Journal Vol – 14 No -5, October 2019

Abstract:

India has been a rapidly urbanizing country despite being known as a country of villages for centuries. Since Independence, India has witnessed the emergence of more than 2500 New Towns across the country, mostly developed through conversion of rural lands. New Town Kolkata in West Bengal being no exception, involved acquisition and conversion of 3075 hectare of rural land of which 68.36% was agricultural land. While such land acquisition led to economic displacement of the local people, it also led to a huge amount of investment in the form of project costs (INR203, 17, 19,887 in 2014-2015) for the development of New Town. This paper aims to determine the direct benefit accrued to the state from the said investment which is achieved in cost of displacement and livelihood changes of local people. For this purpose, the past (before land acquisition) and present economic conditions of these people have been compared. Taking into consideration of almost all sources of income of past as well as present, a cost benefit analysis in present value terms has been done for the period of 1999 (beginning year of land acquisition) to 2014. A quantitative evaluation of cost incurred by the land losers and a comparison with the compensation paid has been made. Also, a qualitative assessment of uncompensated intangible costs incurred by the land losers have been presented. Hence the ethics of the new town planning as practiced in our country is questioned.

Keywords:

Land Conversion,New Town,Opportunity Cost,Cost Benefit Analysis,Gross Profit Ratio,

Refference:

I. Council for Social Development, Indian Social Development Report.
“Development and Displacement”, Oxford University Press. (New Delhi)
(2008),
II. Dey, I. Samaddar, R. and Sen, S. K. “Beyond Kolkata: Rajarhat and the
Dystopia of the Urban Imagination” Routledge (New Delhi) (2013)
III. Kundu, R. “Making Sense of Place in Rajarhat New Town the Village in the
Urban and the Urban in the Village”. Economic & Political Weekly, LI (17).
93-101(2016).
IV. Mallik, C. “Land Dispossession and Rural Transformation: The Case of
Fringe Villages of Kolkata”. Journal of Rural Development. 33 (1). 51-
71(2014).
V. Roy, A. “Development, Land Acquisition and Changing Facets Of Rural
Livelihood: A Case Study From West Bengal”. Journal of Rural
Development. 33(1), 15-32. (2014).
VI. Roy, U. K. “Development of New Townships: A Catalyst in the Growth of
Rural Fringes of Kolkata Metropolitan Area (KMA)”. Annual Conference of
HUDCO. (2005).
VII. Sarkar, A.“Development and Displacement Land Acquisition in West
Bengal”, Economic &Political Weekly, 42 (16): 1435-42. (2007).
VIII. Sengupta, P. & Chattopadhyay, S. “Appraising Compensational Benefit
under “The Right to Fair Compensation and Transparency in Land
Acquisition, Rehabilitation and Resettlement Act, 2013”. Indian Journal of
Regional Science, XLVIII (1), 114 – 119. (2016)
IX. West Bengal Housing and Infrastructure Development Corporation. New
Town Calcutta, Project Report. Kolkata, India. (1999).

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Abstract:

The main reason for the study is SERVQUAL scale couldn't be validated and adapted inside of a retail setting, given the novel dimensions of service in the connection of retail stores when contrasted with "unadulterated" service environment. The paper means to decide the validity of Retail Service Quality Scale (RSQS) as a distinct option for SERVQUAL in the connection of Indian retail environment. Absolutely 450 clients from hypermarkets in Tamilnadu chose accommodation premise. Retail service quality scale RSQS (28 things) was utilized for validation reason. It consolidates particular validity sorts like construct, convergent, discriminant validity. Confirmatory Factor Analysis has been utilized towards validation and advancement of RSQS measurement model. RSQS model in unique structure is substantial in the Indian retail store environment and legitimate RSQS in the Indian retail environment will be an advantage for considering the composed retail settings. The discoveries and proposals will empower retail stores to assemble knowledge into current levels of service quality and in addition to direct occasional "checks" for surveying extension for service change. RSQS could serve as an analytic apparatus for retailers to recognize service zones that are powerless and needing consideration.

Keywords:

RSQS Validation,SERVQUAL,Hypermarkets,Indian Organized Retail Stores,Service Quality,

Refference:

I. Bentler, P. M, “Comparative fit indexes in structural models”, Psychological
Bulletin, Volume: 107, Issue: 2, pp: 238–246. (1990).
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II. Brown, T. A, “Confirmatory factor analysis for applied research”, New
York, NY: Guilford Press., (2006).
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for Retail Stores: Scale Development and Validation”, Journal of the
Academy of Marketing Science, Volume: 24, Winter Issue, pp: 03-16,
(1996).

IV. Gutman J. & Alden S. D., “Adolescents’ Cognitive Structures of Retail Stores
and Fashion Consumption – A Means-End Chain Analysis of Quality”,
Perceived Quality: How Consumers View Stores and Merchandise,
Lexington Books, (1985).
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Analysis “, Prentice Hall Inc., Upper Saddle River, NJ, USA. (7th Edition.)
(2010).
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structure analysis: Conventional criteria versus new alternatives”, Structural
Equation Modeling, Volume: 06, pp: 01–55, (1999).
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VII. Mazursky D. & Jacoby J.,”Forming Impressions of Merchandise and Service
Quality”, Perceived Quality: How Consumers View Stores and Merchandise,
Lexington Books, (1985).
VIII. Mehta, Subhash C., Lalwani A. & Soon Li Han, “Service Quality in Retailing
– Relative Efficiency of Alternative Measurement Scales for Different
Product Service Environments”, International Journal of Retail & Distribution
Management, Volume: 28, Issue: 02, pp: 62-72, (2000).
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XI. Subhashini Kaul, “Measuring Retail Service Quality: Examining
Applicability of International Research Perspectives in India”, Vikalpa,
Volume: 32, Issue: 01, January – March, pp: 15-26, (2007).
XII. Schneider, Benjamin and Susan White, “Service Quality – Research
Perspectives, Foundations for Organizational Science”, California USA, Sage
Publications Inc, (2004).
XIII. Wisniewski M.,”Using SERVQUAL to Assess Customer Satisfaction with
Public Sector Services”, Managing Service Quality, Volume: 11, Issue: 06,
pp: 380-388, (2001).
XIV. Wong A. and Sohal A., “Service Quality and Customer Loyalty Perspectives
on Two Levels of Retail Relationships”, Journal of Services Marketing,
Volume: 17, Issue: 05, pp:495-513, (2003).

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Abstract:

Hollow-core photonic crystal fibers (HC-PCF) are used for high power beam delivery and can deliver ultra-short or compressed pulses at 1550 nm. This paper study the relation between the length of (9 &7) cell HC-PCFsand the full width at half maximum (FWHM) using laser source with centroidwavelength of 1546.7 nm, i.e. almost 1550nm, and FWHM of 286 pm or 10 ns in the time domain.The FWHM in the frequency domain was increased in both (19&7) cell HC-PCFs as the length of Fabry-Perot interferometer increased till it reachesa specific length and then dramatically decreasedto go to the almost same starting point.

Keywords:

Hollow-Core Photonic Crystal Fiber,FWHM,Pulse compression,the compression factor,

Refference:

I. Agrawal, G.P. Application of Nonlinear Fiber Optics, 2nded.; Academic
Press: New York, 2001.
II. D. G. Ouzounov, F. R. Ahmad, D. M¨uller, N. Venkataraman, M. T.
Gallagher, M. G. Thomas, J. Silcox, K. W. Koch, and A. L. Gaeta,
“Generation of megawatt optical solitons in hollow-Core photonic band-gap
fibers,” Science 301, 1702–1704 (2003).
III. D. V. Skryabin, “Coupled core-surface solitons in photonics crystal fibers,”
Opt. Express 12, 4841–4846 (2004).
IV. Diels, J.C.; Rudolph, W. Ultrashort Laser Pulse Phenomena,2nd ed.;
Academic Press: 2006.
V. E. V. Ermolaeva and V. G. Bespalov, “Optimum Condi- tions for Stimulated
Raman Scattering, Compression, and Amplification of Supershort Pulses in a
Plasma with Com- pressed Gases,” Journal of Optical Technology, Vol. 74,
No. 11, 2007, pp. 734-739.
VI. F. G´erˆome, K. Cook, A. K. George, W. Wadsworth, and J. C. Knight,
“Delivery of sub-100fs pulses through 8m of hollow-core fiber using soliton
compression,” Opt. Express 15, 7126–7131 (2007).
VII. F. Luan, J. C. Knight, P. S. J. Russell, S. Campbell, D. Xiao, D. T. Reid, B. J.
Mangan, D. P. Williams, and P. J. Roberts, “Femtosecond soliton pulse
delivery at 800 nm wavelength in hollow-core photonic bandgap fibers,”
Opt. Express 12, 835–840 (2004).
VIII. G. H. He and S. H. Liu, “Physics of Nonlinear Optics,” World Scientific
Publishing Co. Pte Ltd., Singapore, 1999.
IX. G. P. Agrawal, Applications of Nonlinear Fiber Optics (Academic, 2001)
X. H. Q. Quy, “Applied Nonlinear Optics,” Hanoi National University
Publishing, Hanoi, 2007, pp. 214-201.

XI. J. C. Knight, F. G´erˆome, and. J. Wadsworth, “Hollow-core photonic crystal
fibers for delivery and compression of ultrashort optical pulses,” IEEE J.
Quantum Electron. 39, 1047–1056 (2007).
XII. L. V. Tarasov, “Laser Physics,” Mir, Moscow, 1988, pp. 214-335.
XIII. S. O. Atuba, K. Nakkeeran, K. W. Chow, P. Ramesh Babu, A. Manimegalai
& K. Senthilnathan (2016) Generation of a train of ultrashort pulses using
periodic waves in tapered photonic crystal fibers, Journal of Modern Optics,
63:21, 2246-2258, DOI: 10.1080/09500340.2016.1193638
XIV. V. L. Kalashnikov, “Pulse Shortening in the Passive Q- Switched Lasers with
Intracavity Stimulated Raman Scat- tering,” Optics Communications, Vol.
218, No. 1-3, 2003, pp. 147-153. doi:10.1016/S0030-4018(03)01191-X [12]
I. P. Prokopovich and A. A. Khrushchinskii, “Highly Efficient Generation of
Attosecond Pulses in Coherent Stimulated Raman Self-Scattering of Intense
Demtosec-and Laser Pulses,” Laser Physics, Vol. 7, No. 2, 1997, pp. 305-
308.
XV. Weiner, A.M. Ultrafast Optics; John Wiley: Hoboken, NJ,2009.
XVI. Y. Ping, I. Geltner and S. Suckewer, “Raman Scattering,” Physical Review
E, Vol. 67, 2003, Article ID: 016401. doi:10.1103/PhysRevE.67.016401

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Abstract:

Agriculture and its supply chain is one of the major domains of research which need attention for its growth in all developing countries. Food safety and its supply are also drawing the world attention towards its importance and people are focusing on it because of health hazards. In this research, we have presented a model for the uplift of traditional agriculture field to smart farming, considering blockchain with IoT technology. This system promises to provide equal opportunity to all stakeholders involved in the agricultural food supply chain; even they are not interconnected. IoT devices are added to the smart model to reduce human interference for data collection, recording and verification. The validation of our novel model is compared with our own scheme utilizing only IoT devices deployed in the monitoring field without block-chain.  

Keywords:

Agriculture,Blockchain,Novel,IoT,Smart Model,

Refference:

I. Abate, Gashaw Tadesse, Tanguy Bernard, Alan de Brauw, and Nicholas
Minot. “The impact of the use of new technologies on farmers’ wheat
yield in Ethiopia: evidence from a randomized control trial.” Agricultural
economics 49, no. 4 (2018): 409-421.
II. Atzori, Marcella. “Blockchain technology and decentralized governance:
Is the state still necessary?.” Available at SSRN 2709713 (2015).
III. Abeyratne, Saveen A., and Radmehr P. Monfared. “Blockchain ready
manufacturing supply chain using distributed ledger.” (2016).
IV. Calatayud, Agustina, John Mangan, and Martin Christopher. “The selfthinking
supply chain.” Supply Chain Management: An International
Journal 24, no. 1 (2019): 22-38.
V. Chang, Hung-Fu, and Tzu-Kang Lin. “Real-time Structural Health
Monitoring System Using Internet of Things and Cloud Computing.”
arXiv preprint arXiv:1901.00670 (2019).
VI. Colombo, Patricia Eustachio, Emma Patterson, Liselotte Schäfer Elinder,
Anna Karin Lindroos, Ulf Sonesson, Nicole Darmon, and Alexandr
Parlesak. “Optimizing School Food Supply: integrating Environmental,
Health, Economic, and Cultural Dimensions of Diet Sustainability with
Linear Programming.” (2019).
VII. Casado-Vara, Roberto, Javier Prieto, Fernando De la Prieta, and Juan M.
Corchado. “How blockchain improves the supply chain: Case study
alimentary supply chain.” Procedia computerscience 134 (2018): 393-398.
VIII. Chen, Weijie, Guo Feng, Chao Zhang, Pingzeng Liu, Wanming Ren, Ning
Cao, and Jianrui Ding. “Development and Application of Big Data
Platform for Garlic Industry Chain.” Computers,Materials & Continua 58,
no. 1 (2019): 229-248.
IX. Choe, Young Chan, Joowon Park, Miri Chung, and Junghoon Moon.
“Effect of the food traceability system for building trust: Price
premium and buying behavior.” Information SystemsFrontiers 11, no. 2
(2009): 167-179.
X. Dasaklis, Thomas K., Fran Casino, and Constantinos Patsakis. “Defining
granularity levels for supply chain traceability based on IoT and
blockchain.” In Proceedings of the InternationalConference on Omni-
Layer Intelligent Systems, pp. 184-190. ACM, 2019.
XI. Gu, Baojing, Xiaoling Zhang, Xuemei Bai, Bojie Fu, and Deli Chen.
“Four steps to food security for swelling cities.” (2019): 31.
XII. Glunz, Benjamin F., Wayne R. Pearson, and Alfredo F. Munoz. “Method
and system for creating 3D models from 2D data for building information
modeling (BIM).” U.S. Patent 9,817,922, issued November 14, 2017.

XIII. Han, Sangyeal, and Heetae Yang. “Understanding adoption of intelligent
personal assistants: A parasocial relationship perspective.” Industrial
Management & Data Systems 118, no. 3 (2018): 618-636.
XIV. Ivanov, Dmitry, Alexander Tsipoulanidis, and Jörn Schönberger.
“Operations and Supply Chain Strategy.” In Global Supply Chain and
Operations Management, pp. 81-110. Springer, Cham, 2019.
XV. Kamilaris, Andreas, Agusti Fonts, and Francesc X. Prenafeta-Boldύ. “The
Rise of the Blockchain Technology in Agriculture and Food Supply
Chain.” (2018).
XVI. Kumar, Subramania Ananda, and Paramasivam Ilango. “The impact of
wireless sensor network in the field of precision agriculture: a review.”
Wireless Personal Communications 98, no. 1 (2018): 685-698.
XVII. Louis, Joseph, and Phillip S. Dunston. “Integrating IoT into operational
workflows for real-time and automated decision-making in repetitive
construction operations.” Automation inConstruction 94 (2018): 317-327.
XVIII. Meiklejohn, Sarah, Marjori Pomarole, Grant Jordan, Kirill Levchenko,
Damon McCoy, Geoffrey M. Voelker, and Stefan Savage. “A fistful of
bitcoins: characterizing payments among men with no names.” In
Proceedings of the 2013 conference on Internet measurement conference,
pp. 127-140. ACM, 2013.
XIX. Nedovic, Viktor, Ana Kalusevic, Verica Manojlovic, Steva Levic, and
Branko Bugarski. “An overview of encapsulation technologies for food
applications.” Procedia Food Science 1 (2011): 1806-1815.
XX. Perboli, Guido, Stefano Musso, and Mariangela Rosano. “Blockchain in
logistics and supply chain: A lean approach for designing real-world use
cases.” IEEE Access 6 (2018): 62018-62028.
XXI. Patel, Sami, and I. U. Sayyed. “Impact of information technology in
agriculture sector.” International Journal of Food, Agriculture and
Veterinary Science 4, no. 2 (2014): 17-22.
XXII. Ruiz-Garcia, Luis, and Loredana Lunadei. “The role of RFID in
agriculture: Applications, limitations and challenges.” Computers and
Electronics in Agriculture 79, no. 1 (2011): 42-50
XXIII. Sharma, Deepak, Amol P. Bhondekar, Amritesh Ojha, A. K. Shukla, and
C. Ghanshyam. “A technical assessment of IoT for Indian agriculture
sector.” Int. J. Comput. Applic (2016).
XXIV. Sawa, Toshiyuki. “Blockchain technology outline and its application to
field of power and energy system.” Electrical Engineering in Japan 206,
no. 2 (2019): 11-15.
XXV. Timalsina, Tirtha Raj. “Agricultural Transformation around Koshi Hill
Region: A RuralDevelopment Perspective.” NUTA Journal 6, no. 1-2
(2019): 95-101.

XXVI. Tian, Feng. “An agri-food supply chain traceability system for China
based on RFID & blockchain technology.” In 2016 13th international
conference on service systems and servicemanagement (ICSSSM), pp. 1-6.
IEEE, 2016.
XXVII. Trivedi, Shekhar, and Ms Venus Madan. “Amazon Supply Chain
Management: A Case Study.” NOLEGEIN-Journal of Supply Chain and
Logistics Management (2018): 23-27.
XXVIII. Wei, C. H. E. N. “Security Measures Against the Hack Attacks and the
Applications of Hackers’ Technology.” Journal of Hunan University of
Science and Engineering 7 (2010): 029.
XXIX. Zamora-Izquierdo, Miguel A., José Santa, Juan A. Martínez, Vicente
Martínez, and Antonio F. Skarmeta. “Smart farming IoT platform based
on edge and cloud computing.” BiosystemsEngineering 177 (2019): 4-17.
XXX. Zheng, Zibin, Shaoan Xie, Hongning Dai, Xiangping Chen, and Huaimin
Wang. “An overview of blockchain technology: Architecture, consensus,
and future trends.” In 2017 IEEE InternationalCongress on Big Data
(BigData Congress), pp. 557-564. IEEE, 2017.

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Abstract:

Over the recent years we have witnessed an increasing number of applications using deep learning techniques such as Convolutional Neural networks (CNNs), Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN) for remote sensing image classification. But, we found that these models suffer for characterizing complex patterns in remote sensing imagery because of small inter class variations and large intra class variations. The intent of this paper is to study the effect of ensemble classifier constructed by combining three Deep Convolutional Neural Networks (DCNN) namely; CNN, VGG-16 and Res Inception models by using average feature fusion techniques. The proposed approach is validated with 7,000 remote sensing images from Northern Western Polytechnical University – Remote Sensing Image Scene Classification (NWPU- RESISC) 45 class dataset and confirmed as an effective technique to improve the robustness over a single deep learning model.

Keywords:

Image classification,Remote sensing,Feature fusion,Convolutional neural network,Deep CNN and Ensemble classifier,

Refference:

I. Ayhan E, and Kansu O, “Analysis of Image Classification methods for Remote
Sensing experimental Techniques”, Society for Experimental Mechanics, pp.18-
25, 2012.
II. Chan T H, Jia K, Gao S, Lu J, Zeng Z, and Ma Y, “PCANet: A simple deep
learning baseline for image classification?” IEEE Transaction on Image
Processing, pp.5017–5032, 2015.
III. Cheng G, Han J, and Lu X, “Remote Sensing Image Scene Classification:
Benchmark and State of the Art”, Proceedings of the IEEE, pp.1-19, 2017.
IV. Cheng C, and Han J, “A survey on object detection in optical remote sensing
images”, ISPRS Journal of Photogrammetry and Remote Sensing, pp.11-28,
2017.
V. Cheng J, Aurélien B, and Mark van der L, “The relative performance of
ensemble methods with deep convolutional neural networks for image
classification”, Journal of Applied Statistics, pp.1-19, 2018.
VI. Deepan P, and Sudha L R, “Object Detection in Remote Sensing Images: A
Review”, International Journal of Scientific Research in Computer Science
Applications and Management Studies, pp.1-10, 2018.
VII. Grant J, Richard A, Curt H, and Tyler W, “Fusion of Deep Convolutional Neural
Networks for Land Cover Classification of High-Resolution Imagery”, IEEE
Geoscience and Remote Sensing Letters, pp.1-5, 2017.
VIII. Krizhevsky A, Sutskever I, and Geoffrey E, “ImageNet Classification with Deep
Convolutional Neural Networks”, Proceedings, pp.1-9, 2015.
IX. Maarten C, Henri B, Noëlle M, and Klamer S, “Object recognition using deep
convolutional neural networks with complete transfer and partial frozen layers”,
Proceedings of Society of Photo-Optical Instrumentation Engineers (SPIE), pp.1-
7, 2016.
X. Maher Ibrahim S, Biswajeet P, and Omar Saud A, “Classification of Very High
Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural
Networks”, Journal of Sensors, pp.1-12, 2018.
XI. Marmanis D, Datcu M, Esch T, and Stilla U, “Deep Learning Earth Observation
Classification Using Image Net Pre trained Networks”, IEEE Geoscience and
Remote Sensing Letters, pp.105-109, 2016.

XII. McInerney D O, and Nieuwenhuis M, “A comparative analysis of kNN and
decision tree methods for the irish national forest inventory”, International
Journal of Remote Sensing, pp.4937–4955, 2009.
XIII. Mountrakis G, Im J, and Ogole C, “Support vector machines in remote sensing: a
review”, ISPRS Journal of Photogrammetry Remote Sensing, pp.247–259, 2011.
XIV. Muhammet Ali D, “Deep Network Ensembles for Aerial Scene Classification”,
IEEE Geoscience and Remote Sensing Letters, pp.1-4, 2018.
XV. Qi K, Wu H, Shen C, and Gong J, “Land use scene classification in highresolution
remote sensing images using improved correlations,” IEEE Geosci.
Remote Sens. Lett., pp. 2403–2407, 2015.
XVI. Shuying L and Weihong D, “Very Deep Convolutional Neural Network Based
Image Classification Using Small Training Sample Size”, Third IAPR Asian
Conference on Pattern Recognition, pp1-5, 2015.
XVII. Souleyman C, Huan L, Yanfeng G, and Hongxun Y, “Deep Feature Fusion for
VHR Remote Sensing Scene Classification”, IEEE Transaction on Geoscience
and Remote Sensing, pp.1-10, 2017.
XVIII. Taylor L, and Nitschke G, “Improving Deep Learning using Generic Data
Augmentation”, Proceedings, pp.1-6, 2015.
XIX. Thanh Noi P, and Kappas M, “Comparison of Random Forest, k-Nearest
Neighbor, and Support Vector Machine Classifiers for Land Cover Classification
Using Sentinel-2 Imagery”, Journal on the science and technology of sensors,
pp.1-20, 2018.
XX. Williams T, and Li R, “An Ensemble of Convolutional Neural Networks Using
Wavelets for Image Classification”, Journal of Software Engineering and
Applications, pp.69-88, 2018.
XXI. Xingrui Y, Xiaomin W, Chunbo L and Peng R, “Deep learning in remote sensing
scene classification: a data augmentation enhanced convolutional neural network
framework”, Journal of GIScience and Remote Sensing, pp.1-18, 2017.
XXII. Yang L, Yiping G, Zhifeng X, and Qing L, “Accurate Object Localization in
Remote Sensing Images Based on Convolutional Neural Networks”, IEEE
Transactions On Geoscience and Remote Sensing, pp.2486-2498, 2017.
XXIII. Yansheng L, Chao T, Yihua T, Ke S, and Jinwen T, “Unsupervised Multilayer
Feature Learning for Satellite Image Scene Classification” IEEE Geoscience and
Remote Sensing Letters, pp.1-12, 2016.
XXIV. Zhao L, Tang P, and Huo L, “Feature significance- based multi bag-of-visualwords
model for remote sensing image scene classification,” Journal of Appl.
Remote Sens., pp.035-040, 2016.
XXV. Zhiling G, Qi C, Guangming W, Yongwei X, Ryosuke S and Xiaowei S,
“Village Building Identification Based on Ensemble Convolutional Neural
Networks”, Journal of Sensors, pp.1-22, 2017.

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Abstract:

Micro-strip patch antenna has penetrated deep in the market due to its advantages. After studying micro patch antennas, there are some draw backs of it. One of the drawbacks includes the narrowband performance. The primary reason for this is its resonant nature. An E-shaped micro strip patch antenna is used for the broadband applications. This E-shaped antenna is used for the purpose of improving antenna shrinking and information measurement to name a few. This paper shows the detail study of Cowl’s research by using 2 different aspects of micro strip patch antenna. The antenna operated at a frequency of 5GHz. Antennas used were single part narrow band rectangular micro strip patch antenna and slot cut E-shaped micro strip patch antenna. Simulation method included high frequency structure machine (HFSS). Different properties such as Cable loss, information measurement and VSWR were studied using both types of antennas. These properties were then compared between each other.

Keywords:

Micro-Strip,Antenna,Frequency,6.5 GHz,Rectangular patch,

Refference:

I. Ahamed, M. M., Bhowmik, K., & Al Suman, A. (2012). Analysis and design
of rectangular microstrip patch antenna on different resonant frequencies for
pervasive wireless communication. International Journal of Scientific &
Technology Research, 1(5), 108-111.
II. Danideh, A., SadeghiFakhr, R., &Hassani, H. R. (2008). Wideband co-planar
microstrip patch antenna. Progress In Electromagnetics Research, 4, 81-89.
III. Divya, R., &Priya, M. (2013). Design and characterization of E-shape
microstrip patch antenna for wireless communication. ICTACT journal on
communication technology, 4(01).
IV. Hammers tad, E. O. (1975).”Equations for micro strip circuit design,” Proc.
Fifth European Microwave Conf., 268-272.

V. Jan, J. Y., & Tseng, L. C. (2004). Small planar monopole antenna with a
shorted parasitic inverted-L wire for wireless communications in the 2.4-,
5.2-, and 5.8-GHz bands. IEEE Transactions on Antennas and
Propagation, 52(7), 1903-1905.
VI. Kumar, S., & Gupta, H. (2013). Design and study of compact and wideband
microstrip u-slot patch antenna for Wi-Max application. IOSR-JECE, 5(2),
45-48.
VII. Prashanth, K. V., Pavani, T., Srivatsav, N. L., Kumar, C. P., Raja, R., Fields,
G., &Vaddeswaram, G. D. (2017). DESIGN OF INSET FEED
RECTANGULAR PATCH ANTENNA FOR WLAN/WI-FI
APPLICATIONS. International Journal of Pure and Applied
Mathematics, 116(6), 43-48.
VIII. Raj, R. K., Joseph, M., Aanandan, C. K., Vasudevan, K., &Mohanan, P.
(2006). A new compact microstrip-fed dual-band coplanar antenna for
WLAN applications. IEEE transactions on antennas and
propagation, 54(12), 3755-3762
IX. Ramna, A. S. S. (2013). Design of rectangular microstrip patch antenna using
particle swarm optimization. International Journal of Advanced Research in
Computer and Communication EngineeringVol, 2.
X. Rop, K. V., &Konditi, D. B. O. (2012). Performance analysis of a rectangular
microstrip patch antenna on different dielectric substrates. Innovative Systems
Design and Engineering, 3(8), 1727-1729.
XI. Roy, A. A., Mom, J. M., &Igwue, G. A. (2013). Enhancing the bandwidth of
a microstrip patch antenna using slots shaped patch. American Journal of
Engineering Research (AJER), 2(9), 23-30.
XII. Singh, D., Gupta, K. A., & Prasad, R. K. (2013). Design and analysis of Cshaped
microstrip patch antenna for wideband application. VSRD Int. J.
Electr.Electron.Commun.Eng, 3(1).
XIII. Singh, J., Tiwari, M., & Neha, P. (2014). Design and simulation of microstrip
E-shaped patch antenna for improved bandwidth and directive gain. Int. J.
Eng. Trends Technol, 9(9), 23-30.
XIV. Wu, J. W., Hsiao, H. M., Lu, J. H., & Chang, S. H. (2004). Dual broadband
design of rectangular slot antenna for 2.4 and 5 GHz wireless
communication. Electronics Letters, 40(23), 1461-1463.

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Abstract:

The aim of the study was to investigate how office type influences employee performance, and whether this is different for different personalities. Multiple regression was used in order to test the impact of personality and office type on employee performance. The data was collected from 406 employees working in higher educational institutions, with different office types in Bangalore, Karnataka by using convenience sampling technique. Respondents who were emotionally stable, extroverted and conscientious showed higher level of performance. Specially more emotionally stable respondents showed greater performance, specifically those working in flex offices. Extroverts shown greater performance in shared and cell offices than in open plan and flex offices. Conscientious people shown greater performance in shared and open plan offices.

Keywords:

Cell Offices,Open plan Offices,Shared rooms,Flex Offices,Personality,Big five traits,Employee performance,

Refference:

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Abstract:

Corporate governance is aunified, systematic, comprehensive and an integrated mechanism which helps the organization to be transparent. Basically, Its erves as a watch dog for an organization through its monitoring process and create values for itself, share holders and for the society at large. This piece of work aims at a chronological study of corporate governance in Information Technology sector and its impact on society for value creation.A range of studies that have applied in 20-year period are examined in a non-exhaustive review of the literature. These studies are selected from authenticated sources mainly from well-known national and international journals. The paper discusses and summarizes numerous theoretical aspects followed by conceptual criticisms of corporate governance structures/ policies/ framework. Despite these criticisms, the paper concludes that corporate governance remains an useful instrument for industry-oriented research. The author has focused her approach purely from corporate perspective based on her diagnostic studies that is clearly reflected on this paper. The nature and scope of corporate governance is vast and ever evolving. The genesis of the corporate governance reveal that Corporate governance matters are complex that combines its matters like web. The term “governance” designated initially as government dealing with economic and social resources than as a process by which corporate decisions are made or implemented. Corporate governance has become a very effective mechanism which has helped the organizations to create value and stand as a pillar for the growth of Indian economy.The paper provides a useful source of information on corporate governance and its applications. In particular, the paper summarizes a trend of corporate governance over a period of time in India and it is beneficial to the academics, practitioners and researchers.

Keywords:

Corporate Governance,India,CSR,value creation,society,shareholders,

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Abstract:

Relying on the fact that expenses on managing employee turnover costs a lot to the organizations, understanding on the contributors of high turnover becomes crucial. The present paper is focussed on this fact and progresses with an objective to explore the relevant factors influencing employee turnover and put forth their ranking based on their strength of influence. The study successfully concluded four reliable factors – personal, job influencers, environment & working conditions and benefits & welfare measures, as factors influencing employee turnover in the industries selected as sample. The responses of the respondents from manufacturing, mining and services sectors from North east India, were analysed for its reliability and data reduction using SPSS package software. The study further applied Grey Relational analysis method for prioritizing the explored factors for meaningful conclusions.Based on the analysis, the study concludes that statements belonging to employee benefits and welfare measures factor were ranked above all as major influencers for employee turnover in the sample organization represented in the study. The study suggests a roadmap to determine which factors guide towards higher employee turnover and turnover in an organization. They should concentrate on the items for better improvement plans facilitating retention in future.

Keywords:

Employee Turnover,Employee Attrition,Manufacturing,Services,Employee retention,India,

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Abstract:

The Indian management education sector is experiencing a highly competitive and complex environment today. Following which, the Universities and other higher educational institutions have realised the importance of being distinct from their competitors. One of the major pathway to do so is maintaining high standards in educational service quality which will foster developing positive bonding with the students. The present study is carried out with an objective to explore the dimensions influencing the service quality in management education particularly in public university system and to prioritize the dimensions from the perspective of management students. The study engaged exploratory factor analysis and independent RIDIT analysis methodology to analyse the survey responses of 211 management students of public universities. The analysis yielded seven perceived service quality dimensions,namely physical factors, leisure factors, academic factors, industry collaborations, responsiveness, learning outcome and personality development as perceived by the students from EFA. The individual items of these dimensions were then prioritised using RIDIT analysis for further interpretations and business insights. This study may benefit the university decision makers in business studies to formulate policies and strategies to assure superior students satisfaction which can later benefit the university by showing positive behavioural intentions.

Keywords:

Perceived service quality,management education,RIDIT analysis,student satisfaction,higher education,

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