; usually it is required that be at least a poset or lattice. These are usually called L-fuzzy sets, to distinguish them from those valued over the unit interval. The usual membership functions with values in [0, 1] are then called [0, 1]-valued membership functions. These kinds of generalizations were first considered in 1967 by Joseph Goguen, who was a student of Zadeh.[16]

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Tian-SyungLan (2011) In this study, four parameters (cutting depth, feed rate, speed, tool nose runoff) with three levels (low, medium, high) were considered to optimize the surface roughness. Taguchi, orthogonal array , and fuzzy rules The parametric deduction scheme was proposed and applied to achieve the optimum CNC finish turning parameters under the considerations of SR. A confirmation experiment of the optimum deduction parameters was calculated to indicate the effectiveness of the proposed fuzzy deduction optimization method.

Through the confirmation test, the experimental results validate the potency that the SR can be advanced from our parametric deduction optimization technique.[26] Om PrakashVerma, Himanshu Gupta (2012) The work presents in this paper water bath temperature control system which is most widely used process in the process industry. Water bath Fuzzy logic Fuzzy Logic Controller (FLC) based non-linear control for water bath temperature to obtain the desired output water temperature of water bath and to implement them in a real world environment.[20] Rajyalakshmi.G, Venkata Ramaiah. P (2013) This paper discusses the application of the Taguchi method with fuzzy logic to optimize the machining parameters for Wire electrical discharge machining of Inconel 825 with multiple characteristics. A multi-response performance index (MRPI) was used for optimization. The machining parameters viz., pulse on time, pulse off time, corner servo voltage, flushing pressure, wire feed, wire tension, spark gap voltage, servo feed were optimized with consideration of multiple performance characteristics Taguchi, orthogonal array , and fuzzy rules The Taguchi method with fuzzy logic technique using MRPI converts the multiple performance characteristics into single performance characteristics and, therefore, simplifies the optimization procedure.[21] Fuzzy rules may be expressed in terms such as ``If the room gets hotter, spin the fan blades faster'' where the temperature of the room and speed of the fan's blades are both imprecisely (fuzzily) defined quantities, and ``hotter'' and ``faster'' are both fuzzy terms. Fuzzy logic, with fuzzy rules, has the potential to add human-like subjective reasoning capabilities to machine intelligences, which are usually based on bivalent boolean logic.

[Wang, 1993][28] provides an excellent introduction to fuzzy logic and how it can be applied to AI, and [Kosko, 1993] is an excellent popular science book describing all aspects of fuzzy logic. LOTFI A. ZADEH (1965) Introducing of Fuzzy sets Mathematical Information and Control. (1973) New Approach to the Analysis of Complex Systems and Decision Processes Fuzzy logic .The approach provides an approximate and effective means of describing the behavior of complex system.

For the most part, the fuzzy rules that are used in control systems are hand-crafted by the designers of the systems, and machine learning is rarely employed. As such, it can be argued that this human input into the system's design constitutes a homunculus, and that such systems can never be independently intelligent.

This research work aimed to exploit this in order to investigate the effectiveness of machine learning algorithms when compared to human learning processes. The fuzzy rules will serve well as they provide an excellent means of compactly representing what the human has learned in a way that is both accurately human-like and subjective, and can be used in controlled experiments to be compared against standard machine learning algorithms.[21] VI. Conclusion This type of optimization is a difficult method .As this work not only optimization using orthogonal array, but will also be the satisfactory fuzzy technique for improving material removal rate in Micro drilling. The competition of manufacturing industry will then be economically excited through the proposed development in this paper.

VII. References [1] [2].

[3] [4] [5].

[6] [7].

[8] [9].

[10] [11].

Azlan Abdul Rahman,AzuddinMamat,” Effect of Machining Parameters on Hole Quality of Micro Drilling for Brass”Vol 3 no.5 pp.221-230.

AshishBharti,S.K.Moulick ,(2013),” Parametric optimization of multi response factors in micro drilling operation “, International Journal of Scientific & Engineering Research, Volume 4, Issue 7 pp-1157-1163.

B.Y. Lee, H.S. Liu and Y.S. Tarng, (1996). Modeling and Optimization of Drilling Process. Department of Mechanical ManufactureEngineering, National Huwei Institute of Technology, Huwei, 632, pp. 1-9.

B.Y. Lee, H.S. Liu and Y.S. Tarng, (1998). Modeling and optimization of drilling process, Journal of Materials Processing Technology, 74,pp. 149–157 B.K Hinds, G.M Treanor ,(2011)” Analysis of stresses in micro-drills using the finite element method” International Journal of Machine Tools and Manufacture.

C. Lin, S.M. Kang and K.F. Ehmann, (1992). Planar Micro-Drill Point Design and Grinding Methods, Transactions of the North AmericanManufacturing Research Institution of SME, pp. 173–179.

Dong-Woo Kim1, Myeong-Woo Cho , Tae-Il Seo and Eung-Sug Lee (2008).” Application of Design of Experiment Method for Thrust ForceMinimization in Step-feed Micro Drill” Sensors, 8,pp. 211-221 F.H. Jung and D.L Psang, (2002). Mathematical model of multiflute drill point, International Journal of Machine Tools & Manufacture, 42,pp. 1181–1193.

Hongyan Shi, Fumin Song, Lianyu Fu, ,(2011)” Experimental study on drilling force in printed circuit board micro drilling proces” Circuit World (impact factor: 0.44). 02/2011; 37(1):24-29. DOI:10.1108/03056121111101250 J. Zhang, and A. Knoll, “Designing Fuzzy Controllers by Rapid Learning,” FuzzySets and Systems, 101, 287-301, 1999 Jung Soo Nam, Pil-Ho Lee,Sang Won Lee,(2011) “Experimental characterization of micro-drilling process using nanofluid minimum quantity lubrication” 10.1016/j.ijmachtools,

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[13] 14].

[15] [16] [17].

[18] [19].

[20].

[21].

[22].

[23] [24].

[25].

[26] .

[27].

[28].

[29] [30]

J.Pradeep Kumar, P.Packiaraj,(2012), “EFFECT OF DRILLING PARAMETERS ON SURFACE ROUGHNESS, TOOL WEAR, MATERIAL REMOVAL RATE AND HOLE DIAMETER ERROR IN DRILLING OF OHNS “IJAERS/Vol. I/ Issue III/April-June, 2012pp-150-154 .Kamal Hassan, Anish Kumar*, M.P.Garg*, (2012) “Experimental investigation of Material removal rate in CNC turning using Taguchi method “International Journal of Engineering Research and Applications Vol. 2, Issue 2pp.1581-1590.

K.K.Gupta,Prof. TapanJain,ManishDeshmukh ,(2013) ,”Optimization of Process Parameters in HighRPM Micro Drilling Machine”IJIET,Vol 2,pp-128-130.

L.A. Zadeh, “Fuzzy Sets,” Information and Control, 8, 338-352, 1965 L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,” IEEE Transactions on Systems, Man, and Cybernetics, SMC- 3(1), 28-44, January 1973.

Lan T.-S., Lo C. Y., Wang M.-Y. and Yen A-Y, (2008), “Multi Quality PredictionModel of CNC Turning Using Back Propagation Network”, Information M. Black, “Vagueness: An Exercise in Logical Analysis,” Philosophy of Science, 4,427-455, 1937 M.M. Okasha, P.T. Mativenga, N. Tian-SyungLan(2010), Parametric Deduction Optimization for Surface Roughness, American Journal of Applied Sciences 7 pp-1248-1253 Om PrakashVerma, Himanshu Gupta, (2012) Fuzzy Logic Based Water Bath Temperature Control System . International Journal of Advanced Research in Computer Science and software Engineering Volume 2, Issue 4, pp-333-336.Driver, L. Li (2010), Sequential laser and mechanical micro-drilling of Ni superalloy for aerospace application , CIRP Annals - Manufacturing Technology,pp-199-202,00.

Rajyalakshmi.G, VenkataRamaiah.P ,(2013),” Optimization of Process Parameters of Wire Electrical Discharge achiningUsing Fuzzy logic Integrated with Taguchi Method” International Journal of Scientific Engineering ndTechnology (ISSN : 22771581)Volume No.2, Issue No.6, pp : 600-606 Sahoo P., Barman T. K. and Routara B. C., (2008), “Taguchi based practicaldimension modeling and optimization in CNC turning”, Advance in ProductionEngineering and Management, Volume 3, Number 4, pp. 205-217.

Singh H. and Kumar P., (2006), “Optimizing Feed Force for Turned Parts throughthe Taguchi Technique”, Sadhana, Volume 31, Number 6, pp. 671–681.

Singh H. and Kumar P., (2004), “Tool wear optimization in turning operation by Taguchi method”,Indian Journal of Engineering & Material Sciences Volume 11, pp. 19-24.

Singh H. (2008), “Optimizing Tool life of Carbide Inserts for Turned Parts using Taguchi Technique”, Volume II , International MultiConference of Engineers and Computer scientists pp.978-988.

Tian-SyungLan,(2011). Fuzzy Parametric Deduction for Material Removal Rate Optimization, Journal of Mathematics and Statistics,7(1), pp.51-56 “The future is fuzzy,” Newsweek, May 1990 Thamizhmanii S., Saparudin S. and Hasan S., (2007), “Analysis of SurfaceRoughness by Using Taguchi Method”, Achievements in Materials and Manufacturing Engineering, Volume 20, Issue 1-2, pp. 503-505.

Tian-SyungLan,(2011). Fuzzy Parametric Deduction for Material Removal Rate Optimization, Journal of Mathematics and Statistics,7(1), pp.51-56 THIREN G. POKAR ,PROF. V. D. PATEL,(2013) “Optimization and Modeling of Micro drilling Process Parameters” International Journal of Research in Modern Engineering and EmergingTechnologyVol. 1, Issue: 2, pp 26-30.

VIII.

Acknowledgements

The authors would like to express sincere thanks to SantGadgeBaba Amravati University for registration of this research work and also express thanks to College of Engineering and Technology,Akola for availing necessary laboratories facilities.The authors also gratefully acknowledge for necessary financial support of Government College of Engineering,Jalgaon and also grateful to Jawaharlal Darda Institute of Engineering and Technology,Yavatmal for their time to tme help.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Application of Various Multi Criteria Decision Making Methods for the Assessment of Different Waste Water Treatment Systems Case Study: JSPM Hadapsar Campus, Pune Poonam K. Wakchaure1, Dr. Sameer S. Shastri2 Department of Civil Engineering, Sinhgad College of Engineering Savitribai Phule Pune University Vadgaon (Bk), Pune- 411 041 INDIA Abstract: Decision making in Environmental Engineering is a complex task due to the existence of various socio-economic, technical and environmental factors. Multi Criteria Decision Making (MCDM) analysis is used to make comparative assessments of various available alternatives by considering various criteria and sub criteria in complex situation. The paper shows the application of three MCDM techniques viz. - AHP, ANP and TOPSIS to a hypothetical problem concerning the choice of the most sustainable wastewater treatment technology amongst Activated Sludge Process (ASP), Bio Tower and Sequential Batch Reactor (SBR) for JSPM Hadapsar, an educational institute campus in Pune. These models enable to consider social, economic, technical and environmental aspects along with their sub criteria and to compare them to find the best alternative. The AHP, ANP and TOPSIS techniques are applied through specific software packages like Expert Choice 11, Super Decisions and Topsis Solver 2012 respectively. Five stakeholders from various field are consulted and their inputs and judgments are incorporated in the decision making process. A comparison of the judgments and merits obtained from the different models shows that Sequential Batch Reactor emerges as the most sustainable waste water treatment technology for JSPM Hadapsar, Pune.

Keywords: Decision making; Multi Criteria Decision Making; Analytic Hierarchy Process; Wastewater treatment; Activated Sludge Process I. Introduction Industrialization and civilization bring the prosperity but it has got dark side as well. Because of the discharge of untreated waste water from industries & habitations, stress is created on the ecology. According to the estimation of Thermax Waste Water Treatment and Recycling, around 60% of effluent generated from Industries & 74% of sewage generated in India goes untreated into the ecosystem [13]. Lack of waste water treatment systems creates extremely harmful effect on the entire civilization. A government report on Urban and Industrial Water Supply and Sanitation for the Twelfth Five - Year Plan (2012-2017) highlights some points about wastewater in India and are briefed below: According to this report, an estimated 38354 million liters per day (MLD) sewage is generated in major cities of India, but the sewage treatment capacity is only of 11786 MLD. Also the cost is another important factor in this regard. According to the government report, in the mid-1990s, when the first-generation sewage treatment plants were built, they cost Rs 20 lakh to Rs 30 lakh (2-3 Million) per MLD. Today, the same plants cost close to Rs 1 crore (10 Millions) per MLD to build [14].

Considering educational institute’s scenarios in India, either they don’t have sewage treatments plants in their campus or they are not adopting sustainable waste water treatment method. Because, while selection of the waste water treatment technology (WWT), not only the economic factor but also all the other factors like social, technical, political, environmental influence the selection procedure. Due to this, the decision making can be considered as a complex system with several elements that have to be included in the evaluation. In such situations, Multi Criteria Decision Making (MCDM) analyses are used to make comparative assessments of alternative options. These methods allow several criteria to be considered simultaneously in a complex situation and are designed to help Decision Makers or stakeholders to integrate the different options [10].

The case illustrated in this paper refers to the selection of the most sustainable WWT technology for JSPM Hadapsar, an educational institute in Pune, India through the application of different MCDM methods like: Analytic Hierarchy Process (AHP), Analytic Network Process (ANP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). These methods are based on a multi criteria measurements theory.

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II. Materials and Methods Basic MCDM procedure is as follows: 1. Problem/Goal identification 2. Setting evaluation criteria and their weights 3. Defining feasible alternatives 4. Evaluating the alternatives 5. Making a decision A. MCDM Technique- Analytic Hierarchy Process (AHP) The Analytic Hierarchy Process (AHP) is a structured technique for organizing and analyzing complex decisions. It is developed by Thomas L. Saaty. In this analysis, a complex problem can be divided into several sub-problems that are organized according to hierarchical levels, where each level denotes a set of criteria or attributes related to each sub problem. The top level of the hierarchy denotes the goal of the problem and the intermediate levels denote the factors of the respective upper levels. The bottom level contains the alternatives considered when achieving the goal [8] [10] [11].

The analysis is based on three fundamental principles viz; breaking down the problem; pairwise comparison of the various alternatives; synthesis of the preferences.

The first step of the analysis consists in subdividing the decision making problem into several levels in such a way that they form a hierarchy with unidirectional hierarchical relationship between levels. Once the hierarchy is constructed, the decision makers systematically evaluate its various elements by comparing them to one another two at a time, with respect to their impact on an element above them in the hierarchy [4]. In making the comparisons, the decision makers can use their judgments about the elements' relative meaning and importance.

The relative importance values are determined on a 9-point scale, the so- called “Saaty’s Fundamental Scale” [4].

Table I Shows the fundamental scale of pairwise comparison used in AHP Intensity of Importance 1

Definition Equal Importance

Explanation Both the elements are favored equally

3

Moderate Importance

Experience and Judgment moderately favors one element over another

5

Strong Importance

Experience and Judgment strongly favors one element over another

7

Very Strong Importance

One element is favored very strongly over another

9

Extreme Importance

The evidence favoring one element over another is of the highest possible order of affirmation Intensities of 2, 4, 6, and 8 can be used to express intermediate values. Intensities of 1.1, 1.2, 1.3 etc. can be used for elements that are very close in importance.

(Source: Saaty, 1996)

The numerical judgments established at each level of the hierarchy make up pair matrices [4]. After comparison matrixes are created, relative weights of the elements of each level with respect to an element in the adjacent upper level are computed as the components of the normalized eigenvector associated to the largest Eigen value of their comparison matrix. Eigenvector method yields a natural measure of consistency [4].

B. MCDM Technique- Analytic Network Process (ANP) Many decision making issues cannot be structured hierarchically, because they imply interactions and dependencies between the highest elements and the lowest. In order to take into account the complexities of real world problems, Saaty proposed the Analytic Network Process (ANP) model, a generalization of the AHP method [4] [12]. The ANP provides a solution for problems which cannot be structured hierarchically. The ANP represents the decision problem as a network of criteria and alternatives (called elements), grouped into clusters. The ANP requires a network structure to represent the problem, as well as a pairwise comparison to establish the relationships within the structure [12].

The analysis is based on the four fundamental principles: development of the structure of the decision- making process, pairwise comparison, super matrix formation and final priorities. The first step of the analysis consists in subdividing the objective into groups (“clusters”) that are made up of various elements (“nodes”) and alternatives which ultimately forms a network. The relationships between the different parts of the network must be identified [4].Similar to AHP, in ANP also, a series of pairwise comparisons are made to establish the relative importance of the different elements with respect to a certain component of the network. As for the AHP methodology, in ANP also the comparisons are made with the Saaty’s Fundamental Scale [4].

C. MCDM Technique- Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) TOPSIS was presented by Hwang and Yoon. TOPSIS is a multiple criteria method to identify solutions from a finite set of alternatives based upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. TOPSIS can incorporate relative weights of criterion importance. The basic principle of this method is that the chosen alternative must have the shortest distance to the positive ideal solution and the farthest distance to the negative ideal solution [5].

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III. Presentation of the Case The AHP, ANP and TOPSIS methodologies are applied to a hypothetical problem concerning the choice of a waste water treatment technology. Three different waste water treatment technologies have been taken into consideration, namely Activated Sludge Process (ASP), Biotower, and Sequential Batch Reactor (SBR), in relation to JSPM educational institute located at Hadapsar, Pune and the integral part of the study was to determine the most suitable waste water treatment solution at above mentioned location.

These types of methods require consulting experts who can enrich the decision with their judgments regarding the problem. Therefore, five Experts from the sector of waste water treatment were consulted. After a couple of brain storming session and discussions with these experts, some criteria and sub criteria were identified and finalized.

Table II Shows the identified criteria and sub criteria Criteria Economic

Technical

Social

Environmental

Sub-Criteria Initial Cost Operation & Maintenance Cost Improvement aspects Energy saving System Complexity Area requirement Performances Flexibility for up gradation System durability Public opinion Acceptability Effect on surrounding properties Use of natural resources Noise Odour Nuisance Pollution (Solid, Liquid, Air) Ecofriendly

IV. Application of MCDM Methods A. Application of AHP The model has been developed through the use of the specific Expert choice 11 software. Fig. 1 shows the hierarchical structure which represents the decision-making problem in AHP. Figure 1 Hierarchical structure in AHP method Best Suitable WWT Technology for Educational Institute in Urban Area like JSPM Hadapsar Economic Aspect

Technical Aspect

Environmental Aspect

Social Aspect

Initial Cost

System Complexity

Use Of Natural Resources

Public Opinion

Operations & Maintenance Cost

Area Requirement

Noise

Acceptability

Improvement Aspect

Performances

Odor Nuisance

Effect on Surrounding Properties

Energy Saving

Flexibility For Upgradation

Pollution (Solid, Liquid, Air)

Efficiency In Treatment

Eco Friendly

System Durability

Activated Sludge Process

Sequential Batch Reactor

Bio-Tower

Once hierarchy is constructed, pairwise comparison is carried out. Following Table 3 shows the representative table, showing pairwise comparison for AHP model.

Table III Shows the comparison of the relative importance of criteria with respect to: Goal Economic Technical Social Environmental

Economic

Technical

1 1/6 1/7 1/6

6 1 1/8 1/7

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Social 7 8 1 1/7

Environmental 6 7 7 1

Priority vector 0.24 0.63 0.03 0.10

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The performance graph shows the result obtained by combining judgments of all decision makers. Fig.2 shows the performance graph.

Figure 2 Performance graph resulting from the AHP model

Performance graph indicates that in AHP model, Biotower (38.9%) is given highest priority over Sequential Batch Reactor (35.2%) and Activated Sludge Process (25.9%).

B. Application of ANP The ANP application has been developed through the use of the specific Superdecision software version 2.2.3.

Fig.3 shows the network structure in ANP model.

Figure 3 Network structure in ANP method

The central part of this method is a network. Hence, in this method problem is broken down into closely interconnected clusters and nodes with inner and outer dependencies and feedbacks which form a network. In the present problem, there are four distinguished clusters representing main criteria and a cluster representing alternatives. Individual nodes represent sub criteria. All the elements in the clusters are connected to the alternatives.

According to ANP method, the most sustainable waste water treatment solution is SBR (49.6%), followed by Biotower (33%) and ASP (17.4%).

C. Application of TOPSIS In TOPSIS method, pairwise comparison is not carried out as in case of AHP and ANP. Instead of that weights are given to rate each alternative and criteria individually. Following Table 4 shows the qualitative labels used for the ratings of the alternatives according to the importance weighting of each sub criterion for TOPSIS method. Each label is associated to a weight in order to simplify further statistical analyses.

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Table III Shows the linguistic labels and weights used in TOPSIS method Label vL L mL m mH H vH

Description Very low Low Medium low Medium Medium high High Very high

Weights 0 0.1 0.3 0.5 0.7 0.9 1.0

Following Table 5 represents the data of the weights that each expert gave to the group of criteria.

Table V Shows weights for each group of criterion according to experts (E1-E5) and group decision (G) Experts E1 E2 E3 E4 E5 G

Economic aspect 0.9 0.5 0.7 0.9 0.9 0.78

Technical aspect 1.0 0.7 0.9 0.9 0.7 0.84

Criteria Social aspect 0.7 0.1 0.3 0.7 0.9 0.54

Environmental aspect 0.7 0.9 0.3 0.5 1.0 0.68

In TOPSIS method the most sustainable solution refers to SBR (0.592), followed by the ASP (0.499) and Biotower (0.452).

V. Results and Discussion From the analysis of the final results it is possible to note some findingsIn the AHP model, the most suitable solution is the Biotower (38.9%) followed by the Sequential Batch Reactor (35.2%) and Activated Sludge Process (25.9%). The ANP model shows that the most suitable solution is Sequential Batch reactor (49.6%), followed by Biotower (33%) and ASP (17.4%). The TOPSIS model confirms the ANP results and the priority list obtained from the application of the TOPSIS proves that the best technology is represented by the Sequential Batch Reactor (0.592), while ASP has a better score than before (0.499) and the preference for the Biotower is low (0.452).

From the above results, it emerges that the Biotower and SBR systems have high scores in the AHP model but the percentage difference between the two technologies increases in the ANP model, where SBR becomes the most preferable solution. AHP considers one-way interactions while ANP considers interdependencies among the criteria and alternatives that apply additional effect in the model. In fact, AHP suffers from the limitation of only being applicable to straightforward hierarchical structures and ANP can solve complex decision problems that involve several issues. Hence, it is possible to conclude that, the more the model is complicated, the greater are the chances to arrive at the correct decision as several elements related to problem being taken into account.

Also TOPSIS supports the ANP results, though it calculates the priorities in completely different manner than AHP and ANP. But this does not mean that, the results determined from the AHP would necessarily differ from those determined from the ANP and TOPSIS. However, in cases involving interdependence and feedbacks in the problem, the ANP model is suitable method for selecting the best alternative.

VI. Conclusions The results obtained from the development of the three models- AHP, ANP and TOPSIS show that the Sequential Batch Reactor is the most sustainable solution for the waste water treatment, in the case of educational institute in urban area like JSPM Hadapsar.

Also it can be concluded that in this present study, technical criteria and economic criteria continue to dominate above environmental and social criteria.

The present study illustrates some insights of AHP, ANP and TOPSIS methods as well. To start with, the present work indicates that the AHP and ANP analyses are suitable to deal with complex decision-making problems because they allow several criteria to be considered and compared in a systemic way. These approaches make the decision process more traceable, justifiable and reliable and thus help to move forward with more methodological decisions. Secondly, The ANP method overcomes the limitation of AHP of one way interaction as it incorporates the feedbacks and interdependent relationships between elements from different levels or within levels, which are not considered in AHP. Thirdly, the TOPSIS method is useful when quantitative data is available. TOPSIS incorporates the importance of the weightage in the comparison procedures and finally considering the final ranking of the alternatives that come from the AHP and ANP models, the gap among the scores of the options increases as the level of complexity of the technique increases.

Thus ANP gives more consistent results than AHP and these results are confirmed by TOPSIS method though TOPSIS calculates the priorities in a far different manner than AHP and ANP.

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References [1] [2] [3] [4]

[5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Bruntland, G. (Ed.), Our Common Future, Oxford University Press, Oxford 1987.

Chang-Tien Lu,Dechang Chen,Youfeng Kou.:Multivariate Spatial Outliner Detection. International Journal on Artificial Intelligence Tools13 (4), 801-811(2004).

Dr. Ling Xu & Dr. Jian-Bo Yan, “Introduction to Multi-Criteria Decision Making and the Evidential Reasoning Approach” (2001), Manchester School of Management.

Marta Bottero, Elena Comino,Vincenzo Riggio, “Application of the Analytic Hierarchy Process and the Analytic Network Process for the assessment of different wastewater treatment systems” Environmental Modeling & Software 26(2011), 12111224.

Markovic Zoran, “Modification of TOPSIS method for solving of multi criteria tasks”. Yugoslav Journal of Operations Research, (2010).

Murat & Meral Socu., The Analytic Hierarchy & Analytic Network Processes. Hacettepe Journal of Mathematics & Statics .Volume 32 (2003).65-73.

M. D. Gomez-Lopez, J. Bayo, J.M. Angosto, “Decision support in disinfection technologies for treated wastewater reuse”: Environmental Modeling & Software, Journal of Cleaner Production 17(2009), 1504-1511.

Saaty, T.L.,The Analytic Hierarchy Process. McGraw Hill, New York. 1980.

Saaty, T. L. (1996) Decision Making for Leaders: The Analytical Hierarchy Process for Decisions in a Complex World, The Analytical Hierarchy Process Series, Vol. 2, pp 71-74.

Saaty, T.L., Vargas, L.G., 1990.The Analytic Hierarchy Process Series. University of Pittsburg, Pittsburg.

Saaty, R.W., 2003.Decision Making in Complex Environment: The Analytic Hierarchy Process (AHP) for Decision Making and the Analytic Network Process (ANP) for Decision Making with Dependence and Feedback. RWS Publications, Pittsburgh.

Saaty, T.L., Vargas, L.G., 2006. Decision Making with the Analytic Network Process. Springer Science, New York.

Thermax Water & Waste Solutions Applications, Waste Water Treatment & Recycling Twelfth Five Year Plan – 2012-2017, Report of the Working Group on Rural Domestic Water and Sanitation.

Ulukan, H.Z., “Multi-criteria decision making (MCDM) of solid waste collection methods using Life Cycle Assessment (LCA) outputs”: Computers & Industrial Engg., CIE (2009), 584-589.

Acknowledgments We are indebted to the anonymous referees for their critical review and suggestions that enabled us to obtain this paper. We are also thankful to the JSPM Hadapsar, Pune for providing valuable information. We would also like to express our sincere gratitude towards Creative Decision foundation, Expert Choice and TOPSIS team for online availability of student version software and technical support provided during execution of this work.

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American International Journal of Research in Science, Technology, Engineering & Mathematics

Available online at http://www.iasir.net

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

An Experimental Study of Thermal Performance of Concentric Annular Heat Pipe Nishtha Vijra1, Tejinder Paul Singh2 2 1 Assistant Professor, Mechanical Engineering Department, Director and Professor, Symbiosis Institute of Technology (SIT), Symbiosis International University (SIU), Near Lupin Research Park, Gram: Lavale, Taluka: Mulshi, Pune, Maharashtra - 412 115, INDIA Abstract: Higher heat transfer rates and better cooling can be achieved by Concentric Annular Heat Pipes (CAHP) in comparison to conventional heat pipes due to increase in heat transfer area. Experiments were conducted to find the effect of variation in heat input and inclination angle on the performance of CAHP. SS304 was selected as the container material and water as the working fluid for CAHP. Inner surface of the outer pipe was covered with single layer of screen mesh wick. Surface and vapor temperatures were determined for various heat inputs and inclination angles at steady state conditions and isothermal characteristics of CAHP considering uniform heating application were found. Results concluded that with increase in heat input, the vapor temperature difference between the evaporator and condenser section decreases for all inclination angles of CAHP. The temperature difference between the evaporator and condenser end in axial direction is the least for vertical position of CAHP at all levels of heat input. This depicts the isothermal characteristic of concentric annular heat pipe. Thermal resistance decreased possibly due to enhancement in nucleate boiling activity and this improved the heat transfer with increase in heat input. Least thermal resistance was found at vertical position of CAHP. Keywords: Concentric annular heat pipe; thermal resistance; inclination angle; isothermal characteristics I. Introduction Heat Pipe is a heat transfer device efficient enough to transfer heat even around 500 times higher than the best metal conductor available on earth [1]. A conventional heat pipe (CHP) consists of a sealed, evacuated, hollow, pipe like structure having wick material on the inner periphery of the container and working fluid sufficient enough to saturate the wick as shown in fig.1 (a). There are mainly three regions in a heat pipe. One end of the heat pipe is an evaporator region for heat addition, the other end is the condenser region for heat rejection and in between the two, is the adiabatic region [2]. Due to continuous heat addition, the working fluid gets converted to vapor using latent heat of vaporization and flows through the adiabatic section towards the condenser end. Here, the heat dissipation takes place in the form of latent heat of condensation and as a result the phase of the working fluid changes back to liquid. Wick plays a major role to transport the liquid back to the evaporator region by capillary forces. This cycle is repeated till the application of heat is continued. Of late, non conventional heat pipes (NCHP) have been used widely in many applications due to enhanced rate of heat transfer. Concentric Annular Heat Pipe (CAHP) is one such type of NCHP as shown in fig.1(b)[3]. As the name and figure suggest, CAHP has two concentric hollow pipes of different diameters sealed at their ends thereby forming an annular region where the wick structure is placed on the outer periphery of the inner pipe and on the inner periphery of the outer pipe. This enhances the heat flux due to increase in the overall area of the wick and the surface area of heat addition and heat rejection from the pipe [4].

Figure 1(a) and (b)[3] showing working of CHP & CAHP

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Researchers have carried out experiments on CAHP to analyze its performance under various combinations of parameters like material of pipes, their sizes, inclination angles, and working fluids and so on. Vasiliev[5] introduced coaxial heat pipe and analyzed its performance by variation in heat input in radial direction.

Faghri[6] analyzed vapor flow characteristics for steady, 2D and incompressible flow in CAHP. Faghri and Parvani[7] continued their work to study copper – water CAHP vapor flow for 2D, incompressible model and concluded that elliptic model could provide more complete simulation of vapor flow than the parabolic model.

.

The performance characteristics were compared with identical simple heat pipe and an 80% increase was registered in heat flow of CAHP. Faghri and Thomas [8] conducted experiments to find the temperature distribution and analyze the capillary limit for different inclination angles and variable heat supply on Cu-H2O CAHP. Theoretical analysis was done to predict the sonic limit. Layeghi and Borujerdi [9] worked on CAHP with partial heat transfers for laminar, 2D, steady and incompressible vapor flow for variable heat input to predict the pressure and velocity distribution. With increase in radial Reynold’s Number (Re), the performance was increased. Borujerdi and Layeghi [10] also worked to analyze steady state vapor flow and heat transfer in CAHP. For symmetric cases for low to moderate Re, vapor pressure, temperature distribution, vapor reversal and transition to turbulence phenomenon were predicted and results were compared to be in fair agreement with available data. Boo and Park [11] studied the isothermal characteristics and thermal transient response of CAHP with uniform heating for different diameter ratios and different fill ratios. Increase in the diameter ratio resulted in better performance. Effect of fill ratio on thermal resistance was more significant than the diameter ratio variation. Jalilvand et.al. [12] studied the performance of Roll Heat Pipe(RHP) used in copy machines, xerox machines or laser printers. Transient thermal response and internal temperature distribution were observed and analyzed and results were compared with theoretical analysis. Effect of rotation of RHP was studied and comparison was made with existing heat roll of the fusing unit.

The review of literature shows that some work has been done on copper as container material and water as working fluid in concentric annular heat pipe for the vapor flow analysis, thermal analysis, prediction and analysis of sonic and capillary limit for variable heat input, variable inclination angle, different diameter and fill ratio and partial heat transfer condition. Certain computer programs have also been developed to analyze the model. In CAHP, literature in any other combination of container material and working fluid could not be seen.

In the present study, experimental investigations have been done on stainless steel (SS304) as container material and distilled water as the working fluid in CAHP for determining the thermal performance with variation in heat input and inclination angle.

II. Experimental Set Up The main objective of the experimentation was to estimate the effect of variation in inclination angle and variable heat input on the thermal performance of CAHP. Total length of CAHP was 900mm with 100mm evaporator section, 270 mm adiabatic section and 530 mm condenser section. The outer diameter of outer pipe was 53mm and of 2mm thickness and the outer diameter of inner pipe was 15.9mm and of 1.6 mm thickness.

Both the pipes were sealed together to form an annulus. In the experiments performed on the copper –water combination by the researchers, almost similar sizes have been used. However, condenser section area has been increased to permit higher heat rejection [4]. The container material of CAHP was SS304. A single layer, 60 mesh screen wick of 36 gauge and 0.6 mm thickness of SS302 was used and the working fluid was distilled water. Glass wool of 2 inch thickness was used as an insulation material on the adiabatic section of CAHP. Ceramic band heater was the heating source on the outer periphery of the evaporator section of CAHP and cartridge type heater was inserted inside the inner pipe to supply heat. Total heat input was controlled by 4 ampere, closed type dimmer stat and was monitored by digital ammeter (0–5 amp.) and digital voltmeter (0– 300V). SS316 thermo wells were welded in CAHP to permit insertion of K type thermocouples to measure the vapor temperature in axial direction. Five K type thermocouples were mounted at various distances along the

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length of CAHP for surface temperature measurement. Two wooden stands were fabricated to hold CAHP in the horizontal and vertical/inclined positions. Fig. 2 represents the experimental set up for the horizontal, vertical and inclined positions of CAHP. Various levels of heat input were selected from 50W-300W with an interval of 50W. Inclination angle was varied at three different levels of 0˚, 45˚ and 90˚ to the horizontal for each heat input. Condenser section was kept above the evaporator section in the vertical and inclined positions of CAHP. All the experiments for horizontal, inclined and vertical positions were conducted by keeping both the heaters in ON position and temperature readings Figure 2: Heat pipe in Horizontal, Inclined and Vertical Position

were registered with 10 minutes interval till CAHP attained steady state condition. Thermal resistance (R th) of CAHP was calculated from equation (1) Rth = (Tave-Tavc)/Q (1) Where, Tave is the average evaporator temperature, T avc is the average condenser temperature and Q is the total heat input.

III.

Results and Discussion

Table I – Readings of Time Vs Temperature at different locations of CAHP for 200W heat input at horizontal position till achievement of steady state Distance (m)

Time(min.) / Temp.(⁰C)

10

20

30

40

50

60

70

80

90

100

110

120

0.05

TV1

51

80

105

115

125

131

136

138

139

140

141

141

0.103 0.364

TV2 TV3

51 40

78 78

103 103

113 113

123 123

128 128

133 133

135 134

136 136

137 136

138 137

138 137

0.403

TV4

40

78

103

112

122

127

132

134

135

136

137

137

0.496 0.589 0.682

TV5 TV6 TV7

40 40 40

77 77 77

103 102 102

112 112 111

122 121 121

127 126 126

132 132 131

133 133 133

135 135 134

136 135 135

137 136 136

137 136 136

0.775

TV8

40

76

101

111

120

126

130

132

134

135

136

136

0.9 0.05 0.403

TV9 TS1 TS2

40 61 36

75 87 76

100 108 99

111 118 109

120 128 120

125 134 125

130 138 130

132 140 132

133 141 132

134 142 134

135 143 135

135 143 135

0.589

TS3

36

74

98

109

119

124

130

132

132

134

134

134

0.682

TS4

36

74

98

108

118

124

129

130

132

133

133

133

0.775

TS5

36

73

97

108

118

124

129

130

132

133

133

133

Table 1 shows the results of experimentation for 0° inclination angle (horizontal position) and power input of 200W in the evaporator section of CAHP. Vapor and surface temperature distribution along the length was plotted as shown in fig. 3 (a) and (b) with a time interval of 10 minutes. 0.05 meter axial distance indicated the centre of the evaporator region, 0.103 m. and 0.364 m. are the distances at the approximate entrance and exit of adiabatic region and rest of the zone is the condenser region. From the graphs of these figures, the following inferences can be made-(i)The temperature increased continuously with time till the achievement of steady state after 120 minutes from the evaporator till the condenser region at all times.(ii)For an initial period of 70 minutes the increase in the temperature was substantial which reflected that heat supplied was used to heat the working fluid where as with further increase of time, evaporation and condensation processes were clearly visible due to nearly uniform temperature in the respective zones.

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150

0.05m 0.103m 0.364m 0.403m 0.496m 0.589m 0.682m 0.775m 0.9m

Temperature (°C)

100 50 0 0

5 10 Time (10 *min) (a)

15

Temperature (°C)

Such experiments were conducted for different levels of heat input varying from 50W to 300W for horizontal, inclined and vertical positions of CAHP. The trend shown by graphs was found to be similar for all levels of heat input.

Figure 3(a) Vapor temperature distribution and (b) Surface temperature distribution w.r.t. time for 200W heat input in horizontal position 160 140 120 100 80 60 40 20 0

0.05m 0.403m 0.589m 0.682m 0

5

10

15

0.775m

Time (10 min) (b)

142 140 138 136 134 132

Temperature (°C)

Temperature (°C)

Fig.4 (a) depicts steady state temperature distribution of the vapor and figure 4 (b) of the surface with axial distance of CAHP. It is seen that-(a) Surface temperature of the evaporator region was higher than the vapor temperature due to heat supply from the surface where as surface temperature of the condenser region was lower than corresponding vapor temperature due to heat rejection.(b) The temperature decreased continuously from evaporator to condenser region.(c) Adiabatic region showed almost constant temperature due to no heat loss condition. (d) The difference in surface and vapor temperature of the evaporator section was 2°C and condenser section was 3°C at same axial distance. This can be due to very high thermal conductivity of CAHP. (e) For all other levels of heat input a similar phenomenon was observed. (f) Vapor temperature difference from the evaporator to condenser region was only 6°C which showed nearly isothermal behavior of CAHP due to very high heat transfer coefficient during evaporation and condensation. It was of the similar order for all other levels of heat input except for 50W where this was 10°C. This reflected that the size of CAHP was large enough for 50W in horizontal position and it could handle higher heat input. (g) Due to natural convection used for the present experiment, surface heat transfer coefficient was low which was due to the resistance to heat rejection to the surroundings and this was indicated by the almost uniform surface temperature of the condenser region.

Figure 4 (a) Steady state vapor temperature distribution and (b) Surface temperature distribution w.r.t. axial distance for 0° inclination angle 144 142 140 138 136 134 132 130 128 126 0.05m

0.403m

0.589m

0.682m

0.775m

Axial Distance (b)

Axial Distance (a)

Table II – Readings of Time Vs Temperature at different locations of CAHP for 200W heat input at inclined (45⁰) position till achievement of steady state Distance (m) 0.05 0.103 0.364 0.403 0.496 0.589 0.682 0.775 0.9 0.05 0.403 0.589 0.682 0.775

Time(min.)/Te mp.(⁰C) TV1 TV2 TV3 TV4 TV5 TV6 TV7 TV8 TV9 TS1 TS2 TS3 TS4 TS5

10 58 57 57 50 50 45 45 40 40 66 40 40 40 38

20 80 79 79 79 79 78 78 77 75 88 77 76 76 75

30 96 95 95 95 95 94 94 93 91 100 93 92 92 91

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40 113 112 112 112 112 111 111 110 108 115 110 109 109 108

50 128 127 127 127 127 126 126 125 123 130 125 124 124 123

60 138 137 137 137 137 136 136 135 133 140 135 134 134 133

70 140 139 139 139 139 139 138 138 137 142 138 138 137 136

80 144 143 143 143 142 142 141 141 140 146 141 140 140 140

90 146 145 145 145 144 144 144 143 141 148 143 142 142 140

100 147 146 146 146 146 145 145 144 142 149 144 143 143 142

110 148 147 147 147 147 146 146 146 145 150 145 144 144 143

120 148 147 147 147 147 146 146 146 145 150 145 144 144 143

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0

5

10

50 mm 103 mm 364 mm 403 mm 496 mm 589 mm 682 mm 775 mm 900 mm

15

Time (10*min.) (a)

160 140 120 100 80 60 40 20 0

50 mm 403 mm 589 mm 682 mm 10 MIN 20 MIN 30 MIN 40 MIN 50 MIN 60 MIN 70 MIN 80 MIN 90 MIN 100 MIN 110 MIN 120 MIN

160 140 120 100 80 60 40 20 0

Temperature (°C)

Temperature (°C)

Table 2 shows the readings for 45° inclination angle (inclined position) and a power input of 200W in the evaporator section of CAHP. Although, steady state has been achieved in same period of time as for the horizontal position but the temperatures for the inclined position are slightly higher than those for the horizontal position, which depicts that gravity assists in heat transfer.

Figure 5 (a) Vapor temperature distribution and (b) Surface temperature distribution w.r.t. time for 200W heat input in inclined (45⁰) position

775 mm

Time (b)

149 148 147 146 145 144 143 50 103 364 403 496 589 682 775 900 mm mm mm mm mm mm mm mm mm

152 150 148 146 144 142 140 138

Temperature in ˚C

Temperature in ˚C

Fig. 5 shows similar graphs for the position of CAHP inclined at 45° to the horizontal. Time required to achieve steady state was the same as for the horizontal position. At all levels of heat input for inclined position, similar pattern of graphs was obtained.

Figure 6 (a) Steady state vapor temperature distribution and (b) Surface temperature distribution w.r.t. axial distance of CAHP for inclined(45⁰) position

50 mm

403 mm

589 mm

682 mm

775 mm

Axial Distance (b)

Axial Distance (a)

The conclusions that can be drawn from the fig. 6 (a) and 6 (b) are -The difference in the surface temperature and vapor temperature of the evaporator section was 2°C and condenser section was 3°C at the same axial distance as that of the horizontal position. The difference in vapor temperature of the evaporator section and condenser section was found the least as 3°C of all the heat inputs. At all other levels of heat input this was 4°C to 5°C.This was comparatively lower than that for horizontal position. This could be due to gravity assisting working fluid flow inside CAHP. Table III– Readings of Time Vs Temperature at different locations of CAHP for 200W heat input at 90⁰ inclination angle till achievement of steady state Distance (m)

Time(min.)/ Temp.(⁰C)

10

20

30

40

50

60

70

80

90

100

110

120

0.05

TV1

55

80

110

122

133

138

140

142

143

144

145

145

0.103

TV2

55

80

110

121

131

137

140

142

143

144

145

145

0.364

TV3

50

79

109

121

131

137

139

141

142

143

144

144

0.403

TV4

50

79

109

121

131

137

139

141

142

143

144

144

0.496

TV5

45

79

108

120

130

136

138

140

141

142

143

143

0.589

TV6

45

78

108

120

130

136

137

140

141

142

143

143

0.682

TV7

45

78

108

120

130

135

137

139

140

141

142

142

0.775

TV8

45

78

107

119

128

135

137

138

139

140

141

141

0.9

TV9

45

77

107

119

128

134

136

138

139

140

141

141

0.05

TS1

61

87

112

124

136

140

142

144

145

146

147

147

0.403

TS2

45

77

107

119

129

135

137

139

140

141

142

142

0.589

TS3

40

76

106

118

128

134

135

138

139

140

141

141

0.682

TS4

40

76

106

118

128

134

135

137

138

139

140

140

0.775

TS5

40

76

106

117

127

134

135

136

138

138

139

139

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160 140 120 100 80 60 40 20 0

50 mm

Temperature (°C)

Temperature (°C)

Table 3 shows the results of experimentations carried out for 90° inclination angle (vertical position) and power input of 200W in the evaporator section of CAHP. Although the range of temperature at steady state is highest for inclined position as depicted from all of the above readings shown in table (1), table (2) and table (3), but still the temperature difference between the evaporator and condenser end in axial direction is the least for vertical position of CAHP at all levels of heat input.

Figure 7 (a) Vapor temperature distribution and (b) Surface temperature distribution w.r.t. time for 200W heat input in vertical (90°) position 103 mm 364 mm 403 mm 496 mm

160 140 120 100 80 60 40 20 0

5

10

15

Time (10*min.) (a)

.403m .589m .682m

10 30 50 70 90 110 MIN MIN MIN MIN MIN MIN

589 mm 0

0.05m

682 mm

.775m

Time (b)

775 mm

146 145 144 143 142 141 140

Temperature ⁰C

Temperature ⁰C

Figure 8 (a) Steady state vapor temperature distribution and(b)Surface temperature distribution w.r.t. axial distance of CAHP for vertical(90⁰) position for 200W heat input 148 146 144 142 140 138 0 0

500

1000

Axial Distance (mm) (a)

200

400

600

800

1000

Axial Distance (mm) (b)

Thermal Resistance (°C/W)

For the vertical position of CAHP also similar trends were observed as shown in fig. 7(a), 7(b), 8(a) and 8(b).The difference in the vapor temperature of the evaporator section and condenser section was 4°C and was of the same order for all other levels of heat input also. The trend of difference in the vapor temperature was found to decrease from the horizontal position which depicted that gravity effect assisted the flow of working fluid.

Thermal resistance of CAHP was calculated and compared for all the three positions as shown in fig.9. The results that the graph reflects are– (i) with increase in heat input, thermal resistance of the CAHP decreased and heat transfer was more effective. Enhancement of boiling technique could be a possible reason for this as improvement in nucleate boiling activity with increase in heat input in the evaporator region could be reducing the thermal resistance and hence the overall thermal resistance. (ii) Vertical position depicted minimum thermal resistance at all levels of heat input followed by inclined and then the horizontal position of CAHP. (iii) It also depicted that with increase in inclination angle with horizontal, gravity plays an effective role even for heat pipes with wick.(iv) Transportation of the heat flow rate under the driving temperature difference was found to improve with increase in inclination angle due to decrease in thermal resistance.

0.25 0.2 horizontal

0.15 0.1

inclined

0.05

vertical

0 50 100 150 200 250 300 Heat Input (W)

Figure 9 Thermal resistance for various heat input at different inclination angles IV. Conclusions The experimental analysis of an air cooled, concentric annular heat pipe was carried out to determine the thermal performance for various levels of heat input from 50W to 300W with an interval of 50W under steady state condition for three inclination angles and the results were plotted. Stainless steel container material with

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water as a working fluid was tested up to 300W, 150°C for CAHP which was not seen in the literature earlier.

CAHP attained steady-state nearly after 110-120 minutes of operation for various levels of heat input in the evaporation section. The variation of temperature along the length of heat pipe at various time intervals explained the process of evaporation and condensation inside CAHP for different stages of operation. The difference in the vapor temperature of the evaporator and condenser region decreased with increase in heat input for all inclination angles and minimum temperature difference of 3°C was found between the evaporator and the condenser section. This depicts the isothermal characteristic of a heat pipe. With increase in heat input in the evaporator the operating temperature of CAHP also increased due to surface convective resistance of the condenser region. With increase in heat input, heat transfer improved as thermal resistance decreased for all positions of CAHP. One of the possible reasons could be due to improvement in boiling technique. Thermal resistance was found to be the least for vertical position due to the gravitational forces assisting the process of heat transfer.

Encouraging results of the experiments suggest that stainless steel container material with water as a working fluid combination for CAHP can be used for various industrial applications.

In the present case with natural convection using air as the cooling medium in the condenser section, low surface convective heat transfer coefficient was the influencing resistance that affected the performance of the condensing process and eventually the evaporation process in CAHP. For achieving high heat transfer coefficient fins can be attached on the condenser area, surface area of the condenser can be increased or water cooling can be used which will eventually improve the efficiency of operation of concentric annular heat pipe.

References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

P.K.Nag, 2007, “Heat and Mass transfer”, 2nd edition, McGraw Hill Education(India) Private Limited P.D. Dunn and D.A.Reay,1982, “Heat Pipes”, 3rd edition, Pergamon, New York A.Faghri, 1995, “Heat Pipe Science and Technology”, Taylor & Francis, Washington D. C. A. Nouri-Borujerdi and M. Layeghi, 2005, “A Review of Concentric Annular Heat Pipes”, Heat Transfer Engineering, 26(6):45– 58 L.L. Vasiliev, 1973, “Heat and Mass Transfer in Low Temperature Heat Pipes”, Proceedings of The 1 st Heat Pipe Conference, Stuttgart, Germany A. Faghri,1986, “Vapor Flow Analysis in a Double-Walled Concentric Heat Pipe”, Numerical Heat Transfer, vol. 10, no. 6, pp.

583–595.

A. Faghri and S. Parvani, 1988, “Numerical Analysis of Laminar Flow in a Double- Walled Annular Heat Pipe”, Journal of Thermophysics and Heat Transfer, vol. 2, no. 2 A. Faghri & S.Thomas, 1989, “Performance Characteristics of a Concentric Annular Heat Pipe: Part I—Experimental Prediction and Analysis of the Capillary Limit” J. Heat Transfer, Volume 111, Issue 4, 844-851 M. Layeghi & A.N. Borujerdi, 2004, “Vapor Flow Analysis In Partially Heated Concentric Annular Heat Pipes” International Journal of Computational Engineering Science, Vol.5, No. 1, 235 – 244 A.N. Borujerdi and M. Layeghi, 2004, “A Numerical Analysis of Vapor Flow in Concentric Annular Heat Pipes”, ASME Journal of Fluids Engineering, vol. 126, pp. 442–448.

J.H. Boo, S.Y. Park and D.Y Kim, 2005, “An experimental study on the thermal performance of a concentric annular heat pipe” Journal of Mechanical Science and Technology, Volume 19, Number 4, 1036-1043 A. Jalilvand, 2008, “The Study On The Thermal Performance Of Concentric Annular Heat Pipe With The Application In The Heat Roll Of Fusing Unit Of Copy Machine” International Heat Transfer Conference 13 ISBN: 1-56700-225-0 / CD 1-56700226-9

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Stability: Abstract Roadmap of Software Security Kavita Sahu1, Raj Shree2 Department of Information Technology Babasaheb Bhimrao Ambedkar University, Lucknow (U.P.), INDIA 1,2

Abstract: With the advent of new technologies and software, software industry is becoming more competitive. The users are pressurizing software developers to deploy a fully secured system. Increasing requirements of software security often leads to the development of many successive versions of software.

Software products are growing more sophisticated, the writing of latest versions has become complex and time consuming activity. For software security reasons it is necessary to understand the nature of security and the best way to do it is to identify new security factors and analyze them. In this paper stability is discussed as a security factor of software security. Predicting software stability with enhanced new version is a key factor for software security Stability, like most security factor, is a complex phenomenon and its prediction is a real challenge.

Keywords: software security, security attributes, stability, design phase.

I. Introduction Computer and internet are being very popular and basic need for day to day life. These needs include sharing of data globally, transactions etc. With the wide use of computer, software becomes complicated and large-scale.

Complicated software results more security problems. Software security is the ability of software to prevent system from failures when it is attacked [1]. It is evident from literature survey that the plenty of work has been done in the area of software security. In the following section, some relevant works have been discussed with their findings in order to strengthen the fact that need and importance of having security is continuously increasing at the time of development [4]. In 2008, Beznosov and Chess presents a report on security for U.S. An industry perspective on the secure software challenge. This report was based on the state of the practice and recent advances in engineering secure software for the wide range of industrial application domains. Author discussed the needs [4] and importance of security requirements analysis and threat analysis and suggested that developer needs to properly called and analyze security requirements [7].

In software economic and environment factors may affect it, stability and security as well. Software security requirements mainly include data confidentiality, integrity, availability, authentication, authorization, and access control. To develop a fully secured system, all phase contributes the same in security hence this is necessary to secure each and every phase of a software. In the context of all this investigation we considered stability as a security factor.

The approach in this work has been taken for software security and security factors. As security factors effects the security of software directly hence enhancing the factors will improve the quality of software. Stability as a security factor effects the security which is defined later in this paper. Stability is the ability of secured software of being stable for a long time period.

II. Software security Security decisions can be incredibly complex, even when we look at a relatively small and simple problem.

Software security is about developing software to behave as preventive in the presence of various types of attacks, even though in the real world, software failures usually happen unexpectedly that is, without intentional mischief [3]. Software security feature can’t be added through the addition of sets of features, it must be designed and integrated with the every phase of the software development life cycle.[4]. Undesirable threats take advantage of the weaknesses of the software and hardware and affect the factors of security as privacy, authentication, availability and integrity. Software industries are suffering from these undesirable malicious attacks before software deployment. Hence it is necessary to invigilate the each and every factor of security during the design phase.

III. Security factors Security is considered as part of software’s non-functional requirements. Such software quality attributes include security, maintainability, performance, reusability, and reliability [2]. Most of these attributes have been

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studied and measured extensively .The typical model adopted for security has only three security factors which are also called the three pillars of software security these are confidentiality, integrity, availability. These are the three concerns of most endeavors towards providing secure software. These three factors are described as follows.

A. Confidentiality Confidentiality is an application means that the private and sensitive data handled by the application read by anyone who is not explicitly authorized to view it. It also limits the access of information in right direction and prevention of disclosure of information to unauthorized users. Confidentiality is broader concept of privacy which limits access to individual’s personal information which requires a trusted binding mechanism of design and its total supporting services and related components. It ensures that there is no chance of leakage of information [5].

B. Integrity Integrity means that the data processed by an application is modified by any unauthorized channels. In integrity effect the whole information is also validated which insure the changes having not any effect of the integrity. As more and more monetary exchanges occur over the internet, integrity is crucial [8].

C. Availability Availability speaks to the software’s ability to remain operational even in the face of failure or attack. This is typically the hardest attribute to guarantee because there are forces at work that are beyond the control of the system itself. Availability is also related to maximum strength of protection because the information can be traced by intruders to violate security [9].

IV. Stability as a security factor When any change to the security of software is done than its original design is preserved or not, that is called stability. Stability is the state of being steady or ability to maintain a firm position for a time-period. It is usually considered to be the most important dimensions of dependability. If software is unreliable, it is difficult to ensure software safety or security as they may be compromised by software failures. When the stability will be applied to the software it will be auto-stable. Changes to the software are in frequent. Controlled, security, if changes to the software should not invalidate automated. When any software updated then some attributes of that security are changed but some attributes does not change their behavior, because they are stable when they were constructed. Stability is the software attribute that reflects the software’s ability to operate, normally or abnormally without threatening people or the environment. When stability is an important and essential attribute of security which defines all other attributes of software security and components [10].

V. Stability in context of other software attributes A. Stability in context of authorization The process of determining that a person who is requesting for an authorization is allowed to receive a stable service or performance operation. If it is not getting the stable service that means that service will not be secured for a long duration.

Software security modules

Security attributes with stability

Fig. 1: Security and security attributes with modules B. Stability in context of availability Assuring information of data and communication s services will be stable for use when expected. Its information of data must be kept available stable to authorized persons when they need it.

C. Stability in context of reliability Reliability ensures that application performs stable operation without failure in specific environment. Reliability factor is very important in term of stability because as much the system’s reliability increases its security also increases.

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D. Stability in context of survivability Survivability ensures that application of software providing stable and essential resources to users even in the presence of an attack. Application can’t have failure points with stability and it will survive for a long duration.

VI. Impact analysis of security factors Take an example of medicines, as antibiotic medicines prevents the viruses to increase, they do not kill them as well as stability as a security factor gives the security stability. To make the all attributes stable in software security, it should be considered as a factor [11]. As it gives all the security factors stability hence it is complement of every attribute of software security which belongs to C.I.A as showed in the given figure.

Confidentiality, integrity and authentication

Fig. 2: Relationship between Security attributes.

VII. Relation between quality, security and stability The demand for quality software continues to intensify due to our society’s increasing dependence on software and internet. So the software security is being also very important phase in software development. Goal of software development is to deliver high quality software which can be achieved only by measuring and estimating its security factors. Stability as a quality factor relates with its estimation during development phase of a software. As early the software security factors are identified and measured the security of software is assured that much early. Hence it states stability factor plays an important role in defining security as well as quality of a software.

VIII. Discussion Development of security design and architecture for software is not a onetime construction process, it is based on reuse of existing security specifications and sub factors. [6] There is common agreement between researchers and security practitioners to integrate security at the preliminary stage of software development life cycle in order to develop secured software. Security estimation and upgrading is possible with the help of finding new security attributes which directly affects the security features of software. Security attributes are found out and its applications are responsible for the successful delivery of secured software.

IX. Conclusion Software security is a complicated issue. Security attributes play an important role in software security estimation. This research paper studies the report on a security factor which is stability, which helps a developer to understand the security in SDLC to develop more secure software. This paper concludes that development of secured software depends on the software attributes and it finally affects the quality of software. Stability as a security factor effects the quality of a software positively if it is considered in the early stage of development.

References [1] [2]

Gary McGraw, Bruce Potter. “Software Security Testing”. IEEE Security & Privacy, 2004, 2(5):81-85.

A. Sachitano, R. O. Chapman, and J. A. Hamilton, "Security in software architecture: a case study," in Proceedings from the Fifth Annual IEEE SMC Information Assurance Workshop, 2004, pp. 370-376.

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[3] [4] [5] [6] [7] [8] [9] [10] [11]

Gary McGraw, “Software Security: Building Security In”, Addison Wesley Software Security Series, ISBN-13: 9780321356703, Edition: 1, 2006.

S. Chandra and R.A. Khan, 2008. Object Oriented Software Security Estimation Life Cycle-Design Phase Perspective. Journal of Software Engineering, 2: 39-46.

G.H. Walton, T. A. Longstaff, R.C. Linder, Computational Evaluation of Software Security Attributes, IEEE, 1997.

C. Wang and W. A. Wulf, “A framework for security measurement” Proc. Of National Information System Security Conference, 7-10 Oct 1997, pp: 522-533.

Konstantin (Kosta) Beznosov and Brian Chess, “Security for the Rest of Us: An Industry Perspective on the Secure-Software Challenge”, Journal IEEE Software Volume 25 Issue 1, January 2008 Pages 10-12.

R.A.Khan and S.A.Khan, “A Roadmap For security”, IJCSIT, VOL 1, ISSUE 1, JUNE 2010.

S. A. Khan, R. A. Khan, “Securing Object Oriented Design: A Complexity Perspective”, International Journal of Computer Applications (0975-8887) Volume 8- no.13, October 2010.

Charles B. Haley, Jonathan D. Moffett, Robin Laney and Bashar Nuseibeh,”A framework for security requirements engineering”.

In SESS’06, Shanghai, China, ACM, May 2006.

C. Wang and W. A. Wulf, “A framework for security measurement” Proc. Of National Information System Security Conference, 7-10 Oct 1997, pp: 522-533.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Image Filtering and Registration by using Hybrid Area and Feature Based Method 1,2

Kiran P. Dange1, Neha P. Athavale2 Department of Electronics and Communication Usha Mittal Institute of Technology, S.N.D.T. University, Mumbai-49, INDIA

Abstract: During acquisition and transmission, noise can be introduced into images. The main problem of image processing is to effectively remove noise from an image, but keep its features intact. The impulse noise is removed by using Gaussian filter. This filtering process assumes a Gaussian spatial profile in the neighborhood of noisy pixel. Image registration can be defined as the process of transformation of various image data sets into one co-ordinate system. The need of image registration is its ability to compare or integrate the data obtained from the different measurements. For any image registration, filtering is necessary to remove the noise. Image registration is classified into image segmentation and Scale invariant feature transformation. The last process is the template matching. This method provides best filtering at higher noise density. It also provides maximum cross correlation coefficient for registered images.

Key points: Gaussian noise, K-means clustering algorithm, Scale invariant Feature transform, Affine Transform, Template matching.

I. INTRODUCTION Filtering is the process which effectively removes noise or any disturbances while doing the transmission. A fundamental problem of image processing is to effectively remove noise from an image. The nature of the problem depends upon the type of noise added to the image.

Two common types of impulse noise are fixed and random valued noise. Filtering is categorized into linear and non-linear filter. Linear filter could produce very high blurring effect. The non linear filters have been utilized because of improved performance of removal of impulse noise and preserving edge details. The most widely used non-linear filter is standard median (SM) filter which is having tendency to sort the image pixels into descending order and calculate the median. This median replaces the noisy pixel. But the disadvantage of standard median filter is that it does not preserve fine image details. Center weighted and weighted median filters are improved version of median filter. Center weighted median filter (CWM) is a filter which provides weights to the center pixel for the filtering process. Weighted median (WM) filtering provides weights to control the filtering process which preserve image details [10]. Additive Gaussian noise is characterized by adding to each image pixel a value form the zero mean Gaussian distribution. The zero mean property of the distribution allows such noise to be removed by locally averaging pixel values. Ideally, removing Gaussian noise would involve smoothing inside the distinct regions of an image without degrading the sharpness of their edges.

The proposed filtering techniques initially estimates the percentage of corruption and position of impulses in the observed noisy image with the help of an impulse detector. The filtering process estimates the corrupted pixel from the weighted sum of its neighboring non- noisy pixels [10],[11].

It is necessary to form a larger image with a set of overlapping images and so it is required to find the translations to align the images. The process of image registration finds the translations so that two or more overlapping images are aligned properly. It gives the projection from the view point through any position in the sensed images. Image Registration is said to be the process of transforming the different sets of image data into one co-ordinate system [4], [8]. The image which is registered is called as reference image and the another image which has to be matched with the reference image is called as sensed image.

The first step of image registration is image segmentation. Image segmentation is divided into a variety of methods which can be applied to either monochrome or color images [4]. The segmentation can be done by various methods like Segmentation by clustering in color space, edge detection, normalized cuts, gray-level thresholding, surface based segmentation, etc. The thresholding method may be recommended as the simplest and standard method for automatic threshold selection. The key concept behind this method is to obtain an optimal threshold that maximizes a function of threshold level. But the memory required for storage increases.

In Edge based method boundaries formed are not necessarily closed. There is no significant improvement for

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multi-spectral images. Region-based method segmentation uses closed boundary method. Our method makes use of the feature based method for image segmentation in which the hue saturation and intensity value (HSV) color space is analyzed, then the extraction of features is done. Then K-means algorithm is implemented. Our project focuses on segmentation by k-means clustering algorithm. One of the advantage is that the proposed system has low memory requirement. The throughput is also increased. And so the computational efficiency is increased.

The second step in registration is to describe the image features. The class of descriptors, describing these image features are distribution based descriptor, spatial-frequency technique and differential descriptor. The evaluation of the descriptors is performed in the context of matching and recognition of the same scene or object observed under different viewing conditions. Spatial-Frequency Technique describes the frequency content of an image.

The Fourier transform decomposes the image content into the basis functions. However, in this representation, the spatial relations between points are not explicit and the basis functions are infinite; therefore, it is difficult to adapt to a local approach. In Differential Descriptors a set of image derivatives computed up to a given order, approximates a point in neighborhood [6]. The properties of local derivatives are investigated and derived differential invariants, combine the components to obtain rotation invariance. These filters differ from the Gaussian derivatives by a linear coordinate which change in filter response domain.

Our proposed algorithm uses distribution-based descriptor. During the process of image registration, the major problem called as corner clustering occurs which results in increasing false matches as matching the feature point. This is overcome by using this descriptor which is nothing but Scale-invariant feature transform (SIFT) technique [6]. The size of this descriptor is reduced with Principle Component Analysis (PCA) technique. The number of correct matches and correspondences is determined with the overlap error. The overlap error measures how well the regions correspond under a transformation.

The third step involves the template matching algorithm. Feature-based method is usually applied when the structural information matches than the intensity information. They cannot handle image distortions. Harris corner edge detector has rotation invariance, but it is sensitive to scale changes. Area-based approaches are preferable when there are many prominent details and characteristic information such as color size shape etc[2],[3],[12]. Templates and input images must have either statistical dependence or intensity similarity. If they have intensities similarity then correlation methods can be used. In geometric points of view, only small amounts of shifts and rotations are allowed. The proposed method is the hybrid of feature and area based method. This method is focusing best Gaussian filtering at highest 80% noise density and gives maximum cross correlation coefficient for different features such as contrast and brightness.

II. PROPOSED ALGORITHM Following figure shows the block diagram of the proposed system.

Input Image

Gaussian Filtering

Segmentation

Scale Invarient Feature Transform

Template Matching Algorithm

Filtered & Registered image

Figure 1: Block Diagram of Image Filtering and Registration by using Hybrid Area and Feature Based Method It is classified by two algorithms, first Gaussian filtering and second image registration by using segmentation, SIFT and template matching algorithm. In first algorithm, at 80% noise density, the image is filtered and in second stage, maximum cross correlation coefficient is obtained for filtered image by using template matching algorithm [11].

III. STAGE 1: IMAGE FILTERING BY GAUSSIAN FILTER

Figure 2: A Normalized Gaussian Weighting Function

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Here we consider the two dimensional Normalized Gaussian weighting model over the neighborhood of the corrupted pixel under consideration as shown in Figure 2 and given in equation (1).In the first stage, the distribution of impulses is stimulated in terms of their position and quantity using an impulse detector. The second stage involves restoring the corrupted pixels as a weighted sum of their neighboring uncorrupted pixels.

The algorithm of Gaussian filter is explained as follows: Stage I: 1) Get the noisy image S (p, q) of initially 3×3 window size.

2) Classify noisy image into 255 and 0.If it is 255 or 0, then flag matrix f (i, j) =1, otherwise f (i, j) = 0 3) Find the minimum, maximum and median of noisy image i.e. SMin, SMax, SMed and noisy image is S (i, j).

4) For window size selection, if SMin < S(p,q) & S(p,q) < SMax Output (p, q) = S (p, q); Else increase increase the window size Stage II: 1) The filtering of noisy pixel is carried out recursively. Find the Gaussian weights by using Equation (1).

Wi,j =(1/2π){ e |i-p|^2 × e |j-p|^2 } (1) 2) Area under Gaussian curve is unity, so weights are reduced to zero wherever there is a noisy pixel, so we get (2) Wi,j =(1/2π){(1-fi,j × e |i-p|^2 × e |j-p|^2 } (2) 3) All weights are scaled by scaling factor ‘a’ in order to maintain the unit area as shown in (3) Wi,j =a × [(1/2π){(fi,j × e |i-p|^2 × e |j-p|^2 } (3) Note that scaling factor will differ for each noisy pixel. Scaling factor can be calculated by only considering the non noisy pixel. Take the summation of non noisy weights and to maintain unity area, scaling factor will be calculated by dividing 1/(sum of current weights).

4) Replace the noisy pixels as a weighted sum of their neighboring uncorrupted pixels.

IV. STAGE 2: IMAGE REGISTRATION It is classified into following three stages.

A. Segmentation The proposed system uses Segmentation in color space by K-means Clustering Algorithm. Each image point is mapped to a point in a color space. It is many to one mapping. The points in color space are grouped together in clusters. The clusters are then mapped to regions in the image which is represented in HSV color space [1], [4].

The steps of segmentation are as follows.

1.

The properties of the HSV color space are analyzed and studied.

2.

Next the variations in hue saturation and intensity values of an image pixel from viewpoint of visual perception is done.

3.

Then the pixel features are extracted choosing the hue or intensity as dominant property which is based on saturation value of pixel.

4.

Then the segmentation of image by grouping pixels of similar features using K-means clustering algorithm is done.

5.

Given an image of N pixels, the goal is to partition the image into K clusters. The data features give rise to vector space and finds natural clustering by K-means clustering algorithm.

6.

Clusters provide a grouping of pixels. This is dependent on pixel values in the image but not on the location of the image unless the location is a specified property.

7.

The hue and intensity values belong to the same number space, hence are clustered separately, so that the gray value and the color pixels are not considered in the same cluster.

8.

Thus in short K-means clustering steps are as follows. a.

Initialization of Parameter:- Let the image of be of N pixels. Let us denote V(xi ) as the property vector which is associated with the pixel xi. b.

Then values of potential property vectors are initialized. c.

Choose the values of each element of the property vector. This is done randomly from the set of all possible values for that element [4]. d.

Hard Assignment of Pixels to the Clusters:- Each pixel xi of the property vector is given to the cluster having closest mean. The points are all clustered around the centroid as shown in (4).

C i,j (i) = argmin || x i,j (i) - µ j || (4) It has seen that after the process of segmentation, memory required for the storage is reduced. The throughput is increased. The computational efficiency is also increased.

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B. Scale-Invariant Feature Transform (SIFT) Technique The most powerful approach to obtain the local descriptors is obtained by SIFT technique. The SIFT approach transforms image data into scale-invariant co-ordinates relative to local features. It has four major stages such as Scale-Space Extrema Detection, Key-point Localization, Orientation Assignment and Key-point Descriptor [6].

This approach is named the Scale Invariant Feature Transform (SIFT), because it transforms image data into scale- invariant coordinates which has relation with local features. It describes image features which has many properties. These properties makes features suitable for matching differing images of an scene or objects. The features are invariant to image rotation and scaling, and they also invariant to some amount to change in illumination. They are well localized in both the frequency and spatial domains. This reduces the probability of disruption by noise, occlusion and clutter. It is described using following stages C. Scale Space Extrema Detection It detects key-points using a cascade filtering approach [6]. The first step is to search scales and locations which can be repeatably assigned under different views and times of the given object. For obtaining locations that are not variant to changes in scale of the image data, we can search for stable image features at all possible scales, and use a continuous function of scale known as scale space. The scale space kernel is the Gaussian function.

Therefore, the scale space of an image is defined as a function, that is produced from the convolution of a variable-scale Gaussian, with original input image as shown in equation (5) and (6) .

L(x, y, σ) = G(x, y, σ) ∗ I(x, y) (5) G(x,y, σ)=(1/2Π σ2)*e-(x^2+y^2)/2*σ^2 (6) Next Step is to compute the difference of Gaussian as shown in equation (7). It efficiently detects stable keypoint locations in the scale space[6].

D(x,y,σ)=(G(x,y,kσ )-(G(x,y, σ))*I(x,y)=L(x,y,kσ)-L(x,y,σ) (7) Now once the key-points are obtained next step is to obtain stable key-points. So we do Local Extremum Detection. To detect the local maxima and minima of D(x, y, σ1) comparison of each sample point with its eight neighbors in the same image and nine of its neighbors in the scale below and above is done. The sample point is selected only if it is larger or smaller than all of these neighbors. The frequency of sampling is determined in the image and scale domains so that it helps in detecting the extrema. From these extrema obtained not all the key points will be selected but Low Contrast Key-points will be detected and rejected. The function value at the extremum D(x) as shown in equation (8) is used to reject unstable extrema with minimum contrast [6].

D(x)=D+((1/2)(∂DT/∂x))x (8) In equation (8) if the value of LHS is less than equal to 0.09 then that key points are rejected.

Next we have to eliminate edge responses. The difference- of-Gaussian function has a very strong response along the edges. This is because the location along the edge is poorly determined. Therefore it is unstable to small amounts of noise [6]. A poorly defined peak in the difference of Gaussian function has a large principle curvature at the edge. It has small curvature in the perpendicular direction which is found from a 2 × 2 Hessian matrix which is computed across the scale and location of the key-point and is known as H. The derivatives are calculated as we can take differences of neighboring sample points. Now the eigenvalues of H are directly proportional to the principle curvatures. Here we are interested with the ratio of eigenvalues. Let us assume that α is the eigenvalue with the largest magnitude, and β is the eigenvalue with lowest magnitude [9]. Then we compute the sum of the eigenvalue from the trace of matrix and product from the determinant of the obtained matrix. The ratio thus obtained is r and if r is greater than equal to 10 then we can eliminate that key points.

D. Localization of Accurate Key point Now a key point candidate has been found by taking a comparison of a pixel to its neighbors. Then we can perform a detailed fit of the point to the nearby data for scale, ratio of principle curvatures and locations [6]. By this information we can reject points that have low contrast or the points which are poorly localized near an edge. This approach uses Taylor’s series.

E. Assignment of Orientation We can assign a consistent orientation to each key point and this assignment is based on local image properties.

The key point descriptor is represented based on this orientation and hence it is able to achieve invariance to image scaling and rotation [6]. The scale of the key point is used to obtain the Gaussian smoothed image with the closest scale. The computations are performed in a scale-non-variant manner. At this scale for each image sample the gradient magnitude m1 (x,y) and gradient orientation θ1 (x, y) is to be precomputed by using the pixel differences as shown in equation (9) and (10) taken from [6] . m(x,y)=((L(x+1,y)-L(x-1,y))2+(L(x,y+1)-L(x,y-1))2)1/2 (9) θ(x,y)=tan-1((L(x,y+1)-L(x,y-1))/(L(x+1,y)-L(x-1,y))) (10) Here each sample which is added to the histogram is weighted by its gradient magnitude. Peaks in the

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orientation histogram will give the dominant directions of each gradients. The maximum peak in the histogram is observed, and then any other local peak that is within eighty percent of the maximum peak is obtained and is used to create a key-point with that orientation. The locations with multiple peaks of equal magnitude, takes multiple key-points at the same location and same scale but with different orientations.

F. Key-point Descriptor All the operations mentioned above have assigned an image scale, location and orientation to each key-point [5].

The next step is computation of a image descriptor for the local image region. It has to be highly distinctive and also invariant to change in illumination or 3D viewpoint. One method is to sample the intensities of the local image across the key point by using proper scale, and then match these using a normalized correlation measure.

First the image orientations and gradient magnitude are sampled around the location of the key-point . It is done by using the key point scale to select the Gaussian blur level [5], [6]. To achieve orientation invariance, the coordinates of the descriptors and the gradient orientations are rotated in relation to key point orientation.

G. Template matching Algorithm The description of this algorithm is as follows. a. To Obtain Matching Candidates Template Matching is a technique for finding areas of an image that match to template of an image. Here we normalize the output of the matching procedure. First it loads an image[12]. Then we perform template matching. Then we localize the location with highest matching probability. Basically it is process automatically selects lots of detailed and unique templates from one image and locates the templates in another image of the same scene[12]. In this process the centroids of corresponding templates are used as corresponding control points and the best correspondences that minimizes the error criteria are used to determine the scaling, rotational and translational parameters needed to register the images. If the centers of two image volumes correspond to each other and these image data have a small rotational difference, then we can say that the average distance between the corresponding voxels in the image will be larger than the average distance between corresponding voxels in the two sub volumes of these images when the centers of the sub volumes will coincide[12]. So it makes possible to find template correspondences accurately even when the images to be registered have some small rotational differences. Using a set of corresponding control points obtained from the centroids of the corresponding templates in the image data set, it will be possible to determine the translational, rotational and scaling parameters that can bring the image volume into registration[5]. b. Template Selection To achieve highly accurate matches, the templates selected in the target image should represent highly detailed and unique regions[12]. For template to be highly detailed, it should contain a large number of high gradient edges. This is measured by computing the sum of gradient magnitudes in the template. c. Template Matching It is the process to find the locations of the sub-image and are called as templates inside the image. Once we find the number of corresponding templates, then their centers are used as corresponding control points to determine the registration parameters. Template matching involves comparing a given template with windows of the similar size in an image data and identifying the windows that is most similar to the template[12]. d. Affine Transformation The affine transformation class represents a 2D affine transform that performs a linear mapping from 2D coordinates to other 2D co-ordinates that preserves the straightness and paralleness of the lines[]. Affine transformations can be constructed using the sequence of translation, flips, scales, rotations, and sheres [5],[7].

V. EVALUATION PARAMETER One way to characterize uniqueness is to compute the correlation of a template with windows of the same size in its neighborhood [7], [12]. A sharp peak among the correlation coefficient at the template's position is evidence that the template is unique. Here the value of the co-relation coefficient is minimum. The less sharp this peak, the more is the co-relation coefficient value and hence we can say that there is more similarity between the template and the windows in its neighborhood. So when a template that is not locally unique is selected for matching, it may match rather well with many windows in its neighborhood, making distance between the correct matches and an incorrect one difficult[7], [12]. The algorithm finds the co-relation between the original image and the sensed image [8]. It applies the transformation parameters on the sensed images so that the maximum co-relation between original image and the sensed image are achieved. If P(m,n) is the reference image and Q(m,n) is the sensed image then the co-relation coefficient (r) between P and Q is found by equation (11) r =ƩmƩn(Pmn-Po)(Qmn-Qo)/(ƩmƩn(Pmn-Po)2ƩmƩn(Qmn-Qo)2))1/2 (11)

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`VI. RESULTS Some standard images such as Lena, Barbara, Pepper and House of 512×512 are used. Results are found using these standard images by using Visual Studio 2008 with Open CV library. The following figure shows original image, filtered image, segmented image, scale-invariant feature transformed image and registered image. The image matches with itself with different angle of rotation, scaling and also for brightness and contrast. Our result shows maximum cross-correlation coefficient at 45 rotation at 80% noise density.

(a) (b) (c) (d) (e) Figure 3: (a) Original Barbara.jpg (512×512 size) (b) Filtered image at 80% noise density (c) Segmented image (d) SIFT descriptor image (e) Registered Image

(a) (b) (c) (d) (e) Figure 4: (a) Original House.jpg (512×512 size) (b) Filtered image at 80% noise density (c) Segmented image (d) SIFT descriptor image (e) Registered Image

(a) (b) (c) (d) (e) Figure 5: (a) Original Lena.jpg (512×512 size) (b) Filtered image at 80% noise density (c) Segmented image (d) SIFT descriptor image (e) Registered Image

(a) (b) (c) (d) (e) Figure 6: (a) Original Pepper.jpg (512×512 size) (b) Filtered image at 80% noise density (c) Segmented image (d) SIFT descriptor image (e) Registered Image VII. CONCLUSION Thus we can conclude that our image is filtered at 80% noise density and registered with maximum crosscorrelation coefficient. The problem of corner clustering is overcome by using SIFT. Minimum memory is required by using clustering in color space for segmentation. At 80% noise density we get properly registered image. The comparison of various images is shown in the table1.

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Table1: Comparison of three types of registration methods at 80% noise density for different standard images Images

Area-based (Affine Transform)

Feature-based Transform)

(Discrete

Cosine Proposed Hybrid Algorithm

Lena

0.896499

0.879982

0.956845

Pepper

0.920

0.8895

0.96

House

0.8956

0.9021

0.9128

Barbara

0.95

0.91926

0.9568

From the table it is concluded that maximum cross-correlation coefficient is obtained for Pepper image by proposed hybrid algorithm.

ACKNOWLEDGEMENT Both the authors are thankful to Usha Mittal Institute of Technology for the facilities provided.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Shamik Sural, Gang Qian and Sakti Pramanik, “Segmentation and Histogram Generation using the HSV Color Space for Image Retrieval”, IEEE, pp. 589-592 2002.

Ruhina B. Karani and Dr. Tanuja K. Sarode, “Image Registration using Discrete Cosine Transform and Normalized Crosscorrelation”, Int Conf Workshop on Recent Trends in Technology, pp. 28-34 2012.

Ming-Sui Lee and Meiyin Shen and Akio Yoneyama and C-CJay Kuo, “DCT-Domain Image Registration Techniques for compressed Video”, Integrated Media Systems Centre and department of Electrical Engineering 2012.

Hernani Goncalves and Luis Corte, “Automatic Image Registration through Image Segmentation and SIFT,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 49 no 7, pp. 2589-2600 July 2011.

Jimmy Singla and Raman Goyal, “A systematic way of Affine Transformation using Image Registration”, Int Jou of Information Technology and Knowledge Management, Vol. 5 no 2, pp. 239-243 November 2004.

D.G.Lowe, “Distinctive Image Features from Scale Invarient Keypoints”, Int Jou Computer Vis, Vol. 60 no 2, pp. 91-110 November 2004.

Mehfuza Holia and Dr. V.K.Thakur, “Image Registration for Recovering Affine Transformation using Nelder Mead Simplex Method for Optimization”, Int Jou of Image Processing, Vol. 3 no 5, pp. 218-228 November 2004.

Medha V. Wyawahare and Dr. Pradeep M. Patil and Hemant K. Abhyankar, “Image Registration Techniques, An Overview”, In Proc. International Image Processing and Pattern Recognition, pp.11-25 September 2009.

Rafael Gonzalez Richard Woods , “Digital Image Processing”, Pearson Publications.

N. C. Gallagher Jr and G. W. Wise, “A theoretical analysis of the properties of median filters”, IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-29, pp. 1136–1141, Dec. 1981.

Czerwinski, R.N.; Jones, D.L.; O'Brien, W.D., Jr., “Ultrasound speckle reduction by directional median filtering”, International Conference on Image Processing, 1995. Proceedings.

Lijun Ding, Ardeshir Goshtasby and Martin Satter, “Volume Image Registration by Template Matching”,Department of Computer Science and Engineering,Wright State University, Dayton, OH45435.

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

An Investigation on the Potential of Biogas Production from Elephant Grass and Guinea Grass Sindra L Summoogum-Utchanah*1 and Doolar Poorneema1 1 Department of Chemical and Environmental Engineering University of Mauritius Reduit, Moka, Mauritius Abstract: Anaerobic digestion of Elephant grass (EG) and Guinea grass (GG) as well as the co-digestion of EG and GG were studied in a single phase digester at ambient temperature. Six different treatments were set up based on mixture proportions in digesters labelled A-F. The compositions of the digesters were as follows: Digester A contained 2 % total solids (TS) EG, B 2 % TS GG, C 2 % TS mixture of EG and GG, D 6 % TS EG, E 6 % TS GG and F 6 % TS mixture of EG and GG. It was observed that the 6 % TS EG digester produced 3455 ml of biogas compared to only 1725 ml collected for 6 % GG. A mixture of these two grasses is not recommended for anaerobic digestion as the biogas production is lowest compared to the systems using only one grass type. The results for pH, chemical oxygen demand (COD) and soluble chemical oxygen demand (SCOD) tests revealed that the degradation process was taking place well in all digesters.

Keywords: Anaerobic; digestion; co-digestion; elephant grass; guinea grass; total solids

I. Introduction The world is facing a major crisis, namely an energy crisis, due to a worldwide increase in energy demand caused by a rise in population growth, industrialisation and an increasing rate of consumption of fossil fuels.

Consequently, the focus is on renewable sources of energy such as biogas which is obtained from anaerobic digestion (AD). AD is the treatment of several organic wastes by a series of biological process and recovery of bio-energy in the form of biogas, which consists mainly of CH4 and CO2 [1]. Biogas production from agricultural biomass is of growing importance as it offers extensive environmental benefits such as energy savings, recycling of nutrients within agriculture and reduced CO 2 emissions [2]. In AD, co-digestion is also possible where a major amount of a main basic substrate is mixed and digested together with minor amount of a single, or a diversity of supplementary substrate [3].

Grass can be used as a feedstock for biogas production as it is readily available, it does not require any pretreatment and its digestate can be used as fertilizer, thus helping farmers to reduce their energy cost. Even environmental concerns such as deforestation and ecological damage will not be an issue as the grass which will be cut will grow in a short span of time. Moreover, the use of fertilizer produced from the digestate which is of a good quality will not cause significant greenhouse gas (GHG) emissions. During the process of AD, especially that of grass, the use of inoculum is very important. The most essential role of inoculum is a source of microorganisms. This slurry increases the rate of degradation significantly. According to Mshandete et al.

(2004), the co-digestion of different substrates may upgrade the AD process because of the presence of better carbon and nitrogen balance [4]. Moreover, the digestion of more than substrate can give rise to positive synergism and the excess nutrients can boost microbial growth [5]. As such, cow-dung which is a rich source of bacteria is chosen as inoculum. Hence, a mixture of grass and cow-dung would lead to a synergetic effect and as a result the biogas yield would be high.

There are many types of grass which can be used for biogas production namely Elephant grass (Pennisetum Purpureum) and Guinea grass (Panicum maximum). Both types of grass are very common in sugarcane fields and they may be regarded as a source of nuisance. Therefore, AD can be proposed as a solution especially for farmers. Because of this consideration, in this study, we aim to investigate the potential of biogas production from Elephant grass (EG) and Guinea grass (GG), as well the effect of co-digestion of EG and GG. II. Material and Methods Twelve laboratory scale digesters bottles were constructed for the AD process. Six of them were used as digesters for biogas monitoring while the remaining ones were used as duplicates for collection of substrate samples.

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A. Characterization of Grass Sample Substrate in the form of fresh grass was obtained from fields and gardens for characterization purposes. The latter was carried out to estimate the amount of substrate, water and inoculum to be added in each digester to vary the percentage total solids. The first one was the moisture content test wherein samples of the grass were weighed and dried in an oven at a temperature of 60 °C for around 24 hours. The difference between the initial and final weight determined the moisture content. The determination of the percentage volatile solids was the next step which was done using the dried grass samples placed in a furnace at a temperature of around 550 °C for 3 hours. The same experiment was carried out for the inoculum to be used.

B. Experimental Setup The sample was first cut into small pieces of around 20 mm which was then crushed mechanically thus increasing its contact surface area. A mixture was then prepared with the crushed grass, inoculum and water which was fed in the digesters and the duplicate bottles. The system was checked for any possible air leakages and the inverted tube cylinder method was used for biogas collection. The experiment was monitored for a period of 30 days.

C. Tests on Substrate Sample The pH meter was used to measure the pH of the substrate sample from the duplicate digester bottles. The probe was inserted into the sample until stabilization. On the other hand, the COD value was obtained from the following formula: COD mg/l = × D × 1000 Where D: dilution factor; S: titre value for digested tube containing diluted sample (ml); B: titre value for digested tube containing distilled water (ml) and T: titre value for undigested distilled water (ml). Moreover, the procedure for the soluble COD test is similar to the COD test but the only difference is that the sample to be used is first passed through a centrifuge and filtered and then it is diluted. Lastly, the biogas collected in each airtight plastic bag was passed through a gas analyzer to determine the content of the biogas produced.

III. Results and Discussions The pH value of the substrate is an indication of the bacterial activity in an anaerobic digestion system. The pH of 6 % TS EG was higher than the remaining % TS EG, GG and mixes as shown in fig. 1. Moreover, there was a considerable fall in pH value for the 2 % TS EG and GG from days 4 to 14. It was also noted that for both the 6 % TS EG and GG, the pH values were more stable due to the higher substrate and inoculum content.

Fig. 1: Variation of pH against retention time Fig. 2 illustrates the biogas production for the different % TS against retention time. While the 2 % and 6 % TS EG digester yielded the highest yield, the 2 % TS mix gave the lowest amount of biogas. This was due to the fact that the methane bacteria took more time to adapt to the environment in the latter case compared to the others.

The graph of COD against retention time is plotted in fig. 3. At first, the COD value in the digesters were high as the substrates were fresh. Also, the highest values of COD were recorded for both 6 % TS EG and GG. Gradually, a decrease in COD values were noted up to day 11 as the degradation process took place.

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Fig. 2: Variation of biogas production against retention time

Fig. 3: Variation of COD against retention time Fig. 4 below illustrates the change in SCOD as the anaerobic digestion progresses. As seen, all the curves exhibited a decrease in SCOD values up to day 15 and 21 for 2 % TS EG and 2 % TS GG, 2 % TS mix, 6 % TS mix respectively.

Fig. 4: Variation of SCOD against retention time Table 1 below shows the variation in composition of biogas in each digester. The digester containing 6 % TS EG produces the highest amount of methane. On the other hand, the digester containing 2 % TS mixture of both

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grasses produces the least amount of methane gas. These results indicate that EG has a higher potential to produce methane gas compared to the GG alone or when used as a mixture of both grass.

Table 1. Variation in % composition in each digester % Composition Digester

Methane (CH4)

Carbon dioxide (CO2)

Oxygen (O2)

Balance

2 % EG

47.2

10.3

8.4

34.1

2 % GG

40.6

11.9

9.1

38.4

2 % Mix

37.2

12.6

9.5

38.7

6 % EG

52.8

13.9

8.9

24.4

6 % GG

43.4

9.6

10.7

36.3

6 % Mix

39.3

11.4

8.8

40.5

IV. Conclusions In this project, the feasibility of production of biogas from each type of grass was determined by experimentation. Hence, the AD of EG and GG as well as the co-digestion of EG and GG were studied at ambient temperature. It was found that EG was a better option than GG in terms of biogas production. Also, when a mixture of both grass was used the biogas yield was less compared to the individual ones. Following the monitoring of the experiment, it was shown that there was a considerable and gradual decrease in the pH value of each digester indicating that there was a healthy anaerobic environment in the digesters. The results for the COD and SCOD tests concluded that the degradation process was taking place well in all digesters.

V. References [1] [2] [3] [4] [5]

J.Clemens, M. Trimborn, P. Weiland and B. Amon, ‘Mitigation of greenhouse gas emissions by anaerobic digestion of cattle slurry,’ Agriculture, Ecosystems and Environment, vol 112, pp. 171-177, doi:10.1016/j.agee.2005.08.016 A. Kacprzak, L. Krzystek and S. Ledakowicz, ‘Co-digestion of agricultural and industrial wastes,’ Chemistry and Material Science, vol 64 (2), pp. 127-131 R. Braun and A.Wellinger, ‘Potential of Co-digestion,’ http://www.iea-biogas.net/files/daten-redaktion/download/publitask37/Potential%20of%20Codigestion%20short%20Brosch221203.pdf Accessed on 15th March 2014 A. Mshandete, A.Kivaisi, M.Rubindamayugi and B. Mattiasson, ‘Anaerobic batch co-digestion of sisal pulp and fish wastes,’ Bioresource Technology, vol 95, pp. 19-24, doi: 10.1016/j.biortech.2004.01.011 J. Mata-Alvarez, S. Mace and P. Llabres, ‘Anaerobic digestion of organic solid wastes: an overview of research achievements and perspectives,’ Bioresource Technology, vol 74, pp. 3-16, doi: 10.1016/S0960-8524(00)00023-7

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ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629 AIJRSTEM is a refereed, indexed, peer-reviewed, multidisciplinary and open access journal published by International Association of Scientific Innovation and Research (IASIR), USA (An Association Unifying the Sciences, Engineering, and Applied Research)

Discursive Institutionalism: Analysis of the Forest Policy Review Process of Ghana Yaw Amo Sarpong (Assistant Research Fellow/PhDCandidate); Corresponding Author, Bureau of Integrated Rural Development (BIRD, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana Daniel K. B. Inkoom, PhD (Senior Lecturer); Department of Planning, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Ibrahim Latif Apaassongo; Department of Agricultural Economics Agribusiness and Extension, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Abstract: Analysis of ideas and discourses of actors and stakeholders make political scientists understand how ideas and discourses are put across and defended in national policy debate. Stakeholders’ debate at different levels on ideas and issues of local and global concern, ranging from good forest management practices to nature conservation and forest policies. Ghana’s forestry sector is complex with different actors, stakeholders, agencies, and institutions engaged in continuous discussions on concepts, believes, and ideologies for better policy development. This paper answers its central research question by identifying “old” and “new” stakeholders of Ghana’s forestry sector, the ideas they stand for, their discourses, believes, concepts, the interests they want to defend and the ensuing power relations via discursive institutionalism theory. Stakeholders employed diverse means at policy review forums, to influence the review committee to consider their ideas. Evidence of formation of stakeholder coalitions to push across ideas and discourses provided the best audience leading to acceptance and inclusion in the policy output of the forestry sector. It is concluded that discursive institutionalism theory can be used to explain how stakeholders and actors share ideas and discourses in a policy process.

Keywords: Forest policy, discourse, stakeholders, institutional analysis, power relations, ideas, discursive institutionalism I. Introduction The explanatory power of ideas and discourse in relation to what is said; where, when, how, why it was said and which stakeholder or agency said it when formulating new or reviewing old sectoral policies, programs and philosophies is interesting [1-3]. The analysis of the ideas and discourse of different actors and stakeholders make scientists understand how agencies put their ideas across and how they defend them in a national policy debate. Different theories of the policy process can be used in analyzing any policy process [4, 5] but the discursive institutionalism theory lends insight into the role of ideas and discourse in politics while providing a more dynamic approach to institutional change than the other forms of institutionalism theories [1].

Ghana has a total land area of 238,500 km2. The distance from south to north is about 670 km and from west to east is about 560 km. Ghana has two main kinds of vegetation; forest and savanna. Tropical high forest occupies the southern portion and the savanna the northern and southeastern portions of the country. Taxonomically the two are very distinct and very few plants occur naturally in both kinds of vegetation. The savanna covers about 65.5%of the total land area of Ghana and tropical high forest covers about 34.4%of the total land area of Ghana). The approximately 0.1%accounts for the coastal plains and sandy beaches. The total forest cover of the country translates to approximately 0.5%of the world's forests [6-10] .

Ghana’s forest is categorized into unreserved forest and government protected forest reserves. There are about 266 protected forest reserves covering a total land area of about 12,200 km2 and 200 of these are located in the high forest zone. Total forest and wildlife reserves occupy 18,000 km2 or 22% of the tropical high forest zone of Ghana. Unreserved close canopy forest covers an area of 400,000 km2 [8].

Timber is currently being harvested from production areas of about45% of the total area within forest reserves.

The remaining 55% is designated as protection, conversion and research areas in conformity to Ghana's policy for environmental conservation [10, 11].

The country’s vegetation also produces wide range of goods and environmental services aside timber, with national importance including; medicinal plants, and non-timber forest products (NTFPs) such as snails,

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mushrooms, ‘bush meat’, fruits etc. [3, 7]. The forest also produces indirect ecological benefits such as nutrient cycling, water conservation, and serves as carbon sink, it also help to protect biodiversity and landscape. For some forest fringe communities, the economic interest in the forest is huge, from illegal logging, participatory forest management, tree plantation development, game for bush meat, collecting snails and mushrooms to converting forest into cattle pasture.

According to Abebrese (2002), though global forest continues to dwindle in terms of area and quality, the problem is more pronounced in Ghana. The rate of forest lost outside forest reserves is alarming and the fact is that original forest cover are no more except for some parts of the forest reserves, and in small patches of protected areas and sacred groves near villages. The proportion of forests outside forest reserves declined from 20% in 1955 to less than 5 per cent in 1994 [12]. There are about 700 km2 of plantations in Ghana with about 590 km2 in the high forest zone. There are many plantation developments by government and the private sector with some being public-private partnership, example is the ‘tuangya’ going on in the unreserved areas of the forest zone [13-15].

The main cash crops cultivated in the forest zone are cocoa, oil palm and citrus. Other annual crops grown include plantain, cocoyam, yam and cereals (e.g. maize and rice). In all cases of farming, the forest is cleared before crops are planted. Agriculture in Ghana with cocoa being the main cash crop is the highest employer of the citizens employing over 60%of the working population and the second highest foreign exchange earner after mining [16].

The forest policy review process of Ghana is characterized by various actors and stakeholders, some are agencies, international organization and civil society organizations some of whom are in some form of coalitions. These coalitions operate in different arenas, with internal rules, norms, ideas, and concepts they adhere to, example of which is the Forest Watch Ghana (FWG) which is a coalition of some local and international NGOs (CARE, SNV/Netherlands Development Agency, Tropenbus Ghana, Institute for Cultural Affairs, Ricerca e Cooperazione, Green Earth Organization, Friends of the Earth, Friends of the Nation, the Ghana Wildlife Society and many more other environmental NGOs) and individuals interested in forest and nature conservation [9]. Some of these actors and stakeholders have high impact factor in terms of how they articulate their ideas during debates in the forest policy review process than others because of the kind of power they have to convince and influence and change the rules of the game of the review process.

Stakeholders of the forestry sector of Ghana debate at different levels and in different arenas on ideas and issues of both local and global concern. Some of the debate bothers on good forest management practices, new global forest laws and certification programs to nature conservation and forest policies in Ghana.

If a policy review process is not properly conducted by institutions with the right knowledge, to meet and share ideas on best policies to address the current trend of forest degradation through illegal logging, the loss of environmental services may in large part fall on those current and future generations who do not own the forest, live in or near it. Without strong and effective institutions to formulate and implement better policies, the sector might not be able to control forest lose and improved livelihoods for current and future generations [3, 17].

In the year 1994, the forest policy of Ghana which was over 50 years old was updated to reflect the reality of the changing times as well as give direction to sustainable forest management [18]. This policy was to ensure that all segments of society enjoy benefits accruing from the forests in an equitable manner. Additionally, some forestry institutions were restructured and legislative enactments were introduced. The Policy was also intended to serve the greater country objective under the Vision 2020 of maximizing the rate of social and economic development of the country and securing optimum welfare and adequate means of livelihood for all Ghanaians.

One of these legislative instruments restricted the use of chainsaw in milling timber.

This was later followed by a set of interim measures to include traditional authorities and communities in the consultation process for allocating forest concessions, but it was revealed at the forestry policy review forum held in Kumasi on 14th October, 2010 for Paramount Chiefs in Ghana that this has not been the case. The chiefs revealed to the review committee that they are involved but little by the Forestry Commission in the management of the forest and that most concessions are given out without their consent. A conveyance certificate became a requirement for logs in transit to ensure that timber was legally and sustainably harvested [21].

The need for reviewing specific government guidance and control in the forestry sector of Ghana has further become very necessary due to the emerging international conventions, rules, partnership programs and protocols regarding this and other natural resource sectors. The government of Ghana is very keen to sign onto these forest and other natural resource intervention programs, the country was the first to ratify a Forest Law Enforcement, Governance and Trade (FLEGT) Voluntary Partnership Agreement (VPA) with the European Union (EU) aiming to correct the bad forestry practices of the state [19]. As to whether these programs are scrutinized and reviewed in consultation and collaboration with all stakeholders of the economy in order to help achieve better results and impact is yet to be investigated. The current and ongoing wave of global forest laws and other programs has made the forestry sector of Ghana realize the need for a review of the forest and wildlife policy of Ghana which has outlived its usefulness.

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The program REDD, which is Reducing Emissions from Deforestation and Forest Degradation (REDD) an effort to create a financial value for the carbon stored in forests, offering incentives for developing countries to reduce emissions from forested lands and invest in low-carbon paths to sustainable development has been mentioned and considered. The “REDD+” which goes beyond deforestation and forest degradation, and includes the role of conservation, sustainable management of forests and enhancement of forest carbon stocks was also deliberated at some of the stakeholder forums of the policy review process of Ghana. Other interventions and forest management programs like the Clean Development Mechanisms (CDM) have made it important for the policies of the forestry sector of Ghana which was developed in the year 1994 to be reviewed. Under the Clean Development Mechanism, emission-reduction (or emission removal) projects in developing countries can earn certified emission reduction credits. These saleable credits can be used by industrialized countries to meet part of their emission reduction targets under the Kyoto Protocol [20].

Taking into account all the above argument, there is increasing local concern over environmental issues like adverse weather conditions due to climate change and extinction of some indigenous NTFPs due to biodiversity lose which are related to the degradation of forest. Apart from the forestry sector’s role in improving livelihoods and maintaining environmental quality, it also help in the nation’s economy therefore the need to critically review policies governing this crucial sector from time to time to address the changing trends of environmental issues.

II. Objectives Analysis of the explanatory power of ideas and discourse in relation to what is said; where, when, how, why it was said and which stakeholder or agency said it within the forestry sector in formulating new forest policies, programs and philosophies is yet to be conducted.

There have been problems with the 1994 Policy since the annual cut has to satisfy a timber industry that is characterized by excessive installed capacity and low recovery rates, mainly focused on exporting to a hardening market place, and domestic consumption that is mainly fed from illegal sources. At the same time, environmental concerns have increased to the extent where wildlife and biodiversity conservation are demanding equal attention with forest protection and rehabilitation of degraded and understocked areas occupying a third of the forest estate so the need for the review [21].

According to Schmidt (2008), discursive institutionalism (DI) is very unique in its approach to analyzing policies than other forms of institutionalism theories. It is different from the rational choice institutionalism (RI), sociological institutionalism (SI) and historical institutionalism (HI). The DI helps in making sense of the actions of certain actors and stakeholders in the political debate in the forestry sector. DI looks at the role of ideas in the actions taken, the importance of deliberation in democratic legitimacy, the construction of political interests and values, and the dynamics of change in history and culture [1]. The processes involved in the forest policy review (FPR) of Ghana can be analyzed and explained using the DI theory. The ideas and discourse of stakeholders of the forestry sector will be analyzed using DI to determine which stakeholder said what, how, when and where it was said.

The forestry sector is very complex and has lots of actors, stakeholders, agencies and institutions engaged in continuous discussions on their concepts, believes and ideas on how to formulate better policies. It is important to note that the ideas these actors bring into the arena during policy discussions become effective when they are already involved in doing some activities on the ground by way of community advocacy, policy briefs at conferences and workshops, consultancy for the sector etc and not just mere talk [3, 22].

According to some political theorists, DI is the new branch in neo-institutionalism that tries to overcome the traditional institutional thinking like path-dependency and incremental change, the turn to ideas, concepts, narratives and discourse in institutional dynamics and political science [1, 4, 22, 23]. The DI distinguish the difference between discourses and institutions and the effect discourse has on institutions which is a form of ‘analytical dualism’ [4, 22].

The specific objectives of this paper are: i.

To identify actors of the policy review process, their concepts and believes, their interests, and the ideas they want to share ii.

To use the discursive institutionalism theory to explain what is happening in the forest policy review of Ghana The above objectives will help answer the research question of; “how are ideas and discourse articulated by actors and stakeholders of the forestry sector of Ghana?”

III. Institutional Ideas and Discourse in the Forestry Sector of Ghana Policy design, formulation and review in the forestry sector of Ghana involve a lot of stakeholders, some are ‘old’ and have been in existence for so many years whereas others are ‘new’ and only came into existence recently. Irrespective of whether a stakeholder is old or new, their ideas and discourse are expressed in the policy debate in order to formulate better policies which will be beneficial to society and the world at large [24-

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26]. The need to consult with various actors and stakeholders of different sectors of the economy and disciplines allows different ideas and discourses to be expressed resulting in holistic understanding of different views, opinions and interests thereby enhancing systems thinking to bring about the best policy output for the sector [27].

Some important actors, stakeholders, institutions, agencies and commissions that influence policy decisions of the forestry sector of Ghana: Ministries  Environment, Science and Technology  Lands and Natural Resources  Water Resources, Works and Housing  Trade and Industry  Employment and Social Welfare  Tourism  Food and Agriculture Agencies and Commissions  Forestry Commission of Ghana (and all its departments)  Timber Export Development Board  Ghana wood workers association  Timber companies  Forest Products Inspection Bureau  Forestry Research Institute of Ghana  Association of Ghana Industries  Environmental Protection Agency  Lands Commission Secretary  Federation of Ass. of Ghana Exporters  Ghana Energy Commission  Ghana Export Promotion Council  Ghana National Petroleum Corp.  Local and International NGOs  The Mineral Commission of Ghana  The Universities in Ghana  Ghana National Petroleum Corporation  Centre for Development Partnerships (CDP) of the World Bank  The European Union Apart from the above mentioned actors, stakeholders, institutions, agencies and commissions, many more individuals and stakeholders like the traditional authorities, forest fringe communities, non-timber forest products (NTFPs) collectors, charcoal producers, fuel wood users, etc also play vital roles within the Ghana forest policy review (FPR) setting.

There is also a broad coalition of civil society networks and organizations interested in the forestry sector known as the “Contact Group” with members from both local and international NGOs as well as the academia. This group which was formed in 2007 in response to the opportunities that the Voluntary Partnership Agreement (VPA) offered for engaging civil society organizations and other individuals on how to better manage the forest and increase revenue. This and other civil society groups have since extended their work to include other sector initiatives e.g. REDD+ and the overall governance reform which include the FPR. Upon interviewing their leadership, represented by Forest Watch Ghana (FWG), the group now works towards the attainment of sustainable and just forest development and biodiversity conservation in Ghana through campaigns including: i) fair access to forest resources as between different stakeholders and in particular for improved access for forest dependent communities; ii) fair distribution of benefits from forest exploitation as between different stakeholders and in particular for forest dependent communities; iii) greater democratic stakeholder participation in forest policymaking and management particularly for forest dependent communities; iv) greater civil society mobilization around forest and natural resource issues.

Apart from several NGOs, the Contact Group has other members such as organized labor, traditional and other community leaders, research and academic organizations, the FWG, the Domestic Lumber Traders Association, members of District, Regional and National Forest Forums. Using the discursive institutionalism (DI) theory to analyze how this and other groups conveys their ideas, believes and discourse, and the medium and forum through which they do so. Stakeholder groups use different modes of communication and with their power AIJRSTEM 15-187; © 2015, AIJRSTEM All Rights Reserved

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position in relation to others give them an edge in convincing policy-makers in accepting their ideas than those of other competing stakeholders who might share contrary ideas. Even though the Contact Group is not composed of all the civil society groups in Ghana that are interested in forestry, it is believed by the some of the public and some committee members responsible for the FPR that they represent civil society organizations in the sector because of their numbers, position within the sector, interests and how their ideas are argued. 4. Ideas and discourse of the major stakeholders The review of the 1994 Ghana Forest and Wildlife Policy, and the 1996 Ghana Forestry Development Master Plan as well as other relevant documents provided useful insight into Forestry activities in Ghana and how the sector engages stakeholders to solicit for their ideas. The review of these documents gave a better appreciation of challenges of stakeholder engagement processes within the forestry sector of Ghana since there are different kinds of stakeholders involved [10, 11].

Analysis of ideas and discourse of stakeholders is a process of systematically gathering and analyzing qualitative information about stakeholders on what they say, how they say it, what they stand for and determine whose ideas are being considered over the other in a policy debate [1, 4]. It is the identification of the old and new key stakeholders of the sector, an assessment of their interests, and the ways in which these interests are considered in the policy arena [28].

At a stakeholders’ workshop organized by the sector ministry in Accra recently, the invited stakeholders, described stakeholders of the sector as, those directly or indirectly affected by forestry activities and may influence the outcomes of the forest and wildlife policy review. In effect, a stakeholder was considered to be any individual, community, group or organization with an interest in the forestry sector and the outcome of the forestry policy review, either by being affected by the outcomes or not, or by being able to influence the policy implementation in a positive or negative way.

There are major elements in stakeholder categorization. There is the categorization of the ‘old’ stakeholders (those who have been with the sector for several years) and the ‘new’ stakeholders (those who joined the sector recently as a result of their interest or new intervention programs or both). There is also the categorization of Key, Primary and Secondary stakeholders. Key stakeholders are those who can significantly influence or are important to the success of the sector. Primary stakeholders are those individuals and groups who are ultimately affected or impacted either positively or negatively by activities of the sector. Secondary stakeholders are all other individuals or institutions with a stake, interest or intermediary role in the sector. [29-34].

In reality, during a recent field study it was realized that the distinctions were not clear-cut, there are overlaps between these categories; some old- and new- primary or secondary stakeholders may also be key stakeholders.

The stakeholder categorization may not be static as well; a primary can become secondary or key stakeholder depending on changing circumstances and roles in the sector and the policy debate.

At a recent stakeholders’ workshop organized for the traditional authorities (chiefs and elders), they argued that people living in and around the forest should be the ones the new policy should benefit since they depend mainly on the forest for their livelihoods. It also came up that traditional authorities have much interest in relation to the forestry sector compared to all the other community members because of the royalties paid by timber merchants as part of their social responsibilities to them after harvesting timber from their land. The chiefs and elders are considered key stakeholders and are very important and influential since traditionally they are the custodians of the lands on which the forests exist and forestry activities take place.

Forest fringe community members were categorized as primary stakeholders since they are impacted directly by policies of the forestry sector and its activities but have no big influence on the output of policy. The depletion of the forest cover has negative impact on their farms, chain saw activities in and around their farms destroy crops and cost them financially [30, 35].

The secondary stakeholders such as the local and international NGOs do not have much interest in royalties and are not directly affected by illegal chainsaw activities that destroy the forest and farms, but have interest in community and global benefits of forestry activities for example climate change [30, 36]. Table 1below gives a summary of stakeholders and their discursive relations. The table also shows how important and influential a stakeholder is in the policy process as well as their level of participation in the policy debate.

Table 1: Stakeholder Mapping Matrix showing the discursive relations of some Stakeholders of the forestry sector of Ghana Stakeholders

Key Ideas

Ministry of Lands and Natural Resources

 To formulate new forest and wildlife policy that will take into consideration rebuilding the lost forest estate  maintain vital ecology and life-sustaining processes and conserving pools of genetic material  success of sustainable resource management

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Importance to the policy process Very important since it is the sector ministry in charge of the policy review

Influence on policy decisions

Participation

Highly influential to the policy decisions since it is in charge of implementing the policy initiative programs (e.g.

VPA)

Present at all workshops

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Ministry of Environment, Science and Technology

 Scientific research into best practices of forest management and helping the sector ministry to account for the costs of nation’s forest depletion

Forestry Commission of Ghana (and all its departments)

 Develop effective capabilities at the District, Regional and National level in the management, conservation and sustainable development of the nation's forest and wildlife resources  Develop participatory Governance through promoting public awareness and involvement of rural people in conservation so as to maintain life-sustaining systems; preserve scenic areas enhance the potential of recreation, tourism and income-generating opportunities.  Promote public awareness and involvement of rural people in conservation  work towards sustainable and just forest development and biodiversity conservation through campaigns and advocacy  Offer direct engagement with  policy research;  direct engagement with policymakers;  citizen mobilization and capacity building;  mass communication;  lesson learning and experience sharing; and  Partnership or collaboration with national, African and international CSOs.

The Contact Group/Other civil society organizations interest in forest activities

Very important in the review process since it is an allied ministry to the sector ministry Very important in the review process

Influential to the policy process

Very important in the review process

Very influential in the policy process and will still be influential during implementation (e.g. Tropenbus Ghana, Civic Response, CARE Int., SNV etc.)

Their activities is crucial in changing the direction of the forest policy of Ghana at any point in time

Was consulted but did not participate in all the workshops Have been participating in most of the stakeholder workshop organized by the forest review committee of Ghana Participated in the stakeholder workshop designed for just civil society groups. They continue to engage with the sector ministry even after the workshop.

Source: Field research, May – August, 2014 Stakeholder representatives and some individual participants at some of the FPR stakeholders’ workshop recognized that stakeholders have very different levels of power to make their ideas heard and influence decisions to affect the output of the FPR. They also pointed out that stakeholders vary in their importance and potential to affect forestry activities. Potential to contribute to good forest management, good policy statements and livelihood improvement for forest fringe communities and other timber users lays in factors such as knowledge about global forestry, local forestry activities, and type of resources available and international connections.

Some stakeholders have considerable ideas to contribute to sustainable forest management, yet have little power to do so because they are not better situated within a stronger institution with enough power that will help convince others with their ideas. Others, by contrast have lower potential, yet have considerable power to change the current discourse with their argument and support from other coalition members. To understand the power relations of some of the stakeholders of the forest policy review process, a basic “ranking” of stakeholder groups according to power and potential was developed for stakeholders to rank but not themselves in a questionnaire format. The average was deduced and used to rank stakeholders. The following table shows the results. In the table, the main current stakeholder groups are listed and given an indicative ranking along the following lines:  Size of group (5 = very large, 1 = very small): an indication of the number of people in the group (e.g. traditional authorities will have a fewer number than community members but more numbers than the sector ministry)  Potential to contribute to forest policy debate (5 = very high, 1 = very low): an indication of the contribution which the group could make, given their size, with their current role, level of knowledge and expertise – if they had the power to do so  Power to contribute to and influence forest policy decisions (5 =very high, 1 = very low): an indication of the power each group currently has to contribute to the debate on the forest policy review Table 2: Ranking of stakeholder groups’ power and potential to contribute to forest policy review debate Stakeholder Group

Size Of group

Potential to contribute to policy decisions

Ministry of Environment, Science and Technology Ministry of Lands and Natural Resources Ministry of Water Resources, Works and Housing Ministry of Trade and Industry Ministry of Employment and Social Welfare Ministry of Tourism

2 5 2 2 2 4

3 5 2 3 2 2

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Power to contribute to and influence forest policy decisions 3 5 2 4 2 2

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Ministry of Food and Agriculture The Traditional Authorities (Chiefs) The Contact Group/Forest Watch Ghana Forestry Commission of Ghana Timber Export Development Board Ghana wood workers association Timber companies Forest Products Inspection Bureau Forestry Research Institute of Ghana Association of Ghana Industries Environmental Protection Agency Lands Commission Secretary Federation of Ass. of Ghana Exporters Ghana Energy Commission Ghana Export Promotion Council Ghana National Petroleum Corp.

Local and International NGOs The Mineral Commission of Ghana The Universities in Ghana Ghana National Petroleum Corporation Centre for Development Partnerships (CDP) The European Union

2 4 4 4 2 4 3 1 2 4 1 1 3 1 1 1 5 2 3 1 1 4

2 4 4 5 3 2 2 2 2 1 3 2 2 2 2 1 5 2 3 2 2 4

2 4 5 4 2 2 2 2 3 1 4 2 1 1 2 1 5 2 3 1 2 5

At the government organized workshops to solicit for ideas for the new policy, most stakeholders submitted written notes while others did some sort of computer presentation to help influence the committee to consider their ideas on the policy review and to further consult them on how to implement these ideas into programs.

Here is an excerpt from one such note from Civic Response in 2010: “These notes are provided as a preliminary contribution to the Ministry of Lands and Natural Resources proposal to replace the 1994 Forest and Wildlife Policy. We hope that there will be further opportunities to engage the Ministry as the process develops and indeed after any new policy is adopted. Given the opportunity we would be happy to submit a more considered version of this note.”

Analyzing the above statement, it can inferred that civil society groups of which Civic Response is one of their most active actors has a very good strategy to put their ideas and interest across and for continuous engagement with the management of the forestry sector. Their strategy is unique, they have joined coalition forces called Forest Watch Ghana (FWG) and its active members (Tropenbus Ghana, CARE, SNV, Friends of the Earth, Institute for Cultural Affairs, Ricerca e Cooperazione, Green Earth Organization, Friends of the Nation, the Ghana Wildlife Society) and many more other environmental NGOs with particular interest in the forest in order to make their ideas and discourse realized.

IV. Conclusion Ideas and discourses of some stakeholders of the forestry sector of Ghana are accepted and included in the policy output of the forestry sector of Ghana when they are presented with a common voice in the form of a coalition group. The coalition groups formed by stakeholders increase their power to influence policy decisions.

Individual stakeholders even though might have better ideas, fail to change and influence policy decisions when their ideas meet opposition from powerful coalition groups. Discursive Institutionalism theory helps to explain and make sense of how institutions are able to change social norms through ideas and discourses, advocacy, and getting involved in policy debate and processes. The importance of ideas and discourse of actors and stakeholders of the forestry sector of Ghana in shaping the sector through better policies was also noted. The analysis of the power relations and participation of stakeholders in the policy processes make us understand how institutions can influence policy decisions in favor of their ideas and believes. At the policy review forums, some stakeholders submitted written notes while others did computer presentations to help influence the committee to consider their ideas, but those that had formed coalition with other actors had their ideas getting the best audience. From the above analysis and discussions, it is concluded that the discursive institutionalism theory can be used to explain how stakeholders and actors share ideas and discourses in a policy process.

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