Clustering sustainable suppliers in the plastics industry: A fuzzy equivalence relation approach

Nowadays, pure economic supply chain management is not commonly contemplated among companies (especially buyers), as recently novel dimensions of supply chains, e.g., environmental, sustainability, and risk, play significant roles. In addition, since companies prefer buying their needs from a group of suppliers, the problem of supplier selection is not solely choosing or qualifying a supplier from among others. Buyers, hence, commonly assemble a portfolio of suppliers by looking at the multi-dimensional pre-determined selection criteria. Since sustainable supplier selection criteria are often assessed by linguistic terms, an appropriate clustering approach is required. This paper presents an innovative way to implement fuzzy equivalence relation to clustering sustainable suppliers through developing a comprehensive taxonomy of sustainable supplier selection criteria, including supply chain risk. Fifteen experts participated in this study to evaluate 20 suppliers and cluster them in the plastics industry. Findings reveal that the best partitioning occurs when the suppliers are divided into two clusters, with 4 (20%) and 16 (80%) suppliers, respectively. The four suppliers in cluster one are performing better in terms of the capability of supplier/delivery, service, risk, and sustainability criteria such as environment protection/management, and green innovation. These factors are critical in clustering and selecting sustainable suppliers. The originality of this study lies in developing an all-inclusive set of criteria for clustering sustainable suppliers and adding risk factors to the conventional supplier selection criteria. In addition to partitioning the suppliers and determining the best-performing ones, this study also highlights the most influential factors by analysing the suppliers in the best cluster

The Relationship between Organizational Commitment and Job Burnout: a study on staff of selected hospitals affiliated to Kerman University of Medical Sciences

The Relationship between Organizational Commitment and Job Burnout: a study on staff of selected hospitals affiliated to Kerman University of Medical Sciences Tohidi Mahya1, Pour Kiani Masoud2, Amiresmaili Mohammad Reza3, Khosravi Sajad4* 1. Ph.D. Student, School of Medical Sciences, Islamic Azad University, Science and Research Branch, Tehran, Iran. 2. Assistant Professor, Faculty of Management, Islamic Azad University, Kerman, Iran. 2. Associate Professor, School of Management and Medical Informatics, Department of Management, Health Policy and Health Economic, Kerman University of Medical Sciences, Kerman, Iran 3. Ph.D. Student, School of Public Health, Bam University of Medical Sciences, Bam, Iran. *Correspondence: School of Public Health, Campus of Bam, Khalij Fars Highway, Bam University of Medical Sciences. Tel: 034335229985 Email: [email protected] Abstract: Introduction: The presence of committed human resources improves organizational performance and achievement of individual and organizational goals. The objective of this study was to investigate the relationship between organizational commitment and job burnout among staff of the selected hospitals affiliated to Kerman University of Medical Sciences. Methods: This cross-sectional, descriptive-analytic study was carried out in 2015. In this study, 278 employees were selected using quota sampling. In order to collect data, Maslach questionnaires on job burnout (22 questions) and Allen & Meyer organization commitment questionnaire (24 questions) were used. Data analysis was performed through SPSS 16 and using descriptive statistics as well as analytical statistics tests such as Pearson correlation, ANOVA and linear regression. Results: In whole, 74.8% of the participants were female and 40% had less than 10 years of work experience. Mean scores of organizational commitment (98.20) and job burnout (74.58) were in moderate level. Organization commitment and all its dimensions had significant inverse relationship with job burnout (P=0.000). In addition, the relationships of organization commitment with gender and age, and job burnout with employment status were significant (P=0.01). Conclusion: High organizational commitment represents the acceptance of organizational goals and values by the employees. Committed employees have better job performance in the organization and less job burnout. Therefore, managers must attempt to raise the level of organizational commitment and put it in their programs planning. Key¬words: Organizational Commitment, Job Burnout, Educational Hospital, Staff ¬Citation: Tohid M, Pour Kiani M, Amiresmaili MR, Khosravi S. The Relationship between Organizational Commitment and Job Burnout: a study on staff of selected hospitals affiliated to Kerman University of Medical Sciences. Journal of Health Based Research 2016; 2(3): 299-306

High surface area microporous carbon nanocubes from controlled processing of graphene oxide nanoribbons

A new, facile, and template-free method to prepare high surface area microporous carbon nanocubes (CNCs) from a mixture of graphene oxide nanoribbons (NRs), graphene oxide, and carbon dots is reported. The nanoribbons, approximately 30 nm wide and with lengths ranging from a few tens of nanometres up to several micrometres, were obtained from the oxidation of Black Pearls 2000 carbon black in nitric acid solution. The non-purified nanoribbons further contained additional fragments of graphene oxide, and of graphene oxide quantum dots. Slow pyrolysis of the nanoribbon mixture with slow heating rates, e.g., 3 °C/min, yielded carbon nanocubes approximately 250 nm in size with surface areas greater than 900 m2/g. Heating rates of 50 °C/min led to carbons with ∼800 m2/g surface area but bulk morphology. Precipitating the nanoribbons in potassium hydroxide solution, followed by carbonization, yielded microporous nanoparticle aggregates that were 20 nm in size with surface areas greater than 2000 m2/g. The particles exhibited complex, quasi-spherical morphology. Pyrolysis of other products obtained from oxidation in HNO3 of different grades of carbon black, specifically graphene oxide nanoparticles and quantum dots, yielded high surface area microporous carbons but with bulk morphology regardless of the processing conditions. Despite the lower surface area and pore volume of the CNCs in comparison to the nanospheres, the former contained ultramicropores that were highly accessible to CO2 as a molecular probe and had excellent selectivity of CO2 over N2. Hence, CNC materials have promising properties for applications where particle surface-to-volume ratios, high internal surface areas, and abundant super and ultramicropores are desired

Enhanced thermoelectricity in Bi-sprayed bismuth sulphide particles

Bismuth sulphide (Bi2S3), an n-type semiconductor that critically demonstrates the Seebeck effect with Seebeck coefficients of about 300 μVK−1. However, its poor electrical conductivity makes it unsuitable for thermoelectric applications. In this study, we present a facile preparation method for fabricating Bi-sprayed Bi2S3 particles that alters their thermoelectric properties. Samples were created with differing Bi concentrations into the Bi2S3 compound to test for enhanced thermoelectric properties of the resulting Bi/Bi2S3 composites. The incorporation of excess Bi into Bi2S3 significantly improves the compound's electrical conductivity and optimises overall thermoelectric performance. The electrical conductivity of the Bi/Bi2S3 composites improved from 6.5 Scm−1 (for pristine Bi2S3) to 154 Scm−1 (for highest Bi added Bi2S3). Although the Seebeck coefficient of samples decreased with Bi incorporation, a high power factor (∼390 μWm−1K−2) has been achieved for an optimised composition of the composite. Incorporation of metallic Bi has led to an increase in the thermal conductivity of the samples, but the increase is not significant for the optimised composition of the composites where a high thermoelectric performance has been observed. Therefore, enhanced power factor and moderate thermal conductivity have resulted in a peak ZT value of 0.11 at room temperature. The strategy proposed here improves the thermoelectricity in Bi2S3 and shows excellent potential for developing better-performing thermoelectric compounds with excess elemental contents

Novel Synthetic Derivatives of Dichloroimidazole Targeting NorA Efflux Pump against Methicillin-Resistant Staphylococcus aureus

Introduction: Antibiotic resistance has been a major health problem in recent years, which has led to a failure in the treatment of infectious diseases. Therefore, research to synthesize compounds that have antibiotic activity is very valuable. In present study four novel compounds (6a-d), derivatives of dichloroimidazole conjugated with triazole, were synthesized in order to obtain new bacterial efflux pump inhibitors (EPIs). Methods and Results: The derivatives were evaluated for their effects on the minimum inhibitory concentration (MIC) of ciprofloxacin against a methicillin and ciprofloxacin resistant Staphylococcus aureus (MCRSA) clinical isolate. Based on broth microdilution method assay, four derivatives at a minimum effective concentration (MEC) fortified the antibacterial efficacy of ciprofloxacin against MCRSA. MIC of ciprofloxacin decreased in the presence of novel compounds compared to ciprofloxacin alone between 2 to 64 fold. These compounds were then evaluated for their potency as efflux pump inhibitors using a fluorometric assay. Results indicated an increase in accumulation of ethidium bromide (a known fluorescent substrate for the NorA pump) in the presence of each compound, like verapamil (a typical inhibitor of efflux pump), thus these compounds acted as inhibitors of the NorA pump. Moreover, the MTT assay confirmed that novel compounds did not demonstrate any cytotoxic effect against three cancer cell lines, HT-29, MCF-7 and Caco-2, and a normal mouse fibroblastic cell line, NIH-3T3. Conclusion: Collectively, our results propose these derivatives as therapeutic options in combination therapies to tackle antibiotic resistance. Grants: This research has been supported by Grant Number 94-02-33-29506 from Deputy of Research, Tehran University of Medical Science

Development of an intelligent machine vision system for the purpose of online quality measurement of rice paddy

The common methods that are usually used to identify the devoid rough rice from the healthy ones are often time-consuming and expensive. For this reason, in this research, a smart and fast method based on machine vision system coupled with artificial neural networks is presented in order to predict the percentage of devoid/healthy rough rice grains. Digital images of five varieties of paddy were prepared in three states: healthy, devoid, and mixed, in two states scattered and piled. After pre-processing and segmentation, 3 color features and 5 morphological features were extracted for each rice grain. Principal component analysis (PCA) method was then used in order to identify the most effective features in distinguishing devoid rough from healthy rice. In the next step, multilayer perceptron (MLP) algorithm based on the main components obtained by PCA method was used to create models for identifying and classifying the samples. Root Mean Square Error (RMSE), correlation coefficient (R2), specificity and sensitivity were used to evaluate the modeling capability and validation of each algorithm. The obtained results showed that the designed intelligent method can identify devoid rough rice seeds with acceptable accuracy in all cultivars (R2P>0.81, RMSEp0.8 & Specificity>0.98). Therefore, the machine vision system in combination with artificial neural networks can be used as an intelligent and fast method at the entrance of rice bleaching factories to evaluate the quality of harvested rough rice and predict the percentage of unhealthy rough rice

The Differential Impact of Various Injection Pressures on the Exergy of a Diesel Engine Using Biodiesel-Diesel Fuel Blends

Contrary to energy, exergy may be destroyed due to irreversibility. Exergy analysis can be used to reveal the location, and amount of energy losses of engines. Despite the importance of the exergy analysis, there is a lack of information in this area, especially when the engine is fueled with biodiesel–diesel fuel blends under various injection operating parameters. Thus, in this research, the exergy analysis of a direct-injection diesel engine using biodiesel–diesel fuel blends was performed. The fuel blends (B0, B20, B40, and B100) were injected into cylinders at pressures of 200 and 215 bars. Moreover, the simulation of exergy and energy analyses was done by homemade code. The simulation model was verified by compression of experimental and simulation in-cylinder pressure data. The results showed there was good agreement between simulation data and experimental ones. Results indicated that the highest level of in-cylinder pressure at injection pressure of 215 bars is more than that of 200 bars. Moreover, by increasing the percentage of biodiesel, the heat transfer exergy, irreversibility, burnt fuel, and exergy indicator decreased, but the ratio of these exergy parameters (except for heat transfer exergy) to fuel exergy increased. These ratios increased from 46 to 50.54% for work transfer exergy, 16.57 to 17.97% for irreversibility, and decreased from 16 to 15.49% for heat transfer exergy. In addition, these ratios at 215 bars are higher than at 200 bars for all fuels. However, with increasing the injection pressure and biodiesel concentration in fuel blends, the exergy and energy efficiencies increased

The Differential Impact of Various Injection Pressures on the Exergy of a Diesel Engine Using Biodiesel-Diesel Fuel Blends

Contrary to energy, exergy may be destroyed due to irreversibility. Exergy analysis can be used to reveal the location, and amount of energy losses of engines. Despite the importance of the exergy analysis, there is a lack of information in this area, especially when the engine is fueled with biodiesel–diesel fuel blends under various injection operating parameters. Thus, in this research, the exergy analysis of a direct-injection diesel engine using biodiesel–diesel fuel blends was performed. The fuel blends (B0, B20, B40, and B100) were injected into cylinders at pressures of 200 and 215 bars. Moreover, the simulation of exergy and energy analyses was done by homemade code. The simulation model was verified by compression of experimental and simulation in-cylinder pressure data. The results showed there was good agreement between simulation data and experimental ones. Results indicated that the highest level of in-cylinder pressure at injection pressure of 215 bars is more than that of 200 bars. Moreover, by increasing the percentage of biodiesel, the heat transfer exergy, irreversibility, burnt fuel, and exergy indicator decreased, but the ratio of these exergy parameters (except for heat transfer exergy) to fuel exergy increased. These ratios increased from 46 to 50.54% for work transfer exergy, 16.57 to 17.97% for irreversibility, and decreased from 16 to 15.49% for heat transfer exergy. In addition, these ratios at 215 bars are higher than at 200 bars for all fuels. However, with increasing the injection pressure and biodiesel concentration in fuel blends, the exergy and energy efficiencies increased

A stochastic data envelopment analysis model using a common set of weights and the ideal point concept

The efficiency scores of the decision making units (DMUs) in conventional data envelopment analysis (DEA) are between zero and one and generally several DMUs result in having efficiency scores of one. These models generally only rank the inefficient DMUs and not the efficient ones. In addition, conventional DEA models assume that inputs and outputs are measured precisely on a ratio scale. However, the observed values of the input and output data in real-life problems are often imprecise. In this paper, we propose a common set of weights (CSW) model for ranking the DMUs with the stochastic data and the ideal point concept. The proposed method minimises the distance between the evaluated DMUs and the ideal DMU. We also present a numerical example to demonstrate the applicability of the proposed model and exhibit the efficacy of the procedures

The effect of inlet temperature and spark timing on thermo-mechanical, chemical and the total exergy of an SI engine using bioethanol-gasoline blends

Exergy is a quantity of the work potential of energy from a given thermodynamic condition. Unlike energy, exergy can be destroyed, and for gasoline engines, the major source of this destruction is during the combustion process. Therefore, to assess the quality of gasoline engines, the research team examined the effect of inlet temperature and spark timing on chemical, thermo-mechanical and total exergy of fuel using E0, E20, E40, E60 and E85 fuels. Results showed that by advancing the spark timing (20° bTDC), thermo-mechanical exergy has increased but chemical exergy and total exergy have decreased. In addition, advance or delay in spark timing had no effect on the fuel chemical exergy for the compression and expansion strokes. The effect of temperature on exergy parameters indicated that by reducing inlet temperature (320 K), exergy parameters increased. In other words, the fuel chemical exergy at 320 K for E0, E20, E40, E60 and E85 fuels, increased by 7%, 7.1%, 7.2%, 7.2%, 7.3%, respectively, than 350 K and increased by 14%, 14.3%, 14.4%, 14.4% and 14.5% than 380 K