DNA polymorphism in Cab locus of tomato induced by tissue culture

Plants were regenerated from callus induced from leaf disc explants of a tomato F, hybrid heterozygous for three marker loci (a), without anthocyanin (aw), and hairless (hl). Regenerants were studied for somaclonal variation at the phenotypic level by scoring for variation in the marker loci, and at the DNA level by probing geomic DNA blots with a chlorophyll a/b binding protein (Cab-3C) cDNA sequence. While no variation was observed at the phenotypic level in over 950 somaclones studied, DNA polymorphism for the Cab locus could be detected in two out of 17 somaclones tested. Tissue culture induced variation at the phenotypic level for specific loci is very low (less than 0.001 for a, awor hl) but DNA sequence changes are induced at much greater frequency (- 0.1 for a multicopy gene family such as Cab).

Intelligent Use of Emotions in Personal Selling: A Study into the Effect of Emotional Intelligence on the Performance of Sales Executives

School of Management Studies, Cochin University of Science and Technology

A study of information-seeking behavior and user perceptions of academic researchers

The purpose of this paper is to examine the information-seeking behavior of science and social science research scholars, including service effectiveness, satisfaction level on different type of sources and various methods adopted by the scholars for keeping up to dateData were gathered using a questionnaire survey of 200, randomly selected, PhD students of science and social science departments of four universities in Kerala, IndiaAlthough similarities exist between social science and science PhD students with regard to information-seeking behavior, there are significant differences as well. There is a significant difference between science and social science scholars on the perception of the adequacy of print journals and database collection which are very relevant to the research purposes. There is no significant difference between science and social science scholars on the perception of the adequacy of e-journals, the most used source for keeping up to date. The study proved that scholars of both the fields are dissatisfied with the effectiveness of the library in keeping them up to date with latest developments

A Literature Review on the Benefits of Ethnic and Minority Entrepreneurship

Ethnic/Minority entrepreneur is a business owner who does not represent the majority population of a country. Ethnic/Minority entrepreneurship can bring benefits to individual, society, region, economy, and globe too. So, understanding the importance of ethnic/minority entrepreneurship will really be useful to all the stakeholders. The unique culture and value system of ethnic minorities are the most wanted characteristics for any successful entrepreneur in general. Many industrial nations like U.S, U.K., Germany, etc. have utilised the ethnic minorities to build their economy. The future of the minority businesses looks bright as the world economy is booming, availability of experienced and already successful minority entrepreneurs as role models, and institutional support services. In this paper, literature relating to the benefits of ethnic and minority entrepreneurship is reviewed to understand its magnitude of benefits.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.