Determinants of Women's Participation in Self-Help Group (SHG)-Led Microfinance Programme in Tamil Nadu

Factors determining participation of women in microfinance programme in Tamil Nadu have been identified following the multi-stage purposive and random sampling technique and selecting Coimbatore and Ramanathapuram districts for the study. It has been observed that socially backward, landless and marginal farm households participate more in this programme. Possession of livestock and consumer goods by the member households has been found to deter the joining of group. However, the number of households having informal borrowings, especially from moneylenders has been recorded to be higher among the members before joining the group. Analysis of determinants of women’s participation in microfinance programme using probit model has revealed that the age of women and value of productive assets other than land have a significant negative influence on their participation. However, social backwardness, indebtedness and presence of other microcredit programmes in the same or nearby villages have a significant positive influence on women’s participation in this programme.Agricultural Finance,

Structure of the twin-arginine signal-binding protein DmsD from Escherichia coli

The translocation of folded proteins via the twin-arginine translocation (Tat) pathway is regulated to prevent the futile export of inactive substrate. DmsD is part of a class of cytoplasmic chaperones that play a role in preventing certain redox proteins from premature transport. DmsD from Escherichia coli has been crystallized in space group P4_12_12, with unit-cell parameters a = b = 97.45, c = 210.04 Å, in the presence of a small peptide. The structure has been solved by molecular replacement to a resolution of 2.4 Å and refined to an R factor of 19.4%. There are four molecules in the asymmetric unit that may mimic a higher order structure in vivo. There appears to be density for the peptide in a predicted binding pocket, which lends support to its role as the signal-recognition surface for this class of proteins

Food Security and Efficacy of the Intervention Mechanism in India

Rice and wheat are the two major foodgrains in India and their level of production determines the country self-sufficient. Forecasts on cereals supply range from 250 to over 300 million tones in the country by the year 2020. Mere availability of foodgrains is not a sufficient condition to ensure food security but also necessary that the poor have sufficient means to purchase food. Poor economic access leads to food security and it has been checked by the government mechanism through the operation of Targeted Public Distribution System (TPDS) and foodgrains are distributed at subsidized prices to the people living Below Poverty Line (BPL). It is a safety net to more than 330 million poor and those nutritionally at risk and an important delivery channel with a network of half million Fair Price Shops (FPS) catering to the needs of 199 million of ration cardholders. Food subsidy amounting to Rs. 25,800 (approximately US$ 5,730 million) was allotted during the period 2004-05, which rose ten times in the past 15 years. Higher level of procurement, revising the Minimum Support Price (MSP), inventory cost for stocking foodgrains and a lower level of issue price together absorb huge subsidy. Monte Carlo Technique was used to examine the implications of several policy options by simulating the 2002-03 base scenario. Optimum or the normative level of procurement, improving offtake from Food Corporation of India (FCI) godowns, private participation, issuing food stamps and food credit cards are the possible options to improve the efficacy of the system and reduce the budgetary burden due to subsidy.Food Security, Targeted Public Distribution System, Fair Price Shops, Minimum Support Price, Monte Carlo Technique, Food Security and Poverty, J21, H4, H5, O2, Q18,

Feature Diminution by Ant Colonized Relative Reduct Algorithm for improving the Success Rate for IVF Treatment

Infertility is the most common problem faced by today’s generation. The factors like environment, genetic or personal characteristics are responsible for these problems. Different infertility treatments like IVF, IUI etc are used to treat those infertile people. But the cost and emotions beyond each and every cycle of IVF treatment is very high and also the success rate differs from person to person. So, there is a need to find a system which would predict the outcome of IVF to motivate the people both in psychologically and financially. Many Data Mining techniques are applied to predict the outcome of the IVF treatment. Reducing the unwanted features which affects the quality of result is one of the significant tasks in Data Mining. This paper proposes a hybrid algorithm named Ant Colonized Relative Reduct Algorithm (ACRRA) which combines the core features of Ant Colony Optimization Algorithm and Relative Reduct Theory for Feature Reduction. In this work, the proposed Algorithm is compared with the existing related algorithms. It is evident from the results that the proposed algorithm achieved its target of reducing the features to minimum numbers without compromising the core knowledge of the system to estimate the success rate

Pd(II)-doping studies on tris(thiourea)zinc(II) sulphate crystals: Catalytic effect of Pd(II)-doping on SHG efficiency

Single crystals of Pd(II)-doped tris(thiourea)zinc(II) sulphate (ZTS) have been grown from an aqueous solution by conventional slow evaporation solution growth technique. The characteristic functional groups have been identified by FTIR analysis. Crystal stress has been indicated by powder XRD patterns and FTIR analysis. Incorporation of dopant into the crystalline matrix during crystallization process has been evidenced by energy dispersive X-ray spectroscopy and quantified by inductively coupled plasma technique. The surface morphological changes have been observed in the doped specimen. Mechanical stability of the as-grown specimen has been analyzed by Vickers microhardness analysis. Thermal studies reveal no decomposition up to the melting point. Lattice parameters determined by single crystal XRD analysis reveal only minor variations as a result of low doping. Pd(II) doping has a catalytic effect on the second harmonic generation efficiency improvement of zinc thiourea complex

Role of Biofertilizers in Plant Growth and Soil Health

Biofertilizers nowadays have been realised for shifting fortunes in agriculture. It has been proven successful technology in many developed countries while in developing countries exploitation of bioinoculants is hampered by several factors. Scientific knowledge on bioinoculants and its usage will pave way for its effective usage. At the same time overlooking the significance of ensuring and maintaining a high quality standard of the product will have negative impact. Hence a proper knowledge of bioinoculants and its functioning will pave way to tape the resources in a better way. Thus the chapter provide overview knowledge about different bacterial, fungal and algal biofertilizers, its associations with plants and transformations of nutrients in soil. Adopting a rational approach to the use and management of microbial fertilizers in sustainable agriculture thrive vast potential for the future

Measuring a Level of Water Pollution in Sungai Pinang Using a Mathematical Model

Mathematical models are now a popular tool in many applications such as physics, economics and engineering; and provide useful information for decision making and planning. Water pollution in Malaysia has reached a level that needs attention and intervention from environmental department and government. In order to make a strong case, this paper has done a study on measuring the pollution level of Sungai Pinang which is located in Penang Island, Malaysia. Advection-Dispersion equation is one of the mathematical models that often use in evaluating the level of water pollution in the river. This paper applies this model and compares the result of this study with the studies done in the previous research worldwide to evaluate the severity of the pollution. The authors hope that this paper will create awareness to relevant department and also to community in genera

Artificial Neural Network Optimization Modeling On Engine Performance Of Diesel Engine Using Biodiesel Fuel

This paper presents a study of engine performance using a mixture of palm oil methyl ester blends with diesel oil as biodiesel in a diesel engine, and optimizes the engine performance using artificial neural network (ANN) modeling. To acquire data for training and testing of the proposed ANN, a four-cylinder, four-stroke diesel engine was fuelled with different palm oil methyl ester blends as biodiesel, operated at different engine loads. The properties of biodiesel produced from waste vegetable oil were measured based on ASTM standards. The experimental results revealed that blends of palm oil methyl ester with diesel fuel provided better engine performance. An ANN model was developed based on the Levenberg-Marquardt algorithm for the engine. Logistic activation was used for mapping between the input and output parameters. It was observed that the ANN model could predict the engine performance quite well with correlation coefficients (R) of 0.996684, 0.999, 0.98964 and 0.998923 for the incylinder pressure, heat release, thermal efficiency, and volume, respectively. The predicted MSE (mean square error) error was between the desired outputs, as the measured and simulated values were obtained as 0.0001 by the model. Long-term effects on engine performance when running on biodiesel fuel can be further studied and improved