CORPORATE GOVERNANCE: A LITERATURE REVIEW WITH A FOCUS ON PUBLIC SECTOR UNDERTAKINGS IN INDIA

Business dynamic of today’s market has changed and as a result, market need to focus on transparency in the business hence corporate governance has become important for all sectors including government organization.  Corporate Governance is steadily focusing on building the confidence of its various stake holders including Customers, Suppliers, employees, shareholders, Bankers and Society at large. As these public-sector organization (PSU) are socially responsible unit, it becomes utmost important for these organization to adhere to these clauses because these are run by tax payer money for its operation. A company is directed and controlled with the system of rules, practices and process of the corporate governance. The corporate Governance framework of any Public-Sector Undertakings depends upon the four pillars namely Transparency, Full disclosures, Independent monitoring and Fairness to all. Study examines the existing corporate Governance environment, practice and institutional framework in PSUs in India. It is a theoretic review of corporate governance of PSU in India. This paper attempts to understand the various reason for failure to hold governance of the PSUs in India and accordingly provide the solutions to improve the implementation of corporate governance. Though the public sector has unique characteristics but need to adhere the corporate governance to bring the accountability, transparency in business and enhance confidence of the stakeholders. This paper is based on purely secondary research from the various literature available in the journal. It is purely conceptual paper based on the authors recommendations

U.S. Patent Literature Survey of Agrobacterium-Mediated Transformation of Sweet Potato (Ipomoea Batatas)

A team of researchers and patent information scientists at Franklin Pierce Law Center were asked to evaluate the patent and literature landscape related to the Agrobacterium-mediated transformation in sweet potato with respect to the U.S. patents and patent applications. This report provides a patent landscape of the Agrobacterium-mediated transformation of sweet potato. The report includes the applicable methods of transformation and has also included certain patents and patent applications which claim a transformed plant by virtue of these methods. In certain cases, the claim structure covers Agrobacterium-mediated transformation technology via system and composition of matter claims and not the more prevalent method claims. Sweet potato plant (Ipomoea batatas) is adaptable to a broad range of agroecological conditions and fits in low input agriculture. It is highly productive even under adverse farming conditions. Sweet potato is grown in more than 100 countries as a valuable source of food, animal feed and industrial raw material. It is a staple crop in many South East Asian and African countries. Traditional plant breeding has contributed to the improvement of sweet potato, especially in developed countries such as the U.S.A. and Japan. Because of the biological complexities of sweet potato, sexual hybridization strategies have not been very effective in developing improved cultivars. Confidential Therefore, biotechnological tools, such as gene transfer, are very attractive in sweet potato improvement, as they enable direct introduction of desirable genes from other sources into preadapted cultivars

Effect of different feeds and water salinities on the cyst production of brine shrimp, Artemia sp.

Effect of different feeds and salinities on cyst production in Artemia sp. was studied. Among the 12 different feeds used, ragi powder suspension showed the best growth and cyst production. A wide range of salinity from 90 to 210‰ was used to study its effect on cyst production. Among all these treatments, 130‰ salinity was found suitable for induction of oviparity and cyst production in the San Francisco Bay strain of Artemia sp

Gauss-newton Based Learning For Fully Recurrent Neural Networks

The thesis discusses a novel off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. Within the framework of the research presented, a new off-line and on-line variation of RTRL is presented, that is based on the Gauss-Newton method. The method itself is an approximate Newton\u27s method tailored to the specific optimization problem, (non-linear least squares), which aims to speed up the process of FRNN training. The new approach stands as a robust and effective compromise between the original gradient-based RTRL (low computational complexity, slow convergence) and Newton-based variants of RTRL (high computational complexity, fast convergence). By gathering information over time in order to form Gauss-Newton search vectors, the new learning algorithm, GN-RTRL, is capable of converging faster to a better quality solution than the original algorithm. Experimental results reflect these qualities of GN-RTRL, as well as the fact that GN-RTRL may have in practice lower computational cost in comparison, again, to the original RTRL