American International Journal of Research in Formal, Applied and Natural Scienc issue 10 vol1 by iasir journals

ISSN (Print): 2328-3777 ISSN (Online): 2328-3785 ISSN (CD-ROM): 2328-3793

Issue 10, Volume 1 March-May, 2015

American International Journal of Research in Formal, Applied and Natural Sciences

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

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: Germany, Australia, India, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, aijrfans@gmail.com

PREFACE We are delighted to welcome you to the tenth issue of the American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS). In recent years, advances in science, engineering, formal, applied and natural sciences 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. AIJRFANS is publishing high-quality, peer-reviewed papers covering topics such as Biotechnology, Cognitive neurosciences, Physics, Chemistry, Information coding and theory, Biology , Botany & Zoology, Logic & Systems, Earth and environmental sciences, Computer science, Applied and pure Mathematics, Decision Theory & Statistics, Medicine, Algorithms, Anatomy, Biomedical sciences, Biochemistry, Bioinformatics, Ecology & Ethology, Food & Health science, Genetics, Pharmacology, Geology, Astronomy & Geophysics, Oceanography, Space sciences, Criminology, Aerospace, Agricultural, Textile, Industrial, Mechanical, Dental sciences, Pharmaceutical sciences, Computational linguistics, Cybernetics, Forestry, Scientific modeling, Network sciences, Horticulture & Husbandry, Agricultural & Veterinary sciences, Robotics and Automation, Materials sciences and other relevant fields available in the vicinity of formal, applied and natural sciences.

The editorial board of AIJRFANS is composed of members of the Teachers & Researchers community who are actively involved in the systematic investigation into existing or new knowledge to discover new paths for the scientific discovery to provide new logic and design paradigms. Today, modern science respects objective and logical reasoning to determine the underlying natural laws of the universe to explore new scientific methods. These methods

are

quite

useful

develop

widespread

expansion

highâ&#x20AC;?quality common standards and assessments in the formal, applied and natural sciences. 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 formal, applied and natural sciences. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory,

GetCITED,

CRCnetBASE,

Google

DOAJ,

SSRN,

Scholar,

TGDScholar,

Microsoft

Academic

WorldWideScience, Search,

CiteSeerX,

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 AIJRFANS for entrusting us with the important job. We are thankful to the members of the AIJRFANS 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 tenth issue, we received 59 research papers and out of which only 13research papers are published in one volume 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 formal, applied and natural sciences.

This issue of the AIJRFANS 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 formal, applied and natural sciences and may open new area for research and development. We hope you will enjoy this tenth issue of the American International Journal of Research in Formal, Applied and Natural Sciences 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 Formal, Applied and Natural Sciences (AIJRFANS), ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 (March-May, 2015, Issue 10, Volume 1). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune- 411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg, R.K.University,Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana, India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar, Punjab(India) Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Shriram K V, Faculty Computer Science and Engineering, Amrita Vishwa Vidhyapeetham University, Coimbatore, India. Prof. (Dr.) Sohail Ayub, Department of Civil Engineering, Z.H College of Engineering & Technology, Aligarh Muslim University, Aligarh. 202002 UP-India Prof. (Dr.) Santosh Kumar Behera, Department of Education, Sidho-Kanho-Birsha University, Purulia, West Bengal, India. Prof. (Dr.) Urmila Shrawankar, Department of Computer Science & Engineering, G H Raisoni College of Engineering, Nagpur (MS), India. Prof. Anbu Kumar. S, Deptt. of Civil Engg., Delhi Technological University (Formerly Delhi College of Engineering) Delhi, India. Prof. (Dr.) Meenakshi Sood, Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka (India) Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur, India. Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur-313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India. Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women,s College, Gardanibagh, Patna, Bihar, India. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore, India. Prof. (Dr.) Sandhya Mehrotra, Department of Biological Sciences, Birla Institute of Technology and Sciences, Pilani, Rajasthan, India. Prof. (Dr.) Dr. Ravindra Jilte, Head of the Department, Department of Mechanical Engineering,VCET, Thane-401202, India. Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) ABHIJIT MITRA , Associate Professor and former Head, Department of Marine Science, University of Calcutta , India. Prof. (Dr.) N.Ramu , Associate Professor , Department of Commerce, Annamalai University, AnnamalaiNadar-608 002, Chidambaram, Tamil Nadu , India. Prof. (Dr.) Saber Mohamed Abd-Allah, Assistant Professor of Theriogenology , Faculty of Veterinary Medicine , Beni-Suef University , Egypt. Prof. (Dr.) Ramel D. Tomaquin, Dean, College of Arts and Sciences Surigao Del Sur State University (SDSSU), Tandag City Surigao Del Sur, Philippines. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011, India. Prof. (Dr.) Sandeep Gupta, Department of Computer Science & Engineering, Noida Institute of Engineering and Technology, Gr.Noida, India. Prof. (Dr.) Mohammad Akram, Jazan University, Kingdom of Saudi Arabia.

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Prof. (Dr.) Sanjay Sharma, Dept. of Mathematics, BIT, Durg(C.G.), India. Prof. (Dr.) Manas R. Panigrahi, Department of Physics, School of Applied Sciences, KIIT University, Bhubaneswar, India. Prof. (Dr.) P.Kiran Sree, Dept of CSE, Jawaharlal Nehru Technological University, India Prof. (Dr.) Suvroma Gupta, Department of Biotechnology in Haldia Institute of Technology, Haldia, West Bengal, India. Prof. (Dr.) SREEKANTH. K. J., Department of Mechanical Engineering at Mar Baselios College of Engineering & Technology, University of Kerala, Trivandrum, Kerala, India Prof. Bhubneshwar Sharma, Department of Electronics and Communication Engineering, Eternal University (H.P), India. Prof. Love Kumar, Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), India. Prof. S.KANNAN, Department of History, Annamalai University, Annamalainagar- 608002, Tamil Nadu, India. Prof. (Dr.) Hasrinah Hasbullah, Faculty of Petroleum & Renewable Energy Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Bhargavi H. Goswami, Department of MCA, Sunshine Group of Institutes, Nr. Rangoli Park, Kalawad Road, Rajkot, Gujarat, India. Prof. (Dr.) Essam H. Houssein, Computer Science Department, Faculty of Computers & Informatics, Benha University, Benha 13518, Qalyubia Governorate, Egypt. Arash Shaghaghi, University College London, University of London, Great Britain. Prof. Rajesh Duvvuru, Dept. of Computer Sc. & Engg., N.I.T. Jamshedpur, Jharkhand, India. Prof. (Dr.) Anand Kumar, Head, Department of MCA, M.S. Engineering College, Navarathna Agrahara, Sadahalli Post, Bangalore, PIN 562110, Karnataka, INDIA. Prof. (Dr.) Venkata Raghavendra Miriampally, Electrical and Computer Engineering Dept, Adama Science & Technology University, Adama, Ethiopia. Prof. (Dr.) Jatinderkumar R. Saini, Director (I.T.), GTU's Ankleshwar-Bharuch Innovation Sankul &Director I/C & Associate Professor, Narmada College of Computer Application, Zadeshwar, Bharuch, Gujarat, India. Prof. Jaswinder Singh, Mechanical Engineering Department, University Institute Of Engineering & Technology, Panjab University SSG Regional Centre, Hoshiarpur, Punjab, India- 146001. Prof. (Dr.) S.Kadhiravan, Head i/c, Department of Psychology, Periyar University, Salem- 636 011,Tamil Nadu, India. Prof. (Dr.) Mohammad Israr, Principal, Balaji Engineering College,Junagadh, Gujarat-362014, India. Prof. (Dr.) VENKATESWARLU B., Director of MCA in Sreenivasa Institute of Technology and Management Studies (SITAMS), Chittoor. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009, India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University, Coimbatore-641003,Tamil Nadu, India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066 Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057 Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India.

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Prof. (Dr.)B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India. Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India . Prof. Shashikant Shantilal Patil SVKM, MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Engg., Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty, Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT ,Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India.

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Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale, Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman, Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi-835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS-38655, USA Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal-India Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu-India Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India.

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Prof. (Dr.) Meghshyam K. Patil, Assistant Professor & Head, Department of Chemistry, Dr. Babasaheb Ambedkar Marathwada University, Sub-Campus, Osmanabad- 413 501, Maharashtra, INDIA Prof. (Dr.) K. Ramesh, Department of Chemistry, C .B . I. T, Gandipet, Hyderabad-500075 Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics , Bhilai Institute Of Technology, Durg (C.G.) 491001 Prof. (Dr.) Y.P.Singh, (Director), Somany (PG) Institute of Technology and Management, Garhi Bolni Road, Delhi-Jaipur Highway No. 8, Beside 3 km from City Rewari, Rewari-123401, India. Prof. (Dr.) MIR IQBAL FAHEEM, VICE PRINCIPAL &HEAD- Department of Civil Engineering & Professor of Civil Engineering, Deccan College of Engineering & Technology, Dar-us-Salam, Aghapura, Hyderabad (AP) 500 036. Prof. (Dr.) Jitendra Gupta, Regional Head, Co-ordinator(U.P. State Representative)& Asstt. Prof., (Pharmaceutics), Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) N. Sakthivel, Scientist - C,Research Extension Center,Central Silk Board, Government of India, Inam Karisal Kulam (Post), Srivilliputtur - 626 125,Tamil Nadu, India. Prof. (Dr.) Omprakash Srivastav, Centre of Advanced Study, Department of History, Aligarh Muslim University, Aligarh-202 001, INDIA. Prof. (Dr.) K.V.L.N.Acharyulu, Associate Professor, Department of Mathematics, Bapatla Engineering college, Bapatla-522101, INDIA. Prof. (Dr.) Fateh Mebarek-Oudina, Assoc. Prof., Sciences Faculty,20 aout 1955-Skikda University, B.P 26 Route El-Hadaiek, 21000,Skikda, Algeria. NagaLaxmi M. Raman, Project Support Officer, Amity International Centre for Postharvest, Technology & Cold Chain Management, Amity University Campus, Sector-125, Expressway, Noida Prof. (Dr.) V.SIVASANKAR, Associate Professor, Department Of Chemistry, Thiagarajar College Of Engineering (Autonomous), Madurai 625015, Tamil Nadu, India (Dr.) Ramkrishna Singh Solanki, School of Studies in Statistics, Vikram University, Ujjain, India Prof. (Dr.) M.A.Rabbani, Professor/Computer Applications, School of Computer, Information and Mathematical Sciences, B.S.Abdur Rahman University, Chennai, India Prof. (Dr.) P.P.Satya Paul Kumar, Associate Professor, Physical Education & Sports Sciences, University College of Physical Education & Sports, Sciences, Acharya Nagarjuna University, Guntur. Prof. (Dr.) Fazal Shirazi, PostDoctoral Fellow, Infectious Disease, MD Anderson Cancer Center, Houston, Texas, USA Prof. (Dr.) Omprakash Srivastav, Department of Museology, Aligarh Muslim University, Aligarh202 001, INDIA. Prof. (Dr.) Mandeep Singh walia, A.P. E.C.E., Panjab University SSG Regional Centre Hoshiarpur, Una Road, V.P.O. Allahabad, Bajwara, Hoshiarpur Prof. (Dr.) Ho Soon Min, Senior Lecturer, Faculty of Applied Sciences, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Prof. (Dr.) L.Ganesamoorthy, Assistant Professor in Commerce, Annamalai University, Annamalai Nagar-608002, Chidambaram, Tamilnadu, India. Prof. (Dr.) Vuda Sreenivasarao, Professor, School of Computing and Electrical Engineering, Bahir Dar University, Bahirdar,Ethiopia Prof. (Dr.) Umesh Sharma, Professor & HOD Applied Sciences & Humanities, Eshan college of Engineering, Mathura, India. Prof. (Dr.) K. John Singh, School of Information Technology and Engineering, VIT University, Vellore, Tamil Nadu, India. Prof. (Dr.) Sita Ram Pal (Asst.Prof.), Dept. of Special Education, Dr.BAOU, Ahmedabad, India.

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Prof. Vishal S.Rana, H.O.D, Department of Business Administration, S.S.B.T'S College of Engineering & Technology, Bambhori,Jalgaon (M.S), India. Prof. (Dr.) Chandrakant Badgaiyan, Department of Mechatronics and Engineering, Chhattisgarh. Dr. (Mrs.) Shubhrata Gupta, Prof. (Electrical), NIT Raipur, India. Prof. (Dr.) Usha Rani. Nelakuditi, Assoc. Prof., ECE Deptt., Vignan’s Engineering College, Vignan University, India. Prof. (Dr.) S. Swathi, Asst. Professor, Department of Information Technology, Vardhaman college of Engineering(Autonomous) , Shamshabad, R.R District, India. Prof. (Dr.) Raja Chakraverty, M Pharm (Pharmacology), BCPSR, Durgapur, West Bengal, India Prof. (Dr.) P. Sanjeevi Kumar, Electrical & Electronics Engineering, National Institute of Technology (NIT-Puducherry), An Institute of National Importance under MHRD (Govt. of India), Karaikal- 609 605, India. Prof. (Dr.) Amitava Ghosh, Professor & Principal, Bengal College of Pharmaceutical Sciences and Research, B.R.B. Sarani, Bidhannagar, Durgapur, West Bengal- 713212. Prof. (Dr.) Om Kumar Harsh, Group Director, Amritsar College of Engineering and Technology, Amritsar 143001 (Punjab), India. Prof. (Dr.) Mansoor Maitah, Department of International Relations, Faculty of Economics and Management, Czech University of Life Sciences Prague, 165 21 Praha 6 Suchdol, Czech Republic. Prof. (Dr.) Zahid Mahmood, Department of Management Sciences (Graduate Studies), Bahria University, Naval Complex, Sector, E-9, Islamabad, Pakistan. Prof. (Dr.) N. Sandeep, Faculty Division of Fluid Dynamics, VIT University, Vellore-632 014. Mr. Jiban Shrestha, Scientist (Plant Breeding and Genetics), Nepal Agricultural Research Council, National Maize Research Program, Rampur, Chitwan, Nepal. Prof. (Dr.) Rakhi Garg, Banaras Hindu University, Varanasi, Uttar Pradesh, India. Prof. (Dr.) Ramakant Pandey. Dept. of Biochemistry. Patna University Patna (Bihar)-India. Prof. (Dr.) Nalah Augustine Bala, Behavioural Health Unit, Psychology Department, Nasarawa State University, Keffi, P.M.B. 1022 Keffi, Nasarawa State, Nigeria. Prof. (Dr.) Mehdi Babaei, Department of Engineering, Faculty of Civil Engineering, University of Zanjan, Iran. Prof. (Dr.) A. SENTHIL KUMAR., Professor/EEE, VELAMMAL ENGINEERING COLLEGE, CHENNAI Prof. (Dr.) Gudikandhula Narasimha Rao, Dept. of Computer Sc. & Engg., KKR & KSR Inst Of Tech & Sciences, Guntur, Andhra Pradesh, India. Prof. (Dr.) Dhanesh singh, Department of Chemistry, K.G. Arts & Science College, Raigarh (C.G.) India. Prof. (Dr.) Syed Umar , Dept. of Electronics and Computer Engineering, KL University, Guntur, A.P., India. Prof. (Dr.) Rachna Goswami, Faculty in Bio-Science Department, IIIT Nuzvid (RGUKT), DistrictKrishna , Andhra Pradesh - 521201 Prof. (Dr.) Ahsas Goyal, FSRHCP, Founder & Vice president of Society of Researchers and Health Care Professionals Prof. (Dr.) Gagan Singh, School of Management Studies and Commerce, Department of Commerce, Uttarakhand Open University, Haldwani-Nainital, Uttarakhand (UK)-263139 (India) Prof. (Dr.) Solomon A. O. Iyekekpolor, Mathematics and Statistics, Federal University, WukariNigeria. Prof. (Dr.) S. Saiganesh, Faculty of Marketing, Dayananda Sagar Business School, Bangalore, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’S SCHOOL, ATHANI, India Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering , Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, Punjab,India

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Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology, Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura-India Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai, 400103, India, Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, TamilNadu, India Prof. (Dr.) Har Mohan Rai, Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036, India. Prof. (Dr.) Aparna Sarkar, PH.D. Physiology, AIPT, Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP, India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher, Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. .

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Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, India. Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University, Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN. Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV),Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India. Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar, PhD(CS), M.Phil(CS), MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India. Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana), India. Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College, Govind Nagar,Kanpur208006, India. Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura, India. Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura, India. Prof. (Dr.) T Venkat Narayana Rao, C.S.E, Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India. Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India. Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Prof. (Dr.) Chitranjan Agrawal, Department of Mechanical Engineering, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur- 313001, Rajasthan, India. Prof. (Dr.) Rangnath Aher, Principal, New Arts, Commerce and Science College, Parner, DistAhmednagar, M.S. India. Prof. (Dr.) Chandan Kumar Panda, Department of Agricultural Extension, College of Agriculture, Tripura, Lembucherra-799210 Prof. (Dr.) Latika Kharb, IP Faculty (MCA Deptt), Jagan Institute of Management Studies (JIMS), Sector-5, Rohini, Delhi, India. Raj Mohan Raja Muthiah, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts. Prof. (Dr.) Chhanda Chatterjee, Dept of Philosophy, Balurghat College, West Bengal, India. Prof. (Dr.) Mihir Kumar Shome , H.O.D of Mathematics, Management and Humanities, National Institute of Technology, Arunachal Pradesh, India Prof. (Dr.) Muthukumar .Subramanyam, Registrar (I/C), Faculty, Computer Science and Engineering, National Institute of Technology, Puducherry, India. Prof. (Dr.) Vinay Saxena, Department of Mathematics, Kisan Postgraduate College, Bahraich â€“ 271801 UP, India. Satya Rishi Takyar, Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh (PB) Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India.

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Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  Biotechnology  Cognitive neurosciences  Physics  Information coding and theory  Chemistry  Biology , Botany & Zoology  Logic & Systems  Earth science  Computer science  Applied and pure Mathematics  Decision Theory  Statistics  Medicine  Algorithms, and formal semantics  Anatomy  Biomedical sciences  Biochemistry  Bioinformatics  Ecology  Ethology  Food science  Genetics  Health sciences  Pharmacology  Geology  Surface sciences  Astronomy  Geophysics  Oceanography  Space sciences  Criminology  Aerospace  Agricultural  Chemical  Textile  Industrial, Mechanical  Military science  Operations research  Healthcare sciences  Dental sciences  Pharmaceutical sciences  Biostatistics  Computational linguistics  Cybernetics  Forestry  Scientific modeling  Network sciences  Horticulture & Husbandry  Agricultural & Veterinary sciences  Neural and fuzzy systems  Robotics and Automation  Materials sciences

TABLE OF CONTENTS American International Journal of Research in Formal, Applied and Natural Sciences (AIJRFANS) ISSN (Print): 2328-3777, ISSN(Online): 2328-3785, ISSN(CD-ROM): 2328-3793

(March-May, 2015, Issue 10, Volume 1) Issue 10, Volume 1 Paper Code

Paper Title

Page No.

AIJRFANS 15-207

Analytical of the Initial Holy Quran Letters Based On Data Mining Study Anwer Hilal, Dr.Nalla.Srinivas

01-08

AIJRFANS 15-210

Survey of brain tumor detection techniques through MRI images Megha A Joshi, Prof. D.H.Shah

09-13

AIJRFANS 15-215

FERTIGATION IN VEGETABLES CROPS Indu Arora, C. P. Singh, Shant Lal

14-17

AIJRFANS 15-217

"Biomimicry" Innovative Approach in Interior Design for Increased Sustainability Dr. Inas Hosny Ibrahim Anous

18-27

AIJRFANS 15-218

Study of different hormones on callus growth of Nothapodytes foetida and extraction of Camptothecin from callus culture Abhinay Thakre, Pooja Kulkarni, Karishma Datir, Sangeeta Kulkarni, Kailas Choudhari

28-30

AIJRFANS 15-224

Detailed Larval Biology of Indian Gypsy Moth Lymantria obfuscata Walker on Quercus leucotrichophora Roxb. in Himachal Pradesh (India) Bhopesh Thakur, Sumit Chakrabarti and Manoj Kumar

31-34

AIJRFANS 15-232

Centralized Parallel Communicating Non Synchronized Pure Pattern Grammar System with Filters F. Amjad Basha and Sindhu J Kumaar

35-39

AIJRFANS 15-234

MEASURMENT OF RISK IN YIELD OF SOYABEAN P.D.DESHMUKH, K. G. JAYADE, P. G. KHOT

40-42

AIJRFANS 15-239

EFFECT OF STEEL FIBER ON FLEXURE STRENGTH OF CONCRETE Mohammed Yusuf, Prof. S.S. Kadam

43-45

AIJRFANS 15-243

Phylogenetic Diversity among Some Isolates of Ustilago scitaminae Sydow the causal Agent of Whip Smut of Sugarcane in Egypt Sayed Agag, Zeinab Fahmy, Magdy El-Samman and Mostafa Helmy Mostafa

46-55

AIJRFANS 15-249

Petrology Of The Magmatic Rocks In Nakora Area Of Malani Igneous Suite, District Barmer, Western Rajasthan, India Naresh Kumar

56-60

AIJRFANS 15-253

Microsatellite-Based Genetic Diversity Analysis in Grape (Vitis vinifera L) Germplasm and its Relevance with Morphological Characteristics Venkat Rao, P. Narayanaswamy and B.N Srinivasa Murthy

61-66

AIJRFANS 15-254

Studies of complexes of Rb and Cs metal salts of some organic acids with Bis (8hydroxy) – 5 – quinolyl) – methane Dr. Deepali Pal Choudhury, Dr. Shyam Deo Yadav and Dr. Basabi Mahapatra

67-68

American International Journal of Research in Formal, Applied & Natural Sciences

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

ISSN (Print): 2328-3777, ISSN (Online): 2328-3785, ISSN (CD-ROM): 2328-3793 AIJRFANS 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)

Analytical of the Initial Holy Quran Letters Based on Data Mining Study Anwer Hilal, Dr.Nalla.Srinivas Department of Computer Science, Preparatory Year Deanship Prince Sattam Bin Abdul Aziz University Al-Kharj, Kingdom of Saudi Arabia Abstract: The Holy Quran is the reference book for more than 1.6 billion of Muslims all around the world Extracting information and knowledge from the Holy Quran is of high benefit for both specialized people in Islamic studies as well as non-specialized people. This paper initiates a series of research studies that aim to serve the Holy Quran and provide helpful and accurate information and knowledge to the all human beings. Also, the planned research studies aim to lay out a framework that will be used by researchers in the field of Arabic natural language processing by providing a ”Golden Dataset” along with useful techniques and information that will advance this field further. The aim of this paper is to find an approach for analyzing Holy Quran text and then providing statistical information Analyzed by latest data mining techniques. In this paper the holly Quran text is pre-processed and then different text mining operations are applied to it to reveal simple facts about the terms of the holy Quran. The results show a variety of characteristics of the Holy Quran such as its most important words, its word cloud and chapters with high term frequencies. All these results are based on term frequencies that whole data has been executed in data mining technique (WEKA software) where we found in the data set that 11 chapter has fallow under the ascending order category 16 chapter how fallen under descending order category And chapter three have fallen under random usage And j48 algorithm K-mean algorithm where in correctly classified instances where count to be 80% and incorrect classified 20%. Keywords: Holy Quran; Text Mining; Arabic Natural Language Processing, Waikato Environment for Knowledge Analysis I. INTRODUCTION The Holy Quran is the reference book for more than 1.3 billion of Muslims all around the world. Extracting information and knowledge from the Holy Quran is of high benefit for both specialized people in Islamic studies as well as non-specialized people. The Holy Quran is the word of God and hence needs careful handling when processed by automated methods of machine learning, natural language processing and artificial intelligence. The language of the Holy Quran is Arabic which is known to be one of the challenging natural languages in the field of natural language processing and machine learning. This is due to some of its special characteristics such as diacritic, multiple derivations of words. And others [1], [2], [3], [4]. These make dealing with Arabic language a challenging task when applying machine learning and artificial intelligence techniques. Few research studies have considered the Arabic text of Quran [5], [6], instead many studies deal with the translations of the meaning of the words of the holy Quran [7], [8], [9]. Kais and his colleagues have created an open source Quran corpus [10] using both Arabic words as well as translations of these words. To the best of our knowledge, there is no research study that analyzed the Arabic text of the holy Quran using text mining techniques the way it is done in this paper. The aim of this paper is to find an approach for analyzing Arabic text and then providing statistical information might be helpful for the people in this research area. Also, this study aims at providing a framework for future studies in this field of study. The paper used the holly Quran to achieve these aims; first the holly Quran text is pre-processed and then different data mining techniques have been used such as: Oracle, Weka. It is important here to stress that this is not a religious study, instead it is an automated study that gives statistical results. The rest of the paper is organized as the following: Section II is dedicated to explain the process of preparing the text of the Holy Quran, in section III experiments that are applied to the text of the Holy Quran are explained. In section IV Holy Quran Verses text pre-processes and Data mining Methodology. In section V Clustering and Classification using WEKA Software implementing Holy Quran and the results that are obtained in the paper are discussed. In section VI is discussion and section VII concludes the paper. II. PREPARING TEXT The holy Quran has 77,439 words. These words are grouped into verses. A set of verses are grouped into: parts, chapters, group (Hizb) orHizb quarter. The text of the Holy Quran has been first downloaded from Tanzil project website [11] which represents an authentic verified source of the holy Quran text. The downloaded file

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includes the whole text of the Quran without diacritic. The file has been divided semi-automatically into five different set of documents: • 114 Chapter ( Sura), • 30 Part ( Juza), • 60 Group ( Hizb), • 240 Hizb Quarter or • 6236 Verse. After that the encoding of the files has been converted into CP1256 because the original encoding of the files is unreadable by R. The files have then been read as a corpus and cleaned by removing stop words. Rdoes not support stop word removal for Arabic language, hence a list of around 2000 stop words have been created manually and manipulated from different sources including [12]. Also, R does not support stemming for Arabic language, therefore simple cleaning has been applied on the corpus such as normalizing some words by replacing different shapes of the word with its normal form. For example the words: ‫هلل وهللا باهلل فاهلل تاهلل فلله اللهم‬ ‫هللا‬have been replaced by‫هللا‬ Also the words: ‫ربنا ربهم ربكم ربك ربها ربه ربي برب ورب‬have all been replaced by ‫رب‬ The variations of the previous two words are due to the some of the prefixes and suffixes of the Arabic language. Note that both and are considered stems rather than roots for the aforementioned variations for both words. This procedure has been applied to few words because the processing of all shapes of all words (the stemming procedure) is out of the scope of this paper. Although stemming algorithms for Arabic language does exist, but their accuracy still need to be enhanced. For this reason, applying such algorithms is not suitable for the holy Quran as it is the word of God, and hence errors are not tolerated. After that the corpus have been converted into both Term Document and Document-Term matrices as both needed for different type of experimentations. The next section illustrates different experimentation applied to both matrices. III. EXPERIMENTS In this section different set of experimentations are carried out on the text of the holy Quran. These experiments are based on the Term matrices that are built according to two selected partitioning methods: Chapters and Parts. These are chosen as examples because using all partitioning methods will produce numerous results and figures. The experiments will manipulate the text of the holy Quran in order to produce its most frequent terms, word cloud and clusters. Experiments on Chapters of the holy Quran In this subsection the text of the holy Quran is studied based on its 114 chapters. Each chapter in the holy Quran talk’s in general about one theme but it might include different topics. But it is considered the most coherent partitioning methodology. 1) Most Frequent Words: The term-document matrix has been used in one experimentation setup to calculate the frequency of the terms of the holy Quran. There are many frequent terms in the Holy Quran, hence figure 1 depicts the most 30 frequent words. These are calculated using TF measure. Also, the most frequent 30 terms in the holy Quran is calculated based on TF-IDF as shown in figure 2.

Figure 1: Most frequent terms in the holy Quran measured by TF

Figure 2: Most frequent terms in the holy Quran measured by TF-IDF

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Anwer Hilal et al., American International Journal of Research in Formal, Applied & Natural Sciences, 10(1), March-May 2015, pp. 01-08

2) Word Cloud: It is important for specialized as well as non-specialized people in Islamic studies to visualize the words of the Holy Quran. Figures 3-4 show the word cloud of the Holy Quran for the most frequent 100 words measured using TF and TF-IDF measures respectively.

Figure 3: Word cloud for the most frequent 100 words in the Holy Quran measured by TF

Figure 4: Word cloud for the most frequent 100 bi-gram terms

Figure 5: Word cloud for the most frequent 100 words in the holy Quran measured by TF-IDF Itâ&#x20AC;&#x2122;s also important to visualize the word cloud of the holy Quran based on bi-gram, tri-gram and four-gram words. These appear in figures 6-7.

Figure 6: Word cloud for the most frequent 100 tri-gram terms

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Figure 7: Word cloud for the most frequent 100 four-gram terms IV Holy Quran Verses text pre-processing and Data mining Methodology Before building the Quran corpus and finding the similarity among verses the whole extracted chapters and verses is filtered. Arabic function words were detached (i.e. prefixes, suffixes, pronouns, and prepositions) and then our simple stemmer extracted the root of the remaining texts. The holy Quran is composed of 114 chapters. Each chapter consists of a number of verses. The total number of verses in the whole Quran is 6,214 verses. The holy Quran as we all know has 114 chapters starting with Fathiya and ending with Alnas of which 29 chapters are very peculiarly designed After analyzing all the chapters and versus in the holy Quran we have interpreted that these 29 chapters began with a combination of one to five Arabic alphabets and the reason why it has been done so is not known till date In this paper we are trying to analyze the complexity behind this usage. To make it more precise a deep study of our database has revealed us that these 29 chapters which began with particular Arabic alphabet has a specific phenomenon for example this pattern chapter 2 titled “Al-Baqara” started with a combination of 3 Arabic alphabet ‘Alif’ and ‘Lam’ and ‘mim’ unlike the other chapters in the Quran. In this chapter the alphabet ‘Alif’ has been used 4844 times and ‘Lam’ has been used 3205 times and ‘Mim’ has been used 2195 and chapter 14 titled “ibraheem” started with a combination of 3 arabic alphabet like ‘alif’ ‘lam’’ra’ where in the alphabet ‘alif’ has been used 640 times, ’lam’ has been used 160 times when We observe this phenomena it is clearly evident that the combination of these alphabet usage in some of these 29 chapters has been in ascending order and in some chapters it is in descending order and in few chapters these Arabic alphabet has been randomly used. A verse includes words with or without frequencies. The chapter is a set of verses that allows duplicates of the same word. The general assumption is that, frequent terms in a chapter are more important and shows some major subject. We use Salton's equations to measure Holy Quran terms frequency. The weight of each keyword is calculated as follows: Term Weight = wi = tfi * log (D/dfi), where: tfi represents the term frequency (i.e. term counts) or number of times a term i occur in a verse. dfi = verse frequency or number of verses containing the term i, and D = total number of verses in the corpus The dfi/D ratio is the probability of selecting a chapter, then a verse that contains the queried verse from the whole chapters. This perspective reveals a global probability over the entire corpus. Thus, the log (D/dfi) term is the inverse verse frequency, IDFi accounts for global information [16]. A. The Proposed Approach The proposed algorithm and flow chart as shown in figure 8 that is used for searching the Qur’an about Initial Holy Quran Letters and concepts can be described as shown in the following steps. 1. Select the text file that contains Qur’an text as search data. 2. Build the Quran words as table. 3. Build the index table 3.1 Read the text file verse by verse. 3.2 Read the verse word by word. 3.2.1 Read the Letter by Letter. 3.3 IF the Letter is not an Arabic word THEN consider this Word as a useless word. 3.3.1 IF the word contains digits THEN consider this word as a useless word. 3.3.2 During the normalization process remove all diacritics, Normalize the word, Remove prefixes, Remove suffixes. 3.3.2 Take twenty nine chapters from among all Quran chapters and find out frequency of each letter.

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3.3.3Calculating lettersalif,laam,mym,,Raa,Yaa,Syn,Saad,Taa,Ayn,Qaaf,Kaaf,Nuwn,Haa among these chapters. 3.3.4 Normalize the word and made a database with MS SQL server. 3.4 Selecting Quran initial letter like Ya,Sin 3.4.1 And we find out chapter 36 Quran initial letter Ya sin making as order type is descending order. 3.4.2 And we find out chapter 45 Quran initial letter Ha Mim making as order type is Ascending Order. 3.4.3 And we find out chapter 32 Quran initial letter Alif,Lam,Mim making as order type is Random order. 3.5 Clustering and Classification using WEKA Software implementing our data base Depending on Holy Quran Database. 3.5.1 We investigate the Issue of classification Sura’s into Madani, Makki or both implemented in weka software and classified by J48 algorithm. And correctly classified instances 73.33% and incorrectly classified instances 26.66%.

Figure 8: Phases of Holy Quran Text Recognition System V. Clustering and Classification using WEKA Software implementing Holy Quran When the whole data has been executed in special software named ‘data mining techniques’[weka] as shown in figure 9 we found 11 chapters have fallen under Ascending order usage 14 chapters have fallen under descending order usage and 5 chapters have fallen under random usage. The same data has also been put under j48 algorithm in which the correctly classified instances were found to be 80% and incorrect classified instances came to be 20%.

Figure 9: WEKA Classifier Results for Holy Quran To provide an evidence of the usefulness of such process, we investigate the Issue of classification Sura’s into Madani, Makki or both implemented in weka software and classified by J48 algorithm as shown in figure 10. and correctly classified instances 73.33% and incorrectly classified instances 26.66%Relative absolute error 47.4053%,Root relative squared error 70.99%,and total number of instances 30.Ture positive(Tp Rate=0.727 for class A)

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Figure 10: Using J48 Algorithm WEKA Classifier Results for Holy Quran. The goal of clustering is to create clusters, by grouping similar data items to gather as shown in figure 10, x-axis is suraid and Y-axis is alif order type is ascending and descending order and reverse id. Figure 11 illustrated depending database of holly Quran words such as alif, Lam, mym ascending and descending and reverse order

Figure 11: Simple k-means algorithm classified by suraid and alif alphabet. VI. DISCUSSION The results that are obtained based on chapters To make it more precise a deep study of our database has revealed us that these 29 chapters which began with particular as shown in Figure 12, Figure 13, Figure 14.Arabic alphabet has a specific miracle, when We observe this miracle it is clearly evident that the combination of these alphabet usage in some of these 29 chapters has been in ascending order and in some chapters it is in descending order and in few chapters these Arabic alphabet has been randomly used.

Figure 12: Appearance Ascending order pattern for Quran initial letters alphabet usage in some of these 29 chapters.

Figure 13: Appearance Descending order pattern for Quran initial letters alphabet usage in some of these 29 chapters.

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Figure 14: Appearance Random order pattern for Quran initial letters alphabet usage in some of these 29 chapters. According the our Holy Quran pattern results from data base we observed that only three chapters start with single initial letter and among these chapters 38,50,68 initial letters are sad, qaf, nun and we didn’t predict that there is no ordering pattern as shown in the Figure 15 because these chapters start with only single letter.

Figure 15: No ordering pattern for sad, Qaf, Num three chapters from Holy Quran initial letters alphabet usage in some of these 29 chapters. Based on result data mining techniques we can analysis holly Quran words frequency. Therefore according our experiment with database for holy Quran definitely proves that these initials play an important role in the Quran’s mathematical code and serve as proof of God’s infallible scripture. VII. CONCLUSION This study aims to lay out a framework for future work that is related to the application of natural language processing, data mining and text mining to the text of the holy Quran. This is done by initially pre-processing the text of the holy Quran and by considering the different possible partitioning. After all the study and research into these 29 chapters of the holy Quran we have come to a conclusion that it may be some kind of mathematical or structured way of using a language which may be beyond a human perception. Although the results of this study are interesting, however it is based on the original words of the holy Quran. More accurate results will be obtained depending on data mining techniques. Future work may include pre-processing the text of the holy Quran with efficient and accurate algorithm that might give words like stems as light stemmers algorithms do. If such algorithm is developed then further study on the text of the holy Quran will be carried out to extract knowledge and important information that is useful to all humanity using machine learning techniques. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

M. DIAB and N. HABASH, “Arabic dialect tutorial,” in In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (NAACL07), 2007, pp. 29–34. A. Farghaly and K. Shaalan, “Arabic natural language processing: Challenges and solutions,” vol. 8, no. 4, pp. 14:1–14:22, Dec. 2009. [Online]. Available: http://doi.acm.org/10.1145/1644879.1644881 N. Y. Habash, Introduction to Arabic Natural Language Processing, G. Hirst, Ed. Morgan and Claypool Publishers, 2010. M. Saad and W. Ashour, “Arabic morphological tools for text mining,” in 6th International Symposium on Electrical and Electronics Engineering and Computer Science, European University of Lefke, Cyprus, 2010, 2010, p. 112117. K. Dukes, E. Atwell, and N. Habash, “Supervised collaboration for syntactic annotation of Quranicarabic,” Language Resources and Evaluation, vol. 47, no. 1, pp. 33–62, 2013. [Online]. Available: http://dx.doi.org/10.1007/s10579-011-9167-7 M. H. Panju, “Statistical extraction and visualization of topics in the Qur’an corpus,” Master’s thesis, University of Waterloo, 2014. M. Shoaib, M. NadeemYasin, U. Hikmat, M. Saeed, and M. Khiyal, “Relational wordnet model for semantic search in holy Quran,” in International Conference on Emerging Technologies, 2009. ICET 2009. Oct 2009, pp. 29–34. A. A. AliyuRufaiYauri, Rabiah Abdul Kadir and M. A. A. Murad, “Quranic verse extraction base on concepts using owl-dl ontology.” vol. 6, no. 23, pp. 4492–4498, 2013. M. S. HikmatUllah Khan, Syed Muhammad Saqlain and M. Sher, “Ontology-based semantic search in holy Quran,” vol. 2, no. 6, pp. 562 – 566, 2013. K. Dukes. (2014, Dec.) Quranicarabic corpus. [Online]. Available: http://corpus.Quran.com/ Tanzil.net. (2014, Oct.) TanzilQuran text downloads @ONLINE. [Online]. Available: http://tanzil.net/download/

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

T. Zerrouki. (2014, Nov.) Arabic stop words @ONLINE. [ Online ]. Available: http://sourceforge.net/projects/arabicstopwords/ M. Sawalha and E. Atwell, “Comparative evaluation of arabic language morphological analysers and stemmers,” in Coling 2008: Companion volume: Posters. Coling 2008 Organizing Committee, 2008, pp. 107 – 110. N. Thabet, “Stemming the Quran,”´ in Workshop on Computational Approaches to Arabic Script-based Languages.Coling 2008 Organizing Committee, 2008, pp. 28–31. R. J. R. Yusof, R. Zainuddin, M. S. Baba, and Z. M. Yusoff, “Quran´ words stemming,” Arabian Journal for Science and Engineering, vol. 35, p. 38, 2010. Mohammed Akour,IzzatAlsmadi,IyadAlazzam,"MQVC: Measuring Quran Verses Similarity and Sura Classification Using NGram" vol 13, pp.485-491,2014. AnwerHilal received M.Sc. degree in Computer Science, from Sudan University of Science & Technology (SUST), Sudan. He is pursuing Ph.D. In Computer Science from Omdurman Islamic University, Sudan. He is working as Lecturer in Prince Sattam bin Abdul-Aziz University, Riyadh Saudi Arabia. He has 6 years’ experience in teaching in various Educational Institutes. His article and publications published all over the world. His field of interest in Data mining, He took an initiative in Text Mining, Topic Modeling. Dr.Nalla.Srinivas received PhD in computer Science and Engineering from Nagarjuna University Guntur, M.Tech degree in Computer Science engineering, M.Phil (computer Science) from Alagappa University, Tamilnadu, India. He is a member IEEE. He is working as Lecturer in Prince Sattam bin Abdul-Aziz University, Riyadh Saudi Arabia. He worked as Assistant Professor in Sirte University Sirte Libya. He has 12 years’ experience in teaching in various Educational Institutes. His field of interest in Artificial Neural Networks and fuzzy Logic He took an initiative in Artificial neural network, intelligent fuzzy computing.

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Survey of brain tumor detection techniques through MRI images Megha A Joshi1, Prof. D.H.Shah2 PG Student, Instrumentation and Control, Associate Professor 2 1,2 L.D College of Engineering, Navrangpura, Ahmedabad, Gujarat 380015, INDIA. 1

Abstract: The images from the medical imaging technologies like MRI, US, CT are more complex to understand and noisy. Here, the area of interest is tumor detection in brain MRI Images. Todayâ&#x20AC;&#x2122;s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). For tumor detection in brain MRI image segmentation is an important part. Segmentation is the important tool in medical image processing which helps to make a simple format of medical image which is easier and meaningful to analyse. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Keywords: Brain tumor detection, Magnetic resonance image, edema, image segmentation. I. Introduction In the present days, for the human body anatomical study and for the treatment planning medical science is very much depend on the medical imaging technology and medical images[1]. Magnetic resonance (MR) imaging and computer tomography (CT) scanning of the brain are the two most common tests undertaken to confirm the presence of brain tumor and to identify its location for selected specialist treatment options. Specifically for the human brain, MRI widely using. But by nature medical images are complex and noisy [1]. A tumor is a mass of tissue that's formed by an accumulation of abnormal cells [10]. Brain tumor is an abnormal mass of tissue in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells [2]. So, it is very hard to detect tumor in early stage, since accurate measurements in brain tumor diagnosis are quite difficult because of diverse shapes, sizes, appearances of tumor, position of tumor in the brain but once it gets identified the treatment can be done and is curable with technique like chemotherapy, radiotherapy[5]. During the last few years brain tumor segmentation in MRI has become an emergent research field of medical image processing. MRI is an effective tool that provides detailed information about the targeted brain tumor anatomy, which in turn enables effective diagnosis and treatment [5]. This paper presents a review of the methods and techniques used during brain tumor detection through MRI image segmentation. II. Literature Review A. Seed region growing method: It is a simple region-based image segmentation method. Seed based region growing performs a segmentation of an image with respect to a point, known as seed [4]. This approach to segmentation examines neighbouring pixels of initial seed points and determines whether the pixel neighbours should be added to the region.

Figure 1 segmented tumor[1]

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The basic formulation or mathematical description for Region-Based Segmentation is as following [1]. Where, is connected region, . It requires seed points in a region for all .This indicates that the regions must be disjoint for all For example: if all pixels in have the same gray level for any adjacent region & is a logical predicate defined over the points in set and is the null set. This region growing segmentation method without pre-processing require three or four seed point. Manual indication of the seed point with a marker is imprecise in this case [2]. In the case of improved segmentation method one seed point is enough for the appropriate segmentation [1]. B. Thresholding method: Thresholding is the simplest method. This method is on gray level intensity value of pixels [5]. Thresholding is the procedure to determine an intensity value, called the threshold, which separates the desired classes. And segmentation is done by grouping all pixels with intensity greater than the threshold into one class, and all other pixels into another class. It selects a proper threshold and then divide image pixels into many regions and spate objects form the background. Any pixel (x, y) is taken as the part of the object and provided that its intensity is greater than or equal to the threshold value. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. Generally, the threshold selection is done interactively. To get the exact shape and size of tumor, after segmentation the morphological operation and subtraction are used.

Figure 2 Final tumor detected region[5]

C. Watershade segmentation: The basic principle is to transform the gradient of a grey level image in a topographic surface. Where the values of f (m, n) are interpreted as heights and each local minima embedded in an image is referred as catchments basins.

Figure 3 Watershed Segmentation of Input [6]

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If rain falls on the defined topographical surface, then water would be collected equally in all the catchments basins. The watershed transformation can be built up by flooding process on a gray tone image [6]. However, a major problem with the watershed transformation is the over-segmentation. And this over-segmentation is overcome by applying marker based watershade segmentation [6]. Here, watershade internal marker is the only allowed regional minima. External markers found by finding pixels that are exactly midway between the internal markers and is associated with the background[6]. The gradient image is then modified. The next step involves the computation of the watershed transformation of the Marker modified gradient image to produce watershed ridge lines. Finally resulting watershed ridge lines are superimposed on the original image and produce the final segmentation [6]. D. Cohesion self means algorithm: In this algorithm first K-mean algorithm is applied and after that cohesion self-merging algorithm is applied. It is an unsupervised learning technique. Simply speaking it is an algorithm to classify or to group your objects based on attributes/features into K number of group [8]. In the beginning, we determine number of cluster K and we assume the centroid or center of these clusters [8].Then Calculate distance from all the selected initial centroids to all existing points inside the data set then depending upon the minimum distance criterion the clusters have been formed [7]. Next step is new centroids inside the newly formed clusters calculated. Repeat above steps with respect to newly generated new cluster centroids and algorithm continued until the convergence is reached. Although this technique has showed promising results for a few data sets, it needs to prove its potential in practical applications [9].To identify tumor region CSM algorithm has been applied as the measurement of the inter cluster similarity [7]. Basic formulation is as follows. First calculate mean vector& co-variance matrix Estimator ( ).Consider clusters of n points with locations , the values of ( ) of the clusters calculated by:

Where

indicates the (pixel vector) Contains location & grey level information of that pixel

In the next step we consider all the pixels in the image and here we calculate probability density function for each pixel with respect to the mean and covariance of all existing clusters. Assume the location of a point in each cluster follows a multivariate normal distribution, i. e Where d is the dimension of the space. The probability density function is calculated by using the following formula: Where,

Figure 4 An illustration for the meaning of joinability [7]

Where, and are the probability (density) function of the distributions in the above mentioned two clusters ( and ) is being calculated by using the formula as shown below

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Where,

and

the size of the clusters

and

respectively [7].

Figure 5 image wiith tumor[7]

The above figure5 shows the tumor which is detected by applying cohesion self means algorithm and mathematical morphology. Here binary morphological erosion algorithm is used for the noise removal process. After erosion eroded image removes some parts of tumor. Therefore to get back its original size the opposite algorithm of erosion, dilation has been applied. The dilated output represents the exact size and location of tumor in original image. But, cohesion self means algorithm gives not accurate result when tumor is surrounded by edema. Technique Seed region growing

Thresholding

Markerbsed Watreshade segmentation Cohesion self-merging

Table 1: MRI brain tumor method comparison Advantage/Disadvantage  Advantage  Correctly separate the regions that have the same properties we define  Determine the seed points and the criteria we want to make  Disadvantage  It requires manual interaction to obtain seed point  Advantage  Simple method  Tumor is diagnosed at advanced stage  Disadvantage  Also tumor growth can be analysed by plotting graph, which can be obtained by studying sequential images of tumor affected patient.  Advantage  It removes the over segmentation problem , which occurs in watershade segmentation  Advantage  It is a simple method  It has a less computational complexity.  Disadvantage  Performance depends highly on initial cluster centers.

III. Conclusion For accurate diagnosis of brain tumor proper segmentation method is required for MR images to carry out an improved diagnosis and treatment. In this paper we attempted to review some of the worthwhile recent research works done on brain tumor detection and segmentation. Merits and demerits of different segmentation algorithm is discussed. The discussion showed that few methods are working effectively and accurately in regard of brain image analysis but still there exists need for more effective and precise work. But, these methods give not accurate result when tumor is surrounded by edema. IV. References [1] [2] [3]

[4]

[5]

Praveen Kumar E, Manoj kumar V, Sumithra M G “ Tumour Detection In Brain Mri Using Improved Segmentation Algorithm” IEEE – 31661, 4th ICCCNT 2013. Ewelina Piekar, Paweł Szwarc, Aleksander Sobotnicki, Michał Momot” Application Of Region Growing Method-To Brain Tumor Segmentation – Preliminary Results” Journal Of Medical Informatics & Technologies Vol. 22/2013, Issn 1642-6037. Mukesh Kumar, Kamal Mehta,” A Modified Method To Segment Sharp And Unsharp Edged Brain Tumors In 2 D MRI Using Automatic Seeded Region Growing Method”, International Journal Of Soft Computing And Engineering (IJSCE) ISSN: 22312307, Volume-1, Issue-2, May 2011. Aminah Abdul Malek, Wan Eny Zarina Wan Abdul Rahman, Arsmah Ibrahim, Rozi Mahmud, Siti Salmah Yasiran, Abdul Kadir Jumaat “Region and Boundary Segmentation of Microcalcifications using Seed-Based Region Growing and Mathematical Morphology”International Conference on Mathematics Education Research 2010 (ICMER 2010). Natarajan P1, Krishnan.N, Natasha Sandeep Kenkre, Shraiya Nancy, Bhuvanesh Pratap Singh, “Tumor Detection Using Threshold Operation In MRI Brain Images” 978-1-4673-1344-5/12/2012 IEEE.

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[6]

[7] [8] [9] [10]

Pratik P. Singhai, Siddharth A. Ladhake “Brain Tumor Detection Using Marker Based Watershed Segmentation from Digital MR Images” International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-5, April 2013. Subhranil Koley, Aurpan Majumder, “Brain MRI Segmentation for Tumor Detection using Cohesion based Self Merging Algorithm” 978-1-61284-486-2/2011 IEEE. By Kardi Teknomo,PhD,” K-Means Clustering Tutorial” July 2007. D T Pham_, S S Dimov, and C D Nguyen, “Selection of K in K-means clustering” C09304 IMechE 2005 Proc. IMechE Vol. 219 Part C: J. Mechanical Engineering Science. www.webmd.com/cancer/brain-cancer/brain-tumors-in-adult

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FERTIGATION IN VEGETABLES CROPS Indu Arora1, C. P. Singh2, Shant Lal3 Post Doctoral Fellow, (PDF), Department of Horticulture, G. B. P. U. A. & T., Pantnagar, Uttarakhand, INDIA. 2 Professor, Department of Horticulture, G. B. P. U. A. & T., Pantnagar, Uttarakhand, INDIA. 3 Prof. & Head, Department of Horticulture, G. B. P. U. A. & T., Pantnagar, Uttarakhand, INDIA

I. INTRODUCTION India is the brick of a Golden Revolution in Horticulture with a total annual production of 149 Million tonnes. Vegetables are important constituents of Indian agriculture and nutritional security due to their short duration, high yield, nutritional richness, economic viability and ability to generate on-farm and off-farm employment. Our country is blessed with diverse agro-climates with distinct seasons, making it possible to grow wide array of vegetables. Today, India is the second largest producer of the vegetables (90.8 Million tonnes) in the world, contributing 14.45 per cent of the total world production (NHB, 2013). The concept of Horticulture is shifting from maximizing yield to maximizing value with water management assumes paramount importance to reduce the wastage of water to increase the water use efficiency and also ensures evenly distribution. Moisture is maintained in the medium through application of water at critical stage of crop. The main point which considered is that changing from single product to creating value added product through a balanced, crop specific plant nutrition concept. The crop competence for nutrients will become more and more crucial, but even more relevant to transmit the superior knowledge to the end user is essential. Thus, fertigation is an important concept and the key focus of this article is on assisting the horticulturist in general and vegetable crops in particular. II. FERTILIZERS Fertilizers are chemical compounds (liquid or granular) which provides essential plant nutrients to the plants to promote growth. They are either applied through the soil or irrigation water. III. FERTIGATION Fertigation is a method of applying fertilizers, soil amendments and other water soluble products required by the plant during its growth stages through drip or sprinkler irrigation system. In this system fertilizer solution is distributed evenly in irrigation. The availability of nutrients is very high therefore the efficiency is more. In this method liquid fertilizer as well as water soluble fertilizers are used.

Table 1: Nutrient content of common fertilizers suited for fertigation Nutrient

Compound

Urea Ammonium Nitrate Ammonium Sulphate Phosphoric acid Phosphorus (P) Mono Ammonium Phosphate Di Ammonium Phosphate Source: Mangen H. Fert. News (1995) Nitrogen (N)

Nutrient content in solid fertilizers (N:P2O5:K2O) 46: 0: 0 33: 0 : 0 21: 0: 0 12: 61: 0 18: 46: 0

Nutrient content in saturated liquid fertilizers (25Â° C) 21: 0: 0 21: 0: 0 10 : 0: 0 0: 51: 0 4: 18: 0

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

IV. CHARACTERISTICS OF FERTILIZERS USED IN FERTIGATION Full solubility. Quick dissolution in water. Fine grained product. High nutrient content in the saturated solution. Compatibility with other fertilizers, Absence of chemical interaction with irrigation water. Minimum content of conditioning agents. No clogging of filters and emitters. Low content of insoluble (< 0.02 per cent). No drastic change of water pH (3.5< pH> 9.0). Low corrosives for control and head system.

2. 3.

V. TYPES OF WATER SOLUBLE FERTILIZERS Liquid/Fluid fertilizers: Fluid or liquid in which the plant nutrients are in true solution. These are solutions which contain one or more plant nutrients. Suspension fertilizers: Fluid fertilizers that have solid nutrients dispersed throughout the fluid. Water soluble solid fertilizers: Fertilizers that are in solid state plant nutrients are fully water soluble.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

VI. SALIENT FEATURES OF WATER SOLUABLE FERTILIZERS Nutrients are 100 per cent water soluble. Acidic in nature. They correct and maintain the soil pH. Nutrients are applicable in smaller quantity. Nutrients are in readily available form. Free from Na and Cl salts. Apply in precise and uniform form. Correct placement in root zone. There are no chances of fixation of P and K. No soil deterioration. Application as per physiological stages of crop. Improve up take of nutrients present in soil. Efficiency is more than 90 per cent. Improve and fasten the uptake of nutrients in soil.

VII. NEED OF FERTIGATION  Rapid increase in area under micro irrigation, now fertigation is getting momentum in number of the countries. The concept of fertigation is new to the Indian subcontinent growing popularity to accept of this concept making it easy to adopt ‘Fertigation’. Fertigation is the technique to apply water soluble solids or liquid fertilizers through the drip irrigation on weekly or monthly basis so as to reach each and every plant regularly and uniformly. It is the most effective and convenient means of maintaining optimum fertility level and water supply according to the specific requirement (Shirgure, 2000).  Fertigation permits application of a nutrient directly at the site of a high concentration of active roots and as needed by the crop. Scheduling fertilizer applications on the basis of need offers the possibility of reducing nutrient element losses associated with conventional application. Methods that depend on the soil as a reservoir of nutrients thereby increasing nutrient use efficiency. Fertilizer savings through fertigation can be to the tune of 25 - 50 per cent (Haynes, 1985).  Fertilizers and pesticides applied through a drip irrigation system can improve efficiency, save labour and increase flexibility in scheduling of applications to fit crop needs (Rolston et al., 1979). However, all chemicals must meet the following criteria for the successful maintenance of the drip irrigation system (Bucks and Nakayama, 1980).  However, increasing water scarcity and value crops and green houses to ensure higher escalating fertilizer prices may lead to greater efficiency of the two most critical inputs in crop adoption of the technology especially in high production. We should be conscious about that 'per drop more crop. VIII. RULES OF FERTIGATION A few rules should be followed, to achieve maximum benefits of fertigation by Marr, 1993. 1. Type and amount of fertilizers used must be soluble enough to dissolve completely in the fertilizer tank water. 2. Completely charged or pressurized drip irrigation system is required before fertigation begin.

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3. 4.

1. 2. 3. 4.

The fertilizers should be injected ahead of the filters to ensure that any undissolved particles are filtered out before fertilizer enters the drip tape. The period of time in which fertilizer is injected into the system must be at least as long as that required to bring the entire drip irrigation system up to full pressure. This will allow each dispensing orifice in the drip line to have the same contact time with the fertilizer solution as it passes through the system. All fertigation units should be wired to the pump switch control or a flow control switch in the main line to prevent the unit from running when no water flows in the line. IX. PRE-REQUISITE FOR SUCCESSFUL FERTIGATION Scientifically designed and well installed drip irrigation system. Drip system material should be free from residues/deposits and fertilizers must not cause excessive corrosion of irrigation system components. Irrigation system operating pressure variation should be minimum. Selection of most appropriately fertilizer according to soil condition, plant requirement and costs.

X. FERTIGATION EQUIPMENTS Fertiliser can be injected into drip irrigation system by selecting appropriate equipment. Commonly used fertigationequipments are: 1. Venturi pumps 2. Fertiliser tank 3. Fertiliser injection pump 4. External energy driven injector pumps 5. Automatic fertigation controller Venturi Injector This is a very simple and low cost device. Venture consists of a converging section, a throat, a diverging section. A partial vacuum is created in the system which allows suction of the fertilisers into the irrigation system through venturi action. The vacuum is created by diverting a percentage of water flow from the main and pass it through a constriction which increases the velocity of flow thus creating a drop in pressure. When the pressure drops the fertilisers solution is sucked into the venturi through a suction pipe from the tank and from there enters into irrigation stream. Although simple and with greater uniformity of dosing the fertilisers tank the venturi cause a high pressure loss in the system which may results in uneven water and fertiliser distribution in the field. The suction rate of venturi is 30-120 litre per hour. Fertiliser Tank (Flow by pass system) Operational principle is water flow because of pressure gradient between the entrance and exit of the fertilizer tank created by a pressure reducing valve. In this systems part of irrigation water is diverted from the main line to flow through a tank containing the fertiliser in a fluid or soluble solid form, before returning to the main line, the pressure in the tank and the main line is the same but a slight drip in pressure is created between the off take and return pipes for the tank by means of a pressure reducing valve. This causes water from main line to flow through the tank causing dilution and flow of the diluted fertiliser into the irrigation stream. With this system the concentration of the fertiliser entering the irrigation water charges continuously with the time, starting a high concentration. As a result uniformity of fertiliser distribution can be a problem. Fertiliser tanks are available in 90, 120, 160 liters capacity.

Venturi Injector

Fertilizer Tank

Fertigation Pump

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Fertigation Pump These are piston or diaphragm pumps which are driven by the water pressure of the irrigation systems and such as the injection rate is proportional to the flow of water in the system. This injection rate of fertilizer solution is proportional to the flow of water in the system and adjusted to attain the desired level of fertilizer application. A high degree of control over the fertiliser injection rate is possible, no serious head losses are incurred and operating cost is low. Another advantage is that if the flow of water stops, fertiliser injection also automatically stops. This is perfect equipment for accurate fertigation. Suction rates of pumps varies from 40 to 160 litre per hour.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

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

XI. ADVANTAGES OF FERTIGATION In drip fertigation, the amount and form of nutrient supply is regulated as per the need of the critical stages of plant growth. Saving of fertiliser applied, due to better fertiliser use efficiency and reduction in leaching. Optimisation of nutrient balance in soils by supplying the nutrients directly to the effective root zones as per the requirement. Reduction in labour and energy cost by making use of water distribution systems for nutrient application. Better yield and quality of products obtained. Timely application of precise amounts of fertilisers directly to the roots zone, this improves fertiliser use efficiency and reduces nutrient leaching below the root zone. Ensures a uniform flow of water and nutrients. Improves availability of nutrients and their uptake by crop. Safer application method, as it eliminates the danger affecting roots due to higher dose. Soil and water erosion are prevented. XII. DISADVANTAGES OF FERTIGATION Both the components (drip and water soluble fertiliser) are very costly. Maintenance of drip irrigation is difficult. There is possibility of theft and rat infestation. Good quality water is very essential. Clogging of emitters may cause a serious problem. It needs water soluble fertilisers, the availability of these types of fertilisers is limited. Adjustment of fertilisers to suit the need is not easy. Infestation of insect pest and diseases increases. Area under micro irrigation is now increasing mainly because of subsidy in microirrigation, if subsidy is withdrawn, the area under micronutrient may also reduce. Due to fear of yield loss, because of relatively lower dose of fertilisers in fertigation, farmers have the tendency to add additional fertilisers and irrigation water by traditional methods too. This may result in crop lodging (Sugar cane) lower yield and lower profits.

XIII. CONCLUSION Drip fertigation technology is beneficial to the farmers for higher production and quality vegetable production. Achieving maximum fertigation efficiency requires knowledge of crop nutrient requirements, soil nutrient supply, fertilizer injection technology, irrigation scheduling, crop and soil monitoring techniques. Thus, success in using this system will depend on a sound fertility programme based on soil testing and a drip irrigation system that is designed and operated properly. REFERENCES [1]. [2]. [3]. [4]. [5].

Jindal, K. K. 2010. Micro-irrigation and fertigation to enhance water use efficiency in horticulture- Temperate fruit case study. Jain Irrigation System Himachal Pradesh, pp 459-465. Biswas, B. C. 2010. Fertigation in high tech Agriculture: A success story of a lady farmer. Fertiliser marketing news: The Fertiliser Association in India, New Delhi. 41: 10, 4-10. Bucks, D.A. and Nakayama, F. S. 1980. In : Proc. Agri-Turi. Irrig. ConI., California, pp 166-180. Dangler, J. M. and Locasio, S. J. 1990. J Americ Soc for Horticul Sci., 115:585-589. Haynes, R.J. 1985. Fert. Res., 6: 235-255.

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American International Journal of Research in Formal, Applied & Natural Sciences

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"Biomimicry"Innovative Approach in Interior Design for Increased Sustainability Dr. Inas Hosny Ibrahim Anous Lecturer/Department of Interior Design and Furniture Faculty of Applied Arts /Helwan University Arab Republic of Egypt Abstract: The relationship between architecture and nature is one that has brought many questions. Bio mimicry is taking the philosophy behind natures living organisms and uses them to aid in the development of mankind. The application of Bio mimicry is wide; By mimicking a variety of elements from nature, the final composition will not only respond to the activities within the building, but also to the surrounding environment. Biomimicry is a path for a sustainable future. The paper gives an overview of Biomimicry, its approaches and levels of applications in interior design. The analyses shows that using biomimicry as a problem solving methodology will help us discover sustainable and effective solutions to the most important issues in the interior environments: day lighting, thermal comfort, energy efficiency, durability, and productivity. Keywords: biomimicry, sustainable building, bio-inspired design, Biomimicry Interior Design , Biomimicry - Sustainable Design. I. Introduction "The best way to predict the future is to design it[1]. Humans have learned much from nature .Nature has always inspired human achievements and has led to effective materials, structures, processes and the results have helped surviving generations and continue to secure a sustainable future. The field of Biomimicry where flora, fauna or entire ecosystems are emulated as a basis for design, is becoming an increasingly well-known topic and has attracted worldwide interest in the fields of design, engineering, architecture, and business, imitating nature’s designs and processes to solve human problems [2], [3]. The inspiration from nature is driving force in architecture, resulting in majestic works of architecture. For any sustainable building design, need to consider structural efficiency, water efficiency, zero-waste systems, thermal environment, and energy supply; Biomimicry is an alternative solution, Biomimicry is a new way of viewing and valuing nature, based not on what we can extract from the natural world, but what we can learn from it [4] Designers draw their inspiration from multiple sources to address challenging design problems. One method is to study nature, and attempt to comprehend the ways in which it has evolved to address environmental challenges [5]. II. Research problem The lack of a clearly defined approach to biomimicry that interior designers can initially employ, particularly if the goal is to increase the sustainability of the built environment.Interior design uses biology as a library of shapes alone as biomimetics without some biology in it. III. Research objectives Exploring the potential of biomimicry in architecture and interior design .Exploring the application of Biomimicry in current architectural design, resulting in a set of design approaches, levels and principles. Study how to Achieve a sustainable environment with radical increase in resource efficiency by looking to the nature for inspiration" biomimicry".Raise the awareness of interior design students about ‘nature’, ‘sustainability’ and ‘nature inspired design approaches".Ceating a community that will scale the practice of biomimicry and the idea that nature's wisdom is a powerful natural resource we have yet to fully explore. IV. Methodology The paper is divided into five sections: the first gives an overview about Biomimicry. The second explains the relationship between Biomimicry and Nature , The third section discuss the Design approaches and levels of Biomimicry and its design methodology. The last section analyses the Applications of biomimicry in interior design and furniture.The researcher folow the inductive approach through access to the latest scientific literature related to the subject of research and the analytical approach by the analysis of some applications of Biomimicry in interior design.

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V. Fundamentals concepts about biomimicry A. Bionics The term ‘bionics’ (biology + technics) describing the process of “copying, imitating, and learning from biology” was conceived by Jack Steele in as early as 1960 prior to the infamous Bionics Symposium[6]. B. Biomimetics The term is as a derivative of Greek words "bios" meaning life and "mimesis" meaning imitate [6] . It represents the studies and imitation of nature’s methods, mechanisms and processes[7]. It was conceived by Otto H. Schmitt , one of the early giants in biomedical engineering, a founding president of Biomedical Engineering Society and founding vice president of the Biophysical Society, in approximately 1969[6] Biomimetics is the replication of the functionality of a biological structure by approximately reproducing an essential feature of that structure[8] C. Biomimicry Janine Benyus defined the term ‘biomimicry’ as a "new science that provide innovative and sustainable solutions for industry and research Development ".Janine Benyus was known as the founder of the Biomimicry movement. She is a highly accredited biological sciences writer who has inspired and brought forth a new dimension to design by looking to nature as the key source of inspiration. For her Biomimicry is the conscious emulation of nature's genius"[9]. Biomimicry uses an ecological standard to judge the sustainability of our innovations[4]. C. 1Nature-inspired design strategies Nature-inspired design strategies are design strategies that base a significant proportion of their theory on ‘learning from nature’ and regard nature as the paradigm of sustainability. Ingrid de Pauw, Prabhu Kandachar, Elvin Karana, David Peck, Renee Wever[10]. C.2 The philosophy behind Biomimicry Biomimicry is a new discipline that studies nature's best ideas and then imitates these designs and processes to solve human problems[11]. It is a new type of ideology that combines biology and architecture in order to achieve complete unity between the building and nature. The Biomimicry approach based on studying the living organisms -their structures, functions, processes, interactions and relationships among them and their surroundings, in order to learn from their strategies, methods and principles and emulate them to optimize the environmental performance and attitude of the designs. Biomimicry is often described as a tool to increase the sustainability of human designed products, materials and the built environment [12]. C. 3 Importance of Biomimicry research Bio mimicry is considered to be one of the most important design tools that flourished with the dawn of the twenty first century to evolve revolutionary sustainable design solutions .Biomimicry is an approach to innovation that seeks sustainable solutions to human challenges by emulating nature's time- tested patterns and strategies. VI. Biomimicry and Inspiration from nature A. The evolution of mankind relationship with nature [1] Fig. 1: shows diagram showing the evolution of the relationship of mankind with nature

A.1 Nature in Architecture of old civilizations The old civilizations like the Egyptian Pharohs, old Greeks and Romans and Islamic civilization, simulate nature but also apply laws of nature in a scientific way in their designs ''Fig 2". Their designs were: Harmonious with nature. Adapted with the natural environment. Commensurate with the privacy of the place. Use materialsfrom the surrounding environment. Affected with the local environment in the shape, characteristics andperformance. The environment was respectable [12]. Fig. 2: shows the difference between the pharaoh's house, Roman's house and Elsehimy Islamic house

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A.2 The Bio mimicry Evolution Timeline Our history is marked by numerous approaches to the solution of engineering problems based on solutions from nature. All of these approaches are progressions along the same line of thought: "Engineered Biomimicry", which encompasses bioinspiration, biomimetics, and bioreplication [8] .Since the creation of the human , the Raven learned the burial to Cain – who killed his brother . Primitive cultures and ancient civilizations simulate nature to find solutions to their daily needs; Egyptian civilization has inspired a lot of architectural elements (columns, furniture, decorations) from the forms and structures of local organisms.In 15 & 16th century: Leonardo Da Vinci (1452-1519 ): the first researcher in Bionics or Biomimetic mechanical engineering. He studied the formation and movement of bird’s wings , tried to simulate them and invented the flying machine. Mathew Baker (1590)who was one of the leading designers of ships who inspired from nature how to build large ships structures. The design idea of the structure of the ship simulated head and tail of the mackerel fish. In the18th century: John Smeaton (1759) who was a civil engineer. He used the form structure of oak trees as a basis in the designing of Eddystone lighthouse because of its hardness and durability. In the 19thcentury, The natural organic forms were the inspiring basic model for the architect Antoni Gaudi (1883).In La Sagrada Famillia Cathedral in barcheolna he tried to simulate trunks and branches of trees and snails spiral and used them as Structural elements resist to wind and weather conditions and the transfer of loads not only in decoration. Gustave Eiffel (1889)who was a civil engineer , designed Eiffel tower . He simulated the upper part of Femur or Thigh bone as it Is the strongest bone in the human skeletal structure as it act as a carrier to what above it and a holder to the rest of the leg . In the 20th century:Frank Lloyd Wright (1956) took the organic architecture way. It is not only a simulation for natural objects but he uses the nature principles in a new way. Eugene Tsui (1980), an architect, industrial designer, scientist and inventor , stated that human and nature must become partners in the design to create a world of beauty. He invented new way of architecture called it Evolutionary Architecture. In(1997)The biologist and natural Sciences writer Janine Benyus is known as the founder of the Bio mimicry movement. she collected all her theories in a book called “Biomimicry:Innovations Inspired by Nature. In 1998, Janine and Dr.Dayna Baumeister founded The Bio mimicry Guild which uses a deep knowledge of biological adaptation of organisms to helpdesigners, engineers, architects and business men to solve design and engineering problems in a sustainable manner . In 2005 , Janine benyus founded The Biomimicry Institute. In 2008 , The Biomimicry Portal Prototype is produced; it is the first a digital data base for biological organisms that have the strategies to solve problems commensurate with the humanitarian community[12]. B. Nature design principles The nature's unique characteristics and principals that can be applied and help develop architecture and design is as follows:Nature runs on sunlight,Nature uses only the energy it needs,Energy fits form to function,Energy recycles everything,Nature rewards cooperation,Nature banks on diversity,Nature demands local expertise ,Nature curbs excess from within ,Nature taps the power of limits [13]. According to Benyus, ten principles can be identified as underlying nature’s rules for sustaining ecosystems: Use waste as a resource, diversify and cooperate to fully use the habitat, gather and use energy efficiently, optimize rather than maximize, use material sparingly, don’t foul nests, don’t draw down resources, remain in balance with the biosphere, run on information, shop locally . If our products, interior spaces, buildings and cities have designed in accordance with these principles, as Benyus suggests, we would be well on the way to living within the ecological limits of nature, and thus achieving our goal of sustainability[14]. Nature solves the following aspects The economy of constructive materials, Original structures, perfectly adapted to their environment, Aesthetic quality, Principles of nature provide verified information through the natural selection process , Nature is timeproof [15]. C. The way of thinking about nature: Nature as model : Biomimicry studies nature’s perfect models takes inspiration from their designs and processes to solve human problems sustainably. Nature as measure : Biomimicry uses an ecological standard to judge the ‘rightness’ of our innovations, according to natures life principals Nature as mentor : Finally, relationship with nature would change by Biomimicry, from seeing nature as a source of raw materials, to a source of ideas for problem solving, a mentor that has the wisdom and knowledge for survival and living sustainably[1]. VII. Applying Biomimicry in the interior design A. Applying Biomimicry in the design process:Biomimicry Design Spiral The Biomimicry Institute created a Design Spiral methodology as shown in ''Fig 3" to help people learn and practice Biomimicry[16].

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Fig. 3: shows the desidn spiral Methodology

B. Design approaches of Biomimicry B.1 Direct approach: Problem – based approach This approach has different naming "Design looking to biology", “ Top -Down Approach", "Problem –Driven Biologically Inspired Design". In this approach, designers look to the living world for solutions and are required to identify problems and biologists then need to match these to organisms that have solved similar issues [17]. Fig. 4. The steps of problem- based approach

B.2 Indirect approach: Solution- based approach Identifying particular characteristics or behaviors in an organism or ecosystem and then translating that into human designs, ''Fig 5"referred to as "biology influencing design Biomimicry", and "Bottom-Top Approach"[18]. Fig. 5. The steps of solution- based approach

C. Levels of Biomimicry Three levels of Biomimicry determine which aspect of ‘bio’ can be ‘mimicked’ and applied to a design problem, the organism, the behavior and the ecosystem level''Fig 6". Within each of these levels, a further five possible mimic dimensions exist:(form), what it is made out of (material), how it is made (construction), how it works (process) or what it is able to do (function) ''Fig 7" [1] Fig. 6. shows the levels of Biomimicry. Fig. (7) shows the five mimic dimensions

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C.1 Organism level:The Minister of Municipal Affairs & Agriculture (MMAA) in Qatar: The skin of one of the hardiest plantsof the desert" Cactus" is applied to the design of the facade of a desert building , with hundreds of smart shades that open and close depending on the strength of the sun [19]. (a)Design problem : Aesthetics Architects was looking for inspiration to design the (MMAA) in Qatar that would be situated in the hot, dry climate of Qatar , an area that only receives approximately 3.2 inches of rainfall annually''Fig 8" (b) Biological solution: They decided to investigate the Cactus for ideas on a building solution. Cactus, organism that has adapted to arid, dry climates and so unique in the technology it uses in order to survive. The signature characteristic of a cactus is the "spines" that serve more than just one purpose. The obvious purpose for the spines is for protection. It makes it very dangerous and difficult for herbivorous animals to eat the plant. They also serve to channel the rain water down to the base of the plant where it gets collected and stored. But the most important function that the spines serve is to help shade the plant from the intense sun, so the energy-efficient structure was designed by Aesthetics Architects . (c) Design solution: Architects designed the (MMAA) in Qatar using these technologies to create a unique sustainable solution to a complex problem. The botanic dome at the base of the tower will provide sustainable food source irrigated from Grey and black water treatment.In addition , they incorporate sunshades on the exterior of the building witch s act like filters with the sunlight that is penetrating the spaces.These shades have ability to automatically fluctuate up and down, depending on the desired interior temperature, to regulate the amount of sunlight and heat that is transferred into the space, This innovative solution allows this building to lower the size and amount of artificial cooling necessary for the building to operate properly as well as providing a sustainable solution that is aesthetically pleasing[1], [12]. Fig. 8: Fig. 8a shows the Cactus plant, Fig. 8b &Fig. 8c shows the exterior shades and Fig. 8d shows the interior of the buildig A

C.2 Behavior level: Eastgate tower in Zimbabwe (a) Design problem The architect Mick Pearce, was looking for inspiration to design the Eastgate center In Zimbabwe where the temperature outside can vary from 3 °C up to 43 °C and where the air condition plays a significant role ''Fig 9" (b)Biological solution: Mick Pearce looked at "termites" witch have an amazing ability to maintain virtually constant temperature and humidity in their termite mounds in Africa despite outside temperatures that may vary from 35°F to 104°F (3°C to 42°C). Researchers initially scanned a termite mound and created 3-D images of the mound structure, which revealed construction that can influence human building design [20]. The way they construct their mounds to maintain a constant temperature. The insects do this by constantly opening and closing vents throughout the mound to manage convection currents of air - cooler air is drawn in from open lower sections while hot air escapes through chimneys [21]. (c) Design solution: The Eastgate Centre, a mid-rise office complex in Harare, Zimbabwe, uses a form of passive cooling similar to how the termite mound works.It was designed to mimic the heating and cooling systems that termites use in their mounds to Create Sustainable Buildings. The innovative building uses similar behavior in the design, and air circulation planning it stays cool without air conditioning and uses less than 10% of the energy used in similar sized conventional buildings, hence moving towards a more sustainable building [1]. His solution was to have specially designed hooded windows, variable thickness walls and light colored paints as a part of a passive-cooling structure to reduce heat absorption. By doing so Eastegate uses 90% less energy for ventilation than conventional building its size and has already saved the building owners over $3.5 million dollars in air conditioning costs[22].

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Fig. 9: Fig. 9 a :A termite mound, which inspired the design of the Estgate Centre in Zinbabwe in Fig. b B

C.3 Ecosystem Level: the construction of Earthships (a) Design problem Design an Earthships to integrate with nature. (b)Biological solution: Biomimicry design is not only adapting the design from the nature but also considering how to use nature’s effective functions such as heating and cooling system, protecting natural light and ventilation.So the solution is to investigate the natural design principles for ideas on a building solution to mimicking a specific ecosystem which elements and principles are required for it to function successfully ''Fig 10". (c) Design solution: The Earthships are designed to integrate with nature based on six natural design principles(1) Constructed with recycled and local materials: Tiers, sand bags, adobe….etc. (2)Heating and Cooling: From the sun and the earth. (3) Water Harvesting: Caught on the roof from rain and dew mimicking the Namibian beetle bumpy body. (4) Renewable Electricity: Photovoltaic / wind power system. This energy is stored in batteries and supplied to electrical automated outlets, including grid-intertie. (5) Sewage: Gray water from bathing, washing dishes is separated from black water from the toilet. The gray water, is used and filtered for a second time in interior botanical cells. The flush toilet is the third use of the water, which is contained, treated and used a fourth time in exterior botanical cells. (6) Food production: Food is grown inside with botanical planters and outside in landscape irrigated with treated gray water [1]. Fig.10: Fig. 10 a & 10b show Earthships are sustainable homes made of recycled materials, designed to integrate with nature as an example of biomimicry in the Ecosystem level A

Fig. 10c: shows the interior botanical cell in Earthships. Fig. 10 d: shows photovoltaic solar cells for renewable energy. Fig.10 e: shows the Earthship interior bathroom design constructed with natural recyclable materials.

VIII. Applications of biomimicry in architectue , interior design and furniture A. The Habitat 2020 The Habitat 2020 building envisioned for china ''Fig 11"is a future forward example of biomimetic architecture that fuses high-tech ideas with basic cellular functions to create ‘living’ structures that operate like natural organisms. This nature-inspired approach to city living looks at the urban landscape as a dynamic and ever-

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evolving ecosystem. Within this cityscape, buildings open, close, breathe and adapt according to their environment. The Habitat 2020 building radically alters perception of a structure’s surface. The exterior has been designed as a living skin, rather than a system of inert materials used only for construction and protection. The skin (Fig.4) behaves like a membrane which serves as a connection between the exterior and interior of the habitat. Alternatively, the skin may be considered as the leaf surface having several stomata cellular openings involved in gaseous exchange and transpiration in plants. The surface would allow the entry of light, air and water into the housing. It would automatically position itself according to the sunlight and let in light. The air and wind would be channeled into the building and filtered to provide clean air and natural airconditioning.The active skin would be capable of rain water harvesting where water would be purified, filtered, used and recycled. The skin could even absorb moisture from the air. The waste produced would be converted into biogas energy that could be put to diverse uses in the habitat [11], [23]. Fig.11: Habitat 2020, china and the Living skin of Habitat 2020

B. The Esplanade Theater The Esplanade Theater and commercial district in Singapore, designed by DP Architects and Michael Wilford, hosts an elaborate building skin which influenced the look and function of the interiors, inspired by the multilayered Durian plant with its formidable thorn-covered husk ''Fig 12" . The Durian plant uses its semi rigid pressurized skin to protect the seeds inside, just as the building exterior is part of an elaborate shading system that adjusts throughout the day to allow sunlight in but protects the interiors from overheating[20]. Fig. 12: The Esplanade Theater and commercial district in Singapore

C. The Treepods: Carbon-Scrubbing Artificial Trees for Boston City Streets One of the interesting examples of beneficial biomimicry are the Treepods designed by Influx Studio. The inspiration came from The most unique trees in the world, the Dragon Tree because of the large canopy that provides maximum shading which also allows the structure to support solar panels used to power the air cleaning system ''Fig 13".These Treepods are not designed to replace natural trees, but to act like small air cleaning infrastructures, increasing in many times CO2 absorption [11]. The TREEPOD takes the Dragon tree like form to create an important canopy surface that will provide shadow, and that will host a solar pv (sun tracker latest technology) to harvest the energy necessary to powered the air cleaning system and the urban lamp function. The canopy branching structure ends with a myriad of bulbs. They multiplies the contact points between air and the CO², serving as a filter. Working like as alveoli in a human lung, here is where the cleaning gaseous exchange takes place: an alkaline and environmentally friendly resin that reacts with air holding CO²[24].

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Fig. 13: TheTreepods in Boston streets

D. Mimic The Lotus's flower in painting The lotus flower's micro-rough surface naturally repels dust and dirt particles, keeping its petals sparkling clean . A German company, Ispo, spent four years researching this phenomenon and has developed a paint with similar properties. The micro-rough surface of the paint pushes away dust and dirt, diminishing the need to wash the outside of a house [25] E. The honeycomb shape of beeswax in a bee hive Through the process of creating blinds that were ergonomically able to diffuse and keep light out efficiently, designers and scientists adapted the shape and form of the honeybee's honeycomb to keep light contained and properly diffused ''Fig 14" [26]. Fig. 14.shows the design of the blinds inspired by the honeycomb

F. Biomimetic approach to create concept chairs F.1The Bone Chair by Joris Laarman In 2007, Joris Laarman of Amsterdam, Netherlands, explores the Form :one of the core methodologies (form, process, and system) of biomimetic design . Joris Laarman used SKO, a structure optimization algorithm that simulates bone mineralization, to design his innovative Bone Chair. Bone is a smart composite made of specialized cells and protein fibers. As strong as steel and as light as aluminum, it reacts to resist stresses from constantly changing external structural forces ''Fig 14". F.2 The Cellular Chair by Mathias Bengtsson Mathias Bengtsson of London designed the Cellular Chair, in 2011, Designed and Made Based on the Natural Processes of Self-Organization ''Fig 15". The design of the chair explores the process one of the core methodologies -form, process, and system- of biomimetic design: Based on the growth principles of human bones, composed of lightweight epoxy, the material is designed to simulate the regeneration of bone tissue [27]. Fig. 14 shows the Bone chair and Fig. 15 shows the Cellular Chair

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IX. The future of biomimicry in the interior environment Now, biomimicry is still in its infancy in the interior environment. It is expected that it will continue to be applied most wildly in architecture and interior environment in the future, particularly as a tool of sustainable design in terms of day lighting, energy consumption and ecological footprint of new facilities. The architectural and interior design profession are cohesive enough to allow innovative approaches and new technologies to spread rapidly particularly when the profit is clear. As an example, the ability to effectively provide daylight into an interior space that has limited access to it reduces the need for artificial lighting. As a result, less heat is generated and less cooling is necessary, which could reduce cooling equipment’s size (a capital cost). Overall energy use is reduced (a cost of operation), and the dependence on fossil energy is lessened (an environmental cost). This is in addition to the important aesthetic and human benefits that daylight offers. We can say that using biomimicry as problem solving methodology can help create a new sustainable standard for interior spaces, buildings, communities and cities worldwide. For architects and other design professionals, it opens up a whole new world of innovative ideas for transforming the interior environment, while optimizing human wellbeing. And beyond the projects themselves, the principles of biomimicry will help in providing design smarter, and connect the work with the natural environment. In the future, the interior spaces we live in and the workplace we work in might be designed to function like living organisms, specifically adapted to place and able to provide all of their needs for energy and water from the surrounding nature. The architecture and design will have inspiration, not from the machines of the 21th-century, but from the butterfly that flies in the sky or the flower that exists in the landscape that surrounds them. X. BIOMIMICRY TO INCREASE SUSTAINABILITY Biomimicry is often described as a tool to increase the sustainability of human designed products, materials and the built environment . It should be noted however that a lot of biomimetic technologies or materials are not inherently more sustainable than conventional equivalents and may not have been initially designed with such goals in mind .As discussed, most examples of biomimicry are organism biomimetic. While biomimicry at the organism level may be inspirational for its potential to produce novel architectural designs, the possibility exists that a building as part of a larger system, that is able to mimic natural processes and can function like an ecosystem in its creation, use and eventual end of life, has the potential to contribute to a built environment that goes beyond sustainability and starts to become regenerative .This does not prevent organism biomimicry at a detail or material level. A building that is exhibiting form biomimicry, which is stylistically or aesthetically based on an organism, but is made and functions in an otherwise conventional way, is unlikely to be more sustainable than a non-biomimetic building. A building that is able to mimic natural processes and can function like an ecosystem in its creation, use and eventual end of life has greater potential to be part of a regenerative built environment. Both buildings could be termed biomimetic, but the potential for increased sustainability would obviously be quite different. It is suggested that if biomimicry is to be conceived as a way to increase sustainability of an architectural project, mimicking of general ecosystem principles should be incorporated into the design at the earliest stage and used as an evaluative tool throughout the design process [18]. XI. Conclusion 1.Biomimicry which is a multi-disciplinary innovative tool involving a wide diversity of domains like electronics, biology, chemistry, physics design and engineering, studies nature and emulates its creative functions , processes and eco systems using advanced technology to solve human problems in integration with nature. 2. Biomimicry is studying the nature, learning from it and getting the most important principles and characteristics then apply it to solve a specific design problem. The main goal of bio mimicry is sustainability. 3. There is need for future young Architects and designers to Create bio-inspired design adaptations that emulate nature’s best ideas, so that all futuristic buildings will be sustainable. XII. Recommendations 1. To the expansion of biomimetics, education must play a significant role. It should be included in the education syllabus of architecture and design degrees to make them aware of the potential of the approach.Networks,workshops and events could help forge links and transfer knowledge between the designers and the biologists. 2. Build a documented database, in a format of web-page hyperlinks, That would examine biology from an engineering point of view and to catalog nature capabilities including the inventions that have already been used to possibly offer different angles of looking at nature’s innovations to enrich other fields that have not benefited yet. 3. Cooperation between biologists and technologists/engineers as well as the establishment of such an education path in academic institutes that hopefully will also lead to new disciplines of biomimetic science and engineering.

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4.Teach interior architects how to open their eyes to the genius of natural world in an attempt to inspire new paths for living sustainably on earth, therefore changing the evaluation criteria of future designs as well as approaching a different conscious definition and appreciation to nature. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]

Mansour,H. "Biomimicry: a 21st century design strategy integrating with nature in a sustainable way", BUE , FISC 2010 – 12 Pawlyin, M. Biomimicry in architecture. London: RIBA publishing,2011. Drake,C. "Biomimicry: Emulating the closed-loops systems of the oak tree for sustainable architecture" master of architectureuniversity of massachusetts, Department of art, architecture and art history, 2011. Benyus, J. "Biomimicry: Innovation Inspired by Nature". New York: Perennial, 2002. Yurtkurana,S. Kırlı,G. Taneli ,Y. "Learning from Nature: Biomimetic Design in Architectural Education", Procedia - Social and Behavioral Sciences , vol.89 , 2013, pp.633 – 639, doi:10.1016/j.sbspro.2013.08.907) Iouguina,A. "Bionics ≠ Biomimetics ≠ Biomimicry", available online : https://biologytodesign.wordpress.com/2012/05/08/designbiology-linguistics/ Bar-Cohen,Y. "Perspective Biomimetics—using nature to inspire human innovation", Bioinspiration & Biomimetics , vol.1, (2006) pp.1-12, available online :doi:10.1088/1748-3182/1/1/P01 Lakhtakia, A. Jose Martin-Palma, R., Engineered Biomimicry, Elsevier,USA, 2013. The Biomimicry Institute, "Biomimicry Innovation Inspired" , IBE Annual Conference March 6-9, 2008, available online :http://openwetware.org/images/c/c1/IBE_-_biomimicry_lecture.pdf Nature inspired design: strategies towards Sustainability Knowledge , Collaboration & Learning for Sustainable Innovation ERSCPEMSU conference, Delft, The Netherlands, October 25-29, 2010. Rao ,R."Biomimicry in Architecture", International Journal of Advanced Research in Civil,Structural,Environmental and Infrastructure Engineering and Developing ,vol. 1 (3), 08,Apr-2014. Elshapasy,R. "Biomimetic approaches to increase sustainability", Arab Academy for Science, Technology and Maritime TransportCollege of Engineering and TechnologyDepartment of Architectural Engineering and Environmental Design Biomimicry Institute, design resources, available online https://www.biomimicrydesignchallenge.com/p/resources Minsolmaz Yeler,G. "Creating nature awareness in design education", Procedia - Social and Behavioral Sciences , vol.174, 2015, PP.406 – 413 doi:10.1016/j.sbspro.2015.01.682. Giurea, D."A didactic method for transposing natural forms in architecture", Procedia - Social and Behavioral Sciences,vol. 116 , 2014 , pp. 3165 – 3168, doi:10.1016/j.sbspro.2014.01.727 McGregor,S." Transdisciplinarity and Biomimicry" , available online ttp://www.consultmcgregor.com/documents/research/transdisciplinarity_and_biomimicry_as_published.pdf Ashraf Saad El Ahmar ,S." Biomimicry as a tool for sustainablearchitectural design towards morphogenetic architecture" , thesis Faculty of Engineering, Alexandria University, 2011. Zari, M.P. "Biomimetic approaches to architectural design for increased sustainability", School of Architecture, Victoria University,PO Box 600, Wellington, New Zealand. http://www.jetsongreen.com/2009/03/biomimicry-inspired-cactus-tower-by-aesthetics-architects.html Vierra, S,"Biomimicry: Designing to Model Nature", available online : http://www.wbdg.org/resources/biomimicry.php "Learning from Termites How to Create Sustainable Buildings", http://biomimicry.net/about/biomimicry/case-examples/architecture/ Arnarson,P.O" Biomimicry New Technology", Reykjavik University, 2011 http://inhabitat.com/habitat-2020-off-the-grid-future-abode/ Artificial Trees Clean Boston’s Air / Treepods Initiative – Influx Studio available online: http://www.evolo.us/architecture/artificialtrees-clean-bostons-air-treepods-initiative-influx-studio/ http://www.mnn.com/earth-matters/wilderness-resources/photos/7-amazing-examples-of-biomimicry/lotus-paint#ixzz3ZKvu62zF "Biomimicry for designers, What is biomimicry", available online : http://biomimicrymkn.blogspot.com/2011/04/week-1-what-isbiomimicry.html

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Study of different hormones on callus growth of Nothapodytes foetida and extraction of Camptothecin from callus culture 1

Abhinay Thakre1, Pooja Kulkarni1, Karishma Datir1, Sangeeta Kulkarni2, Kailas Choudhari2 Department of Biotechnology, Sinhgad College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India. 2 Sunfruits Ltd., Pune, Maharashtra, India.

Abstract: Camptothecin (CPT) is an anti-cancer and antiviral alkaloid produced by Nothapodytes foetida (Family Icacinaceae). The plant is found in different regions of India including Maharashtra and Karnataka. This plant species has gained considerable importance due to its anticancer alkaloid and is an endangered species. Molecular target of CPT is firmly established to be human DNA topo-isomerase I. CPT is a cytotoxic quinoline alkaloid, first discovered in Camptotheca acuminata. Two CPT analoguesIrinotecan and Topotecan are commercially produced and approved to be used for cancer chemotherapy. Initially organoleptic evaluation of different parts of the plant was carried out, then phytochemical assays were performed using leaf, root and stem extract, which showed the presence of alkaloids, flavonoids and terpenoids in it. Furthermore, leaves ( both young and mature) of the plant were excised and used as explants for the callus culture, which were inoculated in five different media in order to observe which media induces callus formation. Subsequently, the callus was subjected to microwave assisted extraction method. The extracts of leaf, stem, root and callus were analysed by TLC, and solvents used were ethyl acetate and ethanol. Keywords: Nothapodytes foetida, Camptothecin, Callus Culture. I. INTRODUCTION Nothapodytes foetida Syn. Mappia foetida (Family Icacinaceae), a tree species available in Western Ghats of India is an excellent source of quinolone alkaloids, camptothecin (CPT) and 9-methoxycamptothecin (9-OMe CPT). These compounds are clinically used as such or after derivatization as anti-cancer agents for the treatment of solid tumors. Camptothecin (CPT) is a water-insoluble monoterpene derived indole alkaloid produced by the Chinese tree Camptotheca acuminata. It was discovered in the 1960s during screening of plant extracts for antitumor activity and its structure was determined by Wall et al. (1966). At present, four semisynthetic watersoluble CPT analogues—Topotecan, Irinotecan, 9-aminocamptothecin and 9-nitrocamptothecin— are used for the treatment of cancer throughout the world.Plant tissue culture technique, emerged as available route for the production of paclitaxel (taxol), provides a model for production of anti-cancer drugs from woody plants; it can also be applicable for producing CPT. Nothapodytes foetida species has been declared endangered due to its severe exploitation. In this study, effect of different media on callus growth has been observed. II. MATERIAL AND METHODS A. Plant Material used Plant saplings were collected from Om Nursery, Kalewadi, Pune, Maharashtra, India in the month of August and were verified by Dr. Ankur Patwardhan, HOD of Biodiversity, Abasaheb Garware College, Pune, Maharashtra, India. The plants were stored in shade-net in Sunfruits Ltd., Pune, Maharashtra, India. Young leaves were used as explants. B. Preliminary Screening of plant Different parts of plants i.e. stem, roots and leaves were examined to observe morphological characteristics. C. Surface sterilization Young leaves were taken and washed under running tap water for 5 minutes. Each leaf was then subjected to 1 % of Bavistin solution in different bottles. The bottles were shaken continuously for 5 minutes. These leaves were washed with sterile distilled water and then were subjected to 0.1% of carboxyl chloride solution in different bottles. They were shaken continuously for 5 minutes. The leaves were again submerged in solution containing 2-3 drops of Tween 20 in distilled water with constant shaking for 10 minutes. They were again washed with sterile distilled water until the foam was removed. In laminar air flow, the leaves were again surface sterilized with antibacterial solution, 0.5 % mercuric chloride and 0.1% sodium hypochlorite, separately with constant shaking for 5 minutes with continuous washing with distilled water after being shaken in each

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solution. Leaves were then washed again with sterile distilled water. Each leaf was observed in light to check for insect, fungal or bacterial damage. D. Inoculation of plant material After surface sterilization, leaves were cut into small pieces (1-2cm) and the explants were inoculated in glass culture bottles containing 50 ml MS (Murashige and Skoog) medium. Media was prepared 24 hours before and contained different growth hormones according to Table 1. The pH of culture media was adjusted to 5.8 before autoclaving at 120°C, 15lb pressure. Culture bottles were maintained at 25°C for 3 weeks. Callus growth was observed after 3-4 weeks. E. Extraction of Camptothecin Dried powder of leaf, stem, root and callus were subjected to maceration using 90% methanol and were incubated for 24 hours. Extract was filtered and solvent was removed using microwave assisted extraction method. Later, extract was quantified. The dried powder obtained was tested for presence of alkaloids. The CPT obtained was further purified by TLC. III. RESULT AND DISCUSSION A. Organoleptic evaluation The morphology of each part of plant was studied and noted. (Table 2) B. Extractive value The extract obtained was in crude form. Different parts of plant contain different amount of extract. Leaf- 1.57% Stem- 0.83% Roots-1.01% Callus- 0.61% C. Phytochemical Assay Assay was performed to check for the presence of an anti-cancer drug - Camptothecin and other phytoconstituents. Presence of alkaloids in the extract confirmed the authenticity of drug (Table 3). D. TLC TLC analysis of the purified fraction of both leaf and callus showed an Rf of 0.6. E. Callus Induction Explants showed curling, expansion and swelling indicating growth of cells. The leaf from bottle 1, 2, 3, 4 showed cell growth. (Fig. 1) Figure 1

Table 1 Bottle No. 1. 2. 3. 4. 5.

CW (ml) 50 50 50 50 50

BA (mg) 0.5 1.75 0.5 2.5 1

Biotin (mg) 0.25 0.75 -

Kinetin (mg) 0.5 1.75 -

2,4-D (mg) 0.75 1 -

NAA (mg) 0.5 2.5 -

IAA (mg) 0.5

IBA (mg) 0.5

Table 2 Parameter Colour Odour Shape Size

Leaf Green Unpleasant Ovate Length- 8- 10 cm, width 4-6 cm

Stem Light brown Sweet Cylindrical Length – 3-4 m

Roots White Sweet Cylindrical Length- 30-60-cm

Table 3 Sr No. 1.a 1.b 2.a

Test Alkaloids (Meyer’s test) Alkaloids (Wagner’s test) Carbohydrates ( Fehling's test)

Leaf + + -

Stem + + -

Roots + + -

Callus + + -

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2.b 3. 4. 5. 6.

Carbohydrates (Benedict’s test) Saponins Tannins Flavonoids Terpenoids

+ -

Growth of callus in MS media supplemented with different hormones was observed. Callus was grown successfully in media containing plant hormones BA, kinetin, 2, 4-D, Biotin and NAA. Callus growth was not observed in media bottles containing IAA and IBA. Hence, they are not suitable for callus growth. Younger leaf showed optimum growth of callus as compared to mature leaf. In conclusion, it takes about 5-6 weeks for callus to grow. Leaves contain the highest percentage of CPT. Callus produced secondary metabolite CPT, hence Nothapodytes foetida can be cultured to produce CPT. ACKNOWLEDGEMENTS We extend our sense of obligation to Mr. Shivraj Bhosle, the Director of Sunfruits Ltd. Pune, Maharashtra, India for providing us with a well-equipped tissue culture lab. Furthermore, we would like to express a cordial respect to Mrs. Sangeeta Kulkarni and Mr. K. Choudhari for guiding us in plant tissue culture process. We would also like to thank Dr. Ankur Patwardhan HOD, Department of Biodiversity, AGC, Pune, Maharashtra, India for his valuable time and advice on this project.

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

[6] [7]

A. Lorence · F. Medina-Bolivar · C. L. Nessler Camptothecin and 10-hydroxycamptothecin from hairy roots Plant Cell Rep (2004) 22:437–441 DOI 10.1007/s00299-003-0708-4 PHYS I OLOGY AND BIOCHEMISTRY Devanand P. Fulzele, Ramesh K. Satdive Caomparison of techniques for the extraction of the anti-cancer drug Camptothecin from Nothapodytes foetida. K Sundravelan, B desireddy and Veeresham Ciddi Production of camptothecins from callus culture of Nothapodytes foetida (Wight) Sleumer (2003) Indian Journal of Biotechnology Vol 3 July 2004, 452-453 Murashige, T. and Skoog, F., 1962. A revised medium for rapid growth and bioassays with tobacco tissue cultures.Physiol. Plant. 15:473-479. S. R. Thengane, D. K. Kulkarni, V.A. Shrikhande, S.P. Joshi, K.B. Sonawane,and K. V. Krishnamurthy Influence of medium composition on callus induction and camptothecin(s) accumulation in Nothapodytes foetida – 2002 Plant cell, tissue and organ culture 72:247-251, 2003 Surbhi Sharma, Ajay kumar, Ajay Namdeo, Pharmacognostical and phytochemical analysis of Nothapodytes nimmoniana stem (2012) Indian Journal of Pharmacy and Pharmaceuticals Science ISSN-0975-1491 Vol 4, Issue 4, 2012 Tafur et al. (127)

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Detailed Larval Biology of Indian Gypsy Moth Lymantria obfuscata Walker on Quercus leucotrichophora Roxb. in Himachal Pradesh (India) Bhopesh Thakur1, Sumit Chakrabarti2 and Manoj Kumar3 Department of Zoology, University College, Kurukshetra University, Kurukshetra, Haryana, India1 Forest Ecology and Rehabilitation Division, Tropical Forest Research Institute, Madhya Pradesh, India2 Department of Biotechnology, Shoolini University, Solan, Himachal Pradesh, India3 Abstract: Lymantria obfuscata Walker commonly known as Indian Gypsy Moth (IGM), is a serious pest of about 200 broad-leaved tree species throughout India. The pest undergoes complete metamorphosis with six larval instars. The larval stage is the destructive stage as it feeds on the foliage voraciously. The detailed study was done on the larval biology of this pest on the ban oak foliage in Himachal Pradesh. Keywords: Lymantria obfuscata, IGM, larva, feeding, length, width I. Introduction Quercus leucotrichophora Roxb. (Ban oak), is an evergreen tree found between elevations of 1800 to 2300 m in Himachal Pradesh which grow up to 25 m. The distribution and forest cover of ban oak is highest among five indigenous species of oaks and is also a natural broad-leaf associate of many conifers of Himachal Pradesh [1]. The ban oak is of immense importance for the villagers of Himachal Pradesh, as its foliage is the rich source of nutritious green leaf fodder for livestock during the lean winter months and also provides the best firewood for cooking, heating and charcoal. Out of the 90 species of insects that feed on oak, lepidopteran defoliators cause significant damage to oak and the most important are Lymantria obfuscata, L. concolor, L. mathura, Malacosoma indica, Dasychira sp. and Euproctis sp [2]. Lymantria obfuscata Walker commonly known as Indian Gypsy Moth (IGM), is a serious pest of about 200 broad-leaved tree species, throughout India, namely oak (Quercus spp.), willow (Salix spp.), popular (Populas spp.), walnut (Jugulans spp.), apple (Malus spp.), apricot (Prunus spp.), cherry (Prunus cerasus) and almond (Prunus amygdalis) ([3, 4]). It was reported as the major pest of ban oak [5], and of apple trees at Kotgarh, Shimla (H.P.) [6]. In earlier studies, it was reported as one of the most destructive pests of fruit and forest plantations including apple, walnut willows and poplars in Kashmir ([7, 8, 9, 10, 11]). Incidence of L. obfuscata on apples, poplars, willows and other plantations in India has also been reported ([12, 13]). An outbreak was reported from Sarahan and Narag areas in Sirmour (H.P.), where massive defoliation of oak trees took place [14]. Some preliminary observations on the pest biology of L. obfuscata were made in the past ([5, 15, 16, 17]). These observations on its larval biology were either incomplete or were undertaken in different situations in India. Therefore, present work was undertaken to study the detailed larval biology of L. obfuscata Walker on ban oak in Himachal Pradesh, India. II. Materials and Methods The present study on the biological life cycle of L. obfuscata was conducted in the laboratory of Forest Protection Division, Himalayan Forest Research Institute (HFRI), Shimla (H.P.). Thick population of L. obfuscata was found in the village situated at Sarahan (Himachal Pradesh) and the egg-masses were collected and were placed in the laboratory for over-winter storage. The average temperature and relative humidity, during the course of the investigations (2006-2009) in laboratory were recorded as 19.98±4.535ºC and 58.46±16.627%, respectively [Thermo-Hygro Clock M288CTH, Mextech]. After the hatching of eggs, the 1st instar larvae were placed on fresh tender leaves of Quercus leucotrichophora, in the wire-meshed wooden cages of dimensions, 65 cm × 68 cm × 99 cm. Observations on the larval development such as, the number of instars, colour, moulting and feeding habits were recorded periodically. Morphometric studies were made with the help of Radical stereo zoom microscope [RSM-9] with USB Digital Scale 1.1E software in a digital microscopic workstation. A. Preparation of permanent microscopic slides During the period of investigation, larvae of different instars were preserved in 70% alcohol in specimen tubes. 70% alcohol was decanted from the sample and distilled water was added. Thereafter, the sample was kept in

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hot water bath for 5 minutes. Distilled water was decanted from the sample and 10% KOH solution was added and again kept in hot water bath for 15-20 minutes, till the inner tissues of the sample were completely dissolved and appeared clearer. KOH solution was decanted from the sample and distilled water was added and kept in hot water bath for 10-15 minutes. It was repeated till the excess of KOH was removed from the sample. After that dehydration was done in ascending grades of 30%, 50% and 70% alcohol and kept in hot water bath for 10-15 minutes. Finally, a puncture was made in the last abdominal segment and by pressing the larval body gently, the inner dissolved gut tissues came out making the specimen clearer. 70% alcohol was decanted from the sample and 90% alcohol was added and kept in hot water bath for 5 minutes. 90% alcohol was decanted from the sample and absolute alcohol was added and kept in hot water bath for 2-3 minutes. The sample was then dried with the help of blotting paper and Bersale’s mounting media was added to the sample and fixed with cover slip. III. Results and Discussion In the laboratory rearing of L. obfuscata, it was observed that from a single egg-mass around 300-400 neonate larvae emerged in spring just when the new leaves emerged. And rearing of single larvae individually showed that L. obfuscata has six larval instars separated by five moulting stages. The caterpillars were nocturnal and fed gregariously, from the periphery towards the mid rib of the leaves. Young caterpillars spread to new locations by spinning silken thread. Each larval instar had a cylindrical, elongated body that was differentiated into head, thorax and abdomen. The colour changed from greyish-black in the first instar larva to reddish-brown in the last instar larva. The splitting of the exuvae started near the first thoracic segment, and the head capsule got itself split to the opposite direction. The head was hypognathus, black to reddish-brown in colour with small microscopic setae. It was composed of two parietal plates differentiated from each other by a distinct Y-shaped pericardial suture. In the head, the median epicranial sulcus was well-developed and the frons was represented by a pair of narrow oblique plates termed the adfrontals. Both clypeus and labrum were evident and the typical number of ocelli was six which were situated just behind, and a little above the bases of the segmented antennae. The mouthparts were found to be of mandibulate type and each mandible had three teeth, which were powerful and helpful in mastication of food. Larvae bore three thoracic segments which had a pair of legs each which end in a single curved claw. There were ten abdominal segments and 3rd, 4th, 5th, 6th and 10th abdominal segments bore a pair of prolegs each. A typical abdominal leg was a fleshy and was provided with a series of hooks which helped in locomotion. A pair of eversible glands was present on the dorsum of the 6th and 7th abdominal segments. Small hairs were scarcely distributed dorsally over the whole body of the larva and an anal comb was present prior to anus. The armature of the larval body consisted of simple hairs, tubercles and verrucae, bearing tufts of setae. The double row of tubercles on the dorsal surface of matured larvae were more prominent as the first five pairs were bluish and last six pairs brick red in colour. A. First instar larva The freshly hatched first instar larva was very small, active, and greyish in colour, with a small black head. The body of the larva was found to be covered with tufts of hairs, having a pair of small dots on dorsal side of each segment. The mean body length and width of first instar larva measured were 4.65±0.382 mm and 0.65±0.072 mm, respectively. The mean head length and width recorded were 0.78±0.042 mm and 0.75±0.038 mm, respectively, whereas, the mean mandible length and width were 0.18±0.015 mm and 0.16±0.013 mm, respectively. The mean inter-ocular distance was measured as 0.60±0.011 mm. This stage lasted for 11-14 days, with a mean of 12.75±1.50 days. Reference [17] reported that the first instar larvae of L. obfuscata measured 3.26 mm × 0.36 mm and their body was covered with tufts of hairs with a faint light strips, having a pair of small dots on each segment on the back, and this stage lasted for 8.26 days (range 7-10 days). B. Second instar larva The colour of the second instar larva was brown, and the hairs covering the body of the larva were very conspicuous. Its head was dark brown in colour. A double row of small tubercles was found along the back of the larva. The mean body length and width of second instar larva measured were 9.23±1.253 mm and 1.10±0.049 mm, respectively. The mean head length and width larva were recorded as 1.20±0.305 mm and 1.19±0.205 mm, respectively. The mean mandible length and width were 0.24±0.053 mm and 0.23±0.030 mm, respectively, and the mean inter-ocular distance was 0.93±0.183 mm. This stage lasted for 9-13 days, with a mean of 10.75±1.71 days. According to reference [17] the second instar larvae measured 9.5 mm × 2.03 mm and they have a brownish tint, with hairs covering the body. The head of the larva was dark brown in colour. He concluded that the stage was of shortest duration and lasted for 5.46 days (range 4-7 days). C. Third instar larva The third instar larva was dark grey in colour. The dark brown head of this stage was marked by yellow striations. The mean body length and width of third instar larva measured were 13.81±1.787 mm and 1.87±0.227 mm, respectively. Mean head length and width were recorded as 1.71±0.360 mm and 1.78±0.367 mm, respectively. The mean mandible length and width were 0.41±0.084 mm and 0.34±0.070 mm, respectively, and the mean inter-ocular distance was 1.44±0.365 mm. This stage lasted for 12-15 days, with a mean of

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13.75±1.26 days. Reference [17] showed that the third instar larvae measured 19.31 mm × 4.65 mm and were dark grey in colour. The dark brown head of this stage had yellow marking. This stage lasted for 7.6 days (range 5-9 days). D. Fourth instar larva The fourth instar larva was reddish-brown in colour with the double row of tubercles more prominent, and the ventral surface, prolegs and legs were greyish-brown in colour. The body was covered with hairs. A light strip between the tubercles and spiracles on each side of the larva was present. The mean body length and width of fourth instar larva measured were 18.90±0.974 mm and 2.61±0.109 mm, respectively. Mean head length and width were recorded as 3.10±0.360 mm and 3.05±0.268 mm, respectively. The mean mandible length and width were 0.86±0.190 mm and 0.68±0.068 mm, respectively, and the mean inter-ocular distance was 2.52±0.177 mm. This stage lasted for 15-19 days, with a mean of 16.50±1.91 days. Reference [17] observed during the study, that fourth instar larvae measured 26.40 mm × 6.64 mm and the hairy body was brownish in colour with double row of tubercles prominent. A light strip between the tubercles and spiracles on each side of the larva was present. This stage lasted for 10.00 days (range 8-12 days). E. Fifth instar larva The fifth instar larva was bigger than that of fourth instar larva. It had short dorsal and long lateral tufts of hairs. The mean body length and width of fifth instar larva measured were 21.22±1.208 mm and 3.21±0.349 mm, respectively. Mean head length and width were recorded as 3.09±0.440 mm and 3.26±0.285 mm, respectively. The mean mandible length and width were 0.91±0.128 mm and 0.55±0.059 mm, respectively, and the mean inter-ocular distance was 2.72±0.405 mm. This stage lasted for 13-18 days, with a mean of 15.25±2.22 days. According to reference [17] the fifth instar larvae measured 40.17 mm × 11.62 mm and the general appearance and feeding behavior of fifth instar was almost same as that of fourth instar larva. It had short dorsal and long lateral tufts of hair. According to him, the fifth instar lasted for 16.06 days (range 14-19 days). F. Sixth instar larva The sixth instar larvae were the largest of all the previous instars. The mean body length and width of sixth instar larva measured were 36.30±6.764 mm and 5.38±0.869 mm. Mean head length and width were recorded as 4.12±0.424 mm and 4.28±0.049 mm. The mean mandible length and width were 1.06±0.141 mm and 0.70±0.106 mm, respectively, and the mean inter-ocular distance was 3.68±0.120 mm. This stage lasted for 1012 days, with a mean of 11.25±0.96 days. The observations made in the present study, were in complete harmony with [18], according to which the tribe Lymantriini has shorter, more even tufts of hair, arising in clumps from the verrucae and conspicuous yellowishred dorsal glands on sixth and seventh abdominal segments. Earlier, [19] described the full grown larvae of L. obfuscata, [16] the other stages and [20] the head capsule, but only briefly. According to [21], the caterpillars were nocturnal, brown with black specks on the dorsum, head was prominent, brownish-yellow having two dark brown specks on the median suture. Body was covered with tufts of brown hairs. Besides three thoracic, there were four pairs of abdominal pseudolegs and the full grown larva measured 3.3 cm × 0.5 cm. In the present study, the larval period of L. obfuscata was recorded as 76-84 days, with average of 80.25±3.30 days (from mid March to May). It has been observed that the larva moults five times during the larval period. The larvae of L. obfuscata closely resembled those of L. dispar. According to Reference [17], the respective stages 1st, 2nd, 3rd, 4th and 5th lasted for 8.26, 5.46, 7.6, 10.00 and 16.06 days, respectively. Similarly, in a previous study the 1st, 2nd, 3rd, 4th, 5th and 6th instars of L. obfuscata lasted for 7-8, 9-11, 9-11, 10, 8 and 7 days, respectively [3]. During the day the larvae rested either on tree trunks or under stones. When full grown, they seek sheltered spots either in a hollow on the trunk of a tree or in the crevices in stones on the ground. G. Larval feeding The larvae of different instars were observed regarding their feeding habits. The newly hatched larvae rested for sometime on the top of their egg-mass. Thereafter, larvae moved to the leaf surface, where they started feeding on the outer soft parenchyma tissue of the leaf and formed minute holes in the leaf surface. The second instar larvae also preferred softer and fresh leaves and formed patches and holes in the epidermis. Feeding along the margins of the leaves was also observed. In the past it was observed that first instar larvae made small perforations on upper surface of leaves, and late instars devoured the leaves completely leaving the mid-rib of leaves only [17]. The third instar larva fed upon the softer leaf tissues along with the softer veins of the leaves. More larvae were found feeding along leaf margins as compared to previous stages. The fourth and fifth instars were found to cause maximum damage to the foliage by feeding upon the harder tissues of the ban oak leaves. Sixth instar larvae had slow rate of feeding and after sometime they stopped feeding and transformed into the next pupal stage. All the larvae fed gregariously, and moved to the other part of the foliage in search of food, showing their voracious feeding nature. Later instars of gypsy moth move down the trees at dawn and climb back at the dusk. This daily migration appeared to be influenced by environmental conditions, including abiotic factors and sunlight was probably responsible for stimulating large caterpillars to migrate up and down the trees [22]. It was noticed that when larvae were crowded or partially starved, pupal weights were reduced but the period of larval development was extended by 0-3 (crowding) and 8 (starvation) days [23].

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H. Moulting behaviour Before moulting in each instar larvae, the feeding was arrested and the movement slowed down. Shedding of the old skin and head capsule followed different patterns as the head capsule was found intact or broken into two halves occasionally. Shedding of the skin commenced at the first thoracic segment and receded to the rear abdominal end where it was found to be removed off the larval body. Acknowledgements The corresponding author is highly thankful to University Grants Commission, New Delhi, India for providing financial assistance in the form of Research Fellowship (Junior Research Fellowship), during the present study. Acknowledgments are also owed to Himalayan Forest Research Institute, Shimla, Himachal Pradesh, India for providing the necessary laboratory facilities during the course. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14]

[15] [16] [17] [18] [19] [20] [21] [22] [23]

S. S. Negi and H. B. Naithani, Oaks of India, Nepal and Bhutan, International Book Distributors, Dehra Dun, 1995. R. N. Mathur and B. Singh, A list of insect pests of forest plants in India and the adjacent countries, Part 1-9. List of insect pest of plants genera C-Z. Part IV-X-Indian Forest Bulletin, 1958-1961, vol. 171. P. R. Dharmadhikari, G. Ramaseshiah and P. D. Achan, “Survey of Lymantria obfuscata and its natural enemies in India,” Entomophaga, vol. 30, 1985, pp. 399-408. N. D. Rishi and K. A. Shah, “Survey of bio-ecological studies on the natural enemies of Indian gypsy moth Lymantria obfuscata Walker (Lepidoptera: Lymantriidae),” Journal of Entomological Research, vol. 9, 1985, pp. 82-93. C. F. C. Beeson, Ecology and control of the forest insects of India and the neighboring countries, Forest Research Institute, Dehra Dun, 1941. K. A. Rahman, “Occurrence of the gypsy moth Lymantria obfuscata Walker in Shimla Hills,” Indian Journal of Entomology, vol. 3, 1941, p. 338. R. A. Malik, A. A. Punjabi and A. A. Bhat, “Survey and study of insect and non-insect pests in Kashmir,” Horticulture, vol. 3, 1972, pp. 29-44. G. A. Dar, A. G. Sheikh and B. L. Ganjoo, “Relative efficacy of some insecticides in suppressing Lymantria obfuscata Walker on apple trees in Kashmir,” Pesticides, vol. 11, 1977, pp. 27-29. A. G. Sheikh, “The effect of repeated defoliators caused by Lymantria obfuscata Walker on apple in Kashmir,” Indian Journal of Plant Protection, vol. 3, 1975, pp. 170-172. M. A. Masoodi, A. R. Trali and A. M. Bhat, “Suppression of Lymantria obfuscata Walker by sex pheromone trapping of males,” Indian Journal of Entomology, vol. 52, 1990, pp. 414-417. P. A. Kumar, N. Dorjay, M. S. Mir and A. S. Bilal, “Major insect pest associated with forest plantations in cold arid region, Ladakh of Jammu and Kashmir,” Journal of Entomological Research, vol. 31, 2007. H. S. Pruthi and H. N. Batra, Important fruit pests of North-West India, The Indian Council of Agricultural Research, New Delhi, 1960. P. S. Singh and S. S.Singh, Insect pests and diseases of poplar, Forest Research Institute Publications, 1986. R. Singh, S. Kumar, S. Chakrabarti and A. Kumar, “Resurgence of Indian gypsy moth, Lymantria obfuscata Walker (Lepidoptera: Lymantriidae) on ban oak (Quercus leucotrichophora) forests in Rajgarh Forest Division, Himachal Pradesh,” Indian Journal of Forestry, vol. 30, 2007, pp. 83-85. K. A. Rahman and A. N. Kalra, “Apple hairy caterpillars in the Shimla Hills,” Indian Farming, vol. 5, 1944, pp. 312-314. M. L. Roonwal, “Life history and control of the Kashmir willow defoliator Lymantria obfuscata (Lepidoptera: Lymantriidae),” Journal of Indian Academy of Wood Science, vol. 8, 1977, pp. 97-104. M. A. Masoodi, “Biological studies on Lymantria obfuscata Walker (Lepidoptera: Lymantriidae) in Kashmir,” Indian Forester, vol. 177, 1991, pp. 644-651. D. C. Ferguson, The Moths of America: North of Mexico, Noctuoidea, Lymantriidae, E. W. Classey, London, vol. 22. J. C. M. Gardner, “Immature stages of Indian Lepidoptera (1) Lymantriidae,” Indian Forest Record, vol. 3, 1938, pp. 187-212. S. Adhikari, “Studies on the head capsule of the mature larva of Lymantria obfuscata Walker (Lymantriidae: Lepidoptera),” Nepal Journal of Agriculture, vol. 14, 1979, pp. 93-102. T. D. Verma, J. R. Thakur and G. S. Dogra, “Outbreak of Indian Gypsy moth Lymantria obfuscata Wlk., on oak in Himachal Pradesh,” Indian Forester, vol. 105, 1979, pp. 594-597. R. M. Weseloh, “Behavioural responses of gypsy moth (Lepidoptera: Lymantriidae) larvae to abiotic environmental factors,” Environmental Entomology, vol. 18, 1989, pp. 361-367. D. R. Lance, J. S. Elkinton and C. P. Schwalbe, “Components of density-related stress as potential determinate of population quality in the gypsy moth Lymantria dispar (Lepidoptera: Lymantriidae),” Environmental Entomology, vol. 15, 1986, pp. 914-918.

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Centralized Parallel Communicating Non Synchronized Pure Pattern Grammar System with Filters F. Amjad Basha1 and Sindhu J Kumaar2 Research Scholar1 and Associate Professor2 Department of mathematics, B.S. Abdur Rahman University Vandalur, Chennai -48, Tamil Nadu, India 1, 2 Abstract: Motivated by the study of synchronized pure pattern grammar system by Sindhu et al [1], and the study of on non synchronized pure pattern grammars by Amjad et al [2], parallel communicating grammar system by Gh.Paun et al [6] and parallel communicating grammar systems with communication by commands by Csuhaj Varju et al [5]. We begin with centralized parallel communicating non synchronized pure pattern grammar system, besides we proposed a novel approach called centralized parallel communicating non synchronized pure pattern grammar system with filter. Then we could focus and generalize previous results and obtain a sequence of new ones on a variant in communication. It generates a subclass of context free languages, which is compared with already existing languages. Keywords: Communication by command, Filter, Non-synchronized pure pattern grammar and Parallel communication.

I. Introduction Sindhu et al [1] studied synchronized pure pattern grammar which links the studies of pure grammars and pattern grammars. The synchronized Pure Pattern grammar has patterns which are the strings of constants or terminal symbols. The constants are replaced initially by axioms over terminal symbols. The process is continued by replacing at any step the symbols in a pattern with the current set of words derived, there by yielding the associated language. Amjad et al [2] has studied a variant in working of a synchronized pure pattern grammar which we call it as non synchronized pure pattern grammar. Parallel Communicating grammar system with communication by commands by Csuhaj Varju et al [5] represents the first model of networks of language processor where communication is performed through filters. A rewriting step in this system is defined as follows: each grammar generates its own string until it has no more applicable productions. Then the components communicate their strings to each other in the following manner: every grammar tries to send a copy of its string to each of the other grammars, but only those strings are accepted at a component which passes through the filter associated with it. Two important classifications of parallel communicating grammar system concern the communication graph and returning features introduced by S. Demitrescu and G, Paun [7]. In this paper a new generative device, herein after referred as centralized parallel communicating non synchronized pure pattern grammar system with filter is introduced. In this grammar system, all the components except the master are non synchronized pure pattern grammar, but the master is regular or right linear grammar with filter. II. Non Synchronized Pure Pattern Grammar Definition 2.1: Sindhu et al A Non Synchronized pure Pattern Grammar (NSPPG) is a triple where is an alphabet is a finite subset of called axioms and P is a finite subset of called the set of patterns. For a set P and language , let P (L) be the set of strings obtained by replacing uniformly and in parallel, each letter of all patterns in P by strings in L, different occurrences of the same letter in a pattern being replaced by the same string. The difference in the working of a NSPPG is that at the rth step, each letter of the pattern is replaced by words from unlike in SPPG where at the rth step each letter of the pattern is replaced by words The language (NSPPL) generated by G denoted by L (G) is the larger language for which we have . In fact Example 2.1

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In fact initially the axiom a replaces both a and b in the pattern to yield the word a 2. In the subsequent step a and a2 are treated as an axiom which replaces both a and b in the pattern yielding {a2, a3, a4} and the process continues. Here we get the language Example 2.2 Any of the axioms a, b can initially replace independently a as well as b in the pattern ab yielding {a2, b2, ab, ba} the resulting words along with a and b can be used in a similar manner in the pattern ab and the process is continued to yield the language consisting of all possible strings over a, b. Here we get the language . III. Parallel Communicating Grammar System A parallel communicating (PC) grammar system is a construct consisting of several usual grammars, working synchronously, each on its own sentential form, and communicating by request; special (query) symbols are provided, ,with the subscript identifying a component of the system; when a component j introduces a query symbol ,the current sentential form of the component i is sent to the component j, where it replaces the occurrence(s) of in the sentential form of component j. The language generated by a specified component of the system (the master), after a series of such rewriting and communication steps (each component starts from its axiom) is the language generated by the system. Definition 3.1 A parallel communicating grammar system of degree n, n â&#x2030;Ľ 1 is a construct where N, T, K are pair wise disjoint alphabet, with }, N and are finite set of rewriting rules over , ; the elements of N are non terminal symbols, those of T are terminals; the elements of K are query symbols; the pairs are the components of the system. Note that by their indices, the query symbols are associated with the components, For , with , , ; (we call such an n-tuple a configuration), we write if one of the two cases holds 1. If there is no query symbol in then or = , ; 2. If has a query symbol , we write such a string as = for t , ; then y = [and = , ]; For all unspecified i we have = Point (i) defines a rewriting step (component wise, synchronously, using one rule in all components whose current strings are not terminal). Point (ii) defines a communication step; the query symbols introduced in are replaced by the associated string . IV. Parallel Communicating Grammars with Communication by Commands Parallel communicating grammar systems with communication by commands by Csuhaj-Varju et al [4] represent the first model of networks of language processor where communication is performed through filters. A rewriting step in these systems is defined as follows: each grammar generates its own string until it has no more applicable productions. Then the components communicate their strings to each other in the following manner: every grammar tries to send a copy of its string to each of the other grammars, but only those strings are accepted at the component which passes through the filter associated with it. Definition 4.1: Csuhaj Varju et al A parallel communicating grammar system with communication by command and with finite sets of axioms or an FCCPC grammar system (of degree n) is a construct where N and T are disjoint finite alphabets; N is called nonterminal alphabet and T is called terminal alphabet of the system, is a component of , the ith component, where is a non empty finite set called the set of axioms of the components is a finite set of rewriting rules over and is a regular language , called the selector language or the filter of the ith component. The first component is designated as the master component. Example 4.1 Consider the FCCPC grammar with two components , , , , , The first few steps of the derivation are as follows

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Where w is of the form aXbYcZ, with . This word can only be successfully communicated to the first component if it is either of the form w = aAbBcC or w = aA"bB'cC". In the latter case the next rewriting steps results in terminal word abc at the master component, while in the first case the derivation can be continued with a'A'b2B'c2C' at the first component. Analyzing the possible further derivation steps, it is easy to see L ( ) = which is not a context - free language. V. Centralized Parallel Communicating Non Synchronized Pure Pattern Grammar System In this section, we consider a variant in Csuhaj Varju’s model called centralized parallel communicating non synchronized pure pattern grammar system using non synchronized pure pattern grammar system. In a centralized grammar system one component has filter and this component is designated as master component. In this model a regular or right linear grammar is consider as master component. Definition 5.1 A Centralized PC (NSPPG) grammar system is a construct N = Set of non terminals, T = Set of terminals, K = Set of query symbols. Where N T, K are pair wise disjoint alphabets with is a finite set of regular rules over are the components which are NSPPG over T. The rewriting is similar to that of PC grammar systems with the following modifications. Initial configuration is (S0, p1, p2, …, pn) where pi Pi is a pattern of the ith component. The rewriting in the component (Ai, Pi) is done according to NSPPG that is; any word Pin (Aj) is considered in the nth step, until a query is asked. If a query symbol Q j appears in the master component, then the string in the jth component is communicated. After communicating, the components continue working from their axioms if in returning mode ‘r’ or the components continue the processing of the current strings if it is in non returning mode 'nr'. Example 5.1 Where N

Where if f = r; y = if f = nr Here ‘r’ stands for returning mode and ‘nr’ stands for non returning mode. We now define a new generative device, namely parallel communicating non synchronized grammar system with communication by commands where the master component is regular with a filter and the other components are non synchronized pure pattern grammars. VI.

Centralized Parallel Communicating Non Synchronized Pure Pattern Grammar System with Filter In this section a parallel communicating non synchronized pure pattern grammar system with filter is introduced where the first component is designated as master component and the remaining components are non synchronized pure pattern grammar (NSPPG). Definition 6.1 A CCPCNSPPG grammar system is a construct N is a set of non terminals, T is a set of terminals where N and T are disjoint, S0 N, P0 is a finite set of regular rules over N T, R is a regular language called the selector language or the filter of the component, Q N is a special symbol called query symbol. Each (Ai, Pi), i = 1, 2, 3,… is a NSPPG over T. The rewriting in the component (Ai, Pi) is done according to the NSPPG; that is any word in P in (Ai) is considered at any time. If the query symbol appears in the master component then all the other components communicate their strings Pin (Ai) to the master and master component will accept the strings which can passes through the filter associated with it. Let the set of strings sent by the ith component to the master component be defined as , Let

for

be the concatenation of the set of strings sent to the master component that is the

total message received by the master component, and let After a communication step, the obtained language is either the concatenation of the received set of strings with the previous language or it is only previous language, when the components are not involved in communication. Definition 6.2 The language L ( ) generated by a CCPCNSPPG grammar system When is defined as follows:

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Example 6.1 Where , Q = query symbol, T = {a, b} ,

Where Example 6.2 Where

Q = query symbol

Example 6.3 Where ,

y =ab, aa, aaa if f = r;

if f = nr Note If a pattern consists of different symbols with a free axiom, then obviously the language generated from the centralized parallel communicating non synchronized grammar system could be different from the centralized parallel communicating synchronized grammar system. Proposition 6.1 1. CCPC (NSPPL) - PC (NSPPL) 2. CCPC (NSPPL) PC (NSPPL) Proof 1. This is seen from the example 6.2. The language generated by is not a PC (NSPPL), because in PC (PPG) if a query symbol Qj appears in the master component, then the string in the jth component alone is communicated, as only one component can communicate to the master component at a time. But in CCPC (NSPPG), if a query symbol Q appears in the master component, then the strings of the entire set of components are communicated, if they can pass through the filter and are concatenated with the string generated in the master component. Thus the languages generated by CCPC (NSPPG) and PC (PPG) are incomparable. 2. This is seen from examples 5.1 and 6.2. The language can be generated by PC (NSPPG) and CCPC (NSPPG) Proposition 6.2 The class of CCPC (NSPPG) languages in the returning mode coincides with non - returning mode if the master component is regular or linear. Proof Let where is regular with a filter. The number of non terminals in the right side of each rule is one. Hence when the designated terminal of the master component is replaced by a string from the ith component after passing through the filter, we get a terminal word. No further generation is possible along this line. Hence there is no difference between the returning and non - returning modes.

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Proposition 6.3 In a parallel communicating non synchronized pure pattern grammar system (PCNSPPG) if the non synchronized pure pattern grammar components contain patterns of different letters then 1. RL - PC (NSPPL) 2. RL Proof 1. Let L = {w/w is in and w consists of an even number of a’s and even number of b’s. This language is a regular language generated by right linear grammar G = (N, T, P, S) where N = {S, A, B, C}, T = {a, b}, P consists of the rules 1. S  aA 2. A  aS 3. S  bB 4. B  bS 5. A  bC 6. C  aB 7. B  aC 8. C  bA 9. A  a 10. B b. But L PC (NSPPL) since it is non synchronized and even filter is not being used here so it is not possible to get only even number of a’s and b’s in parallel communicating non synchronized pure pattern languages. Therefore both RL and PC (NSPPL) are incomparable. 2. Let L = { / }.This language can be generated by both RL and PC (NSPPL). VII. Conclusion As expected the result of the above combination is a very powerful mechanism. In this model we have used one filter and only one query symbol. Henceforth if the model is adapted by introducing more number of query symbols then the new variant might be of interest. REFERENCES [1] [2] [3] [4] [5] [6] [7]

Sindhu J Kumaar and P.J. Abisha, Parallel communicating synchronized pure pattern grammar system with filters, Proceedings of sixth International Conference on Bio-Inspired Computing: Theories and Applications 2011. Sindhu J Kumaar, P.J. Abisha, D.G. Thomas, N.H. Sarmin and K.G. Subramanian, Languages defined by pure patterns, App. Math. and Comp. Intel., Vol. 2(2) (2013) 195-203. Sindhu J Kumaar and F. Amjad Basha, On non synchronized pure pattern grammars, Applied Mathematical Sciences Vol. 8, no. 137, pp. 6835 – 6841, 2014. J. Dassow, Gh. Paun and G. Rozenberg, Generating languages in a distributed way: Grammar System, in Hand book of Formal Languages (G. Rozenberg, A. Salomaa eds.) Springer-Verlag, Berlin, Heidelberg, 1997. E. Csuhaj –Varju and Gy. Vaszil, On a variant of parallel communicating grammar systems with communication by commands, Formal Models, Languages and Applications, World scientific, Vol. 66, pp. 46-64, July 2006. Gh. Paun and L. Santean, Parallel communicating grammar systems: i.e. regular case, Ann. Univ. Buc, Scries Matcm - Inform. 38, 1989, 55-63. S. Demitrescu and G, Paun, On the power of parallel communicating grammar systems with right-linear components, Theoretical Informatics and Applications, 31(4), pp.331-354, 1997.

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MEASURMENT OF RISK IN YIELD OF SOYBEAN P.D.DESHMUKH1, K. G. JAYADE2, P. G. KHOT3 Assistant Professor (Statistics), College of Agriculture Nagpur, Dr.P. D. K. V., Akola, Maharashtra, India 2 Assistant Professor (Computer Science), College of Agriculture Nagpur, Dr. P. D. K. V., Akola, Maharashtra, India 3 Professor of Statistics RSTM Nagpur University, Nagpur, Maharashtra, India 1

Abstract: The study was conducted to measure the risk in Yield of Soybean crop of Akola District of Vidarbha region of Maharashtra state of India over a period of 21 years from (1990-91 to 2010-2011), Compound growth rates of yield of Soybean was 0.15%. Coefficient of Variation i.e. risk in yield and Expected negative deviation in yield of soybean were 36 % and 132 respectively, there by indicates the risk in production of soybean crop in Akola district of Vidarbha region of Maharashtra state of India. Key words: Risk, Probability of short fall, Probability distribution, Curve fitting I.

INTRODUCTION

The yield uncertainty arising due to vagaries of nature vitiates farmers production programme and causes instability in production and income of the farmers. The measurement of risk involved in Crop production is of paramount importance to suggest remedial measure of technical and social nature. Keeping in view the above aspects it is proposed to measure risk involved in the Yield of Soybean crop in Akola district which is the major growing area of the Vidarbha region of Maharashtra state of India. Measurement of risk in yield of cotton in Amravati division were studied. Three parameters of risk viz. Coefficient of variation, probability of crop failure and crop loss ratio were estimated for risk measurement. The probability of crop failure and crop loss ratio for cotton yield were 0.70 and 21.09% respectively. [1] An attempt was made to measure risk in yield of rice crop in Konkan region of Maharashtra. Three parameters of risk viz. Coefficient of variation, probability of crop failure and crop loss ratio were estimated. [2] Yield and price risk in crop production for social security were studied first time on selected crops for Vidarbha region. The risk associated with production and gross returns have been measured with help of the concept of coefficients of variations after diminution of underlying trends in data and probability of shortfall in yield and gross returns through appropriate underlying probability distributions. [3] II.

MATERIAL AND METHODS

The scope of the present study is limited to the measurement of risk in the yield. The study is based on secondary time series data collected from various official publications of Govt. of Maharashtra. The time series data of soybean yield was collected for the period from the year 1990-91 to 2010-11. Measurement of risk The yield trends were taken for the measurement of risk. Year to year changes in yield represent the variability or risk. The magnitude of risk in yield per hectare for selected crops of Akola districts was measured by coefficient of variation. Coefficient of Variation

Where, Xt = Actual value in tth year = Trend value in tth year = Mean of X n = Total number of years in the study period.

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Year to year fluctuations of a character from it’s trend represents it’s variability or risk. Coefficient of Variation is a commonly used tool to quantify the risk. Different types of trend curves were attempted and the one giving highest R was finally chosen for obtaining data adjusted for trend and for computation of risk. Different types of trend curves have been explored to identify appropriate trend of the variable for Soybean crop. Besides coefficient of variation, the risk in terms of probability of obtaining yields below the 95 per cent of the trend have been computed with the help of one of the appropriate probability distributions for Soybean crop. The average probability was obtained by adopting the appropriate fitted distribution with goodness of fit criteria. Any deviation (positive or negative) from the trend constitutes the risk. It is the negative deviation which is a deep concern to the farmers. Therefore any policy option for the protection of the interest of the farming community should take negative deviations in to consideration. The expected negative deviation worked out on the basis of probability of short fall (by 95 percent of the trend value) and average absolute deviations (from trend) are used to derive “Risk in yield”. (Expected annual = (Average absolute deviation in yield) x (Probability of short fall in yield) Negative deviation) (for yield risk )

Where average absolute deviation in yield = Expected negative deviation indicates an estimated annual loss due to yield risk.[4] III.

Result and discussion

Table 1 gives the fitted model for the soybean along with the value of parameters and value of R. From table 1 it is found that out of the different curve tried the exponential association found to be best fit having R value 0.52. The fitted curve explain 52 % variation in soybean yield. The value of the parameters was used for estimating the trend values. Table 2 gives the fitted probability distribution function along with the probability density function and values of the parameters. From table 2 it is found that Generalized extreme value distribution function is best fitted for the soybean detrend data. The value of the parameters given in the table were used for estimation of expected value of the distribution and for calculation of probability short fall , and Expected negative deviation given in table 3. Table 3 gives the Mean yield, Compound growth rate (%), Coefficient of variation (%) , Probability of short fall and Expected negative deviation for Soybean crop. From the table 3 it is seen that the value of CGR for soybean was found to be very low 0.15 % . The coefficient variations were 36% i.e. Risk in production of soybean. while Probability of short fall and Expected negative deviation were 0.43 and 132 respectively. Similar results were obtained by [5] [6]-[7] Table 1: Fitted curve, Model, Value of parameters and Value of R. Crops

Soybean

Curve fitted

Exponential Association

Model

y= a(1-exp(-b*x)) a and b are parameters

Parameters

a= b= 0.52

R value

1243.765 0.64331

Table 2: Fitted probability distribution, Model and Values of the parameters Crops Probability Distribution fitted Probability Density function

Parameters

Soybean Generalized extreme value Distribution = 1/σ exp(-(1+kz)^-1/k(1+kz)-1-1/k k ≠ 0 = 1/σ exp (-z-exp(-z)) k=0 Domain 1+k(x – μ)/σ > 0 for k ≠ 0 -∞<x<+∞ for k = 0 k = continuous shape parameter σ = Continuous scale parameter (σ > 0 ) μ = Continuous location parameter k =-2.9654, σ =32.807 μ = 1171.8

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Table 3: Mean yield, CGR%, CV%, Probability of short fall and Expected negative deviation of Soybean. Crops Mean Kg/ha. CGR % Coefficient of Variation (C. V.% ) Probability of short fall Annual expected deviation Expected negative deviation

Soybean 1183 0.15 36 0.43 304 132

References [1]

[2] [3] [4] [5] [6]

[7]

P. D. Deshmukh, R. K. Kolhe and A. S. Tingre, 2008 “ Measurement of Risk in Yield of Cotton in Amravati Division paper presented in 65th conference of Indian society of Agricultural Statistics”at Dr. Rajender Prasad Agricultural University, Samstipur Bihar. S.L. Sananse and Borude S. G. 1992 “ Measurement of Risk in Yield of Rice Crop in the Konkan region of Maharashtra” Journal of Maharashtra Agricultural Universities 17 ( 3 ) : 455-457 N. S. Gandhi Prasad and A.S. Tingre, 2006 “Yield and price risk in crop production for social security”. Agresco report Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidypeeth Akola. Suresh Pal and Geeta Bisaria, 1990 “Risk consideration in Product Prices: An Expected Deviation Approach” Indian Journal of Agricultural Economics Vol. 45, No. 4, Oct. Dec. 1990, 503-509. A. M. Degaonkar and P. R. Waghmare, 2007 “Statistical analysis of yield gaps in Kharif sorghum and Tur”, Agresco report Department of Agricultural Economics and Statistics, Marthwada Agricultural University, Parbhani. S. S. Marawar, S. W. Jahagirdar, D. V. Ratnalikar and D. K. Nemade, 2007 “ Management of Risk in Agriculture”. Agresco report Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidypeeth Akola. D. V. Ratnalikar, A. S. Tingre and P. V. Shingrup 2007 “Statistical Analysis of Yield Gaps” Agresco report Department of Agricultural Economics and Statistics, Dr. Panjabrao Deshmukh Krishi Vidypeeth Akola.

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EFFECT OF STEEL FIBER ON FLEXURE STRENGTH OF CONCRETE Mohammed Yusuf Sagri1, Prof. S.S. Kadam2 M.E. Structure Student, 2M.Tech. Structure, Ph.D. (Pursuing) 1,2 SKN Sinhgad College of Engineering, Korti, Pandharpur, Dist. Solapur, Maharashtra, INDIA 1

I. INTRODUCTION Plain concrete possesses a very low tensile strength, limited ductility and little resistance to cracking .Internal micro cracks are present in concrete and its poor tensile strength is due to the propagation of such micro cracks, ultimately leading to brittle fracture of concrete. In the past, attempts have been made to impart improvement in tensile properties of concrete members by way of using conventional reinforced steel bars and also by applying restraining technique; however it does not increase the tensile strength of concrete itself. When concrete members loaded, the cracks propagate and open up, and owing to the effect of stress concentration, additional cracks are form in places of minor defects. The structural cracks proceed slowly because they are retarded by various obstacles, changes of direction in bypassing the more resistant grains in matrix. The development of such cracks is the main cause of inelastic deformation in concrete. It has been recognized that the addition of small, closely spaced and uniformly dispersed fiber to concrete would act as a crack arrester and would substantially improve its static and dynamic properties. This type of concrete is known as fiber reinforced concrete.[1] II.

METHODOLOGY

Ordinary Portland cement of 43 grade (IS 8112) [2] with specific gravity 3.18 was used in making the concrete. The fine aggregate used was sand of zone I and its specific gravity was 2.4. Coarse aggregates used in experimentation were 20m and down size and their specific gravity was found to be 3.1 and fineness modulus of 5.01. [3] Fiber used in the investigation was procured from local market in bundles. The diameter of steel fiber is 1.0mm, young’s modulus is 2.0x105 N/mm2 and unit weight is 78000N/mm3. Concrete mix design for M-20 grade concrete is done by using I.S method and is found to be 1:1.3:3.6 by weight and water/cement ratio is 0.5.Cement, fine aggregates and coarse aggregates are first mixed in dry. Then required volume of fiber is added in 3 stages. After mixing properly in dry condition, required quantity of water is added. Care is taken to check the balling of fibers. Beams and cubes of size 100 mm x 100 mm x 500 mm and 150 mm x150 mmx150 mm respectively were casted. The concrete is poured in three layers by compacting each layer properly with tamping rod. For each volume of the fibers and aspect ratio 3 beams and cubes were casted in order to get average strength. Formwork is removed after 24 hours and beams and cubes are immersed in water for curing up to 28 days. They were later taken to the lab to universal testing machine. Two point loading system is used at a distance 1/3 in order to get pure bending. Cubes are tested in the compression-testing machine by keeping cube perpendicular to the direction of compaction. [4] III. 1.

3. 4.

DISCUSSION

The results of flexural strength tests are tabulated. It was observed from graph 5, 6 that addition of steel fibers to cement concrete, the flexural strength significantly increased. It is seen that addition of 2.5% of fiber with aspect ratio 70, the flexural strength is nearly twice the plain concrete strength. [4] It was observed from graph 7&8 that addition of steel fibers to concrete, the compressive strength is slightly decreased. At aspect ratio 50 & 2.5% volume of fibers shows strength, this is nearer to the strength of plain concrete. [4] It was observed that the addition of fibers decreases the workability. Also it was observed that at constant volume of fiber as aspect ratio increases, the workability is decreased. [4] From graph no’s 1,2,3,4 it is seen that as aspect ratio increases, the deflection increases for the same percentage volume of fibers

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1. 2. 3.

4. 5. 6.

IV. CONCLUSION The addition of a steel fiber into the concrete significantly increases the flexural strength. At constant percentage of fiber =1.5% & by increasing aspect ratio of fiber from 40 to 70, it is observed that the flexural strength is increased from 36.7 % to 58.65 % as compared to plain concrete strength. At constant aspect ratio 70 and by increasing percentage volume of fibers from 0.5 % to 2.5 %, it is observed that the flexural strength is significantly increased from 29.2 % to 119.69 % as compared to plain concrete. By addition of binding wire as a steel fiber to the concrete, it is observed that the compressive strength slightly decreased. The maximum drop in compressive strength (decrease of 31.10% as compared to plain concrete) is observed with the aspect ratio 70 & percentage volume of fiber of 1.5 %. From load deflection curve, it is observed that as the percentage of fiber increases with constant aspect ratio, the deflection of the beam is also increased before failure. The maximum deflection is observed with 2.5% fiber and 70 aspect ratio and it was 3.2 mm. [4] REFERENCES

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

The book on “CONCRETE TECHNOLOGY” by M.S. Shetty. IS: 8112-1989: Specification for 43 Grade Ordinary Portland cement. IS: 383-1970: Specification for coarse and fine aggregates from natural sources for concrete. International Journal of Recent Development in Engineering and Technology (ISSN 2347 – 6435 (Online) Volume 2, Issue 5, May 2014) 13 Study of Flexural Strength in Steel Fiber Reinforced Concrete

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Phylogenetic Diversity among Some Isolates of Ustilago scitaminae Sydow the causal Agent of Whip Smut of Sugarcane in Egypt Sayed Agag*, Zeinab Fahmy*, Magdy El-Samman** and Mostafa Helmy Mostafa**¹ *Maize and sugar crops disease research section, Plant pathology Institute, Agricultural Research Centre,Geiza, Egypt. **Plant Pathology Department, Faculty of Agriculture, Ain Shams University, Cairo, Egypt. Abstract: Sugarcane whip smut disease considers one of the most important fungal diseases in Egypt. This disease is widely distributed among cultivated areas of sugarcane in Upper Egypt. Teliospores of the pathogen were collected from diseased plants cultivated in Minya, Sohag, Quena and Luxor Governorates in order to study their variability either by scanning electron microscopy or by study their genetic pool using six random primers, moreover the pathogen nomenclature was subject of study. No difference in teliospores morphological features was observed between isolates. Scanning electron microscopy revealed that teliospore of the four isolates were morphologically similar, globose in shape , concave on one side and granular on the outer surface, with measure between 5.6 to 6.5 µm. Using bE4 and bE8 as specific primer of Ustilago scitaminae a fragment of 459 bp was amplified by PCR technique, indicted that the pathogen is U. scetaminae. Amplifications of fungal DNA using six random primers indicated the presence of great diversity among tested isolates. Minya isolate was separated in cluster far from the other isolates in five primers out of six primers. These results indicated the presence of separated geographical distribution between isolates.

I. Introduction Sugarcane (Saccharum officinarum L.) is the most sugar producing crop ,cultivated in tropical and subtropical regions up to approximately 35 oN and 35oS (Jannoo et al., 1999). In Egypt , it cultivates from Minya to Aswan Governorates. Sugarcane plants are subjected to attack by many pathogens i.e fungi , bacteria and viruses. One of the most effective fungal diseases is whip smut, caused by Ustilago scitaminea Sydow. It is an important disease of sugarcane that has spread to all of the major cane growing areas of the world. The disease is characterized by a long unbranched whip like structure that develops from plant apex. The whip consists of a hard core of parenchyma and fibrovascular elements surrounded by a mass of dark colored spores encased in a thin silvery membranous sheath leading to stunting of plant which reflected on sugar productivity ( Hoy et al., 1986). This study was aimed to investigate the morphological and molecular differences between teliospores of Ustilago scitaminae isolates collected from different localities of upper Egypt where sugarcane widely cultivate. II. Materials and Methods Spore collection Teliospores were collected separately from four governorates i.e. Minya (Dir-Mowas county, longitude 30.8511 latitude 27.6377N ), Sohag (EL-Maragha county longitude 31.7500 latitude 26.500N ),Quena(Naga-Hammady county longitude 30.8203 latitude 26.0488N )and Luxor (Esna county longitude 32.5522 latitude 25.2899N ) , during growing seasons 2010-2011 of sugarcane variety GT54-C9 . Characterized whips of the disease were collected carefully in plastic sheets and they were left to dry in the lab. at room temperature. The whips were slightly shaken to obtain teliospores . The spores were kept in sterilized test tubes at room temperature until used. Scanning electron microscopy Four dry Teliospores isolates were secured onto brass stubs with double-sided sellotape and coated with gold in an atmosphere of argon in a jeol jfc ion sputtering device for 10 min .Coated sample were examined and photographed with jeol scanning microscopy (SEM ) at 30kv (magnification 7500x) . Singh et al.,(2005).

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III. Molecular studies 1-DNA extraction DNA was extracted from fungal dikaryon cultures growing in a liquid potato dextrose medium. Teliospores of each county were surface disinfected for 48 h. in 1.5% copper sulfate solution , the teliospore suspension was streaked on 9 cm plates containing PDA medium( containing streptomycin sulfate 250mg L -1 ) using sterile loop, the plates were incubated in 30o c for 2 days . The germinated teliospores were streaked into a new plates containing PDA medium, the new plates were incubated in 30 oc for 6 days (Abd El Fattah et al., 2010). Discs of fungal growth were cut from PDA cultures ( 6 days old) then transferred into conical flasks contained potato dextrose broth , the flasks were incubated at 30 oC for 3 weeks . Mycelia were harvested by filtration through two layers of filter paper No.1 , washed several times with sterilized distilled water and blotted dry by Whatman No.1 filter papers. Mycelia were ground in a mortar to fine powder using liquid nitrogen and 0.5 gm of fine powder was transferred into 1.5 ml microcentrifuge tubes. DNA was extracted by Bio Basic DNA extraction kit according to the manufacture technique. Molecular identification of the causal organism PCR amplification of genomic DNA was performed using specific primers bE4(5,-CGCTCTGGTTCATCAACG3,) and bE8 (5, TGCTGTCGATGGAAGGTGT-3,)(Albert and Schenck, 1996). amplification reactions were performed in 25 µl reactions volumes containing 85 ng of purified fungal DNA , 1 µm of each primers and 12.5 μl of 2x PCR master mix(final concentration 1x ( 1.5 mM Mg Cl2). Amplification was performed in applied biosystem thermal cycler, with an initial denaturation at 96 oc for 6 min, followed by 30 cycles each of denaturation at 94 oc for 1 min, annealing at 52 oc for 1 min and extension at 72 oc for 1 min. Final extension at 72 oc for 7 min. Amplification products were separated alongside molecular weight marker (100bp DNA ladder , gene direx ) by electrophoresis on 1 % agarose gel run in 1x TBE ( Tris Boric acid EDTA ) buffer , stained with ethidium bromide and visualized under UV light . Gel photographs were scanned through Gel Doc system . Random amplified polymorphic DNA analysis PCR amplification of genomic DNA was performed using primers listed in table(1)(Wankuan Shen et al,2012) . Amplification reactions were performed in 25 µl reactions volumes containing 85 ng of purified fungal DNA , 1 µl of each primers and 12.5 µl of 2x PCR master mix (Ampli Taq Gold PCR master mix; Applied Biosystem)(final concentration 1x ( 1.5 mM Mg cl2) .Amplification was performed in applied biosystem thermal cycler, with an initial denaturation at 96 oC for 6 min, followed by 35 cycles each of denaturation at 94 oC for 1 min, annealing at 37 o C for 1 min and extension at 72 oC for 2 min. Final extension at 72 oC for 7 min. ( Singh et al., 2005). Amplification products were separated alongside molecular weight marker (100bp DNA ladder , gene direx ) by electrophoresis on 1 % agarose gel run in 1x TBE ( Tris Boric acid EDTA ) buffer , stained with ethidium bromide and visualized under UV light . Gel photographs were scanned through Gel Doc system .The amplification product sizes were evaluated using Gel analyzer 3 software and dendrogram was done using SPSS program( Everitte, 1993). Table (1) Primers used in RAPD reaction. Primers code

(G+C) %

Sequence 5-3

1 2

70 60

CACGGCGAGT GTCGATGTCG

AAGCCTCCCC

CGTCGCCCAT

5 6

60 60

GGGTTTGGCA AGCGAGCAAG

IV. Results Scanning Electron Microscope Teliospore of the four isolates, Minya , Sohag , Quena and Luxor were morphologically similar (Fig.3) , globose in shape , concave on one side .The granular pattern on the outer surface was a characteristic feature in all isolates. Spore diameter for Minya isolate was 6.1µm , Sohag isolate was 5.6 µm , Quena isolate was 6.5 µm and Luxor isolate was 5.8 µm.

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Quena isolate

Minya isolate

Luxor isolate Sohag isolate Fig. (1) Scanning Electron Microscopy(7500x) of teliospores of U.scitaminea collected from different locations. Molecular identification of the causal organism Primers bE4 and bE8 specifically amplified a DNA fragment of 459 bp at annealing temperature of 55 oc in all samples containing U.scitaminea DNA. The PCR product from four isolates of U.scitaminea (Minya , Sohag, Quena and Luxor) are located at 459 bp. M 1 2 3 4

500 kb

459 kb

Fig. (2) Polymerase chain reaction of four isolates of Ustialgo scitaminea,( 1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using specific primer. Random amplified polymorphic DNA analysis RAPD analysis of four isolates of Ustialgo scitaminea using 6 primers was carried out to study the genetic diversity among these isolates . Primer No. 1(CACGGCGAGT). Total number of produced bands were 22 bands , ranged from 3 to 7 .Fig(9).The polymorphic bands were 8 (36% ) ,as indicate in Table(2). Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 3 clusters. Fig.(3) , culster 1 ( Quena and Luxor isolates ) , cluster 2 (Sohag isolate ) and cluster 3 ( Minya isolate ) .

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1500bp

500 bp bp

Fig. (3) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.1(CACGGCGAGT). Table (2) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag, Q=Quena and L=Luxor) generated by primer No.1(CACGGCGAGT). NB

Bands M

1372.905

0.136

0.5

981.109

0.196

501.038

0.275

686.183

0.36

0.75

582.571

0.398

0.5

469.687

0.448

407.447

0.481

0.75

Fig.(4) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.1(CACGGCGAGT). Primer No.2 (GTCGATGTCG). Total number of produced bands were 18 bands , ranged from 3 to 5. Fig(11).The polymorphic bands were 6 (33% ) ,as indicate in Table(3). Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 2 groups .Fig.(5) , cluster 1 ( Sohag ,Quena and Luxor isolates ) , cluster 2 ( Minya isolate ) .

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1500 bp

500 bp

Fig. (5) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.2(GTCGATGTCG). Table (3) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag ,Q=Quena and L=Luxor) generated by primer No.2(GTCGATGTCG). NB

Bands

1073.647

0.267

722.046

0.361

539.625

0.43

367.531

0.521

0.75

250.32

0.612

0.75

Fig.(6) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.2(GTCGATGTCG). Primer No. 3(AAGCCTCCCC). Total number of produced bands were 21 bands , ranged from 2 to 8. Fig(13).The polymorphic bands were 13 (61% ) ,as indicate in Table(4). Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 3 groups. Fig.(7), cluster 1 ( Sohag and Quena isolates ) , cluster 2 (Luxor isolate ) and cluster 3 ( Minya isolate ) .

1500 bp

500 bp

Fig. (7) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.3(AAGCCTCCCC).

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Table (4) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag ,Q=Quena and L=Luxor) generated by primer No.3(AAGCCTCCCC). NB

Bands

2255.781

0.231

M -

2 3 4 5 6 7 8

1756.607 1110.539 885.638 587.226 457.281 335.505 262.822

0.273 0.35 0.388 0.457 0.499 0.551 0.592

+ +

S +

Q +

L +

0.75

+ + + +

+ + + + +

+ + + + + + +

0.75 0.25 0.75 0.5 1 0.25 1

Fig.(8) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.3(AAGCCTCCCC). Primer No. 4(CGTCGCCCAT). Total number of produced bands were 24 bands , ranged from 2 to 8. Fig(9).The polymorphic bands were 16 (66% ) ,as indicate in Table(5) .Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 3 groups. Fig.(10) , cluster 1 ( Quena and Luxor isolates ) , cluster 2 (Sohag isolates ) and cluster 3 ( Minya isolate ) .

1500 bp

500 bp

Fig.(9) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.4(CGTCGCCCAT). Table (5) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag ,Q=Quena and L=Luxor) generated by primer No.4(CGTCGCCCAT). NB

Bands

2076.54

0.263

1600.131

0.316

0.5

1214.965

0.372

0.75

572.562

0.525

0.75

486.798

0.558

0.75

392.089

0.602

306.624

0.652

0.75

193.136

0.746

0.5

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Fig.(10) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.4(CGTCGCCCAT). Primer No. 5(GGGTTTGGCA). Total number of produced bands were 21 bands , ranged from 3 to 7. Fig(11).The polymorphic bands were 13 (61% ) ,as indicate in Table(6). Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 3 groups. Fig.(12) , cluster 1 ( Quena and Luxor isolates ) , cluster 2 (Sohag isolate ) and cluster 3 ( Minya isolate ) .

1500 bp

500 bp

Fig.(11) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.5(GGGTTTGGCA). Table (6) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag ,Q=Quena and L=Luxor) generated by primer No.5(GGGTTTGGCA). NB

Bands

1424.962

0.459

0.5

1343.625

0.522

0.5

1010.599

0.57

0.5

813.451

0.573

0.75

582.149

0.644

0.75

447.87

0.744

265.087

0.704

120.71

0.823

0.25

Fig.(12) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.5(GGGTTTGGCA).

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Primer No. 6(AGCGAGCAAG). Total number of produced bands were 24 bands . Fig(13).There is no polymorphic bands,as indicate in Table(7). Dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into one group. Fig.(20) (Minya, Sohag Quena and Luxor isolates )

1500 bp

500 bp

Fig.(13) Random amplified polymorphic DNA (RAPD) profile of four isolates of Ustilago scitaminea(1) Minya ,(2) Sohag,(3) Quena and(4) Luxor while the first lane (M) is marker using primer No.6(AGCGAGCAAG). Table (7) Profile of DNA bands with their molecular weight four isolates of U.scitamiea(M= Minya , S=Sohag ,Q=Quena and L=Luxor) generated by primer No.6(AGCGAGCAAG). NB

Bands M

1374.005

0.396

855.065

0.487

645.303

0.541

436.508

0.616

319.282

0.676

188.603

0.777

Fig.(15) Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor using primer No.6(AGCGAGCAAG). Final dendrogram of UPGMA cluster analysis revealed that four isolates of U.scitaminea were clustered into 3 clusters,Fig.(16). Cluster one (Quena and Luxor isolates ), cluster two (Sohag isolate) and cluster three(Minya isolate).

Fig.(16) Final Dendrogram of four isolates of Ustilago scitaminea(M) Minya ,(S) Sohag,(Q) Quena and(L) Luxor . V. Discussion Sugarcane smut disease considers one of the most effective disease of this vital crop. The present investigation was planned to study the morphological and molecular features of different isolates collected from four different regions where sugarcane widely cultivated. Scanning electron microscopy revealed that teliospore of the four isolates were

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morphologically similar, globose in shape , concave on one side and granular on the outer surface, with measure between 5.6 to 6.5 µm. This measure are greater than those given by Singh et al., (2005 ) Which are between 4 to 5 µm, but the morphological feature are similar and also the same with Mundkur description , Ustilago scitaminea var.sacchari – officinarum , spores vandyke- brown epispore medium thick ,coarsely echinulate , 6.5 to 11 µm in diameter. Data obtained in this study revealed that teliospores diameters were higher than that reported by AbdElFattah( 1984). This variation in spore scizes may be attributed to many factors i.e degree of spores dehydration or to environment in which this spores were preserved. There is a great conflict on the nomenclature of sugarcane smut disease either U.scitaminea or Sporisorium scitamineum Stoll et al.,(2003). To elucidate this conflict, DNA of the fungus was amplified using specific primers of U.scitaminea . Primers bE4 and bE8 specifically amplified a DNA fragment of 459 bp. The PCR product from four isolates of the pathogen (Minya , Sohag, Quena and Luxor) are located at 459 bp. This results could solve the conflict of the pathogen nomenclature either U.scitmainea or Sporisorium scitamineum. Hence the specific primer of U.scitaminea was reacted positively with the fungal DNA it could say that the whip smut fungal pathogen of sugarcane in Egypt is Ustilago scitaminea . Teliospores of the smut pathogen were collected from four governorates. As found in pathogenecity test (unpublished data) , these isolates were varied for their potentiality to cause the disease whereas ,Luxor isolate has the higher potential of disease incidence .On the other hand , Sohag isolate was the lowest . How one could explain these results, did this isolates differ in their genetic pool or they comprise one isolate. To elucidate this problem RAPD analysis was carried out. Out of the six tested random primers five primers gave very interesting results. The four tested isolates were segregated in three clusters. The first one contained Luxor and Quena isolates, the second one contained Sohag isolate and the third one contained Minya isolate, although the cultivate variety is one in all governorates. It could expected that all of these isolates assemble one isolate because the dominate wind direction in Egypt is North – eastern, this may lead to spread Mynia isolate among Governorates . Results obtained in the present study ensure that there is strict geographical distribution of isolates under study. How the geographic area could induce this variation?. Luxor and Quena is very near district between each other therefore, it was logically to observe that their isolates were segregated in one cluster. Sohag district is relatively far from Luxor – Quena region, therefore, its isolate was segregated in special cluster. The most interesting result is that Minya isolate was segregated in far culster out of the other isolates. What is the meaning of the obtained results? Unfortunately there is not any available literature could explain this phenomenon. Luzaran et al ., (2012) found three major cluster groups ( A ,B and C) generated from 26 isolates of the smut fungus collected from 17 sugarcane growing areas in Philippine. While clustering based on geographic origin was not evident .On the other hand, Xiong et al ., (2004) suggested that the molecular diversity of 18 isolates of Ustilago scitaminea collected from Fujian province in China associated with geographic origins to some degree , but not with host origin. Braithwaite et al .,( 2004) used AFLP to study genetic diversity between 38 isolates of Ustilago scitaminea collected from 13 countries , the technique revealed a low level of variation at the genomic DNA level . Croft and Braithwaite (2006) found that DNA fingerprinting of the smut isolates collected from Western Australia and Indonesia were identical. These results may cause a great difficult to the programs of breeding of sugarcane for the purpose of disease control. References AbdEl-Fattah,M.(1984) .Studies on some disease of sugarcane in Egypt .M.Sc. Thesis , Department of Plant Pathology , Faculty of Agriculture ,Ain Shams University ,Egypt, 105pp. Abd El Fattah,A.L., Almari,S,Abou-Shanab,R.A.L.,and Hafez,E.E.(2010).Fingerprinting of Ustilago scitaminea (Syndow) in Egypt using differential display technique :chitinase gene the main marker.Research Journal of Agricultural and Biology Science ,6(1):8-13. Albert,H.H.,andSchenck,S.(1996).PCR Amplification from a Homolog of the bE Mating –Type Gene as a Sensitive Assay for the Presence of Ustilago scitaminea DNA. Plant Dis.80:1189-1192. Braithwaite, K. S. Bakkeren, G. Croft, B. J. Brumbley, S. M. (2004). Genetic variation in a worldwide collection of the sugarcane smut fungus Ustilago scitaminea. Conference of the Australian Society of Sugar Cane Technologists . Held at Brisbane , Queensland ,Australia , 47 May 2004: 1-9. Croft, B.J. and Braithwaite ,K.S. (2006). Management of an Incursion of Sugarcane Smut in Australia. Australasian Plant Pathology. 35: 113122. Everitt, B. (1993). Cluster analysis (3rd edition). London: Arnold. (library: QD8210 Eve) Hoy,J.W.,Hollier ,C.A.,Fontenot,D.B.,and Grelen,L.B. (1986).Incidence of Sugarcane smut in Louisiana and its effect on yield. Plant Dis.70:5960. Jannoo,N. Grivet,L.Dookun,A.Hont ,A.D.and Glaszmann,J.C.(1999).Evaluaion of the genetic base of sugarcane cultivars and structuration of the diversity at the chromosome level using molecular markers. Food and Agricultural Research Council, Reduit ,Mauritius.123-135. Luzaran, R. T. Cueva, F. M. dela Cumagun, C. J. R. Velasco, L. R. I. Dalisay, T. U. (2012). Variability of Sugarcane Smut Pathogen, Ustilago scitaminea Sydow in the Philippines. Philippine Journal of Crop Science: 37: 2, 38-51. Singh, N., Somai, B.M., and Pillay,D.(2005).Molecular profiling demonstrates Limited diversity amongst geographically separate strains of Ustilago scitaminea.FEMS Microbiology letters.247:7-15 Stoll,M.,Piepenbring, M.,Begerow,D.,Oberwinkler,F.(2003).Molecular phylogeny of Ustilago and Sorisorium species (Basidiomycota ,Ustilaginales ) based on internal transcribed spacer (ITS) sequences.Can.J.Bot.81:976-984.

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Wankuan Shen, Pinggen Xi, Minhui Li , Rui Liu , Longhua Sun, Zide Jiang and Lianhui Zhang (2012). Genetic diversity of Ustilago scitaminea Syd. in Southern China revealed by combined ISSR and RAPD analysis. African Journal of Biotechnology Vol. 11(54), pp. 1169311703. Xiong,Q.,Y., Liping,X., Tang,T.T., and Rukai,C. (2004) . Primary analysis of molecular diversity in populations of the fungus Ustilago scitaminea Syd.(Chinese). Journal of Agricultural Biotechnology, 12:6, 689-689.

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PETROLOGY OF THE MAGMATIC ROCKS IN NAKORA AREA OF MALANI IGNEOUS SUITE, DISTRICT BARMER, WESTERN RAJASTHAN, INDIA Naresh Kumar Department of Geology, Kurukshetra University, Kurukshetra – 136119, Haryana, INDIA Abstract: Rocks of Malani Igneous Suite (MIS) exposed in Nakora area comprise predominantly of felsic volcanic rocks, followed by felsic plutonic rocks and minor amount of mafic rocks. Identification of 44 volcanic flows, volcanic vent and volcano-plutonic associations are the significant features of the present work. The felsic volcanic rocks include rhyolite and trachyte flows. They are mainly dark brown colour and show well developed flow structures, flow bands, vesicles and amygdales. Felsic plutonics are granites. They are hypersolvus-subsolvus, peralkaline and occur in anorogenic setting. Mafic rocks include basalt, gabbro and dolerite. Occurrence of xenoliths of basalt in rhyolite and trachyte indicate that basalt flows are the earliest. Volcanic vent and Luni rift mechanism in NRC suggests that the Nakora Ring Complex is the result of a central volcanic eruption controlled by Luni lineament. Keywords: Petrology, Magmatism, Nakora, Malani Igneous Suite, Rajasthan

I. INTRODUCTION The magmatic rocks exposed in Nakora Ring Complex (NRC) are belonging to the Neoproterozoic Malani Igneous Suite (MIS) in the Trans-Aravalli Block (TAB) of Indian Shield. MIS is the largest (55,000 km2) Atype anorogenic acid magmatism in the Peninsular India. It owes its origin to hot spot tectonics and controlled by NE - SW trending lineaments in the TAB [4, 7]. Except Bhushan and Chandrashekaran [2], there is no published research work available on the field relationships, petrography, petrochemical and petrogenetic aspects of NRC. The present paper focuses on the study of different rock types with some typical field relationships as well as petrographical studies in the Nakora area of MIS. Based on the detailed geological mapping (Fig.1), various magmatic rocks exposed in NRC are grouped into three phases. They are as follows: 1. Extrusive phase: basalt, trachyte, rhyolite, tuff, ash, perlite, breccia and agglomerate 2. Intrusive phase: gabbro and granite 3. Dyke phase: basalt, dolerite, rhyolite and microgranite. II. GEOLOGICAL SETTING In NRC, mainly the acid volcanic rocks are exposed. The rhyolites show various colours (dark brown, light brown and purple) and structures viz. volcanic bands (Fig. 2A), volcanic flows, vesicles, amygdales, shreds, agglomerate and breccia. The volcanic products are volcanic flows, perlite, agglomerate, ash, tuff and explosion breccia. Tuff has sharp contact with rhyolite and basalt (Fig. 2B, 2C) and thus indicates the bimodal and anorogenic nature of the suite. The granites show dark and light shades of pink colour. It is medium grained, massive and compact. Granites show a sharp intrusive contact with basalt and rhyolite (Fig. 2D, 2E) which reflects subvolcanic setting of the magmatism. Various hydrothermal alteration features viz. encrustation of iron oxides, sericite, kaolin, vugs, cavities and geodes are observed in the acid volcanoplutonic rocks. The basalt flows underlie the acid flows and the xenoliths of basalt are observed in the rhyolite (Fig. 2F). Thus the basalt flows are older to the rhyolites in the study area. The gabbros are associated with the acid rocks. Gabbro is dark black colour, coarse grained and massive. Numerous dyke rocks of various compositions (mainly dolerite with minor amount of acid rocks) (Fig. 2G) are cutting across these acid – basic rocks and are trending NE-SW direction. The dolerite is medium grained, black colour, compact and massive. Flow stratigraphy mapping was carried out and has been demarked 44 lava flows (34 of rhyolites, 6 of trachytes and 4 of basalts) [12]. Various primary structures associated with volcanic flows viz. volcanic flows, caves, joints, volcanic vent, vesicles, amygdales, spheroids, orbicular and rapakivi are studied in detail to elucidate the volcanic cooling history and their emplacement mechanism [13]. All volcanic flows show sharp contact to each other. Our field session (December, 2006) in NRC observed a volcanic vent (Fig. 2H) (semicircular to elongated shape and 35 – 40 m wide) (GPS reading: N25o 47’ 923’’ E72o 9’ 627’’) amidst of acidic volcanic terrain at the top of the hill of Nakora Ji hill (Maini hill) [11]. Storm water is

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Naresh Kumar, American International Journal of Research in Formal, Applied & Natural Sciences, 10(1), March-May 2015, pp.56-60

filled in the vent and percolates down as springs through the lava tubes / fractures like network (dimension: 30 m length, 5 m width and 2 m height). III. PETROGRAPHY Under microscope, rhyolite shows flow structures (Fig. 3A) with porphyritic, aphyritic, spherulitic (radiating growth of feldspar and quartz from a common centre), orbicular, rapakivi and perlitic textures. They consist mainly of quartz, orthoclase feldspar and arfvedsonite as phenocrysts and embedded in the quartzofeldspathic groundmass. The microcrystalline aggregates of quartz, alkali feldspar, blue colour amphibole (riebeckite), pyroxene (light green aegirine), blood red aenigmatite, magnetite and hematite are present in groundmass. Thin veins consist of fine quartz crystals with various forms viz. drop like, embayed and fractured pattern cut the orthoclase as well as the groundmass. Phenocrysts of alkali feldspar show Carlsbad twinning whereas altered and fractured orthoclase is observed. The spheroidal rhyolite shows mafic (dark/light brown) and felsic (light grey) layers which represents the flow texture. The mafic layer consists of hematite, magnetite and arfvedsonite and they are fine grained. The felsic layer consists of quartz, orthoclase, perthite and are medium grained. The rapakivi rhyolite shows light brown and dark brown layers. The light brown mainly consists of quartz and orthoclase but dark brown consists of hematite and magnetite. Phenocrysts of quartz are embedded in the dark brown layer and orthoclase is enclosed in light brown layer and both are observed in the fine grained groundmass also. The dark brown layer consists of more black (magnetite) and brown (hematite) colour opaques as compared to light brown layer. The tuff is very fine grained and shows flow bands. The tuffs are micro to cryptocrystalline in nature with flow bands and consist of quartz and alkali feldspar as essential minerals. It contains fine grained angular crystals of quartz and alkali feldspar in the groundmass. Trachyte shows porphyritic texture but sometimes directive flow and parallelism of alongated crystals represent trachytic texture. Trachyte consists essentially of alkali feldspar, quartz, arfvedsonite and riebeckite as phenocrysts and embedded in the quartzofeldspathic groundmass. It contains less amount of quartz but shows same textural and mineralogical features as of rhyolites. Alkali feldspars are mainly of orthoclase and are showing Carlsbad twinning (Fig. 3B). Riebeckite is also observed in groundmass. It is fine grained, needle shape, blue colour, pleochroic (X – light blue, Y- blue, Z- dark blue) and shows extinction angle X ۸ C 3o-5o. The tuffs are micro to cryptocrystalline in nature with flow bands and consist of quartz, alkali feldspar (Fig. 3C). The basalts consist of labradorite and augite as essential minerals and show ophitic and subophitic textures (Fig. 3D). Quartz, hematite and magnetite occur as accessory minerals. Sometimes large vesicles (4–6 mm) in basalt are filled by secondary minerals like quartz and calcite. Granites display hypidiomorphic, granophyric, equigranular and microgranitic textures. They consist essentially of quartz, orthoclase, perthite (hypersolvus type) (Fig. 3E), albite (subsolvus type) and arfvedsonite as essential minerals. The accessory minerals are hematite, magnetite, apatite, zircon, and sphene. Orthoclase displays Carlsbad twinning and albite shows lamellar twinning. Perthite is coarse grained, vein & cloudy types and contains quartz, orthoclase and hematite as inclusions. Also, it is altered to kaolin and sericite which indicate the subsolidus modification processes. Arfvedsonite shows medium grained, pleochroic (X dark bluish green, Y Bluish green, Z yellowish green) and the extinction angle X ^ C of 12° to 15°. Hypersolvus granites of Nakora area fall in the field of alkali granites (except 2 subsolvus samples which fall in the granite field) on QAP diagram (Streckeisen, 1973) (Fig. 4) reflecting the alkaline and peralkaline nature on Lameyre and Bowden (1982) fields which are superimposed on Streckeisen (1973) to distinguish the different plutonic series and their fractionates. Gabbro shows ophitic texture and consists of albite with minor amount of labradorite, augite, olivine and magnetite (Fig. 3F). The original calcic plagioclase feldspar is modified into sodic plagioclase feldspar during subsolidus stage [10]. Interstitial glass is observed between plagioclase feldspar and augite. Dolerite shows ophitic and subophitic textural features but the minerals are finer than gabbro. It consists of untwinned / twinned plagioclase feldspar (labradorite), augite with / without olivine (Fig. 3G). The accessory minerals comprise hematite and magnetite. IV. CONCLUSION On the basis of detailed geological observations during field works viz. identification of the colour, form and structure of rocks and minerals, presence/absence of pyroclasts, volcanic assemblages and pyroclasts etc., 44 volcanic flows are identified in the Nakora rocks. The total thickness of 44 flows is 1776 m out of which 40 flows (1743 m total thickness) are of felsic composition (> 60% SiO 2) and 4 flows are of basaltic composition (total thickness 33 m). The different types of rhyolites (rapakivi, orbicular and spheroidal) show flow bands, porphyritic, aphyritic, spherulitic and perlitic textures whereas granite shows hypidiomorphic, granophyric, equigranular and microgranitic textures. The eruption started with less viscous basic magma with large volatile contents. But at few places pyroclasts mark the beginning of eruption as the sudden and violent type. The nature of NRC is vulcanian type with cyclic characters which depends on the mode of eruption, type of physicochemical compositions. The field and petromineralogical data suggest that the Nakora magmatic rocks are formed by cogenetic process from a comagmatic suite of rocks. The observed hydrothermal and subsolidus processes point towards the rare metals and rare earth mineralization potential of the rocks of Nakora area [4,6].

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‘U’ turn of Luni river is investigated during the field investigations in NRC. It takes sudden ‘U’ turn from West to South direction which indicates the relationship between Nakora volcanic vent and rift dynamics [1]. The fractures and cracks along the Luni lineament may be served as channels for magma rising upto the surface in the study area which advocates a relationship between tectonism and volcanism [3, 5, 8, 9]. It also suggests that the Luni rift served as channel way for the magma rising to the surface as flow at Nakora. ACKNOWLEDGMENTS Authors express their gratitude to University Grants Commission, New Delhi for the grant in the form of Major Research Project (no: F.31-193/2005 SR, dated 31st March 2006) to GV and Project Fellowship to NK. REFERENCES [1] [2] [3] [4]

[5] [6]

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

Bailey, D.K., (1974). Continental drifting and alkaline magmatism. In: T.S.Sorensan (Ed.), Alkaline rocks. John Wiley and Sons, pp. 148-159. Bhushan, S.K. and Chandrasekaran, V., (2002). Geology and geochemistry of the magmatic rocks of the Malani Igneous Suite and Tertiary Alkaline Province of Western Rajasthan. Mem. Geol. Sury. Ind., v. 126, pp. 1-129. Dhont, D., Chorowicz, I., Yurur, T., Froger, L.L., Kose, O. and Gundogdn, N., (1998). Emplacement of volcanic vent and geodynamics of Central Anatolia, Turkey. J. Volcanol. Geotherm. Res., v. 85 (1-4), pp. 33-54. Kochhar, N., (2000). Atttributes and significance of the A-type Malani magmatism, NW Peninsular India. In: M. Deb (Ed.) Crustal evolution and metallogeny in the Northwestern Indian Shield. Narosa Publishing House, New Delhi, Chapter 9, pp. 158188. Korme, T., Chorowicz, I. Collet, B. and Bonavia, F.F., (1997). Volcanic vents rooted on extension fractures and their geodynamic implications in the Ethiopian rift. J. Volcanol. Geotherm. Res., v. 79 (3-4), pp. 205-222. Narayan Das, G.R., Bagchi, A.K., Chaube, D.N., Sharma, C.V. and Navaneetham, K.V., (1978). Rare metal content, geology and tectonic setting of the alkaline complexes across the trans-Aravalli region, Rajasthan. In: V.K.Verma and P.K.Verma (Eds.) Recent Res. Geology, Hindustan Publishing Corporation Ltd, Delhi. v. 7, pp. 201-217. Pareek, H.S., (1981). Perochemistry and petrogenesis of Malani Igneous Suite, India. Geol. Soc. Am. Bull., v. 92, pp. 206-273. Polacci, M. and Papale, P., (1999). The development of compound lava fields at Mount Etna. Phy. Chem. Earth, v. 24, pp. 949952. Toprak, V., (1998). Vent distribution and its relation to regional tectonics, Cappadocian volcanics, Turkey. J. Volcanol. Geotherm. Res., v. 85 (1-4), pp. 55-67. Vallinayagam, G., (1997). Minerals chemistry of Siwana Ring Complex, W. Rajasthan, India. Ind. Mineral., v. 31, pp. 37-47. Vallinayagam, G. and Kumar, N., (2007). Volcanic vent in Nakora Ring Complex of Malani Igneous Suite, Northwestern India. J. Geol. Soc. India., v. 70 (5), pp. 881-883. Vallinayagam, G. and Kumar, N., (2008). Flow Stratigraphy of Nakora Ring Complex, Malani Igneous Suite, Rajasthan, NW Peninsular India. Geol. Sury. India. Special Publication, v. 91, pp. 127-135, Kumar, N. and Vallinayagam, G., (2009). Primary Volcanic Structures from Nakora area of Malani Igneous Suite, Western Rajasthan : Implications for Cooling and Emplacement of Volcanic Flows. Current Science, v. 98(4), pp. 550-557, 2010.

FIGURES CAPTION Fig. 1 Geological map of Nakora area.

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Fig. 2 Field Photographs

Fig. 2A. Flow bands in non porphyritic rhyolite; 2B. Sharp contact between rhyolite and tuff; 2C. Sharp contact between basalt and tuff; 2D. Sharp contact between basalt and granite; 2E. Sharp contact between rhyolite and granite; 2F. Xenolith of basalt in rhyolite; 2G. Dolerite dykes; 2H. Panoramic view of volcanic vent.

Fig. 3 Photomicrographs

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Naresh Kumar, American International Journal of Research in Formal, Applied & Natural Sciences, 10(1), March-May 2015, pp.56-60

Fig. 3A. Flow structures in the rhyolite and phenocrysts of quartz and orthoclase feldspar are are showing parallelism with flow direction, (25x, PPL); 3B. Trachyte is showing porphyritic texture, orthoclase is showing Carlsbad twinning, (40x, XPL); 3C. Tuff is showing quartz and orthoclase as a phenocrysts in fine grained groundmass, (40x, XPL); 3D. Ophitic texture is showing by basalt; labradorite, augite and magnetite are present as a assential minerals in groundmass, (40x, XPL); 3E. Granite is showing microgranitic texture with quartz, perthite, hematite and magnetite, (40x, PPL); 3F. Gabbro is showing ophitic texture with labradorite, augite and magnetite as assential minerals, (100x, XPL); 3G. Dolerite is showing subophitic texture with untwinned labradorite, augite, hematite and magnetite, (40x, XPL).

Fig. 4 Quartz – Alkali feldspar – Plagioclase (QAP diagram of Streckeisen, 1973) diagram with the fields of Lameyre and Bowden (1982) showing the model composition of Nakora granites.

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American International Journal of Research in Formal, Applied & Natural Sciences

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Microsatellite-Based Genetic Diversity Analysis in Grape (Vitis vinifera L) Germplasm and its Relevance with Morphological Characteristics Venkat Rao1, P. Narayanaswamy2 and B.N Srinivasa Murthy3 Assistant Professor of Fruit Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka, India. 2 Director of Research, University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, India 3 Principal Scientist, Division of Fruit Science, Indian Institute of Horticultural Research, Bangalore, India 1

Abstract: The population of grape (Vitis vinifera L), commonly cultivated in India, shows a wide range of ripening periods and fruit quality and is an unexploited resource for breeding programs. The main purpose of this study was to fingerprint these accessions and to construct a molecular database including the cultivars commonly grown in India. A total of 42 genotypes were analyzed using seven microsatellite simple sequence repeat (SSR) markers and their main morphological and agronomic characteristics were compared. A total of 45 alleles in all 42 genotypes were obtained with 7 primers with an average number of 6.4 alleles per locus. The dis-similarity matrix showed that a maximum of 110 units was obtained between the genotypes “Convent Large Black” and “Arka Hans,” while a minimum dissimilarity if 37 units were obtained between the genotypes “Anab-e-Shahi” and “Dilkhush.” In the dendrogram the microsatellite segregated the genotypes into two clusters (A and B) at 476 units with two subclusters each. The subclusters grouped genotypes predominantly as A1 with seeded fruits, A2 with seedless fruits, B1 with pigmented and seedless fruits, and B2 with colorless and seeded fruits. The use of seven polymorphicmicrosatellite markers and the level of genetic variability detected within Indian grapevine germplasm suggested that this is a reliable, efficient, and effective marker system that can be used for diversity analysis and subsequently in crop improvement programs. Keywords: Genetic Diversity, Microsatellites Markers, Morphological Markers, Simple Sequence Repeat, Vitis vinifera L. I. Introduction Grape cultivars (Vitis vinifera L) have a long history of domestication. The world’s vineyards occupy about 8.7 million hectares. More than 9600 grape cultivars exist around the world (Galet, 2000) and almost 16,000 prime names appear in the Vitis International Variety Catalogue (Maul and Eibach, 2003). Some of these are not easily distinguishable by morphology and many cultivars appear to be synonyms, having been distributed around the world and acquiring new names in the process . Moreover, the wide distribution and long history of cultivation have led to the develop-ment of numerous cultivars with many synonyms, resulting in complexity among germplasm collections (Galet, 1990). Grape in India are reported to have been introduced in 620 BC. (Olmo, 1976) and commercial cultivation was started in the beginning of the 20th century. Presently, grapes are successfully grown in India over an area of 60,000 ha with a production of approximately 1.67 million MT (Anonymous, 2005), primarily for use as fresh fruit. Grape breeding had mainly relied on selection among naturally occur-ring spontaneous crosses for ages and to a lesser extent, due to conventional breeding during the last century (Adam-Blondon et al., 2004). The varieties currently available are the results of a selection process by human and eco-geographical conditions. Information on genetic diversity among plant species is important for efficient utilization of genetic resources. The existence of close genetic relationships among cultivars grown in the same region or under similar climatic influence could lead to dilution of genetic resources. Hence, studies on grape have been carried out to charac-terize the commercially important germplasm available in India. Morphological characterization has been attempted among several grape cultivars for identification purposes. However, superiority of molecu-lar markers over morphological characterization in grape cultivars is well established. The usefulness of molecular testing for grapevine identification is widely accepted (Bowers et al., 1996; Bowers and Meredith, 1997; Thomas et al., 1994). In the grape germplasm, characterization of endangered cultivar (Bocccacci et al., 2005), parentage analysis (Sefc et al., 1997), identification of the clones

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(Ye et al., 1998), analysis of genetic diversity (Merdinogluet al., 2000), and molecular mapping (Doligez et al., 2006) is been carried out. Molecular markers like RFLP (Bourquín et al., 1993), RAPD (Grando et al., 1995; Vidal et al., 1999), microsatellite or SSR (Pellerone et al., 2001) and AFLP (Martinez et al., 2003) are successfully used to characterize grape germplasm. In this study, a combination of morphological and microsatellite amplification with appropriate statistical analysis has enabled us to identify the relationships among 42 genotypes. II. Materials and Methods A. Plant Materials Plant material from 42 grape genotypes was collected from Indian Institute of Horticultural Research, Bangalore. Approximately, 50 g of recently matured leaves (15–20 days old) were collected, washed using distilled water, wiped with 70% (v/v) ethanol, then air dried prior to storage in sealed plastic bags at 4 0C. B. Morphological Data Morphological characterization of each genotype with respect to their vegetative and reproductive characters was done in triplicate plants according to IPGRI (International Plant Genetic Resources Institute, Rome) descriptors for grape (Anonymous, 1995). The dendrogram was constructed based on the descriptors value for all genotypes using SPSS for windows, version 11.5.0 (Anonymous 2002). C. DNA Isolation DNA was extracted from the stored leaves of grapevine using a cetyl trime-thyl ammonium bromide (CTAB) method (Simon et al., 2007). 2 g of leaf sample were powdered in liquid nitrogen to extract the DNA. The powder was mixed with 10 ml extraction buffer, preheated to 650C, containing 100 mM Tris-HCl, pH 8.0, 20 mM EDTA, 1.4 M NaCl, 3% (w/v) CTAB, 2% polyvinylpyrrolidone and 1% (v/v) ¾β-mercaptoethanol, then incubated at 650C for 1 h. The mixture was cooled to room temperature, 10 ml cold 24:1 (v/v) chloroform:isoamylalcohol was added, and the contents were mixed well. After centrifugation at 7500 rpm for 12 min at 40C, the superna-tant was transferred to a fresh tube and the chloroform:isoamylalcohol step was repeated until a clear supernatant was obtained. 5 M NaCl was added to the supernatant [0.5 (v/v)] and mixed gently, followed by addition of 1 volume of cold isopropanol to precipitate the DNA. The mixture was incubated overnight at 40C, then centrifuged at 6500 rpm for 5 min. The resulting pellet was washed with 70% (v/v) ethanol, air-dried, and dissolved in TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0). 2 μg RNase (bovine pancreatic ribonuclease; Bangalore Genei, Bangalore, India) was added to each sample which was incubated for 3 h at 370C, mixed with an equal volume of phenol, and centrifuged at 7500 rpm for 12 min at room temperature. This step was followed by washing with an equal volume of 1:1 (v/v) phenol:chloroform, then with chloroform alone. The DNA was precipitated overnight at 4 0C with 0.5 (v/v) 5 M NaCl and 1 volume of cold isopropanol. The resulting pellet obtained after centrifugation was dissolved in TE buffer, analyzed in an agarose gel and quantified using a spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). D. Microsatellite Analyses A total of seven SSR primers characterized in previous studies were used. The primers were VVMD 14, VVMD 25, VVMD 27, VVMD 28, VVMD 31, VVMD 36 (Bowers et al., 1996 and 1999b), and VMC 7f2 (Pellerone et al., 2001). These microsatellites were selected as they were the same core set in the screening programme used to access the target grapevine collections. Primer pairs were synthesized from MWG Biotech, Bangalore, India based on their published gene sequence. PCR was performed in 96-well plates in MJ Research PTC100 thermocyclers (Bio-Rad Laboratories, Bangalore, India). PCR reactions were carried out in 25 μl reactions containing 50 ng of DNA, 5 pmoles of each primer, 10x of Taq polymerase buffer (50 mM KCl, 10 mM TrisHCl, pH 9.0, 0.05% (v/v) NP40, and 0.05% (v/v) Triton X-100), 1.5 mMof MgCl2, 0.5 mM of dNTPs (Finzymes Pvt. Ltd., India), and 1 U of Taq polymerase (Sigma-Aldrich Pvt. Ltd., India). The final volume was adjusted with sterile distilled water. The PCR amplifications were carried out with respect to the protocols for primer sets published in Bowers et al., 1996 and 1999b; Pellerone et al., 2001; and Thomas and Scott, 1993. Amplification was confirmed with agarose gels, and alleles were separated by running on 6% polyacrilamide denaturing gels and electrophoresed in 1 TBE at 55 W for 2 h. The amplified products were visualized with silver staining previously described. III. Results and Discussion The objective of this study was to estimate the extent of the genetic diversity among Indian grapevine lines using SSR markers. Information on genetic diversity in crop plant species is important for efficient utilization of plant genetic resources. The traditional method of identifying species by morpho-logical characters is now accompanied by DNA profiling that is more reliable on proteins, largely because of several limitations of their morphological data (Nayak et al., 2003). Therefore, when investigating organisms with a high tendency for morphological differentiation, studies considering both molecular and morphological characters are highly relevant (Bartish et al., 2000). A constructed dendrogram based on 27 morphological characters clus-tered the grape genotypes into two major clusters (I and II) at 96 units (Fig. 1). Cluster I consisted of 23 genotypes grouped into two subclusters (A and

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B) linked at a distance of 33 units. Subcluster A clustered at a distance of 21 units and consisted of 8 genotypes. The seedless genotypes “Thompson Seedless” and “Flame Seedless” were clustered together, but both differed with com-pact and elongated bunches and pale- and red-colored berries, respectively. The genotypes “Arka Chitra,” “Arka Hans,” “Arka Shweta,” and “Arka Soma” showed seeded berries with greenish yellow color, but varied with their bunch characters. The genotypes “Arka Vathi” and “Arka Neelamani” clustered together showed large and elongated bunches, but contained green- and black-colored berries respectively. Subcluster B

Figure 1: Dendrogram showing the clustering patterns of 42 Indian grapevine genotypes based on morphological characters. consisted of 15 genotypes and clustered into two groups at 23 linkage distances. Group 1 clustered 10 genotypes predominantly characterized by large bunches and dark berry types. The genotype “Angur Kalan” showed golden yellow berries with pinkish tinge, genotype “Sharad Seedless” evidenced a crispy pulp, and genotype E29/7 had spherical berries. Group II consisted of 5 genotypes clustering at 15 units. All genotypes of the group showed medium to large bunches with dark colored berries. The genotypes E-18/12 and “Arka Trishna” were distinctly characterized by cylindrical-bunch and oval-shaped berries, respectively. Cluster II consisted of 19 genotypes forming into two subclusters (C and D) at 43 linkage distance in the dendrogram. Subcluster C was comprised of 7 genotypes at 23 distances. All the genotypes under this subcluster showed colored berries and were seeded, except the genotypes “Sonaka”and “Dilkhush,” which had a pale greenish color, and genotypes “Crimson Seedless” and E-29/6 with a seedless nature. The genotypes “Crimson Seed-less” and “Red Globe” clustered together, respectively showing light-red and wine-red colored berries. Genotypes “Black Champa” and “Convent Large Black” both had spherical berries. The subcluster D with 12 genotypes was clustered at 32 units. The genotypes were predominantly characterized by greenish golden yellow color and seeded berries with the exception of genotypes “Arka Majestic,” “Pusa Navrang,” and “Arka Kanchan” which evidenced a dark color. The remaining genotypes “Centennial Seedless,” “Arka Kanchan,” and E-29/5 produced seeded berries. The genotypes “Pusa Navrang” and “Kali Sahebi” were characterized by berries with reddish pulp, while genotypes “Queen of Vine Yards,” “Kali Sahebi,” and “Cheema Sahebi” were clustered together and characterized by obovate-shaped berries. The genetic variability of this germplasm (Table 1) was evaluated on the basis of the number of alleles (mean: 6.4), gene diversity (GD: 0.71), observed heterozigosity (Ho: 0.849), and probability of coincidence (PC: 0.13). These data indicated the presence of a lower genetic variability in the Indian grapevine germplasm, comparable to the variability found in the Algeria and Mediterranean basin and similar to Spanish grape germplasm (Martin et al., 2003). The most informative locus was VVMD 28 (13 alleles per locus) and the least informative one was VVMD-14 (4) (Table 1). The most informative primers VVMD 28 are VVMD 32 were good candidates to be used for paternity testing due to the high direct count heterozygosity, high number of alleles, and even distribution of allelic frequencies. More-over, in the populations studied, the observed heterozygosity was very

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sim-ilar to the expected heterozygosity at each nuclear SSR locus, suggesting no excess of homozygosity in populations. Since the analysis did not display null alleles hence, the marker should be suitable for a genetic population study among wild relatives and parentage studies (Wagner et al., 2006). Such an absence of null alleles is probably due to the choice of nuclear SSR loci which have revealed very low deviation in the observed heterozygosity from the expected heterozygosity (Bandelj et al., 2004; Khadari et al., 2003). Hence, a choice criterion should be used to avoid loci displaying null alleles being included. Table 1: Descriptive Statistics and Genetic Diversity for Microsatellite Loci at Each of the Seven Sites Among the Indian Grapevine Genotypes Expected Observed hetero-zygosity hetero-zygosity of alleles of alleles

Probability of coincidence (PC)

Probability of nullalleles (r)

Genetic Discrimination diversity (GD) power (d)

Loci

Number

Allele size range (bp)

VVMD 14 VVMD 25 VVMD 27 VVMD 28 VVMD 31 VVMD 36 VMC 7 b1

4 5 7 13 6 5 5

140–165 135–165 135–175 140–200 130–165 180–205 150–205

0.769 0.781 0.840 0.866 0.810 0.856 0.864

0.765 0.793 0.816 0.921 0.947 0.894 0.809

0.08 0.10 0.13 0.15 0.11 0.16 0.20

0.011 0.064 −0.026 −0.045 −0.016 −0.019 −0.015

0.53 0.60 0.64 0.85 0.79 0.78 0.80

0.47 0.61 0.73 0.81 0.84 0.86 0.84

Mean

6.4

130–205

0.827

0.849

0.13

−0.028

0.71

0.74

Pairwise comparisons were made between all genotypes included in this study and the average dissimilarity values were calculated based on microsatellite data. A maximum dissimilarity of 110 units was obtained between the genotypes “Convent Large Black” and “Arka Hans” where the former genotype was characterized by spherical bluish black seeded berries and the later genotype with colorless seeded berries; while a minimum dis-similarity of 37 units were obtained between the genotypes “Anab-e-Shahi” and “Dilkhush,” where both the genotypes were characterized by pale-greenish colored seeded berries. The dendrogram presented demonstrates clearly the ability of microsat-ellites to detect a large amount of genetic variation in genetically closely related genotypes of grapevine and to identify groups with different levels of genetic distance. The markers segregated the genotypes into two major clusters (I and II) at 476 linkage distance (Fig. 2).

Figure 2: Dendrogram showing the clustering patterns of 42 Indian grapevine genotypes based on microsatellite markers. Major cluster I consisted of 30 genotypes grouped into two subclusters (A and B) at 223 linkage dis-tance. Subcluster A with 17 genotypes was grouped into two groups (A1 and A2) with 12 and 5 genotypes, respectively. The genotypes “Anab-e-Shahi” and “Dilkhush” were closely linked in group A1 by 31 linkage distance with both the genotypes showing greenish seeded berries. The genotypes “Pusa Urvasi” and “Cordinal” were closely linked at 39 units and grouped with “Pusa Navrang” at 59 units where “Cordinal” and “Pusa Navrang” were producing pigmented berries. The genotypes “Centenal Seedless” and “Superior Seedless” were

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linked together at 35 units, both with colorless berries. These genotypes were grouped with “Shrad Seedless” (pigmented berries) at 52 units and linked with cultivars “Sonaka” and “Gulabi” at 73 units. The genotypes “Thompson Seedless” and “Flame Seedless” were grouped together at 45 units and differentiated with colorless and red berries, respectively. The five genotypes of group A2 were linked at 71 units, of which “Black Champa” and “Red Globe” were closely linked at 41 units with both pigmented and seeded berries. The “Convert Large Black” (pigmented and seeded berries) was grouped with “Queen of Vineyards” (golden yellow colored and seeded berries) at 56 units. The genotype “Crimson Seedless” (red colored berries) stood separate and linked to the group at 71 units. The subcluster B consisted of 13 genotypes segregated into two groups (B1 and B2) at 145 units in the dendrogram. The group B1 with eight geno-types was clustered at 81 units, of which “Arka Neelamani” and “Arka Shweta” were closely linked at 31 units—both were seedless. The large bunches with colored genotypes, namely, “Angur Kalan” (pinkish) and “Bangalore Blue” (purple) were linked at 33 units. “Bangalore Purple” (purple) and “Kali Sahebi” (reddish pulp) were linked at 63 units. The geno-types “Cheema Sahebi” and “Arkavathi” both with pyramidal shaped and tightly packed bunches were clustered together at 45 units. The group B2 consisted of five genotypes linked at 65 units and divided into two minor clusters. The first minor cluster consists of three colorless and seedless gen-otypes E-29/5, E-31/5, and E-29/7, which are linked at 55 units. The second minor cluster with two genotypes, E-29/6 and E-7/12, is linked at 52 units and both genotypes showed medium-sized bunches and seedless berries. The characteristic feature of cluster I predominantly shows subcluster A with more seeded genotypes and subcluster B with seedless genotypes. IV. Summary In summary, this study, using microsatellite markers on Indian grape vine genotypes, showed considerable genetic diversity existing among the population. This is most likely due to different conditions under which the populations are grown and conserved (Song et al., 2003). The clustering of the genotypes was predominantly based on the pigment and seed charac-ters in fruits. These groupings can be used in selecting diverse parents in breeding improved cultivars and in maintaining variation in the germplasm. Evaluation of genetic diversity among germplasm, particularly of crops, is crucial in utilizing genetic potential to improve traits needed for adaptation to various stress conditions (Amer et al., 2001). To our knowledge, this study is the first attempt in using molecular markers for assessing genetic diversity in Indian grape vine germplasm, and the outcome of this work could be use-ful for future characterization and exploitation of that germplasm. Understanding the spatial organization of genetic diversity within the plant populations is of critical importance for the development of strategies designed to preserve genetic variation (Brown and Briggs, 1991; Hamrick et al., 1991). It has been shown that species with limited gene flow (i.e., with restricted seed and pollen movement) have considerably more among-population variation for total amount of genetic diversity (Schoen and Brown 1991). Since the ex situ collection cannot exceed a limited number of accessions, it is difficult to preserve the evolutionary potential of the species hence, the future of this fruit depends on the selection of high quality cultivars (Tous and Ferguson, 1996). Thus, conservation strategies among grape vine cultivars should be developed with the morphological characters in mind. Taken together, the microsatellites markers can be used for establishing relationships between related species, but their efficiency depends on the amount of variability obtained within the cultivars. References [1] [2] [3] [4] [5] [6] [7] [9] [10] [11] [12]

[13]

[14]

Adam-Blondon, A.F., C. Roux, D. Claux, G. Butterlin, D. Merdinoglu, and P. This. 2004. Mapping 245 SSR markers on the Vitis vinifera genome: A tool for grape genetics. Theor. Appl. Genet. 109:1017–1027. Amer, I.M.B., A. Borner, and M.S. Roder. 2001. Detection of genetic diversity in Libyan wheat genotypes using wheat microsatellite markers. Genet. Resour. Crop Ev. 48:579–585. Anonymous. 1995. IPGRI, Descriptors for grapevine (Vitis vinifera L.). International Plant Genetic Resources Institute, Rome. Anonymous. 2002. SPSS for Windows, version 11.5.0. SPSS, Chicago. Anonymous. 2005. Statistical Databases—Agricultural Data. <http://faostat.fao.org/ site/567/default.aspx#ancor> Bandelj, D., J. Jakse, and B. Javornik. 2004. Assessment of genetic variability of olive varieties by microsatellite and AFLP markers. Euphytica 136:93–102. Bartish, I.V., K. Rumpunen, and H. Nybom. 2000. Combined analyses of RAPDs, cpDNA and morphology demonstrate spontaneous hybridization in the plant genus Chaenomeles. Heredity 85:383–392. Bocccacci, P., D.T. Marinoni, G. Gombino, R. Botta and A. Schneider. 2005. Genetic characterization of endangered grape cultivars of Reggio Emilia province. Am. J. Enol. Vitic. 56:411–416. Bourquín, J.C., L. Otten, and B. Walter. 1993. Restriction fragment length poly-morphism and molecular taxonomy in Vitis vinifera. Theor. Appl. Genet. 87: 157–162. Brookfield, J.F.Y. 1996. A simple new method for estimating null allele frequency from heterozygote deficiency. Mol. Ecol. 5:453–455. Brown, A.H. and J.D. Briggs. 1991. Sampling strategies for genetic variation in ex situ collections of endangered plant species. pp. 99–119. In: D.A. Falk and K.F. Holsinger (eds.). Genetics and conservation of rare plants. Oxford University Press, New York. Doligez, A., A.F. Adam-Blondon, G. Cipriani, G. Di Gaspero, V. Laucou, D. Merdinoglu, C.P. Meredith, [20] Riaz, C. Roux and P. This. 2006. An integrated SSR map of grape-vine based on five mapping populations. Theor. Appl. Genet. 113:369–382. Galet, P. 1990. Cepages et Vignobles de France. Tome. II. L’Ampelographie Francaise, 2nd ed. C. Déhan, Montpellier, France.

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[15] [16] [17]

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Galet, P. 2000. Dictionnaire encyclopédique des cépages. Hachette, Paris. Grando, M., L. De Micheli, L. Biosetto, and A. Scienza. 1995. RAPD markers in wild and cultivated Vitis vinifera. Vitis 34:37– 39. Hamrick, J.L., M.J. Godt, D.A. Murawski, and M.D. Loveless. 1991. Correlations between species traits and allozyme diversity: Implications for conservation biology. pp. 75–86. In: D.A. Falk and K.F. Holsinger (eds.). Genetics and conservation of rare plants. Oxford University Press, New York. Khadari, B., C. Breton, N. Moutier, J.P. Roger, G. Besnard, and A. Berville. 2003. The use of molecular markers for germplasm management in a French olive collection. Theor. Appl. Genet. 106:521–529. Martinez, L., P. Carvagnaco, R. Masuelli, and J. Rodriguez. 2003. Evaluation of diver-sity among [1] Argentine grapevine (Vitis vinifera L.) varieties using morphologi-cal data and AFLP markers. Electron. J. Biotechnol. 6(3):241–250. Maul, E. and R. Eibach. 2003. Vitis international variety catalogue. <http://www. genres.de/eccdb/vitis/>. Merdinoglu, D., G. Butterlin, C. Baur, and J. Balthazard. 2000. Comparison of RAPD, AFLP and SSR (microsatellite) markers for genetic diversity analysis in Vitis vinifera L. Acta Hortic. (ISHS) 528:195–200. Nayak, S., G.R. Rout, and P. Das. 2003. Evaluation of the genetic variability in bamboo using RAPD markers. Plant Soil Environ. 49:24–28. Nei, M. 1973. Analysis of gene diversity in subdivided populations. Am. Nat. 106:238–292. Olmo, H. P. 1976. Grapes. pp. 294–298. In: N.W. Simmonds (ed.). Evolution of crop plants. Longman, London. Paetkau, D., W. Calvert, I. Stirling, and C. Strobeck. 1995. Microsatellite analysis of population structure in Canadian polar bears. Mol. Ecol. 4:347–354. Pellerone, F.I., K.J. Edwards, and M.R. Thomas. 2001. Grapevine microsatellite repeat: Isolation, characterization and use of genotyping grape germplasm from Southern Italy. Vitis 40:79–186. Schoen, D.J. and A.H.D. Brown. 1991. Intraspecific variation in population gene diversity and effective population size correlates with the mating system in plants. P. Natl. Acad. Sci. USA 88:4494–4497. Sefc, K.M., H. Steinkellner, H.W. Wagner, J. Glossl, and F. Regner. 1997. Applica-tion of micro satellite markers to parentage studies in grapevine. Vitis. 36: 179–183. Simon, L., S. Shyamalamma, and P. Narayanaswamy. 2007. Morphological and molecular analysis of genetic diversity in jackfruit. J. Hortic. Sci. Biotech. 82(5):764–768. Sokal, R. R., and P. H. A. Sneath. 1973. Principles of Numerical Taxonomy, W.H. Freeman, San Francisco. Song, Z.P., X. Xu, B. Wang, J.K. Chen, and B.R. Lu. 2003. Genetic diversity in the northernmost Oryza rufipogon populations estimated by SSR markers. Theor. Appl. Genet. 107:1492–1499. Thomas, M.R., P. Cain, and N.S. Scott. 1994. DNA typing of grapevines: A universal methodology and database for describing cultivars and evaluating genetic relatedness. Plant Mol. Biol. 25(6):939–949. Tous, J. and L. Ferguson. 1996. Mediterranean fruits. pp. 416–430. In: J. Janick (ed.). Progress in New Crops. ASHS Press, Arlington, VA. Vidal, J.R., S. Moreno, Y. Gogorcena, A. Masa, and J.M. Ortiz. 1999. On the genetic relationships and origins of six grape cultivars of Galicia (Spain) using RAPD markers. J. Am. Soc. Hortic. Sci. 50:69–75. Wagner, A. P., S. Creel, and S.T. Kalinowski. 2006. Estimating relatedness and rela-tionships using microsatellite loci with null alleles. Heredity 97:336–345. Wagner, H.W. and K.M. Sefc. 1999. IDENTITY 1.0. Centre for applied genetics. University of Agricultural Science, Vienna. Ward, J.H. 1963. Hierarchic grouping to optimize an objective function. J. Am. Stat. Assn. 58:236–239. Ye, G.N., G. Soylemezogln, N.F. Weeden, W.F. Lamboy, R.M. Pool, and B.I. Reisch. 1998. Analysis of the relationship between grapevine cultivars, species and clones via DNA fingerprinting. Vitis 37:33–38.

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

Studies of complexes of Rb and Cs metal salts of some organic acids with Bis (8-hydroxy) – 5 – quinolyl) – methane Dr. Deepali Pal Choudhury1, Dr. Shyam Deo Yadav2 and Dr. Basabi Mahapatra3 1,2,3 Department of Chemistry, Magadh Mahila College, Patna University, Patna, Bihar, INDIA. Abstract: A lot of complexes of alkali metals with oxine and its derivatives have been studied. The present work deals with the chelating ability of Bis (8-hydroxy-5-quinonyl) methane as a ligand. In this work mixed ligands complexes of Rb and Cs were studied having one of the ligand Bis (8-hydroxy-5-quinonyl) methane. These complexes have the general formula (ML)2 H2L1 where M=Rb or cs, L = deprotonatd organic acids such as orthonitro phenol, DNP, TNP etc. H2L1 = Bis (8-hydroxy-5-quinonyl) methane. These complexes were characterized by their elemental analysis, magnetic moment, infrared and electronic spectra. A lot of complexes of alkalimetals were studied with organic ligands1-4. They are highly stable. Sidwick and Brewer studied a lot of complexes of alkali metals with organic lingands having general formula ML – HL1. In these complexes central atom combines with oxygen and nitrogen donar atoms of the ligands. A number of mixed ligands complexes of the composition (ML2)H2L1 have been synthesized by Prakash and et-al5. Where M = L1 Na or K. L = deprotonated 2,4 – dinitrophenol, 2,4,6 – trinitrophenol. H2L1 = Bis (8-hydroxy – 5 – quinolyl) methane. Recently Prakash and Yadav have reported synthesis of four chelate polymers of alkaline earthmetals with Bis (8-hydroxy-5-quinonyl) methane. Key words: I.R, Electronic spectra, molar conductivity. I. Experimental A. Material and methods All the chemicals uses were of AR Grade. For preparing this lignad 14 – 15 gm. 8 – hydroxyquinoline (oxine) was dissolved with stirring in 50 ml concentrated H2SO4, maintaining the temperature 50C. After that 7.2 ml formaldehyde was then added dropwise with stirring the solution over a period of three hours, after which the temperature of the solution was maintained at 0 to 50C for 2 hours and then poured in 6 litre of distilled water maintained at room temperature. After 16 hours the solution was filtered and a bright yellow product reported as hydrogen sulphate salt of Bis (8hydroxy-5-quinonyl) methane was obtained. The yellow precipitate was dissolved in hot distilled water and neutralized with NH4OH to give the desired compound. On recrystallization from dimethyl formamide, a white amorphons powder was obtained that melts at 230C. B. Composition (Percentage) C – 76 H – 4.70 N – 9.07 (C19H4O2N2) Required C – 75.4 H – 4.64 N – 9.26% Preparation of alkali metal salts of some organic acids: (a) Preparation of Rb and Cs metal salts of o-nitrophenol M(ONP). Equimolar proportion of metal hydroxide (i.e. RbOH and CsOH) and o – nitro + phenol were refluxed in absolute alcohol in a conical flask on a water bath for about 20 – 30 minutes. The solution was filtered out, concentrated and cooled. On cooling salts of Rb and Cs were precipitated out. It was filtered washed with ethanol and dried in electric oven at 800C. Composition of RbONP Found % C – 32.16 H – 1.68 N – 6.18 Rb – 38.04 Required (C6H4NO3Rb) C – 32.22 H – 1.79 N – 6.26 Rb – 38.25 (CsONP) Found C – 26.49 H – 1.42 N – 5.06 Cs – 48.85 Required (C6H4NO3Cs) C – 26.58 H – 1.47 N – 5.17 Cs – 49.06 C. Process of complexation Rb or Cs metal salt of organic acid and Bis (8-hydroxy-5-quinonyl) – methane were taken in 2 : 1 (mole) with absolute ethanol in a conical flask. The contents were heated gently with constant stirring till the solid dissolved completely. The conical flask was further heated to get concentrated solution on a sand bath. The clear solution

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was left for cooling, a characteristic coloured crystalline complex was separated out. It was filtered washed and dried in electric oven at 800C. All the mixed ligand complexes are coloured. They are soluble in polar solvents eg – methanol and insoluble in non polar solvents eg – Benzene, ether etc. They are stable in dry air but decompose on exposure to moisture. They were kept in a dessicator over solid fused anhydrous CaCl2. Compound Colour M.P / Decom. / Trans. Temp Bis (8-hydroxy-5-quinolyl) methane (H2L1) colourless 280m (white) (RbONP)2 H2L1 yellow 260t (CsNP)2H2L1 yellow 290t (RbDNP)2 H2L1 deep yellow 250 t (CsDND)2 H2L1 deep yellow 150m Conductivity is taken in methanol solution at 270C.

Molar Conductivity -----7.00 ohm-1 cm2 mole 9.0 9.00 5.9

II. Results and Discussion The low values of molar conductivity indicate the covalent nature of complexes. The absorption band of principal interest in the infrared spectra of Bis (8-hydroxy-5-quinolyl) methane (H2L1) are 3335, 1580 and 1420 cm-1, the moderately strong band at 3335 cm-1 in the spectrum of H2L1 is attributed to the stretching – 0 – H vibration frequency, while the strong band at 1420 cm-1 in its spectrum is probably due to the bending – 0 – H frequency. The absorption band in the region 1580 cm-1 has been assigned to the vibration VC = N group in the quinoline ring. It is evident that the spectrum of the ligand contains a moderately strong band at 3335 cm-1, this band has virtually disappeared (or with its appreciable shifts of about 14 to 94 cm -1) in the complexes, indicating that coordination with oxygen atom of OH group of ligand. A new broad band of weal to medium intensity in the region 2850 – 1950 cm-1 is exhibited by some complexes. This band could be assigned 0–17---- O/N---- H---- 0--- absorption and suggests that H– bonding may be regarded to be an essential feature of these complexes. In infrared spectra of the complexes, the 1580 cm-1 band of the ligand (Vc = N) has appeared as split band at 1589 –1610 cm-1. The splitting and shifting of the 1580 cm1 band of the ligand suggest the coordination of N atom of the C = N group of the quinoline ring with central metal atom. After complexation, shifting of 1420 cm-1 bending – O – H band of the ligand (H2L1) by 10 – 12 cm-1 indicating the chelation of oxygen atom of the – OH group of the ligand. The band in the region 637 – 550 cm-1 in the spectra of all mixed ligand complexes may be assigned to M – 0 band frequency while medium bands in the region 546 – 503 cm-1 is assigned to M – N band frequency. Electronic absorption bands of the complexes are observed in the region 206 – 306 nm and 316 – 360 nm which indicates the π – π* transition and charge transfer. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

M.N. Hughes, “The inorganic Chemistry of Biological system”, Wiley Newyork – 1972. D.A. Phillip, “Metals and Metabolism”, Oxford Univ. Press (1977). O.L. Brady and W.H. Boder, J. Chem. Soc, 952 (1975). V. K. Tiwary, A.N. Gupta, A. Kumar and R.K.Singh: Bull pun Appl. Sci 19 c (2) 75, (2000). D. Prakash, R.N. Prasad, A.K. Gupta & G.S.P.Gupta: Bull, Pure Appl Sci. 19 c (2) 75, (2000). D. Prakash, Y.K.P.Yadev, B. Kumar and A. K. Gupta: Acta Cien. Ind. 18 c (2) 65 (2002). D. Prakash, R. Chandra, B. Kumar and A.K. Gupta: Orient. J. Chem. 17 (I) 139 (2001). D. Prakash, K.B. Sinha, B. Kumar and A.K. Gupta: Asian J. Chem. 13 (1) 211 (2001). D. Prakash and R.N.Shukla A.K.Gupta and A.K. Yadev: Ibid 18 (2) 287 (2002). D. Prakash and A.K.Yadev: Ibid 14 (2) 637 (2002).

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