Effect of the Vulcanization Time and Storage on the Stability and Physical Properties of Sulfur-Prevulcanized Natural Rubber Latex

Prevulcanized natural rubber latex was prepared by the heating of the latex compound at 55°C for different periods of time (2, 4, 6, 8, and 10 h). The changes in the colloidal stability and physical properties were evaluated during the course of prevulcanization. The prevulcanized latex compounds were stored for 300 days, and the properties were monitored at different storage intervals (0, 20, 40, 60, 120, 180, 240, and 300 days). During prevulcanization, the mechanical stability time increased, and the viscosity remained almost constant. The tensile strength increased during storage for a period of 20 days. The degree of crosslinking, modulus, elongation at break, and chloroform number were varied with the time of storage.

Studies on the fracture behaviour of polymer blends with special reference to PP/HDPE and PS/HIPS blends

Investigations on the fracture behaviour of polymer blends is the topic of this thesis. The blends selected are PP/HDPE and PS/HIPS. PP/HDPE blend is chosen due to its commercial importance and PS/HIPS blend is selected to study the transition from brittle fracture to ductile fracture.PP/HDPE blends were prepared at different compositions by melt blending at 180°C and fracture failure process was investigated by conducting notch sensitivity test and tensile test at different strain rates. The effects of two types of modifiers (particulate and elastomer) on the fracture behaviour and notch sensitivity of PP/HDPE blends were studied. The modifiers used are calcium carbonate, a hard particulate filler commonly used in plastics and Ethylene Propylene Diene Monomer (EPDM). They were added in 2%, 4% and 6% by weight of the blends.The study shows that the mechanical properties of PP/HDPE blends can be optimized by selecting proper blend compositions. The selected modifiers are found to alter and improve the fracture behaviour and notch sensitivity of the blends. Particulate fillers like calcium carbonate can be used for making the mechanical behaviour more stable at the various blend compositions. The resistance to notch sensitivity of the blends is found to be marginally lower in the presence of calcium carbonate. The elastomeric modifier EPDM produces a better stability of the mechanical behaviour. A low concentration of EPDM is sufficient to effect such a change. EPDM significantly improves the resistance to notch sensitivity of the blends. The study shows that judicious selection of modifiers can improve the fracture behaviour and notch sensitivity of PP/HDPE blends and help these materials to be used for critical applications.For investigating the transition in fracture behaviour and failure modes, PS/HIPS blends were selected. The blends were prepared by melt mixing followed by injection moulding to prepare the specimens for conducting tensile, impact and flexure tests. These tests were used to simulate the various conditions which promote failure.The tensile behaviour of unnotched and notched PS/HIPS blend samples were evaluated at slow speeds. Tensile strengths and moduli were found to increase at the higher testing speed for all the blend combinations whereas maximum strain at break was found to decrease. For a particular speed of testing, the tensile strength and modulus show only a very slight decrease as HIPS content is increased up to about 40%. However, there is a drastic decrease on increasing the HIPS content thereafter.The maximum strain at break shows only a very slight change up to about 40% HIPS content and thereafter shows a remarkable increase. The notched specimens also follow a comparable trend even though the notch sensitivity is seen high for PS rich blends containing up to 40% HIPS. The notch sensitivity marginally decreases with increase in HIPS content. At the same time, it is found to increase with the increase in strain rate. It is observed that blends containing more than 40% HIPS fail in ductile mode.The impact characteristics of PSIHIPS blends studied were impact strength, the energy absorbed by the test specimen and impact toughness. Remarkable increase in impact strength is observed as HIPS content in the blend exceeds 40%. The energy absorbed by the test specimens and the impact toughness also show a comparable trend.Flexural testing which helps to characterize the load bearing capacity was conducted on PS/HIPS blend samples at the two different testing speeds of 5mmlmin and 10 mm/min. The flexural strength increases with increase in testing speed for all the blend compositions. At both the speeds, remarkable reduction in flexural strength is observed as HIPS content in the blend exceeds 40%. The flexural strain and flexural energy absorbed by the specimens are found to increase with increase in HIPS content. At both the testing speeds, brittle fracture is observed for PS rich blends whereas HIPS rich blends show ductile mode of failure.Photoelastic investigations were conducted on PS/HIPS blend samples to analyze their failure modes. A plane polariscope with a broad source of light was utilized for the study. The coloured isochromatic fringes formed indicate the presence of residual stress concentration in the blend samples. The coverage made by the fringes on the test specimens varies with the blend composition and it shows a reducing trend with the increase in HIPS content. This indicates that the presence of residual stress is a contributing factor leading to brittle fracture in PS rich blends and this tendency gradually falls with increase in HIPS content and leads to their ductile mode of failure.

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.

Provenance, Sedimentation and Geochemistry' of the Modern Sediments of time Mud Banks Off the Centrai Keraia Coast, India

This thesis is an attempt to Provenence, Sedimentetion and Geochemistry of the Modern Sediments of the Mud Banks off the Central Kerela Coast, India. In the present doctoral work, an attempt has been made to study in detail the mud banks of central Kerala, i.e. of Narakkal, Saudi and Purakkad areas which are reported as permanent mud banks, since olden days. The studies have been conducted during the years 1985 and 1986. The important findings of the study is stated as clay mineralogical studies of the rivers, lake and mud bank sediments reveal that the dominant clay mineral is kaolinite followed by montmorillonite, illite and gibbsite. Geochemical analysis of the Vembanad lake and mud bank sediments show that the iron and manganese are widely distributed both in the lake and mud bank sediments

Time and cost overruns of industrial projects in kerala

Poor project planning, implementation and control and the subsequent cost and time overruns are ubiquitous features that have been posing serious concern at all levels - state, national and international. It results in wastage of the nation's scarce resources and retards the socio-economic progress. Although several studies peripheral on project overruns have been made at the national level, no serious attempt has been made at the state level to identify the magnitude of overruns, their causes and impacts on industrial projects. The present study "Time and Cost Overruns of Industrial Projects in Kerala" is an earnest attempt to probe in depth the time and cost overruns and their impact on industrial projects. The study places emphasise on the identification of the real reasons behind the cost and time overruns. It also covers the present project management practices of industrial projects in Kerala.

Some bivariate life time models in discrete time"

The term reliability of an equipment or device is often meant to indicate the probability that it carries out the functions expected of it adequately or without failure and within specified performance limits at a given age for a desired mission time when put to use under the designated application and operating environmental stress. A broad classification of the approaches employed in relation to reliability studies can be made as probabilistic and deterministic, where the main interest in the former is to device tools and methods to identify the random mechanism governing the failure process through a proper statistical frame work, while the latter addresses the question of finding the causes of failure and steps to reduce individual failures thereby enhancing reliability. In the probabilistic attitude to which the present study subscribes to, the concept of life distribution, a mathematical idealisation that describes the failure times, is fundamental and a basic question a reliability analyst has to settle is the form of the life distribution. It is for no other reason that a major share of the literature on the mathematical theory of reliability is focussed on methods of arriving at reasonable models of failure times and in showing the failure patterns that induce such models. The application of the methodology of life time distributions is not confined to the assesment of endurance of equipments and systems only, but ranges over a wide variety of scientific investigations where the word life time may not refer to the length of life in the literal sense, but can be concieved in its most general form as a non-negative random variable. Thus the tools developed in connection with modelling life time data have found applications in other areas of research such as actuarial science, engineering, biomedical sciences, economics, extreme value theory etc.

" Nonlinear Signal Processing of Electroencephalogram .' Application in the Study of Neurodynamics. "

Interfacings of various subjects generate new field ofstudy and research that help in advancing human knowledge. One of the latest of such fields is Neurotechnology, which is an effective amalgamation of neuroscience, physics, biomedical engineering and computational methods. Neurotechnology provides a platform to interact physicist; neurologist and engineers to break methodology and terminology related barriers. Advancements in Computational capability, wider scope of applications in nonlinear dynamics and chaos in complex systems enhanced study of neurodynamics. However there is a need for an effective dialogue among physicists, neurologists and engineers. Application of computer based technology in the field of medicine through signal and image processing, creation of clinical databases for helping clinicians etc are widely acknowledged. Such synergic effects between widely separated disciplines may help in enhancing the effectiveness of existing diagnostic methods. One of the recent methods in this direction is analysis of electroencephalogram with the help of methods in nonlinear dynamics. This thesis is an effort to understand the functional aspects of human brain by studying electroencephalogram. The algorithms and other related methods developed in the present work can be interfaced with a digital EEG machine to unfold the information hidden in the signal. Ultimately this can be used as a diagnostic tool.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.