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

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

Issue 3, Volume 1 & 2 June-August, 2013

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: India, Australia, Germany, 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 third 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

order

develop

and

implement widespread expansion of highâ&#x20AC;?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 third issue, we received 92 research papers and out of which only 36 research papers are published in two volumes as per the reviewersâ&#x20AC;&#x2122; 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.

The 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 third 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 (June-August, 2013, Issue 3, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

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.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, 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, India. 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.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur 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. 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 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.) 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. 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,TamilNadu,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, India. 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. 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.

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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 Eng.,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. 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, India.

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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. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. 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.

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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, 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 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. 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, India. 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

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, 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. . 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) 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 Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura 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.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, 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, Punjab, India. 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. 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 (June-August, 2013, Issue 3, Volume 1 & 2) Issue 3 Volume 1 Paper Code

Paper Title

Page No.

AIJRHASS 13-201

MULTIVARIATE COMPARISON OF CEPHALOMETRIC TRAITS IN IRANIAN AZERIS AND PERSIANS Vahid Rashidvash

01-07

AIJRHASS 13-202

Human and Physical Environmental Factors Affecting Students Utilization of Library and Information Services in Colleges of Education Libraries in Nigeria Lawrence Abraham Gojeh, Lami Ishaya Dutse, Hannatu Daudu

08-16

AIJRHASS 13-204

Strategic 360 Degree Performance Appraisal Model as a Synergy for Strategic Education Planning in Premier HTIs in India Raghunadhan T, Dr. A H Sequeira

17-22

AIJRHASS 13-205

Preserving cultural identity through tribal self governance: The case of Lachenpa and Lachungpa tribes of Sikkim Himalaya (India) Dr. Durga Prasad Chhetri

23-28

AIJRHASS 13-207

STUDY OF WEB 2.0 TECHNOLOGY FOR AGRICULTURAL INFORMATION MANAGEMENT JAYADE, K. G., GAIKWAD, C. J., KHOT, P. G., NIKOSE, S. M.

29-34

AIJRHASS 13-208

A NOTE ON SOME LARGER CLASSES OF OPERATORS Dr.A.RADHARAMANI

35-42

AIJRHASS 13-209

Problems of Literary-Artistic Translation Diler Singh, Dr. Dipali Sharma Bhandari

43-45

AIJRHASS 13-211

ASSESSMENT OF NUTRITIONAL STATUS OF ELDERLY IN SELECTED PAID AND DESTITUTE HOMES IN CHENNAI, INDIA G. Vani Bhushanam, K. Sreedevi and Janaki Kameshwaran

46-49

AIJRHASS 13-212

UNOFFICIAL DIPLOMACY AT WORK: A SAARC PERSPECTIVE Prof. Rajender Gupta, NeelamChoudhary

50-65

AIJRHASS 13-213

Status of Jews at the dawn of the French Revolution Vanishree Radhakrishna

66-70

AIJRHASS 13-214

The Significance of Civil rights movement in America Vanishree Radhakrishna

71-75

AIJRHASS 13-216

Re-engineering of Personality through Language- A study of MBA students in a B-School in Pune Dr. DipaliBiswas, Aditi Kale

76-81

AIJRHASS 13-217

Reach and Academic Use of Various Applications of Social Media: A Survey among the University Students of Tamil Nadu Dr. R.Subramani, D.V.Nithyanandan

82-86

AIJRHASS 13-218

Learning through Mass media is a flawless process in Distance Education system G. PONMENI

87-91

AIJRHASS 13-219

Persian Paradisal Theme on the Wall of Mughal Tombs Rohita Sharma and Dr. Ila Gupta

92-96

AIJRHASS 13-220

Re-engineering Recruitment Strategies with RPO Model for Recruitment Challenges in IT and ITES Industry Ms. Sunita Tank

97-101

AIJRHASS 13-221

Career Development in the Context of Globalization, Privatization and Liberalzation Prof. T.K.Gill

102-116

AIJRHASS 13-222

Employees Ethical value is the key to the competency of the organization Utpalendu Mondal

117-119

AIJRHASS 13-223

Causes of Dropout Rate in Government High Schools (Male) Rani Gul, Gulshan, Arshad Ali

120-125

AIJRHASS 13-225

RESERVATION POLICY AND INDIAN CONSTITUTION IN INDIA DR.SUNIL KUMAR JANGIR

126-128

Issue 3 Volume 2 Paper Code

Paper Title

Page No.

AIJRHASS 13-226

THE DARK SIDE OF HR: EMPLOYEE HARASSMENT Avnish Sharma, Aneesya Sharma

129-133

AIJRHASS 13-228

Special Library: A Gigantic Information Centre for Specials Mr. Prakash Bhairu Bilawar

134-140

AIJRHASS 13-229

Resource Control and Revenue Allocation Problems in Nigeria: Implication for National Peace Adeleke Adegbami

141-148

AIJRHASS 13-230

Literacy Level of Beggars in Aligarh District: A Regional Analysis Dr. Jabir Hasan Khan, Dr. Menka

149-155

AIJRHASS 13-232

ALIENATION AND INCLUSION POLICY: A SOCIOLOGICAL STUDY OF BARARA VILLAGE OF AGRA DISTRICT Prof. Poornima Jain

156-161

AIJRHASS 13-236

Information Common and Emerging Cloud Library Technologies Sangeeta Dhamdhere, Ramdas Lihitkar and Shalini R Lihitkar

162-167

AIJRHASS 13-238

Analysis on Literature review on competency mapping For nurses in healthcare Nalini Devi.S, Dr.N.Panchanathan

168-170

AIJRHASS 13-241

Being to Becoming: A Journey in William Goldings Free Fall Bhargavi D, Rajeshwari C Patel

171-177

AIJRHASS 13-242

Ailing Banks- Failing Economy Dr. A.M.Bhattacharya (Economist)

178-180

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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)

Lossy Transmission Lines Terminated by Parallel Connected RC-Loads in Series Connected to L-Load (II) Vasil G. Angelov, Andrey Zahariev Department of Mathematics, Faculty of Mine Electromechanics University of Mining and Geology “St. I. Rilski” Studentski grad, 1700 Sofia BULGARIA Department of Mathematical Analysis, Faculty of Mathematics and Informatics, blv. “Bulgaria” 236, 4003 Plovdiv, The Plovdiv University “Paisii Hilendarski” BULGARIA Abstract: The present paper is a second part of the previous one (Angelov and Zahariev, 2013) where a lossy transmission line terminated by the same circuit of nonlinear loads is considered. Here we introduce an operator acting in a suitable function space whose fixed point is an oscillatory solution of the neutral system obtained in the first paper cited above. Then we formulate conditions for the existence-uniqueness of an oscillatory solution. The conditions obtained are easily verifiable inequalities. We demonstrate the advantages of our method by a numerical example. Keywords: lossy transmission line, mixed problem for hyperbolic system, neutral equation, oscillatory solution, fixed point theorem, Kirchhoff’s law. I. Introduction The general theory and various applications of transmission line theory can be found in many monographs ([9][17]). In previous papers ([2]-[7]) we have made an analysis of transmission lines (both lossless and lossy ones) loaded by various configurations of nonlinear elements. Motivations for the present considerations in the first part of the paper [8] are given. Since we continue the investigations from [8] this inflicts to recall briefly the main results obtained. Figure 1 Lossless Transmission Line Terminated by Parallel Connected RC-Loads in Series Connected to L-Load

As a consequence of Kirchhoff’s law the boundary conditions are derived (cf. Figure 1) and then the mixed problem for the hyperbolic transmission line system is formulated. The reducing of the mixed problem for the hyperbolic system leads to an initial value problem for neutral equations on the boundary. We also show that natural solutions are oscillatory ones, tending to zero at infinity.The primary goal of the present paper is to introduce a suitable operator whose fixed point is an oscillatory solution of the neutral system. By fixed point method given in [1] we obtain an existence-uniqueness of an oscillatory solution and demonstrate the application of our method to specific problems. The paper consists of five sections. In Section II we recall the formulation of the mixed problem for the hyperbolic system (Telegrapher equations) and give some estimates of the characteristics of the nonlinear loads. In Section III we introduce an operator presentation of the oscillatory

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V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

problem. The key role plays Lemma 3.3. In Section IV we establish existence-uniqueness of oscillatory solution tending to zero at infinity. In Section V we demonstrate the advantages of our method on a numerical example. We point out that for applications it is necessary to verify just simple inequalities. II. The Mixed Problem and Neutral System In the first part (see [8]) we have derived boundary conditions of the mixed problem for lossy transmission line system and we have formulated the mixed problem: to find functions u ( x, t ) and i( x, t ) , satisfying the system u ( x, t ) t i ( x, t ) L t

i ( x, t ) Gu ( x, t ) 0, x u ( x, t ) Ri( x, t ) 0 x

for ( x, t )

x, t

: x, t

(2.1)

and such that the following initial (2.2)

u( x,0) u 0( x), i( x,0) i0 ( x), x 0, and boundary conditions dC0 (uG0C0 ) duG0C0 uG0C0 C0 (uG0C0 ) duG0C0 dt dL0 (i (0, t )) diG0C0

i (0, t )

dC1 (uG1C1 )

uG1C1

duG1C1

i( , t )

di(0, t ) dt

L0 (i (0, t ))

duG1C1

C1 (uG1C1 )

dL1 (i ( , t )) diG1C1

i (0, t ) G0 (uG0C0 ),

(2.3)

i ( , t ) G1 (uG1C1 ),

L1 (i ( , t ))

u (0, t ) uG0C0 (t ) E0 (t ),

di( , t ) dt

u ( , t ) uG1C1 (t ) E1 (t )

(2.4)

are satisfied. Here L, C, R and G are prescribed specific parameters of the line and > 0 is its length. Let us recall that we deal with nonlinear elements whose characteristics are: m m m ~ ~ R p (i) rn( p ) i n , ( p 0,1), L p (i) ln( p ) i n , L p (i) i . L p (i) i. ln( p ) i n , C p (u ) u C p (u ) n 1

such that

~ dL p (i)

n 0

d (i L p (i))

dLp (i) di L p (i) i , dt di

dt ~ dC p (u )

dt d (u C p (u ))

dC p (u ) du C p (u ) u , ( p 0,1). dt du

From Section IV of the first part (see [8]) we have cp h p ~ cp h C p (u ) ; C p (u ) u C p (u ) h h p u dC p (u ) du ~ dC p (u ) du

cp h

h cp h

1 h h

h 1 u h

p p

d 2C p (u )

;

1 h

cp h

du 2 u

; cp

1 h h

h 2~ d C p (u )

;

0; h [2,3] ; u

cp h

1 2h h

2h 2

min

~ dC p (u ) du

2c p h h2

min

h 1 p

0 1 2h

h h 1

ď&#x20AC;¨ C (p1) , u

the derivative

min

~ dC p (u )

h h 1

;

h 1u

1 2 h

0 . Further on we have

;

Since u

min

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V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

~ dC p (u )

cp h

1 h 0 h

du p

~ Since L p (i) i . L p (i)

h 1 h

2~ (1) d C p (u ) ˆ Cp , du 2

cp h h

2h 2

2h 2 h 1

2 p

1 2 h 0

 C (p2) .

ln( p ) i n we have

n 0

~ dL p (i )

dL p (i ) di

Assumptions(C):

L p (i )

nln( p ) i n

n 1

W0 2

n 1 T0

l n( p ) i n

W0 J 0 2Z 0

(n 1)l n( p ) i n .

n 1 0.

It follows u (0, t ) , u ( , t ) , i(0, t ) , i( , t ) 0. ~ dL p (i ) m (n 1)ln( p )i n Lˆ(p1) 0, Assumptions (L): i 0 di n 1 ~ ~ m (1) dL2p (i) m 1 dL p (i) ( p) n (n 1)ln 0 Lp , n(n 1)ln( p ) 0 di di 2 n 1 n 1 m

g n(0) (iR0 L0 ) n , G1 (iR1L1 )

Assumptions (G): G0 (iR0 L0 ) n 1

 L(p2) .

n 1

g n(1) (iR1L1 ) n .

n 1

By the transformation 1

u ( x, t )

R t L W ( x, t )

2 C 1

i ( x, t )

R t L J ( x, t ),

2 C R t L W ( x, t )

R t L J ( x, t )

2 L 2 L under Heaviside condition R / L G / C system (2.1) becomes W ( x, t ) 1 W ( x, t ) J ( x, t ) 1 J ( x, t ) 0, 0 t x t x LC LC with initial conditions W ( x ,0 ) C u ( x ,0 ) L i ( x ,0 ) C u0 ( x ) L i0 ( x) W0 ( x), J ( x ,0 )

C u ( x ,0 )

L i ( x ,0 )

C u0 ( x)

L i0 ( x)

J 0 ( x), x

We assume that the unknown functions are W (0, t ) W (t ), J (t ) J ( , t ) . Then in view of W (0, t ) W ( , t T ), J (0, t T ) J ( , t ) W (0, t T ) W ( , t ), J (0, t ) J ( , t T ) the boundary conditions yield a neutral system: duG0 C0 (t )

R t L W (t )

dt d e dt

R t L W (t )

duG1C1 (t )

T ) 2 LG0 (uG0 C0 (t )) ; ~ 2 L dC0 (uG0 C0 ) / duG0 C0

R (t T ) L J (t

R (t T ) L W (t

dt d e dt

R (t T ) L J (t

T) e

Z0 e

R t L W (t )

R t L J (t )

R (t T ) L J (t

Z 0e

T ) 2 L uG0 C0 (t ) 2 L E0 (t ) ; ~ dL0 (i (0, t )) / diG0 C0

2 LG1 (uG1C1 (t ))

2 L dC1 (uG1C1 ) / duG1C1 R (t T ) L W (t

T) e

R t L J (t )

III.

Z0 e

R (t T ) L W (t

(2.5)

; R

T ) Z 0e L J (t ) 2 LuG1C1 (t ) 2 L E1 (t ) . ~ dL1 (i ( , t )) / diG1C1

Operator Presentation of the Oscillatory

We notice that if the neutral system (2.5) has a periodic solution

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V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

R t L W (t ),

uG0C0 (t ),W (t ), uG1C1 (t ), J (t ) , then functions e

R t L J (t )

are oscillatory ones and vanishing

R R t t   exponentially at infinity. Therefore we put W (t ) e L W (t ), J (t ) e L J (t ) and then we can state the problem for existence-uniqueness of an oscillatory solution vanishing at infinity of the following system (we denote by   W (t ), J (t ) again by W (t ), J (t ) ):

duG0C0 (t )

W (t ) J (t T ) 2 LG0 (uG0C0 (t )) , t [T ; ) ; ~ 2 L dC0 (uG0C0 ) / duG0C0

Z 0 W (t ) Z 0 J (t T ) 2 L uG0C0 (t ) 2 L E0 (t ) , t [T ; ); ~ dL0 (i(0, t )) / diG0C0

dW (t ) dt

dJ (t T ) dt

duG1C1 (t )

W (t T ) J (t ) 2 LG1 (uG1C1 (t ))

2 L dC1 (uG1C1 ) / duG1C1

dJ (t ) dt

(3.1)

, t [T ; );

Z 0 W (t T ) Z 0 J (t ) 2 LuG1C1 (t ) 2 L E1 (t ) , t [T ; ) ~ dL1 (i( , t )) / diG1C1

dW (t T ) dt

uG0C0 (T ) uGT 0C0 , uG1C1 (T ) uGT1C1 , W (t ) W0 (t ), J (t ) J 0 (t ), dW (t ) dW0 (t ) dJ (t ) dJ 0 (t ) , , t [0, T ]. dt dt dt dt We introduce an operator presentation of the oscillatory problem and by a fixed point theorem in uniform spaces given in [1] we establish an existence-uniqueness of oscillatory solution. Now we are able to formulate the main problem: to find a solution of (3.1) with advanced prescribed zeros on ~ ~ [t0 , ), T t0 , where W0 (t ), J 0 (t ) are prescribed initial oscillating functions on the interval [0, t0 ] . ~ ~ Let ST { k }nk 0 , n N be the set of zeros of the initial function, that is, W0 ( k ) 0, J 0 ( k ) 0 such that 0, n T t0 . Besides max{ k 1 k : k 0,1,..., n} T0 . 0 Let S

{t k }k

(C1) lim t k

be a strictly increasing sequence of real numbers satisfying the following conditions (C): ;

(C2) for every k there is s k such that t k It follows, that (C3) 0 inf{tk 1 tk : k 0,1,2,...} sup{tk

t s where t s

T 1

tk : k

0,1,2,...} T0

S. .

Introduce the set C [t0 , ) consisting of all oscillatory continuous and bounded functions differentiable with bounded derivatives on every interval [t k , t k 1 ] . Remark 3.1 Let us note that the left and the right derivative at the point t k of both functions W (.), J (.)

C1[t0 , ) could not be coincided. That is why we introduce a suitable topology for continuous functions with piecewise continuous derivatives. Introduce the sets M0

uG0C0 (.) C 1[t 0 , ) : uG0C0 (t k )

W (.) C 1[t 0 , ) : W (t k )

uG0C0 (t )

0 W (t )

uG1C1 (.) C 1[t 0 , ) : uG1C1 (t k )

J (.) C 1[t 0 , ) : J (t k )

J (t )

W0 e

R t L ,t

uG1C1 (t )

J 0e

U G0 e

R t L ,t

[t k , t k 1 ]; uG0C0 (t 0 )

0 ,

[t k , t k 1 ] ,

U G1 e

R t L ,t

[t k , t k 1 ]; uG1C1 (t 0 )

0 ,

[t k , t k 1 ] .

The following assumptions will be hold:

Page 4

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

~ Assumption (IN): W0 t

W0 e

It follows W t T

W0 e

R (t T ) L ;

R t L

~ J0 t

; J t T

J 0e

R t L

R (t T ) L

, t [0, T ] .

, t [T ,2T ] .

0. 0 , U G0 , U G1 2 C Remark 3.2 It follows that the functions from M 0 , M W , M 1 and M J satisfy the inequalities

Assumption

uG0C0 (t )

(t t k )

UG0 e

, W (t )

(t t k )

W0e

where U G0 , W0 , U G1 , J 0 , , T0

, uG1C1 (t )

UG1 e

(t t k )

, J (t )

(t t k )

J 0e

, t [tk , tk 1] , (k 0,1,2,... )

const. are positive constants.

Introduce the following family of pseudo-metrics: (k )

(uG pC p , uG pC p ) max uG pC p (t ) uG pC p (t ) : t [t k , t k 1 ] , ( p 0,1) ;

(k )

(W ,W ) max W (t ) W (t ) : t [t k , t k 1 ] ;

ˆ ( k ) (uG

p Cp

, uG pCp )

(k )

( J , J ) max J (t ) J (t ) : t [t k , t k 1 ] ;

max uG pCp (t ) uG pCp (t ) : t [t 0 , t k 1 ] , ( p

0,1) ;

ˆ ( k ) (W ,W ) max W (t ) W (t ) : t [t 0 , t k 1 ] ; ˆ ( k ) ( J , J ) max J (t ) J (t ) : t [t 0 , t k 1 ] ; (k )

(k )

(uG p C p , uG p C p )

max e

(W ,W ) max e

(t t k )

(k )

(W ,W )

(k )

(uG pC p , uG pC p )

(k )

 (W , W )

(uG pC p , uG pC p )

ˆ (k ) (uG

pC p

max

(0)

(k )

(t t k )

, uGpC p ) max

(k )

(W ,W ) ;

(t t k )

(uGpC p , uGpC p ),...,

J (t ) J (t ) : t [tk , tk 1 ] ;

(uG pC p , uG pC p ) , ( p 0,1) ;

(k )

(J , J )

 W (t ) W (t ) : t [t k , t k 1 ] ; (0)

0,1) ;

( J , J ) max e

max

( 0)

( J , J ),...,

uG pC p (t ) uG pC p (t ) : t [t k , t k 1 ] , ( p

(t t k )

max e

(k )

(uG pC p , uG pC p ),...,

(W ,W ), ...,

max e

uG p C p (t ) uG p C p (t ) : t [tk , tk 1] , ( p

W (t ) W (t ) : t [tk , tk 1 ] ;

max ( 0)

(t t k )

(k )

 ( J , J )

(k )

(J , J ) ;

0,1) ; (t t k )

max e

 J (t ) J (t ) : t [tk , tk 1 ] ;

(uGpC p , uGpC p ) , ( p 0,1) ;

ˆ (k ) (W ,W ) max (0) (W ,W ),..., (k ) (W ,W ) ; ˆ (k ) ( J, J ) max (0) ( J, J ),..., (k ) ( J , J ) . The following inequalities imply the equivalence of the pseudo-metric families: (k ) (k ) (uG pC p , uG pC p ) (uG pC p , uG pC p ) e 0 (k ) (uG pC p , uG pC p ), (k 0,1,2,...), ( p 0,1) ; (k )

(k )

(W , W )

(W , W ) e

It is easy to verify that ˆ ( k ) (uG C , uG C ) max p

max

ˆ ( k ) (W , W )

( 0)

(W ,W )

(k )

max

The set M 0 MW metrics

( 0) ( 0)

(k )

(W , W ),...,

( J , J ),...,

(k )

(W , W ),

(k )

(W ,W )

(J , J )

max

(0)

(J , J ) e

(k )

( J , J ), (k

0,1,2,...) ;

(uG p C p , uG p C p )

(k )

(J , J ) (k )

(uG p C p , uG p C p ),...,

ˆ ( k ) ( J , J ) max (k )

( 0)

ˆ ( k ) (uG (0)

pC p

, uG p C p ) ;

(W ,W ),...,

( J , J ),...,

(k )

(J , J )

(W , W ) e

ˆ ( k ) (W , W ) ;

ˆ ( k ) ( J , J );

 (W ,W ) and so on.

M1 M J turns out into a complete uniform space with respect to the family of pseudo-

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 max ˆ ( k ) (uG0C0 , uG0C0 ), ˆ ( k ) (W , W ), ˆ ( k ) (uG1C1 , uG1C1 ), ˆ ( k ) ( J , J ),

ˆ (k ) (uG C , uG C ), ˆ (k ) (W ,W ), ˆ (k ) (uG C , uG C ), ˆ (k ) ( J , J ) , (k 0 0 0 0 1 1 1 1 Remark 3.3 Replacing t

0,1,2,...).

t0 in (3.1) we obtain conformity condition (CC):

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V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

W (t0 )

J (0)

J (t0 )

W (0)

If T

~ Z 0 W (t0 ) Z 0 J (0) 2 L uG0C0 (t0 ) 2 L E0 (t0 ) / dL0 (i (0, t0 )) / diG0C0 , ~ Z 0 W (0) Z 0 J (t0 ) 2 LuG1C1 (t0 ) 2 L E1 (t0 ) / dL1 (i( , t0 )) / diG1C1 .

t0 and 0 are zero point of the initial functions and (without loss of generality) one can assume 0, uGT 1C1 0 and (CC) becomes W (t0 ) J (0), J (t0 ) W (0). Then the assumptions W (t0 ) J (0) 0,

uGT 0 C 0

J (t0 ) W (0) 0 imply (CC). In order to avoid the problem with (CC) we define the operator B B0 (uG0C0 ,W , uG1C1 , J ), BW (uG0C0 ,W , uG1C1 , J ), B1 (uG0C0 ,W , uG1C1 , J ), BJ (uG0C0 ,W , uG1C1 , J ) by the formulas t

B0( k ) (uG0C0 ,W , uG1C1 , J )(t ) tk t

BW(k ) (uG0 C0 , W , uG1C1 , J )(t ) :

U ( uG0 C0 , W , J )(s)ds tk

t tk tk 1 tk

B1( k ) (uG0C0 ,W , uG1C1 , J )(t )

U G1 (W , uG1C1 , J )(s)ds tk t

BJ(k ) (uG0C0 ,W , uG1C1 , J )(t ) :

I (W , uG1C1 , J )(s)ds tk

t tk tk 1 tk

UG0 (uG0C0 ,W , J )(s)ds

UG0 (uG0C0 ,W , J )(s)ds, t [tk , tk 1], (k

0,1,2,... ),

U ( uG0 C0 , W , J )(s)ds, t [t k , t k 1 ], (k

0,1,2,... ) ,

t tk tk 1 tk

UG1 (uG1C1 ,W , J )(s)ds, t [tk , tk 1 ], (k

0,1,2,... ),

I (W , uG1C1 , J )(s)ds , t [t k , t k 1 ], (k

0,1,2,... )

where

 1 W (t ) J (t ) 2 LG0 (uG0C0 (t )) U G0 (uG0C0 ,W , J ) , ~ dC0 (uG0C0 ) / duG0C0 2 L   dJ (t ) Z 0 W (t ) Z 0 J (t ) 2 L uG0C0 (t ) 2 L E0 (t ) U ( uG0C0 ,W , J ) , ~ dt dL0 (i(0, t )) / diG0C0  1 W (t ) J (t ) 2 LG1 (uG1C1 (t )) U G1 (W , uG1C1 , J ) , ~ dC1 (uG1C1 ) / duG1C1 2 L   Z 0W (t ) Z 0 J (t ) 2 LuG1C1 (t ) 2 L E1 (t ) dW (t ) I (W , uG1C1 , J ) , ~ dt dL1 (i( , t )) / diG1C1 ~ ~   W0 t T , t [T ,2T ] J 0 t T , t [T ,2T ] K (W ) : W W (t ) , K (J ) : J J (t ) . W (t T ), t [2T , ) J (t T ), t [2T , )

Lemma 3.1 The operator K (.) maps C 1[t 0 , ) into itself. The proof is straightforward. Lemma 3.2 If (uG0C0 ,W , uG1C1 , J ) B0 (t ), BW (t ), B1 (t ), BJ (t )

M 0 MW M1 M J then MW

 M 1 M J , W (t )

R t L ,

e W0 e

 J (t )

J 0e

R t L ,t

[T ,2T ].

Proof: We notice that B0 (uG0C0 ,W , uG1C1 , J ) and B1 (uG0C0 ,W , uG1C1 , J )(t ) are continuously differentiable functions, while BW (uG0C0 ,W , uG1C1 , J )(t ) , BJ (uG0C0 ,W , uG1C1 , J )(t ) are continuous ones. Indeed

lim BW(k ) (uG0C0 ,W , uG1C1 , J )(t )

t tk

lim BW(k

t tk

U ( uG0C0 ,W , J )(s)ds tk

(uG0C0 ,W , uG1C1 , J )(t )

U ( uG0C0 ,W , J )(s)ds tk

tk tk

1 1

tk tk

1 2

U ( uG0C0 ,W , J )(s)ds 0 , tk

tk tk

1 tk

U ( uG0C0 ,W , J )(s)ds 0 ,

Page 6

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

lim BJ(k ) (uG0C0 ,W , uG1C1 , J )(t )

t tk

tk tk

I (W , uG1C1 , J )(s)ds

lim BJ(k

t tk

(uG0C0 ,W , uG1C1 , J )(t ) tk

tk tk

I (W , uG1C1 , J )(s)ds 1

tk tk

I (W , uG1C1 , J )(s)ds 0 , tk

tk tk

1 2

1 tk

I (W , uG1C1 , J )(s)ds 0 .

For the derivatives we obtain tk

U ( uG0C0 ,W , J )(t )

BW (uG0C0 ,W , uG1C1 , J )(t )

tk tk

1 tk

U ( uG0C0 ,W , J )(t )

B J (uG0C0 ,W , uG1C1 , J )(t ) tk

tk tk

I ( W , uG1C1 , J )(s)ds, t [t k , t k 1 ]

U ( uG0C0 ,W , J )(s)ds, t [t k 1 , t k 2 ]

I ( W , uG1C1 , J )(t ) I ( W , uG1C1 , J )(t )

U ( uG0C0 ,W , J )(s)ds, t [t k , t k 1 ]

U ( W , uG1C1 , J )(s)ds, t [t k 1 , t k 2 ] 1 tk

and obviously BW (uG0C0 , W , uG1C1 , J )(t ) , B J (uG0C0 , W , uG1C1 , J )(t ) are continuous on every interval (t k , t k 1 ) but not at t k . Lemma 3.2 is thus proved. Lemma 3.3 Problem (3.1) has a solution (uG0C0 ,W , uG1C1 , J )

M 0 MW

M 1 M J if the operator B has

a fixed point in M 0 MW M1 M J , that is, (uG0C0 , W , uG1C1 , J ) ( B0 (uG0C0 , W , uG1C1 , J ), BW (uG0C0 , W , uG1C1 , J ), B1 (uG0C0 , W , uG1C1 , J ), BJ (uG0C0 , W , uG1C1 , J )).

Proof: Let (uG0C0 ,W , uG1C1 , J )

M 0 MW M1 M J be an oscillatory solution of (3.1). Then after integration

of the equations (3.1) (recalling that u R0 L0 (T ) t

uG0C0 (t )

u R1L1 (T )

0 ) we obtain

U G0 (uG0C0 ,W , J )(s )ds, W (t ) tk

U ( uG0C0 ,W , J )(s )ds, uG1C1 (t )

Therefore

U G1 (W , uG1C1 , J )(s )ds, J (t )

for t [t k , t k 1 ], (k

I (W , uG1C1 , J )(s)ds

2 L Cˆ 0(1) 1 2 L Cˆ 0(1) e

(t k

tk )

( s tk )

e e

( s tk )

n 1 1

ds J 0 e

( s tk )

tk )

uG0C0 ( s) ds tk

n 1 (t k

tk )

2 L

( s tk )

(t k

1 2 L

Cˆ 0(1)

2 L

g n(0) U Gn 0 e ( n

g n( 0) U Gn 0

n 1

M 1 M J . Then

g n(0) U Gn 0

ds 2 L

tk (t k

g n(0)

ds 2 L

ds J 0 e

tk tk

J (tk 1 ) 0 , which yields (3.2).

Conversely, let B have a fixed point (uG0C0 ,W , uG1C1 , J ) M 0 M W  tk 1 tk 1 W ( s) J ( s) 2 LG0 (uG0C0 ( s)) U G0 (uG0C0 ,W , J )( s)ds ds ~ 2 L dC0 (uG0C0 ( s)) / duG0C0 tk tk 2 L Cˆ 0(1)

I (W , uG1C1 , J )(s)ds tk

0,1,2,... ) .

U ( uG0C0 ,W , J )(s)ds W (t k 1 ) 0,

(3.2)

1) (t k

tk )

n 1

Page 7

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

1 2 L

Cˆ 0(1) tk

g n(0) U Gn 0 e ( n

2 L

M G0 ( )

n 1

If we assume

0 then in view of lim M G0 ( )

U G0 (uG0C0 ,W , J )( s)ds

0 we obtain a contradiction. It

tk tk

follows

0 . In analogous way we conclude

U G0 (uG0C0 ,W , J )( s)ds tk

U ( uG0C0 ,W , J )(s)ds

U G1 (W , uG1C1 , J )(s)ds

I (W , uG1C1 , J )(s )ds

, . Differentiating the above equalities we conclude that (3.1) has an oscillatory solution. Lemma 3.3 is thus proved. IV. Existence-uniqueness of the Oscillatory pProblem Theorem 4.1 Let the following conditions be fulfilled: (E) E p (s)

R t L (p

U Ep e

~ 0,1) ; (L) L0 (i)

Lˆ0

(n 1)ln(0) i

0 for i

n 1

Assumption

J0 0

2 C ~ Assumption (IN): W0 t

, U G0 , U G1

W0 e

R (t T ) L ;

~ J0 t

J 0e

R (t T ) L

, t [0, T ] .

Then there exists a unique oscillatory solution of (3.1). Proof: We show that B maps M 0 MW M1 M J into itself. Indeed t

B0 (uG0C0 ,W , uG1C1 , J )(t )

U ( uG0C0 ,W , J )(s ) ds tk

1 2Cˆ 0(1) L

W0 e

2Cˆ 0(1)

1 2Cˆ 0(1) L 1

W0 e

R t L

1 2Cˆ 0(1)

J 0e

R tk L

L e R J 0e R tk L

R (s T ) L ds

R T L

g n( 0)

n 1

e L e R

R t L

2 L

R t L

R tk L

L e R

R t L

R T L

R tk L

J 0e

g n(0) U Gn 0

2 L

R T L

R t L

g n(0) U Gn 0 e

2 L n 1

n 1

uG0C0 ( s ) ds

L e nR

R n tk L

R n s L ds

R n t L

g n(0) U Gn 0

2 L n 1

2Cˆ 0(1)

g n(0) uG0C0 ( s ) ds

e L L L (t e (1) ˆ R 2C0 L e

R s L ds

L e R L

2Cˆ 0(1)

2 L n 1

2Cˆ 0(1) L

R (s T ) L

J 0e

tk t

R s L

L e L R

tk )

1 W0

J 0e

R T L

2 L

g n( 0) U Gn 0

n 1 RT0 L

RT0 L

RT0 L sinh L L R

J 0e

R T L

g n(0) U Gn 0

2 L n 1

J 0e

R T L

2 L

g n(0) U Gn 0

R t L U

n 1

Further on we use the relations

Page 8

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

R s L ds

e tk

R t L

R tk L

L e R R

L L (t k 1 e R

tk )

R tk L

R (t k L

R tk L

L e R

tk )

R t L

e R

L L T0 e R

R t L

R T0 L

R tk L

R t L

L L (t e R

tk )

1 1 e

R (t k L

RT0 2L sinh R L

and then we obtain t

BW (uG0C0 ,W , uG1C1 , J )(t )

t tk tk 1 tk

U ( uG0C0 ,W , J )(s)ds tk

Since t

W1 tk

 dJ ( s) ds ds

 J (t )

t tk

U ( uG0C0 ,W , J )(s)ds W1 W2 . tk

 Z 0 W ( s) Z 0 J ( s) 2 L uG0C0 ( s) 2 L E0 ( s) ds ~ dL0 (i (0, s)) / diG0C0

t t  t 1 Z W ( s ) ds Z J ( s ) ds 2 L uG0C0 ( s) ds 2 L 0 0 Lˆ(1) tk

J 0e

R (t T ) L

1 Z 0W0 ˆ L(1)

Z0 J 0

2 L U G0

2 L U E0

R R t T L e L

R t L

R s L ds

1 Z 0W0 ˆ L(1)

Z0 J0

2 L U G0

2 L U E0

R t L

J 0e

and tk

W2 tk

 dJ ( s) ds ds

1 Z0 ˆ L(1) 0

R T L

1 Z 0W0 ˆ L(01) tk tk

Z0 J 0

2 L U G0

W ( s) ds Z 0

R t L

L R sinh T0 R L

tk 1 tk 1  J ( s) ds 2 L uG0C0 ( s) ds 2 L E0 ( s) ds

2 L U G0 Lˆ(1)

2 L U E0

tk R s L ds

2 L U E0

L L T0 e 1 R

 Z 0 W ( s) Z 0 J ( s) 2 L uG0C0 ( s) 2 L E0 ( s) ds ~ dL0 (i (0, s)) / diG0C0

Z 0W0 ds Z 0 J 0

E0 ( s) ds tk

J 0e

RT0 Z 0W0 2L sinh R L

Z0 J0

2 L U G0 Lˆ(1)

2 L U E0

we have BW (uG0C0 ,W , uG1C1 , J )(t )

R t L

RT0 1 2L sinh Z 0W0 R L Lˆ(01)

J 0e

R T L

J 0e

Z0 J0

R T L

Z 0W0 L R sinh T0 R L

2 L U G0

3L R 1 sinh T0 (1) Z 0W0 R L Lˆ0

Further on we have B1 (uG0C0 ,W , uG1C1 , J )(t )

2 L U E0

2 L U G0

2 L U E0

R t L W . 0

 W ( s) J ( s) 2 LG1 (uG1C1 ( s))

2 L dC1 (uG1C1 ) / duG1C1

U G1 (W , uG1C1 , J )(s)ds tk

2 L U G0 Lˆ(1)

2 L U E0

Z0 J 0

Z0 J0

Page 9

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

2Cˆ1(1)

W0 e

RT0 L

L e L R

J0 R t L

R tk L

L e R

uG1C1 ( s) ds

R T L

g n(1) U Gn1

2 L m

R t L

g n(1) U Gn1 e

2 L n 1

n 1

W0e

tk )

R n tk L

L e nR

R n s L ds

R n t L

g n(1) U Gn1

2 L

R T L

1 W0e

g n(1) U Gn1

2 L n 1

RT0 L

W0e

R T L

2 L

g n(1) U Gn1

n 1

L RT0 sinh L L R

2Cˆ1(1)

R t L

g n(1)

n 1

L L (t e L R

2 L n 1

R tk L

L e R

R t L

2Cˆ1(1) 2Cˆ1(1)

R s L ds

J 0e

R tk L

L e R J0

R tk L

L e R L

R t L

R T L

W0e

2Cˆ1(1) L

g n(1) uG1C1 ( s) ds

W0 e

R t L

2 L

2Cˆ1(1) L

R t L

R (s T ) L ds

W0 e

R s L

n 1 t

2Cˆ1(1) L

2Cˆ1(1)

J 0e

R (s T ) L

W0e

R T L

2 L

g n(1) U Gn1

R t L U

n 1

For the last component we have t

BJ( k ) (uG0C0 ,W , uG1C1 , J )(t )

I (W , uG1C1 , J )(s)ds tk

But t

I1 tk

 dW ( s) ds ds

 W (t )

t tk tk 1 tk

t  t t 1 Z W ( s ) ds Z J ( s ) ds 2 L uG1C1 ( s) 0 0 Lˆ(1)

W0 e

R (t T ) L

I (W , uG1C1 , J )(s)ds

1 Z 0W0 e ˆ L(1)

R R t T L e L

R T L

E1 ( s) ds tk t

Z0 J0

2 L U G1

2 L U E1

R t L

W0 e

and tk

I2 tk

R T L

 dW ( s) ds ds

1 Z0 Lˆ(1) 1

1 Z 0W0 Lˆ(1)

1 Z 0W0 ˆ L1(1) tk

R s L

Z0 J 0

2 L U G1

2 L U E1

Z0 J 0

2 L U G1

L L T0 e 1 R

L R sinh T0 R L

2 L U E1

 Z 0 W ( s) Z 0 J ( s) 2 L uG1C1 ( s) 2 L E1 ( s) ds ~ dL1 (i (0, s)) / diG1C1 tk

W ( s) ds Z 0 tk

e tk

I2 .

2 L

W0 e

 Z 0W ( s) Z 0 J ( s) 2 LuG1C1 ( s) 2 L E1 ( s) ds ~ dL1 (i( , s)) / diG1C1

tk 1  J ( s) ds 2 L uG1C1 ( s) tk

2 L

E1 ( s) ds tk

Page 10

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

R T L

Z 0W0 e

Z0 J 0

2 L U G1

2 L U E1

Lˆ1(1)

R s L

R t L

RT0 Z 0W0 2L sinh R L

Z0 J 0

2 L U G1 Lˆ(1)

2 L U E1

Then

BJ( k ) (uG0C0 ,W , uG1C1 , J )(t )

R t L

W0 e e

R T L

Z 0W0 Z 0 J 0 2 L U G1 2 L U E1 3L R sinh T0 R L Lˆ1(1)

R t L

J0.

It remains to obtain Lipschitz estimates for the components of the operator B.  1 W (t ) J (t ) 2 LG0 (uG0C0 (t )) Indeed, since U G0 (uG0C0 ,W , J ) we get ~ dC0 (uG0C0 ) / duG0C0 2 L ~ ~  dG0 (uG0C0 (t )) dC0 (uG0C0 ) d 2C0 (uG0C0 ) 2 L W (t ) J (t ) 2 LG0 (uG0C0 (t )) duG0C0 duG0C0 U G0 (uG0C0 , W , J ) duG2 0C0 1 2 ~ uG0C0 2 L dC (u ) / du 0

 2 LC0(1)

n g n(0) U G0 n

J0 2 L

n 1

G0C0

 g n(0) U G0 n C0( 2)

n 1

;

2 L (Cˆ 0(1) ) 2

U G0 (uG0C0 ,W , J )

1 ~ 2 L dC0 (uG0C0 ) / duG0C0

G0C0

1 2 L Cˆ 0(1)

;

B0 (uG0C0 , W , uG1C1 , J )(t ) B0 (uG0C0 , W , uG1C1 , J )(t )

U G0 (uG0C0 , W , J )(s ) U G0 (uG0C0 , W , J )(s ) ds tk

t tk t tk

U G0

uG0C0 ( s) uG0C0 ( s) ds

uG0C0

tk t

U G0

uG0C0 ( s) uG0C0 (s) ds

uG0C0

 2 LC0(1)

n g n( 0) U G0 n

W (s) W (s) ds m

2 L

n 1

 g n(0) U G0 n C0( 2)

uG0 C0 ( s ) uG0C0 ( s ) ds tk

1 2 L Cˆ 0(1)  2 LC0(1)

W ( s ) W ( s ) ds tk m

n g n( 0) U G0 n

2 L

n 1

 g n(0) U G0 n C0( 2)

n 1

(k )

2 L Cˆ 0(1)

(s tk )

(W , W ) e

( s tk )

 2 LC0(1)

n g n(0) U G0 n

J0 2 L

n 1

2 L (Cˆ 0(1) ) 2 (t t k )

(uG0C0 , uG0C0 ) e

t (k )

2 L (Cˆ 0(1) ) 2

n 1

2 L (Cˆ 0(1) ) 2

(t t k )

 U G0  J (s) J ( s) ds J

W (s) W (s) ds

1 2 L Cˆ 0(1)

(k )

 g n(0) U G0 n C0( 2) (k )

(uG0C0 , uG0C0 )

(W ,W )

Page 11

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

(t t k )

 2 LC0(1)

n g n(0) U G0 n

J0 2 L

 g n(0) U G0 n C0( 2)

n 1

(k )

2 L (Cˆ 0(1) ) 2

(t t k )

(k )

2 L Cˆ 0(1)

 2 LC0(1)

(t t k )

n 1

(uG0C0 , uG0C0 )

 (W ,W )

n g n(0) U G0 n

 g n(0) U G0 n C0( 2)

J0 2 L

n 1

2 L (Cˆ 0(1) ) 2

2 L Cˆ 0(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 It follows ˆ ( k ) ( B0 (uG C , W , uG C , J ), B0 (uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 e

 2 LC0(1)

n g n(0) U G0 n

J0 2 L

n 1

 g n(0) U G0 n C0( 2)

n 1

2 L (Cˆ 0(1) ) 2

1 2 L Cˆ 0(1)

ˆ ( k ) ((uG C ,W , uG C , J , uG C , W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1   K 0 ˆ ( k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 , W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )).

Further on we have

~ dL0 (i(0, t )) diG0C0

2 L

U ( uG0C0 ,W , J ) uG0C0  2 L L(01)

Z 0 W0

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

~  dL2 (i (0, t )) Z 0 W (t ) Z 0 J (t ) 2 L uG0C0 (t ) 2 L E0 (t ) 0 2 diG0C0 2 ~ dL0 (i(0, t )) / diG0C0  2 LU E0 L(02)

;

U ( uG0C0 ,W , J )

Z0 ~ dL0 (i(0, t )) / diG0C0

Z0 Lˆ(1) 0

But

BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) tk

U ( uG0C0 ,W , J )(s) U ( uG0C0 ,W , J )(s) ds tk

U ( uG0C0 ,W , J )(s) U ( uG0C0 ,W , J )(s) ds W1 W2 ; tk

U ( uG0C0 ,W , J )

 2 L L(01)

uG0C0 Z 0 W0

uG0C0 ( s) uG0C0 ( s) ds

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2 0

Z 0 W0 Z0 J 0 2 L U G0 ( Lˆ(1) ) 2

 2 LU E0 L(02)

Z0 Lˆ(1)

U ( uG0C0 ,W , J )

W ( s) W (s) ds

uG0C0 ( s) uG0C0 ( s) ds tk

Z0 Lˆ(1)

W ( s) W ( s) ds

0 tk

t (k )

(uG0C0 , uG0C0 ) e

(s tk )

t (k )

(W , W ) e

( s tk )

Page 12

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

(1) 1 2 L L0

(t t k )

Z 0 W0

 2 LU E0 L(02)

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

(k )

(uG0 C0 , uG0 C0 )

(t t k )

1 Z0 Lˆ(1)

(k )

 (W ,W )

 2 L L(01)

(t t k ) 2

 2 LU E0 L(02)

Z 0 W0 Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

Z0 Lˆ(01)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 and tk

U ( uG0C0 ,W , J )

uG0C0

 2 L L(01)

Z 0 W0

U ( uG0C0 ,W , J )

uG0C0 ( s) uG0C0 (s) ds

 2 LU E0 L(02)

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

(t k

 2 L L(01)

tk )

Z 0 W0

Z0 Lˆ(1)

uG0C0 (s) uG0C0 ( s) ds tk

W (s) W ( s) ds

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

 2 LU E0 L(02)

W ( s) W (s) ds tk

Z0 Lˆ(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 then

BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) (1)  Z 0 W0 Z 0 J 0 2 L U G0 2 LU E0 L(02) 2e 0 2 L L0 2 ( Lˆ(1) ) 2

Z0 Lˆ(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 It follows ˆ ( k ) BW( k ) (uG C ,W , uG C , J ), BW( k ) (uG C ,W , uG C , J ) 0 0 1 1 0 0 1 1   KW ˆ ( k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )) .

For the third component we have t

B1 (uG0C0 , W , uG1C1 , J )(t ) B1 (uG0C0 , W , uG1C1 , J )(t )

U G1 (W , uG1C1 , J )(s ) U G1 (W , uG1C1 , J )(s ) ds tk

t tk t tk

U G0

uG0C0 ( s) uG0C0 ( s) ds

uG0C0

tk t

U G1

uG1C1 ( s) uG1C1 ( s) ds

uG1C1

 2 LC1(1)

n g n(1) U G1 n

 U G0  W ( s) W ( s) ds W U G1 J

2 L

n 1

 g n(1) U G1 n C1( 2)

J (s) J ( s) ds

n 1

uG1C1 ( s) uG1C1 ( s) ds

2 L (Cˆ1(1) ) 2

1 2 L Cˆ1(1)  2 LC1(1)

U G0

J ( s) J ( s) ds m

J ( s ) J ( s ) ds tk m

n g n(1) U G1 n

n 1

2 L

 g n(1) U G1 n C1( 2)

n 1

L (Cˆ1(1) ) 2

t (k )

(uG1C1 , uG1C1 ) e

(s tk )

Page 13

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

(k )

2 L Cˆ1(1)

(t t k )

(J , J ) e

( s tk )

 2 LC1(1)

n g n(1) U G1 n

J0 2 L

n 1

 g n(1) U G1 n C1( 2)

n 1

(k )

(uG1C1 , uG1C1 )

(k )

(uG1C1 , uG1C1 )

2 L (Cˆ1(1) ) 2 e

(t t k )

1 2 L Cˆ1(1)

 2 LC1(1)

(k )

(J , J )

n g n(1) U G1 n

(t t k ) 2

(t t k )

J0 2 L

n 1

 g n(1) U G1 n C1( 2)

n 1

2 L (Cˆ1(1) ) 2

(k )

2 L Cˆ1(1)

 2 LC1(1)

 ( J , J )

n g n(1) U G1 n

 g n(1) U G1 n C1( 2)

J0 2 L

n 1

2 L (Cˆ1(1) ) 2

1 2 L Cˆ1(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 It follows ˆ ( k ) ( B1 (uG C ,W , uG C , J ), B1 (uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 e

(t t k )

 2 LC1(1)

n g n(1) U G1 n

 g n(1) U G1 n C1( 2)

J0 2 L

n 1

2 L (Cˆ1(1) ) 2

2 L Cˆ1(1)

ˆ ( k ) ((uG C , W , uG C , J , uG C , W , uG C , J ), (uG C , W , uG C , J , uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1   K1 ˆ ( k ) ((uG0C0 , W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )).

Finally for the fourth component we obtain t

B J( k ) (uG0C0 ,W , uG1C1 , J )(t ) B J( k ) (uG0C0 , W , uG1C1 , J )(t )

I (W , uG1C1 , J )(s ) I (W , uG1C1 , J )(s ) ds tk

I (W , uG1C1 , J )(s) I (W , uG1C1 , J )(s) ds

I2 .

Since I1

I (W , uG1C1 , J )

 2 L L1(1)

uG1C1

uG1C1 ( s) uG1C1 (s) ds

I (W , uG1C1 , J ) J

Z 0 W0 Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

 2 LU E1 L1( 2)

 2 L L1(1)

Z 0 W0

Z0 J 0

Z0 Lˆ(1) 1

J (s) J ( s) ds

uG1C1 ( s) uG1C1 (s) ds

tk t

(k )

(J , J ) e

(s tk )

( Lˆ1(1) ) 2

Z0 Lˆ(1)

2 L U G1

J (s) J (s) ds tk

 2 LU E1 L1( 2)

t (k )

(uG1C1 , uG1C1 ) e

(s tk )

Page 14

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

(1) 1 2 L L1

(t t k )

Z 0 W0

 2 L L1(1)

(t t k )

 2 LU E1 L1( 2)

Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

Z 0 W0 Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

2 LU E1

(k )

e (uG1C1 , uG1C1 )

(t t k ) 2

1 Z0 Lˆ(1)

(k )

 ( J , J )

 L1( 2)

Z0 Lˆ(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 and tk

I (W , uG1C1 , J )

uG1C1

(t k

tk tk

 2 L L1(1)

tk )

uG1C1 ( s) uG1C1 ( s) ds

Z 0 W0 Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

I (W , uG1C1 , J )

J ( s) J ( s) ds

 2 LU E1 L1( 2)

Z0 Lˆ(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 we get

BJ(k ) (uG0C0 ,W , uG1C1 , J )(t ) BJ(k ) (uG0C0 ,W , uG1C1 , J )(t ) (1)  Z 0 W0 Z 0 J 0 2 L U G1 2 LU E1 L1( 2) 2e 0 2 L L1 2 ( Lˆ(1) ) 2 1

Z0 Lˆ1(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 It follows ˆ ( k ) BJ( k ) (uG C ,W , uG C , J ), BJ( k ) (uG C ,W , uG C , J ) 0 0 1 1 0 0 1 1   K J ˆ ( k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )) .

For the derivatives we obtain B (u ,W , u , J )(t ) B (u 0

G0C0

U G0 uG0C0

G1C1

G0C0 ,W , uG1C1 , J )(t )

U G0

uG0C0 (t ) uG0C0 (t )

 2 LC0(1)

n g n(0) U G0 n

 U G0  J (t ) J (t ) J

W (t ) W (t ) m

U G0 (uG0C0 ,W , J )(s) U G0 (uG0C0 ,W , J )(s)

J0 2 L

n 1

 g n(0) U G0 n C0( 2)

n 1

uG0C0 (t ) uG0C0 (t )

2 L (Cˆ 0(1) ) 2  2 LC0(1)

n g n(0) U G0 n

J0 2 L

n 1

g n(0) U G0 n

n 1

2 L Cˆ 0(1)

(t t k )

 2 LC0(1)

(k )

 C0( 2) e

2 L (Cˆ 0(1) ) 2 1

(t t k )

n g n(0) U G0 n

J0 2 L

n 1

(t t k )

 2 LC0(1)

(k )

(uG0C0 , uG0C0 )

 g n(0) U G0 n C0( 2)

n 1

2 L Cˆ 0(1)

(k )

(W ,W )

(k )

2 L (Cˆ 0(1) ) 2 e

W (t ) W (t ) 2 L Cˆ (1)

(uG0C0 , uG0C0 )

 (W , W )

n g n(0) U G0 n

J0 2 L

n 1

2 L (Cˆ 0(1) ) 2

 g n(0) U G0 n C0( 2)

1 2 L Cˆ 0(1)

Page 15

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 Then

B 0 (uG0C0 ,W , uG1C1 , J ), B 0 (uG0C0 , W , uG1C1 , J )

(k )

 2 LC0(1)

n g n(0) U G0 n

 g n(0) U G0 n C0( 2)

J0 2 L

n 1

2 L (Cˆ 0(1) ) 2

2 L Cˆ 0(1)

ˆ ( k ) ((uG C , W , uG C , J , uG C , W , uG C , J ), (uG C , W , uG C , J , uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1   K 0 ˆ ( k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )).

Further on we get BW( k ) (uG0C0 ,W , uG1C1 , J )(t )

BW( k ) (uG0C0 ,W , uG1C1 , J )(t ) tk

U ( uG0C0 ,W , J )(t ) U ( uG0C0 ,W , J )(t )

U ( uG0C0 ,W , J )(s ) U ( uG0C0 ,W , J )(s ) ds

W1 W 2 .

But U ( uG0C0 ,W , J )

W1

 2 L L(01)

uG0C0 Z 0 W0

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

Z 0 W0

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

 2 LU E0 L(02)  L(02)

2 LU E0

Z 0 W0

 2 L L(01)

Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2 0

Z 0 W0 Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

W (t ) W (t ) Z0 W (t ) W (t ) Lˆ(1)

uG0 C0 (t ) uG0 C0 (t )

(k )

 2 L L(01)

(t t k )

U ( uG0C0 ,W , J )

 2 L L(01) (t t k )

uG0C0 (t ) uG0C0 (t )

Z0 Lˆ(1)

(k )

(W ,W )e

(t t k )

 L(02)

2 LU E0

(t t k )

(uG0 C0 , uG0 C0 )e (k )

 2 LU E0 L(02)

e (uG0 C0 , uG0 C0 )

(t t k )

Z0 Lˆ(01)

(k )

 (W ,W )

Z0 Lˆ(01)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 and tk

W 2

 2 L L(01)

U ( uG0C0 ,W , J )

uG0C0

Z 0 W0 Z 0 J 0 2 L U G0 ( Lˆ(1) )2

 2 LU E0 L(02)

Z0 1 Lˆ(01) tk 1 tk e

(t k 2

(tk

tk ) 1

U ( uG0C0 ,W , J )

W ( s) W (s) ds

uG0C0 ( s) uG0C0 ( s) ds tk

W (s) W (s) ds tk

(1) 1 2 L L0 tk )

tk 1

uG0C0 ( s) uG0C0 (s) ds

Z 0 W0 Z 0 J 0 2 L U G0 ( Lˆ(1) ) 2

 2 LU E0 L(02)

Z0 Lˆ(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 (1) ( 2) Z 0 W0 Z 0 J 0 2 L U G0 2 LU E0 L0 1 e 0 1 2 L L0 Z0 (1) 2 ˆ (L ) Lˆ(1) 0 0

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )). 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 Therefore

Page 16

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) BW(k ) (uG0C0 ,W , uG1C1 , J )(t ) (1)  Z 0 W0 Z 0 J 0 2 L U G0 2 LU E0 L(02) e (t t k ) 2 L L0 ( Lˆ(01) ) 2

Z0 Lˆ(1) 0

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 (1) ( 2) Z 0 W0 Z 0 J 0 2 L U G0 2 LU E0 L0 1 e 0 1 2 L L0 Z0 (1) 2 ˆ ( L0 ) Lˆ(01) 0 ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 (1) ( 2) Z 0 W0 Z 0 J 0 2 L U G0 2 LU E0 L0 Z0 e 0 1 1 2 L L0 e (t t k ) 1 (1) 2 ˆ (L ) Lˆ(1) 0 0

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) . 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 It follows ˆ ( k ) BW( k ) (uG C , W , uG C , J ), BW( k ) (uG C ,W , uG C , J ) 0 0 1 1 0 0 1 1

  KW ˆ (k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )). Since B1 (uG0C0 ,W , uG1C1 , J )(t ) B1 (uG0C0 ,W , uG1C1 , J )(t ) U G1

U G1

uG1C1 (t ) uG1C1 (t )

uG1C1

 2 LC1(1)

n g n(1) U G1 n

J (t ) J (t )

 g n(1) U G1 n C1( 2)

U G1 (W , uG1C1 , J )(s) U G1 (W , uG1C1 , J )(s)

J0 2 L

n 1

uG1C1 (t ) uG1C1 (t )

2 L (Cˆ1(1) ) 2  2 LC1(1) e

(t t k )

n g n(1) U G1 n

2 L n 1

n g n(1) U G1 n

2 L

n 1

(k )

2 L (Cˆ1(1) ) 2

 2 LC1(1)

n g n(1) U G1 n

J0 2 L

n 1

(uG1C1 , uG1C1 ) e

 g n(1) U G1 n C1( 2)

n 1

(t t k )

(uG1C1 , uG1C1 ) e

 g n(1) U G1 n C1( 2)

(J , J ) 2 L Cˆ1(1)

(t t k )

( J , J ) 2 L Cˆ1(1) (k )

n 1

2 L (Cˆ1(1) ) 2

J (t ) J (t )

(k )

2 L (Cˆ1(1) ) 2  2 LC1(1)

2 L Cˆ1(1)

 g n(1) U G1 n C1( 2)

n 1

(t t k )

2 L Cˆ1(1)

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 then ˆ ( k ) ( B1 (uG C ,W , uG C , J ), B1 (uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 1

 2 LC1(1)

n g n(1) U G1 n

J0 2 L

n 1

2 L (Cˆ1(1) ) 2

 g n(1) U G1 n C1( 2)

1 2 L Cˆ1(1)

ˆ ( k ) ((uG C , W , uG C , J , uG C , W , uG C , J ), (uG C , W , uG C , J , uG C , W , uG C , J )) 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1   K 1 ˆ ( k ) ((uG0C0 , W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 , W , uG1C1 , J , uG0C0 , W , uG1C1 , J )).

Page 17

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

Finally for the fourth component we obtain B J(k ) (uG0C0 ,W , uG1C1 , J )(t ) B J(k ) (uG0C0 ,W , uG1C1 , J )(t ) tk

1 tk

I1 I2 .

I (W , uG1C1 , J )(s) I (W , uG1C1 , J )(s) ds

I (W , uG1C1 , J )(t ) I (W , uG1C1 , J )(t )

Since I (W , uG1C1 , J )

I1

uG1C1

(t t k )

 2 L L1(1)

Z 0 W0

J (t ) J (t )

 2 LU E1 L1( 2)

Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2 1

(k )

Z 0 W0 Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

(k )

(J , J )

e (uG1C1 , uG1C1 )

(t t k )

Z0 Lˆ(1)

(k )

 ( J , J )

Z0 Lˆ(1)

(k )

Z0 Lˆ(1)

 L1( 2)

2 LU E1

(t t k )

(uG1C1 , uG1C1 ) e

 2 LU E1 L1( 2)

Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2 1

 2 L L1(1)

(t t k )

I (W , uG1C1 , J )

uG1C1 (t ) uG1C1 (t )

  ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )) ;

and tk

I2

 2 L L1(1)

I (W , uG1C1 , J )

uG1C1

(t k

(tk

(k )

I (W , uG1C1 , J ) J

J ( s) J (s) ds

uG1C1 ( s) uG1C1 ( s) ds tk

J ( s) J ( s) ds tk

(1) 1 2 L L1 tk )

tk )

1 tk

Z0 1 (1) ˆ t L1 k 1 t k

 2 LU E1 L1( 2)

Z 0 W0 Z0 J 0 2 L U G1 ( Lˆ(1) ) 2

uG1C1 (s) uG1C1 (s) ds

Z 0 W0 Z 0 J 0 2 L U G1 ( Lˆ(1) ) 2

 2 LU E1 L1( 2)

Z0 Lˆ(1) 1

  ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ))

we obtain B J(k ) (uG0C0 ,W , uG1C1 , J )(t ) B J(k ) (uG0C0 ,W , uG1C1 , J )(t ) (1)  Z 0 W0 Z 0 J 0 2 L U G1 2 LU E1 L1( 2) e 0 1 1 2 L L1 1 ( Lˆ1(1) ) 2 0

Z0 Lˆ(1) 1

ˆ (k ) ((uG C ,W , uG C , J , uG C ,W , uG C , J ), (uG C ,W , uG C , J , uG C ,W , uG C , J )). 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 Thus ˆ ( k ) BJ( k ) (uG C ,W , uG C , J ), BJ( k ) (uG C ,W , uG C , J ) 0 0 1 1 0 0 1 1   K J ˆ ( k ) ((uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J ), (uG0C0 ,W , uG1C1 , J , uG0C0 ,W , uG1C1 , J )) .

Therefore if K

max K R0 L0 , K u , K R0 L0 , K u

1 then the operator B has a unique fixed point. It is an oscillatory

solution of the problem stated. Theorem 4.1 is thus proved. V.

Numerical example

We collect all inequalities guaranteeing an existence-uniqueness of an oscillatory solution: 1 Cˆ 0(1)

L RT0 sinh L L R

J 0e

R T L

2 L

g n(0) U Gn 0

U G0 ;

n 1

Page 18

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

J 0e 1 Cˆ1(1)

Z 0W0 3L R sinh T0 R L

L RT0 sinh L L R

W0 e

R T L

n g n(1) U G1 n

 g n(1) U G1 n C1( 2)

Z 0 W0

2 L

 2 LU E1 L1( 2)

Z 0 J 0 2 L U G1 Lˆ(1)

2 L

1 Lˆ1(1)

n g n(0) U G0 n

 C0( 2) W0 Cˆ (1)

n 1

L/C

Z 0 W0

Z0 J 0

n n 1

Z 0 W0

Z0 J 0

1 / 6 10 m . Then f 0 8

104.6.10 LC

0,98.10 ; L / R sinh RT0 / L ~ dL0 (i) / di i dL0 (i) / di

Choose 0 1 then ~ dL0 (i) / di i 1 / 3 i 3

1/ 3

 2 LU E1 L1( 2)

4 mm2 , specific resistance for the

/ S 0,44 . Let L 0,45 H m; C 80 pF m; v 1 / LC 9

6.10

10 Hz

m 6.10 ; RT / L 58,7 ; 7

n 1

100 m, cross-section area S

6.10 .T0

g n(1) U G1 n

2 L

2 L U G1 Lˆ(1)

 2 LU E0 L(02)

n 1

2 L U G0 Lˆ(1)

 C1( 2) W0 Cˆ (1)

g n(1) U G1 n 1

0,0175 . Then R

g n(0) U G0 n

2 L

 2 L L1(1) Lˆ(1)

75 ; T

 C1(1) 2 L (1) Cˆ

 2 L L(01) Lˆ(1)

1 Lˆ(01)

L 0

 C0(1) Cˆ (1)

Consider a transmission line with length

RT0 / L then

2Cˆ1(1)

and T

 2 LU E0 L(02)

n 1

Cˆ1(1)

 2 L L1(1)

2 L

n 1

cuprum

Z 0 J 0 2 L U G0 Lˆ(1)

2Cˆ 0(1) L

n 1

Z 0 W0

 2 LC1(1)

K J

 g n(0) U G0 n C0( 2)

K 0

J0 ;

Cˆ 0(1)

2 L Cˆ1(1)

K W

2 L U E1

n g n( 0) U G0 n

K 0

2 L U G1 Lˆ(1)

 2 L L(01)

2e ˆ L(1)

Z0 J0

n 1

W0 ;

U G1 ;

g n(1) U Gn1

2 L

2 L U E0

n 1

 2 LC0(1)

2 L Cˆ 0(1)

R T L

2 L U G0 Lˆ(1) 0

Z 0W0 3L R sinh T0 R L

2e ˆ L(1)

W0e

Z0 J0

T0 . If G0 i

L0 (i) 6i

;

. Let us check the propagation of waves with length

1 / 6.10

1/ f0

RT / L

G1 i

10 58,7

. Choose

0; RT0 / L 3

1014 , then T0 7

0,98.10 ;

0,028i 0,125i and L0 (i)

10,

sinh RT0 / L

L1 (i) 3i

1 / 12 i 3

~ 1/ 3 i 3 ; d 2 L0 (i) / di 2 6 i 2 .

~ Lˆ(01) 17 / 3 and dL0 (i) / di

6 1/ 3

 ~ L(01) 19 / 3 , d 2 L0 (i) / di 2

 L(02)

Page 19

V. G. Angelov et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), JuneAugust, 2013, pp. 01-20

Let us take C0 (u ) C1 (u ) c0

u , where h 2 , c0

50 pF Cˆ 0

C0 ( 0 ) c0 Then C0 (u ) c0 0/ 0 / 0 u 0 0 ~ dC0 (u )  (1) c0 1 0,5 0 / 0 C0 c0 5.10 11; 3 du 1 0/ 0 2 ~ dC0 (u ) ˆ (1) c0 h 1 C0 1 c0 5.10 0/ 0 3/ 2 du h 1 0/ 0 ~ d 2C0 (u )  ( 2) c0 h 0 2h 2 0 2h 2 h 1 0 C0 2,36.10 9. 2 2 1 2 du h 0

Then for W0 9.10

K W

. 150.10

J0 5

2.10

388,6 8,5.10 14

5.10

F and

0,4 V ;

. 0,4 / 0,4 0,2

0,04 .

3,8.10

;

0,01;U E0

0,45 .2 / 17 10 5 ; K 0

10 ; U G0

5.10

0,01 the above inequalities become: 2,98 10

15.10 4 0,027.10 5,6666

4,5.10 75

VI.

0,038.10

10 2 ;

2,9145 1014

Conclusion

We have noticed that the transmission line loaded by the shown configurations of nonlinear elements (cf. Figure 1) requires different way of derivation of the boundary conditions and respectively leads to different type of differential equations. Here one more difficulty arises. One could not exclude some transitional currents and so we have to solve a system of 4 equations for 4 unknown functions. Our fixed point method, however, is again applicable and we obtain existence-uniqueness of an oscillatory solution for lossy lines. We point out that the procedure for treating of time-varying specific parameters might be applied to the present circuit. VII. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

V. Angelov, Fixed Points in Uniform Spaces and Applications, Romania: Cluj University Press “Babes-Bolyiai”, 2009. V. Angelov, and M. Hristov, “Distortionless lossy transmission lines terminated by in series connected RCL-loads,” Circuits and Systems, vol. 2, pp. 297-310, 2011. V. Angelov, and D. Angelova, “Oscillatory solutions of neutral equations with polynomial nonlinearities,” In “Recent Advances in Oscillation Theory” − special issue published in International Journal of Differential Equations, Article ID 949547, 12 pages, 2011. V. Angelov, “Periodic regimes for distortionless lossy transmission lines terminated by parallel connected RCL-loads,” In “Transmission Lines: Theory, Types and Applications”, Nova Science Publishers, Inc., 259-293, 2011. V. Angelov, “Oscillations in lossy transmission lines terminated by in series connected nonlinear RCL-loads,” International Journal of Theoretical and Mathematical Physics, vol. 2, No. 5, pp. 143-162, 2012. V. Angelov, “Various applications of fixed point theorems in uniform spaces,” Proc. 10Th International Conference on Fixed Point Theory and its Applications, Cluj-Napoca, 2012. V. Angelov, “Lossless transmission lines terminated by in series connected RL-loads parallel to C-load,” International Journal of Theoretical and Mathematical Physics, vol. 3, No. 1, pp. 10-25, 2013. V. Angelov, and A. Zahariev, Lossy transmission lines terminated by parallel connected RC-loads and in series connected L-load (I). International Journal of Modern Engineering Research vol. 3, No. 3, pp. 1410-1418 2013. V. Damgov, Nonlinear and Parametric Phenomena: Theory and Applications in Radiophysical Mechanical Systems. World Scientific: New Jersey, London, Singapore, 2004. J. Dunlop, and D. Smith, Telecommunication Engineering. Chapman & Hall, London, 1994. P. Magnusson, G. Alexander and V. Tripathi, Transmission Lines and Wave Propagation, 3rd ed., CRC Press, Boka Raton, 1992. G. Miano, and A. Maffucci, Transmission Lines and Lumped Circuits, 2-nd ed., Academic Press, New York, 2010. C. Paul, Analysis of Multi-Conductor Transmission Lines, A Wiley-Inter science Publication, J. Wiley & Sons, Inc., New York, 1994. D. Pozar, Microwave Engineering, J. Wiley & Sons, Inc., New York, 1998. S. Ramo, J. Whinnery and T. van Duzer, Fields and Waves in Ccommunication Electronics, J. Wiley & Sons, Inc., New York, 1994. S. Rosenstark, Transmission Lines in Computer Engineering, Mc Grow-Hill, New York, 1994. P. Vizmuller, RF Design Guide Systems, Circuits and Equations, Artech House, Inc., Boston, London, 1995.

VIII. Acknowledgments This paper is supported by Plovdiv University NPD grant NI13 FMI-002.

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

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

FEM Analysis and Optimization of Two Chamber Reactive Muffler by using Taguchi Method Patil SandipS. 1, Patil Sudhir M. 2, Bhattu Ajay P. 3, Sahasrabudhe A.D. 4 Post Graduate Student, Production Engineering Dept., College of Engineering, Pune 2 Associate Professor, Production Engineering Dept., College of Engineering, Pune 3 Associate Professor,Mechanical Engineering Dept., College of Engineering, Pune, India 4 Professor, Director, College of Engineering, Pune 1

Abstract: In the present work, an attempt has been made to study the effect of various parameters such as different radius and lengths of various elements of muffler on the mufflerâ&#x20AC;&#x2122;s capacity of noise reduction (i.e. Transmission loss).The acoustic behavior of a circular two-chamber muffler is investigated in detail by: (1) the finite element method by using LMS Virtual. Lab10-SL 1 and effects of various parameters have been studied, such as (i) the presence of a rigid baffle in the chamber; (ii) the inner hole radius of the baffle; (iii) length and diameter of choke tube; (iv) the extended inlet/outlet and baffle ducts; Some of these effects are shown to modify the acoustic behavior drastically, suggesting potential means to improve the acoustic performance. (2) Validation by experimental work (two-load method). Keywords: FEM, Two-load method, Transmission loss, Two-chamber muffler, Taguchi method, ANOVA. I.

Introduction

Accurate prediction of sound radiation characteristics from reactive muffler is of significant importance in automotive exhaust system design. The most commonly used parameter to evaluate the sound radiation characteristics of muffler is transmission loss (TL). Transmission loss is one of the most frequently used criteria of muffler performance because it can be predicted very easily from the known physical parameters of the muffler [1]. This study proposes an optimal design scheme to improve the muffler capacity of noise reduction of the exhaust system by Taguchi method. Performance of a muffler is measured by performance prediction software (LMS virtual Lab 10-SL 1). In the first stage of a design, effect of extended inlet and outlet lengths along with baffle position and diameter of hole in it are selected as control factors. Then, L-9 table of orthogonal arrays is adopted to extract the effective main factors. In the second stage of a design, effect of extended inlet and outlet lengths, baffle position along the axis, internal choke tube length and its diameter are selected as control factors. Then, L-27 table of orthogonal arrays is adopted to extract the effective main factors. II.

Taguchi method[2]

The Taguchi method is a powerful tool for the design of high quality systems. It provides a simple, efficient and systematic approach to optimize designs for performance, quality, and cost. The methodology is valuable when the design parameters are qualitative and discrete. Taguchi parameter design can optimize the performance characteristics through the settings of design parameters and reduce the sensitivity of the system. Taguchi recommends the use of the Signal to Noise (S/N) ratio to measure the quality characteristics deviating from the desired values. The main principle of measuring quality is to minimize the variability in the products performance in response to Noise factors while maximizing the variability in response to Signal factors. Noise factors are those that are not under control of the operator of a product and the Signal factors are those that are set or controlled by the operator of the product to make use of its intended functions. Therefore, the goal of quality improvement effort can be given as to maximize the Signal to Noise (S/N) ratio for the product. Usually there are three types of quality characteristics in the analysis of the S/N ratio, i.e. the lower-the-better, the-higher-the-better, and thenominal-the-better. Here higher-the-better is used to maximise transmission loss. The S/N ratio for each level of process parameter is computed based on the S/N analysis. Regardless of the category of the quality characteristic a greater S/N ratio corresponds to better quality characteristics. In our case the output is transmission loss. Hence in case of transmission loss, Larger-the-better characteristic is required.

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

III.

Procedure for acoustic analysis

Basic procedure for analysis is started from CAD geometry. Muffler with given dimensions is modelled in Pro-E wildfire 4.0 and exported as neutral file format (.step). HyperMesh software is used for meshing solid models. .step files are imported in HyperMesh and mesh is generated. Meshed files are exported as Nastran bulk file format (.bdf) and then imported in SYSNOISE and harmonic acoustical FEM analysis is done. IV.

Acoustic analysis and optimisation of muffler

i. Two chamber muffler with baffle: Effect of baffle on Transmission loss is analysed by inserting baffle in simple expansion chamber. Figure 1 shows configuration for the two chamber muffler with baffle. In initial stage of design of experimentation four factors are chosen for optimisation. Those are extended lengths of inlet and outlet (L1and L3), axial position of baffle (L2), and diameter of baffle hole (d3). Figure 1: Configuration of two chamber muffler with baffle

ii. Two chamber muffler with choke tube: Further, transmission loss was found to be increasing by inserting choke tube in baffle. Figure 2 shows the configuration for two chamber muffler with choke tube. Effect of choke tube length and diameter, length of extended inlet and outlet and baffle position is analysed and optimised by using Taguchi analysis. Figure 2: Configuration of two chamber muffler with choke tube

Selection of control parameters and levels:[3] Selection of muffler parameters is on the basis of literature review by analysing the effect of parameters on the transmission loss. Levels of factors are also decided on the basis of literature review. Effect of extended inlet and outlet on TL is considerable. Table 1: For Two Chamber Muffler with Baffle: Level→

High

Medium

Low

Factors↓

-1

L1(m)

0.050

0.075

0.100

L2(m)

0.200

0.250

0.300

L3(m)

0.050

0.075

0.100

d3(m)

0.03

0.04

0.05

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

Table 2: For Two Chamber Muffler with choke tube: Level→ High Medium Low Factors↓ 1 0 -1 L1(m) 0.050 0.075 0.100 L2(m) 0.200 0.250 0.300 L3(m) 0.050 0.075 0.100 L4(m) 0.100 0.125 0.150 d3(m) 0.03 0.04 0.05 Results and discussion: Figure 3: Individual effect plot using Minitab[5] Main Effects Plot for TL Fitted Means

40.0

Mean

37.5 35.0 0.050

0.075 L3

0.100

0.20

0.25 d3

0.30

0.050

0.075

0.100

0.03

0.04

0.05

40.0 37.5 35.0

Figure 3 shows the effect of individual parameters of muffler with baffle on TL from L9 which is prepared from Table 1. As L1 and L3 increases TL value decreases but after middle value, TL increases, and for increase in L2 up to middle level TL increases but afterwards TL decreases. Increase in value of d3 there is decrease in TL value. Figure 4: Individual effect plot Main Effects Plot for Means Data Means

52 50

Mean of Means

48 46 44 0.050

0.075

0.100

0.2000

0.2500

0.3000

0.050

0.075

0.100

52 50 48 46 44 0.100

0.125

0.150

0.03

0.04

0.05

Figure 4 shows the effect of individual parameter of muffler with choke tube on TL which is prepared from L27 table. For increase in L1 there is increase in TL and for increase in L2 up to middle level TL increases but afterword‟s TL decreases. As L3 and L4 increases TL value decreases. Increase in value of d3 there is decrease in TL value for two chamber muffler with choke tube. V.

Taguchi analysis:

Taguchi analysis of SN ratio is used for single objective optimization of responses. In Taguchi method, SN ratio is used to measure the quality characteristics deviating from the desired value. The experimental values of various responses are transformed into signal to noise (SN) ratio. „Higher the better‟ characteristics is used for the responses which are to be maximised. „Lower the better‟ characteristic is used for the responses which are to be minimised. The types of Signal to Noise (S/N) ratio are; 1. The-lower-the-better 2. The-nominal-the-better 3. The-largerthe-better. In our case the output is transmission loss. Hence in case of transmission loss, Larger-the-better characteristic is required.

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

The SN ratio for „Higher the better‟ strategy is defined as η = -10 log [(1/n) * ∑ (1/yi2)] The SN ratio for „Lower the better‟ strategy is defined as η = -10 log [(1/n) * ∑ (yi2)] The SN ratio for „nominal-the-better‟ strategy is defined as η = 10 log ((y2)/ 2) Where, η = resultant SN ratio, n = number of observations, y = respective response. Figure 5: S/N ratio graph for TL. Main Effects Plot for SN ratios Data Means

32.4

32.0

Mean of SN ratios

31.6 31.2 30.8 0.050

0.075

0.100

0.20

0.25

32.4

0.30

32.0 31.6 31.2 30.8 0.050

0.075

0.100

0.03

0.04

0.05

Signal-to-noise: Larger is better

From figure 5, the S/N graph for TL, the greater S/N ratio corresponds to the smaller variance of the output characteristic which is desirable. Maximum S/N ratio is for length L1 at level +1, while in case of length L2 it is at level 0. For length L3 it is at level +1 and diameter is at level -1. Thus it is clear that optimal process parameters for the TL are L1 at 0.100, L2 at 0.25, L3 at 0.100, and diameter at 0.03. Figure 6: S/N ratio graph for TL Main Effects Plot for SN ratios Data Means

34.5

Mean of SN ratios

34.0 33.5 33.0 0.050

0.075

0.100

0.2000

34.5

0.2500

0.3000

0.050

0.075

0.100

34.0 33.5 33.0 0.100

0.125

0.150

0.03

0.04

0.05

Signal-to-noise: Larger is better

From figure 6, the S/N graph for TL, maximum S/N ratio is for length L1 at 0.100, while in case of length L2 it is at 0.25. For length L3 it is at 0.050 and length L4 is at 0.100 and diameter d3 at 0.03. Thus it is clear that optimal process parameters for the two chamber muffler with choke tube are L1 at 0.100, L2 at 0.25, L3 at 0.050, L4 at 0.100, d3 at 0.03. Analysis of variance (ANOVA): Table 7: Analysis of variance for TL using Adj. SS for test[8]: Source

Seq. SS

Adj. SS

Adj. MS

% contribution

7.639

3.82

7.29

0.006

0.0161

9.356

4.678

8.93

0.002

0.0197

7.057

3.528

6.74

0.008

0.0149

80.479

40.24

76.83

0.1699

360.67

180.33

344.3

0.7615

Error

8.38

0.524

Total

473.58

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

S=0.723699

R-Sq=98.23%

R-Sq(adj)=97.12%

As seen from ANOVA table % contribution of factors L1, L2, and L3 are negligible compared to other two factors (i.e. L4 and d3). Regression analysis: To determine the best suited equation connecting response with input variables regression technique has been used. Regression coefficient is the measure to indicate how far the established relationship is valid to ensure the values of dependent variable, using the values of independent variables for which readings are not available in the range of minimum and maximum value of independent variables. ANOVA table is used to check the significance of the regression model. The exponential equation established for TL is as follows: For Two Chamber Muffler with Baffle: TL=37.9988*L10.001793*L20.00015*L30.00237*d3-0.03451 R square value comes out to be 0.914376 for TL, therefore relationship established is acceptable. For Two Chamber Muffler with choke tube: TL=48.0606*L10.005874*L2-0.00085*L3-0.00592*L4-0.01809*d3-0.03995 R square value comes out to be 0.942145for TL, therefore relationship established is acceptable. VI.

Experimental validation

Models which got highest TL by S/N ratio analysis are experimentally validated by using two-load method[3]. Experiments are conducted for simple expansion chamber muffler, two-chamber muffler with baffle and for two chamber muffler with choke tube. Experimental setup and procedure [6]: Figure 7: Schematic Diagram of Experimental Setup with Its Components

Figure 7 shows the schematic diagram of experimental setup for two load method [7] to measure transmission loss of muffler. It consists of a noise generation system, noise propagation system and noise measurement system. Figure 8 shows actual experimental setup with its different components. The TL is measured by transfer function method. System consists of following components. 1. Noise source with amplifier: Noise source is speaker which is used to generate noise in system. Sound source used is of high power to produce at least 120 dB of noise. It is attached with amplifier whose function is to increase and adjust the sound level. 2. Impedance tube: Impedance tube is a rigid tube through which sound propagates and reflects from test sample which results in creation of standing waves in it. Main purpose of it is providing guidance to sound wave as required for plane wave propagation. It has measuring locations at specific distances from test sample where the acoustic pressure is measured. We are measuring incident power and reflected power; hence we use two tubes, one at inlet and one at outlet. 3. Data acquisition system: The data acquisition system used is a four channel FFT analyser with an interface for the control and setting of analyser. It collects the pressure data from microphones and feed it to data recording storage system. It also has

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

a single output channel which is fed to speaker through analyser. A random noise signal is generated in the same analyser and directed to the speaker via amplifier. 4. Sound pressure measuring microphones: Pressure field microphones (make PCB) are used for measurement. The two microphones are sufficient as transfer function method is used. Transfer function is evaluated for each set of reading. Figure 8: Experimental setup with and without load ¼‟‟ pressure field microphones

Speaker driver unit

Amplifier OROS OR-34 analyzer Analyzer interface through software package NV Gate 7.0

The experiment is performed for frequency range of 50 to 3400 Hz. The measurements are taken in two slots with two locations 1-1‟ and 4-4‟ as shown in figure 7 respectively to cover desired frequency range. The locations 1-2-3-4 are used for measuring pressure in frequency range 50-400 Hz, while the locations 1‟-2-3-4‟ are used for measuring pressure in frequency range of 400-3400 Hz. The first set of readings is taken for no load condition (figure 8) with both frequency ranges and same procedure is repeated for with load condition. Two microphones are used for measurement, which are sufficient for measurement of transfer function between sound pressures measured at two locations. One microphone is placed at location 3 and other placed at location 1, 2 and 4 respectively to get transfer function H31, H32 and H34 with respective locations. All other locations except locations where microphones are inserted are sealed with pins to avoid sound leakage. The sound leakage is tested and wax is used to seal these leaks. The obtained transfer functions are then directly used in four-pole element calculations to get TL. VII. Comparison of Experimental and FEM Results I Two chamber muffler with baffle: Two-chamber muffler with baffle is analysed by using L9 OA in SYSNOISE and by S/N ratio model shown in figure 9 found out as optimum model which gives high transmission loss among others. This model is manufactured and experiment was conducted on it to calculate average transmission loss. The average transmission loss obtained by experiment is 43.97dB and by FEM 41.0989 dB. Figure 10 shows the comparison between experimental TL curve and FEM TL curve and it matches very well in the entire frequency range. Figure 9: Optimum configuration of two chamber muffler with baffle. 0.500

0.03

Dia 0.35

Ø0.150

0.10

0.10 0.25

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

Figure 10: Comparison of experimental and FEM TL curve of Two chamber muffler with baffle.

II Two chamber muffler with choke tube: Two-chamber muffler with choke tube is analysed by using L27 OA in SYSNOISE and model shown in figure 11 found out as optimum model by using S/N ratio analysis which gives high transmission loss among others. This model is manufactured and experiment was conducted on it to calculate average transmission loss. The average transmission loss obtained by experiment is 54.89dB and by FEM is 56.6491. Figure 12 shows the comparison between experimental TL curve and FEM TL curve and it is seems to be very similar in nature. Figure 11: Optimum configuration of two chamber muffler with choke tube. 0.500 0.10

0.030

Ø0.035

0.10 0.250

Ø0.150

0.05

Figure 12: Comparison of experimental and FEM TL curve of two chamber muffler with baffle and choke tube.

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Patil SandipS et al., American International Journal of Research in Formal, Applied & Natural Sciences, 3(1), June-August, 2013, pp. 2128

VIII. Conclusions Comparison of all models by using FEM analysis shows that there is good agreement between experimental and FEM results. Analysis of two chamber muffler with baffle shows that baffle had a considerable effect on TL. L9 Taguchi OA is used to analyze the muffler. S/N ratio analysis gives optimum value of dimensions of muffler which gives maximum transmission loss. After inserting choke tube in two chamber muffler with baffle, TL increases compared to muffler without choke tube. L27 Taguchi OA is used to find out optimum configuration of muffler. Analysis of variance is done to find out most significant factors, which have an effect on TL. By ANOVA it is clear that, choke tube diameter has most effect (76.15%) on TL, and choke tube length also has an effect (16.99%) on TL. References [1] [2] [3] [4] [5] [6]

[7] [8]

M.L. Munjal, “Acoustics of Ducts and Mufflers”, John Wiley & Sons, New York, 1987. Phillip J. Ross, “Taguchi Techniques for Quality Engineering”, McGraw-Hill Book Company, Singapore, 1989. Dale H. Besterfield, Carol Besterfield-Michna, Glen H. Besterfield and Mary Besterfield-Sacre, “Total Quality Management”, Pearson Education Asia, 2001. Jae-Eung Oh, Kyung-Joon Cha, “Noise Reduction of Muffler by Optimal Design,” KSME International Journal Vol. 14, No.9, 2000, pp. 947-955. Howard S. Gitlow, “Quality Management”, McGraw-Hill Book Company, Inc, 2005. M.B. Jadhav, A. P. Bhattu, “Validation of the Experimental Setup for the Determination of Transmission Loss of Known Reactive Muffler Model by Using Finite Element Method” International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 1, July 2012. Z. Tao and F. Seybert, “A review of current techniques for measuring muffler transmission loss.” SAE 01, 2003, 1653. Ranjit Roy, “A primer on the Taguchi Method”, Van Nostrand Reinhold, New York, 1990.

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

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

Oxidation Behaviour of CoNiCrAlY Bond Coats Produced by High Velocity Oxy-Fuel and Cold Gas Dynamic Sprayed Nitrogen Gas W.S.Rathod1, A.S.Khanna2, J. Karthikeyan3, R.C.Rathod4 Corrosion Science & Engineering Dept, IIT, Bombay Mumbai, India 3 ASB Industries, Ohio, USA, 4 VNIT, Metallurgy Dept, Nagpur, India

1, 2

Abstract: The purpose of the current study was to investigate the microstructure and oxidation behavior of CoNiCrAlY coatings, deposited by the HVOF and CGDS techniques. The quality of the as-sprayed and oxidized bond coats was assessed in terms of their microstructure, especially porosity and oxide inclusion and mechanical properties, especially hardness. Sprayed samples were exposed to isothermal oxidation at 900 0C in air. Oxide growth rates were obtained from a series of mass gain measurements, while oxide scale compositions were determined using SEM, XRD and EDX analysis. Results obtained in this study show HVOF coating features high levels of visible defects, oxide content, spineltype oxide and high oxide growth rate, whereas CGDS coatings show low oxide growth rate as a result of low porosity, oxide content and high hardness. The oxide scale on the CGDS coating after 1000 hrs of oxidation was composed of alumina and initiation of spinal type of oxides. Keywords: Cold Gas Dynamic Spray (CGDS), High velocity oxygen-fuel (HVOF), bond coat I. Introduction Thermal barrier coatings (TBC) are used for gas turbine blades of power plants and aircraft engines for higher efficiency and long term durability. TBC system consists of MCrAlY bond coat and YSZ top coat. Bond coat enhances the adhesion of the ceramic top coat. Working conditions at elevated temperature lead to the oxidation of the bond coat which causes the formation of a thermally grown oxide (TGO) layer at the bond coat/ top coat interface and continues to grow in thickness during thermal cycling by consuming the oxidation resistant element reservoir [1-3]. The oxidation resistance and the TGO quality depend, not only on the chemical composition but also on the technique used to produce the bond coat. CoNiCrAlY is typically coated by VPS (Vacuum plasma spraying), APS (Air-plasma spraying), LPPS (Low pressure plasma spraying) and HVOF (High velocity oxygen-fuel spraying) [4, 6]. The drawback of these techniques is that their inherent elevated temperature inevitably leads to changes in the coating microstructure namely oxide inclusions. APS gives a coating which has porosity and oxide impregnation. Shibata et al. [4] deposited CoNiCrAlY bond coat using APS, LPPS and HVOF and the extent of oxide contamination was 1.8, 0.16 and 0.94 wt. % respectively. F. Tang et al. [7] deposited CoNiCrAlY coating by HVOF and reported that inprocess surface oxidation is detrimental to the TGO growth mechanism as it promotes the onset undesirable fast- growing nonalumina oxides that form protrusions and cause the TBC failure mechanisms. An alternative to above process is cold gas dynamic spray (CGDS) technique. In the CGDS spraying, the kinetic energy, rather than thermal energy is used to produce the bond coat [8-12]. In this process, the fine powders particles (5-42 μm dia) are propelled in a supersonic flow and get deposited on substrate after undergoing sever plastic deformation upon impacting the substrate. CGDS coating operates at lower temperatures and also uses inert gases such as helium and nitrogen which hinder oxidation and grain growth during deposition [13, 15]. Resent researches have showed the potential of CGDS compared to other VPS [23]. P. Richer et al. [16, 17] deposited CoNiCrAlY coating using CGDS sprayed with He gas reported that coating composed of alumina, without the presence of NiO or spinal-type mixed oxides. Q. Zhang et al. [18] studied oxidation behavior of NiCrAlY coatings deposited using CGDS technique. They reported the formation of (αA2O3) at 9000C and 10000C. The aim of the present work, therefore, is to compare the oxidation behavior of the bond coat material (CoNiCrAlY), applied using both techniques. The difference in the coating characteristics is expected due to the large difference in kinetic energy of deposition which in turn can modify the oxidation behavior of the coatings. A . CoNiCrAlY powder characterization Fig.1 shows XRD results of CoNiCrAlY powder, surface morphology and EDX analysis of elemental composition (wt. %) at selected points. This gas atomized powder has a spherical morphology and particle size range (10-42 µm dia.). The observed composition of the powder is in considerable agreement to that supplied by the supplier, except for a small difference in the concentration of yttrium. Since the γ phase is a solid solution of AIJRSTEM 14-206; © 2013, AIJRSTEM All Rights Reserved

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Co, Ni, Cr etc. and has a higher mean atomic number, it appears brighter in BSE mode. Hence, the brighter phase is represented by γ and the darker phase as β-NiAl/CoAl, which has a lower mean atomic number and hence lower contrast in BSE mode, as confirmed in references [16-20]. Fig. 1 XRD pattern of CoNiCrAlY powder revealing a two-phases, γ+β structure

B. Deposition techniques and equipment In cold spray process, the penning effect of incoming high velocity solid particles deforms the deposited material which tends to close any small pores or gaps in the underlying material [9, 11, 15 and 17]. In order to understand the effect of N2 carrier gas was employed to carry and accelerate the CoNiCrAlY powder. The 316L stainless steel plates were cut into 300 x 300 x 2 mm size with a wheel cutter. Before the deposition, the plates were grit blasted with 20- grit alumina at a pressure of 0.3 MPa to increase surface roughness and ultrasonically cleaned in water and ethanol. Finally plates were cut into small pieces of (10 x 10 x 2 mm) size using wire electrical discharge machine for experiment. HVOF coating was carried out using HIPOJET-2700 model, using nitrogen as carrier gas. II. Results A. Characterization of as sprayed Coatings 1. As-sprayed HVOF coating Fig. 2 (a, b, c) show the surface morphology, cross-sectional morphology and dendrites microstructure of assprayed HVOF coating. The microstructure of coating revealed semi-melted particles presented on the top of assprayed condition. The coating surface is rough in the as-sprayed condition due to the presence of melted and unmelted particles. The thicknesses of the coatings measured, is in the range 165-299μm. Coating shows, the dendrites are well packed and well interlocked to each other as shown in Fig. 2 (b). Fig. 2 (c) display relatively higher degree of porosity in HVOF coating. Fig. 2 SEM images of HVOF coating in as-sprayed condition

2. CGDS with N2 gas Figure.3 (a) shows individual powder particles, not properly adhered to the surface, perhaps due to not acquiring the critical velocity required for the deposition. Hence, multi-impacts under N2 carrier gas display relatively higher degree of porosity, portraying low carrying capability of accelerating gas (Fig 3 (b)). This behavior is due to the increase in density of the carrier gas (N2 density is 1.2506 kg/m3), and thereby reduced degree of plastic deformation of the spray particles is expected for the coating. Thus, higher porosity levels, larger splat size, and reduced plastic deformation degree was observed when compared to those of helium-processed coating [19, 20]. Fig.3 (c) shows the surface morphology of the coating, revealing pores and voids and the presence of higher splats attributing to the multi-impacts on the coated layer.

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Fig. 3 SEM image of CGDS coating sprayed with N2 gas in as-sprayed condition

3. Nanoindentation analysis of the coatings Table 1 show the nanoindentation measurements on coatings, CGDS sprayed with N2 career gases and HVOF. A load of 11000, grams applied for 15 seconds and six indentations were made on each sample and the averages results were taken. The coating deposited by N2 carrier gas was denser than that coated by HVOF. The deposition hardness is dependent on the velocities attained by the particle, and thereby the impact to cause plastic deformation on the surface. Hence, the impact velocity, being a function of the ratio of specific heats and inversely to the mass of carrier gas, improves the hardness with lower density and higher specific heat ratio of carrier gas. Hence, pure nitrogen displayed higher hardness in contrast to HVOF processing conditions for deposition coatings [21]. Table 1 Values of Nanoindentation Process Load P max (mN) Hardness Reduced modules Depth of penetration (GPa) Er (GPa) H max (nm) CGDS N2 11000 6.35 151.33 250 HVOF 11000 5.04 119.10 285 B. Characterization of oxidized scale 1. Oxidation Studies 1.1 Kinetics of HVOF and CGDS sprayed with N2 coatings The oxidation behavior of the coated stainless steel was investigated using discontinuous oxidation tests in air. The weight changes vs time plots for the oxidation of coatings in air at 900 0C are given in Fig.4. The linear plots appear to follow parabolic kinetics. After 1000 hrs the parabolic rate constant, Kp calculated using parabolic equation was found to be 1.28x10-8 g2/cm4s-1; for HVOF, 0.510x10-8 g2/cm4s-1; for N2 gas, sprayed with CGDS [17-19, 22]. The results show that the CGDS sprayed with N2 coated samples shows significant lower oxidation rate than the HVOF coated samples. Fig. 4 Mass gain curve obtained during isothermal oxidation of the HVOF and CGDS sprayed with N2 coatings after the oxidation at 900 0C for 1000 hrs

2 Characterization of Oxidized scale of CGDS sprayed with N2

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Fig.5 shows the surface morphology of oxidized sample of CGDS sprayed with N2 carrier gas coating oxidized at 900 0C for 200, 500 and 1000 hrs. The EDX analysis was done on oxidized surface was is rich in Al, and O and small amount of Cr and Co. It is clear from XRD (Fig. 6) result that the intensity of the peaks associated to CoCr2O4 spinal-type oxides also increases, while the intensity of the α-Al2O3 peaks remains relatively low. Fig. 5 Surface morphology of the CGDS coating sprayed with N2 gas after the oxidation at 900 0C for different times

XRD analysis carried out on the Feedstock powder was composed of γ and β-phases as depicted in Fig. 1. Fig.6 shows XRD results for the oxides formed at various stages of oxidation. It is observed that the as-sprayed coating do not retain the typical two-phase microstructure (γ-matrix Co-Ni-Cr solid solution and β-NiAl/CoAl precipitates) initially found in the feedstock powder [16, 17]. The absence of β-phase in as-sprayed coating shows that transformations of microstructure have taken place during deposition. In CGDS coating, the absence of the β-phase is in accordance with findings [16]. Fig. 6 XRD patterns of oxidized CGDS coating sprayed with N2 gas at the temperature of 900 0C for different times

3 Characterization of Oxidized scale of HVOF Figure 7 illustrates surface morphology of HVOF coating oxidized at 900 0C for 200, 500 and 1000 hrs for isothermal oxidation. Surface scale morphology of the oxidized coating it can be seen that the surface is mostly covered with an oxides scale composed of aluminum and oxygen having needle-like (or whisker-like, blade like) morphology which is characteristics of α-Al2O3, and NiO and spinal-type oxides. The EDX analysis was done on oxidized surface was rich in Co, Ni and O rich oxide. Figure 8 shows XRD pattern of coating reveal the existence of NiO oxides, α-Al2O3 and the γ solid solution. As suggested by F. Tang, P. Richer, and Saeidi et al. [7, 17, 23], “spinel” represent wither a mixture of some/all of other spinel mixed oxides such as NiCr 2O4, NiAl2O4, CoAl2O4, CoCr2O4 and NiCo2O4, or a substitutional solid solution of (Ni, Co)(Al,Cr)2O4. From Fig. 8 for HVOF coating the surface morphology of the oxidized samples, it can be seen that the surface is the spineltype oxides a mixture of a (Ni,Co)(Al,Cr)2O4 , α-Al2O3 and also NiO. Oxide layer formed at 1000 hrs reveals the presence of an area with higher Ni concentration, which corresponds to an oxide on the coating surface with block-shape morphology is typical NiO, the presence of this oxide suggests that the coating is nearing the end of its Al-rich lifecycle. These findings are in accordance with those reported in study [7, 23, and 24].

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Fig.7 Oxide scale surface morphology of the HVOF coating after the oxidation at 900 0C for different times

Fig. 8 XRD patterns of oxidized HVOF coating at the temperature of 900 0C for different times

IV. Conclusions Cold-spray deposition of CoNiCrAlY powder using N2 as carrier gas exhibited less dense morphological structure. This effect was attributed to the lower ratio of specific heats and high mass density of nitrogen as carrier gas. The oxide scale for the CGDS coating was predominantly composed of alumina, and initiation of growth of NiO or spinal-type mixed in thermally grown oxide layer. The oxide scale for the HVOF coating shows undesirable NiO and mixed spinal-type oxides during oxidation it suggests that the coating is nearing the end of its Al-rich lifecycle. Acknowledgments The authors wish to thank, ASB Industries, Ohio, USA, and Metallizing Equipment Pvt. Ltd. Jodhpur, India, for thermal spraying the bond coats samples. References 1. 2. 3. 4.

5. 6. 7. 8. 9.

P. K. Wright, A.G. Evans, “Mechanisms governing the performance of thermal barrier coating.’’ Current Opinion in Solid State and Materials Science 4 (1999) pp.255-265. A.G. Evans, D.R. Mumm, J.W. Hutchinson, G.H. Meier, F.S. Petit, “Mechanisms controlling the durability of thermal barrier coatings.’’ Prog. Materials Science 46 (2001) pp.505-553. K. Messaoudi, A.M. Huntz, B. Lesage, “Diffusion and growth mechanism of Al2O3 scales on ferritic Fe-Cr-Al alloys.” Material. Sci. Eng. A 247 (1) (1998) pp. 248-262. M. Shibata, S. Kuroda, H. Murakami, M. Ode, M. Watanabe, Y. Sakamoto, “Comparison of microstructure and oxidation behavior of CoNiCrAlY bond coating prepared by different thermal spray process.” Materials Transactions vol. 47, no.7 (2006) pp.1638-1642. W. Brandl, D. Toma, J. Kruger, H.J. Grabke, G. Matthaus, “The oxidation behavior of HVOF thermal sprayed MCrAlY coating.’’ Surface Coating Technology vol.94–95(1–3) (1997) pp.21-26. D. Toma, W. Brandl, U. Koster, “Studies on the transient stage of oxidation of VPS and HVOF sprayed MCrAlY coating.” Surface Coating Technology vol.120 (1999) pp.8-15. F. Tang, L. Ajdelsztajn, G.E. Kim, V. Provenzano, J.M. Schoenung, “ Effects of surface oxidation during HVOF processing on the primary stage oxidation of a CoNiCrAlY coating.” Surface CoatingTechnology. 185 (2004) pp.228-233. A.P. Alkhimov, A.N. Papyrin, V.F. Kosarev, N.I. Nesterovich, M.M. Shushpanov, US Patent 5 302 414, Gas-Dynamic Spraying Method for Applying a Coating, April 12, 1994. R.C. Dykhuizen, M.F. Smith, “Gas dynamic principles of cold spray.’’ J. Therm. Spray Technol. 7 (2) (1998) pp.205-212.

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T. Stoltenhoff, H. Kreye, H.J. Richter, “An analysis of the cold spray process and its coating.” J. Therm. Spray. Technol. 11 (4) (2002) pp. 542-550. L. Ajdelsztajn, B. Jodoin, G.E. Kim, J.M. Schoenung, “Cold spray deposition of nanocrystalline aluminium alloy.” Metall. Mater. Trans. A 36 (2005) pp.657. C. Borchers, F. Gartner, T. Stoltenhoff, H. Kreye, “Microstructural and macroscopic properties of cold sprayed copper coating.” J. Appl. Phys. vol.93 no.12 (2003) pp.10064-10070. T.H. Van Steenkiste, J.R. Smith, R.E. Teets, “Aluminum coating via kinetic spray with relatively large powder particle.”Surf. Coat. Technol. 154 (2002) pp.237-252. C.J. Li, W.Y. Li, Y.Y. Wang, “Formation of metastable phases in cold sprayed soft metallic deposit.” Surf. Coat. Technol. vol.198 , (2005) pp.469-473. L. Ajdelsztajn, B. Jodoin, P. Richer, E. Sansoucy, E.J. Lavernia, “Cold gas dynamic spraying of iron-base amorphous alloy.” J. Therm. Spray Technol. 15 (4) (2006) pp.495. P. Richer, A. Zuniga M. Yandouzi, B. Jodoin, “CoNiCrAlY microstructural changes induced during Cold Gas Dynamic Spraying.” Surf. Coat. Technol. 203 (2008) pp.364-371. P. Richer, M. Yandouzi, L. Beauvais, and B. Jodoin, “Oxidation Behavior of CoNiCrAlY Bond Coats Produced by Plasma, HVOF and Cold Gas Dynamic Spraying.” Surface and Coatings Technology, 204 (2010) pp. 3962-3974. Q. Zhang, C.J. Li, Y. Li, S. Zhang, X.R. Wang Q. Zhang, G.J. Yang, and C.X. Li, “Thermal Failure of Nanostructured Thermal Barrier Coatings with Cold-Sprayed Nanostructured NiCrAlY Bond Coat”, J. Therm. Spray Technol., 17(5-6), (2008) pp. 838845. K. Balani, T. Laha, A. Agarwal, J. Karthikeyan, N. Munroe, “Effect of carrier gases on microstructural and electrochemical behavior of cold-sprayed 1100 aluminum coating”, Surface & Coatings Technology 195 (2005) pp. 272– 279 Alkimov A.P., Kosarev V.F. et.al., Sov. Phys. Dokl., 1990. 35(12), pp.1047-1049. Giovanni di Girolamo, Marco Alfano, Leonardo Pagnotta, Robert J.K.Wood, Jurgita Zekony, “Depth sensing nanoindentation of oxidized plasma sprayed CoNiCrAlYcoating.” scientific research, 1 (2011) pp.51-53. F. Tang, L. Ajdelsztajn, J.M. Schoenung, Influence of Cryomilling on the Morphology and Composition of the Oxide Scales Formed on HVOF CoNiCrAlY Coatings Oxidation of metals vol.61 no.314, (2004) pp.219-238. S. Saeidi, K.T. Voisey, and D.G.McCartney, “The Effect of Heat Treatment on the Oxidation Behavior of HVOF and VPS CoNiCrAlY Coatings.” J. Therm. Spray Technol., 18(2), (2009) pp. 209-216. L.Ajdelsztain, J.A.Picas, G.E.Kim, F.L.Bastian, J.Schoenung, V. Provenzano, “Oxidation behavior of HVOF sprayed nanocrystalline NiCrAlY powder.”Material Science and Engineerin A 338 (1-2) (2002) pp.33-43.

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

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

Performance Assessment of Modified Irrigation Scheduling for Pench Irrigation Project 1

S. S. Wane1 and M. B. Nagdeve2 Research Scholar, Department of Irrigation and Drainage Engineering 2 Chief Scientist and Professor, AICRP for Dryland Agriculture Dr. Panjabrao Deshmukh Agricultural University, Akola (Mah), INDIA

Abstract: Alternative irrigation scheduling is essential to improve the present low overall efficiency of irrigation projects in India. Existing irrigation scheduling in Right Bank Canal of Pench Irrigation Project located in Nagpur district of Maharashtra was compared with nine developed modified schedules of varied rate rotation (variable discharge, constant duration and constant frequency). Water demand for the Right Bank Canal Command was estimated using CROPWAT. Percent deviation of demand from Existing scheduling was observed to be 2.71, 3.22, 0.93, 4.87, and 0.29 in 5, 5b, 7, 7b and 9 delivery schedules, respectively. Average supply and demand ratio for a period 7 year was estimated to be 0.82, 0.86, 0.90, and 0.84 for existing, 7, 7b, and 9 schedules, respectively. Weighted average (2004-07) of adequacy was obtained as 0.66 for existing schedule which is improved to 0.69, 0.72, and 0.68 for the 7, 7b and 9 schedules, respectively. Weighted average (2004-07) of dependability was obtained as 0.56 for the existing which was reduced to 0.51, 0.48 and 0.52 for the 7, 7b and 9 schedules, respectively. Average of (2004-07) equity value is obtained as 0.33 for the existing and which was reduced to 0.28, 0.24 and 0.29 for the 7, 7b and 9 schedules, respectively. By and large, considering all criteria an alternative delivery schedule ‘7b’ with varied rate rotation having 7 irrigations annually of 12 days canal operation followed by 12 days of canal closure with the starting date September, 25th was found to be the best and can save 6.69 M m3 of water as compared to Existing schedule and maintaining favourable water regime for the crop growth. This irrigation scheduling reduces the gap between supply and demand and results in 4.87 per cent of water saving as compared to existing irrigation scheduling. Keywords: Irrigation Scheduling, CROPWAT, Adequacy, Dependability and Equity I. Introduction Water is the limiting factor in most of the world. Increasing yield with sustainable food production depends mainly on irrigation. Increasing food production with the limited water resources is the main challenges for irrigated agriculture sector in 21st century. Hence monitoring the performance of irrigation system is meaningful. Most of the irrigation projects in India and South Asia perform at the low overall efficiency of 3035% as in [1]. Moreover, lack of financial resources and infrastructure are the major obstacle to improve the efficiency of the system through structural alteration. Efficient operation and management is the only feasible alternative. This realization has shifted the focus of policy makers and researchers to improve the performance of canal irrigation through management suggested in ([2]-[4]). An improved approach to develop irrigation scheduling programme using the water balance method was suggested by [5]. An analysis of water delivery schedule based on a water balance simulation approach using a crop growth simulation model was made. Best modified rotation schedule resulted in 94% increase in yield of crops under the command as compared to on demand module as in [6]. The features of historical delivery schedules in the Right Bank Main Canal system of Kangsabati Irrigation Project was used to prepared nine modified schedules of varied rate rotation. An alternate schedule with three irrigations of 20 to 21 days duration, followed by 20 days of canal closure after the irrigation, was found to be well performed given in [7]. An irrigation water delivery scheduling model was developed to increase irrigation efficiency for a large scale rice irrigation project in Malaysia. Rainfall and evapotranspiration values were used to estimate weekly irrigation water deliveries through the water balance equation given in [8]. To estimate the annual water demand of different crops, the CROPWAT, a decission support system developed by FAO were used in varous irrigation projects and deveoped alternative delivery scedules suggested by ([9]-[13]). Reference [14] shows the development of various indicators to assess the performance of irrigation delivery system in terms of structural and management. This paper describes the performance assessment of modified irrigation scheduling for Right Bank Canal Command of Pench Irrigation Project.

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II. Methodology Command area under Pench Irrigation Project is located between 21º00′ to 21º45′ N latitude and 79º00′ to 79º45′ E longitudes and situated in 11th Agro-Ecological Region of India, K6C3 (Fig. 1). The average annual rainfall of the canal command is 1107 mm with an area of 1044.76km2. The project is serving domestic, industrial and irrigation demand through right and left bank canals. The investigation was limited to right bank canal command area which consist of Right Bank Main Canal (RBMC), Tail Brach Canal (TBC), L4 Branch Canal (L4BC), and Khaperkheda Branch Canal (KBC). The total length of RBMC to Mathni is about 98 km. Fig. 1. Location map of Pench Irrigation Project

Estimation of irrigation demand CROPWAT is used to estimate the irrigation water demand of cotton and wheat crops in the command area. Climatic data viz. rainfall, temeratures, relative humidity wind speed and sunsine hours; crop parameters viz. minimum and maximum root-zone depth, crop growth period, crop coefficients at different stages of crop growth and soil parameters viz. field capacity and wilting point; and irrigation supply dates in scheduling model were given as inputs to the model. Simulations were run for each crop over 10 years (2000-09). For paddy, on irrigation supply day, if water depth of paddy field Wi falls below the minimum water depth (Wmin), then irrigation IR is applied (IRi = Wopt - Wmin). Wopt Optimum water depth in the paddy field, Wmin and Wopt has considered as 3 and 12 cm, respectively suggested by [15] and [16]. A uniform water depth has been considered in the entire field covered under a specific crop. Considering existing and modified delivery schedules to compute daily irrigation water demand for RBC system. The total irrigation water was applied during the canal operation period and then summed up to obtained the total irrigation demand. The average irrigation demand in volumetric terms was then obtained by multiplying the average irrigation demand with ICA of the whole command. A conveyance efficiency of 40% is considered to calculate the irrigation demand at the system source for further comparison with supply. Modified delivery schedule The existing delivery schedule in RBC system is „intermittent‟ (variable discharge, variable duration and variable frequency). Analysis of ten years (2000-09) canal release data of the RBC canal reveals that on an average canal runs for 84 days. It provides average five to nine irrigations of varying duration, frequency and discharge during kharif and rabi season. Development of alternative irrigation scheduling would ensure reliable supply, varied rate rotation scheduling approach (variable discharge, constant duration and constant frequency) was chosen. The numbers of irrigations was varied from five to nine irrigations with fixed duration of supply in each irrigation, followed by equal duration of canal closure. On the basis of moisture depletion study and considering the length of canal operation days, protective irrigation was needed in second fortnight of September. So, to account for the variation, starting dates are shifted by 5 days on either side of September, 20 th . i.e. September 15th and September, 25th. The modified schedules were identified with a numerical character to represent the number of irrigations during a year in the schedule i.e. 5, 7 and 9. Further suffix „a‟ and „b‟ are used with the numeric character to represent the shifting from staring date of September, 20 th e.g. suffix „a‟ represents the starting of September, 20th whereas „b‟ represents the starting date of September, 25th. The details of these developed schedules are presented in Table 1.

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Table 1. Details of alternative delivery scheduling Schedule notation Existing 5

No. of irrigation/y Intermitent Five

Starting date, Varying Sept., 15

Rotation lengh, days Varying 17

Five

Sept., 20

16 17

Five

Sept., 25

16 17

Seven

Sept., 15

16 12

Seven

Sept., 20

Seven

Sept., 25

Nine

Sept., 15

Nine

Sept., 20

4 10

Nine

Sept., 25

4 10

Operative rotioan Period Varying 15 Sept. to 2 Oct.; 19 Oct. to 5 Nov.; 22 Nov. to 9 Dec.; 26 Dec. to 12 Jan. 29 Jan. to 14 Feb. 20 Sept. to 7 Oct.; 24 Oct. to 10 Nov.; 27 Nov. to 14 Dec.; 31 Dec. to 17 Jan. 3 Feb. to 19 Feb. 25 Sept. to 12 Oct.; 29 Oct. to 15 Nov.; 12 Dec. to 19 Dec.; 5 Jan. to 22 Jan. 8 Feb. to 24 Feb 15 Sep. to 27 Sep.; 9 Oct. to 21 Oct.; 2 Nov. to 14 Nov.; 26 Nov. to 8 Dec.; 20 Dec. to 01 Jan.; 13 Jan. to 15 Jan.; 06 Feb. to 18 Feb. 20 Sep. to 02 Oct.;14 Oct. to 26 Oct.; 07 Nov. to 19 Nov.; 01 Dec. to 13 Dec.; 25 Dec. to 06 Jan.; 18 Feb. to 2 Mar.; 11 Mar. to 23 Mar. 25 Sep. to 07 Oct.;19 Oct. to 31 Oct.; 12 Nov. to 24 Nov.; 06 Dec. to 18 Dec. 30 Dec to 11 Jan.;23 Jan. to 04 Feb.;16 Feb. to 28 Feb. 15 Sep. to 25 Sep.; 05 Oct. to 15 Oct.; 25 Oct. to 04 Nov.; 14 Nov. to 24 Nov.; 04 Dec. to 14 Dec.;24 Dec. to 03 Jan.; 13 Jan. to 23 Jan.; 02 Feb. to 12 Feb. 22 Feb. to 26 Feb 20 Sep. to 30 sep.; 10 Oct. to 20 Oct.; 30 Oct. to 09 Nov.; 19 Nov. to 29 Nov.; 09 Dec. to 19 Dec.; 29 Dec. to 08 Jan.; 18 Jan. to 28 Jan.; 07 Feb. to 17 Feb.; 27 Feb. to 03 25 Sep. to 05 Oct.; 15 Oct. to 25 Oct.; 04 Nov. to 14 Nov.; 24 Nov. to 04 Dec.; 14 Dec. to 24 Dec.; 03 Jan. to 13 Jan.; 23 Jan. to 02 Feb.; 12 Feb. to 22 Feb.; 04 Mar. to 08 Mar

Performance assessment Three performance indicators as in [14] were used and presented below Adequacy A measure of performance relative to adequacy for a region or sub-region R served by the system over a period T is given as PA =

1 ∑ T T

1 R

∑ pA

(2)

where, pA= QD/QR if QD ≤ QR pA = 1 otherwise The function PA impose an upper bound on point evaluations of adequacy, that is when QD exceeds QR the delivery was considered as adequate, regardless of the magnitude of excess. Here QD denotes the actual amount of water delivered by the system and QR denotes the amount of water required for consumptive use, leaching, land preparation and farm application and conveyance losses downstream of the delivery point. Dependability It is defined as the temporal uniformity of the ratio of the delivered amount of water to the required or scheduled amount. An indicator of the degree of dependability of water delivery is the degree of temporal variability in the ratio of amount delivered to the amount required over a region. This variability may be measured by PD =

1 QD ∑ CVT Q R R R

(3)

Where, CVT(QD/QR) = temporal coefficient of variation (ratio of standard deviation to mean) of the ratio QD/QR over the time period T. Equity An appropriate measure of the performance relative to equity would be the average relative spacial variability of the ratio of the amount delivered to the amount required over the time period of interest. The measure is given by

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PE =

1 QD â&#x2C6;&#x2018;CVR Q T T R

(4)

Where CVR (QD/QR) = special coefficient of variation of the ratio QD/QR over the region R. This measure describes the degree of variability in relative water delivery from point to point over the region. The closure is the value of PE to zero, the greater the degree of equity (special uniformity) of water delivery. III. Results and Discussion Irrigation water demand Irrigation water requirement for the major crops in RBC system was estimated using CROPWAT (Table 2). Minimum water requirement for the RBC system was 129.39 M m3 in schedule 7b and maximum water requirement was observed to be 146.21 M m3 in schedule 9b. Average irrigation water demand (7 years) 2002-08 at the system source for different delivery schedules were estimated using CROPWAT and presented in Table 2. As evident, five alternative delivery schedules i.e. 5, 5b, 7, 7b and 9 performs better than existing and other developed alternative delivery schedules resulted in lesser demand. Percent deviation of demand from Existing scheduling was observed to be 2.71, 3.22, 0.93, 4.87, and 0.29 in 5, 5b, 7, 7b and 9 delivery schedules, respectively. This shows that only these schedules are capable of judiciously utilizing the canal water in conjunction with the rainfall. Therefore in subsequent analysis only these schedules were considered for further analysis for evaluating the performance. Table 2. Schedule wise Irrigation water requirement for the major crops in RBC system Irrigation Requirement, M m3 % deviation of demand from Schedule Exi. schedule Cotton Paddy Wheat Total Exi 47.63 43.27 45.11 136.01 0.00 5 34.71 50.00 47.62 132.33 2.71 5a 36.79 53.71 47.70 138.20 -1.61 5b 43.40 37.77 50.46 131.63 3.22 7 51.07 36.40 47.28 134.75 0.93 7a 49.66 40.63 51.48 141.77 -4.23 7b 38.91 43.92 46.55 129.39 4.87 9 47.15 44.58 43.89 135.62 0.29 9a 44.55 47.13 47.32 139.01 -2.20 9b 45.82 50.03 50.36 146.21 -7.49 Irrigation water supply and demand ratio Irrigation water supply and demand ratio for the altermative irrigation schedule were calculated to select the best the modified irrigation schedule over existing irrigation schedule. The variation of supply and demand ratio for the existing and modified delivery schedules over 7 years period is presented in Fig 2. In an ideal case this ratio should be one the schedule in which this ratio is on or near to one was selected as the best irrigation schedule. Average supply and demand ratio for a period 7 year were estimated to be 0.82, 0.86, 0.90, and 0.84 for existing, 7, 7b, and 9, schedule, respectively. This clearly shows, superiority of the schedule 7b (Irrigation start date the September, 25th i.e. seven irrigations with 12 days canal operation and 12 days canal closure) followed by 7 (Irrigation start date the September15th, i.e. seven irrigations and with 12 days canal in operation and 12 days canal closure and 9 (Irrigation start date the September 15th, i.e. 9 irrigations with 10 days canal operation and 10 days canal closure over existing schedule. Fig. 2. Supply and demand ratio for the existing and modified delivery schedule 1.10 Exi

Supply/Demand

1.00

0.90 0.80

0.70 0.60 0.50 0.40 2002

2003

2004

2005

2006

2007

2008

Year

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Performance indicators: Three performance indicators viz. adequacy, dependability and equity were selected to assess the performance of the delivery system in different reaches during 2004, 2005, 2006 and 2007. Table 3 shows weighted average values of adequacy for Existing, 7, 7b and 9 schedules are 0.66, 0.69, 0.72 and 0.68, respectively. This is to have an overall idea of adequacy over the entire command of the delivery system. The increase in adequacy values substantiates the narrowing the gap between supply and demand of the system. The dependability values, a measure of temporal distribution of the ratio of supply to demand for the different reaches during 2004, 2005, 2006 and 2007 were estimated and depicted in Table 3. A marked improvement was also seen for schedule 7b over 7, 9 and existing. The four years average weighted dependability was obtained as 0.48, 0.51, 0.52 and 0.56 for 7b, 7, 9 and Existing schedules. The results substantiates that schedule 7b, is better than the 7, 9 and existing. Table 3. Estimated average adequacy and dependability during 2004-2007 Schedule Reach Adequacy Dependability Exi Head 0.79 0.31 Middle 0.60 0.59 Tail 0.51 0.90 Average 0.66 0.56 7 Head 0.81 0.29 Middle 0.66 0.57 Tail 0.55 0.88 Average 0.69 0.51 7b Head 0.82 0.25 Middle 0.68 0.54 Tail 0.59 0.78 Average 0.72 0.48 9 Head 0.80 0.28 Middle 0.63 0.56 Tail 0.56 0.84 Average 0.68 0.52 The equity values, a measure of the spatial distribution of the ratio of supply to demand, during irrigation periods for the four schedules were computed and presented in Table 4. Similar to the adequacy a marked improvement in the equity value is seen for schedule 7b compared to other schedules in all four years. The four years average equity is estimated to be 0.33 for existing schedules which is reduced to 0.28, 0.24 and 0.29 for 7, 7b and 9 schedules respectively. The results substantiates that estimated equity value for 7b is better over other schedules. IV. Conclusions Deviation of water demand from Existing schedule was observed to be positive in 5, 5b, 7, 7b and 9 schedules, which shows the water saving of 2.71, 3.22, 0.93, 4.87 and 0.29 per cent, respectively. Average supply and demand ratio was estimated to be 0.82, 0.85, 0.90 and 0.87 for the Existing 7, 7b and 9 irrigation schedules, respectively. Weighted average (2004-07) of adequacy was obtained as 0.66 for existing schedule which is improved to 0.69, 0.72, and 0.68 for the 7, 7b and 9 schedules respectively. Weighted average (200407) of dependability was obtained as 0.56 for the existing which was reduced to 0.51, 0.48 and 0.52 for the 7, 7b and 9 schedules, respectively. Average equity value (2004-07) obtained as 0.33 for the existing and which was reduced to 0.28, 0.24 and 0.29 for the 7, 7b and 9 schedules, respectively. By and large, considering the performance criteria, an alternative delivery schedule „7b‟ with varied rate rotation having seven irrigations annually with 12 days canal operation followed by 12 days of canal closure with the starting date the September, 25th was found to be the best which can save 6.69 M m3 of water as compared to Existing schedule and maintaining favourable water regime for the crop growth. The irrigation periods also cover the expected dry spell in the region and critical growth stage of the rice crop. Table 4. Estimated equity during 2004-07 Years Schedule Irrigations 2004 2005 2006 2007 Exi 1 0.15 0.38 0.11 0.3 2 0.2 0.29 0.38 0.47 3 0.32 0.31 0.19 0.42 4 0.4 0.53 0.24 0.21 5 0.45 0.48 0.42 0.51 6 0.36 0.36 0.32 0.38 7 0.42 0.26 0.39 0.15 8 0.29 0.22 0.45 0.27 Average 0.32 0.35 0.31 0.34

Average 0.24 0.34 0.31 0.35 0.47 0.36 0.31 0.31 0.33

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1 2 3 4 5 6 7 Average 1 2 3 4 5 6 7 Average 1 2 3 4 5 6 7 8 9 Average

0.13 0.18 0.3 0.38 0.33 0.34 0.4 0.29 0.1 0.15 0.27 0.26 0.29 0.31 0.37 0.25 0.18 0.25 0.25 0.24 0.26 0.35 0.36 0.3 0.24 0.27

0.36 0.27 0.29 0.31 0.36 0.34 0.24 0.31 0.12 0.24 0.26 0.22 0.43 0.31 0.39 0.28 0.13 0.37 0.18 0.34 0.47 0.34 0.2 0.36 0.29 0.3

0.09 0.28 0.37 0.22 0.34 0.3 0.27 0.27 0.18 0.13 0.14 0.19 0.31 0.27 0.14 0.19 0.15 0.24 0.36 0.34 0.38 0.32 0.38 0.24 0.15 0.28

0.28 0.25 0.34 0.19 0.29 0.26 0.13 0.25 0.25 0.24 0.37 0.16 0.46 0.13 0.1 0.24 0.19 0.24 0.34 0.26 0.28 0.34 0.59 0.16 0.37 0.31

0.22 0.25 0.33 0.28 0.33 0.31 0.26 0.28 0.16 0.19 0.26 0.21 0.37 0.26 0.25 0.24 0.16 0.28 0.28 0.30 0.35 0.34 0.38 0.27 0.26 0.29

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

[10] [11]

[12] [13] [14] [15] [16]

K. Sanmugnathan, and P. Bolton “Water management in third world irrigation scheme- lesson from the field” ODU Bull, 11 Hydraulic Research, London, UK, 1988. R. Chamber, “Managing canal irrigation: Practical analysis from South Asia” Cambrige University Press, Cambrige, UK. 1988. R. Lenton, Research and development for sustainable irrigation management. Water Resource Development, 1994, 10(4):417424 R. Loof, B. Das, and G. N. Paudyal, “Improved operation of a large irrigation canal systems in Southeast Asia” Water Resource Development, 1994 vol.10(4): 393-416 S. Pleban, and I. Israeli. “Improved approach to irrigation scheduling programs”. J. Irrigation and Drainage Engineering, 1989, vol. 115(4):577-587. S. Bhirud, N. K. Tyagi, and C. S. Jaiswal, “Rational approach for modifying rotational water delivery schedule” J. Irrigation and Drainage Engineering, 1990, vol. 116(5):632-644 A. Mishra, R. Singh, and N. S. Raghuwanshi, “Alternative delivery scheduling for improved canal system performance” J. Irrigation and Drainage Engineering, 2002, vol. 128(4):244–248 M. A. Haque, M. M. M. Najim and T. S. Lee, “Modelling irrigation water delivery schedule for rice cultivation in East Coast Malaysia” Tropical Agricultural Research, 2004. Vol. 16:204-213. S. F. Kuo, B. J. Lin and H. J. Shieh “Cropwat model to evaluate crop water requirements in Taiwan” Proc. International Commission on Irrigation and Drainage, 1st Asian Regional Conference of ICID, Seoul, Korea Republic, September 2001, pp 16-21. D. K. H. V. Rao, C. S. Krishnakumar, and V. H. Prasad “Irrigation water requirements and supply analysis in Dehradun Region - An integrated remote sensing and GIS approach” J. Indian Society of Remote Sensing, 2001, vol. 29, (l & 2), 59-67. O. Toda, K. Yoshida, S. Hiroaki, H. Katsuhiro and H. Tanji, “Estimation of irrigation water using CROPWAT model at km 35 project site in savannakhet, Laopdr. Proc. Of the International symposium on Role of Water Sciences in Transboundary River Basin Management, Thailand, 10-12 March 2005 F. Zhiming, L. Dengwei, and Z. Yuehong, “Water requirements and irrigation scheduling of spring maize using GIS and CropWat model in Beijing-Tianjin-Hebei Region” Chinese Geographical Science, 2007, vol. 17(1) 056–063. M. Nazeer, “Estimation of irrigation water requirements and yield reduction under different soil moisture depletion levels for Maize using fao cropwat” World J. of Agricultural Sciences 2009, vol. 5(4):394-399. J. D. Molden, and T. K. Gates, “Performance measures for evaluation of irrigation water delivery systems. J. Irrigation and Drainage Engineering, 1990, vol. 116:804-823 R. A. D. Kemchandra and V. V. N. Murty, “Modelling irrigation deliveries for tertiary units in large irrigation systems” Agricultural Water Management, 1992, vol. 21(3):197-214 A. Mishra, H. C. Verma, and R. Singh “Alternative rotational delivery scheduling for better water regime in canal command” J Irrigation and Drainage Engineering, 2008, vol. 34(2):175–184

Acknowledgement Authors gratefully acknowledge to the Department of Science and Technology, Ministry of Science and Technology, Government of India, New Delhi for the financial support under Women Scientist Scheme (WOS-A) for the research.

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Climatic Factors and their Impact on the Stability of the Hydro-Dynamics Model of the Shatt Al-Arab River 30.50 N Adel Jasem Al-Fartusi1, Noori Hussein Noor al Hashimi2 and Samer Adnan Al-taie1 Department of Physics of estuaries and marine water/ Centre for Marine Sciences/ University of Basra/ 2 Department of Physics/college of education for pure science/University of Basra/Basra/Iraq

Abstract: Shatt al-Arab River is one of the rivers affected by the phenomenon of the tides in the area of confluence with the Arabian Gulf. a promising Hydro-dynamics model to simulate the movement of water masses is developed with the help of Mike 11 routine. In this model the different weather factors introduced in order to insure that our model simulate the real conditions and facts of the study area. A field measurement data for the period's 15th-18th oct. 2012 and for the periods 27th-28th may 2013 has been used for the calibration purpose of the simulation result. This study has been carried out for two periods, the first period started on 1st oct. 2012 and end on 31 oct. 2012; the second period started on 1st may 2013 and end on 31 may 2013. The results showed a good agreement in term of qualitatively and quantitatively with the real measurement data, which gives this high-resolution model a chance to simulate the phenomenon and the hydrodynamic interaction between physical and hydrological conditions of the Shatt al-Arab river in Basra. Keywords: Water resources Hydrodynamics, Mathematical Model, Simulation, I. Introduction Shatt al-Arab one of the important river systems in Iraq, which arises from the confluence of the Tigris and Euphrates rivers in the town of Qurna (70 km) North of Basra 30.50N, is the last stage of the river system of the Tigris and Euphrates and the length of the River (204 km) from the confluence of the Tigris and Euphrates to its mouth in the Gulf [1]. Several scientific studies were conducted to examine by setting up a mathematical model based on de-saint-venant equations to predict the hydraulic conditions of the Shatt al-Arab channel see for example [2-5]. Khalaf [6], developed a mathematical model of the selected items under the name of FESTS-VP to represent the flow range in the Shatt al-Arab. Mahmoud etal [7], they used Mike11 routine to simulate the hydrodynamics behavior of the northern part of the Shatt al-Arab. Al-Fartusi etal [1] Construct numerical modeling to stimulate the amount of fresh water inlet and discharge from Shatt al-Arab River in Basra city 30.50 N south of Iraq. Most of the mathematical model used so far based on the numerical solution of the SaintVenant equations [8]. This equation is almost always used to model the different types of flow through the rivers see for example [9-16]. The goal of the present study is to reach out high accuracy in developing the Mathematical model by taking in to account the impact of weather in the calculation and to provide physical and hydrological conditions of the mathematical model for the numerical simulation which match to the reality. The result shows a good agreement between our mathematical model and the local measurement data. II. Theory Water is incompressible, which means that their densities are constant for a wide range of flows. This is a reasonable assumption except for certain extreme situations such as the cases where the fluid is under profound Incompressible Newtonian Fluid pressures. Since the density of river's water is constant, the continuity equation for this type of flow can be consider as incompressible flows which simplified as:

Where ux, uy, uz are the velocities along x, y, z direction. And the momentum equations can be simplified as:

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Where, ρ, P, g, μ, are the density, pressure, acceleration of gravity, and viscosity respectively. The flow of water through the river is a distributed process since the flow rate; velocity and depth vary spatially throughout the river. Estimates of flow rate or water level at certain locations in the river system may be obtained using the above set of equations that define the conservation of mass and momentum along this river. This model is based on the above partial differential equations that allow the flow rate and water level to be computed as a function of space and time. However for most practical purposes, the spatial variations in lateral and transverse directions can be neglected and the flow in a river system can be approximated as a one-dimensional process along the longitudinal direction (i.e., in the direction of flow). Saint-Venant Equations of channel flow that were derived in the early 1870s by Barre de Saint-Venant, may be obtained through the application of control volume theory to a differential element of a river reach. The assumptions made in derivation of Saint-Venant equations of Channel Flow are [1]: 1. The flow is one-dimensional. The water depth and flow velocity vary only in the direction of flow. Therefore, the flow velocity is constant and the water surface is horizontal across any section perpendicular to the direction of flow. 2. The flow is assumed to vary gradually along the channel so that the hydrostatic pressure distribution prevails and vertical accelerations can be neglected. 3. The channel bottom slope is small. 4. The channel bed is stable such that there is no change in bed elevations in time. 5. The Manning and Chezy equations, which are used in assumptions made in derivation of Saint-Venant Equations of Channel Flow the definition of channel resistance factor in steady, uniform flow conditions, are also used to describe the resistance to flow in unsteady, non uniform flow applications. 6. The fluid is incompressible and of constant density throughout the flow. According to these assumptions the inflow to the control volume is the sum of the flow Q entering the control volume at the upstream end of the channel and the lateral inflow q entering the control volume as a distributed flow along the side of the channel i.e. Rate of change of mass = ∑mass inflow-∑mass outflow This general equation of continuity can be given for the particular case of an open channel with an irregular geometry. The conservation of mass then can be written as (1) Applying the assumption of constant density and rearranging produces the conservation form of the continuity equation, which is valid for any irregular cross section (2) And the momentum equation can be written as (4) Where Q = total discharge, A = area of cross section, q = accidental discharge = ' h ' change the water level, n = Mannk factor for roughness, R = hydraulic radius of the Shatt al-Arab, g = gravity, a = motor power factor, x = distance along the riverbed, t = time In order to get realistic and more accurate model, the climatic characteristics are included such as wind (coefficient of friction of the wind) as well as the water temperature. A clear understanding and quantitative description of wind effects on river flow are still outstanding. The generation and growth of wind waves are difficult matters even on standing water (for a discussion see Ch. 12 in [15]). They are further complicated on a river by the flow of the water and by the presence of the banks and the bed. Therefore, this study shall be concerned only with the mean properties of the flow in an open channel with wind blowing parallel to the water surface and in the direction of channel flow. The wind shear stress can be written as: Where, cw, ρa, v10, are the wind fraction factor, air density, and velocity above 10m of the water surface. I.

Result and Discussion

The total lengths of the study area is 240 km, the mesh used in the Mike11 routine is assumed to be Δx =500m in distance step, and Δt = 60sec in time step. To measured the amount of water discharge and calculate the water current in the case of high and low tides for a complete circulation of its range (13-hour) per day we uses the ADCP device (Acoustic Doppler Current Profiler). The measurement is done by the way of the section where the device fixed on a boat and then moves the boat at a constant speed from one of the banks the river to the other side in a straight line and during the process of scanning the cross-section of the river device sends sound waves through the water column to the reception after reflection according to the Doppler effect which allow us to calculate velocities, direction and the discharge as shown in Figure (1). The measurement data are collected for two periods, the first period started on 1st oct. 2012 and end on 31 oct. 2012; the second period

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started on 1st may 2013 and end on 31 may 2013. The study area affected by two main type of wind, First: north westerly wind, which are clear during the cold seasons. Figure (2a) shows the values of velocity and direction of the wind during the period from 1 to 31 October 2012 where we see that the northern winds are common and the maximum values registered of the wind speed is (9 m/s), Second: south easterly moisture winds which are common through the warm seasons, and come in second place in terms of redundancy and speed. Figure (2b) shows the values of speed and direction of the wind during the period from 1 to 31 may 2013. Four different station are chosen for the simulation purpose, the location of these station and the date are chosen to compare our simulation with the true field measurement. These are recorded in table below, and the results of these comparisons are presented in figure (3). One can conclude that our simulations are all most give us a good picture, where we note that there is a match quantitative and qualitative with the field measurement data.

Figure (1) The measurement of the water current by ADCP device. (A) Represent the shape of the measure cross section. (B) Measurement of the Speed, direction, and the discharge.

Figure (2a)

Figure (2b

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Stations AL-Fao Abu Al-Khasibe Al-Siba AL-Qurna

Location Latitude 290 58' 48º 00' 48º15' 47º28'

Longitude 480 28' 30º 27' 30º20' 30º56'

Date 15 October 2012 17 October 2012 18 October 2012 28 may 2013

Location of the different stations and the date of collection data for simulation.

Figure (3a) Al-Fao Station

Figure (3b) Abu-Al-Khasibe Station

Figure (3c) Al-Siba Station

Figure (3c) AL-Qurna Station VI.

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

[6] [7] [8] [9]

References

Adel Jasem Al-Fartusi, Noori Hussein Noor al Hashimi and Samer Adnan Al-taie, (2013); " Hydrodynamic simulation model of the Shatt al-Arab River 30.5N"; International Journal of Emerging Technologies in Computational and Applied Sciences, 4(3), MarchMay, 2013, pp.289- 293 Shatt Al-Arab Project Feasibility Report, (1981) Basrah- Iraq Shahidi , A E , Fatemah Z and Ebrahim J (2008 ); "Modeling of Salinity intrusion under different hydrological conditions in the Arvand River"; Estuary CE-QUAL-W2.can.J.civ.Eng.vol.35,pp.1476-1480. Mahdi, Ayad Abdul Jalil and Abdul Amir al-Asadi, purely, (2007), "no matter algimor vologet properties of the Shatt"; Basra research journal (Humanities), volume 32, number (1-b), Basra University, pp. 88-106. Mahmoud, H K, Al-sayyab, H H, Lacey, D S, Mahmud, I and mutashar, m R; (2011) "hydraulic modeling program Mike11 in one dimension to simulate the hydraulic behaviour of the northern part of the Shatt al-Arab River", Iraq Science magazine, no. 281, pp. 114. Kahlaf W. A. (2003), "Selected items Model to represent the flow range in the Shatt al-Arab", M.Sc. desertation, colloge of eng., Univ. of. Basra, Basra, Iraq. Mahmoud H kh, AL-Siab H A., AL-Miahi D.S., Mhmoud A.B. and Mtasher W.R., (2011), " hydrodynamics behavior of the northern part of the Shatt al-Arab by using Mike 11", journal of Basra. Vol. 281, PP 1-14. Bouchut F., Nonlinear stability of finite volume methods for hyperbolic conservation laws, and well-balanced schemes for sources, Frontiers in Mathematics, Birkhauser (2004). Chalfen, M; Niemiec, A. (1996) "Analytical and Numerical Solution of Saint-Venant Equations"; Journal of hydrology, V86 PP1-13.

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Al-Fartusi et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), June-Aug., 2013, pp. 41-45 [10] SK. Godunov. A difference scheme for numerical computation of discontinuous solution of hydro dynamic equations. Math. Sbornik, 47:271–306, 1959. [11] E. Aldrighetti and P. Zanolli. A high resolution scheme for flows in open channels with arbitrary cross-section. International Journal for Numerical Methods inFluids, 47:817–824, 2005. [12] V. Casulli and P. Zanolli. A conservative semi-implicit scheme for open channel flows. International Journal of Applied Science & Computations, 5:1–10, 1998. [13] B. de Saint Venant. Théorie du mouvement non-permanent des eaux avec application aux crues des rivières at à l’introdution des marées dans leur lit. Acad. Sci. Comptes Rendus Paris, 73:147–154, 1871. [14] T. Tucciarelli. A new algorithm for a robust solution of the fully dynamic Saint Venant equations. Journal of Hydraulic Research, IAHR, 3:239–243, 2003. [15] Moussa R, and Bocquillon C, (2000) "Approximation zones of the Saint-Venant equations for flood routing with overbank flow"; Hydrology and Earth System Sciences, 4(2), PP 251-261. [16] Farge M., and Lacarra J. F., (1988), " The numerical modeling of Saint-Venant equations"; Journal of theoretical and applied mechanics; No.2 V7. [17] Kinsman B (1965). "Wind waves, "Prentice Hall, Englewood Cliffs, N. J.

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ANALYSIS OF FINGERPRINT IMAGE FOR GENDER CLASSIFICATION USING SPATIAL AND FREQUENCY DOMAIN ANALYSIS S. S. Gornale1, Geetha C D2, Kruthi R2, Department of Computer Studies, Government College (Autonomous), Mandya-Karnataka-INDIA. 2 Research Student, DOS in Computer Science, University of Mysore, Mysore-Karnataka-INDIA

Abstract: Gender identification from fingerprints is an important step in forensic anthropology in order to identify the gender of a criminal and minimize the list of suspects search. Fingerprint identification and classification has been extensively researched in the literature however very few researchers have studied the fingerprint gender classification problem. In this paper, gender identification is carried out by using combined features like FFT, Eccentricity and Major Axis Length. Left thumb impression of each sample of the internal database of 450 male samples and 550 female samples of good quality are selected. An optimal threshold for each transform is chosen for better results. It is found that the proposed algorithm produces accurate decision of 80% of male and 78% of female. The overall performance of the system is found to be satisfactory and useful to forensic anthropology. Keywords: WWW; component; formatting; style; styling; insert (Minimum 5 to 8 key words) I. Introduction The science of fingerprint has been used generally for the identification or verification of person and for official documentation. Based on the varieties of the information available from the fingerprint we are able to process its identity along with gender, age and ethnicity. The primary dermal ridges (ridge counts) are formed during the gestational weeks 12-19 and the resulting fingerprint ridge configuration (Fingerprint) is fixed permanently. Variations in ridge dimensions and sex differences in ridge breadth have been reported. Ridges and their patterns exhibit number of properties that reflect the biology of individuals. Dermatoglyphic features statistically differ between the sexes, ethnic groups and age categories. Studies so far carried out in gender determination have used the inked fingerprints and their findings are based on the spatial domain analysis of ridges. Generally ridge related parameters such as fingerprint ridge count, ridge density, ridge thickness to valley thickness ration, ridge width and fingerprint patterns and pattern types were used for gender determination. All the methods proposed based on the fingerprint ridges have given insight about the ridge parameters mentioned about but fails to give accurate method of measuring the parameters. This may be due to the measurement made on the inked fingerprint impressions and manual measurements of the parameters where human error and recklessness is inevitable. Poor impressions are unavoidable due to one or more of the following reasons. Poor, thin or colored ink, poorly maintained inking apparatus, fingers of foreign substances, failure to roll fingers fully, smears and blurred fingerprint due to finger slip or twist while enrolling and poor cooperation of subject. Also, the ridge thickness depends on the pressure applied and may provide false results on gender identification. The traditional methods use ridge related parameters and frequency domain analysis to detect gender. But not much work has been done to detect gender using region properties of the image. Hence, an attempt has been made by proposing a method that utilizes both frequency domain analysis and spatial domain analysis for identification [1][2][3] II. Related work Ahmed Badawi, et al, (June, 2006) proposed a method for Gender classification from fingerprints. A dataset of 10-fingerprint images for 2200 persons of different ages and gender (1100 males and 1100 females) was analyzed. Features extracted were; ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, and ridge count asymmetry, and pattern type concordance. Fuzzy- C Means (FCM), Linear Discriminate Analysis (LDA), and Neural Network (NN) were used for the classification using the most dominant features predicted results of 80.39%, 86.5%, and 88.5% using FCM, LDA, and NN, respectively. [4] Manish Verma, et al, (2008) proposed a method for Gender classification from fingerprints. Features extracted were; ridge width, ridge thickness to valley thickness ratio (RTVTR), and ridge density.SVM is used for the AIJRSTEM 13-212; ÂŠ 2013, AIJRSTEM All Rights Reserved

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classification. This method is experimented with the internal database of 400 fingerprints in which 200 were male fingerprints and 200 were female fingerprints. They found male-female can be correctly classified up to 91%.[5] Jen feng wang, et al, (2008) worked on gender determination using finger tip features. He obtained fingerprints from 115 normal healthy adults in which 57 were male fingerprints and 58 were female fingerprints. They have used ridge count, ridge density, and finger size features for classification. However, the ridge count and finger size features of left little fingers are used to achieve a classification. The best classification result of 86% accuracy is obtained by using ridge count and finger size feature together.[6] Angela Bell proposed a method in contrast to Acreeâ&#x20AC;&#x2122;s method of comparing ridge densities. He compared fingerprint loop ridge counts from 40 male and 40 female subjects. His analysis revealed no significant mean difference in the loop ridge counts across gender represented by these eighty subjects, F (1, 78) =.308, p>.05, MSE= 7.946. There is no difference in the number of loop ridge counts that males have (13.18, SD = 2.735) then did females (13.53, SD = 2.900). He concluded there are no significant differences in loop ridge counts between genders.[7] Dr. Prateek Rastogi, et al, have presented that there is an association between distribution of fingerprint patterns, blood group and gender. A dataset of 200 persons were analyzed (100 male & 100 female) belonging to the age group 18- 25. Results show that each finger print is unique; loops are the most commonly occurring fingerprint pattern while arches are the least common. Males have a higher incidence of whorls and females have a higher incidence of loops. Loops are predominant in blood group A, B, AB and O in both Rh positive and Rh negative individuals except in O negative where whorls are more common. Thus, they concluded that there is an association between distribution of fingerprint patterns, blood group and gender and thus prediction of gender and blood group of a person is possible based on his fingerprint pattern.[8] Gnanaswami P, et al, (2011) have proposed a method for Gender Identification Using Fingerprint through Frequency Domain Analysis to estimate gender by analyzing fingerprints using fast Fourier transform (FFT), discrete cosine transform (DCT) and power spectral density (PSD). A dataset of 400 persons of different age and gender is collected as internal database. Frequency domain calculations are compared with predetermined threshold and gender is determined. They obtained the results of 92.88 % and 78 % for male and female respectively.[9] Gnanaswami P, et al, (2012) proposed a method for Gender Classification from Fingerprint based on discrete wavelet transform (DWT) and singular value decomposition (SVD). The classification is achieved by extracting the energy computed from all the sub-bands of DWT combined with the spatial features of non-zero singular values obtained from the SVD of fingerprint images. K nearest neighbor (KNN) is used as a classifier. This method is experimented with the internal database of 3570 fingerprints finger prints in which 1980 were male fingerprints and 1590 were female fingerprints. They have obtained Finger wise gender classification which is 94.32% for the left hand little fingers of female persons and 95.46% for the left hand index finger of male persons. Gender classification for any finger of male persons tested is obtained as 91.67% and 84.69% for female persons respectively. Overall classification rate of 88.28% has been obtained [10] Ritu Kaur et.al, (2012) have worked on fingerprint based gender identification using frequency domain analysis. The classification is achieved by analyzing fingerprints using Fast Fourier transform (FFT), Discrete Cosine Transform (DCT) and Power Spectral Density (PSD). A dataset of 220 persons of different age and gender is collected as internal database. Frequency domain calculations are compared with predetermined threshold and gender is determined. They obtained results of 90%, and 79.07% for female and male samples respectively.[11][12] Rijo Jackson Tom, et al, (2013) have proposed a method for Fingerprint Based Gender Classification through frequency domain analysis to estimate gender by analyzing fingerprints using 2D Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA).A dataset of 400 persons of different age and gender is collected as internal database. They have used minimum distance method for classification and achieve overall success rate in gender classification of around 70%. III. Proposed Methodology The Proposed method for classification of fingerprint image for gender classification is shown figure-1 it carries four major steps as the functioning blocks of gender identification.

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Figure 1. Proposed methods for fingerprint classification 3.1. Fingerprint Image Acquisition: The fingerprint images of internal database were collected from a fingkey hamster 2nd scanner manufactured by nitgen biometric solution [30 with interface USB 2.0] The images were captured with a resolution of 512. For this work a database with 1000 images of left thumb fingerprints was created which correspond to 80 different people 3.2 Preprocessing. After collecting fingerprint samples, some preprocessing work such as background elimination, cropping, etc. have been carried out All collected fingerprints are resized [200x200] in the bitmap format of same dimensionality. Primarily it is a color image. For computer efficiency, the color image having high dimensionality was converted these color image to binary image, so for this binary image the calculation/recognition time is less and dimensionality will be reduced and it saves the memory. 3.3 Feature Extraction: Feature extraction is a fundamental preprocessing step for pattern recognition and machine learning problems. Various experiments were carried out to extract features using different methods, namely discrete wavelet Transform, Discrete Cosine Transform, Fast Fourier Transform and Region Properties. The promising results are obtained using FFT and Region properties. The experimental analysis, results and conclusion are discussed in sections 4 and section 5 respectively. IV. Experimental Analysis. The various experiments are conducted using DCT, FFT and spatial features like (area, major axis length, minor axis length, eccentricity, orientation, convex area, filled area, Euler number, equivalent diameter, solidity, extent and perimeter) and promising results are obtained and the results have predicted only some dominant features like eccentricity, Major axis length and FFT, which are used to classify the gender (male or female.)

Figure -2 Analysis of algorithm.

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1. 2.

Input from the database is given to the gender identification system. FFT transforms the given input and generates the output. Threshold is set to TH1. Rule is set in such a way that if the fundamental frequency (FF) is greater than TH1 the decision is female and if the FF is less than TH1 the decision will be male. Eccentricity of the given input is computed and generates the output. Threshold is set to TH2. Rule is set in such a way that if the fundamental frequency (FF) is greater than TH2 the decision is female and if the FF is less than TH2 the decision will be male. Major Axis Length of the given input is computed and generates the output. Threshold is set to TH3. Rule is set in such a way that if the fundamental frequency (FF) is greater than TH3 the decision is female and if the FF is less than TH2 the decision will be male. Comparing the decision by all the transforms, if two decisions are male, the result is announced as male and if two decisions are female, the result is announced as female.

An optimal threshold set for each transform is an important part of the gender identification process. Initially 1000 fingerprints of both male and female are examined with FFT, Eccentricity and Major Axis Length were obtained for each case. For FFT, the threshold is set as 500000 and the samples having the fundamental frequency (FF) less than the threshold is identified as male and the samples with FF greater than the threshold is identified as female. For Eccentricity, the threshold as set to 0.6 and the samples having FF less than the threshold is identified as male and the samples with FF greater than the threshold is identified as female. For Major Axis Length, the threshold is set as 250 and the samples having FF less than the threshold are identified as male and the samples with FF greater than the threshold are identified as female as per the figure 2 the analysis of algorithm and the results for combined features are shown in Table-1 and Table-2. FINGERPRINT SAMPLE 1 2 3 4 5 6 7 8 9 10

FFT THRESHOLD > 5000000 5075181.8 5910150.3 8010071.3 6269623.5 6831938.5 5668126.2 5946901 7562407.3 4968828.4 5795622.3

ECCENTRICITY THRESHOLD >0.6 0.8495 0.84051 0.66843 0.60265 0.77789 0.6764 0.6764 0.88811 0.59602 0.86555

MAJOR AXIS LENGTH YHRESHOLD >250 269.22541 261.21347 277.39943 253.10256 273.74287 256.03259 257.11555 250.47639 246.66551 273.17137

Table -1: Results of FFT, Eccentricity, Major Axis Length for female samples FINGERPRINT SAMPLE 1 2 3 4 5 6 7 8 9 10

FFT THRESHOLD > 5000000 4733106.5 1996323.8 4845580.2 2570111.5 4844242.1 4535371.9 5337723.4 4054619.6 4626589 5325927.6

ECCENTRICITY THRESHOLD >0.6 0.5356 0.28955 .25252 0.26499 0.44792 0.19157 0.81617 0.09282 0.19163 0.50575

MAJOR AXIS LENGTH THRESHOLD >250 249.15157 244.83074 244.98633 239.58606 247.83936 129.75999 260.24727 240.87762 245.95536 240.83456

Table-2: Results of FFT, Eccentricity, Major Axis Length for male samples

V. Conclusion and Future Work Fingerprint evidence is undoubtedly the most reliable and acceptable evidence till date in the court of law. Due to the immense potential of fingerprints as an effective method of identification, an attempt has been made in the present work to analyze their correlation with gender of an individual. In this work gender identification is carried out by combined features using FFT, Eccentricity and Major Axis Length for 450 male samples and 550 female samples of good quality. Left thumb impression of each sample of the internal database is tested. An optimal threshold is chosen to achieve better results. It is found that the proposed algorithm produces accurate decision of 80% of male and 78 % of female. In future, the work will be extended to build robust algorithm for frequency domain and region properties to find different parameters (like age group, Rural, Urban people) and different features it can be applied in gender classification which will be more accurate and suitable for all types of applications. AIJRSTEM 13-212; ÂŠ 2013, AIJRSTEM All Rights Reserved

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

13.

References

Davide Maltoni, Dario Maio, Anil K. Jain and Salil Prabhakar, "Handbook of fingerprint recognition" Springer-Verlag, 2003. L.C. Jain, U. Halici, I.Hayashi, S.B. Lee, S. Tsutsui, "Intelligent Biometric Techniques in Fingerprint and Face Recognition", by CRC Press LLC, 1999. RNCOS's Market Research Report “World Biometric Market Outlook (2005-2008)”, 22nd March 2006 (IT Market Research) Ahmed Badawi, Mohamed Mahfouz, Rimon Tadross, Richard Jantz, “Fingerprint-Based Gender Classification.” Biomedical Engineering department, University of Tennessee Knoxville. Manish Verma and Suneeta Agarwal.’’ Fingerprint Based Male-Female Classification.’’ in Proceedings of the international workshop on computational intelligence in security for information systems (CISIS’08), Genoa, Italy, 2008, pp.251-257 Jen feng wang, et al, “Gender Determination using Fingertip Features”. Internet Journal of Medical Update 2008 Jul-Dec;3(2):228. Angela Bell, “Loop ridge count differences between genders”. Nebraska Wesleyan University.( http://www.neiai.org/) Dr. Prateek Rastogi, Ms. Keerthi R Pillai “A study of fingerprints in relation to gender and blood group” J Indian Acad Forensic Med, 32(1), pp-11-14 ISSN 0971-0973 Gnanasivam .P, and Dr. Muttan S, “Fingerprint Gender Classification Using Wavelet Transform and Singular Value Decomposition”. European Journal of Scientific Research ISSN 1450-216X Vol.59 No.2 (2011), pp.191-199 Gnanasivam .P, and Dr. Muttan S, “Gender Identification Using Fingerprint through Frequency Domain analysis” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012 Ritu Kaur and Susmita Ghosh Mazumdar, “Fingerprint Based Gender Identification using Frequency Domain Analysis”. International Journal of Advances in Engineering & Technology, March 2012.©IJAET ISSN: 2231-1963 Ritu Kaur and Susmita Ghosh Mazumdar, Mr. Devanand Bhonsle,“A Study On Various Methods of Gender Identification Based on Fingerprints”. International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Volume 2,Issue 4, April 2012 Rijo Jackson Tom, T.Arulkumaran , “Fingerprint Based Gender Classification Using 2D Discrete Wavelet Transforms and Principal Component Analysis”. International Journal of Engineering Trends and Technology, Volume 4 Issue 2,2013

VI. Acknowledgments This research work is supported by University Grants Commission, SWRO Bangalore. (MRP(S)-162/12-13/KAMY022/UGC-SWRO Dated 29-03-2013). Authors would like thank to Dr. Suresha, Professor DOS in Computer Science University of Mysore for guiding this work.

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

Available online at http://www.iasir.net Paper ID #: AIJRSTEM 13-213

ISSN (Print): 2328-3491, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629

Paper ID #: AIJRSTEM 13-213

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)

Analyze and Design Of Green Computing In IT Organization Performance A.Seenuvasan1 and J.R.Arunkumar2 Computer Science and Engineering Dhaanish Ahmed College of Engineering, Anna University Chennai-601 301, Tamilnadu, India Abstract: Till now, green computing research has mostly relied on few, short-term power capacity to illustrate the energy use of undertaking computing. This paper brings new and inclusive power datasets through power and deployment of the IT systems in the academic building. In this paper we collected some power data from many individual computing devices like CPU, MONITER, PRINTER etc., and have monitored a subset of CPU and network loads. This intense, long-term monitoring allows us to project the data to a detailed breakdown of temperature and electricity use across the building’s computing systems. Our datasets endow with a chance to examine theory universally made in green computing. We show that power inconsistency both between parallel devices and over time for a particular device can lead to cost or savings approximately that are off by 12-17%. Extending the reporting of calculated devices and the duration considerably reduces temperature and electricity by Grid switch mode power supply (C-SMPS). Finally, our occurrence with collecting data and the subsequent analysis lead to a better understanding of how one should go about power classification revision. Keywords: Green computing; AC to DC converter; Centralized SMPS; Distributed power; Reduce conversion losses

I. Introduction Common sense tells us that there are prospect to reduce the energy waste of computing systems. Our electric power system was designed to move central station alternating current (AC) power, via high-voltage transmission lines and lower voltage distribution lines, to organization and businesses that used the power in incandescent computers, laptops, printers, lights other Alter native current devices. Today‟s end user apparatus and tomorrow‟s distributed renewable making requires us to reorganize this model. Electronic devices (such as Personal computers, laptops, changeable speed drives, and business appliances and equipment) need direct current (DC) input. However, all of these DC devices require conversion of the building‟s AC power into DC for utilize, and that conversion classically uses incompetent rectifiers. Moreover, spread renewable production (such as rooftop solar, wind) produces DC power but must be converted to AC to tie into the building‟s electric organism, only later to be re-converted to DC for many end uses. These AC-DC conversions (or DC-AC-DC in the case of rooftop solar) result in substantial energy losses. One possible solution is a centralized DC convertor in DC power grid, which is a DC grid within a organization that minimizes or eliminates entirely these conversion losses. In the DC power grid system, AC power converts to DC when entering the DC grid using a high-competence rectifier, which then allocates the power in a straight line to DC equipment served by the DC grid. On typical, these systems trim down AC to DC conversion losses from an average loss of about 32% down to 10%. From individual converter and other distributed DC generation can be fed directly to DC equipment, via the DC power grid, without the double conversion loss (DC to AC to DC), which would be necessary if the DC production output was fed into an AC system. Evaluate new green computing solutions remains vastly anecdotal. Until now, power characterization studies have either collected data at the macro scale of a whole building, lumping all plug loads into one number, or at the micro scale from a handful of computers and LCD monitors. Data at the macro scale is informative but difficult to act upon – it does not provide visibility into the computing components that can be made more energy efficient. Power data at the micro scale is great at providing a detailed characterization of single device. But fail to show how the individual data point relates to the full building energy use. The green computing research community can benefit from the availability of more extensive power measurements. For example, a single PC power measurement from 2004 has been used in papers as recent as 2010, citing it as a AIJRSTEM 13-213; © 2013, AIJRSTEM All Rights Reserved

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representative value. The aforementioned paper gives the power draw of a 2002 Dell 2350 1.8 Ghz computer as 60 to 85 watts. A 2009 paper measured two desktops (102 and 72 watts, correspondingly) and said their capacity were consistent with prior data, citing. Later the same year, a characterization study used 100 watts per desktop plus LCD for some of its calculations, citing. In 2010, Light Green also referenced, stating that the typical PC draws 80–100 watts when active. The paper goes on to measure one PC (95W active) and uses it to calculate potential energy saving of their proposed solution. If we were to continue on citation trails like the one above, we risk using limited and possibly outdated data for new systems‟ evaluation. Fast-paced improvements in personal computing mean that some newer, more powerful PCs are also more power-hungry than the 60- to 100watt range. In addition, enterprise environments are often heterogeneous and it is beneficial to have power measurements from a larger selection of devices. This paper helps fill the power data gap by characterizing energy data at the individual– and the building–scale levels. The answers to these questions from the fundamental contributions of this paper: Detailed examination of where energy goes reveals that over 50% of the electricity is spent on converter AC to DC converter. PC‟s account for 17% of the bill despite the fact that their utilization is very low. Networking equipment comes at 3.5% and shows no temporal changes despite variations in traffic load. Data analysis shows that estimating saving based on a few isolated desktop measurements is prone to errors due to the wide spread of PC power draws. Assuming that a day of power is representative and using it to calculate yearly values can be off by as much as 20%. Our deployment and data studies expose the relative importance of device coverage versus duration of deployment. Once a deployment is past the first month of data collection, one must prioritize the „what to measure‟ question over the time scale of the study. The rest of this paper reviews the current state of green computing data before diving into the analysis of the Power grid datasets. Along the way, it confirms or refutes a number of anecdotal observations, stressing the need for empirical data. The paper closes with guidelines for the design of future energy characterization studies. II. Background Up until recently, the green computing community has had to rely on limited energy datasets, requiring researchers to make various explicit and implicit assumptions about the energy behavior of computing systems. This section discusses some of the different ways in which related work has procured, used, and analyzed power data in the context of evaluating systems‟ research. At the end of the section, we formulate four common assumptions made in the context of green computing. A modeling approach that takes system subcomponents into consideration was used SMPS in CPU. Instead of collecting measurement with a meter, the authors use hardware components power models and software counters to calculate the power draw of a PC. This methodology was able to predict the power use of one machine based on a different one with 20% accuracy, indicating that even more sophisticated techniques that take device subcomponents into consideration will show error in estimation when assuming that similar equipment has similar power or usage behavior. The industry drive toward less important, lighter and more resourceful electronics has led to the development of the Switch Mode Power Supply (SMPS). There are several topologies commonly used to put into service SMPS. This submission note, which is the primary of a two-part sequence, explains the essentials of unusual SMPS topologies. Applications of unusual topologies and their pros and cons are also thrash out in aspect. This application note will channel the user to decide on an suitable topology for a given function, while given that useful in sequence regarding selection of electrical and electronic mechanism for a given SMPS design. A. Advantages Reliability; security; storage; distributed generation energy efficiency; sustainability; renewable inputs IT/communications leverage/full cyber-security load awareness; demand side management; plug-in vehicles lowering unnecessary barriers to achieving the above Each of these goals can be advanced through the use of DC micro grids and often at Lower cost with greater effectiveness than measures applied to the greater AC grid. The national power grid system in the India and around the world was not designed to handle the energy demands of the modern economy. To meet the contemporary Needs of the grid‟s organizations today, we should consider the tools available through DC micro grids, which can optimize the use of electronic devices, electrical storage, and distributed generation.

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III. Measuring Computing Power Accurately The prior section pointed out four common assumptions in measuring computing power which can lead to inaccurate results. The results from Powernet, however, represent only one point in time. As computing continues to evolve, green computing. Need to periodically re-measure energy consumption and waste. This raises the follow-up question: „Given limited time, money, and effort, how should one measure computing system energy consumption in order to minimize error? ‟ This section presents methodology considerations to guide future green computing research. A. Characterization Our electric power system was designed to move central station alternating current (AC) power, via highvoltage transmission lines and lower voltage distribution lines to Organization Computer equipments. Today‟s organizations equipment and tomorrow‟s distributed renewable generation requires us to rethink this model. Electronic devices (such as computers, florescent lights, variable speed drives, and many other household and business appliances and equipment) need direct current (DC) input. However, all of these DC devices require conversion of the building‟s AC power into DC for use, and that conversion typically uses inefficient rectifiers. Moreover, distributed renewable generation (such as rooftop solar) produces DC power but must be converted to AC to tie into the building‟s electric system, only later to be re-converted to DC for many end uses. These AC-DC conversions (or DC-AC-DC in the case of rooftop solar) result in substantial energy losses. One of possible solution is a DC micro grid, which is a DC grid within a building (or serving several buildings) that minimizes or eliminates entirely these conversion losses. In the DC micro grid system, AC power converts to DC when entering the DC grid using a high-efficiency rectifier, which then distributes the power directly to DC equipment served by the DC grid. On average, this system reduces AC to DC conversion losses from an average loss of about 32% down to 10%.2 In addition, roof top photovoltaic (PV) and other distributed DC generation can be fed directly to DC equipment, via the DC micro grid, without the double conversion loss (DC to AC to DC), which would be required if the DC generation output was fed into an AC system. B. DC micro grid A micro grid possesses independent controls, and intentional islanding takes place with minimal service interruption. These two definitions work easily in both the AC and DC domain, so we will borrow them both. DC micro grids can be deployed in a portion of a building, building-wide or covering several buildings. We will refer to these systems as “DC micro grids” in the balance of this paper. IV. Architecture for Centralized SMPS AC/DC Converter The architecture of the proposed system describes the centralized power architectures can get quite complex and specialized, most are either derived from or combinations of four basic configurations. Figure 1. Centralized SMPS AC/DC Converter

DC / DC Converter DC / DC Converter Centralized SMPS AC/DC

DC / DC Converter DC / DC Converter

Centralized power, such as an approach, represents a converse technique. In a distributed power system the system's power requirements are allocated to a number of smaller power processing units which are then distributed throughout the system in a variety of architectures, usually with the intent of bringing power processing closer to where the power will be used. While the ultimate extension of this concept is the "on-card" regulator or power supply, many other solutions for distributing power processing tasks are common. Before

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discussing the various architectures, however, it is helpful to understand the motivation for considering distributed power. Distributed Power Architectures While distributed power architectures can get quite complex and specialized, most are either derived from or combinations of four basic configurations which are shown in Figure 2. These are: parallel, series, split source, or split load. It should be recognized that in addition to different inter connections, each of these approaches represents a solution to a different Set of objectives. A description of these architectures and their characteristics is given below: Paralleling Paralleling power modules infers a common source and load. This usually means retaining a Central location where a single high power supply is replaced with a grouping of paralleled lower power modules. While the power processing is distributed, it may not be distributed very far. Parallel connections are often generated by the need for standardization and redundancy rather than reducing distribution losses. Figure 2: Paralleling with Current Mode Control Source

Load

Power Stage

REF

With higher reliability as an objective, equalizing stresses by insuring load sharing between modules is usually required. Configuring power converters for current sharing when paralleled is not a trivial problem but ICs for that purpose as well as the use of current-mode control methods provide ready solutions. It should be noted that most approaches to equal current sharing require at least one more interconnection between modules in addition to the common source and load connections. Figure 3 shows current mode control where the output of the voltage sensing error amplifier is used by the PWM modulator to control output inductor current. By using a single error amplifier to control all the paralleled modulators, equal currents from each module can be assured. Load splitting The most common understanding of distributed power assumes split loads Where different portions of the system are each powered by their own power processing unit. An illustration of load splitting is shown in Figure 6. Note that this gives innumerable options in terms of dividing up the loads and the corresponding requirements on the power processing units. Figure 3 -Splitting Loads CHARGER Backup Battery Critical

Load

DC/DC Distribution Bus

DC/DC

The final step is to take the set of biased dimensions and extrapolate to whole system power. Our familiarities with Power net have decorated the need for data additional than power and consumption measurements. If AIJRSTEM 13-213; ÂŠ 2013, AIJRSTEM All Rights Reserved

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extrapolation is to be successful, one also wants metadata in the form of equipment inventories and descriptions. Surprisingly, such metadata is not nearly as complete and readily available as we had hoped. Rather, we had to resort to indirect sources such as cross-correlating networked device registrations with active IPs on the network. In the future, green computing researchers should encourage IT personnel to keep updated and detailed records of what equipment is added to a building. V. Conclusion Characterizing the energy use of organization computing systems is the first step toward identifying opportunities for improvement. Extensive, empirical data allow researchers to better quantify the problems they are tackling and the potential impact of their proposed solutions. Power net has provided such data and has shed light on some of the assumptions that we make when faced with the lack of solid measurements. Despite our best attempts to cover as many computing systems for as long as possible, the Power net data remain but a single study. While the exact breakdown of energy use and waste might shift from building to building, the overarching methodology and data analysis lessons remain. Going forward, green computing research has not only a reference dataset to use but also a blueprint for how to characterize enterprise building power given limited time and resources. The DC power grid concept represents a decentralization of the idea of the grid, and one that advances the goals of the current Smart Grid overhaul. The DC power grids begins to change the paradigm from a centralized generation and distribution system of power delivery to a system that is more flexible and more accommodating of the load that has come to be: one that is more electronic, more ubiquitous, and more essential to our economy and our culture. DC power grids can create power systems that are more efficient and more compatible with the fastest growing segment of the load today: electronic devices. In turn, by catering to the needs of digital devices, we naturally expand the networks in which they operate (both power and control) to benefit from – or indeed require – redundant operation that is primarily available today through the other ubiquitous DC device, the battery. VI. [1] [2] [3] [4] [5] [6]

References

Maria Kazandjieva, Brandon Heller, Omprakash Gnawali, Philip Levis, and Christos Kozyrakis- Green Enterprise Computing Data: Assumptions and Realities, 978-1-4673-2154-9/12/$31.00 c 2012 IEEE. Mohammad Kamil, Microchip Technology Inc.– Switch Mode Power Supply (SMPS) Topologies, 2007 Microchip Technology Inc. Paul Savage, Robert R. Nordhaus, and Sean P. Jamieson, Analyses written at the request of REIL, yale school of forestry & environmental studies. Single Phase Multifunction Energy Metering IC with di/dt Input. Department of Energy, Annual Energy Review, October 2011. Y. Agarwal, S. Hodges, R. Chandra, J. Scott, P. Bahl, and R. Gupta. Somniloquy: Augmenting Network Interfaces to Reduce PC Energy Usage. In Proc. Networked Systems Design and Implementation, 2009.

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

Design of a stir casting machine K. Sekar, Allesu K. and M.A. Joseph Department of Mechanical Engineering, National Institute of Technology, Calicut, Kerala 673601, India ABSTRACT: Non-homogeneous particle distribution is one of the greatest problems in casting of metal matrix composites (MMCâ&#x20AC;&#x2122;s), because of ceramic materials have different density, melting point and boiling point. But other light materials like Aluminium, copper and magnesium etc, have less density of melting point and boiling point, so ceramic particle mixing is very difficult to light materials. This paper deals with design the solid model components of stirrer assembly stand ,screw rod, stirrer rod, nano/micro particle preheater, base stand, furnace stand, electronic assembly stand, electric furnace, heating vessel, path way pipe with heater and also, cooling die and die stand are made by solid model with require dimensions to design and fabricate of stir casting machine. In this machine the nano particle preheater attached in the top of the furnace, because of red hot condition with constant temperature of nano particle injected by push rod into the molten metal. The stirrer rod designed by variation of speed (0-2000rpm) for mixing purpose. We can set any constant rpm or any variation rpm at any time. Here avoid the non wettability of particles or floating or settling of particles. After mixed molten metal transfer into the mould with constant temperature through taper pathway heater pipe into the die .From this cast part improve all the mechanical properties without casting defects Key words: nano particle preheater, stirrer rod, electric furnace, pathway pipe heater, die stand, die. I.

Introduction

Metal matrix composites (MMC) are a range of advanced materials providing properties have not achieved by conventional materials. This stir casting material properties increased strength, high elastic models, higher service temperature, improve wear resistance, decreased part weight, low thermal shocks, high electrical and thermal conductivity, and low coefficient of thermal expansion compound compared to conventional metal and alloys, [1] the excellent mechanical properties of these materials and the relatively low producing cost make them very attractive for a variety of application in automotive and aerospace industries. Technology for electromagnetic stirring of aluminium Reverberatory Furnaces[2] the simplicity of design the capital costs for installation of the equipment can be lower than a water-cooled stirring technology, with the solid design of the stirrer and specially designed control and drive system, low operating costs through low energy consumption can be achieved

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A study of published works showed that design of newly fabricated tribological machine for wear and frictional experiments under dry/wet condition [3], the operating parameters had a significant effect on wear, frictional and interface temperature behaviour of the composite the increase in applied load and or sliding distance increased the weight loss and frictional coefficient Report on design analysis of an electrical induction furnace for melting aluminium scrap [4], the induction furnace design and subsequently its fabrication should be promoted considering the abundant power sources, less maintenance cost and labour requirements. A study of published works showed that design and simulation of component-based manufacturing machine systems [5], the design and building component-based manufacturing systems can dramatically reduce machine/system development lead-times and provide proof-ofconcepts. Results on design and construction of an electrical furnace to fire ceramic product [6], the furnace has automation facility with efficiency of 77%. From the published works, Stir casting methods when used to make the A356/Al2O3 micro and nano composites [7] revealed that the nano composites exhibited better properties in terms of compressive strength, hardness with reduced porosity. Influence of stirring speed and stirring time on distribution of particles in cast metal matrix composite was demonstrated by S. Balasivanandha prabu et.al [8]. From the results, higher the stirring speed and time better the distribution of particles and increase in hardness. From the literature [9], stir cast components are showed with superior mechanical properties, fine microstructure and minimal porosity. The important challenges for design of stir casting machine is analysed in this study. In this machine, all the casted parts show even particle distribution and also increase all the mechanical properties. However the present work elaborates the mechanical property changes in the casting part.

II. Design of stir casting machine components

(a)Stir assembly cover

(b) Stir assembly bottom cover

(d) Stirrer assembly stand Figure 2:

The solid model of stirrer assembly with cover length of 605 mm, width 150 mm, thickness 10 mm, height 230 mm and stirrer rod hole diameter 28 mm are shown in Figure 2a. The stirrer assembly bottom cover length is 625 mm, width 170 mm, thickness 22 mm. Stirrer rod hole diameter is 25 mm is shown in Figure 2b. Screw rod height 1660 mm, diameter vary from 25 mm to 30 mm are shown in Figure 2c. Figure 2d shows stirrer assembly stand with height 1190 mm, width 490 mm, thickness 40 mm. These four components are made of mild steel. This stirrer assembly cover for the purpose of keeping the stirrer motor and stirrer rod. Screw rod for up and down movement of stirrer rod into molten metal for mixing purpose and also stirrer assembly stand for supporting the stirrer assembly cover. The solid model of stirrer rod with total length of 835 mm, diameter 15 mm and bottom of stirrer blade thickness 10 mm and blade height 32 mm are shown in Figure 3a. The nano particle pre heater length 230 mm, diameter 440 mm, top cover diameter 100 mm, and centre push rod holes diameter 17 mm, bottom pipe channel AIJRSTEM 13-214; ÂŠ 2013, AIJRSTEM All Rights Reserved

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diameter 17.5 mm and length 125 mm are shown in Figure 3b. The nano particle push rod wood handle length 110 mm and length of the rod 335 mm at the bottom of sharp angle at 60á´źare shown in Figure 3c.

(a)Stir rod

(b) Nano particle pre heater

Figure 3: These three components are made of AISI 310 grade heat resistant and corrosion resistant steel material. This stirrer rod is used for mixing purpose of liquid metal with solid nano particles. Nano particle pre heater is used for heating the nano particle at different temperature and nano particle push rod is used to push the heated nano particle into liquid metal with uniform flow of nano solid particles are shown in Figures 3b and 3c.

(a) base stand

(b) furnace stand Figure 4:

The 3D solid model of the base stand component having length 710 mm, height 220 mm, and with 500 mm is shown in figure 4a. Similarly, the solid model of furnace stand length 1000 mm and width 410 mm and thickness 75 mm is shown in figure 4b. These two stands were made in mild steel. This base stand used for supporting the furnace.

(a) Electronic assembly stand

(b) Heating vessel cap

Figure 5:

The solid model of electronic assembly stand length 610 mm, width 310 mm and thickness 10 mm is shown in figure 5a. Heating vessel cap outer diameter 220 mm, inner diameter 115 mm, thickness 20 mm and 10 mm diameter holes in 8 numbers on the top of the vessel cap are shown in figure 5b. The electric furnace with length 406 mm, height 330 mm, and inner vessel diameter 115 mm is shown in figure 5c. These three components have

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made in mild steel. This electric assembly stand used for supporting the electronic control panel, heating vessel cap for grip the heating vessel in centre axis of the furnace.

(a) Heating vessel

(b) pathway channel with heater Figure 6:

The solid model of heating vessel having length 206 mm, outer diameter 116 mm and bottom pouring valve diameter 20 mm are shown in Figure 6a. The pathway channel pipe with length of 330 mm and diameter of pipe is 35 mm, with a heater length of 250 mm and diameter 150 mm in the heater. Pathway channel top cover of outer diameter 250 mm, inner diameter 116 mm and 10 mm diameter with three holes are shown in Figure 6b. These two components are made with AISI 310 grade heat resistant and corrosion resistant steel material. The vessel has been heated for producing the molten metal and pathway channel pipe with heater is used for transferring the molten metal into the die with constant temperature of liquid metal.

(a) Electronic assembly cover

(b) Heater assembly cover

Figure 7: The solid model of electronic assembly cover with length 470 mm, width 310 mm, and thickness 10 mm is shown in Figure 7a. Numbers of control switches are connected in the electronic assembly cover are also shown in this figure. The heater assembly cover length 445 mm and length 370 mm, width 370 mm and centre hollow cap width 170 mm are shown in Figure 7b. These two components have made in mild steel. This electronic assembly cover to keep all the electronic control switches inside the cover. And also, the heater assembly cover to keep the nano/micro particle preheating control switch and pathway channel pipe heater control switch.

(a) Cooling die

(b) without cooling die

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The solid model components of cooling die length 260 mm, outer diameter 110 mm, inner diameter 46 mm, water inlet valve diameter 10 mm are shown in Figure 8a.Without cooling die length 260 mm, outer diameter 110 mm, and inner diameter 46 mm are shown in Figure 8b. Die stand length 300 mm, height 690 mm, thickness 130 mm, centre hollow slot length 130 mm, width 105 mm are shown in Figure 8c. Cooling die is made in mild steel material. The die stand is made up of mild steel. This stand is to keep and grip the die for different casting technique.

III. Parts list of complete stir casting machine The following table (Table 1) lists the complete components involved for design of the stir casting machine Table 1: Serial NO:

Name of the component

Material

Quantity

Stir assembly cover

Mild steel

Stir assembly bottom cover

Mild steel

Screw rod

Mild steel

Stirrer assembly stand

Mild steel

Stir rod

310 steel

Nano particle pre heater & injector

310 steel

Nano particle push rod

310 steel

Base stand

Mild steel

Furnace stand

Mild steel

Electronic assembly stand

Mild steel

Electric furnace cover

Mild steel

Heating vessel

310 steel

Pathway channel with heater

310 steel

Electronic assembly cover

Mild steel

Heater assembly stand

Mild steel

Split die

Die steel

Die and squeeze cylinder stand

Mild steel

IV. Assembly of stir casting machine

Figure 7: Assembled view of the stir casting machine.

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The figure 7 shows the assembled view of the stir casting technique machine and the corresponding parts numbers with its materials used for design the machine are listed in the table 1.

V. Design analysis and calculations 1. Stirrer speed Rotation is sensed by micro controller through proximity switch mounted near the stirrer shaft. Rotation every second is multiplied for minute and indicated in digital display. Its speed varies from 0 -2000 rpm 2. Die design Material of the die Die height and diamter A356 Al Alloy density Volume of die (v)

= Mild steel/ Die steel/ Stainless steel = 260 mm and 46 mm = 2.67 g/cc = π/ 4 x D2 x h x ρ = π /4 x (4.6)2 x 26 x 2.67 = 1154 gram 3. Nano particle pre heater design Material = AISI 310 grade heat and corrosion resistant steel Preheater length and diamter = 170 mm and 46 mm Al2O3 nano particle density = 3.9 g/cc Volume of pre heater (V) = π/4 x D2 x h x ρ = π/4 x (4.6)2 x 17 x 3.9 = 1102 gram 4. Heating vessel design Material of the vessel = AISI 310 grade heat and corrosion resistant steel Length of the vessel = 160 mm Diameter of the vessel = 105 mm A356 Al Alloy density = 2.67 g/cc Volume of vessel (V) = π /4 x D2 x h x ρ = π /4 x (10.5)2 x 16 x 2.67 = 3699 gram VI. Conclusions 1. In this study, the design of stir casting machine was successfully designed for the light materials like aluminium, magnesium and copper to melt and for getting cast part with higher mechanical properties. 2. In this machine the nano particle pre heater attached in the top of the furnace, because of red hot condition with constant temperature of nano particle injected by push rod into the molten metal. The stirrer rod designed by variation of speed (0-2000rpm) for mixing purpose. 3. Here avoid the non wettability of particles or floating or settling of particles. After mixed molten metal transfer into the mould with constant temp through taper pathway heater pipe into the die .From this cast part improve all the mechanical properties without casting defects 4. In this design, pour the molten metal into the die with constant temperature before start the crystal growth. Because the furnace and the die connected through the pathway taper pipe channel with heater. The molten metal transfers into the die with constant temperature before start the crystal growth. The stir casting machine has been used for trial product of A356/Al2O3 nano particle composite materials. References [1] S.A. Sajjadi, H.R. Ezatpour, H. Beygi, “Microstructure and mechanical properties of Al Al2O3 micro and nano composites fabricated by stir casting”. In:proceedings of 14th national conference on Journal of materials science and engineering ,Tehran,Iran, 2010, 32532. [2] Alan Peel Ceng, James Herbert”. “Technology for Electromagnetic Stirring of Aluminum Reverberatory Furnaces”. The minerals Metals and Materials society, 2011. [3] B.F. Yousif, “Design of newly fabricated tribological machine for wear and frictional experiments under dry/wet condition”. Materials and Design, 2012. [4] K.C.Bala ,”Design Analysis of an Electrical Induction Furnace for Melting aluminium scrap”. A U J Tq, No. 2, 2005,83-88.

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JosefAdolfsson, Amos Ng, PetterOlofsgard, systems”Mechatronics, No. 12, 2002, 1239-1258.

“Design

and

Simulation

component-based

manufacturing

machine

[6] RamazanBayindir ,“Design and construction of an electrical furnace to fire ceramic product”. Journal of scientific and industrial research, No.66, 2007, 135-140. [7] S.A. Sajjadi, H.R. Ezatpour, H. Beygi, “Microstructure and mechanical properties of Al Al2O3 micro and nano composites fabricated by stir casting”. Journal of materials science and engineering A, No.528, 2011, 8765-8771. [8] S. Balasivanandha prabu, L. Karunamoorthy, S. Kathiresan, B.Mohan, “Inflence of Stirring speed and stirring time on distribution of particles in cast metal matrix composite”. Journal of Material processing technology, No.171, 2006, 268-273. [9] M.R. Ghomashchi, A. Vikhrov, “Squeeze casting: an overview”. Journal of material Processing Technology, No. 101, 2000, 1-9.

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

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

Kinetics and Mechanistic Investigation of Oxidation of Fluoroquinoline Anitibacterial Agent, Norfloxacin, by Diperiodatargentate (III) in aqueous alkaline medium. Heney T Padavathil, Surekha Mavalangi*, S.T.Nandibewoor Department of Engineering Chemistry, The Oxford College of Engineering, Bommanahalli, Bangalore-560 068 Abstract:The kinetics and mechanism of oxidation of Norfloxacin (NF) by Diperiodatoargentate (III) (DPA) in alkaline medium at constant ionic strength of 0.10 mol dm-3 was studied spectrophotometrically. The reaction exhibits 1:1 DPA:NF stoichiometry and is first order in DPA but fractional order in both NF and alkali. The order was found to be negative fractional order with respect to periodate. The main reaction products were identified as hydroxilated NF and Ag(I). A mechanism involving free radicals was proposed. In a composite equilibrium step, norfloxacin binds to DPA to form a complex that subsequently decomposes to the products. The reaction was investigated at different temperatures and activation parameters with respect to the slow step of the proposed mechanism were calculated and discussed. Keywords: Norfloxacin, Kinetics, Oxidation, Diperiodatoargentate (III), Reaction Mechanisms.

I. Introduction: Diperiodatoargentate(III) (DPA) is a powerful oxidizing agent in alkaline medium with the reduction potential[1] 1.74 V. It is widely used as a volumetric reagent for the determination of various organic and inorganic species[2,3]. Jaya Prakash Rao et al.[4,5] has used DPA as an oxidizing agent for the kinetics of oxidation of various organic substrates. They normally found that, order with respect to both oxidant and substrate was unity and [OH-] was found to enhance the rate of reaction. It was also observed that they did not arrive the possible active species of DPA in alkali and on the other hand they proposed mechanisms by generalizing the DPA as [Ag(HL)L] (x+1)-. However, Kumar[6-8] et al. put an effort to give an evidence for the reactive form of DPA in the large scale of alkaline pH. In the present investigation, we have obtained the evidence for the reactive species for DPA in alkaline medium. Norfloxacin, a fluoroquinolone, is a 1-ethyl-6-fluoro-1,4-dihydro-4-oxo-7-(1-piperazinyl)-3quinolinecarboxylic acid. Its empirical formula is C16H18FN3O3. Norfloxacin is a white to pale yellow crystalline powder with a molecular weight of 319.34 and a melting point of about 221Â°C. It is freely soluble in glacial acetic acid, and very slightly soluble in ethanol, methanol and water. Norfloxacin is a synthetic chemotherapeutic antibacterial agent[9,10] occasionally used to treat common as well as complicated urinary tract infections. It also inhibits DNA synthesis and is bactericidal[11-16]. Norfloxacin is a first generation synthetic fluoroquinolone (quinolone) developed by Kyorin Seiyaku K.K. It interacts with a number of other drugs, as well as a number of herbal and natural supplements. Such interactions increase the risk of anticoagulation and the formation of non-absorbable complexes, as well as increasing the risk of toxicity. As a result of their extensive usage, fluoroquinolones may enter the environment via wastewater effluent and biosolids from sewage treatment plants and via manure and litters from food-producing animal husbandry. The presence and accumulation of fluoroquinolone antibiotics in aquatic environments, albeit at low concentrations, may pose threats to the ecosystem and human health by inducing increase and spread of bacteria drug resistance due to long-term exposure. This necessitates development of the various advanced oxidation processes for the transformation of fluoroquinolones in water. In view of potential pharmaceutical importance of norfloxacin and lack of literature on the oxidation of this drug by any oxidant except in two cases[17,18] and the complexity of the reaction, a detailed study of the reaction becomes important. The present investigation is aimed at checking the reactivity of norfloxacin toward Diperiodatoargentate (III) and arriving at a plausible mechanism.

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II. Experiment All chemicals used were of analytical reagent grade, and double distilled water was used throughout the work. The solution of norfloxacin (Sigma-Aldrich ) was prepared by dissolving a known amount of the compound in 6.0 mL of 0.3 mol dm-3 NaOH and further diluted to 100 mL with double distilled water. The required concentration of norfloxacin was obtained from its stock solution. DPA was prepared by oxidizing Ag(I) in presence of KIO4 as described elsewhere[19,20]. The complex was characterized from its UV spectrum, which exhibited three peaks at 210, 254, and 362 nm. These spectral features were identical to those reported earlier for DPA[19]. The magnetic moment study revealed that the complex is diamagnetic. The compound prepared was analysed[21] for silver and periodate by acidifying a solution of the material with HCl, recovering and weighing the AgCl for Ag, and titrating the iodine liberated when excess of KI was added to the filtrate for IO4- . The stock solution of DPA was used for the required [DPA] solution in the reaction mixture. During the kinetics a constant concentration viz. 1.0x10-4 mol dm-3 of KIO4 was used throughout the study unless otherwise stated. Thus, the possibility of oxidation of NF by periodate was tested and found that there was no significant interference due to KIO4 under experimental condition. The total concentrations of periodate and OH- was calculated by considering the amount present in the DPA solution and that additionally added. Kinetics: The kinetic measurements were performed on a Varian CARY 50 Bio UV–Vis Spectrophotometer under pseudo first order condition where [NF] > [DPA] at 25+0.1 0C, unless specified. The reaction was initiated by mixing previously thermostatted solution of the DPA to NF solution, which also contained required concentration of KNO3, KOH, and KIO4; and the progress of reaction was followed spectrophotometrically at 360nm by monitoring decrease in absorbance due to DPA (molar absorbancy index, ‘’ to be 13900+100 dm3 mol-1 cm-1). The application of Beer’s law to DPA at 360 nm had been verified. The reaction was followed more than three half-lifes. The first-order rate constants kobs were evaluated from plots of log [DPA] versus time, and kobs values were reproducible at 360 nm. Kinetics runs were also carried out in N2 atmosphere in order to understand the effect of dissolved oxygen on the rate of the reaction. No significant difference in the results was obtained under a N2 atmosphere and in the presence of air. In view of the ubiquitous contamination of carbonate in the basic medium, the effect of carbonate was also studied. Added carbonate had no effect on the reaction rate. Fresh solutions were used during the experiments. III. Results The reaction orders were determined using the slopes of log kobs versus log (concentration) plots by varying the concentration of norfloxacin, periodate and alkali while keeping other factors constant. With fixed concentrations of NF, 1.0x10-3 mol dm-3,IO4- , 1.0x10-4 and alkali, 0.15 mol dm-3, at constant ionic strength, 0.10 mol dm-3, the DPA concentration was varied in the range of 5.0x 10-5 to 5.0x10-4 mol dm-3. All kinetic runs exhibited identical characteristics. The linearity of plots of log (absorbance) versus time, for different concentrations of DPA, indicates that the order in DPA is unity .This was also confirmed by the constant values of the pseudo first-order rate constants, kobs, for different DPA concentrations (Table 1). The NF concentration was varied in the range of 2.0x10-4 to 5.0x10-3 mol dm-3 at constant alkali, periodate and DPA concentrations and constant ionic strength of 0.10 mol dm-3 at 25 0C. The kobs values increased with increase in NF over the concentration range shown in (Fig. 3). The effect of alkali on the reaction was studied at constant concentrations of NF, periodate and DPA and a constant ionic strength of 0.10 mol dm-3 at 25 0C. The alkali concentration was varied in the range of 5.0x10-2 to 5.0x10-1 mol dm-3. The rate constant increased with increase in alkali concentration (Fig. 4), indicating a fractional-order dependence of the rate on alkali concentration. The periodate concentration was varied in the range of 5.0x10-5 to 5.0x10-4 mol dm-3. The rate constant decreased with increase in periodate concentration (Fig. 2), indicating a negative fractional-order dependence of the rate on periodate concentration. Initially added products did not have any significant effect on the rate of reaction. The order for different species is given in the following equation Rate = k [DPA]1.0 [NF]0.30 [OH-]0.1 [IO4-]-0.18

(1)

A. Stoichiometery and Product Analysis Different sets of reaction mixtures containing varying ratios of DPA to norfloxacin in presence of constant amount of OH-, KIO4 and KNO3, were kept for 3 h in closed vessel under nitrogen atmosphere. The remaining concentration of DPA was estimated spectrophotometrically at 360 nm. The results indicate that 1:1 stoichiometry for the reaction

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as given in Scheme 1. The main reaction products were identified as Ag(I) and 1-ethyl-6-fluoro-2-hydroxy-4-oxo-7piperazin-1-yl-1,4-hydro-quinoline-3-carboxylic acid identical to those obtained for the oxidation of NF with permanganate[18]. The oxidation product of norfloxacin, 1-ethyl-6-fluoro-2- hydroxy-1,4-dihydro-4-oxo-7-(1piperazinyl)-3-quinoline carboxylic acid, was isolated with the help of TLC and other separation techniques and characterized by LC-ESI-MS, FTIR, and 1H NMR spectral studies. The presence of a product was confirmed with molecular ion of m/z 335 (yield 90%) which corresponds to 1-ethyl-6-fluoro-2-hydroxy-1,4-dihydro-4-oxo-7-(1piperazinyl)-3-quinoline carboxylic acid. The IR spectroscopy shows a peak at 1731 cm-1 due to acidic C=O stretching; the peak due to ketonic C=O stretching will appear at 1644 cm-1; 3056 cm-1 is due to NH stretching of the piperzine moiety; and the broad peak at 3424 cm-1 is due to OH stretching 1H NMR (DMSO) shows singlet at 8.9 ppm due to acidic OH, and NH of piperzine moiety singlet appears in the region of 4.6 ppm and the singlet of phenolic OH at 6.6 ppm, which disappears on D2O exchange and confirms the formation of product 1-ethyl-6-fluoro-2-hydroxy-1,4-dihydro-4-oxo7-(1-piperazinyl)-3-quinoline carboxylic acid. B. Effects of ionic strength, dielectric constant and temperature The effect of ionic strength was studied by varying the NaClO4 concentration from 0.01 to 0.10 mol dm-3 at constant concentrations of DPA, NF, periodate and alkali. Increasing ionic strength had no effect on the rate constant. The effect of the dielectric constant (D) was studied by varying the t-butanol–water content (v/v) in the reaction mixture with all other conditions held constant. The rate of reaction increases with increasing t-butanol volume. The kinetics was also studied at four different temperatures with varying concentrations of NF and alkali, keeping other conditions constant. The rate constants were found to increase with increase in temperature. The rate of the slow step was obtained from the slopes and intercepts of 1/kobs versus 1/[NF] and 1/kobs versus 1/[OH-] plots at four different temperatures. The activation energy corresponding to these rate constant was evaluated from the Arrhenius plot of log k versus 1/T from which other activation parameters were also obtained (Table 2). C. Test for free radicals To test for the involvement of free radicals, acrylonitrile was added to the reaction mixture, which was then kept for 2 h under nitrogen. Addition of methanol, resulted in the precipitation of a polymer, suggesting the involvement of free radicals in the reaction. The blank experiment of reacting either DPA and NF alone with acrylonitrile did not induce polymerization under the same conditions. IV. Discussion: A literature survey[19] reveals that the water soluble DPA has a formula [Ag(IO6)2]7- with dsp2 configuration of square planar structure, similar to diperiodatocopper(III) complex with two bidentate ligands, periodate to form a planar molecule. When the same molecule is used in alkaline medium, it is unlike to be existed as [Ag(IO 6)2]7- as periodate is known to be in various protonated forms[21,22] depending on pH of the solution as given in following multiple equilibria:

(2) (3) (4)

Periodic acid (H5IO6) exists in acid medium and also as H4IO6- at pH 7. Thus, under the present alkaline conditions, the main species are expected to be H3IO62- and H2IO63-. At higher concentrations, periodate also tends to dimerise[1]. However, formation of this species is negligible under the conditions employed for this kinetic study. On the contrary, the authors[4,5] in their recent studies have proposed the DPA as [Ag(HL)2]x- in which ‘L’ is a periodate with uncertain number of protons and ‘HL’ is a protonated periodate of uncertain number of protons. This can be ruled out by considering the alternative form[21,22] of IO4- at pH>7 which is in the form H3IO62- or H2IO63-. Hence, DPA could be as [Ag(H3IO6)2]- or [Ag(H2IO6)2]3- in alkaline medium. Therefore, under the present condition, DPA, may be depicted as [Ag(H3IO6)2]-.

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The results suggest that in the prior equilibrium step 1, the [OH-], deprotonates the DPA to give a deprotonated DPA; in the second step, displacement of a ligand, periodate takes place to give free periodate which is evidenced by decrease in the rate with increase in [periodate] (Table 1). It may be expected that lower Ag(III) periodate species such as monoperiodatoargentate(III) (MPA) will be more important in the reaction than the DPA. The inverse fractional order in [H3IO62-] might also be due to this reason. In the pre-rate-determining stage, the MPA reacts with a molecule of NF to give a complex [C], which transfer one electron in slow step to give the free radical and Ag(II) species. The formation of intermediate Ag(II) species is evidenced by earlier work[6-8]. This free radical species combines with another Ag(II) species in a fast step to form hydroxylated norfloxacin compound. On the basis of square planar structure of DPA, the structure of the complex may be proposed as in Fig 1. Spectroscopic evidence for the complex formation between oxidant and substrate was obtained from UV–Vis spectra of NF, DPA, [OH-] mol dm-3 and mixture `of both. A bathochromic shift of about 4 nm from 332 to 336 nm in the spectra of DPA was observed. The rate law for the Scheme 2 could be derived as -d[DPA]

Rate =

kK1K2K3[DPA][NF][OH-]

[H3IO62-] + K1[OH-][H3IO62-] + K1K2[OH-] + K1K2K3[OH-][NF] kK1K2K3[NF][OH-]

dt kobs

[H3IO62-] + K1[OH-][H3IO62-] + K1K2[OH-] + K1K2K3[OH-][NF]

(5) (6)

The rate law can be rearranged to the following form for verification. 1 kobs

[H3IO62-] kK1K2K3[OH-][NF]

[H3IO62-] kK2K3[NF]

+ kK3[NF]

(7)

The rate law may be thus verified by the linear plots of 1/kobs versus [H3IO62-], 1/kobs versus 1/[NF] and 1/kobs versus 1/[OH-], as found in Fig. 2,3 and 4. The slopes and intercepts of such plots lead to the values of K1, K2, K3, and k as (2.50+0.15) dm3mol-1, (5.6+0.25)x10-4 mol dm-3, (3.8+1.22)x 103 dm3 mol-1, and (7.4+0.8) x10-4s-1, respectively. Using these constants the rate constants were calculated and compared with experimental values (Table 1). The experimental rate constants were in good agreement with calculated values, which fortifies the proposed mechanism. The thermodynamic quantities for the first, second, and third equilibrium steps of Scheme 2 can be evaluated as follows. The [NF], [OH-] and [H3IO62-] as given in Table 1 were varied at four different temperatures. The values of K1, K2 and K3 are given in Table 3. A van’t Hoff’s plot was made for variation of K1 with temperature (i.e., log K1 vs. 1/T) and the values of enthalpy of reaction H, entropy of reaction S, and free energy of reaction G298, were calculated (Table 4). Similarly the thermodynamic quantities for the second and third equilibrium steps were also calculated (Table 4). A comparison of the latter values with those obtained for the slow step of the reaction shows that these values mainly refer to the rate limiting step, supporting the fact that the reaction before the rate determining step is fairly slow and involves high activation energy [24,25]. The moderate values of H and S were both favorable for electron transfer processes. The value of S that is within the expected range for radical reactions has been ascribed to the nature of electron pairing and unpairing processes and the loss of degrees of freedom formerly available to the reactants upon the formation of a rigid transition state [26] . The negative value of S indicates that complex (C) is more ordered than the reactants [27,28]. The observed modest enthalpy of activation and a higher rate constant for the slow step indicates that the oxidation presumably occurs via an inner-sphere mechanism [29,30]. This conclusion is supported by earlier observations. V. Conclusion: Among various species of DPA in alkaline medium, [Ag(H2IO6)(H3IO6)]2- is considered as active species for the title reaction. It becomes apparent that in carrying out this reaction, the role of the reaction medium is crucial. The

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overall mechanistic sequence described here is consistent with product studies, mechanistic studies and kinetic studies.

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. 26. 27. 28. 29. 30.

References B. Sethuram (2003) Some Aspects of Electron Transfer Reactions Involving Organic Molecules, Allied Publishers (P) Ltd, New Delhi:151. P. K. Jaiswal, K. L. Yadav (1970) Talanta 17:236–238. P. K. Jaiswal (1972) Analyst 1:503–506. P. Jayaprakash Rao, B. Sethuram, T. Navaneeth Rao (1985) React. Kinet. Catal.29:289–296. K. Venkata Krishna, P. Jayaprakash Rao (1998) Indian J. Chem. A 37:1106. Kumar, P. Kumar, P. Ramamurthy (1998) Polyhedron 18:773–780. Kumar, P. Kumar (1999) J. Phys. Org. Chem. 12:79–85. Kumar, P. Vaishali Ramamurthy (2000) Int. J. Chem. Kinet. 32:286–293. Nelson, JM.; Chiller, TM.; Powers, JH.; Angulo, FJ. (2007) Clin Infect Dis 44 (7): 977–80 Padeiskaia, EN. (2003) Antibiot. Khimioter 48 (9): 28–36 Drug Information 88 (1988) Authority of the Board of Directors of the American Society of Hospital Pharmacists: Bethesda, MD, p 415. J. B. Lippioncott (1988) Drug Facts and Comparisons, Philadelphia,p 1610. Lawrenson, R. A.; Logie, J. W. (2001) J. Antimicrob. Chemother. 48:895–901. Bhaumik, A. (1997) Ind. Vet. J. 74: 246–247. Sumano, L. H.; Ocampo, C. L.; Brumbaugh, G. W.; Lizarraga, R. E. (1998) Br. Poult. Sci. 39:42–46. Domurado, D.; Balazuc, A.-M.; Lagranderie, M.; Pescher, P.;Chavarot, P.; Roseeuw, E.; Coessens, V.; Stern, S.; Schacht, E.; Marchal, G. (2005) J. Controlled Release 101:343–345. Nanda, N.; Mayanna, S. M.; Gowda, N. M. M. (1999) Int. J. Chem. Kinet. 31:153–158. Praveen N. Naik, Shivamurti A. Chimatadar, and Sharanappa T. Nandibewoor (2009) Ind. Eng. Chem. Res.,48:2548–2555. G. L. Cohen, G. Atkinson (1964) Inorg. Chem3:1741–1743. V. Tegginmath, C. V. Hiremath, S. T. Nandibewoor (2007) J. Phys. Org. Chem. 20:55–64. G. H. Jeffery, J. Bassett, J. Mendham, R. C. Denney (1996) Vogel’s Textbook of Quantitative Chemical Analysis, 5th edn, Longmans Singapore Publishers Ltd, Singapore,391:467. E. Crouthamel, H. V. Meek, D. S. Martin, C. V. Banus (1949) J. Am. Chem. Soc. 71:3031–3035. E. Crouthamel, A. M. Hayes, D. S. Martin (1951) J. Am. Chem. Soc.73:82–87. Rangappa KS, Raghavendra MP, Mahadevappa DS, Channegouda.D.(1998) J Org Chem.,63:531. Bilehal DC, Kulkarni RM, Nandibewoor ST.(2001) Can J Chem.79:1926. Walling C (1957) Free Radicals in Solution. Academic Press, New York, p 38. Rangappa KS, Anitha N, Madegouda NM. (2001) Synth React Inorg Met Org Chem. 31:1499. Bugarcic ZD, Nandibewoor ST, Hamza MSA, Heimemann F, van Eldik R.(2006) Dalton Trans, 2984. Hicks KW. (1976) J Inorg Nucl Chem. 38:1381. Farokhi SA, Nandibewoor ST. (2003) Tetrahedron, 59:7595.

APPENDIX According to Scheme 2 The rate law for the Scheme 2 could be derived as kK1K2K3[DPA][NF][OH Rate =k[C] = ] [H3IO62-] [DAP]T =

[DPA]f + [Ag (H3IO6)(H2IO6)2- + [Ag (H2IO6)(H2O)2 + [C] KK 1KK 2[OH ]1 2[OH ]

[DPA]f [ 1+ k1[OH-] +

[H3IO62-]

(I)

K1K2K3[OH-][NF] [H3IO62-]

where T and f are refer to total and free concentrations

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[DPA]T [H3IO62- ] [DAP]f =

[H3IO62-]

(II)

+ K1[OH ][H3IO6 ] + K1K2[OH ] + K1K2K3[OH ][NF]

[OH ]f + [Ag (H3IO6)(H2IO6)2- + [Ag (H2IO6)(H2O)2 ]

[OH]T =

K1K2 [OH-][DPA] -

[OH ]f + K1[DPA][OH ] + +

[H3IO62-]

In view of the low concentration of [DPA] and [H2IO62-] used: [OH]T = [OH]f

(III)

K1K2 K3 [NF] [DPA] [OH-]

Similarly [AMP]T = [AMP]f + [C] = [AMP]f +

2[OH-] [H3IO6 ] In view of low concentration of [DPA], [OH-] and [H3IO62-] used :

[NF]T = [NF]f

(IV)

Substituting the Eqn (II-IV) in Eqn (I) and omitting the subscripts T and f, we get kK1K2K3[DPA][NF][OH-]

-d[DPA] Rate =

Rate =

[H3IO62-] + K1[OH-][H3IO62-] + K1K2[OH-] + K1K2K3[OH-][NF]

(V)

Table 1. Effect of [DPA], [NF], [OH-] and [IO4- ] on the oxidation of Norfloxacin by Diperiodatoargentate(III) in aq.alkaline medium at Temp 25 oC; I=0.25 mol dm-3 [DPA] x 104 (mol dm-3) 0.5 0.8 1.0 2.0 5.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

[NF] x103 (mol dm-3)

1.0 1.0 1.0 1.0 1.0 0.2 0.5 1.0 2.0 5.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

[OH-] (mol dm-3)

0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.05 0.08 0.15 0.2 0.5 0.15 0.15 0.15 0.15 0.15

[IO4-] x104 (mol dm-3)

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.5 0.8 1.0 3.0 5.0

k x104 (s-1) Found

Calculated

5.10 5.18 5.21 5.12 5.20 2.96 4.01 5.18 6.18 6.75 4.60 4.86 5.12 5.31 5.78 5.55 5.25 5.15 4.04 3.50

5.15 5.15 5.15 5.15 5.15 2.35 3.95 5.15 6.08 6.81 4.39 4.79 5.15 5.27 5.49 5.48 5.28 5.15 4.18 3.48

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Table 2: Thermodynamic activation parameters for the oxidation of Norfloxacin by Diperiodatoargentate(III) in aqueous alkaline medium with respect to the slow step of scheme 1 Temperature (K)

k x 104 s-1

Parameters

Values

298

7.4+0.8

Ea (kJ mol-1)

28.72+1.2

303

9.1+0.6

H (kJ mol-1)

26.24+1.0

308

10.8+1.0

S# (J K-1 mol-1)

-115+1.5

313

13.2+0.8

G298# (kJ mol-1)

36.9+1.8

[DPA] = 1.0 x 10 -4 [NF] = 1.0 x10 -3 [OH-] = 0.15 [IO4- ] = 1.0 x 10-4

Table 3: Effect of temperature to calculate K1, K2 and K3 for the oxidation of Norfloxacin by Diperiodatoargentate(III) in aq.alkaline medium * K1 dm mol-1

K2 x 104 mol dm-3

K3 x 10-3 dm3 mol-1

298

2.50+0.15

5.6+0.25

3.8+1.22

303

3.64+0.11

4.6+0.18

5.0+1.4

308

4.90+0.12

3.8+0.20

6.4+1.8

Temperature (K)

313 6.61+0.25 3.0+0.11 8.1+1.5 [DPA] = 1.0 x 10 -4 [NF] = 1.0 x10 -3 [OH-] = 0.15 [IO4- ] = 1.0 x 10-4 Table 4: Thermodynamic quantities using K1, K2 and K3

Thermodynamic quantities

Values from K1

H (kJ mol-1)

45.4+1.8

-33.11+0.8

35.82+1.14

S# (J K-1 mol-1)

108+2.12

-75 +1.75

-44+1.6

G#298 (kJ mol-1)

13.2+1.8

-10.76+1.45

48.93+1.80

[DPA] = 1.0 x 10 -4 [NF] = 1.0 x10 -3 [OH-] = 0.15 [IO4- ] = 1.0 x 10-4 Scheme 1: Formation of hydroxilated Norfloxacin and Ag(I)

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Scheme 2: Proposed mechanism for the oxidation of Norfloxacin by Diperiodatoargentate(III) in aq. Alkaline medium

Fig 1: Structure of Complex C

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Fig 2: Plots of1/kobs Vs [H3I062-] at four different temperatures (Conditions as in Table 1)

Fig 3: Plots of 1/kobs Vs 1/[NF] at four different temperatures (Conditions as in Table 1)

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Fig 4: Plots of 1/kobs Vs 1/[OH-] at four different temperatures (Conditions as in Table 1)

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Laser induced periodic surface modification of polymer: A direct method V Velmurugan, E Srinivasan Center for Nanotechnology Research VIT University, Vellore. INDIA. Abstract: In this paper we present the results of the investigations carried out on SU-8 – a negative tone photoresist for surface modification. The photo polymer is coated and exposed optimally to UV light to create stable structures. The surface modification is carried out with a low power (150 mW) CW laser. The minimum line width of the line pattern that was created by simple set up is 7.1 microns and large area (1mm2) periodic deformation is done by creating laser interference pattern on the polymer surface. Key words: Surface, modification, SU-8, laser, Interference, direct writing.

I. Introduction Laser induced surface modification of polymers is a technique that finds many applications in the areas of food processing, electronics, optoelectronics, aerospace[1], 3-D polymer microlenses[2] , 3D polymer nanostructures for photonics[3]. The use of polymeric structures are exploited for its chemical and thermal resistance property, early processing and low cost. Laser induced surface modification could be of more advantages than other type of sources in terms of the precision, cleanliness of the finished surfaces and throughput. The comparison of the results by different authors in terms of the exposure studies cannot be done because of large trial and error based methods[4] . Excimer laser are predominately used for restructuring polymers, but in this work we used a low power( 532nm, 150mW DPSS, CW) laser to restructure a commercially available negative photoresist (SU-8) and the laser direct writing threshold, polymerization property, surface roughness, pattern width variations were analyzed. The use of low power laser to create uniform periodic structures by direct method on SU-8 is reported for the first time. II. SU8 SU-8 (supplied by MicroChem Corp) is an epoxy based photo resist and sensible to wavelengths in the range of 300nm to 500nm. It is available with different combination that can serve a good recipe for various process steps (Table 1). Table 1 SU-8 properties Property SU-8(2000 series) SU-8(3000 series) Softening point (ºC) 210 200 Thermal Stability in Nitrogen ( ºC @ 5% weight loss) 295 277 Thermal stability in Air ( ºC @ 5% weight loss) 279 Young’s modulus (GPa) 2.0 2.0 Coefficient of thermal expansion (ppm/ ºC) 52 52 Elongation at break (%) 6.5 4.8 Tensile strength (MPa) 60 73 Thermal Conductivity (W/m K) 0.3 0.2 Dielectric strength (V/µm) 112 115 Volume resistivity (Ω/cm) 2.8 x 1016 1.8 x 1016 17 Surface resistivity (Ω/cm) 1.8 x 10 5.1 x 1016 The structure (Fig 1) shows nearly 8 epoxy groups in each molecules and this becomes highly cross linked and allows to form high aspect ratio structures. The UV absorption spectrum (Fig 2) of SU 8 coated on glass plate after UV exposure done by Hitachi U-2800 Double beam Spectro photometer shows maximum absorption at 350nm. It is said to have multifunctional glycidyl ether derivatives of biphenol-A, triarylsulfonium, hexafluoroantimonate salt, photoacid generator and a thinning solvent[4].

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Fig 1. Chemical structure of SU-8.

Fig 2. Absorbance of SU-8

SU-8 upon exposure cross-links in two steps (i) formation of a strong acid during the exposure process, followed by (ii) acid-initiated, thermally driven epoxy cross-linking during the post exposure bake. The average molecular weight is around 7000 and such a low molecular weight allows SU-8 to dissolve in variety of organic solvents. SU-8 has good cohesion to bind to itself but has poor adhesion and bonding with other materials. The typical bonding strength varies from 23MPa to 92MPa. The adhesion is poor on glass and good with Gallium Arsanide. Variation of the process sequence and addition of adhesion promoters will enhance the bond strength with the substrate[5] . SU-8 can be modified based on specific applications, like addition of materials like CNT, Diamondoids, gold[6], Gamma butyrolactone[7]. Several applications like tuning of polymer for biomedical sensing and actuating[7], polarization control devices[8] and photonic structures[3] could be good examples of use of SU-8. The Research efforts could be classified as the variation in the process and applications. The statistics (Fig 3) shows the number of publications based on that. Fig 3. Publications with ‘SU8 process variation’ and SU8 applications’ in their titles based on Scopus data base. Process variations Applications

Number of publications

60 50 40 30 20 10 0 2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Year

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III. Direct writing technique and experimental setup The parameters that govern the process of direct writing are the energy of the incident light, wavelength, type of exposures (pulsed or continuous), absorbity of surface, melting or ablating threshold, reflectance of the surface, heat conductive property, velocity gradient during the time of melting, shape of the pulse, thermal diffusion length, roughness of the surface, etc,. The determination of exact power level for specific application is crucial in surface modification because the final product could be deteriously affected if an excessive or insufficient amount of power is applied. Material absorbs different amount of energies at different wavelengths. If a material is not absorbing sufficient amount of energy at a particular wavelength, increase in the power density can promote the energy absorption by the material at that specific wavelength. Even though SU-8 has the maximum absorption at 350nm ( Fig 1), our experimentation with 532nm source yielded good result due to use of suitable optics. SU-8 is subjected to standard UV exposure and this makes the resist to become transparent to the visible region and also helps in removing the gluing property of the resist. An under dosage of UV light may retain the crossover and thereby fails to form rigid structures[5]. Laser interference based surface modification is a method to produce periodic structures over large area using two interfering highly-coherent light beams. Due to large change in the intensity surface modification can be easily done. IV. Results Surface modification was carried out in two methods (i) by direct focusing of laser beam on the substrate and (ii) by creating interference pattern on the sample. Two sets of samples were prepared by cleaning glass plate with acetone at 55ºC for 10 minutes and finally rinsed in deionized water and dried using nitrogen torch. SU-8 is coated on the glass using spin coater. The optimized values for coating are, the spinning speed: 4700rpm, duration: 100seconds and for every 15 seconds 2 drops of SU-8 were added. The SU-8 film is subjected to UV light exposure for 7 seconds to have complete polymerization. DPSS laser(Compass 315m from Coherent Inc) with a wavelength of 532nm having variable power upto 150mW was used for the experiment. A microscopic objective (45X) was placed at a distance of 10mm from the laser. First set of samples are kept at 0.3mm from the objective. The calculated power density at that position is 235.78 kW mm-2. The sample is moved in one direction at different speeds to create line patterns. Fig 4 shows one of the line pattern formed. For higher speeds, the surface didn’t initiate the modification and at lower speeds (0.25mm/Sec) polymer gets deformed and the line width varies with the exposure timing (Fig 5). The minimum line width obtained is 7.1 microns and the structures were stable for a long period of time. Fig 4. Optical micrscope photographs of the line pattern (8 microns) , (b) 3 D view showing the pumps crateed by the exposure.

(a) Fig 5. Variation of line width for different exposure timings

(b)

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Width(in microns)

9.5 9 8.5 8 7.5 7 1

Time of exposure (Sec)

Second set of samples were restructured by interference method. Mach zender’s interference was tried and the interference pattern obtained is focused on the sample with a concave lens. The patterned samples are characterized by Nano scratch tester and NSOM. For imaging, the sample is sputtered with silver using magnetron sputtering. The image(Fig 6) shows minimum line width of 60 microns and the surface roughness was increased by 1.34 times. Fig. 6. (a) High resolution Interference patterns imaged by NSOM and (b) portion of the interference patterned characterized by nano scratch tester.

(a)

(b)

V. Conclusion In conclusion, low power laser beam interaction with the photoresist has been investigated. Removal of photoactive and gluing property of the resist was done by making a film at optimized coating and the exposure parameters. These values have resulted in better deforming the polymer surface. Periodic patterns formed by direct laser interference on the surface resulted in structures that are stable for long period of time. VI.

References

[1]. Murat Ozdemiry, Hasan Sadikoglu, “A new and emerging technology: Laser- induced surface modification of polymers”, Trends food sci tech. 9, 1998. [2]. R.Llobera, D. Wilke, W.Johnson, S. Büttgenbach,” Polymer Microlenses With Modified Micromolding in Capillaries (MIMIC) Technology”, IEEE Photon. Technol. Lett. 17, 12 ,2005. [3]. Georg von Freymann, Alexandra Ledermann, Michael Thiel, Isabelle Staude, Sabine Essig, Kurt Busch, Martin Wegener, “ThreeDimensional Nanostructures for Photonics”, Adv. Funct. Mater. 20, 2010. [4]. E.F.Reznikova, J.Mohr, H.Hein, “Deep photo-lithography characterization of SU-8 resist layers”, Microsyst Technol. 11, 2005.

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[5]. Maria Nordstro¨m, Alicia Johansson, Encarnacion Sa´nchez Noguero´n Bjarne Clausen, Montserrat Calleja, Anja Boisen,” Investigation of the bond strength between the photo-sensitive polymer SU-8 and gold”, Microelectron Eng. 78-79, 2005. [6]. H.C.Chiamori, J.W.Brown, E.V.Adhiprakasha, E.T. Hantsoo, J.B.Straalsund, N.A.Melosh, B.L.Pruitt, “Suspension of nanoparticles in SU-8: Processing and characterization of nanocomposite polymers”, Microelectr J. 39, 2008. [7]. Yihong Liu, David D. Nolte, Laura J. Pyrak-Nolte, “Large-format fabrication by two-photon polymerization in SU8”, Appl Phys AMaterr. 100, 1, 2010. [8]. Lin Pang, Maziar Nezhad, Uriel Levy, Chia-Ho Tsai, Yeshaiahu Fainman, “Form-birefringence structure fabrication in GaAs by use of SU-8 as a dry-etching mask”, Appl Optics. 44, 12, 2005.

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Human Computer Interaction Using Hand Gesture Shri. S V Manjaragi1, Ashirwad Kulkarni2, Prasad Kulkarni3, Mahesh Chapgaonkar4, Abhishek Tubachi5

Department of Computer Science, Hirasugar Institute of Technology, Nidasoshi-591236 Visvesvaraya Technological University, Belgaum, Karnataka, India. Abstract: We present in this paper a new approach for communication with computer systems using hand gesture. The process is based on extracting features of the hand region and then using these features for communication with computer system. Our approach is defined in three steps (i) Segmentation for hand localization, (ii) Feature extraction and (iii) Feature identification to create a fuzzy rule base. The gesture is a natural way of communication for many people with sign language. In computer systems, gesture recognition is very difficult and complex task since the full recognition system should be able to identify the hand in different scales, positions, orientations, contrasts, funds, luminosity, and others. The gesture communication with machines are enriched by instrumented gloves , which are used to control virtual actors, to describe and manipulate objects on computer screens or even to recognize the sign language. Unfortunately, these gloves are expensive and fragile, and their cables are a hindrance. We present a new system using hand detection technique based on the color classification using fuzzy logic system, image segmentation using edge detection and use Ellipse method and Comparative based method to localize the hands from others objects detected in the image. Keywords: Fuzzy Rule Base, Hand Gestures, Hand Localization.

I. Introduction Computer Imaging can be defined as an acquisition and processing of visual information by computer. Computer representation of an image requires the equivalent of many thousands of words of data, so the massive amount of data required for image is a primary reason for the development of many sub areas with field of computer imaging, such as image compression and segmentation. Another important aspect of computer imaging involves the ultimate “receiver” of visual information in some case the human visual system and in some cases the human visual system and in others the computer itself. Computer imaging can be separated into two primary categories: 1. Computer Vision. 2. Image Processing. In computer vision application, the processed images output for use by a computer, whereas in image processing applications the output images are for human consumption. Image processing [1] is computer imaging where application involves a human being in the visual loop. In other words the images are to be examined and acted upon by people. Computer imaging systems are comprised of two primary components types, hardware and software. The hardware components can be divided into image acquiring sub system (computer, scanner, and camera) and display devices (monitor, printer).The software allows us to manipulate the image and perform any desired processing on the image data. Gesture recognition systems can be categorized into computer imaging systems. There are few ways to perform hand gesture recognition. Let us classify it into two categories. 1) Glove based systems

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This category [2] is involved heavily in hardware parts, employ sensors (mechanical or optical) attached to a glove that transduce finger flexionâ&#x20AC;&#x2122;s into electrical signals to determine the hand posture. Normally, the sensors that used are acoustic or magnetic sensors which are embedded into the glove. Fig 1: Sample Glove based gesture recognition system.

2) Stylus based systems The second category [2] is analysis of drawing gesture, which are involved using special input devices such as stylus. Most of hand gesture recognition currently works by using mechanical sensing, most often for direct manipulation of a virtual environment. But this type of sensing has a range of problems such as accuracy, reliability and electromagnetic noise. These two categories involved external hardware parts. Fig 2: Stylus based gesture recognition system.

3) Vision based systems This category involves the manner in which humans perceive their surroundings. This method is elaborated in the following section. II. Proposed Work The vision based analysis which is based on the way human beings perceive information about their surroundings. Visual sensing has the potential to make gestural interaction more practical and this type of method is most intuitive method to perform hand gesture recognition because it involves no external hardware part, this means it can recognize our hand gesture freely without anything put on our hand. What it needs is just a camera, webcam, camcorder or anything can capture image that is able to interface with computer. We divide the system into two major components: Trainer Module and User Module. 2.1 Trainer Module The Trainer module selects a sample image from any location from the computer system. This selected sample image is then fed to the filtering phase which performs the edge detection for the read image, the segmentation based on elliptic shapes then extracts the hand region from the image. The feature extraction phase selects features like major and minor axis, area from the segmented hand region which is then passed to the fuzzy rule base, which depending on the features assigns the action to be performed. The Trainer module provides the knowledge-base to the system which defines the action to be performed on a particular gesture. Filtering can be performed for noise removal using morphological operations like dilation, erosion etc. The segmentation [2] is carried out through edge detection, to identify objects in the image.

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Fig 3: Training Module workflow overview.

The region of interest (ROI) being hands in the image, providing gestures. Edge detection can be performed using many methods like Sobel filtering, Prewit filtering, Shen operator, Canny operator [2] etc. The canny operator being the most powerful since it uses two different thresholds to identify strong and weak edges. Hand localization is done based on the edges found. The required features of this ROI are extracted and a fuzzy rule base depending on these features can be created which will decide the command to be executed. 2.2 User Module The user module is provided with a real time camera interface which allows the user to pose the gesture to the system. The user can here input the required gesture to the system. The filtering, segmentation, feature extraction phases work similarly to that of the Trainer module. After extracting the required features from the image, the fuzzy rule base then finds for any match in the trained data, if so decides the command to be issued, the system then executes it. The system now poses real time interaction between user and the computer. The user needs to place the gesture in front of the web camera, the filtering phase makes the required background removal for the identification of hand region. This background removal can be done through many techniques like using the skin color space which relies on the color of skin which contain major portion of red color due to blood cells available in the hand. Another technique using fuzzy classification can be done by taking sample images for hand classification. After ROI is identified, the features are extracted which are then passed to the fuzzy rule base created in the Training phase. Depending upon the match command is issued.

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Fig 4: User module workflow overview.

III. Conclusion and Future Work We present in this paper an approach for communication with computer systems with hand gesture. The system requires removal of unnecessary regions from the image except for the hand region. The attributes of the hand region are straight forward for gesture communication, this makes it easy to measure the features of the hand region. Although management of features extracted is quite a complex task. The feature values must be stored and used with caution. The system acts just as an interface for communication with computers, future orientations can be made for a system that can completely communicate with computer system as a single input device.

References [1] Frank Y. Shih, Image processing and pattern recognition fundamentals and techniques (John Wiley and Sons, Inc.) [2] Ahmed BEN JMAA, Walid MAHDI, Yousra BEN JEMAA and Abdelmajid BEN HMADOU, A new approach for digit recognition based on hand gesture analysis, International Journal of Computer Science and Information Security, Vol. 2, No. 1, 2009.

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Machining Parameter Setting For Facing EN8 Steel with TNMG Insert N.E. Edwin paul a,P. Marimuthu b,R.Venkatesh Babu c a

Research Scholar, Department of Mechanical Engineering, Bharath University, Chennai - 73, and Assistant Professor, Department of Mechanical Engineering, GRT Institute of Engineering & Technology, Tirutani – 631209, INDIA. b Professor, Department of Mechanical Engineering and Principal, Syed Ammal Engineering College, Ramanathapuram-623 502, Tamilnadu, INDIA c Dean, Academics, Bharath University, Chennai-73, Tamilnadu, INDIA.

Abstract: Surface roughness is used to determine and evaluate the quality of a final product in faced parts. In order to get better surface finish, the proper setting of cutting parameters is crucial before the process takes place. EN8 is one whose surface hardness is high and it is used for making gudgeon pins, gears etc., is taken as work material for experimental works. This paper describes the Taguchi method based robust design philosophy for minimization of surface roughness in facing. Three parameters namely cutting speed, feed and depth of cut were identified and investigated at three levels to study the effect of parameters on surface roughness. Experimental works were conducted on EN8 material using CNC Lathe based on L9 orthogonal array. Based on the signal to noise ratio analysis, the optimal settings of the process parameters have been determined. Confirmation tests also been performed to predict and verify the adequacy of the additive models for determining the optimal I.surface roughness. Introduction Keywords: EN8, Machining, Facing, Surface roughness, Taguchi Technique Keywords: WWW; component; formatting; style; styling; insert (Minimum 5 to 8 key words) I. INTRODUCTION The drastic increase of consumer needs for quality metal cutting related products (more precise tolerance and better surface finish) has driven the metal cutting industry to continuously improve quality control of the metal cutting processes. The quality of surface roughness is an important requirement of many work pieces in machining operations. Within the metal cutting processes, the facing process is one of the most fundamental metal removal operations used in the manufacturing industry. Surface roughness which is used to determine and evaluate the quality of a product is one of the major quality attributes of a faced product [1]. Surface roughness of a machined product could affect several of the product’s functional attributes such as contact causing surface friction, wearing, light reflection, heat transmission, ability of distributing and holding a lubricant, coating and resisting fatigue. Therefore, surface roughness is one of the important quality aspects in facing operations [2]. Diwahar et al [3] used Taguchi method for turning parameter optimization of AISI 1040 steel. Optimum cutting parameters for turning AISI 1030 carbon steel bar was identified by Nalbant et al [4]. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process takes place. As a starting point for determining cutting parameters, technologists could use the hands on data tables that are furnished in machining data handbooks. Previously the trail – and – error approach could be followed in order to obtain the optimal machining conditions for particular operations. Recently, a Design of Experiment (DOE) has been implemented to select manufacturing process parameters that could result in a better quality product. A systematic approach by Taguchi method for identifying optimum surface roughness in end milling operations was used by John and Joseph [5]. In their study, three independent variables, each with three levels, had total of (33) = 27 experimental runs. Oftentimes, the optimum metal cutting process required studying more than three factors for the cutting parameters. For example, if a DOE setup considered three independent variables, each with at least three levels, then (33) = 27 runs were required in the experiments. Imagining the total cost of these experimental runs, one could conclude that it was very costly for the industry. In addition, the time of these runs could delay any quality resolving actions for the industry. More Industrial Technology graduates are facing challenges to improve the quality of products and processes with minimum cost and time constraints in their

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careers. The Taguchi parameter design techniques have been proved to be successful, economical and viable in meeting this challenge ([5] - [15]). Therefore, there is a need to introduce Taguchi parameter design. Most of the works regarding surface roughness has focused in turning, milling and drilling operations ([3] – [13]). Only a limited amount of research has been reported in the open literature dealing with facing operations ([2], [7]). As well as the research work which uses EN8 metal as a specimen is also very less. This EN8 material is used for making gudgeon pins, gears etc. where surface roughness is an important factor in facing. Hence there is a scope for studying machining parameter setting for facing EN8 steel using Taguchi Technique. II. TAGUCHI APPROACH Taguchi method based robust design approach has produced a unique and powerful quality improvement discipline that differs from traditional practices ([11] – [16]). Taguchi approach can economically satisfy the need of problem solving and product/process design optimization projects in the manufacturing industries. By applying this, it is possible to reduce the time for experimental investigations and improve the process quality. Robust design is a methodology for finding the optimum setting of the control factor to make the product or process insensitive to the noise factors .Robust design is based upon the technique of matrix experiments. Experimental matrices are special orthogonal arrays, which allow the simultaneous effect of several process parameters to be studied efficiently. The purpose of conducting the orthogonal experiment is to determine the optimum level for each factor and to establish the relative significance of individual factors in terms of their main effects on the response. Taguchi suggests signal to noise ratio (S/N) as the objective function for matrix experiments. The S/N ratio is used to measure the quality characteristics and is also used to measure the significant machining parameters through the analysis of variance (ANOVA). Taguchi classifies objective function into three categories, such as smaller the better type, larger the better type, nominal the best type. The optimum level for the factor is the level that results in the highest value of signal to noise ratio in the experimental region [9]. The main objective of this paper is to study the different process parameters during facing of EN8 steel using robust design methodology. This paper attempts to introduce how Taguchi parameter design could be used in identifying the significant processing parameters and optimizing the surface roughness of facing operations. III. PLANNING FOR EXPERIMENTATION A.

Selection of factors, levels and orthogonal arrays

Three operating factors, viz., depth of cut, feed and cutting speed have been selected for parametric optimization and each parameter has three levels [12]. These factors and three levels are given in Table 1. Orthogonal array gives more reliable estimates of factor effects with less number of experiments, when compared to the traditional methods, such as one factor at a time experiments. With three factors at three levels, the total degrees of freedom are 6. Hence, L9 orthogonal array having 6 degrees of freedom was selected for the controllable factors. L9 indicates 9 trials considered for experimentation, the array along with factor assignment to the columns is given in Table 2. TABLE 1 FACTORS AND LEVELS

Levels 2

0.8

1.0

1.2

Feed (F)

mm/rev

0.15

0.20

0.25

Cutting Speed (V)

m/min

200

240

280

Sl.No

Factors

Units

Depth of Cut (DOC)

2 3

Conducting the experiments and measurement of responses

Experiments were conducted on CNC Fanuc Oi-TA Horizontal Turning Centre using EN8 alloy steel rods of 30mm diameter as dictated by L9 orthogonal array. According to the hardness of the material the TNMG 160404 EN-TF CTC 2135 inserts was selected. Soluble cutting oil emulsion in water was used as a coolant. One specimen for each trial condition was prepared. Thus nine specimens were faced and the surface roughness was

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measured at three different locations in each specimen and the average value was taken. The surface roughness was measured using Mitutoyo Surf Tester 211 with a cut off length of 0.25mm. The results of the experiments for nine trial conditions are reported in Table 2. TABLE 2 L9 ORTHOGOAL ARRAY WITH FACTORS, RESPONSES AND S/N RATIOS Average Surface S/N Sl. DOC F V Roughness Ratio No. Ra (μm) 10.75 1 1 1 1 0.29

0.73

2.73

0.64

3.87

0.60

4.44

0.54

5.35

0.99

0.09

0.46

6.74

0.59

4.58

0.61

4.29

Analysis for optimization of operating parameters

In Taguchi method, the signal to noise ratio (S/N) analysis has been carried out to determine the optimal parametric condition of facing process. For that, smaller-the-better type category responses are considered for the present analysis ([12], [13]). The S/N ratio for smaller-the-better category is

1 n 2   yi   n i 1 

  10log10 

…… (1) where, y is the response and n is the number of replications of each trial i. The S/N ratios were computed using Eq. (1) for each of the 9 trials and the values are reported in Table 2. The data analysis procedure using Taguchi experimental framework involves Analysis of Means (ANOM) and Analysis of Variance (ANOVA). The ANOM helps in identifying the optimal factor combinations. Based on ANOM, the optimum levels for each factor resulting from matrix experiment are shown in Table 3. The ANOM table for the raw data of surface roughness is indicated in Table 4. TABLE 3 ANOM FOR S/N RATIO 1

Levels 2

5.78

3.29

5.20

5.78 (V1)

7.31

4.22

2.75

7.31 (F1)

DOC

5.14

3.82

5.32

5.32 (DOC3)

Factors

Optimum

TABLE 4 ANOM FOR RAW DATA Levels

Factors

Optimum

0.55

0.71

0.57

0.55 (V1)

0.45

0.62

0.74

0.45 (F1)

DOC

0.62

0.64

0.54

0.54 (DOC3)

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FIGURE 1 S/N RESPONSE GRAPH FOR MACHINING PARAMETERS

S/N ratio

S/N response graph for machining parameters 8 7 6 5 4 3 2 1 0

Cutting Speed Feed Depth of Cut

Levels

FIGURE 2 MEAN RESPONSE GRAPH FOR MACHINING PARAMETERS

Mean response graph for machining parameters Surface Roughness

0.8 0.7 0.6 0.5 0.4

Cutting Speed Feed

0.3 0.2

Depth of Cut

0.1 0 1

Levels

From table 3 and 4 and Figure 1 and 2, it is observed that lower level of cutting speed and feed and higher level of depth of cut gives the optimum cutting parameters for facing EN8 steel with TNMG inserts (i.e) Cutting speed (V1) = 200m/min, Feed (F1) = 0.15mm/m and Depth of cut (DOC3) = 1.2mm ANOVA establishes the relative significance of factors in terms of their percentage contribution to the response. ANOVA is needed for estimating the error variance for the effects and variance of the prediction error. In Table 5, the ANOVA for S/N ratio are shown and in Table 6, the ANOVA for raw data of surface roughness are indicated. It is clear from ANOVA for raw data that feed influences 45.50 % of the surface roughness, cutting speed contributes 16.55 % of the surface roughness and depth of cut influences 5.56 % of the surface roughness. TABLE 5 ANOVA FOR S/N RATIO

Factors

DoF

Sum of squares

Mean squares

% Contribution

10.19

5.09

15.16 48.33

32.49

16.24

DOC

4.03

2.01

5.99

Error

20.52

10.26

30.52

Total

67.23

8.40

100.00

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Edwin paul et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), June-August, 2013, pp. 87-92 TABLE ANOVA FOR RAW DATA

Factors

DoF

Sum of squares

Mean squares

% Contribution

0.048

0.024

16.55

0.132

0.066

45.5

DOC

0.016

0.007

5.56

Error

0.095

0.048

32.39

Total

0.290

0.036

100.00

Determination of optimal surface roughness

The optimal surface roughness is predicted at the selected optimal setting of process parameters. The significant parameters with optimal levels are already selected as: V1, F1, and DOC3. The estimated mean of the response characteristic can be computed as:

μ Ra =V1  F1  DOC3  2* TRa

…… (2) where TRa = overall mean of surface roughness. V1, F1, and DOC3 are the mean values of the surface roughness with parameters at optimum levels. Hence the predicted optimum value for S/N ratio is μS/N = 8.876 and the predicted optimum value for the raw data is μRa = 0.3381. A confidence interval for the predicted optimum S/N ratio on a confirmation run can be calculated using the following equation:

 1 1 C.I. = F 1, f e  Ve     neff R 

…… (3) Where, Fα (1, fe) is the F – ratio required for α, α is the risk, fe is the error DOF, Ve is the error variance, neff is the effective number of replications and is equal to

neff 

N 1  Total DOF

…… (4), R is the number of repetitions for confirmation experiment and N is the total number of experiments ([7], [10]). Using the values of S/N ratios Ve = 10.26 and fe = 2 from Table 5, the C.I. was calculated. Total DOF associated with the mean (μRa) = 2 * 3 = 6. Total trials = 9; N = 1 * 9 = 9; neff = 9/ (1+6) = 1.286. α = 0.05. F0.05 (1,2) = 18.5 (tabulated). The calculated C.I. for S/N ratio =  18.37. The predicted mean of the S/N ratio is 8.876μm. The 95 % confidence interval of the predicted optimum S/N ratio is:

 Ra  C.I.  Ra  N   Ra + C.I.

-9.49 < μS/N < 27.246 For optimum surface roughness: -0.912 < μRa< 1.588 3.5 Confirmation Experiments Verification experiments were conducted to check, how close the optimum conditions suggested by the manual experiment was related with the real ones. It was conducted at the optimal setting of facing process parameters recommended by the investigation. The mean value of surface roughness was found to be 0.28μm. The difference between the predicted optimum surface roughness value by Taguchi method and the verification experiment value is low. This result was within C.I. of the predicted optimal surface roughness. IV. CONCLUSION Taguchi method is used and is a powerful tool for optimization, which provides a systematic and effective methodology for the design optimization of cutting parameters. The percentage contributions of parameters in affecting variation in surface roughness while machining EN8 steel with TNMG 160404 EN-TF CTC 2135 insert are: The feed has greater influence on the surface roughness followed by the cutting speed. From the analysis it is

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revealed that the feed, cutting speed and depth of cut are prominent factors which affect the facing operations. Confirmation test results proved that the determined optimal combination of machining parameters satisfy the real requirements of machining operations in the facing of EN8 materials. 5. REFERENCES [1] Jacques Masounave, Youssef [2] [3]

[4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

[15]

[16]

A. Youssef, Yves Beauchamp, Marc Thomas, "An experimental design for surface roughness and built-up edge formation in lathe dry turning", International Journal of Quality Science, vol. 2 Issue 3, pp. 167 – 180, 1997. W. Pedersen., M . Ramulu, “Facing SiCp/Mg metal matrix composites with carbide tools, Journal of Material Processing Technololgy”, vol. 172, pp. 417-423, 2006. V . Diwahar Reddy, D. Bhanu Praksh, G. Bhanodaya and G. Krishnaiah, “Sensitivity analysis of surface roughness of AISI 1040 carbon steel in dry turning operation with PVD tool using Taguchi method”, Manufacturing Technology Today, vol.1, pp. 5-11, 2012. M. Nalbant, H. Gokkaya and G. Sur, “Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning”, Materials and Design, vol. 28, No. 4, pp. 1379-1385, 2007. John L. Yang and Joseph C. Chen, A systematic approach for identifying optimum surface roughness in end – milling operations. Journal of Industrial Technology, vol. 17, No 2, pp. 1-8, 2001. W. H. Yang and Y.S. Tarng, “Design optimization of cutting parameters for turning operations based on Taguchi Method”, Journal of Material Processing Technology, vol. .84, pp. 122 – 129, 1998. Yusuf Sahin and Riyaz Motorcu, “Surface roughness prediction models in machining of carbon steel by PVD coated cutting tools”, merican Journal of Applied Sciences, vol. 1, No.1, pp. 12-17, 2004. Hari Singh. and Pradeep Kumar, Optimizing cutting force for turned parts by Taguchi’s Parameter design approach, Indian Journal of Engineering and Material Sciences, vol. 12, pp. 97-103, 2005. V.N. Gaitonde, B.T. Achyutha and B. Siddeswarappa, “Burr size minimization in drilling using Taguchi Technique”, Indian Journal of Engineering and Material Sciences, vol. 12, pp. 91-96, 2005. M. Aksoy and A. Inan, “Study of tool wear and surface roughness in machining of homogenized SiC-p reinforced aluminium metal matrix composite”, Journal of Material Processing Technology, vol. 164-165, pp. 862 – 867, 2005. G. Taguchi, Introduction to quality engineering, Mc-Graw Hill, New York, 1986. P.J. Ross, Taguchi Techniques for Quality Engineers, McGraw-Hill Company, Singapore, 1988. M.S. Phadke, Quality Engineering using robust design, Prentice Hall Int., New Jersey, 1989. A. Noorul Haq, P. Marimuthu and R. Jeyapaul, “Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method”, International Journal of Advanced Manufacturing Technology, vol. 37, pp. 250–255, 2008. N.H.M Nor, N. Muhamad, M.H.I. Ibrahim, M. Ruzia and K.R. Jamaludin, “Optimization of injection molding parameter of Ti-6Al-4V powder mix with palm stearin and polyehylene for the highest green strength by using Taguchi method”, International Journal of Mechanical and Materials Engineering, vol. 6, No.1, pp 126-132, 2011. A.M. Zaharudin, R.J. Talib, M.N. Berhac, S. Budin. And M.S. Aziurah, “Taguchi method for optimizing the manufacturing parameters of friction materials”, International Journal of Mechanical and Materials Engineering, vol. 7, No. 1, pp 83–88, 2012 .

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Optimal control of an N-policy two-phase MX/M/1 queueing system with server startup subject to the server breakdowns and delayed repair V. Vasanta Kumar1, T.srinivasa Rao2 K . L . University, Vaddeswaram, Guntur(Dist.), Andhra Pradesh, INDIA. A.Anjaneyulu3, V.S.R & N.V.R College, Tenali, Guntur(Dist.), Andhrs Pradesh, INDIA. Abstract: This paper investigates the economic behaviour of the two-phase Mx/M/1 queueing system with Npolicy, server startup time and unreliable server, consisting of breakdown and delay periods. The service is in two phases of which the first phase is batch service provider to all customers waiting in the queue and the second phase is individual provider to each customer in the batch. The server is turned off and takes a vacation whenever the system is empty and turned on when the total number of customers in the system reaches the threshold N (Nâ&#x2030;Ľ1), and starts preparatory work before providing the batch service. While the server is working with any phase of service, it may breakdown at any instant and the service channel will fail for a short interval of time. There may be delay in repair due to non-availability of the repairing facility. The startup times, batch service times, individual service times, breakdown and delay periods are assumed to follow an exponential distribution. The closed form of expressions for the performance measures of interest is obtained. The mean queue waiting time by heuristic interpretation and the reliability indices of this model are also derived. The total expected cost function per unit time is developed to determine the optimal threshold of N at a minimum cost. The sensitivity analysis is presented through numerical illustration. Keywords: Two-phase, vacation, breakdowns, N-policy, repair time, delay time, cost function. I. Introduction. A. Literature review In most queueing systems the server may be subjected to lengthy and unpredictable breakdowns while serving a customer. For instance, in manufacturing systems the machine may breakdown due to malfunction or job related problems; in computer systems, the machine may be subject to scheduled backups and predicted failures. In these systems, server breakdown results in a period of server unavailability time until it is fixed. Therefore, it is necessary to see how the breakdowns affect the level of performance of the system. Regarding the queueing systems with two phases of service Krishna and Lee [5] and Doshi[4] studied the distributed systems where all customers waiting in the queue receive batch service in the first phase of service followed by individual service in the second phase. Selvam and Sivasankaran [7] introduced the two phase queueing system with server vacations. Kim and Chae [6] investigated the two phase system with Npolicy.The server startups correspond to the preparatory work of the server before starting the service. In some actual situations, the server often requires a startup time before starting each service period. Baker[1]first proposed the N-policy M/M/1 queueing system with exponential startup time. Several authors have investigated queueing models with server breakdowns and vacations in different frameworks in recent past. Wang [13] for the first time proposed the Markovian queueing system under the N-policy with server breakdowns. Vasanta Kumar and Chandan [8] presented the optimal operating policy for the two-phase Mx/Ek/1 queueing system under N-policy. Vasanta Kumar et, al. [9,10]. presented the optimal control of Mx/ M/1 and M/Ek/1 gated queueing systems with server startup and break downs. Vasanta Kumar et al.[11,12] presented the optimal operating policy for a two-phase Mx/M/1 and Mx/Ek/1 queueing systems under N-policy with server breakdowns and without gating. Choudhury and Tadj [2] considered an M/G/1 model with an additional phase of optional service with the assumption that the server is subject to breakdowns and delayed repair. Later Choudury et al. [3] investigated such a type of model, where the concept of N-policy is also introduced along with a delayed repair for batch arrival queueing system. In the two-phase Mx/M/1 queueing system without gating analyzed by Vasanta Kumar et al.[11] breakdowns are considered in second phase of service, without taking into consideration the concept of delayed repair.

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Hence the present paper aims at the study of economic behavior of an N-policy Mx/M/1 queue in which service is in two-phases and the server is typically subjected to unpredictable breakdowns in both phases of service and delayed repair. B. Model description and assumptions We consider an Mx/M/1 queueing system with server startup, two-phases of service, unreliable server, and delay in repair due to non-availability of the service facility operating under N-policy. Assumptions of the model (i). The arrival process is a compound Poisson process (with rate λ ) of independent and identically distributed random batches of customers, where each batch size X, has a probability density function {a n: an = P(X=n), n≥1}. Batches are admitted to service on a first come first served basis. (ii).The service is in two phases. The first phase of service is batch service to all customers waiting in the queue. On completion of batch service the server proceeds to second phase to serve all customers in the batch individually. Batch service time is assumed to be exponentially distributed with mean 1/β and is independent of batch size. Individual service times are also assumed to be exponentially distributed with mean 1/μ. On completion of individual service, the server returns to the batch queue to serve the customers who have arrived. If customers are waiting, the server starts the batch service followed by individual service to each customer in the batch. If no customer is waiting, the server takes a vacation. (iii).Whenever, the system becomes empty, the server is turned off. As soon as the total number of arrivals in the queue reaches to a predetermined threshold N the server is turned on and is temporarily unavailable for the waiting customers to restart service. It needs a startup time which follows an exponential distribution with mean 1/θ. As soon as the server finishes startup, it starts serving the first phase of waiting customers. (iv).The customers who arrive during the pre-service and batch service are also allowed to enter the same batch which is in service. (v).The server is subject to breakdowns at any time with Poisson breakdown rates α 1 for the first phase of service and α2 for the second phase of service where it is working. Whenever, the server fails, it is sent for repair during which the server stops providing service and waits for the repair to get started. The waiting time for repair is defined as delay time and is assumed to be exponentially distributed with mean 1/δ. Repair time in any phase of service is assumed to be exponentially distributed with mean 1/γ. (vi).In case the server breaks down while serving customers, it is sent for repair and that particular batch of customers or the customer who is just being served should wait for the server to come back to complete the remaining service. Immediately after the server is repaired, it starts to serve and the service time is cumulative. A customer who arrives and finds the server busy or broken down must wait in the queue until a server is available. Customers continue to arrive during the delay and repair periods of the broken server. The primary objectives of the paper: (i) To establish the steady state equations and to obtain the steady state probability distribution of the number of customers present in the system. (ii) To derive diverse system characteristics such as expected number of customers in different states of the server: in vacation, in startup, in batch service, in individual service, in breakdown and delay states. An attempt is also made to estimate the expected length of the queue for the geometric batch size distribution. (iii) To obtain the mean queue waiting time by heuristic interpretation and reliability indices. (iv) Further, the paper aims to formulate the total expected cost function per unit time and determine the optimal value of the control parameter N. (v) Finally it proposes to perform a sensitivity analysis on the optimum value of N and the minimum expected cost through numerical experiments. II. Steady state results In steady state the following notations are used. P0, i, 0 = The probability that there are i customers in the batch queue when server is on vacation, i= 0,1, 2,...

P1, i, 0 = The probability that there are i customers in the batch queue when server is doing pre-service (startup work) , where i = N , N+1 , N+2 , … P2, i, 0 = The probability that there are i customers in the batch which is in batch service, i = 1, 2, 3…

P3, i, 0 = The probability that there are i customers in the batch which is in batch service, but the server is found to be broken down and waiting for repair , i = 1, 2, 3,…

P4, i, 0 = The probability that there are i customers in the batch which is in batch service, but the server is under repair , i = 1, 2, 3,…

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P5, i, j = The probability that there are i customers in the batch service queue and j customers in the individual service queue when the server is in individual service, i = 0, 1, 2,…, and j = 1, 2, 3,…

P6, i, j = The probability that there are i customers in the batch service queue and j customers in the individual service queue when the server is in individual service , but found to be broken down and waiting for repair , i = 0,1,2 …, and j = 1,2,3….

P7, i, j = The probability that there are i customers in the batch service queue and j customers in the individual service queue when the server is in individual service, but the server is under repair, i =0,1,2…, and j = 1,2,3…. The steady state equations satisfied by the system size probabilities are as follows:

λ P0, 0, 0 = μ P5, 0,1 .

(2.1)

λ P0, i, 0 = λ  a l p 0, i  , 0 ,

 i  N-1.

(2.2)

l 1

(λ  θ) P1, N, 0 = λ  a l p 0, N l, 0 ,

 1.

(2.3)

l 1

i- N

(λ  θ)P1, i, 0 = λ  a l p1, i l, 0  λ l 1

a p

l i -(N -1)

0,i -l, 0

i=N+1,N+2,N+3,….

(2.4)

(λ  β  α1 ) P2, i, 0 = λ  a l p 2, i l, 0  μ P5, i, 1  γ P4, i, 0 ,

1  i N-1.

(2.5)

l 1 i

(λ  β  α1 ) P2, i, 0 = λ  a l p 2, i l, 0  μ P5, i, 1  γ P4, i, 0  θ p1,i,0 , i  N.

(2.6)

(λ  δ) P3,1, 0 = α1 P2,1, 0 .

(2.7)

l 1

(λ  δ) P3, i, 0 = α1 P2, i, 0  λ  a l, p 3, i l, 0 ,

i  1.

(2.8)

l 1

(λ  γ) P4,1, 0 = δ P3,1 ,0 .

(2.9) i

(λ  γ) P4, i, 0 = δ P3 ,i ,0  λ  a l P4, i l, 0 ,

i  1.

(2.10)

l 1

(λ  α 2  μ) P5, 0, j = μP5, 0, j1  β P2, j, 0  γ P7, 0, j ,

j  1.

(2.11)

(λ  α 2  μ) P5, i, j

μ P5, i, j1  λ a l P5, i l, j  γ P7, i , j ,

i  1, j  1.

(2.12)

l 1

(λ  δ) P6, 0, j = α 2 P5, 0, j ,

j1.

(2.13)

(λ  δ) P6, i, j = α 2 P5, i, j  λ a l P6, i l, j ,

i1, j1.

(2.14)

l 1

(λ  γ) P7, 0, j = δ P6, 0, j ,

j1.

(2.15)

(λ  γ) P7, i, j = δ P6, i, j  λ a l P7, i l, j ,

i1 , j1.

(2.16)

l 1



Let A(z) =

a z l 1

be the probability generating function of the arrival batch size random variable X

and Al (z), All(z) represent the first and second order derivatives of A(z) respectively. The following generating functions are used to solve the equations (2.1) to (2.16)

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G q (z)   Pq, i, 0 z i , 0 ≤ q ≤ 4, where P0,i,0 = 0, if i ≥ N and P1,i,0 = 0, if i < N. i 0



G r (z, y)   Pr, i, j z i y j , r = 5,6,7. i  0 j 1



R j (z)   p 5,i, j z i , i 0



Tj (z)   p 7,i, j z i .

S j (z)   p 6,i, j z i , i 0

i 0

Solving equations (2.1) to (2.16) the following generating functions are obtained: N -1

G 0 (z) =

P0, 0, 0 y N (z) , where y N (z)   y i z i , yi = i 1

N 1

a y l 1

i -l

, y0 = 1,

(2.17)

[λ ( 1 - A(z) )  θ ] G1 (z) = λ P0, 0, 0  λ ( A(z) - 1 ) G 0 ( z ) ,

(2.18)

λ ( 1  A(z) )  β  α1  G 2 (z) = μ R1 (z)  γ G 4 (z)  θ G1 (z)  λ P0, 0, 0 , λ ( 1  A(z) )  δ G 3 (z) = α1 G 2 (z) , λ ( 1  A(z) )  γ G 4 (z) = δ G 3 (z) , λ y ( 1  A(z) )  α 2 y  μ ( y  1 ) G 5 (z, y) =  μy R1 (z)  γy G 7 (z, y)  βy G 2 (y) ,

(2.19) (2.20) (2.21)

(2.22)

λ ( 1  A(z) )  δ G 6 (z, y) = α 2 G 5 (z, y), λ ( 1  A(z) )  γ G 7 (z, y) = δ G 6 (z, y) .

(2.23) (2.24)

The total probability generating function G(z, y) is given by

G(z, y)  G 0 (z)  G1 (z)  G 2 (z)  G 3 (z)  G 4 (z)  G 5 (z, y)  G 6 (z, y)  G 7 (z, y) .

(2.25)

The normalizing condition is

G(1,1)  G 0 (1)  G1 (1)  G 2 (1)  G 3 (1)  G 4 (1)  G 5 (1,1)  G 6 (1,1)  G 7 (1,1) =1. (2.26) This condition yields,

R1 (1)  λA (1) / μ. 1

Under steady state conditions, let

Pv , Ps , Pb , Pbb , Pdb , Pi , Pbi and Pdi be the probabilities that the

server is in vacation, in startup, in batch service, waiting for repair during batch service, under repair during batch service, in individual service, waiting for repair during individual service and under repair during individual service states respectively. Then

Pv  G 0 (1)  y N (1) P0, 0, 0 ,

(2.27)

Ps  G1 (1)  ( λ P 0, 0, 0 / θ ) ,

(2.28)

Pb  G2 (1)  ( λ A1 (1) / β ) ,

(2.29)

Pbb  G 3 (1)  ( α 1 / ) ( λ A1 (1) / β ) ,

(2.30)

Pdb  G 4 (1)  ( α 1 / γ ) ( λ A (1) / β ) ,

(2.31)

Pi  G 5 (1,1)  ( λ A1 (1) / μ ) ,

(2.32)

Pbi  G 6 (1,1)  ( α 2 / δ ) ( λ A1 (1) / μ ) ,

(2.33)

Pdi  G 7 (1,1)  ( α 2 / γ ) ( λ A1 (1) / μ ) ,

(2.34)

l l where P0, 0, 0  (1  ρ) , and ρ  λA (1) 1  α1  α1   λA (1) 1  α 2  α 2  is the    



δ 



δ 

utilizing factor of the system.

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A. Expected number of customers in different states Using the probability generating functions expected number of customers in the system at different states are derived in this section.

L v , L s , L b , L bb , L db , L i , L bi and L di be the expected number of customers in the system

Let

when the server is in vacation, in startup, in batch service, waiting for repair during batch service, under repair during batch service, in individual service, waiting for repair during individual service and under repair during individual service states respectively. Differentiating the generating functions Gq(z), q = 0,1,2,3,4,5,6,7 given in equations (2.17) to (2.24) and substituting z =1 we get L v  G 0 (1)  y N (1) P0, 0, 0 1

(2.35)

L s  G1 (1)  [λ A1 (1)  λ  θ y N (1)  / θ 2 ] P0, 0, 0 1

(2.36)

L b  G 2 (1)  λ A1 (1) / β ,

(2.37)

 λα1 A (1)  λ A (1)  1    L bb  G 3 (1)   1     βδ δ    1

L db 



i P

4, i, 0

i 1

 G4

(2.38) ,

λ α1 A1 (1)  λ A1 (1) λ A1 (1)    (1)  1    γβ γ δ  

(2.39)

L i  G 5 (1,1) 1



ρ1 λ A 1 (1) ρ 2 λ A 1 (1)  1 λ A 1 (1)  λ  θ y N (1)    P    0, 0, 0  μ (1  ρ 2 ) θ2   (1  ρ 2 ) (1  ρ 2 ) (1  ρ 2 ) 

1 1    γ δ



y (1) ( λA1 (1) ) 2 α 1  λ A 11 (1) δ γ  λ  θ y N (1)  P0,0,0   P0, 0, 0  N 1 1  γ  δ  θ (1  ρ ) β γ δ (1  ρ ) 2 A (1)(1  ρ 2 )     2 2



A 11 (1) (ρ1  ρ 2 ) ( λA1 (1) ) 2  μ (1  ρ 2 ) 2 A 1 (1)(1  ρ 2 )

 1 1  ( λA1 (1) ) 2 α 2        γ δ  μ γ δ (1  ρ 2 ) 

L bi  G 6 (1,1) 

( λA1 (1) ) 2 α 2 α 1  2 G 5 (1,1) , 2 δ μ δ

L di  G 7 (1,1) 

( λA1 (1) ) 2 α 2 μ γ

1 where ρ  λA (1) 1  α 1  α 1 1  β γ δ 

  

(2.40)

(2.41)

1 1 δ 1     G 5 (1,1) , γ δ γ α α λA1 (1)  1  2  2 , ρ2  μ γ δ 

(2.42)  and  

G q ( . ) denotes the first order derivative of G q ( . ) . Finally, the expected number of units in the system L(N)  L v  L s  L b  L bb  L db  L i  L bi  L di



y (1) ρ λ A 1 (1)  λ  θ y N (1)   N P0, 0, 0    P0, 0, 0 (1  ρ 2 ) (1  ρ 2 ) (1  ρ 2 )  θ2 



( λ A 1 (1) ) 2 α1 β γ δ (1  ρ 2 )



λA1 (1)ρ 2 (1  ρ 2 )



λ A 11 (1) ρ 2  λ  θ y N (1)  A 11 (1) ρ 2 ρ P    0, 0, 0 θ 2 A 1 (1) (1  ρ 2 )  2 A 1 (1) (1  ρ 2 ) 

 γ δ  (λ A 1 (1) ) 2 α 2 1     δ γ μγδ 

 γ δ 1    δ γ 

 1 1  λA1 (1)  ( A 1 (1)) 2 α 2 ρ 2      ρ 2  μ μγδ(1  ρ 2 )  γ δ  

(2.43)

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III. Some other system performance measures In this section, expected length of vacation period, startup period, batch service period, delay period during batch service, waiting period for repair during batch service ,individual service period, delay period during individual service and waiting period for repair during individual service are presented. The expected length of a busy cycle Ec is given by

Ec  E v  Es  E b  E b b  Ed b  Ei  E b i  Ed i

(3.1)

The long run fractions of time the server is in different states are respectively,

E v / E c  Pv  y N (1) P0, 0, 0 ,

(3.2)

E s / E c  Ps  ( λ/ θ ) P0, 0, 0 ,

(3.3)

E b / E c  P b  λ A1 (1) / β ,

(3.4)

 λ A1 (1)   , E bb / E c  Pb b  ( α1 / δ)  β  

(3.5)

 λ A1 (1)   , E db / E c  Pdb  ( α1 / γ )  β  

(3.6)

E i / E c  Pi  λ A1 (1) / μ ,

(3.7)

 λ A (1)   , E bi / E c  Pbi  ( α 2 / δ )  μ   1

 λ A1 (1)   , E di / E c  Pdi  ( α 2 / γ )  μ   Expected length of vacation period is given by

Ev  N / λ ,

(3.8) (3.9)

(3.10)

Substituting this in equation (3.2 )

E c  1 /( λ P 0, 0, 0 ) ,

(3.11)

Heuristic approach to waiting time in the queue Let Wq be the waiting time of the test customer until his individual service. An arbitrary customer waits different time amounts according to the state of his arriving epoch. First, we divide the regeneration cycle into eight parts of the idle period, the startup period, the first phase batch service period, waiting time for repair and repair period due to breakdown in first phase, the second phase individual service period, waiting time for repair and repair period due to breakdown in second phase, and the repair period with respective probabilities ' ' ' ' '   y N (1) P0,0,0 ,  P0,0,0 ,  A (1) , α1   A (1)  , α1   A (1)  ,  A (1) , α 2   A (1)  and      δ     μ δ     γ  

  A ' (1)  .      That is the system state that the arriving customer sees determines his waiting time. The test customer has to wait during the individual service times for those already waiting (except the ongoing individual service) in the system. In addition to it, (i) If the server is idle, the customer has to wait the remaining idle period, startup period, the first phase batch service period. (ii) If the server is in the startup state, the customer has to wait the remaining startup period, the first phase batch service period. (iii) If the server is in the first phase, the customer has to wait the remaining time of the ongoing batch service. (iv) If the server is in the breakdown waiting state of batch service, the customer has to wait the remaining waiting period and repair period. (v) If the server is in the repair state due to breakdown during batch service, the customer has to wait the remaining repair period. (vi) If the server is in the second phase, the customer has to wait the remaining time of the ongoing individual service plus the batch service. α2 γ

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(vii) If the server is waiting for repair due to breakdown during individual service, the customer has to wait the remaining waiting period and the repair period, the first phase batch service period. (viii) If the server is in the repair state due to breakdown during individual service, the customer has to wait the remaining repair time period plus the first phase batch service period. Thus  1 1  λ P0,0,0  λ A ' (1)  E(Wq) = L(N  1) 1   N  1  1  1  y (1)P     N 0,0,0 ' θ  β     μ  θ β2    2 λ A (1) θ β   

+  1  1  α1 δ γ δ  

'  λ A (1)   α1    2  β   γ

+  1  1  1  α 2 δ γ β δ   =

1 1 r

 λ A ' (1)   λ A ' (1)  1 1         β μ     μ β 

'  λ A (1)   1 1  α 2       β    γ β  γ

 λ A ' (1)  .   μ  

 N  1  1 1  λ P0,0,0   λ A ' (1)  1 1        y (1)P  N 0,0,0 2 θ  β    '     θ     β   2 λ A (1) θ β 

+  1  1  α1 δ γ δ  

'  λ A (1)   α1    2  β   γ

+  1  1  1  α 2 δ γ β δ  

 λ A ' (1)   λ A ' (1)  1 1         β μ     μ β 

'  λ A (1)   1 1  α 2       β    γ β  γ

 λ A ' (1)  .   μ  

' ' where r =  λ A (1)    λ A (1)  .

 

 

 

 

B. Reliability indices In this section two reliability indices of the system viz.- The system availability and failure frequency under the steady state conditions are discussed. Let Aν(T) be the system availability at time t, that is the probability that the server is working for a customer or in idle period or in startup period, or in batch service such that the steady state availability of the server will be Aν =

Lt A ν (t)

t 

The steady state availability of the server will be given by G0 (1)+ G1 (1) + G2 (1) + G3 (1,1) =

= 1-

λ  y N (1) θ  θ

λ A ' (1) β

P0,0,0

λ A ' (1) λ A ' (1) + + μ μ

 α1 α1  λ A ' (1)    δ  μ  γ

 α2 α2     δ   γ

The steady state failure frequency of the server is given by

λ α1 A ' (1) λ α 2 A ' (1) + . β μ IV. Optimal Cost Structure In this section, the optimal value of N is determined that minimizes the long run average cost of twophase Mx/M/1, N-policy queue with server break downs and delay in repair. To determine the optimal value of N the following linear cost structure is considered. AIJRSTEM 13- 220; © 2013, AIJRSTEM All Rights Reserved

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Let CA(N) be the average cost per unit of time , then E E E  C A (N)  C h L(N)  C 0  b  i   C m  s Ec   Ec  Ec  E  E db  E bi  E di  C b  bb Ec 

  

   C 

 1  E c

   C 

E  E

v c

  . 

(4.1)

where

C h  Holding cost per unit of time spend for each customer present in the system, C 0  Cost per unit of time for keeping the server on and in operation,

C m  Start up cost per unit time, C s  Setup cost per cycle, C b  Break down cost per unit of time for the unreliable server, and

C r  Reward per unit of time as the server is doing secondary work during vacation. From equations (3.4) to (3.9) it is observed that

E b / E c , E bb / E c , E db / E c , E i / E c , E bi / E c , E di / E c are not functions of the decision variable N. Hence, for determination of the optimal operating N-policy , minimizing CA(N) in equation (4.1) is equivalent to minimizing  λ   1 (1  ρ) C h YN (1)  (1  ρ 2 ) C m  λ C s  C r YN (1) θ    TA (N)  . ( 4.2) (1  ρ 2 ) (y N (1)  (λ / θ) ) It is hard to prove that TA (N) is convex. But a procedure that makes it possible to calculate the optimal threshold N* is presented below. V. Result Under the long run expected average cost criterion, the optimal threshold N* for the model is the best value of ‘k’ given by N  λ(1  ρ 2 )  C m  C r kλ   (4.3) N*  min.k  1/  ( k  j ) y j    C s   θ C θ    j  0 h   and it is one of the integers surrounding N. Proof: See ref. (11). Sensitivity analysis 

In this section, sensitivity analysis is performed on the optimum threshold N based on changes in the system parameters and the cost elements numerical illustrations. Let the batch size X have geometric distribution with mean batch size 1/p. Then aj = P(X=j) = p(1-p)j-1, 0 < p < 1, j=1,2,3,… with the probability generating function A(z) = p z / [ 1-(1-p) z ] and E(X) = A1(1) = 1/p, E[ X (X-1) ] = A11(1) = 2 (1-p) /p2 . The expected number of customers in the system is given by

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V. Kumar et al., American International Journal of Research in Science, Technology, Engineering & Mathematics, 3(1), June-August, 2013, pp. 93-102 N 1

L( N ) 

ρ  (1  ρ 2 ) 

i y P i

i 0

0,0,0

(1  ρ 2 )

λ 2 α1 p β γ δ (1  ρ 2 ) 2

N 1    λ   yi  λ i 0  P   0,0,0 p (1  ρ 2 )  θ2    

 γ δ λ2 α2  γ δ 1     2 1    δ γ p μ γ δ δ γ   

N 1   λ   yi λ(1 - p ) ρ 2  i 0  p (1  ρ 2 )  θ  



λ ρ 2  1 1  λ  λ 2 α 2ρ 2    ρ 2    2 ρ (1  ρ 2 )  δ γ  p μ  p μγδ(1  ρ 2 )

N 1

where yi =

   1  p  ρ2 ρ P    0,0,0  p  (1  ρ 2 )  

y l 1

i -l

, y0 = 1, aj = p(1-p)j-l , ρ  λ 1  α1  α1  , ρ  λ 1  α 2  α 2 2 1 p μ  γ δ pβ γ δ    . θ

P0,0,0  (1  ρ1  ρ 2 )

,  

N 1    λ  θ  yi  i 0  

The varying details of optimal threshold N* and the minimum cost C A(N*) for specified values of the system parameters and the cost elements are presented in the following tables. Table1: The optimal N*and minimum expected cost CA(N*)with various ( , γ) =4, =3, γ=3,m=2,1=0.2,2=0.5, =2, Ch=5, C0 =50, Cm =100, Cb =100, Cr =40, Cs =1000 (λ,μ)

CA(N*)

(λ,μ)

CA(N*)

(0.2,3.5) (0.3,3.5)

11 13

30.78 47.04

(0.4,2.0) (0.4,2.5)

11 12

66.32 62.28

0.4,3.5)

58.31

(0.4,3.0)

59.92

(0.5,3.5) (0.6,3.5)

15 15

65.32 68.15

(0.4,3.5) (0.4,4.0)

14 14

58.31 57.14

From Table 1 it may be observed that (a) N* shows increasing trend for increase in the values of λ and μ, (b)CA(N*) increases with increase in λ and decreases with increase in μ. Table2: The optimal N*and minimum expected cost CA(N*)with various (,) 

,

, γ=3,m=2,1=0.2,2=0.5, =2, Ch=5, C0 =50, Cm =100, Cb =100, Cr =40, Cs =1000

(,)

CA(N*)

(,)

CA(N*)

(3.0,3.0) (4.0,3.0)

14 14

58.71 58.31

(4.0,2.0) (4.0,2.5)

14 14

58.90 58.55

(5.0,3.0)

58.07

(4.0,3.0)

58.31

(6.0,3.0) (7.0,3.0)

14 14

57.91 57.80

(4.0,3.5) (4.0,4.0)

14 14

58.14 58.01

It can be seen from Table 2 that (a) N* is insensitive to the values of β and θ, (b) with increase in the values of β, CA(N*) decreases. Conversely it decreases with increase in the values of θ. Table 3: The optimal N*and minimum expected cost CA(N*)with various(ch,co) =4, =3, γ=3,m=2,1=0.2, 2=0.2,=2,Cm =100, Cb =100, Cr =40, Cs =1000 (ch,co)

CA(N*)

(ch,co)

CA(N*)

(4.0,50) (6.0,50) (8.0,50)

16 13 11

52.71 63.44 72.62

(5.0,40) (5.0,60) (5.0,80)

14 14 14

54.02 62.60 71.17

(10,50)

80.88

(5.0,100)

79.74

(12,50)

88.41

(5.0,120)

88.31

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It can be observed from table 3 that (a) N* is insensitive to increase in C0 where as it decreases with increase in Ch and (b) CA(N*) increases with increase in the values of Ch and C0 . Also, it is observed from the numerical computations (which are not presented) that (a) N* is insensitive to increase in the values of γ, δ, α1, α2, Cm and Cr (b) CA(N*) decreases with increase in the values of γ, δ and Cr and increases with increase in the values of α1, α2 and Cm . VI. Conclusions In this paper, some important performance measures are derived for the N-policy, Mx/M/1 queueing system with two phases of service, server startup, server breakdowns and delayed repair. As the convexity of the expected cost function cannot be proved theoretically, a heuristic approach is chosen to determine the optimal threshold. Sensitivity analysis is performed between the optimal value of N, the specific system parameters and cost elements for the specific batch size distribution, geometric. REFERENCES [1] Baker, K.R., “Note on operating policies for the queue M/M/1 with exponential startups”, INFOR, Vol, 11, 1973, pp. 71-72. [2] Choudhury, G. and Tadj, L, “An M/G/1 queue with two phases of service subject to the server breakdown and delayed repair”, Applied Mathematical Modelling, Vol. 33, 2009, pp. 2699-2709. [3] Choudhury, G., Ke, J.-C. and Tadj, L, “The N-policy for an unreliable server with delaying repair and two phases of service”, Journal of Computational and Applied Mathematics, Vol. 231, 2009, pp. 349-364. [4] Doshi, B.T, “ Analysis of a two-phase queueing system with General Service Times”, Intergral Transforms and Special Functions, Operations Research Letters, Vol. 10, 1991, pp. 265-272. [5] Krishna, C.M. and Lee, Y.H, “A study of two-phase service”, J. Operations Research Letters, Vol. 9, 1990, pp. 91-97. [6] Kim, T.S. and Chae, K.C, “A two-phase queueing system with threshold”, Global Telecommunications Conference, 1998, GLOBECOM98, The Bridge to Global Integration, IEEE, Vol. 1, 1998, pp. 502-507. [7] Selvam, D. and Sivasankaran, V, “A two-phase queueing system with server vacations”, Operations Research Letters, Vol. 15, No. 3, 1994, pp. 163-169. [8] Vasanta Kumar, V. and Chandan, K, “Cost Analysis of a Two-Phase Mx/Ek/1 Queueing System with N-policy”, OPSEARSCH, Vol. 45, No. 2, 2008, pp. 155-174. [9] Vasanta Kumar, V, Hari Prasad, B.V.S.N, Chandan, K, “Optimal strategy Analysis of an N-policy two-phase Mx/M/1 gated queueing system with server startup and breakdowns”, International journal of open problems computer science and Mathematics, Vol. 3, No. 4, 2010, pp. 563-582. [10] V,Vasanta Kumar, B.V.S.N. Hari Prasad, K.Chandan, K.P.R.Rao, “Optimal Strategy Analysis of an N-policy two-phase M/Ek/1 queueing system with server breakdowns and gating”, Applied Mathematical Sciences, Vol. 4, No. 66, 2010, pp. 3261-3272. [11] Vasanta Kumar Vemuri, Venkata Siva Nageswara Hari Prasad Boppana, Chandan Kotagiri, Ravi Teja Bethapudi, “Optimal Strategy Analisys of an N-policy two-phase Mx/M/1 queueing system with server startup and breakdowns”, OPSEARCH, Vol. 48, No. 2, 2011, pp. 109-122. [12] Vasanta Kumar, V., Chandan, K., Ravi Teja, B. and Hari Prasad, B.V.S.N. “Optimal Strategy Analisys of an N-Policy two-phase Mx/Ek/1 queueing system with server startup and breakdown”, Quality Technology and & Quantitative Management, Vol. 8(3), 2011, pp. 285-301. [13] Wang, K.-H, “Optimal operation of a Markovian queueing system with removable and non-reliable server”, Microelectronics Reliability, Vol. 35, 1995, pp. 1131-1136.

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Multiclass Tumour classification by using SVM classifiers P.K.Srimani1and Shanthi Mahesh2 Dept. of Computer Science &Maths, Bangalore University, Director R&D, BU. Bangalore-560078, Karnataka, India. 2 Dept. of Information Science & Engineering, Atria Institute of Technology, Bangalore-560024, Karnataka, India. 1

Abstract: Brain cancer is a disease in which cells grow in an uncontrolled fashion in the brain. Therefore, a proper diagnosis of the disease is absolutely required so that effective treatment could be provided to the patients. This paper presents brain tumour classification of five classes using SVM classifier. The classes considered are Medulloblastoma, Malignant glioma, Normal cerebellum and PNET from the data set Brain_tumor1.mat), which includes 90 sample, each sample has 5920 genes. From the experiment conduct in Matlab using SVM classifier, the result was found to be Normal cerebellum has sensitivity of 1, specificity of 0.5000 and prevalence of 95.6%.This chapter presents brain tumour classification of five classes using SVM classifier. The classes considered are Medulloblastoma, Malignant glioma, Normal cerebellum and PNET from the data set Brain_tumor1.mat), which includes 90 sample, each sample has 5920 genes. From the experiment conduct in Matlab using SVM classifier, the result was found to be Normal cerebellum has sensitivity of 1, specificity of 0.5000 and prevalence of 95.6%. Key Words: Brain Tumour, SVM classifier, Sensitivity, Specificity, Prevalence. I. Introduction Tumour is basically as theformatting; abnormal style; growthstyling; of the insert tissues. Brain tumour an words) abnormal mass of tissue Keywords: WWW;defined component; (Minimum 5 to 8iskey in which cells grow and multiply uncontrollably. Brain tumours can be primary or metastatic and either benign or malignant. A metastatic brain tumour is a cancer that can spread from anywhere in the body to the brain. The knowledge of volume of a tumour plays an important role in the treatment of malignant tumours. Manual segmentation of brain tumours from Magnetic Resonance images is a challenging and time consuming task. This paper presents a novel technique for the detection of tumour in brain using segmentation and histogram thresholding. The proposed method can be successfully applied to detect the contour of the tumour and its geometrical dimensions. This technique is proved to be a handy tool for physicians engaged in this field. Brain cancer is one among the most deadly and intractable diseases. Tumours may be embedded in regions of the brain that are critical to orchestrating the bodyâ&#x20AC;&#x2122;s vital functions, while they shed cells to invade other parts of the brain, forming more tumours too small to detect using conventional imaging techniques. Brain cancerâ&#x20AC;&#x2122;s location and ability to spread quickly makes treatment with surgery or radiation like fighting an enemy hiding out among minefields and caves. Brain cancer is a disease in which cells grow uncontrollably in the brain. Brain tumours are of two main types: (i) Benign tumours (ii) Malignant tumours. Benign tumours are incapable of spreading beyond the brain itself. Benign tumours in the brain usually do not need to be treated and their growth is self limited. Sometimes they cause problems because of their location, and surgery or radiation can be helpful. Malignant tumours are typically called brain cancer. These tumours can spread outside the brain. Malignant tumours of the brain are most harmful which may remain untreated and an aggressive approach is almost always warranted. Detection of Brain tumour is a serious issue in medical science. Brain tumour is one of the major causes for the increase in mortality among children and adults. Imaging plays a central role in the diagnosis and treatment planning of brain tumour. Imaging of the tumours can be done by CT scan, Ultrasound and MRI etc. The MR imaging method is the best due to its higher resolution. But there are many problems in the detection of brain tumour in MR imaging as well. An important step in most medical imaging analysis systems is to extract the boundary of an area we are interested in. Many of the methods are there for the MRI segmentation. Till today histogram thresholding is used for pre-processing only in many of the segmentation methods. This paper shows that it can be used as a powerful tool for segmentation also. The image captured from an affected tumours brain shows the place of the infected portion of the brain. The image does not give the information about the numerical parameters such as area and volume of the infected portion of the brain. However, after the preprocessing of the image, the image segmentation is done first. The segmented image shows the unhealthy portion clearly. From this image the infected portion (tumour) is selected by cropping the segmented image.

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From this cropped image, statistical analysis viz., mean, standard deviation, correlation and the area or dimension of the tumour is calculated. II. Related work In this section, a brief review of the literature is presented. Quite a good amount of literature pertaining to the application of SVM classification different area is available. Authors [1] have proposed an approach that uses mirror point pairs and a multiple classifier system to reduce the classification time of a SVM. A greedy strategy is then used for fine selection of member classifiers. Authors[2] have proposed a parallelization of support vector machine learning for shared memory systems. The learning algorithm relies on a decomposition scheme, which in turn uses a special variable projection method, for solving the quadratic program associated with SVM learning. Authors[3], have presented Pattern recognition approaches, such as the SVM, have been successfully used to classify groups of individual based on their patterns of brain activity. This approach is an application of one-class SVM to investigate if patterns of fMRI response to sad facial expression in depression patients would be classified as outliers in relation to pattern of health control subjects. Authors[4] presented a SVM classifier based on Rough Set(RS) is researched in order to enhance the predicting performance. Authors[5], have used Diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment(MCI). This method shows the potential of a DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Authors[6], have made a study to use SVM in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms and shown that SVMâ&#x20AC;&#x2122;s had significantly less training time. Authors[7] have presents a novel learning method, SVM, is applied on different data which have two multiclass and comparative results are shown using different kernel function. Authors[8], have proposed a primary method to overcome SVM drawback with respect to classification speed which is due to the number of support vectors being large. Authors[9] , have presented and implement SVM using R tool. Authors[10], have introduced an alternative implementation technique for SVM to address the classification problem with small-size training sample set. It has been empirically proven that the effectiveness of the introduced implementation technique which has been evaluated by benchmark dataset. Authors[11], have proposed a convergence of a generalized version of modified SMO for SVM classifier design is proved, the results are also extended to modified SMO algorithms for solving v-SVM classifier problem. Authors[12] have used machine learning approaches to analyze multidimensional experimental data and integrate results from many studies, yet they have not been applied to specifically tackle the task of predicting and classifying self renewal and pluripotency gene memberships. III. Dataset Description BrainTumour Data (Brain_Tumor1.mat) The dataset comes from a study of 5 human brain tumor types and includes 90 samples. Each sample has 5920 genes: a. Medulloblastoma: Medulloblastoma is a highly malignant primary brain tumor that originates in the cerebellum or posterior fossa. Previously, medulloblastomas were through to represent a subset of primitive neuroectodermal tumor (PNET) of the posterior fossa. However, gene expression profiling has shows that medulloblastomas have distinct molecular profile and are distinct from other PNET tumors. b. Malignant glioma: A glioma is a type of malignant brain tumor. A malignant tumor is a mass of abnormal cells that is cancerous. Tumors can develop in any part of the brain or its nerves and covering tissues. The two major types of brain tumor are primary and secondary. Primary brain tumors start in the brain. Secondary brain tumors start in another part of the body, then spread to the brain. A glioma is a primary brain tumor, accounting for 45% of cancers that begin in brain cells. The three main types of glioma include: astrocytoma, ependymoma, and oligodendroglioma. Each of these types can be assigned a grade, either low grade or high grade, with high grade being more malignant and aggressive. Astrocytomas are named for the cells where they originate, the astrocytes. These tumors can either show clear borders between normal brain tissue and the tumor (called focal) or no clear border (called diffuse). Focal astrocytomas are most common in children and are not often found in adults. Ependymomas begin in cells called ependymal cells that are found lining certain areas of the brain and spinal cord. These cells help repair damaged nerve tissue. They usually occur in children and young adults. Oligodendrogliomas form in oligodendrocyte cells, which produce a fatty substance called myelin that protects the nerve. More common in adults, these tumors may move to other parts of the brain or spinal cord. c. AT/RT (atypical teratoid/rhabdoidtumours):

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Atypical teratoidrhabdoid tumor (AT/RT) is a rare tumor usually diagnosed in childhood. Although usually a brain tumor, AT/RT can occur anywhere in the central nervous system (CNS) including the spinal cord. About 60% will be in the posterior cranial fossa (particularly the cerebellum).Figure 1 shows MRI of AT/RT and figure 2 shows the cerebellum with surrounding skull and spinal fluid occupies the bottom 1/3 of the axis of MRI image.. One review estimated 52% posterior fossa, 39% sPNET (supratentorial primitive neuroectodermal tumors), 5% pineal, 2% spinal, and 2% multi-focal.

Figure 1: MRI of AT/RT

Figure 2: The cerebellum with surrounding skull & spinal fluid occupies the bottom 1/3 of this axis MRI

d. Normal cerebellum The cerebellum is part of the brain. It lies under the cerebrum, towards the back, behind the brainstem and above the brainstem. The cerebellum is largely involved in "coordination". Persons whose cerebellum doesn't work well are generally clumsy and unsteady. They may look like they are drunk even when they are not. The main clinical features of cerebellar disorders include incoordination, imbalance, and troubles with stabilizing eye movements. There are two distinguishable cerebellar syndromes -- midline and hemispheric. Midline cerebellar syndromes are characterized by imbalance. Persons are unsteady, they are unable to stand in Romberg with eyes open or closed, and are unable to well perform tandem gait. Severe midline disturbance causes "trunkal ataxia" a syndrome where a person is unable to sit on their bed without steadying themselves. Some persons have "titubation" or a bobbing motion of the head or trunk. Midline cerebellar disturbances also often affect eye movements. There may be nystagmus, ocular dysmetria and poor pursuit. Hemispheric cerebellar syndromes are characterized by incoordination of the limbs. There may be decomposition of movement, dysmetria, and rebound. Dysdiadochokinesis is the irregular performance of rapid alternating movements. Intention tremors may be present on an attempt to touch an object. A kinetic tremor may be present in motion. The finger-to-nose and heel-to-knee tests are classic tests of hemispheric cerebellar dysfunction. While reflexes may be depressed initially with hemispheric cerebellar syndromes, this cannot be counted on. Speech may be dysarthric, scanning, or have irregular emphasis on syllables. e.

PNET(primitive neuroectodermaltumours): PNET(pronounced pee-net) stands for a group of tumours known as Primitive Neuro Ectodermal Tumours. PNETS develop from cells that are left over from the earliest stages of a bady’s development in the womb. Normally these cells are harmless. But occasionally they turn into a cancer. These cancers are more common in children than adults.

Doctors use the term PNET to classify the tumour. They are divided into two main groups: a. PNETs of the brain and central nervous system b. Peripheral PNET (outside the brain and nervous system) PNETs of the brain or spinal cord Primitive neuroectodermal tumours that occur in the brain and spinal cord include: Medulloblastoma (develops in the back part of the brain – the hindbrain) Pineoblastoma (develops in the pineal region of the brain) Non pineal supratententorial IV. Methodology Support vector machines (SVM) is a class of linear classification algorithms which tries to maximize the margin of confidence of the classification on training data set and has been considered as well-suited to the noise and AIJRSTEM 13-221; © 2013, AIJRSTEM All Rights Reserved

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high-dimensionality of microarray data. In its simplest form, the decision boundary of SVM is a hyperplane that separates boundary training data of different classes with maximum confidence. Unlike LDA, if no separating hyperplane exists, SVMs will map the samples to a higher-dimensional space where the data become linearly separable.

Figure 3: The optimal hyperplane for the separation of balls and diamonds has the maximal margin between the two classes. SVM classifiers are based on the class of hyperplanes (w-x) + b = Q,wG RN, b ϵ R, corresponding to decision functions f(x) = sign((w-x)+b). This is a convex, quadratic programming problem which is approached using Lagrangian. Figure (3) shows that the optimal hyperplane is defined as that with the maximal margin of separation between the two classes. SVMs map the data into some other dot-product space (called the feature space) F via a nonlinear map Φ: RN—>F, and try to solve the same optimization problem in F. The mapping is not explicitly needed if some kernel function K is used such that Φ(i)- Φ {xj)=K(xi,xj). The mapping is accomplished by raising the dot-product kernel x • y to a positive integer power. For instance, squaring the kernel yields a convex surface in the input space. Raising the kernel to higher powers yields polynomial surfaces of higher degrees. SVMs are more powerful than linear discriminant methods, since they are able to classify non-linearly separable classes by mapping the samples into higher-dimensional space. Moreover, SVMs are different from other nonlinear classifiers in that they pay special attention to the boundary of separation between the regions corresponding to each class, and this may yield small improvements of the classification prediction rate. V. Experiments and Results Experiments were conducted using MATLAB, figure 4a and 4b show variable and Sample profiles of the brain tumour data.

(a) (b) Figure 4: (a) Sample Profile, (b) Variable Profile

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Sl.no 1 2 3 4 5

Table:1: Classified rate, sensitivity and specificity prediction Tumor type Last Error Rate Classified Rate Sensitivity Medulloblastoma 0.3556 1 0.0667 Malignant glioma 0.1111 1 1 AT/RT Normal cerebellum PNET

0.1111 0.0222 0.1111

1 1 1

Specificity 0.9333 0

1 1 0.9524

1.5 1 0.5 0

0 0.5000 0

specificity sensitivity

Figure 5: Comparison of specificity and sensitivity of 5 classes of tumor Figure 5 gives the graphical representation of the results corresponding to the five classes of tumor. Table 2 gives the accuracy (prevalence) prediction while figure 6 gives its graphical representation. The results show that the performance of SVM classifier is excellent.

Sl.no 1 2

Tumor type Medulloblastoma Malignant glioma

3 4 5

AT/RT Normal cerebellum PNET

Table 2: Accuracy Prediction Positive predictive NPV 0.3333 0.6667 0.8889 NaN 0.8889 0.9773 0.9302

NaN 1 0

PL 1 1

NL 1 NaN

Prevalence 0.3333 0.8889

1 2 0.9524

NaN 0 NaN

0.8889 0.9556 0.9333

Form table 1, it is evident that the two tumour types viz., Malignant glioma and AT/RT have the sensitivity equal to 1 and specificity as 0, while Normal cerebellum has sensitivity equal to 1 and specificity equal to 0.5.

Prevalence 100 50 0

Prevalence

Figure 6: Prevalence comparison VI. CONCLUSION Gene expression data can be classified on both genes and samples. As a result, the SVM classification algorithm performs extremely well. From the present investigation it is concluded that SVM classifier is the best classifier AIJRSTEM 13-221; ÂŠ 2013, AIJRSTEM All Rights Reserved

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with (Sensitivity=1, specificity=0) when compared to other classification techniques. Normal cerebellum has sensitivity of 1, specificity of 0.5000 and prevalence of 95.6%. The results of this investigation are obtained by implementing the CODE in the MATLAB and they throw light on the qualitative as well as the quantitative aspects of the problem. VII. References [1] [2] [3] [4] [5]

[6] [7] [8] [9] [10] [11] [12] [13] [14]

Jiun-Hung Chen and Chu-Song Chen, ―Reducing SVM classification Time Using Multiple Mirror Classifiers‖ IEEE Transaction on Systems, Man and Sybernetics, Vol. 34, N0.2, April 2004. Tatjana Eitrich and Bruno Lang, ―Data Mining with Parallel Support Vector Machine for Classification‖, ADVIS 2006,LNCS 4243, pp.197-206, 2006 C Springer-Verlag Berlin Heideberg 2006. Janaina Maurao,David R Hardoon, Tim Hahh, Andre F Marquand, Steven Williams and Michael Brammer, ―Patient classification as an outlier detection problem: an application of the one-class support vector machine‖, pp.793-804, NeuroImage, Vol.58, 1 Oct 2001. Guojun Zhang and Jixiong Chen, ―A SVM Classifier Research Based on RS Reducts‖, Information Management, Innovation Management and Industrial Engineering, 2009 Internatioanl Conference. Dec 26th and 27th 2009. Vol.4 Laurence O'Dwyer, Arun L. W. Bokde, Michael Ewers, Yetunde O. Faluyi, Colby Tanner, Bernard Mazoyer, Desmond O'Neill, Máiréad Bartley, D. Rónán Collins,Tara Coughlan, David Prvulovic and Harald Hampel, ―Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive Impairment‖ PLOS ONE, February 23, 2012. Harris Drucker, Donghui Wu and Vladimir N.Vapnik, ―Support Vector Machines for Spam Categorization‖, IEEE Transactions on Neural Networks, vol.10, No. 5, September 1999. Durgesh KSrivastava and Lekha Bhambhu, ―Data Classification Using SVM‖, 2005-2009 JATIT. WWW.jatit.org Sathiya Keerthi, Olovier Chapelle and Dennis DeCoste, ―Building Support Machine with Reduced Classifier complexity’, Journal of Machine Learning Research 1493-1515, 7 (2006). Alexandros Karatzoglou, David Meyer and Kurt Hornik, ―Support Vector Machine in R‖, Journal of Statistical Software, Vol.15, Issue 9. April 2006. Mingmin Chi, Rui Feng and Lorenzo Bruzzone. ―Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem‖ ScienceDirect, Advance in space Research 41(2008) 1793-1799, Keerthi S.S and Gilbert E.G, ―Convergence of a Generalized SMO algorithm for SVM Classifier Design‖, Kluwer Academic Publishers, Machine Learning, 46, 351-360, 2002. Huilei Xu, Ihor R Lemischka and Avi Maayan. ―AVM classifier to predict genes important for self-renewal and pluripotency of mouse embryonic stem cells‖. BMC System Biology 2010, 4:173. Srimani P.K and Shanthi Mahes, ―Knowledge Discovery in Image-Segmentation Data set Using Decision Tree Classifiers ― International Journal of Current Research, Vol.4, Issue, 09, pp.135-140, september, 2012 Srimani P.K and Shanthi Mahes, ―Knowledge Discovery Process in Image-Segmentation―, International Journal of Knowledge Engineering, Vol.3, Issue, 3, Issue 2, pp.188-192, 2012

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Enhancing database access control policies Trilochan Tarai1, Pradipta Kumar Mishra2 Department of Computer science & Engineering, Centurion University of Technology & Management Bhubaneswar, India Abstract â&#x20AC;&#x201C; Now a days Public and private organizations increase their database system requirement for day-to-day business. Hence database security becomes more crucial as the scale of database is growing. A signified approach for protecting information which enforcing access control policies based on subject and object and their characteristics. There are many security models for database systems. The database security systems have developed a number of different access control policies for assuring data confidentiality, integrity and availability. In this paper we survey the concepts under access control policies for database security. We review the key access control policies such as Mandatory Access Control policy(MAC), Discretionary Access Control Policy(DAC), and Role Based Access Control Policy(RBAC) and propose a concept on RBAC policy that is instead of access control through role assigned to the users, the users are assigned by some level of access control. Keywords-Database Security, Access Control Policy, MAC, DAC, RBAC. I. Introduction Since organizations are implementing database systems for daily business and make decision, database security becomes more crucial. These functions are inventory management and budgeting, payroll, and various types of forecasting. If these important data will lose or misuse, then it will affect on user and application and also affect on entire organization[5]. A complete solution to data security must provide the following three requirements: 1) secrecy or confidentiality refers to the protection of data against unauthorized disclosure, 2) integrity refers to the prevention of unauthorized and improper data modification, and 3) availability refers to the prevention and recovery from hardware and software errors and from malicious data access denials making the database system unavailable. These three requirements arise practically in all application environments. Consider a database that stores payroll information. It is important that salaries of individual employee not be released to unauthorized users, that salaries be modified only by the users that are properly authorized, and that paychecks be printed on time at the end of the pay period. Similarly, consider the web site of an airline company. Here, it is important that customer reservations only be available to the customers they refer to, that reservations of a customer not be arbitrarily modified, and that information on flights and reservations always be available. In addition to these requirements, privacy requirements are of high. Though the term privacy is often used as a synonym for confidentiality, the two requirements are quite different. Techniques for information confidentiality may be used to implement privacy; however, assuring privacy requires additional techniques, such as mechanisms for obtaining and recording the consents of users. [1]. Data protection is ensured by different components of a database management system (DBMS). In particular, an access control mechanism ensures data confidentiality. Whenever a subject tries to access a data object, the access control mechanism checks the rights of the user against a set of authorizations, stated usually by some security administrator. An authorization states whether a subject can perform a particular action on an object. Authorizations are stated according to the access control policies of the organization. Recently most relational database management systems(RDBMS) provide only some limited security techniques. They range from the simple password protection offered by Microsoft Access to the complex user/role structure supported by advanced relational databases like Oracle Server. There are some functional areas for database security models such as security policies, security mechanisms and security system assurance. Security policy describes what the security system is expected to do. Security mechanisms explain how the security systems should achieve the security goals. System assurance is used to provide consistency and integrity of the security mechanisms[8]. II. Access Control Policies The policies through which the user access data object, called access control policies. In access control policies, access control mechanisms are used for securing databases. Whenever a user tries to access a data object, the access control mechanism checks the rights of the user against a set of fixed authorization. Basically there are two main access control policies, such as Mandatory Access Control policy and Discretionary Access Control Policy. Now a days another policy is used that is RBAC(Role Based Access Control) policy which is most popular access control policy and has been used for many applications, such as grid and multilevel database security system. AIJRSTEM 13-224; ÂŠ 2013, AIJRSTEM All Rights Reserved

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III. Mandatory Access Control (MAC) Policy MAC policy is based on the classification of subject and object. Through this classification, this policy controls the access. The security levels of subjects and objects are classified into TopSecret(TS), Secret(S), Confidential(C), and Unclassified(U) in the relations such that TS>S>C>U. This access control policy defines two rules :  A subject can read only objects in the equal or lower level than itself.  A subject can read and write objects, that means record the objects when the level of subject is equal or higher than level of object. This policy is usually applied to mass data which generally needs to be strong protection. MAC policy is wellknown implemented in Multilevel Security (MLS), which traditionally has been available mainly on computer and software systems deployed at highly sensitive government organizations such as the intelligence community or the U.S.Departmant of defence [8]. IV. Discretionary Access Control (DAC) Policy DAC policy controls the data access according to the user’s identification and authorization. These authorizations are known as rules. These rules specify the access modes for each users or group of users and each object in the system. This policy can be defined as a means of restricting access to objects according to the identity of subjects or groups to which they belong. This policy specifies the decision that who can access information at the discretion of the information creator. That means owner of data or database administrator. Implementation of Security policy is based on granting and revoking privileges. Access is granted or denied according to the identification o the user. The authorization administration policy supervises this function in DAC. There are two types of Common Administration Policies in DAC, such as Centralized Administration and Ownership Administration. In Centralized Administration, only some privileged subjects may grant and revoke authorizations while in Ownership Administration grant and revoke operations on data objects are entered by the creator(or owner) of the object. User-level privileges in DAC defines access permissions based on the general account information of user [1][8]. The flexibility of DAC policy make suitable for a variety of systems and applications. DAC policy has the drawback that they do not provide real assurance on the flow of information in a system. V. Role-Based Access Control Policy (RBAC) This policy is one of the important policy which is recently innovated and widely used in organization. It direct represents access control policies of organizations and simplify authorization administration. Role based policies manage user’s access to the information on the basis of the activities of the users. That means this policy is based according to the role of the users. A role is nothing but a specific function in an organization or some set of actions or responsibilities associated with this function. All authorization needed to perform a certain activity are granted to the role associated with that activity. The user access to object is regulated by roles. That means each user is authorized to play certain roles and on the basis of these roles, a user can perform access to the object. This policy consists two parts: one which assigns access rights for object to roles. This represent management of security. Another important point is suppose user responsibilities changed that means the user’s current role can be taken away and new roles assigned as appropriate for the new responsibilities[3][4]. A. NIST RBAC Reference Model In recent years, vendors have begun implementing role-based access control (RBAC) features in their database management, security management. Several RBAC models have been proposed without any attempt at standardizing salient RBAC features. For an individual user based on the role, the RBAC reference model takes the access decision. The access rights are grouped by role, and the access to a resource is granted only to users authorized to play the associated role. The NIST RBAC model defines four components : Core RBAC, Hierarchical RBAC, Static Separation of Duty Relations(SISD), and Dynamic Separation of Duty Relations(DSD). Core RBAC embodies the essential aspects of RBAC. There are five basic data elements of the Core RBAC component: Users, Roles, Resource, Permissions and Sessions. A user is defined as a human being. Although the concept of a user can be extended to include machines, networks, or intelligent automated agents, for simplicity reasons we limit a user to a person in this article. A role is a job function within the context of an organization with some associated semantics regarding the authority and responsibility conferred on the user assigned to the role. Permission is an approval to perform an operation on one or more RBAC protected objects. Session is the mapping between a user and a subset of roles enabled for the user. An operation is an executable image of a program, which upon invocation executes some function for the user. The types of operations and objects that RBAC controls are dependent on the type of system in which they will be implemented. For example, within a file system, operations might include read, write, and execute; within a database management system, operations might include insert, delete, append, and update. The basic concept of RBAC is that users are assigned to roles, permissions are assigned to roles, and users acquire permissions by being members of roles.

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Core RBAC includes requirements that user-role and permission role assignment can be many-to-many. Thus the same user can be assigned to many roles and a single role can have many users. Similarly, for permissions, a single permission can be assigned to many roles and a single role can be assigned to many permissions. Core RBAC includes requirements for user-role review whereby the roles assigned to a specific user can be determined as well as users assigned to a specific role. A similar requirement for permission-role review is imposed as an advanced review function. Core RBAC also includes the concept of user sessions, which allows selective activation and deactivation of roles. Finally, Core RBAC requires that users be able to simultaneously exercise permissions of multiple roles [2][7][9]. The following figure-1 represents the elements and relations specific to each component.

Hierarchical RBAC is the Core RBAC enhanced with the role hierarchy. It adds requirements for supporting role hierarchies. A hierarchy is mathematically a partial order defining a seniority relation between roles, whereby senior roles acquire the permissions of their juniors, and junior roles acquire the user membership of their seniors. They are many to many relations and define inheritance relations among roles that is role X inherits role Y if all permissions granted to role Y are also granted to role X. Roles can have overlapping capabilities; that is, users belonging to different roles may be assigned common permissions. Furthermore, within many organizations there are a number of general permissions that are performed by a large number of users. As such, it would prove inefficient and administratively cumbersome to specify repeatedly their general permission role assignments. To improve efficiency and support organizational structure, RBAC models as well as commercial implementations include the concept of role hierarchies. This constraint is inherited also within a role hierarchy. Then the model component, Static Separation of Duty Relations, adds relations among roles with respect to user assignments. The constraints on the relations between elements take the form of Static Separation of Duty(SSD) relations and Dynamic Separation of Duty(DSD) relations. The SISD relation specifies the constraints on the assignment of users to roles. Once a role is authorized to a user, then the user canâ&#x20AC;&#x2122;t be the member of a second role. DSD relations place constraints on the roles that can be activated in a userâ&#x20AC;&#x2122;s session. If one role that takes part in a DSD relation is activated, the user cannot activate the related (conflicting) role in the same session [2][3]. From the above discussion we knew as per RBAC model, access rights are provided to group of users based on the role and governed by Dynamic Separation of Duty (DSD) relations. This model never described the category of roles under different levels of system i.e. level based role categories. In this paper we propose to assign different category of roles under some levels of a system with the concept in view that a particular level can be granted authorization up to a certain maximum level described by Database Administrator. B.

Comparative study of access control policies Mandatory Access Control Policy(MAC)

Discretionary Control Policy

Access of information

Accessed by defining two rules

Through owner of information

By assigning roles

Access Based on

Classification of subject and object Low

Human interpretation of good and bad user High

Classification of roles High

By assigning roles under some levels Classification of levels Higher

Yes

No ,but it occurs through some modifications

Yes

Flexibility for accessing information Support for multilevel database system Support for grid database system data privacy

Access

Role Based Control Policy

Access

Level Wise Roll Based Access Control Policy(proposed)

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VI.

Proposed Model

Level 1

Level 2

Level 3

Manager/ Administrator<figure-1>

User Level m

<figure-1> Level 1

Role 1

Role 2

Role 3

Role n

Role 1

Role 2

Level m

Role 3

Role n

<figure-2>

Roles

Includes

Manager/Administrator

Assigned <figure-3>

Users

Access Resource

Validation <figure-3>

Figure-1 represents that the DBA creates different levels for users and will indicate that which user belongs to which level. After that figure-2 represents that role is assigned to the user that must under a level. In this way level and role is categorized. The figure-3 represents the mechanism of the modified RBAC policy(Level Wise

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Roll Based Access Control Policy). The mechanism is first the administrator assign the role to the user according to the level wise. Then the user is validated by the admin, then after validation the user will access the resource. Here in this policy, data privacy is maintaining. Because it is not possible that if a user of one level want to access the role assigned of another level. VII. Conclusion Now a days the implementation of database systems are the key concept of data management for dayto-day operations of any organization. The scale of database is becoming larger and the user access control is complicated. So security of data management of system becomes crucial. So there are lots of requirements of access control mechanisms to achieve secrecy, integrity, and availability of data. In this paper we reviewed some access control models such as: Mandatory Access Control (MAC), Discretionary Access Control (DAC), and Role Based Access Control (RBAC) model. Especially we are focusing RBAC model. In this paper we propose a policy that is Level Wise Roll Based Access Control Policy (LWRBAC) to assign different category of roles under some levels of a system with the concept in view that a particular level can be granted authorization up to a certain maximum level described by Database Administrator. The proposed policy represents that according to the level wise, the role is assigning to the user by administrator. If the user is valid, then the user will access the resource. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

Betrino Elisa and Sandhu Ravi,”Database Security-Concepts, Approaches, and Challenges”, IEEE Transactions on Dependable and Secure Computing, Vol.2, No.1, January-March 2005. Marius ConstantinLeahu, Mare Dobson, and Giuseppe Avolio, “Access Control Design and Implementation in the ATLAS Experiment”, IEEE Transactions on Nuclear Science, Vol.55, No.1, February 2008. Anil L. Pereira, VineelaMuppavarapu and Soon M. Chung, “Role-Based Access Control for Grid Database Services Using the Community Authorization Service”, IEEE Transactions on Dependable and Secure Computing, Vol.3, No.2, April-June 2006. Ravi S. Sandhu, Edward J. Cope, Hal L. Feinstein, Charles E. Youman, “Roll Based Access Control Models”, IEEE journals, February 1996. Feikis John, “Database Security”, IEEE Journals, February-March 1999. Ravi S. Sandhu and PierangelaSamarati, “Access Controls Principle and Practice”, IEEE Communication Magazine September 1994. Akshay Patil and B.B.Meshram, “Database Access Control Policies”, International Journal of Engineering Research and Applications, Vol.2, May-June 2012. Min-A Jeong, Jung-Ja Kim and Yonggwan Wan, “A Flexible Database Security System Using Multiple Access Control Policies”, IEEE Journals, November 2003. D.Ferraiolo et al., “Proposed NIST standard for role-based access control”, ACM Trans. Inf. Syst. Security, vol.4, no.3, pp.224274, Aug,2001.

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

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

Kinetic Study of Catalyzed and Uncatalyzed Esterification Reaction of Acetic acid with Methanol M.B. Mandakea, S.V. Anekarb, S.M.Walkec Department of Chemical Engineering, Tataysaheb Kore Institute of Engineering and Technology, Waranangar, Kolhapur 416113, India. b Department of Chemical Engineering, Tataysaheb Kore Institute of Engineering and Technology, Waranangar, Kolhapur 416113, India. c Department of Chemical Engineering, Bharati Vidyapeeth college of Engineering, Navi Mumbai, India. a

Abstract: The kinetics of esterification of acetic acid with methanol will be investigated extensively for homogenous and heterogeneous catalyzed reactions. The experiments will be carried out in a batch stirred reactor at different temperatures by using ion exchange resin as a catalyst. The concentration â&#x20AC;&#x201C; time profile will be observed for different sets of conditions. The effects of temperature, Catalyst loading and feed molar ratios on reaction kinetics will also be studied. The activation energy and equilibrium constants for this reaction will be determined experimentally at different temperatures. The temperature dependency of the constants appearing in the rate expression will also be determined. Key Words: Esterification, Kinetics, Heterogeneous, Catalyst, Methyl Acetate, Ion exchange resin. I. Introduction There is an increasing the inclination of chemical industries toward new processes that should meets requirement such as generation of nearly zero waste chemicals, less energy, and sufficient uses of product chemicals in various application. Hence the esterification is widely employed reaction in the organic process industry. The reaction is carried out between carboxylic acid and alcohol at with or without present of different acid catalyst under such specific conditions. Esters are used as solvents of paints, adhesives and organic media instead of aromatic compounds. Esterification reactions are extremely slow; it requires several days to attain the equilibrium in the absence of a catalyst. To accelerate the reaction rate, catalysts are always employed in liquid phase esterification. Despite the strong catalytic effect, the use of homogenous catalysts, such as Sulphuric acid and p-toluenesulphonic acid suffers from several draw-backs, such as the existence of side reactions, corrosion of the equipment and the need to deal with acidic wastes. Homogenous catalysts may also result in sulphur contamination of the final ester product. In this situation, the use of solid catalysts has received increasing attention in the past years. Many solid-acid catalysts have been used, such as iodine, zeolite-T membrane, ZSM5, HY zeolite, zeolite beta, acid treated clays, heteropolyacids, copper catalysts, sulphated oxides and zirconia. Ion exchange resins, however, are the most commonly used solid acid catalysts instead of liquid ones. The goal of this work was to study the esterification kinetics of acetic acid with methanol in the presence of catalyst and in the absence of catalyst. The effect of the reaction temperature, the initial molar ratio of the reactants, the catalyst loading and the stirrer speed on esterification kinetics were studied. CH3COOH + CH3OH CH3COOCH3+ H2O Most reactions catalyzed by ion exchange resins can be classified as either a quasihomogenous or a quasiheterogeneous. The kinetics of this model reaction catalyzed by Amberlyst-15 was described as a quasihomogenous and a quasiheterogeneous model. II. Experimental A. Chemical and catalysts All chemicals and catalysts were procured from firms of repute and used without further purification: Acetic acid (S.D. Fine Chemicals, Mumbai India), Methanol (Merck India), Amberlyst-15 (Rohm and Hass, USA). The physical properties of these resins are reported in Table 1. The macroporous resin was dried at 70 0C for 6 hrs in vacuum drier before use. In all the experiment, the dried catalyst was used. Table 2.1:- Properties of Amberlyst-15 ion exchange resin Shape Average particlesize (mm)

Beads 0.5

Internal surface area (m 2 /gm) Capacity (meq /gm) Cross-linking density (% DVB) Porosity (%) Max.operating temperature ( C)

55 4.7 20-25 36 120

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B. Reaction Procedure The reaction was carried out in a 500 ml glass reactor of 7.5 cm I.D. equipped with four equally spaced full baffles and four- bladed turbine impeller. The reaction temperature was maintained by means of a thermostatic water bath in which the reaction assembly was immersed. Reactions were carried out in the presence and in the absence of Amberlyst-15 resin catalyst. The reaction mixtures were allowed to reach the desired temperature and the zero time sample was collected. Agitation speed was then commenced at sufficiently high speed measured with a tachometer. In most of the cases, alcohol was taken in far molar excess over acetic acid to drive the equilibrium away towards the ester formation. C. Analysis For kinetic measurements, samples were withdrawn periodically and analysed by instrumental methods. The product distribution in the sample was determined by Gas Chromatography (GC) on a CHEMITO 7610 gas chromatograph machine (Thermo Fisher Pvt. Ltd, India) equipped with Flame Ionisation Detector. A 3 m long stainless steel column of 0.3175 cm I.D., packed with OV-17 on chromosorb was used for the analysis. III. Results and discussion To shift the equilibrium towards the formation of the desired product, excess methanol was used in the reaction. The preliminary experiments were con ducted under otherwise similar conditions of reactant concentration (or mole ratio), catalyst loading, speed of agitation, and temperature except the type of catalyst. Kinetics for Uncatalyzed Reaction: A. Elimination of mass transfer resistance Preliminary experiments were carried out to evaluate the influence of the different mass transfer resistances. The batch (solid-liquid and solid-liquid-liquid) reactions were carried out over a wide range of speed of agitation 800-1200 RPM to study the external mass resistance. It was observed that beyond 1000 RPM there was no appreciable change in the rate of the reaction. Hence all the reactions were conducted at 1000 rpm to ensure that there is no resistance to mass transfer at any existing solid-liquid and liquid-liquid interface. B. Effect of temperature The study of the effect of the temperature is very important since it is useful to calculate the activation energy of the reaction. The effect of temperature was studied over a temperature range of 308 K to 328 K. The experiments were carried out at three different temperatures i.e. 308K, 318K and 328K with 1:3 molar ratios of acetic acid and methanol, and 1000 rpm speed for uncatalyzed reactions. Results obtained in the Fig.3.1 indicate that for the limiting component the rate of reaction and conversion are sensitive to a change in temperature. Fractional Conversion of Acetic acid increases with increase in temperature for uncatalyzed reactions.

B X(1:3,308k,1000rpm) C X(1:3,318k,1000rpm) D X(1:3,328k,1000rpm)

0.12

Frac. Conversion

0.10

0.08

0.06

0.04

0.02

0.00 0

100

120

140

160

180

200

Time (min)

Figure 3.1: Effect of Temperature on Acetic Acid in Uncatalyzed reaction: AA: MeOH mole Ratio = 1:3, rpm=1000 C. Effect of mole ration of reactants For uncatalyzed reactions the experiments were conducted at different mole ratios like 1:1, 1:2, and 1:2 of acetic acid and methanol at 55 C, 1000 rpm speed. Results obtained in the Fig.3.2 shows the effect of mole ratio of alcohol to acetic acid on the reaction kinetics. As expected the rate and equilibrium conversion of the limiting

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reactant increase with increase in mole ratio. It was observed that fractional conversion of acetic acid goes up to high as 14% for the mole ratio of 1:3, 550C, and 1000 rpm for uncatalyzed reactions.

B X(308K,1:1,1000rpm) C X(318K,1:2,1000rpm) D X(328K,1:3,1000rpm)

Frac. Conversion

0.15

0.10

0.05

0.00 0

100

120

140

160

180

200

Time (min)

Figure 3.2: Effect of Mole ratio on Acetic Acid in Uncatalyzed reaction: Temperature = 328 K, rpm=1000 Kinetics for Catalyzed Reaction D.

Effect of presence of catalyst

The catalytic activity of the resin was first tested qualitatively through two experiments with different catalytic conditions. In the first one, a non-catalyzed reaction was conducted in a homogeneous liquid phase; in the second, the commercial resin Amberlyst-15 was used. Both the reactions are carried out at 55 C, 1:3 molar ratios of acetic acid and methanol at 1000 rpm speed of agitator. For the reaction with catalyst 10wt% of catalyst dried under vacuum for 8 hrs at 343 K was used. Esterification reaction can take place even in the absence of the catalyst due to the weak acidity of acetic acid itself. However, it was observed that the reaction is extremely slow.

B (With Catalyst) C (Without Catalyst) 0.6

Frac. Conversion

0.5

0.4

0.3

0.2

0.1

0.0 0

100

120

140

160

180

200

Time(min)

Figure 3.3: Effect of presence of catalyst: Temperature= 328 K, AA: MeOH mole ratio=1:3, rpm= 1000, catalyst loading=10WT% After about 3 hrs, when the catalyzed processes have already reached equilibrium, the conversion achieved in the non-catalyzed reaction was 14% while in catalyzed reaction it was 54%. E. Effect of Catalyst Loading The experiments were conducted with different catalyst loadings i.e. 2.5 wt%, 5 wt% and 10wt% at 55 C, 1:3 molar ratio of acetic acid and methanol and 1000 rpm speed. It was found that with increase in catalyst loading results in an increase in the rate of reaction and fractional conversion of acetic acid because of an increase in number of active sites, which again indicates that mass transfer resistances are absent and the reaction is only

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controlled by intrinsic kinetics. At higher catalyst loading the rate of mass transfer is excessively high and therefore there is no more increase in the rate. B(2.5wt%,328K,1:3,1000 rpm) C(5wt%,328K,1:3,1000 rpm) D(10wt%,328K,1:3,1000 rpm)

0.60 0.55 0.50

Frac. Conversion

0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0

100

120

140

160

180

200

Time (min)

Figure 3.4: Effect of catalyst loading on Acetic Acid: Temperature= 328 K, AA: MeOH mole ratio=1:3, rpm=1000 F. Effect of Temperature The effect of temperature was studied over a temperature range of 308 K to 328 K. The experiments were carried out at three different temperatures i.e. 308K, 318K and 328K with 1:3 molar ratios of acetic acid and methanol, 10 wt% catalysts, 1000 rpm speed. Results obtained in the Fig.3.5 indicate that the rate of reaction and conversion are sensitive to a change in temperature. It was seen that Conversion of limiting reactant increases with increase in temperature. B X(308K,1:3,10Wt%,1000rpm) C X(318K,1:3,10Wt%,1000rpm) D X(328K,1:3,10Wt%,1000rpm)

0.6

Frac. Conversion

0.5

0.4

0.3

0.2

0.1

0.0 0

100

120

140

160

180

200

Time (min)

Figure 3.5: Effect of Temperature on Acetic Acid: AA: MeOH mole ratio=1:3, rpm=1000, Catalyst loading= 10 wt% G. Effect of Mole Ratio The concentration of methanol has an influence on the reaction rate and on the concentration. The experiments were conducted at different mole ratios like 1:1, 1:3, and 1:5 of acetic acid and methanol at 55 C, 10 wt% catalysts, 1000 rpm speed. Results obtained in the Fig. 3.6 shows the effect of mole ratio of alcohol to acetic acid on the reaction kinetics. As expected the rate and equilibrium conversion of the limiting reactant increase with increase in mole ratio. It was observed that fractional conversion of acetic acid goes up to high as 54% for the mole ratio of 1:3, 550C, and 1000 rpm.

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B X(328K,1:1,10Wt%,1000rpm) C X(328K,1:2,10Wt%,1000rpm) D X(328K,1:3,10Wt%,1000rpm)

0.60 0.55 0.50

Frac. Conversion

0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 0

100

120

140

160

180

200

Time (min)

Figure 3.6: Effect of Mole ratio on Acetic Acid: Temperature= 328 K, rpm=1000, catalyst loading= 10 wt% 3.8 Reaction Kinetics Since the external mass transfer resistance and the intraparticle diffusional resistance were absent, the reaction was kinetically controlled. The catalyst used is a macroporous ion-exchange resin. This is because, in a macroporous resin, the pores are so large (>400 A0) that the reactants are able to diffuse into the pores and the products to diffuse out without any resistance. This is the so called pseudo homogeneous model. a. Kinetic model Esterification of Acetic acid with Methanol is given as CH 3 COOH

CH 3 OH

CH 3 COOCH

H 2O

(1)

Esterification reactions are known to be second- order reversible reactions. Therefore, for the bimolecular-type second order reactions. sA B (2) E W With the restrictions that C AO C BO and C EO CWO , the rate can be written as

dC A dt

C AO

dX A dt

k1C A C B

2 k1C AO (1 X A ) 2

k 2 C E CW

(3)

k 2 (C AO X B ) 2

Where A, B, E, and W refer to Acetic Acid, Methanol, Methyl Acetate and Water, respectively. At the equilibrium, rA 0 . Hence, from the above equations, we find the fractional of A at the equilibrium conditions by

C Ee CWe C Ae C Be

2 X Ae

1 X Ae

(4)

And the equilibrium constant by

k1 k2

(5)

Combining the above three equations, in terms of the equilibrium conversion, we obtain dX A dt

k1C AO 1

X Ae X Ae

X A2

(6)

With conversions measured in terms of XAe, this may be indicated as a pseudo second-order reversible reaction which, on integration, gives X Ae 2 X Ae 1 X A 1 (7) ln 2k1 1 C BO t X Ae X A X Ae Temperature and Reaction Rate: We can examine the variation of the rate constant with temperature by an Arrheniusâ&#x20AC;&#x2122; law relationship.

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EA RT

k10 exp

(8)

This is conveniently determined by plotting lnk1 versus 1/T. b. Kinetics for Catalyzed Reaction The experimental data collected at temperatures of 308 K, 318K and 328K at molar ratios of acetic acid to methanol of 1:1 and at a stirring speed of 1000 rpm were used to plot the left-hand side (LHS) of Eq. (7) versus time to get a straight line passing through the origin, thus suggesting that the model developed is adequate to represent the system. B X(308K) C X(318K) D X(328K)

LN((XAe-(2*XAe-1))/(XAe-XA)) v/s Time

LN((XAe-(2*XAe-1))/(XAe-XA))

3.2 3.0 2.8 2.6 2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 0

100

120

140

160

Time(min)

Figure 3.7: Typical kinetic plots for the effect of temperature on Catalyzed Reaction. Catalyst loading = 10wt%, speed of agitation = 1000 rpm, mole ratio of acetic acid to methanol = 1:1 The optimized values of the heterogeneous reaction rate constants are presented in Table 3.1 Table 3.1: Reaction rate constants for heterogeneous catalyzed esterification Temp., K 308

K1 L.mol-1 .min-1 0.060013

K2 L.mol-1 .min-1 0.807277

318

0.081278

0.901893

328

0.147012

1.25339

c. Kinetics for Un-Catalyzed Reaction For the kinetic study of the uncatalyzed esterification reaction, the experiments are carried out at 308K, 318K, and 328K at molar ratios of acetic acid to methanol of 1:1 and at a stirring speed of 1000 rpm respectively.

B(308K) C(318K) D(328K)

LN((XAe-(2*XAe-1))/(XAe-XA)) v/s Time

3.5

LN((XAe-(2*XAe-1))/(XAe-XA))

3.0

2.5

2.0

1.5

1.0

0.5

0.0 0

100

120

140

160

Time (min)

Figure 3.8: Typical kinetic plots for the effect of temperature on Un-catalyzed reaction. Speed of agitation = 1000 rpm, mole ratio of acetic acid to methanol = 1:1

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The optimized values of the Un-catalyzed reaction rate constants are presented in Table 3.2 Table 3.2: Reaction rate constants for Un-catalyzed Esterification Temp., K 308 318 328

K3 L.mol-1.min-1 0.015088 0.020076 0.024358

K4 L.mol-1.min-1 2.723251 3.4285 3.707542

The optimized values of the activation energies for Un-catalyzed and Catalyzed reactions are presented in Table 3.3 Table 3.3: Activation Energies for Un-catalyzed and Catalyzed Esterification Reactions Type of Un-Catalyzed Reaction Catalyzed Reaction Reaction Forward Backward Forward Backward E KJ/mol 20.12 12.95 37.626 18.473 IV. Conclusion The esterification reaction of acetic acid with methanol was successfully carried out over Amberlyst-15 as cationic exchange resin as a catalyst and in the absence of catalyst also in stirred batch reactor. The effect of temperature, molar ratios, and catalyst loading on the overall rate of reaction as well as concentration-time profiles were investigated. For catalyzed reaction various experiments were carried out in the temperature range of 308-328K and it has been observed that at 328K conversion was maximum, also catalyst loading varied from 2.5 to 10 wt% and at 10 wt% catalyst loading conversion becomes maximum, similarly if molar ratios varied from 1:1 to 1:3 it has been observed that at molar ratio of 1:3 conversion achieved was maximum. For Uncatalyzed reactions also conversion achieved was maximum at 323K, 1:3 mole ratio. A stirrer speed â&#x2030;Ľ 800 rpm was found sufficient for eliminating external diffusion. Internal diffusion was negligible under the employed esterification reaction conditions. The conversion of acid increased with increasing temperature and catalyst loading and initial amount of alcohol. The conversion achieved in the non-catalyzed reaction was 14% while in catalyzed reaction it was 54%. To develop the model, reactions are carried out in the temperature ranging between 308 and 328K and molar feed ratio of 1:1. This reaction was intrinsically controlled. A pseudo homogenous kinetic model was employed to fit the experimental data. The activation energies for Un-catalysed forward and backward reactions were evaluated as 20.12 KJ/mol and 12.95 KJ/mol, for the heterogeneous forward and backward reactions were evaluated as 37.626 KJ/mol and 18.473 KJ/mol. V. Nomenclature CA - Concentration of acetic acid mol/lit CB - Concentration of methanol mol/lit CE - Concentration of ester mol/lit CW - Concentration of water mol/lit CAO - Concentration of acetic acid at t=0, mol/lit CBO - Concentration of methanol at t=0, mol/lit E - Activation energy KJ/mol K1 - Rate constant for the heterogeneous forward reaction, lit/mol min K2 - Rate constant for the heterogeneous backward reaction, lit/mol min K3 - Rate constant for the uncatalyzed forward reaction, lit/mol min K4 - Rate constant for the uncatalyzed backward reaction, lit/mol min kc - Equilibrium constant rA - Initial rate mol/lit min R - Real gas constant T - Reaction temperature, K XA - Fractional conversion of acetic acid XB - Fractional conversion of methanol XAe - Fractional conversion of acetic acid at equilibrium VI. 1. 2.

References

Ronnback.R, Salmi. T, Vuori. A, Haario H., Lehtonen J., Development of a Kinetic model for the esterification of acetic acid with methanol in the presence of a homogenous acid catalyst, Chemical Engineering Science,1997, 52, 3369-3381. Yijun Liu., Edger Lotero., James G. Goodwin., A comparison of the esterification of acetic acid with methanol using heterogeneous versus homogenous acid catalysis, Journal of Catalysis, 2006, 242, 278-286.

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17. 18. 19. 20.

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Modified Approach for Object Detection in Video Sequences Rajni Nema1, Dr. A.K.Saxena2 Shri Ram College of Engineering and Management Banmore1, 2 Gwalior INDIA Abstract: Object detection in video sequence processes is the basic and starting steps for more complex processes, such as video context analysis and multimedia indexing A simple method of object detection for static camera movies is proposed. In this paper. First, Canny Edge operator applies on video sequence. It detects the video objects by edge detection. Then some morphological processes give the object in video sequence.This paper proposed a modified approach for object detection in video sequence. The software is developed using MATLAB R2010a. I. Introduction Video object segmentation, detection and tracking processes are the basic, starting steps for more complex processes, such as video context analysis and multimedia indexing. Object tracking in videos can be defined as the process of segmenting an object of interest from a sequence of video scenes. This process should keep track of its motion, orientation, occlusion and etc. in order to extract useful context information, which will be used on higher-level processes [1]. The procedure of moving object tracking is to decide whether there exist objects moving into video and to position the target basically and recognize it. A video sequence is made of basically a series of still images at a very small interval time between each capture. As the video sequence consists of frame sequences which have certain temporal continuity, the detection for moving objects in video is conducted in a way that frame sequences are extracted from the video sequence according to a definite cycle [2]. Therefore, moving object detecting has something similar to object detection in still images. Only moving object detecting is more relying on the motion characteristics of objects, i.e. the continuity of time, which is the difference between moving objects and object detection in still images. The need of real-time object detection for video surveillance has spawned a huge amount of our daily life, especially in some domains where it has received. In the literature, the previous works concentrated mainly on moving-object D&T in videos. One can find bunch of methods dedicated to generic-object D&T in video processing like Background Subtraction (BS) [5, 6], Mean-Shift (MS) and/or Continuously Adaptive Mean-Shift (CMS) [7-9], Optical Flow (OF) [10, 11], Active Contour Models (i.e. Snakes) [12, 13] and etc. Template matching is an essential-object D&T method, but it is simpler than others, and is generally based on matching a given template as an object in giving a frame. In this paper, we propose an efficient algorithm for detecting a moving object using canny edge operator and some morphological process. Section 2 focused on the methodology. Section 3 focused on canny edge detection method and morphological process. Section 4 provides comparable results, and finally the conclusion is drawn in section 5. II. Methodology In the present algorithm, we assume that the background is stationary for the video clips considered. The architecture and modeling of the proposed algorithm are shown in Figure 1. Input

Input Video Clip

Convert into Gray Scale Image

Apply Canny Edge Operator

Morphological Process

Object Identification

Figure 1. Architecture for Object Identification

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In the first step we collect video clips and generate the frames. In the second step we convert each frame into the grayscale image. It is necessary for third step that is edge detection of the image using the canny operator. Edge detection is one of the most frequently used techniques in digital image processing. The boundaries of object surfaces in a scene often lead to oriented localized changes in intensity of an image, called edges [3]. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is at the forefront of image processing for object detection. The result of canny edge algorithms uses for morphological process, where the difference of two successive video frames indicates the motion between them, the resulted non-black image zones representing the moving regions. In this step provides the object. III. Canny Edge Detection Algorithm The Canny edge detection algorithm is known to many as the optimal edge detector. Canny's intentions had been to enhance the many edge detectors already out at the time he started his work. He was very successful in achieving his goal and his ideas and methods can be found in his paper, "A Computational Approach to Edge Detection"[14]. In his paper, he followed a list of criteria to improve current methods of edge detection. The first and most obvious is low error rate. It is important that edges occurring in images should not be missed and that there be no responses to non-edges. The second criterion is that the edge points are well localized. In other words, the distance between the edge pixels as found by the detector and the actual edge is to be at a minimum. A third criterion is to have only one response to a single edge. This was implemented because the first two were not substantial enough to completely eliminate the possibility of multiple responses to an edge. Based on these criteria, the canny edge detector first smoothes the image to eliminate and noise. It then finds the image gradient to highlight regions with high spatial derivatives. The algorithm then tracks along these regions and suppresses any pixel that is not at the maximum (nonmaximum suppression). The gradient array is now further reduced by hysteresis. Hysteresis is used to track along the remaining pixels that have not been suppressed. Hysteresis uses two thresholds and if the magnitude is below the first threshold, it is set to zero (made a non edge). If the magnitude is above the high threshold, it is made an edge. And if the magnitude is between the 2 thresholds, then it is set to zero unless there is a path from this pixel to a pixel with a gradient above T2 [15]. Step 1: - In order to implement the canny edge detector algorithm, a series of steps must be followed. The first step is to filter out any noise in the original image before trying to locate and detect any edges. And because the Gaussian filter can be computed using a simple mask, it is used exclusively in the Canny algorithm. Once a suitable mask has been calculated, the Gaussian smoothing can be performed using standard convolution methods. A convolution mask is usually much smaller than the actual image. As a result, the mask is slid over the image, manipulating a square of pixels at a time. The larger the width of the Gaussian mask, the lower is the detector's sensitivity to noise. The localization error in the detected edges also increases slightly as the Gaussian width is increased. Step 2: - After smoothing the image and eliminating the noise, the next step is to find the edge strength by taking the gradient of the image. The Sobel operator performs a 2-D spatial gradient measurement on an image. Then, the approximate absolute gradient magnitude (edge strength) at each point can be found. The Sobel operator [6] uses a pair of 3x3 convolution masks, one estimating the gradient in the x-direction (columns) and the other estimating the gradient in the y-direction (rows). They are shown below:

1 0 1 Gx

2 0 2 1 0 1

The magnitude, or edge strength, of the gradient is then approximated using the form

Gx 2 Gy 2

Step 3: - The direction of the edge is computed using the gradient in the x and y directions. However, an error will be generated when sum x is equal to zero. So in the code there has to be a restriction set whenever this takes place. Whenever the gradient in the x direction is equal to zero, the edge direction has to be equal to 90 degrees or 0 degrees, depending on what the value of the gradient in the y-direction is equal to. If Gy has a value of zero, the edge direction will equal 0 degrees. Otherwise the edge direction will equal 90 degrees. The method for finding the edge direction is just:

tan

Gy Gx

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Step 4: - Once the edge direction is known, the next step is to relate the edge direction to a direction that can be traced in an image. So if the pixels of a 5x5 image are aligned as follows: xxxxx xxxxx xxaxx xxxxx xxxxx Then, it can be seen by looking at pixel "a", there are only four possible directions when describing the surrounding pixels - 0 degrees (in the horizontal direction), 45 degrees (along the positive diagonal), 90 degrees (in the vertical direction), or 135 degrees (along the negative diagonal). So now the edge orientation has to be resolved into one of these four directions depending on which direction it is closest to (e.g. If the orientation angle is found to be 3 degrees, make it zero degrees). Think of this as taking a semicircle and dividing it into 5 regions. Therefore, any edge direction falling within the yellow range (0 to 22.5 & 157.5 to 180 degrees) is set to 0 degrees. Any edge direction falling in the green range (22.5 to 67.5 degrees) is set to 45 degrees. Any edge direction falling in the blue range (67.5 to 112.5 degrees) is set to 90 degrees. And finally, any edge direction falling within the red range (112.5 to 157.5 degrees) is set to 135 degrees. Step 5: - After the edge directions are known, non-maximum suppression now has to be applied. Non-maximum suppression is used to trace along the edge in the edge direction and suppress any pixel value (sets it equal to 0) that is not considered to be an edge. This will give a thin line in the output image. Step 6: - Finally, hysteresis [11] is used as a means of eliminating streaking. Streaking is the breaking up of an edge contour caused by the operator output fluctuating above and below the threshold. If a single threshold, T1 is applied to an image, and an edge has an average strength equal to T1, then due to noise, there will be instances where the edge dips below the threshold. Equally it will also extend above the threshold making an edge look like a dashed line. To avoid this, hysteresis uses 2 thresholds, a high and a low. Any pixel in the image that has a value greater than T1 is presumed to be an edge pixel, and is marked as such immediately. Then, any pixels that are connected to this edge pixel and that have a value greater than T2 are also selected as edge pixels. If you think of following an edge, you need a gradient of T2 to start but you don't stop till you hit a gradient below T1. IV. Morphological Process Morphology is a broad set of image processing operations that process images based on shapes. Morphological operations apply a structuring element to an input image, creating an output image of the same size. In a morphological operation, the value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors [4] The difference of two successive video frames indicates the motion between them, the resulted non-black image zones representing the moving regions. V. Result We apply this proposed work on two video clips. Figure2 (a, b, c, d, e, f) show the object (football) location is changed in frames.

(a) (b)

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(f) Figure 2: Results of object detection in video sequence

(c)

Figure3 (a, b, c, d, e, f) shows the object (football player) movement change in frames.

(d) (a)

(e) (b)

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(c)

(d)

(e)

(f)

Figure 3: Results of object detection in video sequence VI. CONCLUSIONS In this paper, we propose an efficient algorithm for detecting a moving object using a canny edge detector and morphology process . It can be seen from the analysis and examples that the computer language Matlab has the characteristics of simple programming, easy operation and high processing rate, etc. when used in series of processing of moving object detecting algorithm. Initially, we convert the frame image to edge frames then frame difference between two consecutive input frames give the location of the moving object. Then, each incoming frame is compared to the previous frame. Finally, the resulted non-black image zones representing the moving regions. As a result, the implementation of the algorithm becomes very fast. References [1] Bahadir KARASULU, " Review and evaluation of well- Known methods for moving object detection and tracking in videos", JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIE, JULY 2010 VOLUME 4 NUMBER 4 (11-22) [2] Kauleshwar Prasad, Richa Sharma and Deepika Wadhwani, "A Review on Object Detection in Video processing"' International Journal of u- and e- Service, Science and Technology Vol. 5, No. 4, December,2012 [3] Senthilkumaran.N and R.Rajesh ,“Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing Approaches”,School of Computer Science and Engieering, Bharathiar University, Coimbatore -641 046, India [4] R. C. Gonzalez and R. E. Woods. “Digital Image Processing”. 2nd ed. Prentice Hall, 2002. [5] Carmona, E. J., Martínez-Cantos, J., and Mira, J., “A new video segmentation method of moving objects based on blob-level knowledge”, Pattern Recognition Letters, Vol. 29, No. 3, pp. 272-285, 2008. [6] Kim, J.B., Kim, H.J., “Efficient region-based motion segmentation for a video monitoring system”, Pattern Recognition Letters, Vol. 24, No. 1–3, pp. 113–128, 2003. [7] Bradski, G. “Computer Vision Face Tracking For Use in a Perceptual User Interface”, In Intel Technology Journal, (http://developer.intel.com/technology/itj/q21998/ articles/art_2.htm, (Q2 1998). [8] François, A. R. J., "CAMSHIFT Tracker Design Experiments with Intel OpenCV and SAI", IRIS Technical Report IRIS-04-423, University of Southern California, Los Angeles, 2004. [9] Comaniciu, D., Meer, P., "Mean Shift Analysis and Applications", IEEE International Conference Computer Vision (ICCV'99), Kerkyra, Greece, pp. 1197-1203, 1999. [10] Jodoin, P.M., Mignotte, M., “Optical-flow based on an edge-avoidance procedure”, Computer Vision and Image Understanding, Vol. 113, No. 4, pp. 511-531, 2009. [11] Pauwels, K., Van Hulle, M. M., “Optic flow from unstable sequences through local velocity constancy maximization”, Image and Vision Computing, (The 17th British Machine Vision Conference (BMVC 2006)), Vol. 27, No. 5, pp. 579-587, 2009. [12] Kass, M., Witkin, A., and Terzopoulos, D., “Snakes: active contour models”, International Journal of Computer Vision, Vol. 1, No. 4, pp. 321–331, 1988. [13] Dagher, I., Tom, K. E., “WaterBalloons: A hybrid watershed Balloon Snake segmentation”, Image and Vision Computing, Vol. 26, pp. 905–912, doi:10.1016/j.imavis.2007.10.010, 2008. [14] J. F. Canny. “A computational approach to edge detection”. IEEE Trans. Pattern Anal. Machine Intell.,vol. PAMI-8, no. 6, pp. 679697, 1986 [15] Maini Raman and Agrawal Himanshu, “Study and Comparison of Various Image Edge Detection Techniques”, Punjabi University, Patiala-147002(Punjab), India.

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