Synergistic solvent extraction of Thorium(IV) and Uranium(VI) with β-diketones in presence of oxo-donors

The thesis entitled “Synergistic solvent extraction of Thorium(IV) and Uranium(VI) with β-diketones in presence of oxo-donors” embodies the results of the investigations carried out on the extraction of thorium(IV) an uranium(VI) with heterocyclic β-diketones in the presence and absence of various macrocyclic ligands and neutral organophosphorus extractants. The objective of this work is to generate the knowledge base to achieve better selectivity between thorium(IV) and uranium(VI) by understanding the interactions of crown ethers or neutral organophosphorus extractants with metal-heterocyclic β-diketonate complexes. Para-substituted 1-phenyl-3-methyl-4-aroyl-5-pyrazolones, namely,1-phenyl-3-methyl-4-(4-fluorobenzoyl)-5-pyrazolone (HPMFBP) and 1-phenyl-3-methyl-4-(4-toluoyl)-5-pyrazolone (HPMTP) were synthesized and characterized by elemental analysis, IR and H NMR spectral data. The synthesized ligands have been utilized for the extraction of thorium(IV) and uranium(VI) from nitric acid solutions in the presence and absence of various crown ethers. Thorium(IV) and uranium(VI) complexes with HPMPP(1-Phenyl-3-methyl-4-pivaloyl-5-pyrazolone) and neutral organophosphorus extractants were synthesized and characterized by IR and P NMR spectral data to further understand the interactions of neutral organophosphorus extractants with metal-chelates. Solid complexes of thorium(IV) and uranium(VI) with para-substituted 4-aroyl-5-isoxazolones and crown ethers were isolated and characterized by various spectroscopic techniques to further clarify the nature of the extracted complexes.

AN ARTIFICIAL NEURAL NETWORK FOR SONAR TARGET DETECTION

Neural Network has emerged as the topic of the day. The spectrum of its application is as wide as from ECG noise filtering to seismic data analysis and from elementary particle detection to electronic music composition. The focal point of the proposed work is an application of a massively parallel connectionist model network for detection of a sonar target. This task is segmented into: (i) generation of training patterns from sea noise that contains radiated noise of a target, for teaching the network;(ii) selection of suitable network topology and learning algorithm and (iii) training of the network and its subsequent testing where the network detects, in unknown patterns applied to it, the presence of the features it has already learned in. A three-layer perceptron using backpropagation learning is initially subjected to a recursive training with example patterns (derived from sea ambient noise with and without the radiated noise of a target). On every presentation, the error in the output of the network is propagated back and the weights and the bias associated with each neuron in the network are modified in proportion to this error measure. During this iterative process, the network converges and extracts the target features which get encoded into its generalized weights and biases.In every unknown pattern that the converged network subsequently confronts with, it searches for the features already learned and outputs an indication for their presence or absence. This capability for target detection is exhibited by the response of the network to various test patterns presented to it.Three network topologies are tried with two variants of backpropagation learning and a grading of the performance of each combination is subsequently made.

Bayesian Inference in Exponential and Pareto populations in the presence of Outliers

This thesis Entitled Bayesian inference in Exponential and pareto populations in the presence of outliers. The main theme of the present thesis is focussed on various estimation problems using the Bayesian appraoch, falling under the general category of accommodation procedures for analysing Pareto data containing outlier. In Chapter II. the problem of estimation of parameters in the classical Pareto distribution specified by the density function. In Chapter IV. we discuss the estimation of (1.19) when the sample contain a known number of outliers under three different data generating mechanisms, viz. the exchangeable model. Chapter V the prediction of a future observation based on a random sample that contains one contaminant. Chapter VI is devoted to the study of estimation problems concerning the exponential parameters under a k-outlier model.

Web Presence Of The Engineering College Libraries In Kerala: An Analysis Of Content

Websites of academic institutions are the prime source of information about the institution. Libraries, being the main provider of information for the academics, need to be represented in the respective homepages with due importance. Keeping this in mind, this study is an attempt to understand and analyze the presence and presentation of libraries of Engineering Colleges (EC) in Kerala in their respective websites. On the basis of the reviewed literature and an observation of libraries of nationally important institutions imparting technical education in India, a set of criteria were developed for analyzing the websites/web pages. Based on this an extensive survcy of the websites of ECs were done. The collected data was then analyzed using Microsoft Excel. The library websites were then ranked on the basis of this analysis. It was observed that majority of the websites of ECs in Kerala have least representation of their respective libraries. Another important observation is that even the highest scoring libraries satisfy only half of the criteria listed for analysis.

ICT Tools for Teaching & Learning

Page 1. ICT Tools for Teaching & Learning G Santhosh Kumar Cochin University Page 2. Agenda • What is ICT? • Why integrate ICT in Education? • What are the Challenges? • What are the good resources? Page 3. Questions # “Despite the increasing use of ICT, the need for teachers is as great as ever” # “Placing ICT in schools will automatically improve the quality of education that children receive” #”The Internet is unsafe for children to use because there is so much dangerous material available on it” Page 4. What is ICT? • ICT is short for ..

A CRITICAL STUDY OF THE I/W-'ll/EA/6'1.-' OF GANDHIJI AND HIS TEACHING ON MODERN HINDI POETRY

Hindi

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.