Journal Vol – 14 No -5, October 2019

Abstract:

In the present study we analyze the farmers’ perception towards adoption of technology such as ITC for better productivity in farming. The considered constructs are adopted from Technology adoption model (TAM). A total sample of 800 farmers from the Guntur district are collected through simple random technique and out of which survey respondents irregular responses are eliminated finally 756 samples are determined for statistical analysis. Chi-square test was performed to determine the association between perceptions and model constructs. Results are reported and discussions are made as per the results and in correlation between results and previous literature and finally, suggestions and future indication for extension of the study are proposed.

Keywords:

Technology,Farming,Ease of Use,Usefulness,Intention,

Refference:

I. ALI, S. (2005). Total Factor Productivity Growth and Agricultural Research and
Extension : An Analysis of Pakistan ’ s Agriculture , 1960 – 1996. The Pakistan
Development Review, 44(4), 729–746.
II. Amin, K., & Li, J. (2016). Applying Farmer Technology Acceptance Model to
Understand Farmers’ Behavioral Intention to use ICT Based Microfinance
Platform: A Comparative analysis between Bangladesh and China. The
Thirteenth Wuhan International Conference on E-Business—IT/IS Technology
for E-Business, (July), 123. https://doi.org/10.13140/RG.2.1.3832.9363
III. Barker, R., Dawe, D., & Inocencio, A. (2003). Economics of Water Productivity
in Managing Water for Agriculture. Economics of Water Productivity in
Agriculture, 19–35.
IV. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi region
of Andhra Pradesh. International Journal of Innovative Technology and
Exploring Engineering.8(7), pp. 1708-1714.
V. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology.8(4), pp. 1913-
1920.
VI. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities
market with reference to GUNTUR city, Andhra Pradesh.International Journal of
Engineering and Technology(UAE). 7(2), pp. 1104-1106.
VII. Jain, P. (2017). Impact of Demographic Factors : Technology Adoption in. SCMS
Journal of Indian Management, 3(September), 93–102.
VIII. Jin, S., Huang, J., Hu, R., Rozelle, S., Jin, S., Huang, J., … Rozelle, S. (2019).
The Creation and Spread of Technology and Total Factor Productivity in China ’
s Agriculture. Agricultural & Applied Economics Association, 84(4), 916–930.
IX. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International Journal
of Recent Technology and Engineering.8(1), pp. 2874-2879.
X. Mahadevan, R. (2003). PRODUCTIVITY GROWTH IN INDIAN
AGRICULTURE : THE ROLE OF GLOBALIZATION AND. Asia-Pacific
Development Journal, 10(2), 57–72
XI. Manukonda et al. (2019).What Motivates Students To Attend Guest Lectures?.The International Journal of Learning in Higher Education.Volume 26,
Issue 1. 23-34.
XII. Mittal, S., & Tripathi, G. (2009). Role of Mobile Phone Technology in
Improving. Agricultural Economics Research Review, 22, 451–459.
XIII. Mukherjee, A. N., & Kuroda, Y. (2003). Productivity growth in Indian
agriculture : is there evidence of convergence across states ? Agricultural
Economics, 5150(03), 43–53. https://doi.org/10.1016/S0169-5150(03)00038-0
XIV. Reddy, P. K. (2005). A framework of information technology-based agriculture
information dissemination system to improve crop productivity, 88(12), 1905–
1913.
XV. Shahabinejad, V., & Akbari, A. (2010). Measuring agricultural productivity
growth in Developing Eight. Journal of Development and Agricultural
Economics, 2(9), 326–332.
XVI. Singh, G. (2010). Replacing Rice with Soybean for Sustainable Agriculture in
the Indo-Gangetic Plain of India : Production Technology for Higher
Productivity of Soybean. International Journal of Agricultural Research, 5(5),
259–267. https://doi.org/10.3923/ijar.2010.259.267
XVII. Stiroh, B. K. J. (2019). Information Technology and the U . S . Productivity
Revival : What Do the Industry Data Say ? American Economic Association,
92(5), 1559–1576
XVIII. Sivakoti Reddy, M. (2019).Impact of RSERVQUAL on customer satisfaction: A
comparative analysis between traditional and multi-channel retailing.
International Journal of Recent Technology and Engineering. 8(1), pp. 2917-
2920
XIX. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes. International
Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1720-
1728
XX. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail service
quality in food and grocery retailing: A comparative study between traditional
and multi-channel retailing. International Journal of Management and Business
Research. 9(2), pp. 68-73.
XXI. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016).Saga of silicon
plate: An empirical analysis on the impact of socio economic factors of farmers
on inception of solar plants. International Journal of Control Theory and
Applications. 9(29), pp. 257-266.
XXII. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving factors
and their impact over employee satisfaction – An empirical evidence from Indian
it industry.International Journal of Mechanical Engineering and Technology.
9(4), pp. 725-732.

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Abstract:

The Consumer Price Index (CPI) is a measure that examines the weighted average of pricesof a basket of consumergoods and services, such as transportation, food, and medical care. It is calculated by taking pricechanges for each item in the predeterminedbasket of goods and averaging them.The main objective of this study to check howconsumer price index affects the BSE sensex. In this paper null hypothesis is taken and to prove that hull hypothesis is correct for this correlation and regression analysis tools are used for the analysis.

Keywords:

Consumer price Index,Hypothesis,Sensex,

Refference:

I. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.
II. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
III. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
IV. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). Saga of
silicon plate: An empirical analysis on the impact of socio economic factors
of farmers on inception of solar plants. International Journal of Control
Theory and Applications. 9(29), pp. 257-266.
V. Manukonda et al. (2019). What Motivates Students To Attend Guest
Lectures?. The International Journal of Learning in Higher Education.
Volume 26, Issue 1. 23-34.
VI. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi
region of Andhra Pradesh. International Journal of Innovative Technology
and Exploring Engineering. 8(7), pp. 1708-1714.
VII. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards
commodities market with reference to GUNTUR city, Andhra Pradesh.
International Journal of Engineering and Technology(UAE). 7(2), pp. 1104-
1106.
VIII. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1913-1920.
IX. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Affect of worksite
wellness interventions towards occupational stress. International
Journal of Recent Technology and Engineering. 8(1), pp. 2874-2879.
X. Neelima, J., Koneru, K.(2019). Assessing the role of organizational culture in
determining the employee performance – empirical evidence from Indian
pharmaceutical sector. International Journal of Innovative Technology and
Exploring Engineering. 8(7), pp. 1701-1707.

XI. Suhasini, T., Koneru, K. (2019). Employee engagement through HRD
practices on employee satisfaction and employee loyalty: An empirical
evidence from Indian IT industry.
International Journal of Engineering and Advanced Technology. 8(4), pp.
1788-1794.
XII. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving
factors and their impact over employee satisfaction – An empirical evidence
from Indian it industry. International Journal of Mechanical Engineering and
Technology. 9(4), pp. 725-732.

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Abstract:

Applications of Nanotechnology has widened in diverse fields such as, agrifood processing, food packaging, cosmetics and many more. In this paper we defined a research model constitutes consumers’ willingness to pay for NCPs (Nanotechnology enabled cosmetics products) in fulfillment of physical wellness which is studied from observed variables perceive risk, trust and perceived benefit for the past literature. A total of 139 consumer sample data was taken to conduct the study. It is observed through hierarchical regression that perceived risk is more associated with cosmetics products enabled with nanotechnology and perceived benefit is also a significant predictor i.e., at a benefit forthcoming consumers are comprised to pay for NCPs and followed by trust component in predicting the behavior. It is also observed that consumer’s education qualification (control variable) was having a significant positive association on the behavioral aspect willing to pay for nanotechnology enabled products. Inclusion of a variable educational qualification as control variable the explained variance of the model has increased.

Keywords:

Nanotechnology,wellness,cosmetic products,perceived benefit,perceived risk,trust,

Refference:

I. Hettler, B. (n.d.). Defining wellness: The six dimensional model of wellness. The
National Wellness Institute. Retrieved from
http://www.nationalwellness.org/index.php?id=391&id_tier=381
II. Hymavathi, C.H., Koneru, K.(2019). Investors perception towards Indian
commodity market: An empirical analysis with reference to Amaravathi region of
Andhra Pradesh. International Journal of Innovative Technology and Exploring
Engineering. 8(7), pp. 1708-1714.
III. KishanVarma, M.S., Koneru, K., Yedukondalu, D.(2019). Effect of worksite
wellness interventions towards occupational stress. International Journal of
Recent Technology and Engineering. 8(1), pp. 2874-2879.
IV. Hymavathi, C., Koneru, K. (2019). Role of perceived risk in mutual funds
selection behavior: An analysis among the selected mutual fund investors.
International Journal of Engineering and Advanced Technology. 8(4), pp. 1913-
1920.

V. Hymavathi, C.H., Koneru, K.(2018). Investors’ awareness towards commodities
market with reference to GUNTUR city, Andhra Pradesh. International Journal of
Engineering and Technology(UAE). 7(2), pp. 1104-1106.
VI. Indian cosmetics Industry report (ICIR) 2017. A short perspective document on
the cosmetics retail sector. http://redseer.com/wp-content/uploads/2017/10/118-
Cosmetics-Industry-Report_Final_July2017.pdf. Accessed on May 30th ,2018.
VII. John Besley (2010). Current research on public perceptions of nanotechnology.
Emerging Health Threats Journal, 3:1, 7098, DOI: 10.3402/ehtj.v3i0.7098
VIII. Kim, A.J., Ko, E. (2012). Do social media marketing activities enhance customer
equity?An empirical study of luxury fashion brand. J. Bus. Res. 65 (10) 1480–
1486.
IX. Lisa A. DeLouise (2012). Applications of Nanotechnology in Dermatology,
Journal of Investigative Dermatology (2012) 132, 964–975
X. Nunnally, J.C.(1978). Psychometric Theory, 2nd ed., McGraw-Hill, New York,
NY.
XI. NSDC & KPMG, (2017). “Human Resources and Skill Requirements in Beauty
and Wellness Sector”. Retrieved from
http://www.nsda.gov.in/skill%20gap%20report/sector%20skill%20gap%20report
/Beauty_and_Wellness.pdf , Vol. 4. Accessed on march 10, 2018.
XII. Priyanka Singh &Arun Nanda (2012). Nanotechnology in cosmetics: a boon or
bane?, Toxicological & Environmental Chemistry, 94:8, 1467-1479, DOI:
10.1080/02772248.2012.723482
XIII. Pastrana, H., Avila, A. & Tsai, C.S.J. Nanoethics (2018). Nanomaterials in
Cosmetic Products: the Challenges with regard to Current Legal Frameworks and
Consumer Exposure. Springer Netherlands, 1871-4757, DOI
https://doi.org/10.1007/s11569-018-0317-x
XIV. Roosen, Jutta&Bieberstein, Andrea &Blanchemanche, Sandrine & Goddard,
Ellen &Marette, Stephan &Vandermoere, Frédéric. (2015). Trust and willingness
to pay for nanotechnology food. Food Policy. 52. 10.1016/j.foodpol.2014.12.004.
XV. Siegrist, M. (2000). The influence of trust and perceptions of risks and benefits
on the acceptance of gene technology. Risk Analysis, 20, 195–203.
XVI. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer satisfaction: A
comparative analysis between traditional and multi-channel retailing.
International Journal of Recent Technology and Engineering. 8(1), pp. 2917-
2920.
XVII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes. International
Journal of Innovative Technology and Exploring Engineering. 8(7), pp. 1720-
1728.

XVIII. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail service
quality in food and grocery retailing: A comparative study between traditional
and multi-channel retailing. International Journal of Management and Business
Research. 9(2), pp. 68-73.
XIX. Sivakoti Reddy, M., Naga Bhaskar, M., Nagabhushan, A. (2016). Saga of silicon
plate: An empirical analysis on the impact of socio economic factors of farmers
on inception of solar plants. International Journal of Control Theory and
Applications. 9(29), pp. 257-266.
XX. Suhasini, T., Koneru, K. (2019). Employee engagement through HRD practices
on employee satisfaction and employee loyalty: An empirical evidence from
Indian IT industry.International Journal of Engineering and Advanced
Technology. 8(4), pp. 1788-1794.
XXI. Suhasini, T. Koneru, K. (2018). A study on employee engagement driving factors
and their impact over employee satisfaction – An empirical evidence from Indian
it industry. International Journal of Mechanical Engineering and Technology.
9(4), pp. 725-732.

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Abstract:

This paper tries to investigate the role of ethics in managing organization. This study is divided into three parts, Firstly, the introduction of ethics in management context, secondly, its relevance and challenges in implementing ethics in any institutions and lastly, the ways to get rid of challenges with the help of model in step wise construction.

Keywords:

Business,ethics,workplace,

Refference:

I. Ahad Faramarz GharaMalaki, “professional ethic in civilization of Iran &
Islam”, 2008, Anvar Danish Pub.
II. Dr. Princi Gupta and Nisha Singh A Comparative Study of the Strategies and
Lessons of Two Great Indian Epics: Mahabharata and Ramayana, ISSN
2250-0588, Volume 9, April 2019
III. GhasemVaseghi “The Lessons of ethic Management”, 2005, Amir Kabir
PUB.
IV. HasanKamali,” ManifestatImam of Imam Ali ‘s Management”, 2001,
Modaber pub.
V. Nisha Singh, Dr.Princi Gupta A Study on Effects of Safety and Welfare
Measures on the Motivation of Employees with respect to Balrampur Chini
Mills Limited, International Journal of Research in Engineering, IT and
Social Sciences, ISSN 2250-0588.
VI. Sharma Dr.Gaurab Kumar, Singh Nisha,” Indo-US Trade & Defence
Relations: Opportunities & Challenges for Narendra Modi Government”
April 2019, 4th International Conference On Recent Trends in Humanities,
Technology,
VII. Shames Afagh Yavari, “Professional ethic in Management”2007, Fra pub.

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Abstract:

Online retail growth drivers are many in number but it all depends on the extent of shopper’s traffic and choice of preference, to achieve this, online stores need to improve on its productivity by ensuring high level of conversion rate from casual visitors to patron customers. This conversion is possible by impacting the patronage behaviour using the variables within the control of the on line retailers. From online shoppers’ perspective, apparel may be a risky product to buy in any one of the online shop due to the uncertainty of apparel quality and non suitability of the various dimensions expected by the shoppers. There are various behavioural theories to explain how an individual forms his intentions, and how intentions relate to actions. Among them the most widely used is multi-attribute model developed by fishbone and ajzen in the year of 1975 i.e., Theory of Reasoned Action and after few years(1985, 1991) ajzen was come up with addition of TRA i.e Theory of Planned Behaviour. The primary purpose of this research study was to identify and investigate the factors and proposed suitable model that affect on-line apparel shoppers’ store patronage behaviour. To attain these objectives, researcher used two diverse tools, i.e., SPSS &AMOS was used for dimension model analysis and structural equation model to test the anticipated hypothesized model.

Keywords:

Shoppers’ Patronage Behaviour,Online Apparel Retailing,Theory of Reasoned Action,Theory of Planned Behaviour,

Refference:

I. Allard, T., Babin, B.J. and Chebat, J.C. (2009), “When income matters:
Customers evaluation of shopping malls’ hedonic and utilitarian orientation”,
Journal of Retail Consumer Service, Vol. 16, pp. 40–49.
II. Anderson, N. H. (1974). Cognitive algebra: Integration theory applied to
social attribution In L. Berkowitz (Ed.,), Advances in experimental social
psychology (Vol. 7, pp. l-101). New York: Academic Press.
III. Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: An
integrated model of trust, user experience and branding. Computers in Human
Behavior, 61, 103- 113.
IV. Bilgihan, A. (2016). Gen Y customer loyalty in online shopping: An
integrated model of trust, user experience and branding. Computers in Human
Behavior, 61, 103- 113.
V. Davis, L., Wang, S., &Lindridge, A. (2008). Culture influences on emotional
responses to on-line store atmospheric cues. Journal of Business Research,
61(8), 806–812.
VI. Dawson, S., Bloch, P.H., and Ridgway, N.M. (1990), “Shopping motives,
emotional states, and Retail outcomes”, Journal of Retailing, Vol. 66, No. 4,
pp. 408-427.
VII. Garbarino E, Strabilevitz M (2004). Gender Differences in the Perceived Risk
of Buying Online and the Effects of Receiving a Site Recommendation. J.
Bus. Res. 57: 768-775.

VIII. Gefen, D., Straub, D. W., and Boudreau, M.-C. .Structural Equation
Modeling and Regression: Guidelines for Research
Practice,.Communications of the AIS (4:7), 2000, pp. 1-70.
IX. Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption:
Emerging concepts, methods and propositions. Journal of Marketing, 46(3),
92–101.
X. Johnson, K.K.P., Lennon, S.J., Jasper, C., Damhorst, M.L. and Lakner, H.B.
(2003), “An application of Rogers’ innovation model: use of the internet to
purchase apparel, food, and home furnishing products by small community
consumers”, Clothing and Textiles Research Journal, Vol. 21 No. 4, pp. 185-
96.
XI. K.L. Keller, Conceptualizing, measuring and managing customer based brand
equity, Journal of Marketing 57 (1993) 1–22.
XII. Lysonski, S. and Durvasula, S. (2013), “Consumer decision making styles in
retailing: evolution of mindsets and psychological impacts”, The Journal of
Consumer Marketing, Vol. 30, No. 1, pp. 75-87.
XIII. Magee, M. (2003), “Boom or bust for e-shopping”, The Sunday Tribune,
available at: http:// web.lexis-nexis.com/universe/document (accessed 22
December 2003).
XIV. Osman, M. Z. (1993). A conceptual model of retail image influences on
loyalty patronage behaviour. International review of retail, distribution and
consumer research, 3(2), 133-148.
XV. Pantano, E., &Priporas, C. V. (2016). The effect of mobile retailing on
consumers’ purchasing experiences: A dynamic perspective. Computers in
Human Behavior, 61, 548-555.
XVI. Rowley, M.L. and Okelberry, A. (2000), “Shopping.com: analysis of a
trend”, Journal of Family and Consumer Sciences, Vol. 92 No. 1, pp. 9-10.
XVII. Sivakoti Reddy, M. (2019). Impact of RSERVQUAL on customer
satisfaction: A comparative analysis between traditional and multi-channel
retailing. International Journal of Recent Technology and Engineering. 8(1),
pp. 2917-2920.
XVIII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
XIX. Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole.
XX. V. W. Mitchell, “Consumer perceived risk: Conceptualizations and models,”
European Journal of Marketing, vol. 33, no.1, pp. 164-196, 1999.

XXI. Vijayasarathy, L.R., & Jones, J.M. (2000) ‘Intentions to shop using Internet
catalogues:Exploring the effect of the product types, shopping orientations,
and attitudestoward computers’, Electronic Market, 10(1), 29-38.
XXII. Vroom, V. H. (1964). Work and motivation. New York: Wiley.
XXIII. W. Huang, H. Schrank, and A. J. Dubinsky, “Effect of brand names on
consumers’ risk perceptions of online shopping,” Journal of Consumer
Behavior, vol. 4, no.1, pp. 40-50, 2004.
XXIV. Yim, M.Y., Yoo, S., Sauer, P.L. and Seo, J.H. (2014), “Hedonic shopping
motivation and co- shopper influence on utilitarian grocery shopping in
superstores”, Academy of Marketing Science Journal, Vol. 42, No. 5, pp.
528-544.
XXV. Zeithaml, V. A., Berry, L. L., and Parasuraman, A. .The Behavioral
Consequences of Service Quality,.Journal of Marketing (60:April), 1996, pp.
31-46.

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Abstract:

The banking sector in India plays a vital role in the economic growth of the country. Hence, the performance of banks has got a decisive role in controlling the pace of economic development of the whole nation. Performance of banks, in turn, depends on the performance of their human resources (HR) – the most sensitive and most valuable among all resources of an organization. Effective management of HR along with proper adoption and utilization of technological advances particularly those in the field of Information and Communication Technology, (ICT) has become an imperative for banks for their survival and growth. Likewise, thrust on the promotion of bank products particularly using modern philosophies like e-CRM side by side with provision of excellent quality customer service is another imperative. At the centre of all these lies Human Resources (HR); because a well-trained and techno-savvy workforce alone can provide customer service matching with the expectations of today’s discerning customers. As India’s banking sector is passing through a highly turbulent world characterized by VUCA (Volatility, Uncertainty, Complexity, Ambiguity), this paper seeks to study the relative performance of the Old generation Private sector Banks (OPBs) based in Kerala with a focus on their HR productivity and allied HR-related performance parameters.

Keywords:

Old Private sector Banks (OPBs),ICT,CRM,HRM,Employee Productivity,

Refference:

I. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
II. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
III. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.

IV. Ashok Kumar. (2019), “Mutual Fund an Electronic Inference”, Eurasian
Journal of Analytical Chemistry, 47, 49-53.
V. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VI. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VII. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
VIII. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
IX. Ashok Kumar (2018), “Illicit Financial Flows on Africa’s Democratic Chain
Governance”, International Journal of Supply Chain Management, Vol 7, No
5, 894-902.
X. Manukonda et al. (2019). What Motivates Students To Attend Guest
Lectures?. The International Journal of Learning in Higher Education.
Volume 26, Issue 1. 23-34.
XI. Official websites of the Reserve Bank of India (RBI), www.rbi.org.in
XII. Sivakoti Reddy, M., Venkateswarlu, N.(2019). Customer relationship
management practices and their impact over customer purchase decisions: A
study on the selected private sector banks housing finance schemes.
International Journal of Innovative Technology and Exploring Engineering.
8(7), pp. 1720-1728.
XIII. Sivakoti Reddy, M., Murali Krishna, S.M.(2019). Influential role of retail
service quality in food and grocery retailing: A comparative study between
traditional and multi-channel retailing. International Journal of Management
and Business Research. 9(2), pp. 68-73.
XIV. Y. V. Rao and Srinivasa Rao Budde. Banking Technology Innovations in
India: Enhancing Customer Value and Satisfaction. Indian Journal of Science
and Technology, Vol 8(33), DOI: 10.17485/ijst/2015/v8i33/78280, December
2015.1-10.

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Abstract:

In this paper, the key generators generated by using (Brüer generator, Geffe generator, and Linear generator), then improved these key generators (Brüerand Geffe). In this research was the focus on the frequency test and then compares the outputs with results in a chi-square.

Keywords:

Cryptography,Stream Cipher,Frequency,LFSR,

Refference:

I. C. Paar, J. Pelzl, 2010,”Understanding Cryptography”, Springer, Verlag
Berlin Heidelberg.
II. A. Klein, 2013, “Stream Ciphers”, Springer Verlag London.
III. Fardous Eljadi, 2017, “Dynamic Linear Feedback Shift Registers: A Review”,
Kuala Lumpur, Malaysia.
IV. Alice Reinaudo, 2015, “Empirical testing of pseudo random number
generators based on elliptic curves”, Linnaeus University, Sweden.
V. Yassir Nawaz, 2007, “Design of Stream Ciphers and Cryptographic
Properties of Nonlinear Functions”, University of Waterloo, Canada.
VI. Hossam El-din H. Ahmed, Hamdy M. Kalash, and Osama S. Farag Allah,
2005, “An Efficient Chaos-Based Feedback Stream Cipher (ECBFSC) for
Image Encryption and Decryption”, Menoufia University.
VII. M. Robshaw and O. Billet (Eds.), 2008, ” Design of a New Stream Cipher—
LEX”, Springer-Verlag Berlin Heidelberg.
VIII. Roy Ward, Tim Molteno, 2007, “Table of Linear Feedback Shift Registers”,
University of Otago.
IX. Abdullah Ayad Ghazi,Faez Hassan Ali,2018, “Robust and Efficient Dynamic
Stream Cipher Cryptosystem”,Iraqi Journal of Science,University of
Baghdad.
X. Xingyuan Wang, Xiaojuan Wang, Jianfeng Zhao, Zhenfeng Zhang, 2010,”
Chaotic encryption algorithm based on alternant of stream cipher and block
cipher “, Springer, Dalian University of Technology.

XI. Gordon Meiser, 2007,” Efficient Implementation of Stream Ciphers on
Embedded Processors”, Ruhr-University Bochum (Germany).
XII. Rusol M. Shaker Alzewary,Ayad G. Naser Al-Shammar,2016, “Designof
High Efficiency Non-linear Keys Generator Based on Shift Registers”, Iraqi
Journal of Science,University of Baghdad.

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Abstract:

In this paper dealt with estimating the reliability in the (S-S) stress-strength of Kumaraswamy function distribution using different estimation methods, Maximum likelihood, Moment method, Shrinkage method depend on to Monte Carlo simulation Comparisons between estimation methods have been using mean square error criteria.

Keywords:

Reliability,Stress-Strength (S-S),Kumaraswamy distribution,Maximum likelihood estimator,Moment estimator and Shrinkage estimator,

Refference:

I. A. N. Salman, T.A. Taha, On Reliability Estimation for the Exponential
Distribution Based on Monte Carlo Simulation, Ibn Al-Haitham Journal for
Pure and Applied science, 10.30526/2017, P.P.(409-419).
II. Dreamlee Sharma, Tapan Kumar Chakrabarty, On Size Biased
Kumaraswamy Distribution, Statistics, Optimization, and Information
Computing, Vol 4, Sep 2016, pp 252-264
III. Kumaraswamy, P. A Generalized probability density function fordoublebounded
random processes. Journal of Hydrology 1980,46(1), 79-88.
IV. Mostafa Mohie Eldin1, Nora Khalil2, Montaser Amein, Estimation of
parameters of the Kumaraswamy distribution based on general progressive type II censoring, American Journal of Theoretical and Applied Statistics,
2014; 3(6): 217-222.
V. Mohamed A. Hussian, Estimation of P[Y<X] for the class of Kumaraswamy-
G distributions, Australian Journal of Basic and Applied Sciences, 7(11) Sept
2013, Pages: 158-169.
VI. Muna Shaker Salman, Comparing Different Estimators of two Parameters
Kumaraswamy distribution, Journal of Babylon University/Pure and Applied
Science/ no.(2)/vol.(25):2017,395-402.
VII. Weerahandi, S., and Johnson, R.A.: Testing reliability in a stress-strength
model when X and Y are normally distributed. Technometrics, 1992; 38: 83–
91.

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Abstract:

This paper proposes a novel method for genome editing using cellular automata evolutions of adjoints of Adenine, Thymine, Guanine, and Cytosine. The adjoints of the given a genome sequence are the characteristic binary string sequences. For example, the adjoint of Adenine of a given genome sequence is a binary string consisting of 0’s and 1’s where 1’s corresponds to the presence of Adenine in the genome sequence. So, one can have four adjoint sequences of Adenine, Thymine, Guanine, and Cytosine corresponding to a given genome sequence. Onedimensional three neighborhood binary value cellular automata rules can be applied to an adjoint sequence and the desired number of evolutions could be obtained. This rule is defined by a linear Boolean function and one can have 256 such linear Boolean functions. Genome editing is carried out by superimposing the evolved adjoint sequence on the original genome sequence or on its successive evolutions. In this manner, one can have four ways of genome editing using four adjoint sequences and evolutions.

Keywords:

Genome Editing,Cellular Automata,Evolutions of Adjoints,Linear Boolean functions,

Refference:

I. Carey, Nessa (2015). Junk DNA: A Journey Through the Dark Matter of the
Genome. Columbia University Press. ISBN 9780231170840.
II. Chris M. Rands, Stephen Meader, Chris P. Ponting and Gerton Lunter
(2014). “8.2% of the Human Genome Is Constrained: Variation in Rates of
Turnover across Functional Element Classes in the Human Lineage”. PLoS
Genet 10 (7): e1004525. doi:10.1371 /journal.pgen.1004525.
PMC 4109858. PMID 25057982.
III. Costa, Fabrico (2012). “7 Non-coding RNAs, Epigenomics, and Complexity
in Human Cells”. In Morris, Kevin V. Non-coding RNAs and Epigenetic
Regulation of Gene Expression: Drivers of Natural Selection. Caister
Academic Press. ISBN 1904455948.
IV. Dan Graur, Yichen Zheng, Nicholas Price, Ricardo B. R. Azevedo1,
Rebecca A. Zufall and Eran Elhaik (2013). “On the immortality of
television sets: “function” in the human genome according to the evolutionfree
gospel of ENCODE” (PDF). Genome Biology and Evolution5 (3):
578–90. doi:10.1093/gbe/evt028. PMC 3622293. PMID 23431001.
V. Doolittle, W. Ford (2013). “Is junk DNA bunk? A critique of ENCODE”.
Proc Natl Acad Sci USA110 (14): 5294–5300.
Bibcode:2013PNAS..110.5294D. doi:10.1073/pnas.1221376110.
PMC 3619371. PMID 23479647.
VI. Elgar G, Vavouri T; Vavouri (July 2008). “Tuning in to the signals:
noncoding sequence conservation in vertebrate genomes”. Trends Genet. 24
(7): 344–52. doi:10.1016/j.tig.2008.04.005. PMID 18514361.

VII. Ehret CF, De Haller G; De Haller (1963). “Origin, development, and
maturation of organelles and organelle systems of the cell surface in
Paramecium”. Journal of Ultrastructure Research. 9 Supplement 1: 1, 3–42.
doi:10.1016/S0022-5320(63)80088-X. PMID 14073743.
VIII. Gregory TR, Hebert PD; Hebert (April 1999). “The modulation of DNA
content: proximate causes and ultimate consequences”. Genome Res. 9 (4):
317–24. doi:10.1101/gr.9.4.317 (inactive 2015-02-01). PMID 10207154.
IX. Hawkins, JS.; Kim, H.; Nason, JD.; Wing, RA.; Wendel, JF. (Oct 2006).
“Differential lineage-specific amplification of transposable elements is
responsible for genome size variation in Gossypium”. Genome Res 16 (10):
1252–61. doi:10.1101/gr.5282906. PMC 1581434. PMID 16954538.
X. International Human Genome Sequencing Consortium (February 2001).
“Initial sequencing and analysis of the human genome”. Nature 409 (6822):
879–888. Bibcode:2001Natur.409..860L. doi:10.1038/35057062.
PMID 11237011.
XI. Li M, Marin-Muller C, Bharadwaj U, Chow KH, Yao Q, Chen C; Marin-
Muller; Bharadwaj; Chow; Yao; Chen (April 2009). “MicroRNAs: Control
and Loss of Control in Human Physiology and Disease”. World J Surg 33
(4): 667–84. doi:10.1007/s00268-008-9836-x. PMC 2933043.
PMID 19030926.
XII. Mattick JS, Dinger ME (2013). “The extent of functionality in the human
genome”. The HUGO Journal 7 (1): 2. doi:10.1186/1877-6566-7-2.
XIII. Morris, Kevin, ed. (2012). Non-Coding RNAs and Epigenetic Regulation of
Gene Expression: Drivers of Natural Selection. Norfolk, UK: Caister
Academic Press. ISBN 1904455948.
XIV. Marshall CR, Raff EC, Raff RA; Raff; Raff (December 1994). “Dollo’s law
and the death and resurrection of genes”. Proc. Natl. Acad. Sci. U.S.A. 91
(25): 12283–7. Bibcode:1994PNAS…9112283M.
doi:10.1073/pnas.91.25.12283. PMC 45421. PMID 7991619.
XV. Nelson, PN.; Hooley, P.; Roden, D.; Davari Ejtehadi, H.; Rylance, P.;
Warren, P.; Martin, J.; Murray, PG. (Oct 2004). “Human endogenous
retroviruses: transposable elements with potential?”. Clin Exp Immunol 138
(1): 1–9. doi:10.1111/j.1365-2249.2004.02592.x. PMC 1809191.
PMID 15373898.
XVI. Nielsen H, Johansen SD; Johansen (2009). “Group I introns: Moving in new
directions”. RNA Biol 6 (4): 375–83. doi:10.4161/rna.6.4.9334.
PMID 19667762.
XVII. Palazzo, Alexander F.; Gregory, T. Ryan (2014). “The Case for Junk
DNA”. PLoS Genetics10 (5): e1004351. doi:10.1371/journal.pgen.1004351.
ISSN 1553-7404.
XVIII. Pennisi, E. (6 September 2012). “ENCODE Project Writes Eulogy for Junk
DNA”. Science 337 (6099): 1159–1161.
doi:10.1126/science.337.6099.1159. PMID 22955811.
XIX. Petrov DA, Hartl DL; Hartl (2000). “Pseudogene evolution and natural
selection for a compact genome”. J. Hered. 91 (3): 221–7.
doi:10.1093/jhered/91.3.221. PMID 10833048.

XX. Piegu, B.; Guyot, R.; Picault, N.; Roulin, A.; Sanyal, A.; Saniyal, A.; Kim,
H.; Collura, K.; et al. (Oct 2006). “Doubling genome size without
polyploidization: dynamics of retrotransposition-driven genomic expansions
in Oryza australiensis, a wild relative of rice”. Genome Res 16 (10): 1262–
9. doi:10.1101/gr.5290206. PMC 1581435. PMID 16963705.
XXI. Ponting, CP; Hardison, RC (2011). “What fraction of the human genome is
functional?”. Genome Research21: 1769–1776. doi:10.1101/gr.116814.110.
PMC 3205562. PMID 21875934.
XXII. Ponicsan SL, Kugel JF, Goodrich JA; Kugel; Goodrich (February 2010).
“Genomic gems: SINE RNAs regulate mRNA production”. Current
Opinion in Genetics & Development 20 (2): 149–55. doi:10.1016/j.gde.
2010.01.004. PMC 2859989. PMID 20176473.
XXIII. Robin McKie (24 February 2013). “Scientists attacked over claim that ‘junk
DNA’ is vital to life”. The Observer.
XXIV. Sean Eddy (2012) The C-value paradox, junk DNA, and ENCODE, Curr
Biol 22(21):R898–R899.
XXV. Smith MA; et al. (June 2013). “Widespread purifying selection on RNA
structure in mammals”. Nucleic Acids Research 41 (17): 8220–8236.
doi:10.1093/nar/gkt596. PMC 3783177. PMID 23847102.
XXVI. The ENCODE Project Consortium (2012). “An integrated encyclopedia of
DNA elements in the human genome”. Nature 489 (7414): 57–74.
Bibcode:2012Natur.489…57T. doi:10.1038/nature11247. PMC 3439153.
PMID 22955616..
XXVII. Tutar, Y. (2012). “Pseudogenes”. Comp Funct Genomics 2012: 424526.
doi:10.1155/2012/424526. PMC 3352212. PMID 22611337.
XXVIII. Visel A, Rubin EM, Pennacchio LA (September 2009). “Genomic Views of
Distant-Acting Enhancers”. Nature 461 (7261): 199–205.
Bibcode:2009Natur.461..199V. doi:10.1038/nature08451. PMC 2923221.
PMID 19741700.
XXIX. Wahls, W.P.; et al. (1990). “Hypervariable minisatellite DNA is a hotspot
for homologous recombination in human cells”. Cell 60 (1): 95–103.
doi:10.1016/0092-8674(90)90719-U. PMID 2295091.
XXX. Waterhouse, Peter M.; Hellens, Roger P. (25 March 2015). “Plant biology:
Coding in non-coding RNAs”. Nature 520 (7545): 41–42.
doi:10.1038/nature14378.
XXXI. Walters RD, Kugel JF, Goodrich JA; Kugel; Goodrich (Aug 2009).
“InvAluable junk: the cellular impact and function of Alu and B2 RNAs”.
IUBMB Life 61 (8): 831–7. doi:10.1002/iub.227. PMC 4049031.
PMID 19621349.
XXXII. “Worlds Record Breaking Plant: Deletes its Noncoding “Junk” DNA”.
Design & Trend. May 12, 2013. Retrieved 2013-06-04.
XXXIII. Zheng D, Frankish A, Baertsch R; et al. (June 2007). “Pseudogenes in the
ENCODE regions: Consensus annotation, analysis of transcription, and
evolution”. Genome Res. 17 (6): 839–51. doi:10.1101/gr.5586307.
PMC 1891343. PMID 17568002.

 

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