Investigations on The Finite Size Effects of Some Inverse Spinels and Studies on Their Composites Based on Nitrile Rubber

Magnetism and magnetic materials have been an ever-attractive subject area for engineers and scientists alike because of its versatility in finding applications in useful devices. They find applications in a host of devices ranging from rudimentary devices like loud speakers to sophisticated gadgets like waveguides and Magnetic Random Access Memories (MRAM).The one and only material in the realm of magnetism that has been at the centre stage of applications is ferrites and in that spinel ferrites received the lions share as far as practical applications are concerned.It has been the endeavour of scientists and engineers to remove obsolescence and improve upon the existing so as to save energy and integrate in to various other systems. This has been the hallmark of material scientists and this has led to new materials and new technologies.In the field of ferrites too there has been considerable interest to devise new materials based on iron oxides and other compounds. This means synthesising ultra fine particles and tuning its properties to device new materials. There are various preparation techniques ranging from top- down to bottom-up approaches. This includes synthesising at molecular level, self assembling,gas based condensation. Iow temperature eo-precipitation, solgel process and high energy ball milling. Among these methods sol-gel process allows good control of the properties of ceramic materials. The advantage of this method includes processing at low temperature. mixing at the molecular level and fabrication of novel materials for various devices.Composites are materials. which combine the good qualities of one or more components. They can be prepared in situ or by mechanical means by the incorporation of fine particles in appropriate matrixes. The size of the magnetic powders as well as the nature of matrix affect the processability and other physical properties of the final product. These plastic/rubber magnets can in turn be useful for various applications in different devices. In applications involving ferrites at high frequencies, it is essential that the material possesses an appropriate dielectric permittivity and suitable magnetic permeability. This can be achieved by synthesizing rubber ferrite composites (RFC's). RFCs are very useful materials for microwave absorptions. Hence the synthesis of ferrites in the nanoregirne.investigations on their size effects on the structural, magnetic, and electrical properties and the incorporation of these ferrites into polymer matrixes assume significance.In the present study, nano particles of NiFe204, Li(!5Fe2S04 and Col-e-O, are prepared by sol gel method. By appropriate heat treatments, particles of different grain sizes are obtained. The structural, magnetic and electrical measurements are evaluated as a function of grain size and temperature. NiFel04 prepared in the ultrafine regime are then incorporated in nitrile rubber matrix. The incorporation was carried out according to a specific recipe and for various loadings of magnetic fillers. The cure characteristics, magnetic properties, electrical properties and mechanical properties of these elastomer blends are carried out. The electrical permittivity of all the rubber samples in the X - band are also conducted.

Evidence for intergranular tunnelling in polyaniline passivated α-Fe nanoparticles

Nanoparticles are of immense importance both from the fundamental and application points of view. They exhibit quantum size effects which are manifested in their improved magnetic and electric properties. Mechanical attrition by high energy ball milling (HEBM) is a top down process for producing fine particles. However, fineness is associated with high surface area and hence is prone to oxidation which has a detrimental effect on the useful properties of these materials. Passivation of nanoparticles is known to inhibit surface oxidation. At the same time, coating polymer film on inorganic materials modifies the surface properties drastically. In this work a modified set-up consisting of an RF plasma polymerization technique is employed to coat a thin layer of a polymer film on Fe nanoparticles produced by HEBM. Ball-milled particles having different particle size ranges are coated with polyaniline. Their electrical properties are investigated by measuring the dc conductivity in the temperature range 10–300 K. The low temperature dc conductivity (I–V ) exhibited nonlinearity. This nonlinearity observed is explained on the basis of the critical path model. There is clear-cut evidence for the occurrence of intergranular tunnelling. The results are presented here in this paper

Learning From Cross Country Experiences In Housing: A Micro Finance Approach For Inclusive Housing In India

Housing is one of the primary human needs. It is second only to the need for food and clothing. From a macro perspective, housing is an industry that can prove itself to be a growth engine for a nation, particularly a developing nation like India. Housing has been one of the top priorities for the various governments in India since the seventies. The need for housing has been increasing at a phenomenal pace in India and so also the need for housing finance. Since the growth in supply of housing could not keep pace with the growth in its demand, housing shortage has been on the rise over the years. Housing finance industry which was relatively dormant till the early nineties underwent sweeping changes ever since the initiation of financial sector deregulation measures. Financial deregulation measures brought about several changes in this industry, the first and foremost being the fast growth rate in the industry coupled with cutthroat competition among the industry players. This trend has been quite prominent since the entry of commercial banks into this arena. Accordingly, there has been a surge in the growth of retail (personal) loans segment, particularly in respect of housing loans. This is evident from the fact that housing loans disbursed by banks as a percentage of their total loans has increased from just 2.79% as of end-March 1997 to as high as 12.52% as of end-March 2007. Thus, there has been an unprecedented growth rate in the disbursement of housing loans by banks, and as of 31 March 2007 the outstanding balance of housing loans by all banks in India stands at Rs.230689 Crore, as against just Rs.7946 Crore as of 31 March 1997, the growth rate being 35.82 %CAGR (for the eleven years’ period, FY 1997-‘2007). However, in spite of the impressive growth in housing finance over the years, there are growing apprehensions regarding its inclusiveness, i.e. accessibility to the common man, the underprivileged sections of the society to housing finance etc. Of late, it is widely recognized that formal housing finance system, particularly the commercial banks (CBs) – most dominant among the players – is fast becoming exclusive in operations, with nearly 90% of the total housing credit going to the rich and upper middle income group, primarily the salaried class. The case of housing finance companies (HFCs) is quite similar in this regard. The poor and other marginalized sections are often deprived of adequate credit facilities for housing purpose. Studies have revealed that urban housing poverty is much more acute than the rural probably because of the very fast process of urbanization coupled with constant rural to urban migration

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.