Ecotoxic Metal Records and its Degree of Geochemical Symphony in the Niche of Cochin Estuarine System- A Theraptic Perspective

The heavy metal contamination in the environment may lead to circumstances like bioaccumulation and inturn biomagnification. Hence cheaper and effective technologies are needed to protect the precious natural resources and biological lives. A suitable technique is the one which meets the technical and environmental criteria for dealing with a particular remediation problem and should be site-specific due to spatial and climatic variations and it may not economically feasible everywhere. The search for newer technologies for the environmental therapy, involving the removal of toxic metals from wastewaters has directed attention to adsorption, based on metal binding capacities of various adsorbent materials. Therefore, the present study aim to identify and evaluate the most current mathematical formulations describing sorption processes. Although vast amount of research has been carried out in the area of metal removal by adsorption process using activated carbon few specific research data are available in different scientific institutions. The present work highlights the seasonal and spatial variations in the distribution of some selected heavy metals among various geochemical phases of Cochin Estuarine system and also looked into an environmental theraptic/remedial approach by adsorption technique using activated charcoal and chitosan, to reduce and thereby controlling metallic pollution. The thesis has been addressed in seven chapters with further subdivisions. The first chapter is introductory, stating the necessity of reducing or preventing water pollution due to the hazardous impact on environment and health of living organisms and drawing it from a careful review of literature relevant to the present study. It provides a constricted description about the study area, geology, and general hydrology and also bears the major objectives and scope of the present study.

Science and technology of rubber reclamation with special attention to NR-based waste latex products

A comprehensive overview of reclamation of cured rubber with special emphasis on latex reclamation is depicted in this paper. The latex industry has expanded over the years to meet the world demands for gloves, condoms, latex thread, etc. Due to the strict specifications for the products and the unstable nature of the latex as high as 15% of the final latex products are rejected. As waste latex rubber (WLR) represents a source of high-quality rubber hydrocarbon, it is a potential candidate for generating reclaimed rubber of superior quality. The role of the different components in the reclamation recipe is explained and the reaction mechanism and chemistry during reclamation are discussed in detail. Different types of reclaiming processes are described with special reference to processes, which selectively cleave the cross links in the vulcanized rubber. The state-of-the-art techniques of reclamation with special attention on latex treatment are reviewed. An overview of the latest development concerning the fundamental studies in the field of rubber recycling by means of low-molecular weight compounds is described. A mathematical model description of main-chain and crosslink scission during devulcanization of a rubber vulcanizate is also given.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.