Aijrfans vol2 print by iasir journals

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

Issue 6, Volume 1 & 2 March-May 2014

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: India, Australia, Germany, 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 sixth 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,

INSPEC,

CiteSeerX, 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 sixth issue, we received 108 research papers and out of which only 34 research papers are published in two 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 sixth 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, 2014, Issue 6, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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

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

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

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:  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 (March-May, 2014, Issue 6, Volume 1 & 2) Issue 6 Volume 1 Paper Code

Paper Title

Page No.

AIJRFANS 14-205

Subspecies identification in aphids (Homoptera: Aphididae) by application of partial sequence of cytochrome c oxidase subunit I (COI) gene: a view on the potential of method Nina V. Voronova

01-06

AIJRFANS 14-208

Efficacy of aqueous plant extracts on the seed quality of pea (Pisum sativum L.) during storage Kiran Rana, K. C. Sharma and H. S. Kanwar

07-11

AIJRFANS 14-209

Genetic Diversity Analysis in Grape (Vitis vinifera L) Germplasm using Microsatellite Markers Venkat Rao, P. Narayanaswamy and B.N Srinivasa Murthy

12-18

AIJRFANS 14-210

Behaviour of low rank high moisture coal in large stockpile under ambient conditions Naveen Chandralal, D. Mahapatra, D. Shome and P. Dasgupta

19-26

AIJRFANS 14-214

Dermatoglyphics as a genetic tool and bio-indicator to detect high risk factor in recurrent pregnancy loss Warda Nazir Qazi, Geetha Viswanathan

27-31

AIJRFANS 14-215

Hybridity studies in Indian Grape (Vitis vinifera L) Germplasm using Microsatellite Markers Venkat Rao, P. Narayanaswamy and B.N Srinivasa Murthy

32-36

AIJRFANS 14-219

Effect of elevated CO2 over Sugarcane crop Kapil Madan, D.S.Shukla, Richa Tripathi, Akanksha Tripathi, H.D.Dwivedi

37-38

AIJRFANS 14-223

Austenitic Stainless Steel Weld Inspection Ashish Bijalwan

39-44

AIJRFANS 14-224

Red Mud as Low Cost Adsorbent for Zn(II) ion â&#x20AC;&#x201C; Kinetic, Thermodynamic and Equilibrium Study Sujata Kumar, Dhanesh Singh, Saroj Kumar

45-50

AIJRFANS 14-227

Antibacterial Investigations & Spectral Characterization of the Complex of Se4N3Br with Co (II) Compound Govind Kumar Gupta & S.P.S. Jadon

51-54

AIJRFANS 14-228

Compatibility of Bradyrhizobium japonicum isolates with agrochemicals V.V.Deshmukh, B.T.Raut, S.S.Mane, R.W.Ingle, M.S.Joshi

55-62

AIJRFANS 14-231

Effect of Fin Configuration on Heat Transfer of an Annulus Tube Dipti Prasad Mishra, Kailash Mohapatra

63-69

AIJRFANS 14-233

Physico-Chemical Analysis of Ground Water in Sangrampur Tehsil of Buldana District, Maharashtra D. L.Bhade and R.E.Khadsan

70-72

AIJRFANS 14-242

Distribution of ABO and Rh (D) Allele Frequency among Five Endogamous Groups of Haryana, India Manisha Saini & Abhay Singh Yadav

73-75

AIJRFANS 14-243

(3a, 16a)-Eburnamenine-14-Carboxylic acid Ethyl ester Mishra Bharti, Tiwari R.K.

76-81

AIJRFANS 14-244

Biochemical effects on Protein and Free Amino acid metabolism in Catla catla and Labeo rohita due to Pallisentis nagpurensis infection Dr. P.Anil Kumar

82-85

AIJRFANS 14-245

A STUDY ON THE EFFECT OF SOLID WASTE DUMPING ON GEO-ENVIRONMENT AT BILASPUR Pratima Rani Dwivedi, Dr M.R. Augur

86-90

AIJRFANS 14-246

Molar volume and rheology of anionic surfactants in non-aqueous media Suman Kumari, Mithlesh Shukla, and R.K Shukla

91-94

Issue 6 Volume 2 Paper Code

Paper Title

Page No.

AIJRFANS 14-248

Phytochemical Analysis of Tridax Procumbens L. Prof.Vaishali.N.Agme

95-97

AIJRFANS 14-256

Behaviour of low rank high moisture coal in small stockpile under controlled ambient conditions-A statistical approach Naveen Chandralal, D. Mahapatra, D. Shome and P. Dasgupta

98-108

AIJRFANS 14-257

Gene effects for pod yield and related traits in French bean ( Phaseolus vulgaris L.) population developed through induced mutation Meenakshi Sood and N. K. Pathania

109-113

AIJRFANS 14-259

Crystal structure of [(2E)-6,6-dimethylhept-2-en-4-yn-1-yl](methyl)(naphtha-1-ylmethyl)amine( Terbinafine) Tiwari R.K., MishraBharti

114-118

AIJRFANS 14-260

Introduction of Vogtia malloi syn. Arcola malloi as biocontrol agent of Water Hyacinth (Eichhornia crassipes) in Devipatan Division (U.P.). Richa Tripathi, D.S. Shukla, Akanksha Tripathi, ,H.D.Dwivedi

119-120

AIJRFANS 14-264

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A Newer Approach to Green Earth - Solar-Induced Hybrid Biomass Fuel Cell Dr. Vanita Kumari Sapra

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Length-weight relationship and condition factor of Tetraodon cutcutia (Ham) from Neematighat, Jorhat P. Karmakar & S.P.Biswas

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An Analytical and Practically Feasible improvisation over representation of Sky-View-Factor Rajesh Gopinath, Jagdeep Singh, Dharmender Singh, Ghanshyam Kumar and Navneet Singh

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Synthesis, Characterization and Study of Optical Constant of 4-(4-N,NDimethylaminobenzylideneamino) Phenyltellurium Tribromide Adil Ali Al-Fregi, Ghufran Mohammad Shabeeb

161-171

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)

Phytochemical Analysis of Tridax Procumbens L. Prof.Vaishali.N.Agme & Prof.Rupali N.Agme Dept of Applied Science, BVCOE,Navi Mumbai, Maharashtra, India Abstract: The current study is aimed on the replacement of anti-viral, antibacterial, anti-fungal, anti-biotic, anti-Cancer, anti-ulcer, anti-pyretic drugs by use of medicinal herb Tridax Procumbens L. Tridax Procumbens L. is Commonely called as “Coat Buttons” or wild daisy or Tridax daisy. Medicinal properties of plants are due to presence of some bioactive Chemical Constituents. The present study of Tridax Procumbens L. suggest hereby that all natural products can be turned bioactive molecules as every diverse molecule possessing one kind or multiple kinds of biological & pharmacological activities. This photochemical analysis of Tridax Procumbens L. was Carried out by author shows that Tridax Procumbens L. Contains element like sulphur, Iron, Sodium, & Chlorines as well as gluoside, Amino acids, Flavanol, Synergic acid ,Tannin, Steroids, polysaccharides, Pectin, Hemicellulose, Phenols, Alkaloids, fats & Volatile oils etc.& shows presence of some elements which are also observed in few drugs like anti-viral, antibacterial, anti-fungal, anti-biotic, anti-Cancer, anti-ulcer, anti-pyretic, anti- healing, anti-dandruff, hypotensive etc. Keywords: Tridax Procumbens L., Coat Button, Photochemical analysis, Anti-dandruff, Anti-healing. I. INTRODUCTION Tridax Procumbens L. is a Common grass found in tropical southern part of Nigeria growing primarily during rainy seasons. It is annual herb with leaves opposites, incised toothed, broadly lance late, acute & with prostate ascending stems. S/N 1 2 3 4 5

Countries/Regions United state Florida California India English

Traditional Names Coat Button Coat Button Coat Button Kambarmodi/Ghamara Tridax daisy,wild daisy

Systematic Classification: Kingdom Plantae Subkingdom Tracheobionta Super division spermatophyta Division Magnoliophyta Order Asteridae Family Asteraceae Species Tridax Procumbens L . Distribution: Tropic & subtropics throughout the world. II. Methods/Techniques Phytochemical analysis of leaves, stems, roots & flowers of Tridax Procumbens L. has been made to investigate some elements & organic Compounds present in leaf, stem, root & flower of Tridax Procumbens L. as well as to study important medicinal & pharmaceutical properties. It was carried out by subdividing into two parts. Part-I: Elemental Analysis [test for elements in ash Content were performed by two methods i) ash dissolved in 20% HCl ii) ash dissolved in 20% NaOH] Part-II: Functional groups analysis. First of all sample of plants were Collected & roots, stems, flowers & leaves were separated out. After drying at room temperature for 15days, then fine powder was treated as sample of root, stem, leaves, & flowers. In this method moist ash & Cold water, hot water, 1%NaOH, 1%HCl solubility of Tridax Procumbens L .have been investigated. In proximate analysis, elements, functional groups & individual Compounds identification of leaves, stems, roots & flowers of Tridax Procumbens L. have been studied. These were extracted in distilled water, ethanol, benzene, aq NaOH & HCl separately then Phytochemical Analysis was carried out. Observations: A) Ash dissolved in 20% HCl-test for Elements:

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Vaishali.N.Agme et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May 2014, pp. 95-97 S/N

Elements

Ash of leaf sample

1 2 3 4 5 6

Mg Ca S Fe Na Cl

Absent Absent present present present present

Ash of sample Absent Absent present present present present

stem

Ash of sample Absent Absent present present present present

root

Ash of sample Absent Absent present present present present

flower

stem

Ash of sample present present present present present present present present present present present present present

root

Ash of flower sample Present Present present present present Present Present Present Present Present Present Present Present

Test for groups: S/N

Groups

Ash of leaf sample

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

Glucoside Cynogenic glucoside Acubin type glucoside. Phenol Flavanol Amino acid Alkaloids Steroids Tannin Fats Volatile oils Synergic acid Polysaccharides, Pectin,hemicellulose

present present present present present present present present present present present present present

Ash of sample present present present present present present present present present present present present present

III. Result The sample of Tridax Procumbens L. have been reported to have presence of various elements like Sulphur, Sodium, Iron, Chlorine as well as groups like Volatile oils, fats, Synergic acid, Alkaloids, Phenols, Flavanol, Cynogenic Glucoside,Acubin gluCoside, Polysaccharides, Pectin,hemicelluloses etc. because of which Tridax Procumbens L. can be used for the treatment of anti-bacterial, anti-viral, anti-dandruff, emollient, anti-healing, analgesic ,anti-AIDS, & can be used against various disorders. It was found that the drugs which are prescribed by allopathic Practitioner for the treatment of healing, hypotension & anti-biotics properties contains particular types of functional groups & nucleus those were also observed during this analysis. IV. Conclusion Medical & biochemical literature survey reveals that Nimesulide, Paracetamol ,Iboprufen are used as analgesic, while Amoxicilin, cefrofloxin as antibiotics whileAbacavir, Amantadine, Rumantadine, Lamivudine, Stavudine, Rabin are used as anti-viral drugs, Abelcet,Cytovene, Daunoxome, Eraxis, Taxol, Vistide are used as anti-

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AIDs, while Lomustine, Arimidix,,Tagretin as anti-cancer & Ofloxacin as anti-bacterial & ketocanazole as anti â&#x20AC;&#x201C;fungal , all the above said drugs are best for respective treatments but have some side effects, during this study it is observed that tridax procumbens L.is having curing capaity of above said diseases which further is studied by NMR & IR spectroscopy.

Page 97

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

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

Behaviour of low rank high moisture coal in small stockpile under controlled ambient conditions-A statistical approach Naveen Chandralal1, D. Mahapatra1*, D. Shome2 and P. Dasgupta3 1 PT Trimex International Indonesia, Jakarta, Indonesia 2 Dept. of Geological Sciences, Jadavpur University, Kolkata, India 3 Cultivation of Sciences, Jadavpur, Kolkata, India

Abstract: The low rank high moisture coal have a great tendency to drain out moisture from their pores under suitable ambient atmospheric conditions. The influence of rain has a negative impact which needs to be protected all round. Stage processing of coal with time gap confirms an enhanced rate of moisture loss in low rank coal. Use of multivariate analysis has given a good insight to understand inter-relation of coal properties, specially the control of moisture with time. Key Words: Low rank Coal, Stockpile, Total Moisture, Equilibrium moisture, ambient drying, XRD, FTIR, DTA, SEM, Factor Analysis, Cluster Analysis I. Introduction Low rank coals have high total moisture contents in the range of 30-70wt%. Due to such a high moisture content of the coal, moisture removal is the first and essential step in almost any process for upgrading or utilizing them. Since the moisture removal is known to have a significant effect on the physical and chemical properties of dried coal, understanding of moisture removal is important. A number of studies have been carried out on the drying behavior of coals from a fundamental viewpoint. Moisture, which can be removed by heating the coal up to a temperature of 100°C, may be retained in various forms: 1. as a film, on the surface of each coal particle, and in the interstices between particles, retained by capillary forces. 2. Or "occluded" inside the coal particles. This occluded moisture may be either free moisture (as in a sponge), or hygroscopic moisture which varies with atmospheric conditions, (also called "regain "). These latter forms of moisture are particularly common in "young" coals (subbituminous and lignite). With the demand of coal by the power sector, the use of low rank coal (LRC) becomes inevitable. This has forced the consumers to look for high moisture thermal coal. Also to improve the efficiency many research work progressed to dry the coal. Below is the outline of few coal drying methods being adopted by power plant or at advanced stage of research by different institutes as explained below. 1. MTE –the mechanical-thermal procedure [1,2] 2. Convective drying in rotational dryers [3-5] 3. Drying followed by briquetting – [6-9] 4. Autoclave Drying – [10-13] 5. Thermal drying 1. vibrating fluid bed dryers;: [14,15] 2. fluid bed dryers;: [16-18] 3. dryers with in-bed heat exchangers; [19] 4. Conventional flash / Tornesh flash dryers. [20] 5. Pulsed Combustion Drying [21] 6. Microwave Drying [22-24] 7. Chemical drying [25-26] 8. Fluidised Bed drying – [27] 9. Coal drying in bubbling fluidized beds [28-33] 10. Hydrothermal Dewatering: [34-38] 11. CWS Process [39-42] 12. Integrated drying gasification Process [43-45] 13. The Limax™ Coal Drying System [46] 14. WCT, Australia – Binderless briquetting[47] 15. High velocity air flow grinding/drying - DevourX mill : DXCoal Pty Ltd Australia, LF Pumping Ltd, England For the high moisture low rank thermal coal of East Kalimantan, Indonesia (current work in TOP Mine Coal in

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Muarawahau region), all the above options were carefully examined. It was found all these processes have inherent shortcomings considering its applicability due to1. Most of the processes are designed and adopted in power plants in situ utilisation 2. No uniform applicability to all types of coal 3. Capacity limitations of each process 4. High Power and capex requirement etc. 5. Space constraint in mining operations because of larger plant foot print 6. Adaptability to very lager volumes or production 7. Limited applicability to Indonesian coals 8. Readsorption of moisture 9. Uneconomical due to Indonesian thermal coal selling price It is very clear that the moisture present in coal as chemically bound is not easy to remove unless sufficient energy is induced externally; however other types of moistures are easy to remove. A comprehensive overview on the fundamental understanding of water in brown coal and lignite, including the physical and chemical structure of coal, the forms of water present in low rank coals, migration of water during drying, coal structure changes during moisture loss, moisture re-adsorption, and effects of water removal on subsequent applications including combustion, gasification and liquefaction has been reported. [48]. Allardice [49] reported the relationship between water content and heat of desorption for brown coal sample as About 20% of the water is bound more strongly to the coal than the water molecules are to each other.  For 80% of the water, the heat of desorption is simply the latent heat of evaporation. For the remaining 20%, it is assumed that hydrogen bonding has occurred. This tends to happen within the fine pore structure.  In the course of drying, the varying strength of the water bond will produce different evaporation behaviour.  Lignite and some LRC differ significantly from bituminous coal where most of the moisture is present on the surface and loosely bound. In lignite and LRC, a high proportion of the water is held in the pores. The natural drying would be more suitable and economic choice for taking care of 80% of total moisture which is available in surface with large pore structure, so experiment was aiming to the moisture which are physically bound in nature or freely adhered to coal as loosely bound coal-water matrix. A testing and sampling regime was established, with a degree of confidence, the potential moisture loss via natural drainage and drying of a large sample mined and crushed on the site. The methodology is based on evaluating results from four levels of testing to facilitate the scale up to full production. II. Experimental Laboratory scale analysis of coal properties like –Total Moisture (TM), Proximate, Calorific Value, Ultimate, Petrographic, Ash Analysis, Ash Fusion Temperatures, Moisture Holding Capacity (EQM equilibrium moisture), Bulk Density, Relative Density, drop shatter test, and Spontaneous Combustion propensity, XRD, DTA/TG, FTIR, and SEM etc. has been carried out. The small scale tests were conducted to investigate the potential to drain moisture. The tests show a consistent drainage profile for all tests. This has allowed a defined drainage relationship as discussed below. As these tests were conducted in the ideal conditions of no stockpile segregation, maximum gravity effect and shelter from rain re-wetting, they give indication of the maximum potential for moisture reduction via natural drainage. The results from the small scale drying tests indicate a strong potential to significantly reduce the “as mined” moisture content of coal. All tests showed consistent losses over time with an average weight loss of 27% for the 24 days test period. These test show the maximum possible natural drying potential with no impediments to drainage and no additional moisture load from rainfall. But the test results of large stockpile under ambient conditions had shown that rainfall have got very high detrimental effect on coal dehydration process [50]. Small stockpile natural drying tests –using different granulometry with sheltered and unsheltered conditions in addition of aging process to establish the natural drying optimization with staging process of storage and crushing. In continuation to understand the behaviour of coal moisture loss in ambient conditions, the present work covers in small stockpiles of coal (test plan shown in Table 1). Table 1: Details of test plan and granulometry of sample Test Days ROM Sample under shelter Pile No ROM Sample without shelter pile No

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Naveen Chandralal et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May 2014, pp. 98-108

-100 mm Crushed sample under shelter -100mm Crushed sample without shelter -50 mm Crushed sample under shelter -50mm Crushed sample without shelter

  

with different granulometry (ROM- Run of Mines, top size 100mm and top size 50mm), Under shelter and without shelter We have added staging crushing o ROM will be stockpiled and monitored for TM and EQM o when it was found that TM has come to EQM level of 35% in ROM, the next sample was confirmed for the same level of TM, then ROM will be crushed to <100mm and continue the experiment o Further <100mm will be crushed after a time gap to <50mm when TM of -100mm reaches~30% and will be monitored further.  All samples were tested (ASTM Method) for TM, Proximate analysis, Ultimate Analysis and equilibrium moisture and screened for granulometry change (drop shatter test), HGI. III. Results and Discussion 3.1 X ray Diffraction pattern shows a strong base line shifting due to amorphous nature of TOP Coal (Figure 1). This is possibly due to the early stages of coalification process [51], where the wood tissues have not broken due to aging process or not undergone compaction due to overburden to make ordered packing of macromolecules [52] as seen in bituminous coal. Amorphous nature of coal will carry more surface moisture in pore spaces than any occluded moisture. Due to less compaction, the original pore structure of vegetation remains as interconnected. 3.2 Combustion behavior of the sample Figure 1 : X-Ray Diffraction pattern of TOP Coal was studied with the help of Simultaneous Thermal Analyzer, model STA 409 C (NETZSCH, Germany) having DSC/TG sample carrier device. Accurately weighed samples (in the range of 20 ± 1 mg) were loaded in an Al2O3 crucible of a simultaneous Thermal Analyzer. The experimental run was performed with air flow rate 50 ml/ min and rate of heating was maintained at 10 ◦C/ min. Four key characteristics of the DTG curve are used when analyzing a burning profile (as shown in Figure 2). (a) The ignition temperature (IT) is the Figure 2 Thermogravimetric Analysis results of TOP Coal temperature at which pyrolysis is initiated has been found as 250.7 ◦C. The ignition temperature increases with decreasing volatile matter of coal [53]. The ignition temperature is assumed to be the average temperature in the last time interval where both on pyrolysis and combustion curves coincide [54]. (b) The fixed carbon initiation temperature (ITFC), which may be defined as the temperature at the initial phase of combustion where the rate of weight loss reaches 1.0%/ min, cannot be isolated precisely but estimated as 338.1 ◦C. The IT region and the ITFC region overlap because releasing volatiles from the coal sample creates conditions encouraging combustion. (c) The peak maximum temperature (PT) is simply the temperature at the peak of the DTG curve noting the

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Naveen Chandralal et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May 2014, pp. 98-108

temperature at which maximum weight loss occurs found to be 349.9 to 393.6 ◦C. (d) The burnout temperature (BT) is the temperature at which the weight loss has ended and a baseline weight has once again been reached was 424.6 ◦C (BT = Temperature ◦C at which loss rate reduces to 1.0% /min at the terminal phase of combustion process). The TGA curve shows a loss of 11% by weight at a temperature of around 200 ◦C is mostly the surface moisture. DTA results of combustion which allows evaluating about the type of ignition. Flatter curves indicate homogeneous combustion, while sharp peaks are indicative of heterogeneous combustion [55]. The TOP coal shows a very flatter peak in the exothermic event, indicating a predominant homogeneous combustion. 3.3 Fourier transform infrared (FTIR) spectroscopy is a widely used analytical technique for determining the different functional groups of a coal structure. Due to the structural complexity of coal, some studies on FTIR [56] have done assignments of particular bands to various functional groups. Analysis of FTIR spectra in absorbance mode for low rank coal sample reveal some dominant absorbance peaks at certain wave numbers (Figure 3). All assignments were made according to Mayo, Muller & Hannah [57] and the FTIR peaks have been characterized as below.    



    

Figure 3: FTIR Spectra of TOP Coal

Absorbance bands within 3600 cm-1 to 3800 cm-1 are mainly due to OH groups present in clay minerals. 3400 cm-1 and 3600 cm-1 are assigned as hydrogen bonded -OH stretching and N – H stretching. A weak sharp absorption band at contain a small amount of carboxyl groups at 3733 cm-1. The zone of 2800 cm-1 to 3100 cm-1 is significant for aliphatic stretching. The FTIR spectra of most coals show several resolved bands between 2800 and 3000 cm-1 and a well-resolved band between 3000 and 3100 cm-1. The former are assigned to the aliphatic C-H stretching modes of methyl or methylene groups, and the latter is assigned to the aromatic C-H stretching mode [58]. From 2750 to 3000 cm-1 the bands are attributed to CH3 and CH2 (aliphatic), and after 3000 cm-1 the bands are attributed to CH aromatics. The bands near 2921cm-1 and 2850 cm-1 are assigned to asymmetric and symmetric stretching of sp3–CH2 groups implying presence of long aliphatic chains associated with the coal structures. The bands at 2853, 2870 and 2890 cm-1 have been assigned to the aliphatic C-H symmetric stretching of methylene, methyl and tertiary CH groups respectively. The band at 2830 cm-1 has been assigned to the C-H stretching frequency of the methoxy group [59]. The sharp peaks of FTIR spectra in the region 2855-2921 cm-1 wave length region suggest the coal is immature. With the progress of coalification process the peaks will shift to a lower region. Absorbance peak near 1700 cm-1 is indicative of carboxylic acids and Ketone groups while –COO-1 groups are described by the peak near 1610 cm-1. This zone of oxygen-containing functional groups is characterized by a very intense peak at 1618–1622 cm−1, which is attributed either to C=O or C=C aromatic ring stretching. The C=C bands, which should be placed between C−O and C=O bands, were not definitely distinguished, since low rank coals have high oxygen content and these bands almost masked the C=C structures [60]. Band at or near 1650cm-1 denotes conjugated Ketonic structures (Quinones). The C=C groups and the C=O groups are recognizable at 1550 cm-1. Presence of 1440 cm-1 bands indicate, in general, the CH2 groups in bridges, but the same wave number may also indicate the presence of aromatic C=C and bending mode of H-bonded O-H groups. The bands at 1541 cm-1 and 1442 cm-1 is normally present in immature coals with more lignin content [61]. The band near 1260 cm-1, which is very common in low rank coal, is assigned to arylether structure. At the 1200–1000 cm−1 region, a sharp, intensive peak is common for low rank coals. The 1032–1047 cm−1 may also result from silicate minerals (Si−O bonds) from Kaolinite and Illite [62]. Si–O–Si stretching vibration is represented by the absorbance bands within 1100 cm-1 to 400 cm-1.Presence of quartz and other clay minerals may be responsible for these bands. C=O stretching, O-H band in phenoxy structure, aliphatic ethers and alcohols are responsible for the absorbance bands within 1000 cm-1 to 1300 cm-1 zone. Amorphous carbon bands: moderate absorption at 1540 and 1465 cm-1 bands could represent aromatic ring systems typically found in amorphous carbon materials. The band at 1355 cm-1 to benzene or condensed benzene rings in amorphous carbon. The band at 1180 cm-1 to sp3- rich structures in amorphous carbon

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With enhancement in coalification process, the content of oxygen as COOH decreases and that as OH increases [63]. The comparison of peat and lignite analysis reveals a degradation of methoxy groups, carbohydrates and carboxylic groups during early coalification, whereas the aliphatic carbons were less affected [64]. The above spectral analyses point to the presence of dominant functional groups associated within coal structure of lower rank coals. 3.4 From the proximate and ultimate analysis results (Table 2), TOP coal was found to be low rank high with moisture and very low sulphur & low ash content, which is typical of Muarawahau formation in East Kalimantan, Indonesia. The sample found to have an equilibrium moisture content of 34.2%adb. Hardgrove index (HGI) of the sample was 61 was considerably harder although very young coal. The relative ignition temperature was found to be 132 ◦C is also quite high for propensity to self-combust. Table 2: Chemical analysis of coal samples (air dried basis) Parameters

Units

Average

Total Moisture

%ar

47.42

Moisture in Analysis Sample

%adb

15.12

Ash Content

%adb

1.96

Volatile Matter

%adb

42.81

Fixed Carbon

%adb

40.12

Total Sulfur

%adb

0.13

Calorific Value (adb)

Kcal/kg

5583

Carbon

%adb

42.72

Hydrogen

%adb

2.64

Nitrogen

%adb

0.80

Oxygen

%adb

13.21

HGI- Hard Groove Index

Index

Moisture Holding Capacity %

% adb

34.2

Relative Ignition Temperature

◦

132

3.5 In the younger coals like TOP Coal, the coalification process has not advanced so far, and a substantial amount of water is present in the pore structure (SEM Photo, Figure 4) Although the overall porosity of TOP Coal is similar to bituminous coal, about 0.1ml/g, there are larger interconnected pores of over 5 mm diameter in TOP Coal. A larger proportion of the porosity is accounted for in the larger diameter interconnected pores, and in turn these Figure 4: SEM Photograph of TOP Coal with large hold a large fraction of the total moisture interconnected pores content. The macro-pore system in coals is strongly influenced by overburden pressure. By contrast, the micro-pore structure depends on the chemical make-up of the coal and is largely dependent of the confining stress during the formation stage The drying rate increased with air velocity, Thus, the larger drying rates associated with the larger particles, are due to higher air velocities and not to any inherently higher rates of drying due to particle size. This suggests that, in this particle size range, drying rate is controlled by the internal pore structure of the coal. 3.6 The ash chemistry (table 3) found to have enrichment of Fe2O3, apart from SiO2 and Al2O3 as major constituent. From the XRD, we have detected Goethite in few samples which are carrier of iron in ash. Although the iron content seems high in ash but considering the low ash % in coal, contribution of iron in total coal will be very low. Table 3: Ash chemistry and fusion temperature Parameters

Units

Result

Ash Analysis Silicon as SiO2

% adb

46.00

Aluminum as Al2O3

% adb

20.30

Iron as Fe2O3

% adb

17.45

Calcium as CaO

% adb

5.60

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Magnesium as MgO

% adb

4.37

Sodium as Na2O

% adb

0.12

Potassium as K2O

% adb

0.68

Titanium as TiO2

% adb

0.71

Manganese as Mn3O4

% adb

0.15

Sulfur as SO3

% adb

3.52

Phosphorus as P2O5

% adb

0.37

Barium as BaO

% adb

0.19

Strontium as SrO

% adb

0.08

Zinc as ZnO

% adb

0.05

Ash Fusion - Oxidizing Deformation Temperature

◦

1240

Spherical Temperature

◦

1260

Hemispherical Temperature

◦

1280

Flow Temperature

◦

1340

Deformation Temperature

◦

1130

Spherical Temperature

◦

1140

Hemispherical Temperature

◦

1150

Flow Temperature

◦

1330

C C C C

Ash Fusion - Reducing C C C C

The characteristic melting temperatures are related to the transformations and reactions of ash and these are interpreted in terms of their importance to fouling and slagging in furnaces. Ash fusion temperatures typically are measured at four defined points under both reducing and oxidizing conditions. Generally, a temperature under reducing conditions should be equal to or lower than the corresponding temperature under oxidizing conditions. The difference in these temperatures generally increases with increasing iron content in the ash. The initial deformation temperature of 1240 ◦C with flow temperature of 1340 ◦C in oxidizing and 1330 ◦C in reducing atmosphere is considerably accepted as a suitable blend for boiler operations. IV. Statistical data analysis The data generated in the experiment having 50 variables and 45 data columns for this study. To analyze such huge data with so many variables, first we tried to use a simple linear correlation method. With this we could able to reestablish the same understanding what we had earlier between the variables. It was very difficult to understand the inter-relationship among the variables taking all at a time. This has demanded us to use multivariate approach. For this we have selected Cluster analysis and Factor analysis package and the software used was Minitab version 17. A. Factor analysis Factor analysis generates a table in which the rows are the observed raw indicator variables and the columns are the factors or latent variables that explain as much of the variance in these variables as possible. The cells in this table are factor loadings, and the meaning of the factors must be induced from seeing which variables are most heavily loaded on which factors. These techniques are commonly used when developing a questionnaire to see the relationship between the items in the questionnaire and underlying dimensions. It is also used in general to reduce a larger set of variables to a smaller set of variables that explain the important dimensions of variability. There are several different types of factor analysis, with the most common being principal components analysis (PCA), R-mode factor analysis. R-mode is by far the most common. In R-mode, rows are cases, columns are variables, and cell entries are scores of the cases on the variables. In R-mode, the factors are clusters of variables on a set of other entities, at a given point of time. Rotation serves to make the output more understandable, and is usually necessary to facilitate the interpretation of factors. Factors are rotated according to various possible criteria, with the object of making the factors each relatively independent of the independent variables, consistent with the other objectives. The sum of Eigen values is not affected by rotation, but rotation will alter the Eigen values of particular factors and will change the factor loadings. Varimax rotation is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, which has the effect of differentiating the original variables by extracted factor. That is, it minimizes the number of variables that have high loadings on any one given factor. Each factor will tend to have either large or small loadings of particular variables on it. A Varimax solution yields results that make it as easy as possible to identify each variable with a single factor. The results are summarized in Table 4; factoring loading values below ±0.40 are ignored considering their low influence.

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A.1 Factor 1: Constitutes all the original chemical components of coal. Total moisture: all of the moisture in and on a sample of coal; commonly determined quantitatively by air drying a sample and then assaying residual moisture in the air-dried sample; thus, total moisture is the sum of the air-dry loss and the residual moisture adjusted to an as-received basis (ASTM D-2961; ASTM D-3302). Both air dry loss (ADL) and Total Moisture (TM) are negatively correlated to all other components of coal which is a natural phenomenon process of coalification. The association of ADL and TM under one factor clearly confirms the major influence and contribution of ADL towards the total moisture of coal. Residual moisture: moisture remaining in the sample after air drying; assayed by determining the mass lost from drying the sample at 104 to 110 ◦C (219 to 230◦F) at speciﬁed conditions of residence time, atmosphere, particle size, sample mass, and equipment conﬁguration (ASTM D-3173; ASTM D-3302). The residual moisture in not covered under this factor is quite surprising. This means residual moisture is of different category possibly of different nature than the other moisture available in coal. The residual moisture is that moisture that is still locked up in the coal after air-drying. The higher ranked coals that ASTM standards were based on possess this well-defined split between the air-dried or surface moisture and the residual or near EQ moisture. This is not the case for low rank coals. The sponge-like or wood like nature of low rank coal make the split between surface moisture and inherent moisture a rather fuzzy line [65]. Total sulphur to be part of this factor indicates the source of the sulphur in this type of coal is organic, which is from plant debris not mineral matter constituents from ash of coal. From the X-ray diffractograms also we have not observed any sulphur bearing minerals presence confirms this observation. Table 4 showing results of Factor Analysis Variables

Days ADL % ar Residual Moisture %adb Total Moisture, %ar EQM, %ar Moisture in Sample, %adb Ash, %adb VM, %adb FC, %adb TS, %adb CV, Kcal/kg adb CV, Kcal/kg daf HGI Carbon, %daf Hydrogen, %daf Nitrogen, %daf TS, %daf Diff + 50 mm Diff. -50 + 31.5 Diff. - 31.5 + 22.4 Diff. -22.4 +16 Diff. -16 +11.2 Diff.-11.2 +8 Diff.-8+4 Diff.-4+2 Diff.-2+1 Diff. -1+0.5 Diff.-0.5 % Variance Total Variance

0.68 -0.45

-0.73 -0.92

-0.52

-0.60 -0.53 -0.92 -0.61

0.61 0.48 0.88 0.96 0.60

0.67 0.80 0.76 -0.75

0.77 0.46 0.87 0.87 -0.95 0.65 0.73 0.56 0.79 0.61 0.56

20.1

16.5

15.9 72.4

-0.65 -0.90 -0.88 -0.77 10.9

9.0

A.2 Factor 2: This is very important observation. The number of days (ambient condition drying time) is correlated negatively with the equilibrium moisture (EQM). This means, EQM will drop with increased exposure time. Also EQM is positively correlated with +50mm fraction and all other fractions have negative correlation with the EQM. This has given a good indication that major contributor of EQM is +50mm fraction. In other words suitable granulometry change with time can be optimized to have a better control of EQM. Decrepitation to smaller grain sizes of the coal with time (Figure 5), that the smaller the grain sizes the larger the surface area and the more contact with oxygen, and heat was continuously accumulated in the medium and could not be taken out [66], helps in lowering of equilibrium moisture further. The rewetting of partly dry coal regarded as a more serious trigger for the production of heat with time. Laboratory measurements have shown that such heats of wetting can range as high as 85 to 105 J/ g, sufficient to raise the oxidation temperature of the coal by 25 to 30°C and increase oxidation rates six- to eight-fold [67]. Wetting of coal takes place when stockpiled coal is exposed to rain after a period of dry, sunny weather, or when wet coal is placed on a dry pile. Porosity and Equilibrium moisture of coal are closely interrelated [68] and low rank coals with larger pores may

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experience evaporation from pores at high humidity [69]. It is shown that larger particle sizes give rise to a higher drying rate. The loss of EQM will help the low rank coal to reduce the propensity of self-ignition. Generally when the moisture content of low rank coal decreases by 1%, the coal calorific value will increase by 88 kcal/kg, [70] improving the techno-economic suitability.

Figure 5: Ambient condition decrepitation of coal granule with loss on surface moisture A-47% TM, B-38% TM and C-34% TM and shrinkage in volume A.3 Factor 3: Residual moisture is separated out in this factor. May be the pore distribution and macerals & inorganic constituent’s chemistry plays a role in the residual moisture content of coal. Hydrogen found to have negative correlation with residual moisture. The H concentrations increase with increasing of residual moisture, hydrated minerals, and methane in coals [71]. However, we have observed an opposite relation. The increased contents of H are normally more characteristic of lower-rank coals. Volatile matter does not contain the moisture of coal but it contains water that is formed from the hydrogen and oxygen of coal during the decomposition. Free water may exist in the coal as adsorbed on the surface, condensed inside fine capillary network and as bound to the coal molecule by chemisorption and hydrogen bonding. Water molecules bond to the coal on oxygen functional groups (OH and COOH) using oxygen in the coal and hydrogen bonds in the water molecules [72] was also considered. The possibility is that part of the hydrogen was a byproduct of the oxidation of the coal and has negative correlation with residual moisture. A.4 Factor 4: All the weathered components of the granulometry below 4 mm have been grouped together because of their common origin, oxidation followed by decrepitation of coal. They have no influence on the TM as the nature of moisture available in the decrepitated product may be of different nature due to weathering and partial oxidation. Low-rank coals can lose 25 to 30 percent of their original mass during air drying [67]. During ambient drying, desorption of moisture from external surface of coal will always be faster than desorption from its inner layer. Such de-hydration is accompanied by extensive, partially irreversible volume shrinkage, and the internal stresses set up quickly cause the coal to lose its cohesion and to disintegrate into progressively smaller pieces. This disintegration is known as decrepitation or slaking, which results in more surface area of coal. Depending on the humidity of atmosphere the coal will continue to adsorb and desorb moisture and these disintegrated fractions play a major role in this process. These products of decrepitation have changed pore structure and moisture contents than the original mass and have distinctly different behaviour [73], which are indicted from their separate grouping into one factor. A.5 Factor 5: Ash in coal found to be influencing the TM of coal. As the ADL component is also correlated with ash, this indicates the ash is carrying moisture is external contamination not inherent with coal may be natural weathering as the sample was in the out crop area of the seam. Also we have seen HGI of coal is influenced by ash as well as TM of coal. As the TM increases the HGI values also goes up indicating coal is softer. The Hardgrove Grindability index value is influenced by petrographic composition of coal. The analysis of Grindability of British coal confirmed wide relation of HGI values between the quality group of coal, volatile combustible matter contents, carbon and hydrogen [74]. The increase of contents of volatile combustible matter improves the grindability up to the contents of volatile combustible matter of approximately 30%, beyond which the grindability deteriorates. Similarly the HGI value increases with the growth of carbon contents. The grindability then drops rapidly with the contents of carbon exceeding approximately 92% that is inherent moisture decreases with increasing coal rank. It was found that as the inherent moisture in coal increased the HGI values also increased [75]. The grindability is significantly influenced also by the contents of ash in coal [76, 77] and found to correlate well with residual moisture [78]. B. Cluster analysis The underlying and basic difficulty, of course, is that factor analysis has no way of distinguishing between “true correlations” and “error correlation” reported the empirically greater usefulness of cluster analysis rather than factor analysis. Cluster analysis identifies and classifies object individuals or variables on the basis of the similarity of the characteristics they possess. It seeks to minimize within-group variance and maximize betweengroup variance. The result of cluster analysis is a number of heterogeneous groups with homogeneous contents. There are substantial differences between the groups, but the individuals within a single group are similar. Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type. The term cluster analysis actually

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encompasses a number of different classification algorithms, which organize observed data into meaningful structure. In general, whenever one needs to classify a "mountain" of information into manageable and meaningful piles, cluster analysis is of great utility. The result of cluster analysis is shown in figure 6. There are four major clusters of variables could be seen Cluster 1 : following Carbon, Total Moisture is major one Cluster 2A : Number of days and residual moisture Cluster 2B : Calorific Value, daf Cluster 2C : Ash Cluster 3A1 : VM and TS Cluster 3A2 : FC and TS daf Cluster 3B : CV adb Cluster 3C : HGI Cluster 3D :H Cluster 3E : Moisture in sample by TGA Cluster 4A : Air dried loss (ADL) Cluster 4B : Equilibrium moisture EQM Cluster 4C : Granulometry Cluster analysis clearly separated total moisture as the major variable amongst the 50 variables considered in the study. The three dimensions of control of total moisture can be understood by the three cluster dendograms shown in figure 6. From the cluster 2 it can be seen that number of days and residual moisture has shared in one cluster. This means exposure time affects the residual moisture content and ultimately the TM. To some extent it was seen that ash is also playing a role in residual moisture content of the coal. The moisture in sample is present in Cluster 3 comprises of all the test results of TGA, so they are grouped together. Cluster 4 components are very important to understand. Both ADL and EQM in one side and all the granulometry fractions on the other side of the cluster. This indicates very clearly that both ADL and EQM is a function of the granulometry a product of oxidation. This is very important observation to apply in the field at different process levels in order to achieve the required ADL and EQM which in turn is reflecting the TM in coal and ultimately the calorific value of coal.

Figure 6: Cluster Analysis Dendograms V. Conclusion It is evident from the available literatures that processing or upgrading technologies for low rank high moisture coals at the power plant end may be a suitable solution but at the mine may not be technically feasible due to many techno-commercial limitations. Best way is to utilize the natural climatic conditions in a controlled manner. Because the low rank high moisture coals carry majority of their moisture in surface and inside the pore structure, good dry weather helps in lowering of total moisture. In summary, TOP coal with average 47% TM with 30-35% ADL which is surface moisture, a significant part of this moisture gets evaporated in ambient atmospheric conditions under shelter, with proper staging and suitable sizing methods. The TOP Coal reaches to equilibrium stage of 27% TM in small scale test. However, optimization of the TM content could be achieved to 35-40% in large scale tests. Stage processing under controlled ambient condition of low rank coal stockpile results in boosted lowering in moisture and enhancement in calorific value. Both Factor analysis and Cluster

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analysis has given a very good insight on the inter-correlation of various chemical and physical parameters of low rank coal. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

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

Gene effects for pod yield and related traits in French bean ( Phaseolus vulgaris L.) population developed through induced mutation. Meenakshi Sood1 and N. K. Pathania2 Assistant Professor of Vegetable Science, College of Horticulture, Mysore, University of Horticultural Sciences, Bagalkot, Karnataka, India. 2 Professor of Vegetable Science, College of Agriculture, CSK Himachal Pradesh Krishi Vishvavidyalaya, Palampur (H. P.) India. 1

Abstract: Present study was carried out in French bean where mutation was induced through physical mutagen (gamma irradiation), chemical mutagen (ethyl methane sulphonate) and combination of these two mutagens. Observations were recorded for days to first flowering, days to first picking, pods/ plant, green pod yield/ plant, pod length, plant height, seeds / pod and seed yield / plant in parental variety, M2 and M3 generations. Genetic component analysis indicated the induction of genetic variation due to both additive and dominance gene effects. The role of over dominance was observed for most of the traits suggesting that the selection should be deferred to the later generations. Key words: Phaseolus vulgaris L., induced mutation, gamma radiation, EMS I. Introduction French bean (Phaseolus vulgaris L.) is one of the most important legume vegetables. Amongst the 150 species of Phaseolus, it is the most widely cultivated form. It is cultivated throughout the world for tender pods and dry beans, and also grown on a large scale for processing. This crop has many medicinal properties and enriches the soil by fixing atmospheric nitrogen. Phaseolus vulgaris L. is believed to have originated in the Central America (Southern Mexico, Guatemala and Honduras), where it was domesticated about 7,000 years ago. It was introduced into the Europe in the 16th century, and since then it has become a common legume crop almost throughout the world. The bush type French bean is not found wild. The determinate (bush) habit was most likely derived from mutants. Similarly, natural mutations in single genes of tomato are completely or mostly responsible for its determinate growth habit (Pnueli et al., 1998), resistance to powdery mildew (Bai et al., 2008), and yield heterosis (Krieger et al., 2010). Exploiting natural or induced genetic diversity is a proven strategy in the improvement of all major food crops, and the use of mutagenesis to create novel variation is particularly valuable in those crops with restricted genetic variability (Parry et al., 2009). The use of mutagenesis in conventional breeding involves forward genetic screens and the selection of individual mutants with improved traits and their incorporation into breeding programmes. Mutation breeding has been an effective approach to producing horticultural varieties with improved traits (Ahloowalia et al., 2004).Use of mutations is a valuable supplementary approach to plant breeding. It creates variability, qualitative and quantitative, required for selection. About 3000 varieties in different crops have been developed or improved using mutagenesis after the initiation of mutation breeding (Joint FAO/ IAEA, 2011). Many mutants have been released directly as new varieties while others were used as parents to derive new varieties. Tulmann Neto (1990) reported that mutagen based research work in beans is being carried out in 17 countries with the objective of obtaining mutants possessing resistance to diseases, higher protein content, changed growth habits and higher productivity. During the 1970â&#x20AC;&#x2122;s and 1980â&#x20AC;&#x2122;s, most of the white seeded bean cultivation areas in Michigan (North America) were covered by bush mutants and at present 40% of the area is still covered under mutant derived bean cultivars (Nichterlein, 1999). As in hybridization, in mutation breeding too, it is imperative to know the nature and magnitude of components of induced genetic variation to choose appropriate breeding methodology for maximum exploitation of induced variability. II. Materials and methods A. Mutagenied populations Mutations occur spontaneously in nature but the frequency of such mutations is too low to rely on alone for accelerated plant breeding. Therefore, mutations can be induced by physical and chemical mutagens. Mutations may be gross, resulting in large-scale deletions of DNA, or only involve point mutations. Mutation can be induced by using physical or chemical mutagens. Mutations at single nucleotide pairs are generally of great interest to breeders because large-scale changes to chromosome structures usually have severely negative results. However, the use of mutagens that alter chromosome structure to increase the number of recombination

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events and break undesirable linkages is also extremely valuable. Experimental material for the present study was developed by treating the samples of 350 healthy French bean seeds of uniform size of variety ‘Contender’ with different doses of physical and chemical mutagens, and their combinations. The doses administered were, 5kR, 10kR, 20kR, 30kR and 40kR gamma -ray; 0.1%, 0.2% and 0.3% ethyl-methane sulphonate (EMS) solutions, and 5kR+0.1%EMS, 5kR+0.2%EMS, 10kR+0.1%EMS and 10kR+0.2% EMS combination treatments. Gamma irradiation was done in 60 Co gamma chamber @ 1800 R/ min at National Physical Laboratory, Indian Agricultural Research Institute, New Delhi. For treatment with EMS, three hundred and fifty seeds for each treatment were first soaked in water at room temperature for 8 hours and then treated with freshly prepared buffered EMS solution of different doses for four hours followed by one hour washing of the seeds under running tap water. These M 1 seeds were then sown in the field during the first season. Normal looking M 1 plants were harvested individually at maturity to obtain M 2 seed. Single M 2 seed from each harvested M 1 plant was bulked treatment wise for planting along with M 3 generation. Remaining M 2 seed was sown during second season in plant to progeny rows along with the parent in Augmented Block Design to generate M 3 seed. In the third season, final trial was conducted where M 0 (untreated seeds), M 2 and M 3 were raised simultaneously and observations were recorded. B. Genetic component analysis for induced variation By considering the variation that arises within the progenies of individual M1 plants and treating different M 1 plants more or less as F 1 replicates between hypothetical plants, one can analyze the contribution to the variation of those loci which have become heterozygous following mutagenic treatment of a pure breeding line. The properties of the selfed progenies of the individual M 1 plants can then be specified by using usual selfing series formulae of Mather and Jinks (1971) since M 2 = F 2, M 3 = F 3 and so on. Genetical expectations of the means (Mn) for the n th generation produced by selfing following mutagenic treatment of a pure breeding line (n=0) as per Mather and Jinks (1971) and further suggested by Yonezawa (1979) are as follows:

in u, v system where frequencies of three genotypes namely, AA, Aa, aa in respect of a single gene difference are u 2 , 2 uv and v 2 and ‘m’ is the parental mean . C. Estimation of genetic components The genetic components were estimated by using first degree statistics as per the following procedure given by Yonezawa (1979) and their respective standard errors were worked out by imperial way:

Where, M 0 , M 2 and M 3 are the mean values of parents, M 2 and M 3 generations, respectively. The standard errors were worked out as:

For testing their significance, ‘C’ test was used. Approximate average degree of dominance or potence ratio was estimated as the ratio of [h]m/ [d]m. III. Results and discussion The objective of this study was to estimate the additive and dominance effects, and their ratio in induced mutagenized populations. This information is important for making selection of genotypes with improved yield and related components as selection can strengthen and stabilize a mutated population (Sharma and Sharma, 2004).

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Estimates of induced additive [d]m, dominance [h]m effects and [h]m/[d]m ratio under different gamma-irradiations Estimates of induced effects are given in Table 1. It was inferred that in 5kR dose, additive effects [d] m were significant for pods per plant, plant height and seeds per pod. The dominance effects [h] m were also significant for all these traits and days to first flowering. Average degree of dominance was found in the range of overdominance for plant height and seeds per pod, while it was below 1 for pods per plant indicating the presence of partial dominance in the inheritance of this trait. In 10kR dose, significant additive effects were observed for pods per plant, pod length, plant height, seeds per pod and seed yield per plant, while significant dominance effects were observed for days to first flowering, days to first picking, plant height and seeds per pod. Over dominance was observed for plant height and seeds per pod. Additive effects were found significant for all the traits studied except for days to first flowering and seeds per pod in 20kR, whereas significant dominance effects were observed for pods per plant, green pod yield per plant, pod length and plant height. Over dominance was noticed for all these traits where dominance effects were significant. Under 30kR dose, significant additive and dominance effects along with presence of over dominance were observed for days to first picking, plant height and seed yield per plant. However, significant additive effects were observed for pods per plant. In 40kR dose, significant additive effects were observed for days to first flowering, days to first picking, pods per plant, plant height and seed yield per plant. The dominance effects were significant for pod length, plant height and seed yield per plant. Over dominance was observed for plant height. But in case of seed yield per plant, the approximated average degree of dominance was in the range of partial dominance. B. Estimates of induced additive [d]m and dominance [h]m effects and their ratio under treatments involving combinations of physical and chemical mutagens The 5kR+0.1% EMS combination treatment exhibited significant additive effects for pod length, plant height, seeds per pod and seed yield per plant (Table 44) with significant dominance effects for days to first flowering, plant height and seeds per pod. The ratio of dominance to additive effects revealed the presence of over dominance for plant height and seeds per pod. Significant additive effects were observed for pod length, plant height and seeds per pod whereas significant dominance effects were observed for pods per plant, plant height and seeds per pod in 5kR + 0.2% treatment. Over dominance was noticed for plant height and seeds per pod. Under 10kR+0.1% EMS treatment, neither additive non dominance effects were significant for any of the traits studied. Additive effects were significant for pods per plant, green pod yield per plant, plant height and seed yield per plant; and dominance effects were significant for days to first flowering, days to first picking, pods per plant and plant height under 10kR+0.2% EMS treatment. In this case, over dominance was observed for pods per plant and plant height. C. Estimates of induced additive [d]m and dominance [h]m effects and their ratios under different EMS treatments As given in Table1, additive effects were significant under 0.1% EMS treatment for pod length and seeds per pod, while dominance effects were significant for seeds per pod only. Over dominance was noticed for seeds per pod. In 0.2% EMS treatment, significant additive effects were observed for days to first picking, pod length, plant height, seeds per pod and seed yield per plant. Dominance effects were significant for plant height and seeds per pod; and the average degree of dominance was observed in the range of over dominance for these two traits. Dominance effects have been positive and significant for number of pods per plant in 5kR, 20kR and 10kR+0.2% EMS, for green pod yield per plant and pod length in 20kR, for seeds per pod in 5kR, 10kR, 5kR+0.1% EMS, 5kR+0.2% EMS, 0.1% EMS and 0.2% EMS, and for seed yield per plant in 30kR and 40kR indicating more number of increasing alleles to be dominant than decreasing alleles. Pre-ponderance of nonadditive gene effects has also been observed for yield components in mutagenized populations of other crops like sorghum( Larik et al., 2009). However, more number of decreasing alleles have been found dominant for pods per plant in 5kR+0.2% EMS, pod length in 40kR, and for plant height in all the treatments except 10kR+0.1% EMS and 0.1% EMS treatments where the dominance was non-significant. Approximate average degree of dominance or potence ratio has been calculated where both additive and dominance effects have been found significant. This ratio reveals the role of over dominance for pods per plant, green pod yield per plant, pod length, plant height, seeds per pod and seed yield per plant in different treatments except for pods per plant under 5kR and seed yield per plant under 40kR, where partial dominance has been observed. However, for days to first flowering and days to first picking the potence ratio could not be calculated because no single treatment gave significant estimates of both additive as well as dominance effects. Presence of over dominance in most of the cases can be due to the fact that the chances of getting an allele mutated at one locus are much more than that of both the alleles getting mutated in a pure breeding line. The presence of over dominance suggests that the selection should be deferred to the later generations so that the additive effects become more pronounced and fixed.

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Table 1: Estimates of induced additive [d] m and dominance [h]m effects under different mutagenic treatments Trait

5kR

Days to first Days to first Pods/ flowering picking plant [d]m [h]m

10 kR

[h]m/[d]m [d]m [h]m

20 kR

[h]m/[d]m [d]m [h]m

30 kR

[h]m/[d]m [d]m [h]m

40 kR

[h]m/[d]m [d]m [h]m

[h]m/[d]m 5 kR +0.1% [d]m EMS [h]m [h]m/[d]m 5 kR +0.2% [d]m EMS [h]m [h]m/[d]m 10 kR +0.1% [d]m EMS [h]m [h]m/[d]m 10 kR +0.2% [d]m EMS [h]m

0.1% EMS

[h]m/[d]m [d]m [h]m

0.2% EMS

[h]m/[d]m [d]m [h]m

[h]m/[d]m * Significant at P≤0.05

0.39 ±0.34 2.06* ±0.73 0.76 ±0.41 3.36* ±1.04 -0.2 ±0.40 1.3 ±0.96 -0.59 ±0.69 1.70 ±1.71 -2.96* ±0.97 -4.36 ±2.36 -0.04 ±0.38 2.08* ±0.91 -0.31 ±0.39 1.56 ±0.82 -0.53 ±0.83 0.98 ±1.76 0.43 ±0.61 3.88* ±1.53 -0.36 ±0.42 0.16 ±0.78 -0.25 ±0.47 0.56 ±1.03 -

0.33 ±0.38 1.06 ±1.10 0.41 ±0.48 4.12* ±1.58 -1.08* ±0.50 -0.06 ±1.43 -3.26* ±0.59 -3.36* ±1.55 1.03 -3.53* ±0.44 -2.14 ±1.35 -0.81 ±0.92 1.00 ±2.06 -0.17 ±0.79 2.96 ±1.87 -0.75 ±0.44 0.38 ±1.40 -0.31 ±0.47 3.22* ±1.43 -0.57 ±1.07 -0.88 ±2.16 -1.41* ±0.50 -1.74 ±1.07 -

2.33* ±0.32 1.90* ±0.69 0.82 2.12* ±0.99 2.20 ±3.64 3.55* ±0.88 6.16* ±2.96 1.74 2.36* ±0.75 4.58 ±2.85 3.48* ±1.31 3.66 ±3.92 1.62 ±0.89 -1.28 ±1.98 -0.36 ±0.79 -5.76* ±2.50 0.57 ±2.20 -1.74 ±4.98 5.46* ±1.01 6.64* ±2.87 1.22 0.46 ±0.94 -1.32 ±2.80 0.49 ±0.83 -1.44 ±2.24 -

Green pod Pod length Plant height Seeds/ pod yield / plant (cm) (cm) (g) 10.49 -0.12 -6.67* 0.51* ±8.39 ±0.61 ±0.76 ±0.08 -18.28 -1.08 -17.24* 0.98* ±19.61 ±1.22 ±1.58 ±0.20 2.58 1.92 12.40 0.43* -4.35* 0.23* ±10.11 ±0.17 ±0.99 ±0.09 -1.08 0.20 -9.50* 0.54* ±29.74 ±0.33 ±2.16 ±0.23 2.18 2.35 28.55* 0.78* -3.47* 0.05 ±8.76 ±0.19 ±0.79 ±0.09 44.72* 0.98* -5.04* -0.24 ±19.54 ±0.37 ±1.71 ±0.26 1.56 1.26 1.45 16.58 0.19 -7.18* -0.08 ±11.68 ±0.30 ±0.97 ±0.10 29.06 -0.50 -15.90* -0.50 ±24.45 ±0.70 ±2.24 ±0.31 2.21 10.46 -0.25 -11.04* 0.06 ±15.53 ±0.27 ±1.14 ±0.11 9.76 -1.30* -20.66* -0.52 ±34.72 ±0.61 ±2.80 ±0.30 1.87 12.97 0.34* -2.82* 0.45* ±8.00 ±0.17 ±0.76 ±0.07 -13.98 0.00 -5.20* 0.96* ±16.43 ±0.44 ±1.39 ±0.16 1.84 2.15 1.63 0.51* -4.40* 0.42* ±9.59 ±0.18 ±0.81 ±0.09 -32.04 0.52 -10.76* 0.96* ±25.39 ±0.38 ±1.54 ±0.22 2.45 2.24 10.40 0.14 0.43 0.10 ±20.13 ±0.20 ±1.09 ±0.11 18.90 -0.28 -1.38 0.10 ±48.49 ±0.43 ±2.10 ±0.23 36.63* 0.19 -4.43* 0.13 ±10.01 ±0.23 ±0.72 ±0.10 29.74 -0.62 -12.02* 0.16 ±22.83 ±0.46 ±1.60 ±0.24 2.71 5.24 0.36* -0.75 0.25* ±9.58 ±0.16 ±0.80 ±0.09 -6.92 -0.60 -2.78 0.66* ±22.60 ±0.33 ±1.65 ±0.20 2.64 -5.05 0.51* -3.54* 0.33* ±9.22 ±0.19 ±1.01 ±0.08 -35.06 0.68 -7.50* 0.76* ±18.13 ±0.40 ±1.97 ±0.16 2.12 2.30

Seed yield/ plant (g) 2.90 ±2.05 -2.02 ±5.65 7.67* ±2.61 13.12 ±8.15 8.68* ±2.35 13.46 ±7.10 13.74* ±2.33 21.72* ±5.61 1.58 12.08* ±1.86 11.28* ±4.01 0.93 6.12 ±1.85 3.64 ±3.83 3.46 ±2.01 -3.00 ±4.33 1.94 ±3.14 1.28 ±6.37 10.04* ±2.24 7.94 ±4.50 2.03 ±2.29 -0.86 ±5.40 4.52* ±2.00 4.04 ±4.26 -

References [1] [2]

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

Ahloowalia, B.S., Maluszynski, M. and Nichterlein, K. 2004. Global impact of mutation derived varieties. Euphytica 135 : 187204. Bai, Y., S. Pavan, Z. Zheng, N. F. Zappel, A. Reinstädler, C. Lotti, C. De Giovanni, L. Ricciardi, P. Lindhout, R. Visser, K. Theres, and R. Panstruga. 2008. Naturally occurring broad-spectrum powdery mildew resistance in a Central American tomato accession is caused by loss of Mlo function. Mol. Plant- Microbe Interactions 21:30-39. Joint FAO / IAEA (2011). Database of mutant varieties and Genetic stock. Krieger, U., Z. B. Lippman, and D. Zamir. 2010. The flowering gene SINGLE FLOWER TRUSS drives heterosis for yield in tomato. Nat. Genetics 42: 459-465 Larik, A.S., Memon, S. And Soomro, Z.A.2009. Radiation induced polygenic mutations in Sorghum bicolor L. Journal of Agricultural Research. Vol 47 (1): 11-19. Mather, K. and Jinks, J.L. 1971. Biometrical Genetics – The study of continuous variation. 2nd Edition. Chapman and Hall, London, New York.

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[7] [8]

[9]

[10] [11]

Nichterlein, K. 1999. The role of induced mutations in the improvement of common bean (Phaseolus vulgaris L.). Mutation Breeding Newsletter 44: 6-9. Pnueli, L. Carmel-Goren, D. Hareven, T. Gutfinger, J. Alvarez, M. Ganal, D. Zamir, and E. Lifschitz. 1998. The SELFPRUNING gene of tomato regulates vegetative to reproductive switching of sympodial meristems and is the ortholog of CEN and TFL1. Development 125:1979â&#x20AC;&#x201C;1989. Sharma, B and Sharma, S.K. 2004. Induced mutations and selection techniques for quantitative traits. In: Plant BreedingMendelian to molecular approaches. (eds) H K Jain and M C Kharwal. Narosa Publishing House, New Delhi, India. 2004,pp 647-656. Tulmann Neto, A. 1990. Genetic improvement of beans (Phaseolus vulgaris L.) through mutation breeding. In: Genetic Improvement of Pulse Crops. pp. 296-327, Nizam, J. (Ed.). Premier Publishing House, Hyderabad. Yonezawa, K. 1979. Some additional considerations on the method of genetical analysis for induced continuous variation of self fertilizing plants. Heredity 43: 191-204.

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

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

Crystal structure of [(2E)-6,6-dimethylhept-2-en-4-yn-1yl](methyl)(naphtha-1-ylmethyl)amine( Terbinafine) Tiwari R.K., MishraBharti Department of Physics, Jiwaji University-474011, Gwalior (M.P.), India Abstract: The title molecule [(2E)-6,6-dimethylhept-2-en-4-yn-1-yl ](methyl)(naphtha-1-ylmethyl)amine, C21H25N (Terbinafine) is characterized by X-ray single crystal diffraction analysis. The compound crystallizes in the monoclinic space group P21/n with a=5.9181(3)Å, b=29.4239(14)Å, c=11.429 β=97.92˚(0), Volume 1971.24(16) Å3 and Z=4. The two Benzene rings in the structure are essential planer, but the side chain is inclined with a gle of 7.21˚. There is i teresti g observ tio i this structure. During crystallization, two water molecules have shown their presence in the unit cell. These water molecules are engaged in hydrogen bonding and thus providing the stability to the structure. Keywords: Single crystal diffraction, Hydrogen bond, Crystallization etc. I. Introduction Title compound (Terbinafine) C21H25N became available first time in 1991 in Europe and in 1996 in USA. It is a synthetic allylamine antifungle compound. It is recently introduced, orally active, antifungal belonging to the allylamines class1 of synthetic antifungal agents. The structurally related topical antifungal naftifine 2 was the prototype of these compounds from which it was developed during a programme of chemical synthesis3. The biological and clinical properties of the allylamines have recently been reviewed4. Title compound in common with naftifine and related allylamines, acts by blocking fungal ergosterol biosynthesis5-7 . Terbinafine is highly lipophilic in nature and tends to accumulate in skin, nails and fatty tissues. It prevents conversion of squalene to lanosterol. The present paper is related with its three- dimensional structure. II. Experimental Details Nice beautiful colorless crystals of title compound were grown by the slow evaporation from its solution in Acetone at room temperature. The density of the crystals was measured by floatation method in a mixture of Benzene and Carbon tetra chloride. The measured density was 1.054 mg/m3 whereas calculated density is 1.0966 mg/m3. The molecular weight of the sample is 291.43 g/mol. and melting point is 193˚C. The IUPAC name of Terbinafine is [(2E)-6, 6-dimethylhept-2-en-4-yn-1-yl] (methyl) (naphtha-1-ylmethyl) amine. The unit cell parameters were determined by a computerized automatic Bruker axs Kappa apex 2 CCD diffractometer at Sardar Patel University, Vallabh Vidyanagar, Gujurat e ell pa amete s a e a 91 1 3 9 39 1 11 93 and β 97 9 ˚ 0 us t e spa e g oup was dete mined to e P 1/n with monoclinic crystal system and Z=4. The preliminary crystal data is given in Table 1. III. Data collection and structure solution The complete three dimensional intensity data collection was done at Sardar Patel University, Vallabh Vidyanagar, Gujarat on a computerized automatic Bruker axs Kappa apex 2 CCD diffractometer. The temperature of crystal during data collection was 293K. The X- ay adiation used was Mokα (0.7107 Å). All the data were corrected for Lorentz and polarization effects but no absorption correction was done because of very small absorption coefficient. The data collection was done y a θ ange of 1 to 7 ˚ e enti e data we e collected where h varies from -6 to 7, k from -38 to 38 and l from -14 to 7. In all 17396 reflection were measured out of which 4512 were unique reflections. Each intensity measurement involved in a scan over the reflection peak height. The structure was solved by direct method using SHELXS-978. IV. Refinement The positional parameters which were obtained from SHELXS-97 and their isotropic temperature factors were subjected to refinement by SHELXL-979 refinement program. After 4 cycles of refinement the R factor dropped to 0.1154. Further refinement of the structure was carried out with individual anisotropic temperature factors of the form: Exp.[(U11h2+U22k2+U33l2+2U12hk+2U23kl+2U13hl)] reduced R-factor to 0.1007. At this stage the hydrogen atoms were fixed by geometrical considerations and refined subsequently with isotropic temperature factors which were taken from the corresponding non- hydrogen atoms.

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Refinement of the structure was terminated after three more cycles of refinement when all the s ifts in t e pa amete s e ame mu small t an t e o esponding e s d’s e final R-value was 0.0951 for all the 17396 observed unique reflections. The final positional and thermal parameters of non-hydrogen atoms are listed in Table 2. V. Results and discussion The ORTEP10 view of the molecule with numbering scheme is shown in Fig 2. The bond lengths and angles involving non-hydrogen atoms are listed in Table 3. The two Benzene rings in the structure are essential plane ut t e side ain is in lined wit an angle of 37 1˚ The bond lengths and angles in the Benzene ring have usual variations, but the C(1)-C(11) bond in unusually long of 1.499(4) Å. This elongation may be due to extension of Benzene ring electrons delocalization along this bond. Similarly the chain from C(11)-C(23) is a stretched one having normal bond distances. There is also nothing unusual about the tetrahedral geometry around C(23). All the bond lengths and angles are normal. The relevant torsional angles are shown in Table 4. There is an interesting observation in this structure. During crystallization, two water molecules have shown their presence in the unit cell. These water molecules are engaged in hydrogen bonding and thus providing the stability to the structure. The possible hydrogen bonds are listed in Table 5. The packing of the molecules viewed along a, b and c axes are shown in Figs 3, 4, 5 and 6 respectively.

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

References G.Petranyi, N.S.Ryder, A.Stutz, Allylamine derivatives new class of synthetic antifungal agents inhibiting fungal squalene epoxidase Science, 1984, 224, 1239-41 A.Georgopoulos, G.Petranyi, H.Mieth, J.Drews, in vitro activity of naftiline, a new antifungal agent, Antimicrob agents chemother, 1981, 19, 386-9 A.Stutz, Synthesis and structure- activity correlations within allylamine antimycotics, Ann N Y Acad Sci, 1988, 544, 46-62 N.S.Ryder, H.Mieth, Allylamine antifungal drugs Curr Top Med Mycol, 1991, 4, 158-88 N.S.Ryder, G.Seidl, P.F.Troke, Effect of the antimycotic drug naftifine on growth of and sterol biosynthesis in candida albicans, Antimicrob Agents Chemother, 1984, 25, 483-7 N.S.Ryder, Specific inhibition of fungal biosynthesis by SF 86-327,a new allylamine antimycotic agent, Antimicrob Agents Chemother, 1985, 27, 252-6 N.S.Ryder, M-C Dupont, Inhibition of squalene epoxidase by allylamine antimycotic compounds, a comparative study of the fungal andmammalian enzymes Biochem J, 1985, 230, 765-70 G M S eld i “ SHELXS-97 P og am fo t e solution of ystal st u tu e” 1997 G M S eld i “SHELXL-97 P og am fo ystal st u tu e dete mination” 1997 C K Jo nson “OR EP Repo t ORNL-3794 Ook Ridge National La o ato y ennessee U S A” 19

Fig.1 Molecular structure of compound

Table 1: Crystallographic data for title compound Empirical formula Formula weight Temperature Wavelength Crystal system, space group Unit cell dimensions Volume Z, Calculated density Absorption coefficient F(000) Theta range for data collection Limiting indices Reflections collected / unique Completeness to theta Refinement method

C21 H25N 325.45 293(2) K 0.71073 Å (Mokα monoclinic, P21/n a=8.89 aa a=5.9181(3) Å, b=29.4239(14)Å 11 93 β 97 9 0 ˚ 1971.24(16) Å3 4, 1.0966 Mg/m3 0.070 mm-1 704 1.4 to 27.5 deg -6<=h<=7, -38<=k<=38, -14<=l<=7 17396 / 4512 [R(int) = 0.027] 27.5 99.0 % Full-matrix least-squares on F2

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Data / restraints / parameters Goodness-of-fit on F2 Final R indices [I>2sigma (I)] R indices (all data) Largest diff. peak and ho

4512 / 0 / 233 1.065 R1 = 0.0683, wR2 = 0.1939 R1 = 0.0951, wR2 = 0.2146 0.53 and -0.32 e.Å

Fig. 2 ORTEP view of molecule

Fig. 3 Packing seen down a-axis

Fig. 4 Packing seen down b-axis

Fig. 5 Packing seen down c-axis

Fig. 6 Packing of the unitcell

2 Table 2: Atomic coordinates (x 104 x103) for non-hydrogen. U (eq) is defined as one third of the trace of the orthogonalized Uij tensor.

OW(2) OW(1) C(12) N(1) C(13) C(6) C(1) C(16) C(15) C(11) C(14) C(17) C(7) C(8) C(3)

X 0.740 (2) 0.743 (2) 1.1656 (5) 0.9532 (3) 0.8008 (4) 0.7372 (4) 0.8127 (5) 0.7217 (5) 0.7940 (5) 1.0157 (4) 0.7379 (5) 0.6573 (5) 0.8548 (5) 0.7725 (7) 0.5005 (7)

Y 0.2441 (5) 0.2414 (5) 0.23898 (10) 0.26658 (6) 0.24985 (7) 0.36422 (7) 0.34646 (8) 0.12353 (9) 0.16986 (9) 0.31568 (8) 0.20124 (9) 0.08516 (9) 0.35660 (9) 0.37409 (11) 0.38507 (11)

Z 0.139 (2) 0.147 (2) −0 0 61 3 −0 071 17 −0 17 3 −0 1 −0 1060 −0 1 −0 1 −0 0 −0 1690 3 −0 1 3 −0 319 −0 3 −0 0316

Ueq 0.050 (3) 0.0426 (17) 0.0595 (7) 0.0447 (5) 0.0462 (5) 0.0523 (6) 0.0522 (6) 0.0613 (7) 0.0573 (7) 0.0541 (6) 0.0534 (6) 0.0645 (7) 0.0608 (7) 0.0808 (10) 0.0849 (11)

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C(5) C(18) C(10) C(2) C(9) C(4) C(21) C(19) C(20)

0.5344 (5) 0.5693 (6) 0.4556 (7) 0.6968 (6) 0.5696 (8) 0.4209 (6) 0.6751 (15) 0.6519 (17) 0.3262 (9)

]

Table 3: C(12)—N(1) N(1)—C(13) N(1)—C(11) C(13)—C(14) C(6)—C(7) C(6)—C(5) C(6)—C(1) C(1)—C(2) C(1)—C(11) C(16)—C(17) C(16)—C(15) C(15)—C(14) C(17)—C(18) C(7)—C(8) C(8)—C(9) C(3)—C(4) C(3)—C(2) C(5)—C(10) C(5)—C(4) C(18)—C(20) C(18)—C(19) C(18)—C(21) C(10)—C(9) C(7)—C(6)—C(5) C(7)—C(6)—C(1) C(5)—C(6)—C(1) C(2)—C(1)—C(6) C(2)—C(1)—C(11) C(12)—N(1)—C(13) C(12)—N(1)—C(11) C(13)—N(1)—C(11) C(14)—C(13)—N(1)

−0 391 −0 99 −0 3 30 −0 01 1 −0 −0 1 07 −0 3 13 −0 13 9 −0 7

0.39082 (8) 0.03833 (9) 0.40692 (11) 0.35758 (9) 0.39871 (12) 0.40035 (10) 0.0120 (2) 0.01448 (19) 0.03818 (18)

1.491 (3) 1.497 (3) 1.501 (3) 1.485 (3) 1.415 (4) 1.424 (4) 1.432 (4) 1.371 (4) 1.499 (4) 1.190 (4) 1.429 (4) 1.311 (4) 1.471 (4) 1.371 (4) 1.393 (6) 1.349 (6) 1.407 (5) 1.404 (5) 1.414 (5) 1.427 (6) 1.473 (6) 1.505 (7) 1.345 (6) 118.2 (3) 123.1 (2) 118.7 (3) 119.6 (3) 118.8 (3) 112.14 (19) 109.24 (19) 112.00 (18) 112.8 (2)

3 3

0.0665 (8) 0.0725 (9) 0.0894 (12) 0.0687 (8) 0.0960 (13) 0.0820 (10) 0.198 (4) 0.217 (5) 0.311 (8)

7 6 1

˚] f

- hydrogen atoms

C(6)—C(1)—C(11) C(17)—C(16)—C(15) C(14)—C(15)—C(16) C(1)—C(11)—N(1) C(15)—C(14)—C(13) C(16)—C(17)—C(18) C(8)—C(7)—C(6) C(7)—C(8)—C(9) C(4)—C(3)—C(2) C(10)—C(5)—C(4) C(10)—C(5)—C(6) C(4)—C(5)—C(6) C(20)—C(18)—C(17) C(20)—C(18)—C(19) C(17)—C(18)—C(19) C(20)—C(18)—C(21) C(17)—C(18)—C(21) C(19)—C(18)—C(21) C(9)—C(10)—C(5) C(1)—C(2)—C(3) C(10)—C(9)—C(8) C(3)—C(4)—C(5)

121.6 (2) 178.0 (3) 124.9 (3) 113.30 (19) 123.1 (3) 178.0 (3) 120.4 (3) 120.7 (4) 120.0 (3) 122.5 (3) 118.8 (3) 118.7 (3) 110.6 (3) 111.9 (6) 109.9 (3) 111.5 (6) 109.2 (3) 103.4 (6) 121.7 (4) 121.2 (3) 120.2 (3) 121.7 (3)

Table 4: Torsion angles [deg] C(12)—N(1)—C(13)—C(14) C(11)—N(1)—C(13)—C(14) C(7)—C(6)—C(1)—C(2) C(5)—C(6)—C(1)—C(2) C(7)—C(6)—C(1)—C(11) C(5)—C(6)—C(1)—C(11) C(17)—C(16)—C(15)—C(14) C(2)—C(1)—C(11)—N(1) C(6)—C(1)—C(11)—N(1) C(12)—N(1)—C(11)—C(1) C(13)—N(1)—C(11)—C(1) C(16)—C(15)—C(14)—C(13) N(1)—C(13)—C(14)—C(15) C(15)—C(16)—C(17)—C(18) C(5)—C(6)—C(7)—C(8) C(1)—C(6)—C(7)—C(8) C(6)—C(7)—C(8)—C(9) C(7)—C(6)—C(5)—C(10) C(1)—C(6)—C(5)—C(10) C(7)—C(6)—C(5)—C(4) C(1)—C(6)—C(5)—C(4) C(16)—C(17)—C(18)—C(20) C(16)—C(17)—C(18)—C(19) C(16)—C(17)—C(18)—C(21) C(4)—C(5)—C(10)—C(9) C(6)—C(5)—C(10)—C(9)

56.5 (3) 179.7 (2) 176.2 (2) −3 3 − 1 3 175.9 (2) 110 (10) 76.9 (3) −10 3 −17 56.3 (3) −17 7 3 −117 3 − 16 −0 7 179.2 (2) −1 2.2 (4) −177 7 (2) −177 3 2.8 (4) 6 (9) −11 9 129 (9) 177.9 (3) −1 6

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C(6)—C(1)—C(2)—C(3) C(11)—C(1)—C(2)—C(3) C(4)—C(3)—C(2)—C(1) C(5)—C(10)—C(9)—C(8) C(7)—C(8)—C(9)—C(10) C(2)—C(3)—C(4)—C(5) C(10)—C(5)—C(4)—C(3) C(6)—C(5)—C(4)—C(3)

1.9 (4) −177 1.1 (5) −0 6 6 2.2 (5) − −179 3 0.3 (4)

Table 5: Possible Hydrogen Bonds (Å) D-H

D….A

H...A

∟D-H......A

C(12)-H(12A) 0.960(.003)

C(12 )...OW(2) (1) 3.734(.013)

H(12A)...OW(2) (1) 2.968(.012)

C(12)-H(12A) ...OW(2) (1) 137.65( 0.33)

C(12)–H(12B) 0.960(.003)

C(12)...OW(2) (2) 3.736(.018)

H(12B)...OW(2) (2) 2.891(.017)

C(12)-H(12B)...OW(2) (2) 147.41( 0.37)

C(12)-H(12B) 0.960(.003)

C(12)...OW(1) (2) 3.628(.018)

H(12B)...OW(1) (2) 2.779(.017)

C(12) -H(12B)...OW(1) (2) 147.76( 0.38)

C(13)-H(13B) 0.970(.003)

C(13)...OW(2) (2) 3.561(.016)

H(13B)...OW(2) (2) 2.661(.015)

C(13) –H(13B)...OW(2) (2) 154.50( 0.35)

C(13)-H(13B) 0.970(.003)

C(13)...OW(1) (2) 3.509(.016)

H(13B)...OW(1) (2) 2.622(.015)

C(13)-H(13B)...OW(1) (2) 152.17( 0.35)

H(7)...OW(2) (2) 2.975(.014)

C(7)-H(7)...OW(2) (2) 149.33( 0.32)

C(7)-H(7) 0.930(.003)

C(7)...OW(2) (2) 3.805(.014)

C(7)-H(7) 0.930(.003)

C(7)...OW(1) (2) 3.738(.014)

H(7)...OW(1) (2) 2.893(.013)

C(7)-H(7)...OW(1) (2) 151.68( 0.32)

C(13) -H(13A) 0.970(.003)

C(13)...OW(2) (3) 3.691(.013)

H(13A)...OW(2) (3) 3.000(.013)

C(13)-H(13A)...OW(2) (3) 129.29( 0.30)

C(13)-H(13A) 0.970(.003)

C(13)...OW(1) (3) 3.605(.013)

H(13A)...OW(1) (3) 2.889(.013)

C(13)-H(13A)...OW(1) (3) 131.42( 0.30)

Equivalent positions: ( 1) x+1,+y,+z ( 2) x+1/2,-y+1/2,+z-1/2 ( 3) x-1/2,-y+1/2,+z-1/2

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Introduction of Vogtia malloi syn. Arcola malloi as biocontrol agent of Water Hyacinth (Eichhornia crassipes) in Devipatan Division (U.P.). Richa Tripathi+, D.S. Shukla+,Akanksha Tripathi++, ,H.D.Dwivedi+++ + Botany Deptt, MLK PG College, Balrampur, U.P., India ++ Zoology Department, M.L.K.P.G. College, Balrampur, U.P., India +++ Botany Department, S.V.N.P.G. College, Kalan Sultanpur, U.P., India Abstract: Eichhornia crassipes (Water hyacinth) is a major aquatic weed of India. It is very tough to control is by chemicals because of their harmful side effects. Present study shows the easy and ecofriendly management of this weed by the insect Vogtia malloi syn. Arcola malloi. I. Introduction Weeds can be defined as plants growing out of place and of course they are the biggest natural enemies of biodiversity. Originally these weeds were introduced as ornamental plants for ponds because of their attractive flowers or foliage, or their ability to grow quickly. The intentional and unintentional dumping of pond plants, water and unwanted fish has led to the infestation of natural waterways. These weeds often out-compete local species and seriously affect the local ecology. Not only do they affect native wildlife but can also impact on recreational activities such as swimming, boating and fishing and can ruin the aesthetic appeal of the water body. As a weed management method, biological control offers an ecofriendly approach that complements conventional methods. Vogtia malloi was introduced to the United States in the 1970s to attack the alligator weed (Alternanthera philoxeroides)1. Materials and Methods About the WeedSeven species of the genus Eichhornia are found worldwide 2. In India Eichhornia crassipes is the most noxious aquatic weed. Eichhornia crassipes (Water hyacinth) is a free floating perennial aquatic plant which is native to tropical and subtropical South America. It has been widely introduced throughout North America, Asia, Australia, and Africa. It can be found in large water reservoirs allover I ndia. About the InsectThe moth Vogtia malloi (Pyralidae: Phycitinae) was named in 1961 when Jose A. Pastrana 3 described it as a new genus and species. Vogtia may be recognized by the following characteristics: large labial palpi, three times the diameter of the eye, pointing forward with loose, thick scales and an obtuse third joint; no maxillary palpi are present; ocelli are present; the front wing has smooth scales, a slightly curved edge, and ten veins. The wingspan is 20-22 mm and the wings are straw-colored, dashed with brown scales on the edge and tip of the wing. This insect was discovered by George Vogt in his surveys in South America for natural enemies of alligator weed for possible introduction into the United States. Vogtia was one of the four insects considered by Vogt4 (1961) as a major suppressant of alligator weed in South America. II. Experimental Methodology Mass cultivation of Vogtia malloiInoculam of Vogtia malloi5 (Fig.1) was obtained from the ‘Mewalal Pond’ of Balrampur, U.P. One pair of adult Vogtia malloi was placed each on ten plants in the cultivation pond of Suaon Nalah near Naharbalaganj, district Balrampur, U.P. Vogtia malloi inserted eggs either singly or in group of up to 25 inside the petiole. About 30°C is optimal for feeding and oviposition. Eggs were hatched in 17 days and larvae fed and developed inside the petioles. Fully grown larvae were moved into the water near the upper root zone, created a ball around them formed from lateral roots and attached to the main root axis. The larvae possibly use roots as Oxygen source. After 75 days, adult insects were harvested twice a week. Introduction of Vogtia malloi Vogtia malloi was introduced in the ‘Test Pond pond of MLK PG College, Balrampur, U.P., as well as over 4 plants of Eichhornia in the aquarium of Zoology department, M.L.K. P.G. College. The pond was highly infested with Water hyacinth (Eichhornia crassipes). One pair of adult Vogtia malloi was placed each on ten plants in the test pond of ‘MLK PG College, Balrampur, U.P. Observations-

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After 25 days there were seen larvae of the insect. The adult was seen after 15 days thereafter. Being a nocturnal moth Vogtia malloi oviposited on terminal leaves. Larvae tunneled into stems, and later exit at irregular intervals, re-entered and thereby damaged a number of stems as they passed through five instars. Pupation was inside the hollow stem, and there were 3-5 generations per year. Extensive stem collapse resulted from the feeding of V. malloi and it developed satisfactorily on both rooted and free floating plants. III. Results and Discussion Very encouraging results were obtained to manage this noxious weed in the form of multiple lesions and necrosis of leaves as well as total feeding of leaves of Water hyacinth. Preliminary quantitative data from test pond indicate a reduction in leaf length, leaf laminar area and fresh weight at several sites and general increases in larval mining, feeding scars and adult weevil density. About 48% reduction in shoot biomass was recorded in the Eichhornia plants grown in the aquarium of the Zoology Department, M.L.K. P.G. College as well as in the test pond of MLK PG College, Balrampur. [1]. [2]. [3]. [4]. [5].

Coombs, E. M., et al., Eds. (2004). Biological Control of Invasive Plants in the United States. Corvallis: Oregon State University Press, 146. Holm LG, Plucknett DL, Pancho JV, Herberger JP. 1977. The world's worst weeds: distribution and biology. Honolulu: University Press of Hawaii. 609 pp. Pastrana, J. A. 1961. Una nueva Phycitidae (Lep.) parasito de la â&#x20AC;&#x153;lagunilla. Revista de Investigaciones Agricolas 15: 265-272. Vogt, G. B. 1961. Exploration for natural enemies of alligator weed and related plants in South America. U. S. Dep. Agric, Agric. Res. Serv., Entomol. Res. Div., Special Report PI-5, 50p. Brown, J. L. and N. R. Spencer. 1973. Vogtia malloi, a newly introduced phycitine moth (Lepidoptera: Pyralidae) to control alligatorweed. Environmental Entomology 2: 519-523.

Fig.1 - Adult Vogtia malloi

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Spectroscopic and Micellization of Uranyl Hexanoate in Organic Solvent Suman Kumaria*, Mithlesh Shuklaa, and R.K Shuklab Department of Chemistry, Agra College, Agra-282004, India b Department of Chemistry, R.B.S. College, Agra-282004, India *Email: vermasuman03@yahoo.co.in Abstract: Micellization behavior of uranyl hexanoate in non-aqueous solvent was studied by using conductometric measurements. The critical micellar concentration (CMC), molar conductance and degree of ionization have been determined. The molar conductance, of the solutions of uranyl hexanoate decreases with increasing solute concentration. The decrease in molar conductance may be due to the combined effects of ionic atmosphere, solvation of ions, decrease of mobility and ionization and formation of micelles. The results show that the uranyl hexanoate behaves as a simple electrolyte in nonaqueous solvent below the CMC and the addition of Sudan dye increases the specific conductance of the alkanoates solution but the general behavior of the solution remains unaltered. The physico-chemical characteristics of uranyl hexanoate in solid state were investigated by FT-IR analysis. The IR results revealed that the fatty acids exist in dimeric state through hydrogen bonding and uranyl hexanoate possess partial Abstract : ionic character. Key words: Uranyl hexanoate, Sudan dye, critical micellar concentration, Specific conductance, molar conductance, degree of ionization. a

I. Introduction Surface active agents are characterized by the possession of both polar and non-polar regions in the same molecule. This dual nature is responsible for the phenomenon of surface activity, and micellization and solublization. The dual nature of a surfactant is typified by metal soaps or alkanoates, can be called association colloids, indicating their tendency to associate in solution, forming particles of colloidal dimensions. Inspite of wide applications in many industries, the physico-chemical characteristics of rare earth alkanoates have not been thoroughly investigated. Wu et al1 and Kanai2 developed new technologies to synthesize metal alkanoates. Workers3-10 studied the spectroscopic and thermal behavior of metal alkanoates. Sawada et al11 characterized the fine metal alkanoate particles by x-ray diffraction, differential scanning calorimetery and specific surface area analysis. A number of workers12-24 studied their micellar behavior using conductometric, ultrasonic, viscosity and density measurements. In the present work, the results of FT-IR analysis have been used to obtain structural information of uranyl hexanoate in solid state. Micellization behavior of uranyl hexanoate in DMF and effect of sudan dye have been studied by conductometric investigations. II. Experimental All chemicals used were of BDH/AR grade. Solvent DMF was purified by distillation under reduced pressure. Uranyl hexanoate was synthesized by direct metathesis of corresponding potassium alkanoates as mentioned in our earlier publications12-13. The insoluble deep yellow precipitate of uranyl hexanoate was digested for 1-2 hour and separated from the mother liquor by filtering through a Buchner funnel under reduced pressure and washed with water and then alcohol. The uranyl hexanoate thus obtained was dried in an air oven at 50-60oC and final drying of the alkanoate was carried out under reduced pressure. The purity of uranyl hexanoate was checked by elemental analysis and the absence of hydroxylic group was confirmed by FT-IR analysis. The infrared absorption spectra of hexanoic acid and their corresponding uranyl hexanoate were recorded with a Perkin-Elmer ‘Model 577’ grating spectrophotometer in the region 4000-200 cm-1 using the potassium bromide disc method. A digital conductivity meter (Toshniwal CL 01.10A) and a dipping type conductivity cell with platinized electrodes (cell constant 0.895) were used for measuring the conductance of uranyl hexanoate solution at 40±0.05 oC.

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Specific conductance x103 (mhos)

III. Results and Discussion Infrared spectra The infrared spectra of uranyl hexanoate shows marked differences with the spectrum of their corresponding hexanoic acid in some spectral regions. In the spectra of uranyl hexanoate characteristics v O-1 -1 -1 H stretch (2650-2660cm ), vC=O (1690-1700 cm ), vC-O+O-H (1468-1470cm ) stretch+ in plane deformation -1 and vO-H out of plane deformation (930-950cm ) vibrations of free acids which are characteristic bands of dimeric carboxylic acids were found completely absent with the absorption maxima near 690cm-1 and 550cm-1 associated with carboxyl group bending and wagging modes. Beside it, two absorption bands are observed near 1480cm-1 and 1370cm-1 corresponding to asymmetric and symmetric vibration of carboxylate ion as pointed out by Duval, Lecomte and Douville 10 with metal- oxygen bond near 435 cm-1. In uranyl hexanoate stretching frequencies of the UO22+ entity were also observed near 810 and 740 cm-1. Specific conductance, k (mhos cm -1) and CMC The specific conductance, k (mhos cm-1) clearly depends on the concentration of the alkanoate. The Specific conductance, k of the dilute solutions of uranyl hexanoate in DMF increases with increasing solute concentration, C (mol dm-3). The increase in the specific conductance, k with the increase in solute concentration may be due to the ionization of uranyl hexanoate into simple metal cations, UO2++ and fatty acids anions, C5H11COO- in dilute solutions and the formation of micelles at higher concentrations of alkanoate. The values of critical micellar concentration, CMC (0.03M) of the uranyl hexanoate. have been determined by k-C plot (Fig.1). The concentration at which micelles formation starts known as critical micellar concentration (CMC), beyond this concentration the bulk properties of the surfactant, such as osmotic pressure, turbidity, solublization, surface tension, viscosity, ultrasonic velocity and conductivity changes abruptly. If the micelles are formed in organic medium the aggregates are called “reversed micelles” in this case the polar head groups of the surfactant are oriented in the interior and the lyophilic groups extended outwards in to the solvent. It is suggested that the uranyl hexanoate is considerably ionized in dilute solutions and the anions begin to aggregate to form micelles. The addition of a surface active agent, i.e., sudan dye has no effect on the CMC value of uranyl hexanoate as apparent from the plot k-C (Fig.1). When the concentration of dye is increased from 10 -4M to 10-2M, the specific conductance, k (mhos cm-1) increases but CMC remains unchanged (Fig.2). Fig. 1 Specific Conductance vs. Concenteration of Uranyl Hexanoate at 40±0.05℃ 0.8 0.6 0.4

Uranyl hexanoate in DMF

0.2 Uranyl hexanoate in DMF and Red Sudan

Concenteration (mol dm -3)

Specific conductance x103 (mhos)

Fig. 2 Specific Conductance vs. Concenteration of Uranyl Hexanoate at 40±0.05℃ 1 0.8 0.6 0.4 0.2 0

Uranyl hexanoate in DMF and Suan Red (Con 10-2) Uranyl hexanoate in DMF and Red Sudan( Conc 10-4)

Concenteration ( mol dm -3)

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The molar conductance, Λ (cm 2 mol-1) and ionization constant

The molar conductance, Λ of the solutions of uranyl hexanoate decreases with increasing solute concentration. The decrease in molar conductance may be due to the combined effects of ionic atmosphere, solvation of ions, decrease of mobility and ionization and formation of micelles. Since the molar conductance, Λ of the solutions of uranyl hexanoate solution does not vary linearly with the square root of alkanoate concentration, the Debye-Huckel-Onsager’s equation25 is not applicable to these solutions. Molar conductance results show that solution of uranyl hexanoate behaves as simple electrolyte and ionization of uranyl hexanoate solution may be explained by Ostwald’s manner. If C (mol dm-3) is the concentration and α is the degree of ionization of uranyl hexanoate solution, molar concentration may be represented as follows:

The ionization constant,

(C5H11COO)2UO2 UO2++ + 2 C5H11COOC(1- α) C α 2C , for this equilibrium may be expressed as follows:

The ionic concentrations are low in dilute solutions, so interionic effects are almost negligible. Therefore, the solution of alkanoate does not deviate appreciably from ideal behavior and the activities of ions can be taken as almost equal to the concentrations. The degree of ionization, α may be replaced by the conductance ratio, Λ Λ where Λ and Λ (cm2 mol-1) are the molar conductance at finite and infinite dilution, respectively. By substituting the value of α and rearranging, equation (1) can be written as: Λ Λ Λ The values of ionization constant, , and limiting molar conductance, Λ were obtained from the slope, Λ Λ and intercept Λ of the linear part of the plot (Fig.3) of Λ below critical micellar concentration. The value of limiting molar conductance, Λ was found to be 35.0. Λ

100 90 80 70 60 50 40 30 20 10 0

Uranyl hexanoate in DMF

3.47 3.48 3.57 3.64 3.68 3.74 3.79 3.83 3.95 3.96 4.04 4.15 4.27 4.33 4.47 4.67 4.96 5.2

Ʌ2C2 x 102

Fig.3 Ʌ2C2 vs. 1/T

1/Tx 102 The values of degree of ionization, α have been evaluated by assuming α as equal to the conductance ratio, Λ Λ . The values of the degree of ionization lie between 0.513 and 0.802 (Table I), thereby confirming the fact that the uranyl hexanoate behaves as a simple electrolyte. The degree of ionization decreases rapidly in dilute solutions with the increase in uranyl hexanoate concentration (Table. I). It may thus conclude that the addition of Sudan dye increases the specific conductance, k (mhos cm-1) of the alkanoate solution (Table I and II) but the general behavior of the alkanoate remains unaltered.

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Table I: Conductance of uranyl hexanoate in DMF at 40±0.05oC Concenteration, C (mol dm-3) 0.0100 0.0108 0.0119 0.0131 0.0147 0.0166 0.0192 0.0227 0.0277 0.0357 0.0500

Specific Condctance, k (mhos cm-1)

Molar Conductance, Λ (cm2 mol-1)

0.301 0.320 0.342 0.359 0.393 0.433 0.484 0.547 0.640 0.765 0.960

30.10 29.62 28.74 27.40 26.73 26.08 25.21 24.09 23.10 21.43 19.20

C2×102

09.06 10.24 11.69 12.88 15.44 17.33 23.42 29.90 40.94 58.47 92.16

×102

2.53 3.37 3.48 3.64 3.74 3.83 3.96 4.15 4.33 4.67 5.20

Degree of Ionization, α 0.798 0.791 0.743 0.732 0.714 0.697 0.674 0.644 0.617 0.572 0.513

Table II: Conductance of uranyl hexanoate in Sudan dye (Conc. 10-4) and Sudan Red (Conc. 10-2) in DMF at 40±0.05oC Concenteration, C(mol dm-3) 0.0100 0.0108 0.0119 0.0131 0.0147 0.0166 0.0192 0.0227 0.0277 0.0357 0.0500

(Conc. 10-4) Specific Condctance k×103 0.202 0.217 0.234 0.254 0.277 0.300 0.346 0.389 0.452 0.544 0.635

(Conc. 10-2) Specific Condctance

k×103

0.327 0.352 0.377 0.410 0.443 0.490 0.562 0.664 0.675 0.975 1.140

Acknowledgements The authors are thankful to UGC, New Delhi for the financial assistance. References [1]. [2]. [3]. [4]. [5].

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

[10]. [11]. [12]. [13]. [14].

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K.N. Mehrotra, R.K. Shukla, Mithlesh Chauhan, Tenside Sur. Det. 29(6), 432 (1992). K.N. Mehrotra, R.K. Shukla, M. Chauhan, J. Appl. Poly. Sci. 39, 1745 (1990). K.N. Mehrotra, R.K. Shukla, Mithlesh Chauhan, Monatshefte für Chemie (Austria) 121, 461 (1990). K.N. Mehrotra, R.K. Shukla, Mithlesh Chauhan, J. Electrochem. Soc. India. 14(39), 147 (1990). K.N. Mehrotra, R.K. Shukla, M. Chauhan, J. Am. Oil Chemists Soc. 67(7), 446 (1990). K.N. Mehrotra, R.K. Shukla, M. Chauhan, J. Phys. Chem. Liq. 21, 237 (1990). K.N. Mehrotra, R.K. Shukla, M. Chauhan, J. Colloid and Surfaces, 119, 67 (1996). R. K. Shukla, M. Shukla, Vikas Mishra, Physics and Chemistry of Liquids 43(3), 345-349(2007). K.N. Mehrotra, R.K. Shukla, M. Chauhan, J. Phys. Chem. Liq. 25, 7 (1992). K.N. Mehrotra, M. Anis, Tenside Surf. Det 38(2), 116-119(2001). I. N. Levine, Physical Chemistry, Fourth Edition, New York: McGraw-Hill Inc (1995).

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EFFECT OF DENSITY ON GROWTH AND PRODUCTION OF LITOPENAEUS VANNAMEI OF BRACKISH WATER CULTURE SYSTEM IN WINTER SEASON WITH ARTIFICIAL DIET, INDIA Danya Babu. Ravuru1 and Jagadish Naik. Mude2 Department of Zoology and Aquaculture, Acharya Nagarjuna University, Guntur, Andhra Pradesh–522510, INDIA Abstract: The White leg shrimp Litopenaeus vannamei (Boone, 1931) is an Ecological important tropical and euryhaline species. The culture was conducted from three ponds each one of 0.5hac for the study. Semi Intensive culture system was selected in Chinaganjam village, Prakasam District under Brackish water conditions. Stocking densities of L.vannamei (post larvae) were taken from three samples, each one contains (2, 50,000) 50 species/m2 and its survival was 82%, 84% and 86%. The artificial diet was provided 4times/day with Manamei feed pellets (Protein 35 and 34%).In winter season in month of November to February, the water quality parameters were measured fortnightly in a month at 7a. m. The production was 3287, 3472 and 3554kg and FCR was1.43, 1.51 and 1.46 and the final growth was 26.5, 27.0 and 27.5g for P1, P2 and P3/95, 98 and 101 days, respectively. Key words: L. vannamei, Temperature, Salinity, Density, Feed, Growth and Production I. Introduction Litopenaeus vannamei (Boone, 1931), is the most important penaeid shrimp species farmed worldwide (Alcivar – Warren et al., 2007). Because of the high demand for shrimps in Japan, the United States and Europe, shrimp aquaculture has expanded rapidly in all around the world, especially in tropical areas, such as Southeast Asia and Latin America (Lombardi et al., 2006). Among all species of shrimp, L. vannamei, which represents over 90% of shrimp culture in the Western hemisphere, is the most commonly cultured shrimp in Central and South American countries, China and Thailand (Frias- Espericueta et al., 2001; Mc Graw et al., 2002; Saoud et al., 2003). India ranks second next to china in shrimp production. India has the one of the longest coastal line of 8118 km. About 90percent of the total landings has commercially most importance for the shrimp culture all over the world. Andhra Pradesh has the second longest coast line 972 km distributed in India. Prakasam District has distributed 102 km coast line in Andhra Pradesh. The L.vannamei is growing much better than Penaeus monodon. The recent trends in shrimp culture shows a considerable increase of farming of L. vannamei replacing P. monodon culture. The optimal stocking density varies depending on the farm system and management practices. In India the production of L.vannamei culture about 18247 (MT) from 2930 ha culture in 2010–11, the production of shrimp 48430.00 (MT). II. Material and Methods All ponds were pumped with creek water. The pond shape is rectangular. The post larvae (PL15) of L.vannamei were 15 days old for beginning the study. The PL15 collected from BMR hatchery (Iscapalli village) situated about 20 km of Nellore District in Andhra Pradesh. Cost of seed Rs. 50 paisa for each. Water depth maintained 7ft. In the summer season, L.vannamei (post larvae) stocking densities were taken for culture in three ponds, each one contains (2, 50000) 50 species/m2 and also, survival was 82, 84 and 86% (2, 05,000; 2, 10,000; 2, 15,000), respectively. The temperature, salinity and DO ranges up to 16±2 0C, 12±2ppt and 4.0ppm/day. The artificial diet was given made by Manamei feed pellet (Protein% 35 (Feed No. 1, 2, 3 and 3S) and Protein% 34(Feed No. 3M)).The methodology includes standard techniques to measure the water quality parameters. III. Results In the experiment the stocking density was influenced by the water quality parameters (see Table1) and also, indicated the reduction of survival rate at higher densities. The species L.vannamei was well grownup to 20 gm body weight from 2.0–3.5, 2.0–3.5 and 2.0–4.0g for P1, P2 and P3/15 days in Indian climate conditions, which is better than other countries. In the culture system the growth rate increased due to the artificial feed supplementation in the season. The oxygen consumption was higher in the large size groups than in the smaller shrimp. More the feed is given; more the Ammonia and H 2S gas are released. When the electrical aerators and probiotics are used, the shrimp growth rate was increased due to lack of Dissolved Oxygen (DO). The shrimp culture of the mean average weights of the shrimp were 16.5, 17.0 and 17.5g (Tables 1,2 and 3), survival were

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82, 84 and 86%.The given feed 2352, 2359.4, 2486.7 kg/ 95, 98 and 101 days; FCR was1.43, 1.51 and 1.46 for P1,P2 and P3 (Table 1); production was 3287, 3472 and 3554 kg, respectively. Cost of the feed Rs.71.84/kg and Cost of the species at harvesting time Rs. 380, 390 and 400/kg for P1, P2 and P3. IV. Discussion The statistical analysis method was applied “ANOVA” test, comparison of the survival, production, growth rate and FCR in P1, P2, and P3. The maintenance of good water quality is essential for optimum health, survival and growth of shrimp. The present study was concluded that L.vannamei culture is successful in brackish water environments and the growth is directly related to stocking density. The shrimp was relatively inactive about 200C and exhibited low food consumption comparatively at about 35 0C. The shrimp maintained at 350C had the highest rate of food consumption (Araneda et a., 2008) recorded the average growth rate of 0.38 g/wk in the 90 shrimp/m2 and lowest in the180 shrimp/m2 (0.33 g/wk).Despite the growth variation observed, all values of the parameters meet the water quality requirements for shrimp production (Cawthorne, Beard, Devenport and Wickins,1983; Allan and Maguire, 1991; Garcia and Brune, 1991; Lee and Wickins, 1992; Prado-Estepa, Llobrera,Villaluz and Saldes, 1993); early morning Dissolved Oxygen concentration was between 3.0 to 4.5 mg1-1; salinity was about 14% during the first week of grow out pond, which is preferable for post larvae (PL). The initial lower temperatures would have reduced metabolism and diet intake of the shrimp (Lester and Pante 1992), consequently slowing growth during the first weak. The growth rate of L.vannamei at higher salinities of 50ppt and more, showed the possibility of commercial production. The optimum feeding rate and frequency of presentation must, therefore, be determined for individual feeds and farms by carefully monitoring feed consumption, growth and feed efficiency over several growing seasons (Tacon, 1993). As one of key factors for culture shrimp, water quality not only affects the shrimp growth and survival rate, but also affects the accuracy of the experiment result (Chim et al., 2008). During the course of the attachment, a large number of shrimp could be assembled on the pond bottom from the artificial substrates (Zhang et al., 2010). Protein requirement has been defined by Guillaume (1997) as the minimum or the maximum amount of protein needed per animal per day. Protein requirements change with respect to changes in biotic factors (e.g. species, physiological state, size) and dietary characteristics (e.g. protein quality, energy: protein ratio). Abiotic factors such as temperature and salinity may also affect the protein requirement (Guillaume, 1997). The protein requirement of a given species is often based on the response (e.g. weight gain, feed efficiency, protein conversion efficiency) of the animal to varying levels of dietary protein under a given set of circumstances. Pro“W” Probiotic is provided to all 3 ponds depending on biomass control of dead shrimps. Minerals are provided to all three ponds depending on biomass i. e. EDTA 2.5 kg/0.5ha for molting of the species, Burunt lime to enhance the water quality. Sugar 7.5 kg/0.5 ha for hardening the shell. “Gasonex” to lift of the gas (while it is black soil, it will be given after 70 days). “Boonin” (do not use of above 15ppt of salinity) for deficiency of minerals. “Opti Oxygen” controls the DO. “AQ lite” for bottom clears. Potash 15kg/0.5/ha for control the body gram of species.P1 the survival rate was decreased comparatively with P2, P3 and P2 Food Conversion Ratio was high compared with P1,P3 (Table1) and P3 the growth was increased in P1,P2(Table 2, 3 and 4). Table 1: Pond performance Details Pond Details

Area (ha)

DOC

Stocking date

PL stocking (days)

Density(m2) & Initial stocking

P1 P2 P3

0.5 0.5 0.5

95 98 101

02/11/2012 02/11/2012 02/11/2012

PL15 PL15 PL15

50=2,50,000 50=2,50,000 50=2,50,000

Survival (%) & Numbers 82=2,05,000 84=2,10,000 86=2,15,000

FCR 1.43 1.51 1.46

Table 2: Pond 1 Water parameters & Growth performance (g) in winter season DOC 15 30 45 60 75 95

Temperature (0C) 13.0±2 14.0±2 14.5±2 15.0±2 16.0±2 15.5±2

Salinity (ppt)

DO (ppm)

9.0±2 10.0±2 10.5±2 11.0±2 12.0±2 11.5±2

3.4 3.6 3.7 3.8 4.0 3.9

Giving feed (%) – 6.5 5.0 4.5 3.5 3.5

Feeding/day (kg) 120.00 54.90 41.00 36.90 28.70 28.40

Total growth (gm) 2.00 4.00 7.00 10.00 13.00 16.50

AVG/ fortnightly (gm) 2.00 2.00 3.00 3.00 3.00 3.50

Mean 14.6±2 10.5±2 3.7 Total production=3382kg; Dead shrimps=95kg; Final production=3287kg; Total feed=2352kg

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Line Graph1: Pond 1 Water parameters & Growth performance (g) in winter season

140 120 100 80 60 40 20 0

Temperature Salinity (ppt) DO(ppm) Giving feed (%) Feeding/day (kg) Total growth (gm) AVG (gm)

Table 3: Pond 2 Water parameters & Growth performance (g) in winter season DOC

Temperatur e (0C)

Salinity (ppt)

DO (ppm)

Giving feed (%)

Feeding/da y (kg)

Total growth (gm)

AVG/ fortnightly (gm)

13.0±2

9.0±2

3.4

–

120.0

2.00

13.5±2

9.5±2

3.5

6.5

54.6

4.50

2.50

14.0±2

10.0±2

3.6

4.5

37.8

7.50

3.00

15.0±2

11.0±2

3.8

4.0

33.6

10.50

3.00

75 98

16.0±2 15.5±2

12.0±2 11.5±2

4.0 3.9

3.5 3.5

29.4 29.2

13.50 17.00

3.00 3.50

Mean 14.5±2 10.3±2 3.7 Total production=3570kg; Dead shrimps=98kg; Final production=3472kg; Total feed=2359.40kg Line Graph 2: Pond 2 Water parameters & Growth performance (g) in winter season

140 120

Temperature

100

Salinity (ppt)

DO(ppm)

Giving feed (%)

Feeding/day (kg) Total growth (gm)

AVG (gm)

0 15

Table 4: Pond 3 Water parameters & Growth performance (g) in winter season DOC

Temperatur e (0C)

Salinity (ppt)

DO (ppm)

Giving feed (%)

Feeding/da y (kg)

Total growth (gm)

AVG/ fortnightly (gm)

15 30 45 60 75 101

13.5±2 14.0±2 14.5±2 15.0±2 16.0±2 15.5±2

9.5±2 10.0±2 10.5±2 11.0±2 12.0±2 11.5±2

3.5 3.6 3.7 3.8 4.0 3.9

– 6.5 4.5 4.0 3.5 3.5

120.0 55.90 38.70 34.40 30.10 30.00

2.00 4.50 7.50 10.50 13.50 17.50

2.00 2.50 3.00 3.00 3.00 4.00

Mean 14.7±2 10.7±2 3.7 Total production=3655kg; Dead shrimps=101kg; Final production=3554kg; Total feed=2486.70kg

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Line Graph 3: Pond 3 Water parameters & Growth performance (g) in winter season

140 120 100 80 60 40 20 0

Temperature Salinity(ppt) DO(ppm) Giving feed (%) Feeding/day (kg) Total growth (gm) AVG growth (gm)

101

Note: P=Pond, DOC=Days of Culture, PL=Post Larvae, FCR=Food Conversion Ratio and DO=Dissolved Oxygen, AVG= Average growth V. Conclusion In the present study, it has been observed, Temperature, Salinity, Dissolved oxygen, Density and Survival have been observed and the shrimp Growth rate and Production were increased with artificial Manamei feed when compared with control. VI. Acknowledgements Authors are thankful to the Farmer and Owner of the culture ponds K. Ramana (Neeli Aqua Pvt, Ltd.) in Chinaganjam Village, Prakasakm District, for their encouragement and provided facilities up to harvest of the L.vannamei culture. References 1. 2. 3.

4. 5. 6. 7. 8. 9. 10. 11.

12.

13. 14. 15.

16.

Allen G.L., & Maguire G.B. (1991) lethal levels of low dissolved oxygen and effect of short-term oxygen stress on subsequent growth of juvenile Penaeus monodon. Aquaculture 94, 2–37. Arenda.M., E.P.Perez, and E.Gasca–Leyva .2008 white shrimp Penaeus vannamei culture in fresh water 3densities; condition state based on length and weight. Aquaculture 283; 13–18. Alcivar–Warren AD, Meehan–Meola S, Won Park, Xu Z, Delaney M, Zuniga G. (2007). Shrimp Map: a low-density, microsatellite-based linkage map of the Pacific white leg shrimp, Litopenaeus vannamei: identification of sex-linked markers in linkage group 4. Journal of Shellfish Research 26(4): 1259–1277, http://dx.doi.org/10.2983/0730-8000(2007) 26[1259: SALMLM] 2.0.CO; 2 Chim, L., M. Castex, D. Pham, P. Lemaire, P. Scmidely and M. Mariojouls, 2008. Evaluation of floating cages as an experimental tool for marine shrimp culture studies under practical earthen pond conditions. Aquaculture, 279: 63–69. Cawthorne, D.E., Beard T., Davenport, J and Wickins, J. (1983). Response of juvenile Penaeus monodon Fabricius to natural and artificial sea water of low salinity. Aquaculture 32.165–174. Frías–Espericueta, M.G, Voltolina, D. and Osuna–López, J.I, 2001. Acute toxicity of cadmium, mercury and lead to white leg shrimp (Litopenaeus vannamei) post larvae. Bulletin of Environmental Contamination and Toxicology, 67: 580–586. Garcia, A and Brune, D.E. (1991). Transport limitation of oxygen in shrimp culture pond. Aquaculture engineering 10,269–279. Guillaume, J., 1997. Protein and amino acids. In: D’Abramo, L.R., Conklin, D.E., Akiyama, D.M. (Eds.), Crustacean Nutrition. World Aquaculture Society, Baton Rouge, LA, pp. 26–50. Lee D.O.C. & Wickins J.E. (1992). Crustacean forming Black Well Scientific Publications. Oxford. Lombardi, J.V., M.H .L. De Almeida, L.P.R. Toledo, B.O.J. Salee and E.J. De Paula, 2006. Cage Polyculture of the Pacific white shrimp Litopenaeus vannamei and the Philippines Sea weed Kappaphycusalvarezii. Aquaculture, 258: 412–415. Lester, L. J. and M. J. Pante. 1992. Penaeid temperature and salinity responses. Pages 515–534 in A. W. Fast and L. J. Lester, editors. Marine shrimp culture: principles and practices. Elsevier Scientific Publishing Company, Elsevier, New York, New York, USA. Mc Graw, W.J., Davis, D.A., Teichert–Coddington, D and Rouse, D.B, 2002. Acclimation of Litopenaeus vannamei post larvae to low salinity: influence of age, salinity, endpoint and rate of salinity reduction. Journal of the World Aquaculture Society, 33: 78–84. Parado–Estpa E.E.D, Llobera A., Villaluz, A and Saldes, R. (1993). Survival and metamorphosis of Penaeus monodon Larvae at different salinity levels. Israel Journal of Aquaculture 45, 3–7. Saoud, I.P., Davis, D.A. and Rouse, D.B, 2003. Suitability studies of inland well waters for Litopenaeus vannamei culture. Aquaculture, 217: 373–383. Tacon, A.G.J. 1993. Feed formulation and on-farm feed management. In M.B. New, A.G.J. Tacon and I. Csavas, eds. Farm-made aquafeeds, p. 61–74. Proceedings of the FAO/AADCP Regional Expert Consultation on Farm–Made Aquafeeds. Bangkok, FAO– RAPA/AADCP. Zhang, B., W.H. Li, J.R. Huang, Y.J. W and R.L. Xu. (2010). Effects of artificial substrates on the growth, survival and spatial distribution of Litopenaeus vannamei in the intensive culture condition. Iran. J. Fish. Sci., 9: 293–304.20.

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Synthesis And Structural Investigation Of Some Trivalent Lanthanide Complexes Of Cloxacillin Rajesh Kumar Mishra1 & B.G.Thakur2 Department Of Chemistry, C.M.Sc. College L.N.M.U Darbhanga, Bihar-846004 North to Badri Narayan Mandir, New Colony Shubhankarpur, Darbhanga, Bihar-846006 INDIA 1,2

Abstract: Complexes of some trivalent lanthanides with Cloxacillin have been synthesized.The complexes have been formulated as [Ln(Clox)2(H2O)2]Cl Where Ln = La(III), Pr(III), Nd(III), Sm(III), Dy(III), Ho(III) and Er(III). The ligand and its metal complexes were characterized by their elemental analysis, molar conductance, magnetic susceptibility, IR and electronic spectral studies. Elemental analysis indicate 1:2 stoichiometry of synthesized complexes. In all the complexes, cloxacillin acts as a tridentate ligand with coordination involving the carboxylate-O, endocyclic-N of the ď ˘-Lactam ring and N-of amide. Complexes are eight coordinated. Finally the complexes have been screened for their antibacterial activity against E.Coli, K.Pneumoniae, S.Aureus and P.Aeruginosa....etc and found to be more potent against uncomplexed Cloxacillin. (Keywords : Ln(III)-Cloxacillin complexes, IR, Electronic, Antibacterial, Disc-Diffusion Method)

I. INTRODUCTION Cloxacillin (Clox) ,(2S,5R,6R)-6-{[3-(2-chlorophenyl)-5-methyloxazole-4-carbonyl]amino}-3,3-dimethyl-7oxo-4-thia-1-azabicyclo[3.2.0]heptane-2-carboxylic acid) [Figure-1] is a commonly used biologically important drug which has been shown to exert pronounced biological effects on various bacterial strains. Cloxacillin is used against staphylococci that produce beta-lactamase, due to its large R chain, which does not allow the betalactamases to bind.This drug has a weaker antibacterial activity than benzylpenicillin, and is devoid of serious toxicity except for allergic reactions. Cloxacillin is white crystalline powder, freely soluble in water, methanol and soluble in alcohol. H3C O H N

H3C

N O

O OH

Figure-1: Cloxacillin

Most living systems contain metal ions for their proper functioning1-4. Many studies concerning the biochemical and pharmaceutical effects of antibiotics when complexed with metal ions have been a subject of great interest for many scientist5-20. Based on these observations, we report here the synthesis, characterization and antimicrobial activity of a few Lanthanide(III)-Cloxacillin complexes. II.

EXPERIMENTAL

Chemicals used for synthesis were of AR grade and used without further purification. Metal salts i.e, LaCl3, PrCl3,NdCl3, SmCl3, DyCl3,HoCl3 & ErCl3 (of 99.97% Purity) were purchased from Indian Rare Earth Udyog Mandal, Kerala, India and the ligand i.e, Cloxacillin was purchased from CDH. Molar conductance of the newly synthesized metal complexes was measured by Systronics Conductivity Meter Model-304 in 1x10-3 M DMF Solution. Melting points of the complexes were obtained in sealed glass capillary and are still uncorrected. Magnetic Moment of the complexes were measured Gouys method in Bohr-Magneton unit using Hg[CO(NCS)4] as the calibrant in INORGANIC RESEARCH LABORATORY, L.N.M.U

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Darbhanga.Molecular weight of the complexes were determined by Camphor Rast Method. C, H & N were determined at CDRI Lucknow. Chloride in the complexes was estimated using Volhards Method 19. IR Spectra of the complexes were recorded by the cortesy of CDRI using KBr Pellets on a Perkin Elmer IR Spectrometer in the range of 4000-400 cm-1. Electronic spectra of the complexes were recorded by the courtesy of Dept. of Chemistry, IIT Delhi in the ranges of (10-35)kk. III.

BIOLOGICAL ACTIVITY

For determining antibacterial activity , the synthesized metal complexes have been screened against E.Coli, K.Pneumoniae, S.Aureus, P.aeruginosa using Agar-Plate diffusion technique21. Two to eight hours old bacterial inoculums containing approximately 104-106 colony forming units (CFU)/ml were used in these assays. The wells were dug in the media with the help of a sterile metallic borer with centers at least 24mm. Recommended concentration (100l) of the test sample (1mg/ml in DMSO) was introduced into the respective wells. Other wells suplemented with DMSO and reference antibacterial drug, imipenum served as negative and positive controls respectively. The plates were incubated immediately at 37 0C for 20h. Activity was determined by measuring the diameter of zones (mm) showing complete inhibition. Growth inhibition was compared with the standard drug. In order to clarify any participating role of DMSO in the biological screening, separate studies were carried out with the solutions of DMSO alone which showed no activity against any bacterial strains. IV.

PREPARATION OF METAL COMPLEXES

For the preparation of [Ln(Clox)2(H2O)2]Cl Complexes, Cloxacillin(5mmol, 2.1794g) was mixed with 2.5mmol of Ln(III) chlorides in a mixture of water-ethanol(25ml, 1:1v/v). The pH of the solution was adjusted to 7-8 with sodium aceate using digital pH meter. The mixture was refluxed for 1h on a water bath and concentrated to half volume. Then on cooling to room temperature, the colored complexes got precipitated slowly, which was filtered, washed repeatedly with distilled water and ethanol. Now, the complexes were dried over anhydrous calcium chloride in dessicator. V.

RESULTS AND DISCUSSION

All the Ln(III) complexes were obtained in powder form with characteristic color. All these complexes are nonhygroscopic. Analytical data, Magnetic moment, %yield, Molar conductance, Decomposition temperature, Melting points and color of all the seven complexes are reported in Table-1. At room temperature magnetic moment of the complexes are in good agreement with the theoretical values calculated by Van-Vleck22. Complexes are insoluble in common organic solvents, only soluble in DMF and DMSO. All the metal complexes decomposed above than 3000C. VI.

IR SPECTRAL STUDIES

IR Spectra of cloxacillin and their Ln(III)-complexes comparing mainly the IR frequencies of free and complexed cloxacillin are reported in Table-2. The IR Spectra of cloxacillin and their Ln(III) complexes were recorded in the range of 4000-400cm-1. The IR Spectra of all the complexes shows band at 3450-3400 cm-1 indicate the involvement of water molecule in the coordination sphere 23. Ligand exhibits strong absorption bands at 1185 cm-1, 2988 cm-1 due to C-N(-Lactam) and  (N-H) of Amide stretching vibrations which was shifted in the range of (1350-1040) cm-1 and (2950-2900) cm-1 respectively. The band at 1748 cm-1 assigned due to C-O of carboxylic acid of thiazolidine nucleus of cloxacillin which was shifted to lower frequencies in the range of (1630-1590) cm-1 in the spectra of all the Ln(III) complexes. A comparison of the IR Spectra of free ligand and complexed ligand provide evidence in support of mode of bonding i.e, Shifting of these bands in all the Ln(III) complexes indicate that there is a coordinate covalent bonding through endocyclic N of -Lactam, N of Amide , carboxylate-O of cloxacillin and ‘O’ of water molecule with Ln(III) central metal ion 24,25. All of the IR–spectral data confirms coordination number eight of the synthesized metal complexes. VII.

ELECTRONIC SPECTRAL STUDIES

Electronic spectral data for the solution of Ln(III)-Cloxacillin complexes investigated in CH3CN are reported in Table-3. For comparison, the spectral data for the corresponding aqueous salt solution are also given in the same table. Lanthanum(III) has no significant absorption in the UV-Visible region. The absorption bands of the Pr3+, Nd3+, Sm3+, Dy3+, Ho3+, & Er3+ in the visible and near infrared region appears due to the transitions from ground

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state i.e, 3H4, 4I9/2, 6H5/2, 6H15/2, 5I8 and 4I15/2 respectively to the excited states i.e, J-levels of 4fn-configuration26. The Nephlauxetic ratio () has been determined by the method of JØrgensen27 using the relation :

aquo - complex (1-) =

aquo

The covalence factor (b1/2), metal – ligand covalency % i.e, sinhas parameter (%) and covalency angular overlap parameter () have been calculated by using the following relations28 : 1/2 1 b1/2 = 2 [(1-) ]  

(1- )



x 100  =

1-1/2 1/2

The +ve values of (1-) and % supports the evidence of covalent bonding in all the synthesized Ln(III)complexes. The spectral profile of hypersensitive bands of Nd(III), Ho(III) and Er(III) complexes closely resembles that of eight coordinated complexes reported by Karrakar29,30 and is in good agreement with the other physico-chemical investigations. VIII.

MAGNETIC SUSCEPTIBILITY STUDIES

Except La(III) all the Ln(III) complexes are paramagnetic showing close agreement with the calculated values except for Sm(III), indicating an insignificant participation of the 4f-electrons in the bonding. Unlike the delectrons of the transition metal ions, the f-electrons of the lanthanide ions are almost unaffected by the chemical environment and the energy levels are same as in the free ion due to very effective shielding by the overlying 5s2 and 5p6 shells. The relatively high value obtained in the case of samarium(III) complex, which may be due to small J-J separation, which leads to the thermal population of the higher energy levels and show susceptibilities due to first order Zeemann effect31. IX.

BIOLOGICAL EVALUATION

A comparison of the diameter of inhibition zone of complex investigated showed that all the Ln(III) complexes exhibits higher antibacterial activity than the uncomplexed cloxacillin (Table-4). Table-1 : Analytical data of “Ln(III)-Cloxacillin” Complex. S.No

Complex

M. Formulae

M. Wt (Obs./Cal.)

Cloxacillin

C19H18ClN3O5S

435.88/435

[La(Clox)2 (H2O)2]Cl [Pr(Clox)2 (H2O)2]Cl [Nd(Clox)2 (H2O)2]Cl [Sm(Clox)2 (H2O)2]Cl [Dy(Clox)2 (H2O)2]Cl [Ho(Clox)2 (H2O)2]Cl [Er(Clox)2 (H2O)2]Cl

C38H36N6O12S2LaCl3

1076.25 (1076) 1078.15 (1078) 1081.23 (1081) 1087.11 (1087) 1099.09 (1099) 1102.21 (1102) 1104.07 (1104)

52.47 (52.41) 42.41 (42.37) 42.35 (42.30) 42.24 (42.18) 42.01 (41.95) 41.57 (41.49) 41.40 (41.37) 41.36 (41.30)

4.19 (4.13) 3.41 (3.34) 3.35 (3.33) 3.37 (3.33) 3.38 (3.31) 3.32 (3.27) 3.34 (3.26) 3.29 (3.26)

3. 4. 5. 6. 7. 8..

C38H36N6O12S2PrCl3 C38H36N6O12S2NdCl3 C38H36N6O12S2SmCl3 C38H36N6O12S2DyCl3 C38H36N6O12S2HoCl3 C38H36N6O12S2ErCl3

% Obs.(Cal.) N

9.69 (9.65) 7.84 (7.80) 7.82 (7.79) 7.82 (7.77) 7.74 (7.72) 7.68 (7.64) 7.64 (7.62) 7.65 (7.60)

8.07 (8.04) 9.81 (9.75) 9.78 (9.74) 9.78 (9.71) 9.69 (9.65) 9.59 (9.55) 9.54 (9.52) 9.53 (9.51)

......... 12.95 (12.91) 13.12 (13.07) 13.34 (13.32) 13.83 (13.79) 14.82 (14.74) 15.02 (14.97) 15.18 (15.12)

S.No

Complex

% Yield

Color

Decomposition Temp(0C)

M.Pt (0C)

m (Ohm1 cm2mol-1)

eff.(in B.M.)

1. 2.

Cloxacillin [La(Clox)2 (H2O)2]Cl [Pr(Clox)2 (H2O)2]Cl

........ 64

White Yellowish White Pale Yellow

........... 330

........ 260

.......... 10.7

......... Dia

335

263

14.3

5.64

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[Nd(Clox)2 (H2O)2]Cl [Sm(Clox)2 (H2O)2]Cl [Dy(Clox)2 (H2O)2]Cl [Ho(Clox)2 (H2O)2]Cl [Er(Clox)2 (H2O)2]Cl

5. 6. 7. 8.

Yellow.

332

273

13.4

3.67

Deep Yellow.

347

267

10.3

1.63

Light Yellow

342

266

17.4

11.5

Pale Yellow

377

275

15.9

10.17

Yellow

384

278

12.8

9.58

Table-2: IR Spectral data (in cm-1) of ligand and complexes : Functional Group

-Lactam

Ligand Cloxacillin

Complexes La(III)

Pr(III)

Nd(III)

Sm(III)

Dy(III)

Ho(III)

Er(III)

1185

1182

1178

1072

1317

1329

1105

1038

1748

1605

1592

1628

1614

1619

1626

1623

O -NHAmide

2988

2918

2907

2944

2934

2925

2947

2938

Table-3: Electronic spectral data along with band-assignment (in cm-1) and related bonding parameters of “Ln (III)- Cloxacillin” Complex. Complex

[Pr(Clox)2 (H2O)2]Cl

[Nd(Clox)2 (H2O)2]Cl

[Sm(Clox)2 (H2O)2]Cl

[Dy(Clox)2 (H2O)2]Cl [Ho(Clox)2 (H2O)2]Cl

[Er(Clox)2 (H2O)2]Cl

Band Assignments

H4 → 3P2 → 3P1 → 3P0 → 1D2 4 I9/2→4G9/2 →4G5/2,2G7/2 →4F9/2 →2S3/2,4F7/2 →4F5/2,4H9/2 6 H5/2→7F5/2 →4H7/2 →4I7/2 →4I15/2 6 H15/2→6F5/2 →4I15/2 →4G11/2 5 I8 → 5G3 → 5G5 → 5F2 → 5F3 → 5F4 → 5F5 4 I15/2→(2G,4F)9/2 → 4F7/2 → 4H11/2 → 4S3/2 → 4F9/2 3

Bands of Ln3+aqua ions (in kk)

Bands of Complex (in kk)

22.5 21.7 20.8 16.7 19.3 17.5 14.6 13.8 12.8 33.8 29.0 26.6 22.7 12.4 22.8 23.6 23.87 22.24 21.5 20.8 18.92 15.86 24.6 20.7 19.6 18.5 15.8

22.2 21.3 20.2 16.4 18.9 17.1 14.2 13.4 12.3 33.21 28.7 26.1 22.2 12.0 22.3 23.1 23.6 22.0 21.01 20.5 18.4 15.5 24.2 20.5 19.2 18.1 15.2

Calculated Bonding Parameter

(1- )



b1/2

(%)



0.0134 0.0185 0.0289 0.0180 0.0208 0.0229 0.0274 0.0290 0.0391 0.0175 0.0104 0.0188 0.0221 0.0323 0.0220 0.0212 0.0114 0.0108 0.0228 0.0145 0.0275 0.0227 0.0163 0.0097 0.0205 0.0217 0.0380

0.9866 0.9815 0.9711 0.9820 0.9792 0.9771 0.9726 0.9710 0.9609 0.9825 0.9896 0.9812 0.9779 0.9677 0.9780 0.9788 0.9886 0.9892 0.9772 0.9855 0.9725 0.9773 0.9837 0.9903 0.9795 0.9783 0.9620

0.0578 0.0680 0.0850 0.0670 0.0721 0.0756 0.0827 0.0851 0.0988 0.0661 0.0509 0.0685 0.0743 0.0898 0.0741 0.0728 0.0533 0.0519 0.0754 0.0602 0.0829 0.0753 0.0638 0.0492 0.0715 0.0736 0.0974

1.3581 1.8848 2.9760 1.8329 2.1241 2.3436 2.8171 2.9866 4.0691 1.7811 1.0509 1.9160 2.2599 3.3378 2.2494 2.1659 1.1531 1.0917 2.3331 1.4713 2.8277 2.3227 1.6570 0.9795 2.0929 2.2180 3.9501

0.0068 0.0093 0.0148 0.0091 0.0106 0.0117 0.0139 0.0149 0.0201 0.0088 0.0053 0.0095 0.0113 0.0165 0.0112 0.0108 0.0058 0.0055 0.0116 0.0073 0.0140 0.0116 0.0082 0.0049 0.0105 0.0111 0.0195

Table-4. Antibacterial activities of the ligand & its Ln(III) complexes [Diameter(mm) of Zones Showing complete inhibition of growth] Compound Cloxacillin [La(Clox)2 (H2O)2]Cl [Pr(Clox)2 (H2O)2]Cl [Nd(Clox)2 (H2O)2]Cl [Sm(Clox)2 (H2O)2]Cl [Dy(Clox)2 (H2O)2]Cl

Pseudomonas Aeruginosa 24 24 29 32 25 32

S. Aureus

E. Coli

Klebs. Pneumoniae

22 25 27 28 27 36

18 19 23 22 25 24

13 15 18 24 16 23

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[Ho(Clox)2 (H2O)2]Cl [Er(Clox)2 (H2O)2]Cl

27 36

25 29

27 26

26 31

CONCLUSION

On the basis of above discussion coordination number eight has been assigned for all the Ln(III)-Cloxacillin complexes. The tentative structure of the synthesized complexes may be as shown in the figure-2. H3C H3C

H N

H3C

O N Cl

C O

N O

O O

OH2 Ln

.Cl

H2O O O

O O Cl

C N O

N N

CH3 S

CH3

H CH3

Figure-2: Proposed structure of Ln(III)-Cloxacillin Complexes.. Where Ln(III)= La(III), Pr(III), Nd(III), Sm(III), Dy(III), Ho(III) and Er(III)

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] [27] [28] [29] [30] [31]

B.Norden, P. Lincoln, B. Akerman, In E. Tuitec : Sigel A, Sigel S, Editors : Metal ions in biological systems, New York: Marcel Dekker: vol 33, (1996) DR Williams: The metals of life, London: Van Nostrand (1971) H Sigel, DB Mc Cormick : On the discriminating behavior of metal ions and ligands with regard to their biological significance. Acct Chem Res :3(6), 201-207 (1970) DR. Williams : Metals, ligands & Cancer: Chem Rev, 72(3): 203-213, (1972) A Albert.: The variety of effects of chelating agents on organisms. Aust J Sci : 30(1), 1-7, (1967) S Kirschner, Y K Wei, D Francis, JG Bergman : Anticancer and potential antiviral activity of complex Inorganic Compounds : J Med Chem: 369-372: 9 ; (1966) Bhupinder Singh Sekhon : Metalloantibiotics and antibiotic mimics- an overview: J Pharm Educ Res Vol 1, 1, (June 2010) Li-June Ming : Structure and function of Metalloantibiotics, Med Res Rev, Vol. 23, No. 6, 697-762, (2003) JK Barton: Metal Nucleic Acid Interactions, Bio-Inorganic Chemistry, London, University Science Books, (1994) J Hinton, RE Koeppe : Complexing properties of Gramicidins. Metal Ion Biol Syst, 173: 19: (1985) CA Claussen, EC Long : Nucleic acid recognition by metal complexes of bleomycin. Chem Rev, 2797[32]: 99, (1999) G.S. Shields, H. Markowitz, G.E. Cartweight and M. Wintroe, Metal binding in medicine, J. Lippincott Co., 259, 1960 S. Krischner, Y.K. Wei, D. Frances, and J.G. Berjmann: J. Med. Chem., 9, 369, 1966 J .M. Clear : Coord. Che. Rev, 12, 249., 1974 J.R. Anacona: J. Coord. Chem.,54, 355, 2001 R.D. Stefano, M. Scopelliti, C. Pellerito, G. Casella, T. Fiore, G.C. Stocco, R. Vitturi, M. Colomba, L. Ronconi, I.D. Sciacca, and L. Pellecito: J. Inorg. Biochem, 98,534,2004 Rajesh Kumar Mishra, Parashuram Prasad Singh, B.G. Thakur : J. Chemtracks, 15(2), pp. 405-410, 2013 Parashuram Prasad Singh, Anjit Kumar Thakur, Rajesh Kumar Mishra, B.G.Thakur: J. Chemtracks, 15(2),pp. 453-458, 2013 B Thirumagal, Suman Malik and Bharti Jain, J. Ind. Council Chem. : Vol. 27, No. 1, pp. 76-79, 2010 A.I.Vogel:A textbook of quantitative Inorganic analysis, 5th edition (Longman group, London),(1989) H. Adams, N.A.Bailoy, D.E.Fenton, R. Mood and J.M.Latour: Inorg. Chim. Acta., 135, L1, (1987) J.H.Van Vleck and N. Frank : Phys. Rev., 34, 1494 (1929) K. Nakamoto: Infrared Spectra of Inorganic and Coordination Compounds, 2nd Edn, New York, Wiley Interscience(1970) J.R.Ferraro: Low frequency vibrations of Inorganic and Coordination Compounds: Plenum Press, New York (1970) L.J. Bellamy : The Infrared spectra of complex molecules. Methuen, London (1964) R.K. Agarwal and Himanshu Agarwal: Synth. React. Inorg. Met.-Org. Chem. 31, 263, (2001) C.K. Jorgenson and B.R. Judd: Mol. Phys., 8,281, (1964) S.P.Tandon, P.C. Mehta: J. Chem. Phys. 52, 4313, (1970) D.G. Karrakar, Inorg. Chem,6,1863., 1967 D.G. Karrakar, Inorg. Chem, 7, 473, 1968 J.P. Phillips and L.L. Merritt: J. Am. Chem. Soc., 71, 3984 (1949)

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ACKNOWLEDGEMENT The authors gratefully acknowledge to Professor L. K. Mishra, Science College, Patna University for providing valuable suggestions for carrying out this work. Authors also expresses their heartiest thankful to Prof. S. Jha, University PG Dept. of Chemistry, L.N.M.U Darbhanga for fruitful discussion of the results. For the more recent work concerning Elemental and Spectral Analysis of ligand & complexes, Courtesy of CDRI & Department of Chemistry, IIT Delhi is highly acknowledged. One of the author (B.G.Thakur) expresses their sincere thanks to UGC New Delhi for providing Major Research Project Under XIth Plan.

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

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Synthesis, Characterisation and Thermal studies of polymeric Cu(II), Zn(II) and Cd(II) complexes with 4-{(E)-1-(pyrimidin-2-ylimino)ethyl}-6((z)-1-(pyrimidin-2-ylimino)ethyl)benzene-1,3-diol and 4-{(E)-1-(ptolylimino)ethyl}-6-((z)-1-(p-tolylimino)ethyl)benzene-1,3-diol L. B. Roya, Pragya Kumarib, Madhu Bala* Department of Civil Engineering, National Institute of Technology, Patna-800005,Bihar (INDIA) b Department of Life Science (Chemistry), United Institute of Technology, Industrial Area, Nainy, Allahabad, U.P (INDIA) * Department of Chemistry, National Institute of Technology, Patna-800005, Bihar (INDIA) a

Abstract: Polymeric copper(II), zinc(II) and cadmium(II) complexes of polydentate ligand 1,5-bis(2pyrimidineaminoethylidene)-2,4-dihydroxy benzene (H2bispdb) and 1,5-bis(p-tolylaminoethylidene)-2,4dihydroxy benzene (H2bistdb) of compositions [CuL(H2O)2] n and [ML] n, (M= ZnII or CdII and H2L= H2bispdb or H2bistdb) were synthesised and characterised by analytical results, magnetic susceptibility, 1 HNMR, IR and electronic absorption studies. The thermal stability of zinc(II) and copper(II) complexes were studied and discussed. Keywords: Synthesis, Characterisation Polymeric Metal Complexes1,1’-4,6-dihydroxy-1,3phenylene)diethanone Schiff bases I. INTRODUCTION The design of polymeric architecture in coordination compounds by ligand assisted reaction have aroused considerable interest due to multidimensional utility of polymers in industrial Technology and catalysis[1-3]. An additional interest arose when the polydentate bridging ligands possess relevant importance in biological processes, because their coordination to metal serves as model of reference in Bioinorganic chemistry [4]. The most important stereochemical models for biological function and polydentate Schiff bases are considered to be the important donor molecules for coordination chemistry[5-6]. The Schiff base ligand containing nitrogen, oxygen and sulphur donor sites are of prime importance due to their strong ability for formation of coordination complexes of biological potentiality, catalytic activity and photochromic properties [7-8]. In present investigation we have designed quadridentate Schiff bases 4-{(E)-1-(p-tolylimino)ethyl}-6-((z)-1-(ptolylimino)ethyl)benzene-1,3-diol (H2bistdb) and 4-{(E)-1-(pyrimidin-2-ylimino)ethyl}-6-((z)-1-(pyrimidin-2ylimino)ethyl)benzene-1,3-diol (H2bispdb), capable of forming polymeric complexes with metal ions and reported the synthesis and characterisation of their Cu(II), Zn(II) and Cd(II) complexes. II. EXPERIMENTAL These ligands were prepared by condensing 1,1’-4,6-dihydroxy-1,3-phenylene)diethanone[8,9] (acdp) with appropriate amine, p-tolylamine and 2-aminopyrimidine in 1:2 molar proportion in ethanol containing a few drops of acetic acid.

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1,1’-4,6-dihydroxy-1,3-phenylene)diethanone (acdp) was prepared by Fries rearrangement in anhydrous ZnCl 2 on reacting dry acetic anhydride and resorcinol by reported method [8,9] . Preparation of Schiff bases Preparation of H2bistdb and H2bispdb About 19.5 gm (.01 mole) 1,1’-4,6-dihydroxy-1,3-phenylene)diethanone was taken in 100 ml ethanol and refluxed with (0.2 mole) of appropriate amines (p-toludine or 2-aminopyrimidine) for three to four hours on a steam bath by adding 2 ml acetic acid. The cream yellow Schiff bases began to separate slowly. The refluxate was concentrated and cooled to ice temperature. The product separated was collected on a filter, washed with cold ethanol and dried in a dessicator over CaCl2. The dried samples were recrystallised from THF ethanol mixture (1:1) and dried samples were analysed for carbon, hydrogen and nitrogen contents. The ligand H 2bistdb was found to contain C; 77.22%, H; 6.65%, N; 7.43% and it (C24H24N2O2) requires C; 77.42%, H; 6.45%, N; 7.52%. Melting point recorded 2430c (uncorrected). The compound H2bispdb was found to contain C; 61.88%, H; 4.68%, N; 23.96%. The compound H2bispdb (C18H16N6O2) requires C; 62.06%, H; 4.59% and N; 24.14%. Melting point of H2bispdb recorded 2610c. Preparation of complexes [CuL(H2O)2] and [ML]n, (M= Zn2+ or Cd2+ and H2L= H2bistdb or H2bispdb) About 0.05 mole of metal acetate was dissolved in 30 ml aqueous ethanol and added slowly with stirring to appropriate ligand (0.05 mole) dissolved in hot THF and ethanol mixture, when tary product separated slowly. The products were titurated with ether when fine powdered products were obtained. The products were collected on filter, washed with methanol and ether and dried in a desiccators over CaCl2. The analytical results of complexes are recorded in Table-A. Materials and Physical measurements: All solvents and chemicals used were E.Merck or BDH products. Metal acetates were E.Merck extra pure chemicals. The magnetic susceptibility of the complexes were determined by Gouy method at room temperature. The i.r spectra of ligands and their complexes were recorded as KBr optics in the range of 4004000cm-1 on Shimadzu 8201 FTIR spectrophotometer at IIT Patna. The electronic absorption spectra of ligands and their complexes were recorded on Shimadzu U-V 2500 PC series spectrometers. 1HNMR spectra of ligand were recorded in DMSO-d6 solution with Brucker AV 300 NMR spectrometer. Mass spectra were recorded on GEOL G.C Mate spectrometer at IIT Chennai. The results of C, H, N and TG, DTA analyses were obtained from BIT Mesra, Ranchi. Table-A: Elemental analysis and physical data of complexes (Molar electrical conductance value in DMF at 310c) Compound

Colour

Ωαohm1 mol-1cm-2

[Cu(bistdb)(H2O)2]n [Cu(bispdb)(H2O)2]n [Zn(bistdb)]n

Brick red Brick red Light cream

% Elemental analysis Found (Calc.) Metal Carbon Hydrogen 13.43(13.53) 61.19(61.33) 5.66(5.54) 14.17(14.26) 48.31(48.55) 3.82(4.04) 14.92(15.02) 66.01(66.14) 5.13(5.05)

[Zn(bispdb)]n

Light cream

15.73(15.89)

52.41(52.50)

3.48(3.40)

20.18(20.41)

[Cd2(bistdb)]n [Cd2(bistdb)]n

Creamyellow Creamyellow

23.18(23.30) 24.38(24.52)

59.53(59.78) 48.49(48.55)

4.69(4.56) 3.27(3.14)

5.94(5.80) 18.13(18.32)

3 4

Nitrogen 6.01(5.96) 18.62(18.85) 6.51(6.43)

4 6 5

III. RESULTS AND DISCUSSION The proton NMR spectrum of 4-{(E)-1-(p-tolylimino) ethyl}-6-((z)-1-(p-tolylimino)ethyl)benzene-1,3-diol (H2bistdb) shows two sharp singlet (1HNMR-Fig I) located at δ= 2.527 ppm and δ= 4.282 ppm assigned as tolyl

CH3proton and ethylidene proton signals. The multiplets observed in 1HNMR spectrum of H2bistdb between (δ= 7.346-7.874 ppm) are assigned as phenyl ring proton signals. The proton signals at δ= 8.001 and 8.027 ppm are attributed to phenolic proton signals. The 1HNMR spectrum of 4-{(E)-1-(pyrimidin-2ylimino)ethyl}-6-((z)-1-(pyrimidin-2-ylimino)ethyl)benzene-1,3-diol (H2bispdb) shows one strong singlet at δ= 4.268 ppm can be assigned to ethylidene CH3 proton signal. The multiplets between δ= 7.105 and 7.935 ppm are assigned to phenyl and pyrimidine ring (CH) proton signals. The phenolic proton signals of H 2bispdb were observed at δ= 8.145 and 8.195 ppm. The mass determination of ligand H 2bistdb shows molecular mass peak (Fig-M-T-1) at 373 for M++1 peak supporting molecular mass 372 for ligand. The base peak at 105 indicated the formation of toluidine fragment. The mass spectrum of ligand 4-{(E)-1-(pyrimidin-2-ylimino)ethyl}-6-((z)-1(pyrimidin-2-ylimino)ethyl)benzene-1,3-diol(H2bispdb) show M++1 peak at 349 supporting molecular mass to be 348. The base peak at 79 indicated the formation of pyrimidine fragment. The mass and 1HNMR spectra of ligand H2bistdb and (H2bispdb) are consistent with their assigned structure.

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The ligands are potent quadridentate (N-O) donor coordinating molecules capable of forming bridging group in polymeric complexes. The elemental analysis of the complexes correspond to composition [CuL(H 2O)2]n, (H2L= H2bistdb or H2bispdb) and [ML]n, (M= ZnII or CdII and H2L= H2bistdb or H2bispdb). The complexes are quite stable in air at elevated temperature. The complexes are insoluble in water, methanol and ethanol but dissolve appreciably in DMF and DMSO. The complexes partially dissolve in dioxan and THF. The DMF solutions of complexes are almost non conducting (Ωα = 4-5 ohm-1 mol-1 cm2) supporting their non ionic characters[10]. As expected zinc(II) and cadmium(II) complexes are diamagnetic and copper(II) complexes are paramagnetic. The effective magnetic moment value of [Cu(bistdb)(H2O)2] and [Cu(bispdb)(H2O)2] at room temperature are 1.87 and 1.89 B.M respectively occur in the range of magnetically dilute distorted octahedral copper(II) complexes [1112] . The electronic absorption spectrum of H2bistdb in ethanol shows electronic bands at 234, 262 and 330 nm assigned as σ π *, ππ* and nπ* transitions. The ligand H2bispdb shows electronic transitions at 228, 256 and 305 nm assignable as σ π *, ππ* and nπ* transitions. These transitions are obscured in complexes due to strong charge transfer transitions of complexes. The electronic absorption spectrum of DMF solutions of zinc(II) and cadmium(II) complexes show strong absorption below 390 nm due to charge transfer absorption. The Cu(II) complexes [Cu(bistdb)(H2O)2] shows a medium band at 520 nm and weak broad band at 680-690 nm attributed to 2B1g2B2g and 2B1g2A1g , 2Eg transitions. The brick red copper(II) complex [Cu(bispdb)(H 2O)2] shows strong absorption below 400 nm due to charge transfer transition. The medium band at 530-540 nm observed has been attributed to 2B1g2B2g and a broad band at 670-700 nm to 2B1g2A1g , 2Eg transitions.

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The i.r. spectra of ligands and their complexes display characteristic IR vibrations of phenolic OH and ethylideneimino (C=N) groups. The i.r spectrum H2bistdb shows ν(OH) vibration at 3501 and broad band at 3190-2973 cm-1 due to hydrogen bonded phenolic OH group. The methyl (CH 3) group stretching band can be assigned to i.r. band at 2973 cm-1. The phenolic group stretching band of ligand disappears in its complexes supporting deprotonation of (OH) proton on coordination. The ligand (IR-Fig-A3) show ν(C=N) vibration at 1642 cm-1 which is shifted to lower vibrations and observed near 1600±5 cm-1 supporting coordination of ligand through (C=N) nitrogen. A large number of i.r. bands in finger print region are assigned to phenyl group and ethylidene part skeletal vibrations.

Diaquo copper(II) complex [Cu(bistdb)(H2O)2] shows a broad strong band at 3409 cm-1 for ν(H2O) vibration and a medium band at 663 cm-1 for rocking band of coordinated H2O group. The ν(C=N) of ligand was shifted to lower wave number and located at 1604 cm-1 supporting coordination of (C=N) nitrogen to copper (II).The ligand H2bispdb shows phenolic group ν(OH) at 3388 cm-1 and broad band near 3195 cm-1 due to hydrogen bonded phenolic group. The ν(C=N) of ligand (IR-Fig-M2) was observed 1634 cm-1 which is shifted to lower frequency in almost all complexes and observed at 1600±5 cm-1. The phenolic group ν(C-O) of H2bispdb was assigned to a band at 1156 cm-1 which is shifted to higher wave number and observed near 1350±10 cm-1 supporting coordination of deprotonated phenolic oxygen atom.

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The TGA and DTA studies of complexes were performed in the range of 40 0-7200c in static air. The TG curve of copper(II) complex [Cu(bistdb)(H2O)2] starts loss in weight at 1600c giving DTA maxima at 1800c and DTG peak at the same temperature and give stable product at 210 0c. The weight loss corresponds to 2H2O per copper atom supporting coordination of both H2O molecule for each copper(II) in complex. The product formed at 2100c is very stable and remains stable upto 5400c without loss in weight showing an exothermic DTA maxima 4800c indicating phase change in complex. The complex started slow decomposition with weight loss and showing DTG maxima at 5700c and an exothermic DTA peak at 5750c. The loss in weight continues upto 6100c giving stable product probably CuO. The weight of residue required is 16.94 and observed for formation of CuO is 17.02%. The TG curve shows that the product [Cu(bispdb)(H2O)2] is also stable upto 1250c and starts loss in weight giving DTG maxima at 1700c and an exothermic DTA peak at 1700c. The loss in weight continues upto 1900c giving stable product [Cu(bispdb)]n. The loss incurred is 8.24% and calculated for loss of two coordinated water is 8.08%. The TG curve shows that product is stable upto 490 0c but shows an exothermic DTA maxima at 3800c attributable to change in phase structure of complexes. The TG curve shows that on heating after 490 0c the complex starts decomposes showing an exothermic peak at 5300c and DTG maxima at 5250c. The loss continues giving stable metal oxide at 6000c. The loss in weight corresponds to expected loss 82.15% for formation of CuO. The Zn(II) complex, [Zn(bistdb)] n is stable upto 3300c with an exothermic DTA maxima at 3500c indicating a phase change forming octahedral environment around metal from tetrahedral one. TG curve shows that complex starts decomposing slowly after 440 0c giving metal oxide at 520-5300c. A broad DTA maxima at 4900c indicated burning and decomposition of complex between 440-5200c. The cadmium(II) complex [Cd(bistdb)]n is also stable to heat below 4200c with phase change at 3800c as indicated by an exothermic DTA maxima. The complex starts decomposing at 450 0c as indicated by weight loss in TG curve. The complex decomposes completely between 450-520 in static air giving stable metal oxide (CdO). The observed weight of residue 26.82% corresponds to expected weight of CdO, 26.61%. The decomposition process is exothermic, showing DTA maxima at 480 0c. The exceptionally high thermal stability of complexes supported polymeric structure of complexes. The copper(II) complexes have higher stability than Zn(II) and Cd(II) complexes indicating strong coordination of Cu(II) than that of Zn(II) and Cd(II) complexes. Thus from the studies of molecular composition and physical data, the following polymeric structure is suggested for H2bistdb or H2bispdb complexes of Cu(II), Zn(II) and Cd(II).

Conclusion The ligand H2bistdb and H2bispdb forms thermally stable polymeric complexes with Cu(II), Zn(II) and Cd(II). These ligand coordinates as N, O, donor chelating molecule forming bridge between metal atoms. Acknowledgement: Thanks are due to authority of IIT Patna for IR and UV spectral measurement and B.I.T Mesra for C, H, N analysis, TG and DTA measurements. References: [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

A. I. Balch, Prog. Inorg. Chem. vol.41, 1994, pp. 239. G. R. Newkome, Chem. Rev., vol.93, 1993, pp. 2067. N. H. Tarte, Hyun Yong. Cho, and IhI. Woo. Seong, Macromolecules vol. 40, 2007, pp. 8162-8167. P. A. Vigato and S. T. Tamburine, Coord. Chem. Rev., vol. 248, 2004, pp. 1717. S. R. Collinson and D. E. Fenton, Coord. Chem. Rev., vol. 19, 1996, pp. 148. M. Calligaris and L. Randaccio, “Schiff bases as acyclic polydentate ligands” In comprehensive coordination chemistry, Eds. G. Wilkinson, R. D. Gillard, and J. A. Mc Cleverty, Pergaman Press, Oxford, vol. 2 1987, pp.715. Dikonda, S. Rani, V. A. Parupalli, Lakshami and V. Jagadyaraju, Trans. Met. Chem. vol.19, 1994, pp.75. Anjaneyalu and A. S. R. Prasad, Current science, vol. 48 1979, pp. 300. A. Balasubramanian and P. Sankaran, Indian. J. Chem., Sec B vol. 20B(11), 1981, pp. 989. W. J. Geary, Coord. Chem. Rev. vol. 7 1971, pp.81. E. Foster and D. M. L. goodgame, Inorg. Chem., vol. 4, 1965,pp. 823, A. B. P. Lever “Inorganic Electronic Spectroscopy” Elsevier, Amsterdam, 1968. M. Joseph, A. Sreekanth, V. Suni, and M. R. P. Kurup Spectro Chim. Acta. vol. 74, 2009, pp. 907. K. B. Deepa and K. K. Aravindakshan, Synth. React. Inorg. Metal. or Chem., vol. 30, 2000, pp. 1601. K. Nakamotto “Infrared and Raman Spectra of Inorganic and coordination compounds” 3 rd Ed, John Wiley & sons, New York, U.S.A, 1978.

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A NEWER APPROACH TO GREEN EARTH - SOLAR-INDUCED HYBRID BIOMASS FUEL CELL Dr. Vanita Kumari Sapra HOD Chemistry, DAVCC, Faridabad, Haryana, India Abstract: Green energy, sustainability, renewable energy should be the future of next generation. Sustainable energy is energy which has minimal negative impacts; both in its production and consumption on human health and the environment, and that can be supplied continuously to future generations. With growing demand for better energy, this paper describes a new type of low-temperature fuel cell that directly converts biomass to electricity with assistance from a catalyst activated by solar or thermal energy - The hybrid fuel cell. Keywords: Green, Hybrid, Biomass, Photo catalyst, Fuel I. INTRODUCTION The new hybrid fuel cell is developed by researchers at the Georgia Institute of Technology .The hybrid fuel cell is a new type of low-temperature fuel cell that directly converts biomass to electricity with help of a catalyst activated by solar or thermal energy. The hybrid fuel cell can use various types of biomass sources, including starch, cellulose, lignin – and even switch grass, powdered wood, algae and waste from poultry processing. The device could be used in small-scale units to provide electricity for developing nations, as well as for larger facilities to provide power where significant quantities of biomass are available. II. PROBLEMS RELATED WITH BIOMASS FUEL CELL The challenge for biomass fuel cells is that the carbon-carbon bonds of the biomass – a natural polymer – cannot be easily broken down by conventional catalysts, including expensive precious metals. To overcome that challenge, microbial fuel cells in which microbes or enzymes break down the biomass are developed. But that process has many drawbacks: 1. Power output from such cells is limited. 2. Microbes or enzymes can only selectively break down certain types of biomass. 3. The microbial system can be deactivated by many factors. III. SOLUTION TO PROBLEMS: HYBRID FUEL CELL Solutions to the problems is hybrid fuel cell which is a new system in which chemistry is altered to allow an outside energy source to activate the fuel cell’s oxidation-reduction reaction. The fuel cell uses polyoxometalates as the photocatalyst and charge carrier to generate electricity at low temperature. This solarinduced hybrid fuel cell combines some features of solar cells, fuel cells and redox flow batteries. IV. WORKING In the new system, the biomass is ground up and mixed with a polyoxometalate (POM) catalyst in solution and then exposed to light from the sun – or heat. A photochemical and thermochemical catalyst, POM functions as both an oxidation agent and a charge carrier. POM oxidizes the biomass under photo or thermal irradiation, and delivers the charges from the biomass to the fuel cell’s anode. The electrons are then transported to the cathode, where they are finally oxidized by oxygen through an external circuit to produce electricity. The biomass and catalyst will not react at room temperature. But the reaction begins when exposed to light or heat. The POM introduces an intermediate step because biomass cannot be directly accessed by oxygen.

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IV. WORKING OF HYBRID FUEL CELL A. POWER GENERATED The power density of the solar-induced hybrid fuel cell powered by cellulose reaches 0.72 mW cm−2, which is almost 100 times higher than cellulose-based microbial fuel cells and is close to that of the best microbial fuel cells reported in literature. ADVANTAGES 1. The system gives major advantages, including combining the photochemical and solar-thermal biomass degradation in a single chemical process, leading to high solar conversion and effective biomass degradation. 2. It also does not use expensive noble metals as anode catalysts because the fuel oxidation reactions are catalyzed by the POM in solution. 3. The hybrid fuel cell can use unpurified polymeric biomass without concern for poisoning noble metal anodes because the POM is chemically stable. 4. Unlike most cell technologies that are sensitive to impurities, the cell reported in this study is inert to most organic and inorganic contaminants present in the fuels. 5. The system can use soluble biomass, or organic materials suspended in a liquid. In experiments, the fuel cell operated for as long as 20 hours, indicating that the POM catalyst can be re-used without further treatment. 6. Beyond the ability to directly use biomass as a fuel, the new cell also offers advantages in sustainability – and potentially lower cost compared to other fuel cell types. V. FUTURE PROSPECTIVE This system can be optimized by having a better understanding of the chemical processes involved and its improvement. Then this type of fuel cell could have an energy output similar to that of methanol fuel cells in the future. VI. CONCULSION Sustainable materials can be used without any chemical pollution. Solar energy and biomass are two important sustainable energy sources available to the world today. This system would use them together to produce electricity while reducing dependence on fossil fuels. Thus, the new HYBRID FUEL CELL is an amazing and beneficial innovation for future. REFERENCES [1]. [2]. [3]. [4].

Rühl, C., Appleby, P., Fennema, J., Naumov, A. & Schaffer, M. Economic development and the demand for energy: a historical perspective on the next 20 years Tollefson, J. & Monastersky, R. The global energy challenge: Awash with carbon Deng, W., Zhang, Q. & Wang, Y. Polyoxometalates as efficient catalysts for transformations of cellulose into platform chemicals. Dalton Transact .Article in the journal Nature Communications

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Length-weight relationship and condition factor of Tetraodon cutcutia (Ham) from Neematighat, Assam (India) 1

P. Karmakar & 2S.P.Biswas Dept. of Life Sciences, Dibrugarh University, Assam- 786004, INDIA Abstract: The length-weight relationship and condition factor for Tetraodon cutcutia were carried out from Neematighat of Jorhat district of Assam between April, 2011and March, 2013. This paper throws light on the changes in the ponderal index (K) and growth coefficient (b), (length-wise, month-wise and seasonally). Length-weight relationship and relative condition factor (K) for a sample size of 285 specimens were calculated on monthly basis. A wide fluctuation in growth coefficient (b) in the fishes was observed. The ‘b’ value ranged from 1.13-2.33 in male and 2.362-7.048 in female; seasonally the b value varied from 0.476 (pre-monsoon) to 1.622 (post-monsoon) in case of male and 0.98 (winter) to 3.086 (monsoon) in female. Similarly, the K value ranged from 3.864 (monsoon) to 6.279 (winter) in male and from 3.196 (pre-monsoon) to 3.628 (winter) in female. Key words: Tetraodon cutcutia, length-weight relationship, condition factor, Assam.

I. Introduction The study of length-weight relationship is of paramount importance in fishery science, as it assists in understanding the general well being and growth patterns in a fish population. According to Bashir et al. (1993) the length-weight relationship of fish varies depending upon the condition of life in aquatic environment. The study of the condition factor is thus important for understanding the life cycle of a fish species and contributes to adequate management of the fish species and, therefore, to the maintenance of equilibrium in the ecosystem. Length-weight relationship is of great importance in fishery assessments (Garcia et al., 1998; Haimovici and Velasco, 2000). The mathematical parameters of the relationship between the length and weight of fish furnish further information on the weight variation of individuals in relation to their length (condition factor, K). This factor estimates the general well-being of the individual and is frequently used in three cases: (a) Comparison of two or more co-specific populations living in similar or different conditions of food, density or climate; (b) Determination of period and duration of gonadal maturation and (c) Observation of increase or decrease in feeding activity or population changes, possibly due to modifications in food resources. Tetraodon cutcutia (Hamilton-Buchanan) is considered as a trash fish in the Indo-Gangetic basin. It belongs to the family Tetraodontidae under order Tetraodontiformes. Commonly known as Gangatop in Assam, the species is widely distributed throughout the plains of N.E. India, region and the fish is utterly neglected as it has no food value. However, this is a potential aquarium fish. It is a small sized fish, measures about 6 to 9.2cm in length (Nath and Dey, 2000). It mainly occurs in riverine habitats but also found in the beels (wetlands). Like other parts of the Brahmaputra basin, T. cutcutia has a well established population at Neematighat and its adjoining areas in Jorhat District of Upper Assam. The present communication deals with the length-weight relationship of the species. II. Material and methods Samples were collected from Neematighat of Jorhat district. Monthly samplings were carried out from April 2011 through March 2013. The specimens were measured to the nearest cm and weighted to the nearest gm. The length-weight relationship was based on 285 specimens collected during the study period. Length-weight relationship of T. cutcutia was calculated following Le Cren (1951) -W=aLb , where W= weight, L= length and ‘a’ and ‘b’ are initial growth and growth coefficient respectively. The values of constant a and b were estimated from log transformation values of length and weight:-Log W = log a + b log L. The correlation coefficient (r) was also estimated to determine the degree of linear relationship between the length and weight of samples. For estimation of general well being of the fish, ponderal index or condition (K) factor was used. Condition factor (K) was calculated from the expression (Bagenal, 1978): K=100W/L 3 where, W is the whole body weight in gm and L the total length in cm.

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Table1: Length-weight relationship and condition factor of Tetraodon cutcutia Length group (cm) 2.5-3.5 3.5-4.5 4.5-5.5 5.5-6.5 6.5-7.5 3-4.5 4.5-6 6-7.5 7.5-9 9-10.5

Sex

Log W=Log a+ b log L

M M M M M F F F F F

5.271 6.722 4.38 3.227 2.66 7.048 4.071 3.09 2.362 2.611

1.13 1.83 1.70 2.33 1.88 1.19 3.25 2.74 5.32 2.93

Log -3.642+1.13 log L Log -2.962+1.83 log L Log -2.734+1.7 log L Log -7.027+2.329 log L Log -2.561+1.879 log L Log -0.325+1.19 log L Log -11.183+3.252 log L Log -9.04+ 2.74 log L Log -3.52+5.32 log L Log -0.923+2.932 log L

0.65 0.65 0.37 0.62 0.04 0.69 0.60 0.43 0.53 0.49

Table 2: Seasonal variation of length-weight relationship and K-factor of T. cutcutia Season Winter (Dec-Feb) Pre-monsoon May) Monsoon (Jun-Aug) Post-monsoon Nov)

(Mar-

(Sept-

Sex M F M F M F M F

K 6.279 3.628 4.221 3.196 3.864 3.479 4.452 3.357

b 0.986 0.98 0.476 1.297 1.307 3.086 1.622 2.837

Log W=Log a+ b log L Log-0.173+0.986 log L Log -0.223+0.98 log L Log-3.101+0.476 log L Log-0.142+1.297 log L Log-0.985+1.307 log L Log-3.31+3.086 log L Log-2.584+1.622 log L Log-9.358+2.837 log L

r 0.95 0.95 0.83 0.72 0.81 0.82 0.84 0.74

Table 3: Month-wise length-weight relationship and K-factor in T. cutcutia Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

Sex M F M F M F M F M F M F M F M F M F M F M F M F

K 6.695 4.502 6.36 2.984 4.67 3.08 3.81 3.099 4.35 3.485 3.68 3.166 3.82 3.845 4.05 3.42 3.96 3.669 4.36 3.328 5.37 2.579 5.77 3.539

b 0.822 1.082 1.028 0.024 2.872 1.75 1.196 1.026 1.09 1.976 1.285 2.854 1.28 3.386 1.71 2.598 1.57 2.485 1.32 1.054 4.74 1.185 1.17 1.082

LogW=Log a+b logL Log-0.856+0.822 logL Log-0.328+1.082 logL Log-0.14+1.028 logL Log-5.857 + 0.024 logL Log-8.686+2.872 logL Log-3.245+1.75 logL Log-0.63+1.196 logL Log-1.207+1.026 logL Log-0.36+1.09 logL Log-4.031+1.976 logL Log-0.938+1.285 logL Log-9.521+2.854 logL Log-0.81+1.28 logL Log-11.374+3.386 logL Log-3.10+1.71 logL log-7.51+2.598 logL Log-2.44+1.57 logL Log-5.506+2.485 logL Log-0.91+1.32 logL Log-0.40+1.054 logL Log-6.29+4.74 logL Log-0.204+1.185 logL Log-0.58+1.17 logL Log-0.407+1.082 logL

r 0.96 0.95 0.95 0.91 0.69 0.92 0.89 0.79 0.82 0.69 0.76 0.86 0.81 0.82 0.87 0.83 0.66 0.93 0.90 0.69 0.94 0.91 0.98 0.96

Legend: r = Coefficient of correlation, K= condition factor, a =initial growth; b =growth coefficient III. Results The growth coefficient (b) values showed seasonal fluctuation. In case of male, it ranged from 1.13-2.33 and in female it varied from 1.19-5.32 (Table-1). The b value was found lowest (0.476) in pre-monsoon and highest (1.622) in post-monsoon for male (Table 2) whereas in female it ranged from 0.986 (winter) to 3.086 (monsoon). The value in male was recorded minimum (0.822) in January and that of maximum (4.74) in November in male while in female the minimum value (0.024) was observed in February and that of maximum (3.386) in July (Table3). The condition factor (K) value, growth coefficient (b) and coefficient of correlation (r) value were calculated sex wise and seasonally. The K value ranged from 2.66-6.722 in case of males whereas it ranged from 2.362-7.048 in female specimens (Table 1). Seasonally, the value ranged from 3.864 (monsoon) to 6.279 (winter) in male and from 3.196 (pre-monsoon) to 3.628 (winter) in female (Table 2). Incidentally, K value was recorded highest in January in both the sexes (Table 3).

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The coefficient of correlation (r) ranged from 0.04-0.65 in males and from 0.43-0.69 in females (Table 1). Seasonally, r varied from 0.81 (monsoon) to 0.95 (winter) in male specimens (Table2) and that of female, it fluctuated between 0.72 (pre-monsoon) and 0.95 (winter). The coefficient was very high in December for both males and females (Table 3). IV. Discussion Arslan et al. (2004) stated that it is usually easier to measure length than weight and weight can be predicted later on using the length-weight relationship. In this study variability was found between the exponent (b) and condition factor. These differences might have been caused by the methods of measurement, and/or seasonal fluctuations, or variability in sampling. According to Le Cren (1951) the variation in ‘b’ value is due to environmental factors, seasons, food availability, sex, life stage and other physiological factors. The lengthweight relationship of T. cutcutia exhibits highly positive correlation. A characteristic of length-weight relationship in fishes is that the value of the exponent (b) is 3 when growth is isometric (without changing shape). If b value is different from 3, growth is said to be allometric (fish changes shape as it grows larger). The variations in fish sizes indicate that the fish population ranged from immature specimens to fully matured ones. This also suggests differences in their growth (Forta et al., 2004). Fish specimens of a given length, exhibiting higher weight are said to be in better condition (Anyanwu et al., 2007). The ‘b’ values of T. cutcutia exhibited allometric growth. Allometric growth may be negative (b<3) or positive (b>3). According to Wooton (1992) allometric growth is negative if the fish gets relatively thinner as it grows larger and positive if it gets plumber as it grows. In biological studies, L-W relationships enable seasonal variations in fish growth to be followed and the calculation of condition indexes (Richter et al. 2000). In the present study, a high correlation value (r) between length-weight of T. cutcutia indicate a strong associationship between these body parameters. The condition factor is an indicator of the environmental suitability for the resource. In this study variability was found between the exponent (b) and means of condition factors (K). These differences might have been caused by the methods of measurements, and/or seasonal fluctuations or variability in sampling (Safran 1992). The condition factor helps in the study of functional relationship between length and weight and the well-being of the fish. The condition factor of fishes influenced by a number of factors such as the onset of maturity (Hoda, 1987), spawning (De-Silva and Silva, 1979; Al-Daham and Wahab, 1991), sex and maturity (Gowda et al., 1987; Doddamani and Shanbouge 2001) and pollution (Bakhoum, 1999 and Devi et al., 2008). The condition factor (K) reflects, through its variations, information on the physiological state of the fish in relation to its welfare. However, condition factor also showed variability that might have been caused by several environmental and physiological factors. Bakare (1970) and Fagade (1979) opined that condition factor decreased with increase in length. Similarly, Welcome (1979) viewed that K- factor influenced the reproductive cycle in fish. V. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

Al-Daham, N. K and N. K. Wahab.: Age, growth and reproduction of the greenback mullet, Liza subviridis (Valenciennes), in an estuary in Southern Iraq. J. Fish Biol., (1991) 38: 81-88pp. Anyanwu, P.E., B.C.Okoro,; A.O. Anyanwu,.; M.A. Matanmi,; B.I Ebonwu,.; I.K. Ayaobu- Cookey,; [2] M.B. Hamzat,;. F. Ihimekpen,; and S.E. Afolabi,: Length-weight relationship, condition factor and sex ratio of African mud catfish (Clarias gariepinus) reared in indoor water recirculation system tank. Res. J. Bilo. Sci., (2007) 2(7) 780-783pp. Arslan, M. A.; Yildirim and S. Bektas,: Length-weight relationship of Brown trout (Salmo trutta L), inhibiting Kan stream, Coruh Basin, North-Eastern Turkey. Turk. J. Fish. Aquatic Sci., (2004) 4: 45-48pp. Bagenal, T. B.. Aspects of fish fecundity. In: S.D. Gerking (Ed) Ecology of Freshwater fish Production. Blackwell Scientific Publications., Oxford: (1978) 75- 101pp. Bakare, O: Bottom Deposits as Food of Inland Fresh Water Fish. In: Kainji, A Nigerian Man-Made Lake. S. A. Visser, (Ed.), (1970) Kanyi Lake Studies Vol. 1. Ecology Published for the Nigerian Institute. Bakhoum, S. A: Comparative study on length-weight relationship and condition factor of the genus Oreochromis in polluted and nonpolluted parts of lake Maruit, Egypt. Bull. Nat. Inst. Oceanogr. Fish (Egypt). (1999) Bashir, Z. I; Z. A. Bortolotto,; C. H Davis,; N; A. Berretta, J. Irving,; A.J Seal,; , J. M Henley; D. E Jane,; J. C Watkins, and G. L. Collingridge: Induction of LTP in the hippocampus needs sympatric activation of glutamate metabotropic receptors. Nature, 363: (1993) 347-350pp. De, Silva, S. S and E. I.L Silva.: Biology of young grey mullet, Mugil cephalus (L). Populations of a costal lagoon in Srilanka, J. Fish Biol. 15: (1979) 9-20pp. Devi, J. O; T. S Nagesh,; S. K Das, and B. Mandal. : Length-weight relationship and relative condition factor of Pampus argenteus (Euphrasen) from Kakdwip estuarine region of West Bengal. J. Inland Fish. Soc. India.40(2): (2008) 70-73pp. Doddamani, M. T. J. R and S. L. Shanbhogue.: Length-weight relationship and condition factor of Stolephorus bataviensis from Mangalore area. Indian J. Fish. 48(3): (2001) 329-332pp. Fagade, S. O.,: Observation of the biology of two species of Tilapia from the Lagos lagoon Nigeria. Bull. Inst. Fond Afr. Nore (Ser. A) 41: (1979) 627-658pp. Forta, L. O.; P.A.S. Costa and A.C. Braga,: Length-weight relationship of marine fishes from the central Brazilian coast. NAGA, ICLARM Q. 27(1&2): (2004) 20-26pp. Gowda, G; S.L Shanbougu,. and K. S. Udupa,: Length-weight relationship and relative condition of Grey mullet, Valamugil sechi (Forscal), from Mangalore waters. Indian J. Fish. 34(4): (1987) 340-342pp. Garcia, C. B.; J. O. Buarte,; N. Sandoval,; D. Von Schiller; Mello, and P. Najavas,: Length-weight Relationships of Demersal Fishes from the Gulf of Salamanca, Colombia. Fishbyte, 21: (1989) 30-32pp. Haimovici, M. and G.Velasco,: Length-weight relationship of marine fishes from southern Brazil. The ICLARM Quarterly 23 (1): (2000). 14-16pp.

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P. Karmakar et al., American International Journal of Research in Formal, Applied & Natural Sciences, 6(2), March-May, 2014, pp. 143146 [16] Hoda, S. M. S: Relative growth of body parts and length-weight relationships in Boleopthalmus dussumieri and B. dentatus of Karachi coast. Indian J. Fish. 34(1): (1987) 120-127pp. [17] Le-Cren E. D. The length-weight relationship and seasonal cycle in gonad-weight and condition in the perch (Perca fluviatilis). J. Anim. Ecol., 20: (1951): 201-219pp. [18] Nath and Dey: Fish and Fisheries of Eastern India. Narendra Publishing House, Delhi, 250. (2000). [19] Richter, H.C.; C. Luckstadt,; U Focken,. ; K Becker,.: An improved procedure to assess fish condition on the basis of length-weight relationships. Arch. Fish Mar. Res. 48.(2000) 255-264pp. [20] Safran, P.;: Theoretical analysis of the weight-length relationships in the juveniles. Mar Biol., 112: (1992) 545-551pp. [21] Simon, K. D. and A. G. Malan. : Length-weight and Length-length Relationships of Archer and Puffer fish species. The Open Fish Science Journal. 1, (2008) 19-22pp. [22] Welcome, R. L.,: Fisheries Ecology of Flood Plain Rivers, Longman Press, London, (1979). 317pp. [23] Wooton, R. J., Fish ecology. Tertiary level biology. Blackie. New York, (1992): 212pp.

VI- Acknowledgement Authors are grateful to the Head, Dept. of Life Sciences, Dibrugarh University, Assam, India for giving necessary permission to carry out the study.

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An Analytical and Practically Feasible improvisation over representation of Sky-View-Factor Rajesh Gopinath1, Jagdeep Singh2, Dharmender Singh2, Ghanshyam Kumar2 and Navneet Singh2 1 Assistant Professor, 2Project Scholars Department of Civil Engineering, Acharya Institute of Technology, Bangalore, INDIA Abstract: Sky-View-Factor (S.V.F.) as an urban canyon factor is of most significance to climatic studies. The available techniques to measure the same is sufficiently inadequate to provide representativeness and are either cumbersome or uneconomical. The present study proposes a newer, practically feasible and logical means of ascertaining and representing S.V.F. for a wide spread highly variant canyon. Twelve locations possessing variable degree of green cover, water body, open spaces, paved/unpaved surfaces and built-up spaces were selected for the study. The study is based on an author introduced modification of the Oke’s methodology of determination of angles of elevation about absolute ground level, using a theodolite. The novelty of the present technique to determine S.V.F.is the inclusion of two extra angles of elevation perpendicular to road and with the height of instrument considered as focal point. Eventually the comparative results from Oke’s method and the author modified method were subjected to review from practical point of view. Keywords: Sky View Factor; cumbersome; urban; canyon; climatic; I. Introduction Street canyon geometry is the most relevant urban parameter responsible for microclimatic changes occurring in a street canyon, due to its potential to influence solar access and airflow at street level [1]. A typical urban canyon is a basic urban surface unit comprised of the walls of adjacent buildings, the ground (street) between, and the air volume enclosed within. As observed from figure 1, it can be visualised as a relatively narrow street with buildings lined up continuously along both sides [2]. A commonly used indicator to describe this typical urban geometry is the ‘Sky View Factor’ (S.V.F.), a dimensionless measure between 0 and 1, representing totally obstructed and free spaces, respectively. Herewith owing to its role in radiation balance schemes, S.V.F. is widely studied by climatologists for variations in surface and air temperature. Hence in this context, it is utmost vital to represent canyon geometry most appropriately.

Figure 1: Sky-View-Factor Profile [3].

Figure 2: A FC-E8 fish-eye lens equipped NIKON Coolpix 4500 camera, alongside a typical fish eye photograph [4].

Some common methods that have evolved for estimating or calculating S.V.F. are scale models, analytical method (angle measurements, H:W), estimation by graphics signals, evaluation of G.P.S. signals from G.P.S. receivers, surveying techniques, manual and computer evaluation of fish-eye photos snapped with fish-eye lens, thermal fish-eye imagery and computer evaluation of a 3d-database describing surface geometric elements (G.I.S.) environment [5]. During the 1980's, studies were mostly based around the geometrical modelling of

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canyons. Also referred to as Analytical method; this is suitable for simple and well represented structures and could be used for algorithm testing and parametric analysis [6]. As compared to the other methods, it is also time consuming but is easy to understand and economical, and doesn’t need too many skills. The photographic methods (Figure 2) use a fish-eye lens to take onsite photographs that project the hemispheric environment onto a circular plane. It is only since the 1980s that photographic methods have received enough attention in determining S.V.F. in urban climatology [7]. However, as this method requires image generation and processing, it is uneconomical and often time-consuming. In contrast to the above-mentioned methods, which are based on direct calculation, the G.P.S. method was developed with the aim of measuring S.V.F. in real-time using proxy data. The G.P.S. component was integrated with a fish-eye lens photo capturing and processing module on a mobile platform to give simultaneous calculation and approximation of S.V.F. in real-time [8]. This method has been proved to be ‘significantly faster’ than the vector-based method [9]. However here, prediction equation depends on the accuracy G.P.S. equipment used. Hence though there are several methods to ascertain S.V.F., each has its advantages and disadvantage, and their application would be specific to environment and skills. II. Scope of Study Reviewed literatures have unveiled that the differences of intra-urban surface air temperatures have been shown to be strongly dependent on the S.V.F, when especially taken from the ground than those taken at sensor level [10]. Nevertheless it may be affirmed that there is no conclusive findings that highlight the true nature of correlation between canyon geometry with ambient air temperature, while some have shown good relationship, other studies showed the opposite. This gap in research thereby necessitates auxiliary studies on framework for more accurate determination and precise representation of S.V.F. Keeping this in mind, and by explicating from previous section, Geometrical Method would be the most suited and economical technique for undertaking research at Graduate level so as to resolve the current concern for S.V.F . III. Study Area The present research objective primarily proposes a logical modification in the basic formulae of computation of S.V.F., as an analytical and practically feasible improvisation over the geometric determination by Oke’s method (Figure 3) [5]. Under the circumstances, upon choosing a location which has no dead-ends, the results computed by Oke and present methodology would remain the same. However in reality such conditions need not necessarily apply in townships or cities ill-planned or inorganically developed, and therefore do not apply to Oke’s methodology. Therefore great care was taken in identifying locations wherein the author suggested modification projected significance. Also most researches cited in literatures for assessing Urban Canyon Geometry, were limited to the city centre or only some urban canyons of cities. In the context of present study, to ascertain true and representative results, the present study involved measurement of 12 distinct and different urban canyons for Bangalore city, each within a Radius of 250m.

Figure 3: Oke’s Logic [5]

Figure 5: Typical Wide landscape S.V.F.

Figure 4: Typical narrow landscape.

Figure 6: S.V.F of narrow landscape

Figure 7: Typical closed valley landscape

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Generally all the stations comprised of distinct features such as immediate lakes in eutrophicated state, outward looking Buildings, low density-low rise developments with colonial bungalows on large plots, tight constructions which cut deep canyons through the area (Figure 4), wide pavements (Figure 5), narrow streets projecting skewed S.V.F. (Figure 6), closed spaces (Figure 7), low built-up density with green spaces interspersed between them, low rise structures, with mainly single, double and triple storey structures, scattered residential spaces and coconut grooves, Single-family homes, interlaced several open spaces etc. IV. Experimental Methodology While twelve calibrated Theodolites were made uses of in the physical surveying for the measurement of ‘angles of elevation’ and ‘height of each building’; a measuring tape was made use of in determining the ‘width’ of each road. Standard practices for calibration and angle measurement was adopted [11]. As all the observations were taken acknowledging height of the instrument; this study hence postulates the point of measurement as the instrument height and not the ground as suggested by Oke [5]. The present case-study herewith hence revises the positional concept and considers representative measurement as applicable to instrument height (or 1.5m) only. This was evolved as a practical and effective change from the actual procedure, to support the W.M.O. (World Meteorological Organization) guidelines which consider all climatic (including ambient air temperature) measurements only between 1.5-2.0m [12].

Figure 7: Angles of Elevation as per Oke’s 1998 Method

Figure 8: Angles of Elevation as per Current method.

The basic formula for finding S.V.F. was adapted from the analytical method of Oke (1988). In this geometric method, only 2 elevation angles (Equations 1 & 2) to the top of buildings were measured normal to the axis of streets in both directions (Figure 7), using a 1.5m high theodolite. Eventually the S.V.F. was determined using Equation 3 [3]. X1 = (1-Cos α1)/2 …Eqn. 1 X2 = (1-Cos α2)/2 …Eqn. 2 S.V.F. Oke = [1- (X1 + X2)] …Eqn. 3 X3 = (1-Cos α3)/2 …Eqn. 4 X4 = (1-Cos α4)/2 …Eqn. 5 S.V.F. modified = [(1- (X1 + X2)) + (1- (X3 + X4))]/2 …Eqn. 6 However, the present study postulates the above equations with a slight author desired modification that not only two, but all the four directions shall contribute to the S.V.F.; hence 2 extra angles of elevation, perpendicular to the axis of streets (Figure 8) were measured (Equations 4 & 5), and finally the S.V.F. was arrived at by using Equation 6. To achieve the study objectives, all the 4 angles of elevations were measured for each point for observable changes in the topography, about all the streets within the radius of 250m at each monitoring station. If there were open spaces, parks, forests or water surface in a particular direction, then 0º was assigned as an angle value. In present study, the dataset so obtained shall represent an entire radius, thereby bringing clarity in true representativeness of S.V.F. for a vast area, achieved with an averaged S.V.F. across each station, by referring the Google maps (downloaded from a common altitude) with the field work data. The plan-view was divided into equal number of square cell. For every complete cell, which depicted an entire open space or waterbodies, the S.V.F. was assigned 1 whereas in case of built-up spaces, it was accorded ‘0’ for it occupied the entire cell. For each cell which represents fractions of both, proportionality was introduced in assigning final S.V.F. Hence, the computation also took into account even the contribution of 0’s and 1’s from the cells where practically surveying wasn’t carried out. This exercise is of top priority as no standard measure has been found in literature review to arrive at a single representative value for S.V.F.

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V. Results and Discussion Using the author modified methodology; a map of continuous S.V.F. (Figure 9) at instrument height for all the 12 stations within 250m radius was generated. Generally speaking, though the 2 extra ‘angles of elevation’ may be 0° for a wide open road junction, however it would definitely exhibit certain values when there is dead-end encountered in the form of a building or tree or other land-use uses. This concept is most applicable for a city like Bangalore which has grown without any proper town planning.

Figure 7: Map of continuous S.V.F. within 250m radius

Chart 1: Comparison of S.V.F values from both techniques.

As observed from Chart 1, there is a distinct deviation among S.V.F values computed from ‘Oke’s Method’ and ‘Author Modified Method’. This is found to be higher in stations wherein the street alignment was highly irregular and non-uniform with several dead-ends marking the station, and practically this makes absolute sense. Statistically speaking this variation will have a leading invariable impact on inferential relationships established, than that using values referred from Oke’s method. Hence the study proposes the application of present methodology over Oke’s technique. VI. Conclusion Surface geometry has a complex influence on the urban atmosphere. In the present research a logically modified and simpler analytical approach has been described which allows the accurate estimation of S.V.F. for the present inorganic urban scenario. When compared to the Oke’s Method, a significant deviation was revealed in the final S.V.F. values, capable of strategically influencing studies on relationship with climatic parameters. Therefore the proposed methodology confirms the value added significance of the additional angles of elevation measured perpendicular to road, alongside the consideration of height of instrument as focal point. Eventually the current study clarified the significance of incorporating the contribution of every cell for true representativeness of S.V.F. VII. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Y. Nakamura and T. Oke, “Wind, temperature and stability conditions in an east-west oriented urban canyon,” Atmospheric Environment, Vol. 22, 1988, pp. 2691-2700. Nicholson Sharon E., “A Pollution Model for Street-Level Air”, Atmos. Environ., Vol. 9, 1975, pp.19-31. http://www.atmosphere.mpg.de. Watson I.D. and Johnson G.T., “Graphical Estimation of Sky View Factors in Urban Environments”, International Journal of Climatology, Vol. 7, 1987, pp. 193-197. Oke T.R., “The urban energy balance”, Progress in Physical Geography, Vol. 2(4), 1988, pp. 471- 508. Johnson, G.T. and I.D. Watson, ‘The determination of view factors in urban canyons”, Journal of Climatology and Appl. Meteor., Vol. 2, 1984, pp. 329-335. Steyn D.G., “The calculation of view factors from fish-eye lens photographs”, Atmosphere-Ocean Vol. 18, 1980, pp. 254-258. Chapman L., Thornes J.E. and Bradley A.V., “Sky-view factor approximation using G.P.S. receivers”, International Journal of Climatology, Vol. 22, 2002, pp. 615-621. Gal T., Lindberg F. and Unger J., “Computing continuous sky view factors using 3D urban raster and vector databases: comparison and application to urban climate”, Theoretical and Applied Climatology, Vol. 95, 2009, pp. 111-123. Svensson M.K., “Sky view factor analysis - implications for urban air temperature differences”, Meteorological Applications, Vol. 11, 2004, pp. 201-211. B.C. Punmia, Ashok Kumar Jain and Arun Kumar Jain, “Surveying-II”, Laxmi Publications (P) Ltd., 12th Edition, 1994. Tim R. Oke, “Initial Guidance to Obtain Representative Meteorological Observations At Urban Sites, (Canada), Instruments and Observing Methods”, Report 81, World Meteorological Organization, 2006.

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Study of trace elements in groundwater of in and around Hingoli Region, Maharashtra, India Godbole Mahendra T. & Patode Hari S. School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded (M.S.), India Abstract: The present paper deals with trace elements geochemistry from the groundwater of Hingoli area, Maharashtra, India. Over a period of three years from 2009 to 2012, during the post-monsoon and pre-monsoon seasons. Fifty three groundwater samples were collected and analysed for trace metals with SPECTRO XEPOS, Advanced XRF Spectrometer, at environmental magnetic studies lab, Indian Institute of Geomagnetism, New Panwel New Mumbai. Trace elements such as Fe, Cu, Ni, Pb, Mn, Cd, As, Se, Co, Hg, Zn, Cr etc. are detected by XRF Spectrometer. Concentration of Fe, Mn, Cd, Se, Co, Hg, Zn are well within the permissible limit. Trace element analyses show high concentration levels for Cr, Ni and As in almost all groundwater samples. (as per WHO guidelines for drinking water quality 2011 and BIS 1991).Except Cr, Ni and few localities of As overall groundwater quality is suitable for drinking and agriculture. The elevated concentrations of trace elements are combined effects of geogenic, sources as well as excessive use of chemical fertilizers. It is recommended to control anthropogenic activities adequately in order to minimise the pollution problems. Key words: Trace elements, groundwater, Higoli, Maharashtra India.

I. Introduction Water is the most essential substance for living things and it supports the life processes and without water it would not have been possible to sustain life on this plane (Javid Hussain et.al.,2012). Water is one of the most vital resources for the sustenance of human, plants and other living beings. It is required in all aspects of life and health for producing food, agricultural activity and energy generation. Groundwater is rarely treated and presumed to be naturally protected, it is considered to be free from impurities, which are associated with surface water, because it comes from deeper parts of the earth (R.N. Tiwari, et. al., July,2013).The total quantity of water on earth is approximately14 trillion cubic meters (Parveen F.,U. Asghar and T.H.Usmani,2007)Trace elements are the important part of the material basis of medical effects (Guo et al., 2005).Heavy metals are sometimes called trace elemes of the periodic table (Javid Hussain et. al., 2012). Heavy metals are among the most persistent pollutants in the aquatic ecosystem because of their resistance to decomposition in natural conditions (Khan A.T., 2011). It has long been recognized that large area of the globe contain human population characterized by having trace elements deficiency, or excess including chronic poising (Romic D.M. 2012). Sediments and suspended particles are also important repositories for trace metals, e.g Cr, Cu, Mo, Ni, Co and Mn (Javid Hussain et. al., 2012). Heavy metal pollution is an important factor in the present environmental deterioration. The heavy metals can be absorbed by the medicinal plants and into our human bodies, which can cause great harm (Ying Guo et. al., 2011). So it is very important for us to determine the content of these heavy metals in the groundwater. Complex processes control the distribution of trace elements in ground water, which typically has a large range of chemical composition (Hem,1970, Drever,1982, Appelo and Postama, 1993).The trace element composition of groundwater depends not only on natural factors such as the lithology of the aquifer, the quality of recharge waters and the types of the interaction between water and aqufer, but also an human activities, which can alter these fragile groundwater system, either by polluting them or by changing the hydrogeological cycle (Helena et.al,.2000). Contamination of the environment with toxic heavy metal has become one of the major causes of concern for the human kind (Aweng et.al., 2011). Chemical substances such as heavy metals are one of the factors which contribute to environmental pollution, and it was believed that it can disturb the living ecosystem (Kobata Pendias et. al. , 2011). Some metals present in trace concentration are important for physiological functions of living tissue and regulate many biochemical processes (Fakhare Alam and Rashid Umar, 2013). The same metals, however, in higher concentrations may have severe toxicological effects on human being (Chapman, 1992). At the same time the deficiency of trace elements is equally harmful. But the trace metals can be toxic and even lethal to humans even in relatively low concentrations because of their tendency to accumulate in the body (Domenico and Schwartz, 1998). Groundwater contamination and its management have become need of the hour, because of their far reaching impacts on human health. Many naturally occurring major,minor and trace elements in drinking water may have

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significant effect on human and animal health either through its deficiency or through excessive intake (Frengstad et al., 2001). II. STUDY AREA The study area is a part of Kayadhu basin, bounded by latitudes 19 042’ & 190 44’ and longitudes 770 7’ & 770 10’ the area includes Hingoli City, is a head quarter of district located on the bank of Kayadhu River. Apart from this, the area consists of seven villages like Devulgaon Rama, MIDC area Hingoli, Andharwadi, Gadipura, Ganeshwadi, Warud Gawali etc. The study area covers an area of about 36 sq.Km2 (Toposheet 56E/2; Fig.1). The major sources of employment are agriculture, horticulture and animal husbandry. The MIDC area of Hingoli consists of some Industries like, PVC pipe Industry; oil industry etc.

Fig.1: Location of study area

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III. MATERIAL AND METHODS During the post-monsoon season from 2009, 2010 & 2011 and pre-monsoon season from 2010, 2011 & 2012, 53 groundwater samples were collected from bore and dug wells analysed for trace metals with SPECTRO XEPOS, Advanced XRF Spectrometer, at environmental magnetic studies lab, Indian Institute of Geomagnetism, Mumbai. The SPECTRO XEPOS HE uses a 50 Watt end-window X-ray tube to excite the samples. The target changer, with up to 8 polarization and secondary targets, offers many different excitation conditions ensuring optimum determination of the middle to heavy elements. The application range covers the elements from Na to U. A shutter improves the stability of the system by enabling the sample to be changed without having to turn off the X-ray tube. Consistent X-ray tube performance is ensured by the uninterruptible power supply (UPS) that compensates for power fluctuations. Measurements can be conducted in a He gas atmosphere or in a vacuum; many applications even in air. A. Sample Presentation Not only powerful analytical components, but also exact sample presentation is critical for exceptional analytical performance. This was a special consideration during development of the SPECTRO XEPOS HE. The precision of the sample changer and a new generation of sample trays dramatically reduce the effects of mechanical and physical fluctuations; improving analytical results. The analyzer can handle samples with diameters of 32 mm, 40 mm and 52 mm. The sample chamber can be equipped with a sample spinner for 40 mm sample cups to further improve the measurement results for inhomogeneous samples or irregular surfaces. IV. Result and Discussion A. Hydrogeology The area consist of Deccan Trap, it contains different types basaltic flows, separated by red bole. The occurrence of groundwater mostly found in shallow and deep aquifer. The Deccan Trap consists of four types of rocks like compact, amygdaloidal, vecicular, and tachylitic basalt. The groundwater mostly found in compact basalt due to the presence of secondary porosity i e. Fracture and joints in the rock. Depth of the dug wells is from 5.18-54.87 mt. and 12.19-122 mt. for bore wells. The soil thickness is about 0.2 mt to 12.19 mt. and average is 2.94 mt. The chemical composition of groundwater of the study area is shown in Table 1. The chemical composition of the groundwater is controlled by, nature of geochemical reaction, velocity and volume of groundwater flow, lithology, precipitation and role of human activity (Matthes and Harvey,1982; Reddy, Subba Rao and Reddy 1991, Bhatt and Sakalani, 1996.) Table 1. Statistical measures of trace elements of Hingoli Region Maharashtr, India Parameters Fe Cu Ni pb Mn Cd As Se Co Hg Zn Cr

Min

Max

0.2 <1.0 1.4 0.00033 <1.0 0.00001 0.2 0.000003 <1.8 0.00004 0.02 1.1

1 0 2.6 0.00013 0 0.0006 0.3 0.00009 3.5 0.00012 0.7 5.8

WHO (1994) Highest Desirable Max. Permissible level level 0.1 0.01 0.01 -

ISI(1994) Highest Max. Permissible Desirable level level 0.05 0.05 0.1 0.03 0.1 0.01 0.01 0.01 0.05 0.05 0.01 0.1 0.1 0.001 5.0 0.05 0.05

Distribution of Trace elements-Post monsoon 2009

ppm

6 Min

Max

2 0 Fe Cu Ni pb Mn Cd As Se Co Hg Zn Cr

Graphical representation of trace elements in study area. B. Trace Elements

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Iron Iron is an essential element in human body (Moore, 1973) and is found in groundwater all over the world; higher concentrations of iron cause bad taste, discoloration, staining, turbidity, esthetic and operational problem in water supply systems (Dart, 1974; Vigneshwaran and Vishwanathan, 1995). Deficiency of iron results in hyochromic macrobiotic anemia; one of the world's common health problems (R.N. Tiwari, et. al,2013). The limit of concentration of iron in drinking water ranges between 0.3 (desirable limit) to 1.0 mg/L (permissible limit). In the study area, all the samples shows (<1.0 mg/L) of iron is within the WHO limit. Copper Copper is an essential element, concentrated in several enzymes, and its presence in trace concentrations is essential for the formation of haemoglobin (R.N. Tiwari, et. al., July, 2013). An over dose of copper may lead to neurological complication, hypertension, liver and kidney dysfunctions (Krishna and Govil, 2004; Khan et al., 2010). Ingestion of copper causes infant death, short lived vomitting diarrhea etc (Barzilay, 1999). In the present study its value ranged from 0.2 to 1mg/L, and average is 0.81mg/L which is above the permissible limit as suggested by WHO (1993) and BIS (1991). Nickel Nickel is present in a number of enzymes in plants and microorganism (R.N. Tiwari, et. al., July, 2013). In the human body, nickel influences iron adsorption, metabolism and may be an essential component of the haemopoiitic process. Acute exposure of nickel in the human body is associated with a variety of chemical symptoms and signs such as nausea, vomiting, headache, giddiness etc. (Barzilay, 1999). The BIS (1991) has recommended 0.02 mg/L as maximum permissible concentration in drinking water. In the study area, Ni concentrations range from 1.4 to 2.6mg/L, and average is 1.83 6mg/. The primary source of nickel in drinking water is leaching from metals in contact with drinking water such as pipes and fittings However, it may also be present in some ground waters as a consequence of dissolution from nickel ore bearing rocks (R.N. Tiwari, et. al., July, 2013). The geogenic source appears to be responsible mainly for the nickel concentrations in groundwater of the study area (Tiwari and Dubey, 2012). Lead Lead occurs geologically in association with sulphide minerals and may be present in generally elevated concentration in areas with ores and coal (Reimanne and Decarital, 1998). Lead is toxic to the central and peripheral nervous system causing neurological and behavior effects. The consumption of lead in higher quantity may cause hearing loss, blood pressure and hypertension and eventually it may be prove to be fatal. In the present study, lead concentration ranged from 0.00003 mg/l to 0.00013 mg/l. It is observed that all groundwater samples have lead values within the permissible limit (BIS, 1991). Manganese Manganese is one of the most abundant elements in the earth's crust, it usually occurs together with iron and is widely distributed in soil, sedimentary rocks and water (R.N. Tiwari, et. al., July, 2013).The most abundant compounds of manganese are sulphide, oxide, carbonate and silicate ( In the present study, the concentration of is observed <1.0 desirable limit however they do not exceed the permissible limit (0.3 mg/L). Manganese is regarded as one of the least toxic elements but its excess amount in the human body may cause growth retardation, fever; fatigue and eye blindness, and may affect reproduction (R.N. Tiwari, et. al., July, 2013). Cadmium Cadmium is a cumulative environmental pollutant and its exposure to the body results damage of the kidney, and causes renal dysfunction, arteriosclerosis, cancer etc. (Goel, 1997; Robards and Worsfold, 1991). In the present study, the concentration of cadmium ranged from 0.00001 mg/L to 0.00016 mg/L. which is well within the permissible limit as recommended by BIS (1991) and WHO (1993) respectively. The concentration of cadmium in water samples of the study area may be attributed to the runoff from the agricultural sector where pesticides as well as cadmium phosphatic fertilizer are being used (R.N. Tiwari, et. al., July, 2013). ArsenicArsenic is a semi-metallic element found in soils, groundwater, surface water, air, and some foods ( U.S. Agency for Toxic Substances and Diseases Registry (ATSDR), 2005). Arsenic occurs naturally in the earth’s crust, with higher concentrations in some geographic areas, and in some types of rocks and minerals ( U.S. Agency for Toxic Substances and Diseases Registry (ATSDR), 2005). When combined with elements other than carbon, it is called “inorganic arsenic.” Arsenic and inorganic arsenic compounds can be emitted into air and then deposited into water and soil during industrial operations such as ore mining and smelting, and during volcanic eruptions and forest fires (U.S. Agency for Toxic Substances and DiseasesRegistry (ATSDR). 2005; U.S. Environmental Protection Agency. 2000). Chronic inorganic arsenic exposure is known to be associated with adverse health effects on several systems of the body, but is most known for causing specific types of skin lesions (sores, hyper pigmentation, and other lesions) and increased risks of cancer of the lung and skin ingesting contaminated foods or soil (predominantly via hand-to-mouth activity) (U.S. Agency for Toxic Substances and Diseases Registry (ATSDR). 2005; U.S. Environmental Protection Agency. 2003; Subcommittee on Arsenic in Drinking Water, N.R.C. 1999.). Groundwater can be contaminated with arsenic from natural sources of arsenic, or by mining and smelting

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operations (U.S. EPA, Toxicity and Exposure Assessment for Children’s Health).The arsenic in study area ranging from 0.2 mg/l to 0.03mg/l and, average is 0.22 mg/l. All the water samples of study area are below the WHO standards. Selenium (Se): Selenium in the study area ranges from 0.00003mg/l to 0.00009mg/l. All water samples had measurable concentrations of Selenium. However, all are below the WHO limit. WHO permissible limit for selenium is 0.04 (V. Hanuman Reddy, 2012). V. Conclusion The Ni, Cr, and As is generally elevated in most localities, originating from geologic formation. The continuous higher intake Ni and Cr may cause toxic effects to the human health. The elevated concentrations of trace elements are combined effects of geogenic, sources as well as excessive use of chemical fertilizers. It is recommended to control anthropogenic activities adequately in order to minimise the pollution problems. Except Ni, Cr and As concentration. Overall groundwater quality is good for drinking and agricultural purposes. Acknowledgements First author of this research paper, Godbole Mahendra Turaram is thankful to Prof. Nathani Basavaiah, Professor –F Environmental Geomagnetism and Paleomagnetism Studies Solid Earth Geomagnetism Division, Indian Institute of Geomagnetism, New Panvel, New Mumbai. For his kind inspiration and cooperation for use of his Geomagnetism and Paleomagnetism Studies Solid Earth Geomagnetism Division Lab. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14].

Fakhare Alam and Rashid Umar, 2013.Trace Elements in Groundwater of Hindon-Yamuna Interfluve Region, Baghpat District, Western Uttar Pradesh, Journal of Geological Society of India. Vol.81, March 2013, pp.422-428. CHAPMAN, D. (1992) Water Quality Assessments, Published on behalf of UNESCO/WHO/UNEP, Chapman & Hall Ltd., London, 585p. DOMENICO, P.A. and SCHWARTZ, F.W. (1998) Physical and Chemical Hydrogeology, IInd Edtn. John Wiley and Sons, INC, 495p. FRENGSTAD, B., BANKS, D. and SIEWERS, U. (2001) The chemistry of Norwegian groundwaters: IV. The pH-dependence of element concentrations in crystalline bedrock groundwaters. Science of the Total Environment, v.277, pp.101-117. Moore CV (1973). Iron: Modern nutrition in health and disease, Philadelphia. Lea and Fiibeger, p. 297. Krishna AK, Govil PK (2004). Heavy metal contamination of soil around Pal. Industrial area, Rajasthan, India. Environ. Geol. 47:38-44. Khan MQMA, Umar R, Latch H (2010). Study of trace elements in groundwater of Uttar Pradesh, India. Sci. Res. Essays 5(20):3175-3182. Tiwari RN, Dubey DP (2012). A study of Bauxite deposit of Tikar Plateau Rewa district, M.P. Gond. Geol. Soc. sp. 13:111118. Reimanne C, Decaritat P (1998). Chemical elements in the environment. Springer Verlag, p. 398. U.S. Agency for Toxic Substances and DiseasesRegistry (ATSDR). 2005. "Toxicological Profile for Arsenic." http://www.atsdr.cdc.gov/toxprofiles/tp2.pdf. U.S. Environmental Protection Agency. 2000. "Arsenic Occurrence in Public Drinking WaterSupplies." http://www.epa.gov/OGWDW/arsenic/pdfs/occurrence.pdfEPA-815-R-00-023 Subcommittee on Arsenic in Drinking Water,N.R.C. 1999. Arsenic in Drinking Water." http://www.nap.edu/catalog/6444.html. National Academy Press, Washington, DC. V. Hanuman Reddy, P. M. N. Prasad, A. V. Ramana Reddy and Y. V. Rami Reddy. Determination of heavy metals in surface and groundwater in and around Tirupati, Chittoor (Di), Andhra Pradesh, India. Mohammad Muqtada Ali Khan et., al. Study of trace elements in groundwater of Western Uttar Pradesh, India.

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STUDY OF VEGETATIVE TRICHOMES IN PETREA VOLUBILIS L. (VERBENACEAE) Ingole Shubhangi N Department of Botany Bar. R.D.I.K. and N.K.D. College Badnera-Amravati, Maharashtra INDIA Abstract: Petrea Volubilis L. is an extensive perennial liana with ash coloured stem covered with greyish pubescence, leaves elliptic, scabrous, pubescent on nerves beneath, undulate, acute or shortly acuminate. Flowers showy, purple in pendulous 15-19 cm long axillary racemes, calyx star shaped, large persistent. Trichomes are reliable taxonomic markers. In present attempt trichomes on all vegetative parts are studied. They are found of two types non-glandular and glandular, which vary in minute details on different parts. They are ranging from papillae, unicellular to multicellularar uniseriate type with diverse hair bases . The epidermal surface is papillate in almost all organs. Trichomes are not only specific but suggestive of their functional significance . Keywords: Petrea volubilis, trichomes, hair bases, non -glandular trichomes, vegetative trichomes. I. Introduction Trichomes are reliable taxonomic markers as they are diveres types and are diagnostic characters not only helpful in identification of particular plant species but also of crude drug and detection of adulterants. Many have provided useful information on structure, development, function and classification of trichomes in may angiospermic families and they proved more useful at generic and specific level. [15],[11].[4],[5],[10],[6],[7],[8],[12][14],[9],[1],[3]. Petrea volubilis is an extensive perennial liana with ash coloured stem covered with grayish pubescence, leaves elliptic, scabrous, pubescent on nerves beneath, undulate, acute or shortly acuminate. Flowers showy, purple in pendulous 15-19 cm long axillary racemes, calyx star shaped, large persistent. It is grown as an ornamental, native of tropical America.

Petrea volubilis L. II. Materials and methods Plant material for the present study collected from various localities from Amravati District and identification is confirmed with standard floras. To get an integrated picture of trichome types and their organographic distribution, mature vegetative parts including stem, petiole, leaves were used varied temporary micropreparations were made by  Epidermal peels  Mounts using sodium hydroxide (aq.) and 2% acetic acid treatment

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 Scrapping of trichomes  Transverse sections. Trichomes were stained in safranin (1%aqueous) and mounted in glycerine. Camera lucida sketches were made. Measurements were taken III. Observations Stem Non-glandular uniseriate filiform (Plate –I Figs -1-3, 5-12) 1. Unicellular conical 1.1 Body-unicellular, papilloform, 32x28µ, tapering above, subacutely pointed at apex; contents- hyaline; wall-moderate thick, surface smooth, lumen- broad. Seated upon single or vertical division-wall between2adjoining epidermal cells. 1.2 Body- short , 100x25 µ, acutely pointed at apex; base-flat; contents- thin, hyaline; wall moderate thick or thin. Seated upon single epidermal cell. 1.3 Body –straight-conical, 225x25 µ, acutely pointed at apex; base-swollen, surrounded by ring of 9-10 arched adjoining epidermal cells or seated upon vertical division- wall between 2 adjoining cells. 1.4 2. Multicellular conical 2.1 body- 2 celled in length, short, papilloform, 140x16 µ, pointed at apex; basal cell swollen, as long as broad terminal cell longer, contents-hyaline, walls- lateral of basal cell little convex, of terminal- straight; crosswalls –straight, surface-smooth Seated upon vertical division-wall between 2 adjoining epidermal cells. 2.2 Body -3-4 celled, 200x20 µ, tapering, subacutely pointed at apex; cells of varied length; basal cell bulbous, as long as broad; lateral walls- convex, lower, intermittent cells unequal, longer than broad; terminal cell longest; contents –hyaline; walls-straight. Hair base surrounded by 5-8 arched adjoining epidermal cells, hyaline or containing dense contents, protruding. Frequent on angular portions 2.3 Body -3 celled, 264x16 µ; intermittent cell slight short, foot- multicellular, multiseriate, cells juxtaposed, protruding . Seated on verticaldivision- wall between 2-adjoining epidermal cells. Frequent on angular portions Uniseriate glandular capitate (Plate- I Figs 4,13,14) 1. Capitate sessile or shortly stalked 1.1 Foot-1- celled, not sunken; contents- hyaline; stalk -1-2 celled, narrower than base of head; cells squarish; lower cell longer than broad. Basally broader, contents –hyaline; head- spherical, many (9-10) celled; contents –dense, 40x20 µ 1.2 Head- peltate, many-celled, contents-hyaline 2. Long stalked Foot-l-celled; stalk-3-celled,56x20 µ, lower cell much longer, intermittent, subterminal cells short, almost equal, squarish, contents- hyaline; head-globose ,2- celled; contents-dense. Petiole: Non glandular uniseriate filiform ( Plate –II Figs.15 - 20) 1. Unicellular conical Papillae-similar to those of stem, 32x28 µ 2. Multicellular conical 2.1 Body -2- celled, 225x15 µ contents- granular Similar to those of stem. 2.2 Body 6-celled in length, 225x25 µ, acutely pointed at apex; cells almost equal, longer than broad, basal cell longer or short, round; terminal cell shorter or longer, Hair base surrounded by narrow ring of 12 small distinguished cells, 2.3 Body -3-4 celled, much narrower, 650x25 µ; foot- multicellular, multiseriate similar to those of stem Uniseriate glandular capitate (Plate -II Fig.21) 1. Capitate sessile or shortly stalked Similar to those of stem. Slight differeing in stalk-cells equal; head- globose 2-4-6celled, 50x20 µ. IV. Lamina Non-glandular uniseriate filiform (Plate –II Figs.22 - 28) 1. Unicellular conical 1.1 Papillae- similar to those of stem, petiole, 40x30 µ Frequent adaxilly, marginally 1.2 Body- short, ovate-conical, papilla form, 60x24 µ acutely pointed at apex; base-round; contentshyaline. Seated upon single distinguished, roundish eqidermal cell, containing dense contents, surrounded by 4- adjoining cells arranged cross –wisely. Frequent abaxially.

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1.3

1.4

2. 2.1 2.2

Body-Straight- conical, 130x15 µ acutely pointed at apex, base-flat or angular, contents- thin, hyaline.. Seated upon single distinguished or vertical division wall between 2-adjoining epidermal cells Frequent abaxially Body- ovate- conical, shorter, broad, 60x32 µ obtulsely pointed at apex base- bulbous, with dense contents; contents- hyaline . Hair base surrounded by about 8-adjoing ordinary epidermal cells, in rosette. Frequent adaxially Multicellular conical Body-2-celled in length, 140x12 µ terminal cell onger Similar to those of stem. Frequent abaxially Body 3-4-celled, 200x16 µ, acutely pointed at apex, similar to those of stem.. Frequent abaxialy

Uniseriate glandular capitate (Plate- II Figs.29 - 33) 1 Capitate sessile or shortly stalked 1.1 Similar to those of petiole, 50x12 µ 1.2 Head- spherical or peltate, 7-8 celled, contents –dense, 36x36 µ 2. Long stalked Foot-1-Celled, distinguished, roundish, contents-hyaline; stalk-2 celled 228x40 µ narrower than base of head, lower cell ong, broad, end walls arched, lateral walls- straight, ; contents- hyaline, subterminal cell small, short, much narrower; head- globose, 6-8 celled, contents-dense Frequent adaxially. V. Discussion Detailed descriptions of trichomes are available in the literature for many commercially important genera [13]. The distribution and structure of trichomes on plant surfaces contribute to the control of transpiration and temperature of organ. Trichome density affords the organ protection. Trichomes function in plant defence or act as attractants to facilitate pollination.[16] VI. Conclusion Hence trichomes are suggestive of their functional significance. They differ in their details and are special and typical for particular taxon and particular organ and surface. In present study some specific additional ltypes are found. These trichomes are very specific for particular species. References [1]. [2]. [3]. [4]. [5]. [6]. [7]. [8]. [9]. [10]. [11]. [12]. [13]. [14]. [15]. [16].

Al-shammary and R.J. Gornall “Trichome anatomy of the saxifragaceae S. L. from Southern Hemishphere,” J. Linn. Soc. Bot., 1992;114:131. A.M. Bosabalidis. “Structural features of origanum species.”. In: Kintzios Se ed, Oregano: The genera origanum and Lippia; 1st edition, London, Taylor & francis, 2002: 11-64. S. Combrink G.W.DUPlooy R.I.McCrindle and B.M.Botha . “Morphology and histochemistry of glandular trichomes of Lippia scaberrima (Verbenacea).” Annals of Botany, 2007, 99: 1111-1119. V.M.Cowan . The Rhododendron leaf: Botany. A study of Epidermal appendages London 1950. M. Farooq “Trichomes of the flowers of Utricularia,” J. Indian Bot. Soc., 1963; 45:242-248. J.A. Inamdar “Studies of Trichomes of some oleaceae, Structure and ontogeny”, Proc. Indian acad. Sci., 1967; 66: 164-177. J.A. Inamdar “Trichomes and nectarines on the floral organ,” Beitr. Bio. Pflanz., 1968; 45:39-47. J.A. Inamdar , R.C. Patel “Structure, Ontogeny and classification of trichomes in some polemoniales.” Feddes Repert., 1973;83:473-478. Lowell and T.W. Lucansky “Vegetative anatomy and morphology of Ipomoea hederifolia (convolvulaceae,)” Bull Torrey. Bot, Club., 1986; 113(4): 382-397 C.R.Metcalfe L.Chalk .Anatomy of Dicotyledons. Vol. I & II Oxford, 1950 F.Netolitzk In handbuch der pflazenanatomie (ed.k.Linbauer) Abt. 1. Teil. 2 Hautgewebe. Band IV. Die pflazenhaare Gebruder Borntraeger, Berlin, 1932. N.Ramayya “Classification and phylogeny of the trichomes of angiosperms” in: Research trends in plants anatomy, Tata Mc Graw Hill, Bombay, 1972; 91-102. S.Sharma , N.S.Sangwan and R.S. Sangwan “Developmental Process of essential oil glandular trichome collapsing in menthol mint,” Current science, 2003; 84; 544-550. V.Sing , D.K.Jain & M.Sharma “Epidermal studies Ipomoea (convolvulaceae )” systematic Anatomy of the Dicotyledons ( Transt. L. A. Boodle and F.E.Fritsch), Oxford Uni. Press, London, 1974. H.Solereder. Systematic anatomy of the dicotyledons. Oxford, Clarendon Press, 1908. E.A.Weiss Essential oil crops, New York, NY CAB International, 1997.

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PLATE-I 4

Fig. 1 â&#x20AC;&#x201C; 14 Trichomes on stem Fig-1 stem epidermis shwoing hair bases Fig-2 Bicellular Trichomes Fig-3 Unicellular papilla Fig-4 Sessile glandular Trichome Figs -5,7,9,11 Multicellular Uniseriate Trichomes Figs-6 ,8,10,12 Unicellular Trichomes with hair bases Fig -13 long Stalked glandular Trichome

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PLATE -II

30 23 24

29 Figs-15-21 Trichomes on petiole Fig -18Unicellular papilla Figs -15,16,17,19,Multi cellular Non-glandular Trichomes Fig -20 Multiseriate Trichome base Fig 21 Short stalked glandular Trichome Figs-22-33 Trichomes on lamina Fig-22 Marginal Papillae, Figs-23,24,25, 28 â&#x20AC;&#x201C;Unicellular Non-glandular Trichomes with various hair bases Fig- 27 Multicellular Non-glandular Trichome on abaxial surface Fig -29,30,31,33, Glandular Trichomes with multicellular heads

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

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

Synthesis, Characterization and Study of Optical Constant of 4-(4-N,N-Dimethylaminobenzylideneamino)Phenyltellurium Tribromide Adil Ali Al-Fregi1, Ghufran Mohammad Shabeeb2 Chemistry Department, College of Science, University of Basrah, Basrah, IRAQ 2 Physics Department, College of Education for Pure Sciences , University of Basrah, Basrah, IRAQ 1

Abstract: 4-(4-N,N-dimethylaminobenzylideneamino) phenyltellurium tribromide synthesed by reaction of ehtanolic solutons of 4- aminophenylmercuric chloride with 4-N,N-dimethylaminobenzaldehyde then with tellurium tetrabromide. The prepared compund 4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide was charecterized by several techniques such as elemental analysis ; FTIR, UV-Visible, 1H and 13 C- NMR spectroscopies ; X-Ray diffraction and molar conductivity. Optical absorption of 4-(4-N,Ndimethylaminobenzylideneamino)phenyltellurium tribromide was measured. The X-ray analysis revealed that these films are amorphous nature. Then films were deposited using cast method. Transmittance measurements in the wavelength range(190-900)nm are used to calculate the refractive index(n), the absorption index(k) and the optical energy gap. Keywords: optical absorption; refractive index; optical energy gap; organotellurium compounds; azomethine group; Schiff base. I. Introduction Tellurium is one of members of group 16 in the periodic table. Tellurium lies between selenium and polonium and have electronic configuration [Kr]4d 10 5s2 5p4, it bears resemblance to this group especially to selenium in many of its properties and reactions [1]. Tellurium and its compounds have several applications in different fields. Organotellurium compounds have been mainly used as antioxidant agents [2,3], polymerization catalysts [4,5], antitumors and pharmaceutical agents [6-8], organic super conductors [9,10], synthetic intermediate [11-13] and as ligands with many transition metal ions [14,15]. In recent years there has been a growing interest in studing the organotellurium compounds which have Nâ&#x2020;&#x2019;Te intramolecular interaction [16,17]. There are several types of organotellurium compounds containing nitro, amino and azomethine groups which show such intramolecular interaction [18-29]. One of these compounds represent in organotellurium compounds which contain azomethine groups [20,30,31]. Organyltellurium trihalides (RTeX3 where X= Cl, Br, I; R = alkyl, aryl) constitute a large and a well studied class of compounds [5,10]. Generally, the stability of alkyltellurium trihalides are lower than aryltellurium trihalides, thus, methyltellurium trihalides (CH3TeX3 ; X = Cl, Br and I) are very sensitive to light and moisture, and decomposed in solutions while the corresponding phenyltellurium trihalides are stable under the same conditions [32]. Generally, the structures of organyltellurium trihalides are connected into infinite chains [3234] or dimeric structures by halogene bridges [32-35]. The important methods for preparing organyltellurium trihalides are reaction of tellurium tetrahalides (TeX 4; X = Cl, Br) with several compounds such as alkenes [36], alkynes [37], ketones [38] and activated aromatic compounds [33]. In some cases, the direct substitution of tellurium tetrahalides with aromatic compounds is not feasible or give low yield. The substitution reaction of arylmercuric chloride with tellurium tetrahalides gave the corresponding aryltellurium trihalide in good yield [39,40]. Several attempts to prepare tellurated Schiff bases by reacting Schiff bases with tellurium tetrabromide were failed and leading to ionic products [41,42]. Singh and McWhinnie [43] prepared {4-substituted-2-(phenyliminomethyl) phenyl}tellurium tribromide by reacting 4-substituted-2-(phenyliminomethyl) phenyl mercuric chloride with tellurium tetrabromide, Scheme1.

Al-Rubaie et al [30] reported the synthesis of a new series of tellurated Schiff bases compounds of formula ArTeBr3 (Ar =5-RC6H3N=CHC6H5, R=Cl, Br, CH3O and NO2) by reaction of tellurium tetrabromide with the corresponding arylmercuric chloride in 1:1 mole ratio, Scheme 2 .

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In the present work, attempts will be made to prepare and characterization a new organyltellurium tribromide compound containing azomethine group (-CH=N) namely 4-(4-N,Ndimethylaminobenzylideamino)phenyltellurium tribromide and study of optical constant of it by using thin films technique. Both of the optical constant, n(refractive index) and k (extinction coefficient), represent fundamental properties of a material not only because of their relation to the electronic structure but also due to their applications in many integrated optical devices. Thus, calculating n and k, of material are the key parameters for a device design [44]. The refractive index provides the information about the chemical bonding and electronic structure of the material [45]. The evaluation of refractive indices of optical material is of considerable importance for application in integrated optical devices such as switches, filters and modulators [46]. II. Experimental Section A. Chemicals All chemicals used in this study were supplied from the commercial sources by famous chemical companies . Bromine, 4-N,N-dimethylaminobenzaldehyde, p-toluenesulfonic acid and mercuric acetate were supplied by British Drug House (BDH). Absolute ethanol, aniline, benzene and tellurium 99.5% were supplied from Fluka company. Diethyl ether, dioxane, hexane and sodium chloride were supplied by Reidal de Hean company. Argon gas 99.995% was purchased from Jordan Gases company. Tellurium tetrabromide which used in the present work was prepared by a literature method [47] 4aminophenylmercuric chloride was prepared by the literature methods [48]. All the prepared compounds gave the correct melting points and infrared spectra. B. Purification of Solvents The solvents were obtained from commercial sources and are analytically pure solvents. Some solvents such as dioxane and ethanol were cautiously purified and drying according to literature methods and were kept over molecular sieve type A4 and stored in clean dark containers [49,50]. C. Physical Measurements Melting points of all solid compounds were determined by using a Gallenkamp Thermo point apparatus. Elemental analysis for carbon, hydrogen, nitrogen and sullphur were performed at AL al-Bayt Univrsity, AlMafraq, Jordan using a Euro vector EA 3000A Elemental Analysis (Italy). Infrared spectra for the synthesed compounds were recorded as KBr disk or thin film supported on KBr disk using a FT-IR spectrophotometer Shimadzu model 8400S in range 4000-400 cm-1 at Department of Chemistry, College of Science, University of Basrah. UV-Vis spectra for the synthesized compounds were recorded at Department of Chemistry, College of Science, University of Basrah by using Scan 80D (England) at range 200-800 nm using ethanol or chloroform as a solvents and 1cm3 pathway quartz cells. 1H-and 13C NMR spectra were recorded at Al al-Bayt University, Jordan by using a Bruker 300 MHz (Germany). Chemical shift of all 1H-and 13C NMR spectra were recorded in δ(ppm) unit downfield from the internal reference tetramethylsilane (TMS), using DMSO-d6 solvent. The molar conductivity for synthesized compounds were measured in 1x10 -3M solutions of dimethylsulfoxide solvent at room temperature using a Konduktoskop model 365B using standard conductivity cell with constant equal to 0.81 cm-1. D. Synthesis of 4-(4-N,N-dimethylaminobenzylideneamino)phenylmercuric chloride A mixture of 4-aminophenylmercuric chloride (2.43 g , 8.00 mmol) in 50 mL of ethanol and 4-N,Ndimethylaminobenzaldehyde (1.19 g,8.00 mmol) in 50 mL of ethanol containing 0.1 g of p-toluenesulfonic acid was refluxed with stirring for 5h. After cooling, the precipitate was collected by filtration and washed several times with ethanol. The solid product was twice recrystallized from a mixture of ethanol and benzene (3:2) to give a pale yellow solid. Yields 88%, melting point 191-193 0C , Table 1. E. Synthesis of 4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide A mixture of tellurium tetrabromide (1.78 g, 4.00 mmol) in 35 mL of dry dioxane and 4-(4-N,Ndimethylaminobenzylideamino)phenyl mercuric chloride (1.83 g, 4.00 mmol) in 30 mL of dry dioxane was refluxed with stirring for 6h under argon atmosphere. The resulting solution was filtered hot and on cooling deposited 2:1 complex of dioxane and mercuric chloride as white plates, which was filtered off. The filtrate was evaporated by a rotary evaporator to give a yellow precipitate. Recrystallization of the crude product from a mixture of diethyl ether and hexane (7:3) gave a yellow solid. Yield 60% and melting point 155 – 156 0C , Table 1.

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Comp. No.

Compound Stracture

Colour

Melting Point(oC)

Yield%

Molar conductivity

Elemental analysis Found (calculated)

Pale yellow

191-193

33.05

40.01 (39.22)

3.26 (3.29)

6.08 (6.10)

Yellow

155-156

28.99

30.44 (30.50)

2.52 (2.56)

4.73 (4.74)

Table 1 : Some physical properties and elemental analysis of new organometallic compounds containing azomethine groups 1 and 2 in cm-1 unit. III. Results and Discussion X-ray diffraction (XRD) studies were carried out to get an idea about the structural changes produced in the investigated 4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide thin films. The diffracted intensity as a function of the reflection angle was, measured automatically by the X-ray diffractopmeter. The absence of a peak in X-ray spectra confirmed the amorphous nature of 4-(4-N,Ndimethylaminobenzylideneamino)phenyltellurium tribromide samples.

Fig. (1): (XRD) of4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide thin film. The elemental analysis (CHN) of compounds 1 and 2 are in good agreement with the calculated values ,Table 1. The IR spectra of the two new synthesized compounds 1 and 2 display common feature in certain region and characteristic bands in the fingerprint and other regions. Table 2 shows the important functional groups vibration bands and some representive IR spectra are shown in Fig. 2 and 3. The IR spectra of compounds 1 and 2 show a strong band at 1668 and 1618 cm-1 respectively, can be attributed to CH=N stretching . These values are in good agreement with previous works [51-53]. The IR spectra of mercurated Schiff bases 1 shows no stretching bands vibration of carbonyl groups of at 1715 cm-1 and stretching band of amino groups of mercurated anilines (i.e 4-aminophenylmercuric chloride at 3450-3100 cm-1 range [51,53,54] this indicates the complete condensation between carbonyl and amino groups. The IR spectra of aryltellurium tribromides 2 is quite similar to those of the mercurated Schiff base 1. This means that telluration has occurred at the point of mercuration. The IR spectra of compounds 1 and 2 show a weak bands in the range 3110-3060 cm-1 due to aromatic C-H stretching while weak bands were appeared at 2921- 2817 cm-1 range due to stretching of aliphatic C-H bands [53,54]. Also, compounds 1 show two strong bands appeared in 1598 and 1365 cm-1, while compound 2 at 1541 and 1325 cm-1 can be attributed to asymmetrical and symmetrical stretching of aromatic (C=C) [53,54].

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Furthermore, several variable bands between 815 – 700 cm-1 range can be assigned to aromatic C-H bending while the band at 1365-1317 cm-1 due to aliphatic C-H bending [53,54]. The IR spectra of compounds 1 and 2 show a strong band at 1232 and 1200 cm-1 respectively, attributed to ν(C-N) [53,54].

Comp.No.

Aromatic ν(C-H)

Aliphatic νas (C-H) νs (C-H)

ν(C=N)

νas(C=C) νs (C=C)

Bending νara(C-H)

ν(C-N)

3110 w

2912 w 2817 w

1668 s

1598 s 1365 s

815 s 721 m

1232 s

3066 w

1618 s

1541 s 1325 s

813 m 840 m 7500 m

1200 s

2921 w

Table 2 : Selected infrared bonds vibration of new organometallic compounds containing azomethine groups in cm-1 unit.

Fig. (2): showed the FtIR- spectrum of 4-(4-N,N-dimethylaminobenzylideneamino)phenylmercuric chlorid .

Fig. (3): showed the FTIR spectrum of 4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide. .

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The 1H NMR spectra of compounds 1 and 2 were measured in DMSO-d6 solvent and are represented in Fig. 4 and 5 respectively and summarized in Table 3. In general, 1H NMR spectra of the 1 and 2 show the expected signals in proper intensity ratio, Table 3.

Fig. (4): 1H NMR spectrum of compound 1.

Fig. (5): 1H NMR spectrum of compound 2. The 1H NMR spectra of compounds 1and 2, Fig.3 and 4, respectively gave another evidences for forming azomethine group (-CH=N-) by showing a singlet signal at 8.33 and 8.32 ppm respectively, Table 3. These values are in agreement with previously reported data [53,56]. The 1H NMR spectrum of compound 1 and 2, Fig. 6 and 7, respectively, show a multple signals at between 7.44 - 6.18 ppm can be assigned to aromatic protons of phenyl groups [53-55]. Furthermore, these spectra show a singlet signal at 2.27 ppm can be attributed to methyl groups [53-55]. Comp. No.

Compound Structure

Chemical Shift (ppm) TMS = 0 ppm 8.33 ( s , 1H , CH=N) 7.43 â&#x20AC;&#x201C; 6.18 ( m, 8H, Ar-H) 2.27 ( s, 6H, 2 CH3)

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8.32 (s , 1H , CH=N) 7.44 â&#x20AC;&#x201C; 6.18 ( m, 8H, Ar-H) 2.27 ( s, 6H, 2 CH3)

Table 3 : 1H NMR data for organometallic compounds containing azomethine groups 1 and 2. The 13C NMR spectra of some synthesized compounds 1 and 2 were recorded using DMSO-d6 solvent. Fig. 5 and 6 represented 13C NMR spectra of synthesized compounds while 13C NMR data were summarized in Table 4.

Fig. (6): 13C NMR spectra of compound1.

Fig. (7): 13C NMR spectra of compound 2. 13

C- NMR spectra proved further evidences about characterization of synthesized compounds . The 13CNMR spectra of 1 and 2 show the signal of methine carbon atoms (C7) at 169.12 and 169.44 ppm, respectively which are in agreement with previous reported works [53-56],Table 4. 13 C NMR spectra of compounds 1 and 2 show a lowfield signal at (155.69 and 155.62) ppm and ( 145.56 and 145. 60) ppm, respectively can be assigned to aromatic carbon atoms which attached with nitrogen atom (C4) and (C11) , Table 4 . The high chemical shifts for these carbon signals attributed to presence of high electronegativity of nitrogen atom [53-56]. The 13C NMR spectra of 1 and 2 show a high field signal at 113.18 and 113.19 ppm respectively can be assigned to tellurated carbon atoms (Te-C) (C1) [53-56]. The low chemical shift, comparatively, for carbon atoms bearing tellurium atom compared with other aromatic carbon atoms may be attributed to the polarity of Te-C bond [56]. Generally, the other signals between 145.60 - 120.09 ppm in 13C NMR spectra of 1 and 2 can be assigned to aromatic carbon atoms, Table 4. The high field signals at 47.43 and 47.84 ppm in both 13C- NMR spectra of compounds 1 and 2, respectively are due to methyl groups (C14 and C15) [53-56].

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Comp. No.

Chemical shift (ppm) TMS = 0 ppm

Compound structure

169.12 (C7) , 155.69 (C4) 145.56 (C11) , 130.55 (C2, C6) 130.16 (C9,C13 ) , 127.74 (C3,C5) 122.08 (C8) , 120.09 (C10,C12) 113.18 (C1) , 47.43 (C14, C15)

169.44 (C7) , 155.62 (C4) 145. 60 (C11) , 130.16 (C2, C6) 129.12 (C9,C13 ) , 128.30 (C3,C5) 122.31 (C8) , 120.10 (C10,C12) 113.19 (C1) , 47.84 (C14, C15)

Table 4 : 13C -NMR data for organometallic compounds containing azomethine groups 1 and 2. The UV-Vis spectra of compounds 1 and 2 were measured at 1 x 10-4 M using chloroform as a solvent. Fig. 8 and 9 have shown the UV-Vis spectra for the synthesized compounds Table 5.

Fig. (8): UV-Vis. spectrum of compound 1.

Fig. (9): UV-Vis. spectrum of compound 2.

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In general, the UV-Vis spectra of compounds 1 and 2 showed two strong bands , The first band at 245 nm with molar extraction (Є) ranged 13000 and 11500 M-1.cm-1, respectively is attributed to π→π* transitions of the aromatic rings [53,54]. The second band observed at 295 nm (molar extinction Є = 12250 and 10100 M -1.cm-1, respectively) which may attributed to π→π* transitions of azomethine groups [53,54]. Comp.No.

Wavelength nm (molar extinction M-1.cm-1)

245 (13000) , 295 (12250)

245 (11500) , 295 (10100)

Table 5 : UV-Vis data of compounds 1 and 2 The molar conductivities were determined for compounds 1 and 2 in 1 x 10-3 M of DMSO solvent at room temperature, Table 1. The molar conductances of 1 and 2 were found at 33.05 and 28.99 ohm-1cm2mol-1 respectively, Table 1. This indicates that these compounds behave as 1:1 electrolyte which are in agree well with previous work in DMSO solution [57-59]. This observation may be due to ionic character of one Te-Br bond in these compounds. IV. Optical Constant The optical parameters for 4 -(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide films deposited on glass substrate have been investigated . The analysis of the absorption coefficient has been carried out to obtain the optical energy gapand also, the analysis of the refractive index n with the help of the absorption index k has been carried out to obtain the optical conductivity. The values of n and k are determined from the transmittance and reflectance spectra of the thin films. The reflectance and refractive index of any solid certain wavelength are expressed as [60]. K=

(1)

(2)

The spectral distributions of the mean values of n and k versus wavelength for the investigated-(4-N,Ndimethylaminobenzylideneamino)phenyltellurium tribromide are shown in Fig.10.

Fig. (10): Dependence of the mean values of the refractive index n on the wavelength.

Fig. (11): Dependence of the mean values of the absorption index k on the wavelength. The absorption coefficient have been estimated after correction for the reflection losses. The absorption coefficient is given by [61]:

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)*A

(3)

Where d is the thickness of the sample and Ais the absorption after correction. The absorption edge can be divided into two regions depending on the value of absorption coefficient . For <104cm-1, there is usually an Urbach tail [62] in which depends exponentially on photon energy, h .Fig.(22) shows a plot of log( ) as a function of photon energy h .

Fig. (22): Dependence of the absorption coefficient Îą on the photon energy. For 104 106 cm-1 in the high-absorption region( where absorption is associated with inter-band transitions). The following relation [63,64] is obeyed: h = (h -Eopt)n (4) Where is the band tailing parameter, Eopt is the optical band gap and n is an index which can assume values 1,2,3,1/2 and 3/2 depending on the nature of the electronic transition responsible for the absorption [65]. The relation between ( h )1/2 and photon energy shown in Fig. (23).

Fig. (13): Dependence of the ( h )1/2 on the photon energy. The absorption coefficient

can be used to calculate the optical conductivity

opt

as follows [66].

opt=

Where c the velocity of the light. Fig.(14) shows the variation of optical conductivity photon energy.

(5) opt

as a function of

Fig. (24): Dependence of the opt on the photon energy. The increased of optical conductivity at high photon energy is due to the high absorbance of 4-(4-N,Ndimethylaminobenzylideneamino)phenyltellurium tribromide thin films and also may be due to the electron excited by photon energy [67].

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V. Conclusions 4-(4-N,N-dimethylaminobenzylideneamino)phenyltellurium tribromide thin films have been deposited on glass substrate by cast method, and X-ray diffraction shows that it is structure. The optical constant of A1 was calculated from the transmittance spectra. The estimation of the corresponding band gap E g is 2 eV. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48]

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Spatial Patterns of Malaria among Bharia Tribe of Tamia, Chhindwara (M.P.), India *Arvind Solanki & Prof. Kailash Choubey** **Ret. Professor, Department of Geography, Dr. H. S. Gour Central University, Sagar (M.P.)-470003, India Abstract: The study was carried out to determine the spatial malaria patterns of Bharia tribe. Tamia is a tribal community block of Chhindwara district, and has more than 85 percent unique tribal population of Bharia tribe. Geographically, Tamia is situated between 22° 20’ northern latitude and 78° 40’ in eastern longitude. It extends over an area of 1268.02 km 2, which is about 9.32 percent of the district geographical area. Malaria is a febrile disease caused by the four species of plasmodium parasites to host by the bite of an infected female mosquito of the genus Anopheles. Early symptoms of malaria include fever, shivering, aches, and pain in the joints and headache. Bharia is one of the important tribal groups of Tamia, Chhindwara district and suffered with malaria from a very far time due to the environmental factors of Tamia block. Tamia is surrounded by hills and forests and provide favorable environmental conditions for malaria occurrence. This study was based on secondary data collection from PHCs, District hospital and malaria office and their analysis Key Words: Malaria, Bharia-tribe, plasmodium parasite, mosquitoes.

I. Introduction This study is mainly concerned on malaria among Bharia tribe of Tamia, Chhindwara (M.P.). Malaria is the most devastating disease in India nearly 300-500 million clinical cases and 1.5-2.7 million deaths every year [1]. Tribal community is the last ladder of socio-economic development and always suffered with malaria. In central India malaria is complex because of vast tracks of forests with tribal settlements. The tribal community contributed 30% of total malaria cases, 60% of total falciparum cases and 50% of total malaria deaths in the country [2]. Bharia is the Dravidian tribe, mainly reside in Patalkot valley of Tamia [3] and have frequent malaria prevalence. The Tamia block is considered for national Malaria Control Program (NMCP/NMEP) from 1960s, but a huge amount of population is always suffered with the disease [4, 5]. So it is necessary to study the environmental factors of Tamia block on malaria disease and its spatial distribution patterns among Bharia tribe. Malaria is a major health problem in India and its dynamics vary from place to place [6]. Malaria is a febrile disease caused by the four species of plasmodium parasites to host by the bite of an infected female anopheles mosquito. The malaria is mainly characterized four types such as: Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale and Plasmodium malariae. Early symptoms of malaria include fever, shivering, aches, pain in joints and headache. Plasmodium falciparum malaria infected red cells can obstruct the blood vessels of the brain, causing cerebral malaria, which is often lethal [1-3, 7]. In the study area mainly Plasmodium falciparum and Plasmodium vivax malaria are found. I.

Area Profile

Chhindwara is a tribal district of the state Madhya Pradesh. It is situated from 21°28 to 22°49’ north Longitude and 78°40’ to 79°24’ East Latitude and spread over an area of 11,815 square km. Tamia is one of the tribal community block of Chhindwara district. It has more than 85% unique tribal population of Bharia tribe. Tamia is situated between 22° 20’ northern latitude and 78° 40’ in eastern longitude. It extends over an area of 1268.02 square km, which is about 9.32 % of the district geographical area. II.

Bharia Tribe

Bharia is a Dravidian tribe. The Bharia tribe is mainly concentrated in the Patalkot valley, tamia and its adjoining regions. The Bharia is one of the indigenous tribe of the region. The name bhumia meaning “Lord of soil” is another name of this tribal group. It is one of the scheduled tribes of the Indian subcontinent. The Bharia tribe has adapted to the profession of cultivation mainly shifting cultivation has been practiced in order to

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sustain their livelihood. Apart from cultivation, Bharia tribes also collect various forest products like tubers, roots, and fruits and thus meet the demands of their day to day living. The Bharia tribe also works as labourers in the forest department. The field survey data on occupational positions of bharia tribe has thrown some light on the variety of occupations i.e. about 38.7 percent of the people are farmers, agriculture labour comprises 37 percent. The rest around 24.3 percent work as forest labourers. Since the whole region of Patalkot valley are quite rich in plants of medicinal values. The Bharia tribe is also going to depend on these plants to meet various purposes. Bharia tribe has set up their own system of treatment for all the health hazards and illness. Bharia tribe pays least attention to education and learning only 11.6% passed primary education and about 66.4% of the Bharia tribes are illiterate. The Bharia tribes have maintained their originality without adopting the modern culture. They follow the structure of nuclear family and live in beautiful households built by their own hands. Bharia people strongly follow birth and funeral rites and they are highly religious by nature. III.

Approaching Procedure

In this work we try to search the distribution of spatial patterns of malaria cases like species wise, gender wise and on the basis of different age groups among Bharia tribe of Tamia, Chhindwara. The study of malaria is based on secondary data collection due to extensive field work. The secondary data on malaria disease are collected from, district hospital, district malaria office and primary health center (PHC). The collected data are analyzed on the basis of species, gender and age groups. IV.

Environment of Tamia

Tamia is surrounded by hills forests and its area consists of ridges and valleys. Tamia is a treasure of forests and herbal wealth. The average temperature of the region lies in between 20º C to 39º C in summer and 9ºC to 25ºC in winter. The Doodhi and his supporting rivers flow in the study region are crates suitable breeding and hiding places for mosquitoes. The surroundings to Tamia and their environmental conditions are favourable for malaria incidence and prevalence among bharias of the area.

Results and Discussion:

The study of malaria cases of the duration 2006-2010 is based on various segments like species wise, gender wise and age wise malaria cases and their distribution in five year duration. The study shows the trends of malaria existence and its variation year by year. The results are mentioned in three sections as follows:A.

Species Wise Malaria Cases:

The total malaria cases and species wise plot of malaria cases versus years show in figure-1. The plot of total malaria cases from 2006-2010 show that the malaria cases increase from 2006 to 2007. In the year 2008 the total malaria cases are the least and again increase up to 2010. It is cleared that the total malaria cases of 2010 are approximate five times of 2006 total malaria cases; even the anti-malarial activities are running in the study area. The reason is that the due to continuous use of anti-malarial drugs and spray chemicals the development of resistance capacity in mosquitoes and in malaria parasite. Hence the used drugs are ineffective in malaria control in the study area; there is a need of new anti-malarial drugs and chemicals for malaria control. Figure 1: Total malaria cases and species wise distribution versus years

The figure-1shows that the Pf cases (Plasmodium Falciparum) malaria cases are very large and nearly equals to total malaria cases while the Pv (Plasmodium vivax) malaria cases are very low among bharia people. It is due to the environmental conditions like temperature and humidity of tamia block of Chhinwara district are more favourable in spread and growth of Plasmodium falciparum parasite in mosquitoes than plasmodium vivax parasite.

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Age Wise Malaria Cases:

The plot of age wise distribution of malaria cases with years shows in figure-3. The figure shows that in the year 2008 the malaria cases and their age wise distribution are the lowest and in 2010 are the highest. The figure-2 also show that the adult age group is more suffered with malaria. The age group 5-15 ranks second while children age group is ranks last. The reason is that the age group 15 & above years is working in agriculture fields, forests and spent more time in mosquitogenic conditions. The age group 5-15 is generally school going children, playing in open fields hence, exposed for mosquito bites. But the children of age group 0-5 years are always in observation of households, hence less malaria patients are of this age group. Figure 2: Age wise distribution of malaria cases versus years

C. Gender Wise Malaria Cases: The plot of gender wise distribution of malaria cases versus years is shown in figure-3. The figure shows that during 2006-07 female malaria patients are more than male patients while from 2008 to 2010 the male patients are more than female patients. The figure shows that from 2008 the malaria patterns in the study area are reversed. It is due to the development of resistivity in mosquitoes and malaria parasite against anti-malaria drugs and chemicals makes them more susceptible to men. The reason is that men are working in agriculture fields and forests, hence spent more time in mosquito-genic conditions. Another reason is that men wear fewer cloths to women hence maximum part of their body is opened for mosquito bites. Figure 3: Gender wise malaria cases

VI. Conclusion Bharia tribe is one of the scheduled tribe of Indian subcontinent and mainly resides in the Patalkot valley of Tamia block of Chhindwara district, Madhya Pradesh (India). The study shows the distribution of malaria patterns among Bharia tribe from 2006-2010. The total malaria cases suddenly rise during 2010 and almost five times of 2006 malaria cases due to development of resistance in mosquitoes and plasmodium parasite against anti-malarial chemicals and drugs. The Pf cases are more than Pv cases because the environmental conditions are suitable for spread and growth of plasmodium falciparum parasite. The adult age group i.e. 15 & above years is more suffered with malaria specially men due to their clothing habits and the environmental conditions of their working place.

VII. 1. 2.

References

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