The Fishing Industry in Kerala - Problem and Potentials

importance of fishing and other allied industries in the economy was realised only very recently. Consequently only very few studies are available on the subject. Here an attempt is made to survey the available literature on the subject.

Industrial Laws In Transnational Corporations And Their Impact On The Economic Conditions Of Labour: Acase Study Of Pharmaceutical Industry In India

This study proposes to verify the hypothesis relating to labour legislation in the industrial sector.Here there are as many as fifty enacments of the central government alone.These legislations indicating the growth of this branch of law over a period of more than half a centuary cover a wide spectrum of interests of workers both individuals and collective in different areas of employment.However this study relates mainly to a)trade unions act,b)industrial employment c)industrial disputes.

Export trend of Indian ornamental fish industry

Inspite of having two hotspots of biodiversity India is way long back in the ornamental fish trade. Large number of species can only foster the needs of the industry. The study aims to (1) to find the various indigenous, exotic ornamental fish species and ornamental shrimp species being exported from India, (2) to provide an overview of the trends in the Indian ornamental fish export industry. 287 indigenous fish species, 92 exotic fish species and 44 ornamental shrimps have been found to get exported from India. The export trend of the industry for the past ten years shows a declining state which is also reflected in the annual and compound annual growth rate. Ornamental fish industry has enormous potential in tropical countries like India. To expand trade, new technologies and policies will have to be developed which will help in attaining a sustainable industry.

Linking sustainability indicators of Indian wild caught ornamental fish industry

The Indian ornamental fish industry is divided into two- the domestic market and the export market. 90% of the freshwater ornamental fish exported from India are wild caught indigenous species. The study formed the criteria and indicators assessing the sustainability of wild caught ornamental fish exported from India. These indicators were then analyzed for their interactions, connections, linkages and relationships using cognitive mapping. The work is first of its kind in the ornamental fisheries

Marine Bacteria As Source Of Pigment For Application As Dye In Textile Industry

The textile industry is one amongst the rapidly growing industries world wide, which utilizes enormous amounts of synthetic dyes. Consequently, the effluent from these textile industries poses serious threat to the environment which is often very difficult to treat and dispose. This has become a very grave problem in environment conservation and hence natural pigments have drawn the attention of industry as safe alternative. In this context, in the present study an attempt was made to bioprospect marine bacteria towards isolation of a suitable and ideal pigment that could be used as a natural dye. A marine Serratia sp. BTWJ8 was recognized to synthesize enormous amounts of a prodigiosin-like pigment. The pigment was isolated and characterized for various properties. The pigment was evaluated for application as a dye in the textile industry. Results of the studies indicated that this pigment could be used as a natural dye for imparting red-yellow colour to various grades of textile materials. The colour was observed to be stable after wash performance studies

Challenges Faced by Kerala Tourism Industry

Tourism being a smokeless industry is now a multi-billion, multi-sectoral and multi-dimensional activity in the world. Twenty first century tourism has reached up to space when a Russian rocket carried the space vehicle of Dennis Tito, an American businessman and the world’s first space tourist, to the space station. Time is not too far to carry tourists to moon and other planets in specially launched vehicles. Tourism is being considered as an agent of social change bridging gaps among nations, regions and people and helping them to open up. It is a promoter of development-material and spiritual both at macro and micro level. The General Assembly of the United Nations, in designating the year 1967 as ‘International Tourism Year’ recognized the importance of international travel as a means of fostering understanding among people, and giving them a knowledge of the rich heritage of the past civilizations, a better appreciation of the values of different cultures, thus contributing to the strengthening of world peace. It adopted the theme “Tourism-Passport to peace”. Our veteran national leader and the first Prime Minister of India, Jawaharlal Nehru had said” welcome a tourist and send back a friend” which indicates the need for extending friendly hospitality to the in bound tourists. Modern transportation has removed the obstacles of distance enabling people to appreciate each other engage in the exchange of ideas and commerce. Tourism can help overcome real prejudices and foster bonds. Tourism can be a real force of world peace. Considering the vast and varied potential of tourism in the state and its impact on the economic, social and cultural environment of the state, a detailed study is found to be relevant and imperative

ICT Industry In India: A Swot Analysis

The objectives of this paper are mainly three (i) to make an overall study of the global experiences and trends in respect of ICT industry, (ii) to study the performance of the Indian ICT industry (including, its strengths, weaknesses, opportunities and threats), and lastly (iii) to make constructive suggestions as to ensure superior performance of the Indian ICT industry in the light of the latest developments and trends in the field.

Assessment of Energy Conservation in Indian Cement Industry and Forecasting of Co2 Emissions

Cement industry ranks 2nd in energy consumption among the industries in India. It is one of the major emitter of CO2, due to combustion of fossil fuel and calcination process. As the huge amount of CO2 emissions cause severe environment problems, the efficient and effective utilization of energy is a major concern in Indian cement industry. The main objective of the research work is to assess the energy cosumption and energy conservation of the Indian cement industry and to predict future trends in cement production and reduction of CO2 emissions. In order to achieve this objective, a detailed energy and exergy analysis of a typical cement plant in Kerala was carried out. The data on fuel usage, electricity consumption, amount of clinker and cement production were also collected from a few selected cement industries in India for the period 2001 - 2010 and the CO2 emissions were estimated. A complete decomposition method was used for the analysis of change in CO2 emissions during the period 2001 - 2010 by categorising the cement industries according to the specific thermal energy consumption. A basic forecasting model for the cement production trend was developed by using the system dynamic approach and the model was validated with the data collected from the selected cement industries. The cement production and CO2 emissions from the industries were also predicted with the base year as 2010. The sensitivity analysis of the forecasting model was conducted and found satisfactory. The model was then modified for the total cement production in India to predict the cement production and CO2 emissions for the next 21 years under three different scenarios. The parmeters that influence CO2 emissions like population and GDP growth rate, demand of cement and its production, clinker consumption and energy utilization are incorporated in these scenarios. The existing growth rate of the population and cement production in the year 2010 were used in the baseline scenario. In the scenario-1 (S1) the growth rate of population was assumed to be gradually decreasing and finally reach zero by the year 2030, while in scenario-2 (S2) a faster decline in the growth rate was assumed such that zero growth rate is achieved in the year 2020. The mitigation strategiesfor the reduction of CO2 emissions from the cement production were identified and analyzed in the energy management scenarioThe energy and exergy analysis of the raw mill of the cement plant revealed that the exergy utilization was worse than energy utilization. The energy analysis of the kiln system showed that around 38% of heat energy is wasted through exhaust gases of the preheater and cooler of the kiln sysetm. This could be recovered by the waste heat recovery system. A secondary insulation shell was also recommended for the kiln in the plant in order to prevent heat loss and enhance the efficiency of the plant. The decomposition analysis of the change in CO2 emissions during 2001- 2010 showed that the activity effect was the main factor for CO2 emissions for the cement industries since it is directly dependent on economic growth of the country. The forecasting model showed that 15.22% and 29.44% of CO2 emissions reduction can be achieved by the year 2030 in scenario- (S1) and scenario-2 (S2) respectively. In analysing the energy management scenario, it was assumed that 25% of electrical energy supply to the cement plants is replaced by renewable energy. The analysis revealed that the recovery of waste heat and the use of renewable energy could lead to decline in CO2 emissions 7.1% for baseline scenario, 10.9 % in scenario-1 (S1) and 11.16% in scenario-2 (S2) in 2030. The combined scenario considering population stabilization by the year 2020, 25% of contribution from renewable energy sources of the cement industry and 38% thermal energy from the waste heat streams shows that CO2 emissions from Indian cement industry could be reduced by nearly 37% in the year 2030. This would reduce a substantial level of greenhouse gas load to the environment. The cement industry will remain one of the critical sectors for India to meet its CO2 emissions reduction target. India’s cement production will continue to grow in the near future due to its GDP growth. The control of population, improvement in plant efficiency and use of renewable energy are the important options for the mitigation of CO2 emissions from Indian cement industries

The impact of the working of the Kerala State Bamboo Corporation in the development of the bamboo industry in Kerala

Cochin University of Science & Technology

Concurrent Aggregates (CA): An Object-Oriented Language for Fine-Grained Message-Passing Machines

Fine-grained parallel machines have the potential for very high speed computation. To program massively-concurrent MIMD machines, programmers need tools for managing complexity. These tools should not restrict program concurrency. Concurrent Aggregates (CA) provides multiple-access data abstraction tools, Aggregates, which can be used to implement abstractions with virtually unlimited potential for concurrency. Such tools allow programmers to modularize programs without reducing concurrency. I describe the design, motivation, implementation and evaluation of Concurrent Aggregates. CA has been used to construct a number of application programs. Multi-access data abstractions are found to be useful in constructing highly concurrent programs.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Properties of Support Vector Machines

Support Vector Machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed Support Vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this paper we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending only on the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m+1 margin vectors and observe that m+1 SVs are usually sufficient to fully determine the decision surface. For relatively small m this latter result leads to a consistent reduction of the SV number.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

From Regression to Classification in Support Vector Machines

We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.