Sustainability of Medicinal Plants in Kerala Economic Considerations in Domestication and Conservation of Forest Resources

The present study on the sustainability of medicinal plants in Kerala economic considerations in domestication and conservation of forest resources. There is worldwide consensus on the fact that medicinal plants are important not only in the local health support systems but in rural income and foreign exchange earnings. Sustainability of medicinal plants is important for the survival of forest dwellers, the forest ecosystem, conserving a heritage of human knowledge and overall development through linkages. More equitable sharing of the benefits from commercial utilization of the medicinal plants was found essential for the sustainability of the plants. Cultivation is very crucial for the sustainability of the sector. Through a direct tie-up with the industry, the societies can earn more income and repatriate better collection charges to its members. Cultivation should be carried out in wastelands, tiger reserves and in plantation forests. In short, the various players in the in the sector could find solution to their specific problems through co-operation and networking among them. They should rely on self-help rather than urging the government to take care of their needs. As far as the government is concerned, the forest department through checking over- exploitation of wild plants and the Agriculture Dept. through encouraging cultivation could contribute to the sustainable development of the medicinal plant sector.

Production of Alkaline Protease by Free and Immobilised Cells of VIBRIO sp. Under Different Fermentation Systems and Its Application on Deproteinisation of Prawn Shell Waste for Chitin Recovery

This thesis presents a detailed account of a cost - effective approach towards enhanced production of alkaline protease at profitable levels using different fermentation designs employing cheap agro-industrial residues. It involves the optimisation of process parameters for the production of a thermostable alkaline protease by Vibrio sp. V26 under solid state, submerged and biphasic fermentations, production of the enzyme using cell immobilisation technology and the application of the crude enzyme on the deproteinisation of crustacean waste.The present investigation suggests an economic move towards Improved production of alkaline protease at gainful altitudes employing different fermentation designs utilising inexpensive agro-industrial residues. Moreover, the use of agro-industrial and other solid waste substrates for fermentation helps to provide a substitute in conserving the already dwindling global energy resources. Another alternative for accomplishing economically feasible production is by the use of immobilisation technique. This method avoids the wasteful expense of continually growing microorganisms. The high protease producing potential of the organism under study ascertains their exploitation in the utilisation and management of wastes. However, strain improvement studies for the production of high yielding variants using mutagens or by gene transfer are required before recommending them to Industries.Industries, all over the world, have made several attempts to exploit the microbial diversity of this planet. For sustainable development, it is essential to discover, develop and defend this natural prosperity. The Industrial development of any country is critically dependent on the intellectual and financial investment in this area. The need of the hour is to harness the beneficial uses of microbes for maximum utilisation of natural resources and technological yields. Owing to the multitude of applications in a variety of industrial sectors, there has always been an increasing demand for novel producers and resources of alkaline proteases as well as for innovative methods of production at a commercial altitude. This investigation forms a humble endeavour towards this perspective and bequeaths hope and inspiration for inventions to follow.

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Reinforcement Learning approaches to Economic Dispatch problem

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out.

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses