Improved Mechanical Properties of NR/EPDM and NR/Butyl Blends by Precuring EPDM and Butyl

Ethylene-propylene-diene rubber (EPDM) and isobutylene-isoprene rubber (IIR) were compounded, precured to a low degree, and then were blended with natural rubber (NR). The compounding ingredients for NR were then added and the final curing was done. NR/ EPDM and NR/IIR blends, prepared using this method, were found to possess much improved mechanical properties as compared to their conventional counterparts. The optimum precuring crosslink density that has to be given to the EPDM and IIR phases has been determined.

Bioremediation of Zinc Using Bacillus sp. Isolated from Metal- Contaminated Industrial Zone

The production of heavy metals has increased quickly since the industrial revolution. Heavy metals frequently form compounds that can be toxic, carcinogenic, or mutagenic, even in very small concentrations. The usual techniques of removing metals from wastewaters are in general expensive and have many restrictions. Alternative methods of metal removal and recovery based on biological materials have been measured. Among various agents, the use of microbes for the removal of metals from industrial and municipal wastewater has been proposed as a promising alternative to conventional heavy metal management strategies in past decades. Thus, the present study aims to isolate and characterize bacteria from soil, sediment, and waters of metal-contaminated industrial area to study the zinc resistance patterns and the zinc bioaccumulation potential of the selected microorganism. Zinc analysis of the samples revealed that concentrations varying from 39.832 m g/L to 310.24 m g/L in water, 12.81 m g/g to 407.53 m g/g in soil, and 81.06 m g/g to 829.54 m g/g in sediment are present. Bacterial zinc resistance study showed that tolerance to Zn was relatively low (<500 m g/ml). Ten bacterial genera were represented in soil and 11 from water, while only 5 bacterial genera were recorded from sediment samples. Bacillus, Pseudomonas , and Enterobacter were found in soil, sediment, and water samples. Highly zincresistant Bacillus sp. was selected for zinc removal experiment. Zinc removal studies revealed that at pH 5 about 40% reduction occurs; at pH 7, 25% occurs; and at pH 9, 50% occurs. Relatively an increased removal of Zinc was observed in the fi rst day of the experiment by Bacillus sp. The metal bioaccumulative potential of the selected isolates may have possible applications in the removal and recovery of zinc from industrial ef fluents.

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.