The present paper deals with the characterization of ion exchange resins Dowex 550A LC and Indion-930A based on kinetics of ion-isotopic exchange reactions for which the short lived radioactive isotopes 131I and 82Br were used as a tracers. The study was performed for different concentration of ionic solution varying from 0.001 mol/L to 0.004 mol/L and temperature in the range of 30.0 °C to 45.0 °C. The results indicate that as compared to bromide ion-isotopic exchange reaction, iodide exchange reaction take place at the faster rate. For both the ion-isotopic exchange reactions, under identical experimental conditions, the values of specific reaction rate increases with increase in the ionic concentration and decreases with rise in temperature. It was observed that at 35.00C, 1.000 g of ion exchange resins and 0.002 mol/L labeled iodide ion solution for iodide ion-isotopic exchange reaction, the values of specific reaction rate (min-1), amount of ion exchanged (mmol), initial rate of ion exchange (mmol/min) and log Kd were 0.270, 0.342, 0.092 and 11.8 respectively for Dowex 550A LC resin, which was higher than the respective values of 0.156, 0.241, 0.038 and 7.4 as that obtained for Indion-930A resins. From the results, it appears that Dowex 550A LC resins show superior performance over Indion-930A resins under identical experimental conditions.

New silica-supported yttrium trinitrate was prepared, characterized, and used as a reusable catalyst for the three-component synthesis of quinazolinones/thiones via the Biginelli condensation under solvent-free conditions. Non-toxic reagent, short reaction times, simple work-up procedure, and high yield of the products are some of the important advantages of present method.

Evaluating the capability of electrospinning method as an efficient versatile approach for fabrication of fibers, a central composite design (CCD) model was used to design an experimental program for investigation of effective in production of poly (vinyl alcohol) (PVA) fibers fabricated from aqueous solutions of PVA. The studied variables were polymer solution concentration, applied voltage, distance between nozzle and collector plate and flow rate of solution injection. The influence of these parameters on diameter and morphology of obtained PVA fibers was studied by SEM analysis. Among all factors, concentration would strongly affect the fiber diameter.

Optimal method were developed for the green synthesis of quinoxaline derivatives based on the highly efficient and simple condensation reaction of various aromatic 1,2-diketones and 1,2-diamines in nearly quantitative yields in water. In this method we did not use any catalyst. The very mild reaction conditions, the high yields of the products, and the absence of any catalyst make this methodology an efficient and green route for synthesis of quinoxalines.

A tautomeric equilibrium exists for quinoline-2-thiol and quinoline-2(1H)-thione. Quantum mechanical calculations predict the thione is the major tautomer and this is confirmed by the absorption spectra. The utility of quinolone-2-thiol/quinoline-2(1H)-thione as a chromophore for developing fluorescent sensors is explored. No fluorescence is observed when excited at absorption maxima, however a fluorescence increase is observed when exposed to HNO, a molecule of import as a cardiovascular therapeutic. Alkylated quinoline-2-thiol derivatives are found to be fluorescent and show a reduction in fluorescence when exposed to metals and changes in pH.

Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

Performance measurement plays essential role on improving the performance of business units and their efficiencies. During the past few years, there have been tremendous development in banking systems and the primary focus of many managers is to improve the quality of services for market retention. Performance measurement in banking industry is normally involved with various qualitative as well as quantitative criteria, which leads to the implementation of multiple criteria decision making techniques. This paper presents a hybrid grey relational analysis and K-means to cluster and measure the performance of banking system. The proposed study uses different criteria, clusters banks into various segments and ranks 43 different banks in city of Semnan, Iran.

Today, the world facing with huge flood of data and the recent advances in computer technology have provided the capability to process significant amount of data. On the other hand, analyzing the information requires resources that most institutions do not have, independently. To handle such circumstances, grid computing has emerged as an important research area where the calculation of distributed computing and clustering are different. In this study, we propose a grid computing architecture as a set of protocols that use the cumulative knowledge of computers, networks, databases and scientific instruments based on the implementation of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) technique. The results of the implementation of the proposed algorithm on grid systems indicate the superiority of the proposed approach in terms of validation criteria scheduling algorithms, such as task completion time and the performance compared with some alternative method.

Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.

Managers always look for systems with minimum hazards, which cause problems for performance of projects. The largest and the most important hazards of working underground mines can be associated with health, safety and environmental Failure mode and effects analysis (FMEA) is a widely used technique to identify the potential failure modes for measuring reliability of a product or a process. FMEA is performed by developing a risk priority number (RPN), which is the product of severity, occurrence, and detection ratings. On the other hand, with regard to uncertainty in the decision-making, fuzzy theory can help model the inherent uncertainty involved in the underground mining projects. Fuzzy FMEA provides a tool that can work in a better way with vague concepts using insufficient information compared with conventional FMEA. The comparison between the results of the conventional FMEA with those of the proposed model shows that the fuzzy model has a high potential to formulate the level of risk.