Improving energy efficiency considering reduction of CO2 emission of turnip production:A novel data envelopment analysis model with undesirable output approach

Modern Turnip production methods need significant amount of direct and indirect energy. The optimum use of agricultural input resources results in the increase of efficiency and the decrease of the carbon footprint of turnip production. Data Envelopment Analysis (DEA) approach is a well-known technique utilized to evaluate the efficiency for peer units compared with the best practice frontier, widely used by researches to analyze the performance of agricultural sector. In this regard, a new non-radial DEA-based efficiency model is designed to investigate the efficiency of turnip farms. For this purpose, five inputs and two outputs are considered. The outputs consist turnip yield as a desirable output and greenhouse gas emission as an undesirable output. The new model projects each DMU on the strong efficient frontier. Several important properties are stated and proved which show the capabilities of our proposed model. The new models are applied in evaluating 30 turnip farms in Fars, Iran. This case study demonstrates the efficiency of our proposed models. The target inputs and outputs for these farms are also calculated and the benchmark farm for each DMU is determined. Finally, the reduction of CO2 emission for each turnip farm is evaluated. Compared with other factors like human labor, diesel fuel, seed and fertilizers, one of the most important findings is that machinery has the highest contribution to the total target energy saving. Besides, the average target emission of turnip production in the region is 7% less than the current emission

Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model

Given the rapid development of the banking sector, it is reasonable to expect that the performance of banks has become the centre of attention among bank managers, stakeholders, policy makers, and regulators. In order to maximizing the share-holders’ satisfactory level, two bank efficiency measurement approaches, i.e. the production approach and the user cost approach, which are financial evaluations, are employed. The evaluations are done by means of data envelopment analysis method. The proposed methodology is run on the 15 privet bank branches in Markazi province. By using this approach, four regions that show the various performances are obtained. In addition the status of returns to scale for each bank branch is calculated

Quality of life model in multiple sclerosis: personality, mood disturbance, catastrophizing and disease severity

Objective: The aim of current study was to investigate the interaction between factors such as personality, catastrophizing, mood disturbance and disease severity, which may affect the quality of life in patients with multiple sclerosis. The result of this study can identify the factors that have an impact on quality of life among these patients and hopefully it may lead to improve the services provided for these patients. Design: One hundred and thirteen participants with multiple sclerosis completed the following questionnaires: Type D Personality (DS-14), Hospital Anxiety and Depression (HADS), Illness Perception (Brief-IPQ) and Quality of Life (SF-36). The Expanded Disability Statue Scale (EDSS) assessed disease severity. Main Outcome Measures: Data was analyzed in structural equation modeling. Results: Type D personality was associated with quality of life and the relationship was mediated by disease severity, catastrophizing and mood status. Conclusion: Results showed a significant relationship between Type D personality and QOL. However, when the variables were added to the model, the relationship ceased to exist. These results suggest that personality traits are indirectly associated with QOL, mediated by another variable. Quality of Life Model in Multiple Sclerosis: Personality, Mood Disturbance, Catastrophizing and Disease Severity

Inverse Data Envelopment Analysis Model in the Present of Non- Discretionary and Discretionary Data to Preserve Relative Efficiency Values: The Case of Variable Returns to Scale

ABSTRACT This paper studies the proposed models by S

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

The security to lead: a systematic review of leader and follower attachment styles and leader–member exchange

Attachment styles can predict the quality of organizational relationships, particularly in reference to leader–member exchange (LMX). However, there is much work to be done in articulating and summarizing these findings and in detecting gaps in the literature. This systematic review fills a critical niche by providing a review of the attachment/LMX relationship. Using the PRISMA framework, this review integrates research on attachment styles and LMX by evaluating associations between secure, anxious, and avoidant attachment styles with LMX for leaders and followers. Across 10 studies, we review the evidence for associations between leader and follower attachment and LMX. We seek to investigate if secure attachment is associated with high-quality LMX and if insecure attachment is associated with lower quality LMX. Our review in general provides mixed support for these propositions, although the association of avoidant attachment for followers with LMX received consistent support. Furthermore, our results highlight the need to consider potential moderating and mediating factors within the attachment/LMX relationship. Based on the patterns of these relationships and the methodological gaps in the literature, we discuss the managerial implications for attachment styles in work and organizational psychology and suggest several directions for future research on the attachment–LMX relationship

Identifying the links between trauma and social adjustment: implications for more effective psychotherapy with traumatized youth

Background: Past research has highlighted the role of trauma in social adjustment problems, but little is known about the underlying process. This is a barrier to developing effective interventions for social adjustment of traumatized individuals. The present study addressed this research gap through a cognitive model. Methods: A total of 604 young adults (aged 18–24; living in Australia) from different backgrounds (refugee, non-refugee immigrant, and Australian) were assessed through self-report questionnaires. The data were analyzed through path analysis and multivariate analysis of variance. Two path analyses were conducted separately for migrant (including non-refugee and refugee immigrants) and Australian groups. Results: Analyses indicated that cognitive avoidance and social problem solving can significantly mediate the relation between trauma and social adjustment (p 0.95). According to the model, reacting to trauma by cognitive avoidance (i.e., chronic thought suppression and over-general autobiographical memory) can disturb the cognitive capacities that are required for social problem solving. Consequently, a lack of effective social problem solving significantly hinders social adjustment. There were no significant differences among the Australian, non-refugee immigrant and refugee participants on the dependent variables. Moreover, the hypothesized links between the variables was confirmed similarly for both migrant (including refugee and non-refugee immigrants) and Australian groups. Conclusion: The findings have important implications for interventions targeting the social adjustment of young individuals. We assert that overlooking the processes identified in this study, can hinder the improvement of social adjustment in young adults with a history of trauma. Recommendations for future research and practice are discussed

Group Decision Making Process for Supplier Selection with TOPSIS Method under Interval-Valued Intuitionistic Fuzzy Numbers

Supplier selection is a fundamental issue of supply chain area that heavily contributes to the overall supply chain performance, and, also, it is a hard problem since supplier selection is typically a multicriteria group decision problem. In many practical situations, there usually exists incomplete and uncertain, and the decision makers cannot easily express their judgments on the candidates with exact and crisp values. Therefore, in this paper an extended technique for order preference by similarity to ideal solution (TOPSIS) method for group decision making with Atanassov's interval-valued intuitionistic fuzzy numbers is proposed to solve the supplier selection problem under incomplete and uncertain information environment. In other researches in this area, the weights of each decision maker and in many of them the weights of criteria are predetermined, but these weights have been calculated in this paper by using the decision matrix of each decision maker. Also, the normalized Hamming distance is proposed to calculate the distance between Atanassov's interval-valued intuitionistic fuzzy numbers. Finally, a numerical example for supplier selection is given to clarify the main results developed in this paper

Deriving Weights of Criteria from Inconsistent Fuzzy Comparison Matrices by Using the Nearest Weighted Interval Approximation

Deriving the weights of criteria from the pairwise comparison matrix with fuzzy elements is investigated. In the proposed method we first convert each element of the fuzzy comparison matrix into the nearest weighted interval approximation one. Then by using the goal programming method we derive the weights of criteria. The presented method is able to find weights of fuzzy pairwise comparison matrices in any form. We compare the results of the presented method with some of the existing methods. The approach is illustrated by some numerical examples