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In a new approach, copper ferrite (CuFe2O4) nanoparticles were easily synthesized using a microwave-ignition reaction and then coated by extracted chlorophyll (extracted from mulberry green leaves) to amplify the photocatalytic and adsorption qualities. The FE-SEM, TEM, Raman, BET, VSM, XRD, EDX and FT-IR techniques were applied to analyze the structure and properties of CuFe2O4 and Chlorophyll/CuFe2O4. The average sizes for nano-catalysts were estimated about 90 nm for CuFe2O4 and 120 nm for Chlorophyll/CuFe2O4. A shift in the band gap from 1.45 eV for CuFe2O4 to 1.3 eV for Chlorophyll/CuFe2O4 exhibited an increase in photocatalytic performance of nanoferrites after coating by the chlorophyll molecules. Photocatalytic degradation studies were performed through UV–Vis technique to measure the removal content of methylene blue (MB) from aqueous samples. The removal efficiency for CuFe2O4 and Chlorophyll/CuFe2O4 were found to be 35.1% and 94.0%, respectively, at certain test conditions. Thereupon, the results confirmed that photocatalytic activity and decomposition capability to remove the MB contaminant increased considerably for chlorophyll coated ferrite. Finally, the nanoparticles were easily separated by an external magnet, reused for several times, and illustrated a negligible decrease in their activity after each cycle. [ABSTRACT FROM AUTHOR]

Medical data mining is considered as a new solution to the analysis of medical data and further expansion of the relative knowledge. Medical data mining has a high potential to discover the hidden patterns. Recently, some studies have been conducted on the relationship between the individuals' environmental surroundings and their exposure to diseases, thus clearly confirming the considerable impact of the quality of environmental indicators such as environmental pollutants on diseases. In this respect, the current study investigated the effect of the environmental conditions on the chance of diabetes based on medical data mining technique. Given that diabetes is a global threat to the human health, an Ensemble Classifier based on Genetic Algorithm (ECGA) method was designed to study the environmental conditions in diabetes. Decision tree, random forest, K-nearest neighbor, and naive were included in the designed ensemble classifier. It was found that ECGA was more accurate than the base classifier algorithms. For study purposes, three datasets were collected from different regions in Iran with different climatic conditions and it was found that environmental conditions had a significant impact on diabetes. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA), is a non-parametric mathematical programming technique to evaluate the efficiency of a set of homogeneous decision-making units (DMUs), so that DMUs are evaluated into two groups, efficient and inefficient. According to the staggering costs in order to managing DMUs or organizations, maintaining some loss-making organizations are not cost-effective. Therefore, one of the concerns of managers in the discussion related to the financial problems of organizations is the maintenance or merger or elimination of ineffcient organizations (inefficient DMUs). However, this article focuses on the performance of ineffcient units. Therefore, we measure the productivity of ineffcient DMUs using the revised Malmquist productivity index (MPI) to make a decision based on the maintenance or merger or elimination of these DMUs by decision makers. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.09.018 Byline: M. Rabbani (a), S.M.T. Fatemi Ghomi (b), F. Jolai (a), N.S. Lahiji (a) Keywords: Project scheduling; Criticality index; Cruciality index; Stochastic networks; Critical chain Abstract: This paper presents a newly developed resource-constrained project scheduling method in stochastic networks by merging the new and traditional resource management methods. In each project, the activities consume various types of resources with fixed capacities. The duration of each activity is a random variable with a given density function. Since the backward pass method is implemented for feeding-in resources. The problem is to determine the finish time of each activity instead of its start time. The objective of the presented model is defined as minimizing the multiplication of expected project duration and its variance. The values of activities finish times are determined at decision points when at least one activity is ready to be operated and there are available resources. If at a certain point of time, more than one activity is ready to be operated but available resources are lacking, a competition among ready activities is carried out in order to select the activities which must be operated first. This paper suggests a competition routine by implementing a policy to maximize the total contribution of selected activities in reducing the expected project duration and its variance. In this respect, a heuristic algorithm is developed and compared with the other existing methods. Author Affiliation: (a) Department of Industrial Engineering, University of Tehran, Tehran, Iran (b) Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran Article History: Received 26 March 2004; Accepted 14 September 2005

Usage of graph theory to formulate distribution functions, for structural mechanism to reduce network completion time in stochastic networks, is described.

In today's world, production and distribution of products in supply chain systems should be done with careful consideration of the environmental and social issues as global concerns about the emission of greenhouse gases within the manufacturing processes and overlooking the major needs of the public are rising. In this regard, the present paper proposes a new multi-objective model for the Closed-Loop Supply Chain (CLSC) problems by incorporating lot sizing and considering lean, agility, and sustainability factors, simultaneously. Furthermore, a robust possibilistic programming approach was applied for handling the uncertainty of the model. To increase the responsiveness of the system, a Fuzzy C-means Clustering Method (FCCM) was employed in order to select the potential locations based on the proximity to local customers. A new hybrid metaheuristic algorithm was developed in order to improve efficiency of the model in dealing with large-size problems and assess the impact of using a single-based initial solution as the income for the second phase of the proposed hybrid algorithm. In addition, to ensure effectiveness of the proposed algorithm, another well-known metaheuristic algorithm was developed. The results achieved by experiments on different test problems approved the superiority of the hybrid metaheuristic algorithm in achieving proper solutions. [ABSTRACT FROM AUTHOR]

Clostridium perfringens is a Gram positive anaerobe which makes spores that help it to persist under different environmental conditions. Spores of C. perfringens have the potential to remain in the fecal matter and soil for many years due to which control of transmission of pathogen becomes difficult. The pathogen is responsible for various diseases in both humans and animals. We determined the prevalence of C. perfringens in the soil of selected districts of Punjab province of Pakistan along with potential association of different categorical variables. A total of 970 samples were processed for real time PCR based screening of genome corresponding to cpa gene (alpha toxin gene) of C. perfringens. Only n=96 samples were found positive [9.89% with CI: 95% (8.018-11.78), p-value< 0.05]. Highest prevalence was found in district "Chakwal" followed by district "Attock" and "Lahore". The villages found positive for C. perfringens were evaluated for different risk factors including distance from the main road, animal market and water source; animal density, number of houses in the village and animal and human interaction at the sampling site. Analysis of the data revealed that animal and human interaction was the associated risk factor in most of the districts followed by distance from the water source, animal density and human population of the village. The study concludes that C. perfringens is prevalent in the soil of Punjab province and the prevalence varies with several risk factors in different districts. Further studies are required to find out most prevalent type of C. perfringens as well as effect of different categorical variables on prevalence of C. perfringens in other parts of the country. [ABSTRACT FROM AUTHOR]