Chronic kidney disease leads to significant functional limitations AND severe disability, which requires the application of an appropriate physical rehabilitation strategy that helps patients achieve social well-being AND significant health benefits. Data sources MEDLINE, PubMed, Google Scholar AND Web of Science were electronically searched, by using search terms: physical exercise, hemodialysis, barriers, risks of exercise, expected benefits. The contraindications for exercise are recent myocardial infarction, uncontrolled arrhythmia AND hypertension, unstable angina pectoris, unregulated diabetes mellitus, the presence of neurological AND muscular dysfunctions, malignancies, AND pregnancy. The implementation of the physical exercise program for hemodialysis patients confirmed all the expected benefits: improvement of cardiovascular functions, stabilization of blood pressure, increased muscle strength, improvement of nutritional status AND quality of dialysis, reduction of negative emotions, anxiety AND depression, as well as social interaction of patients AND their families. Despite the fact that a large number of studies have proven the beneficial effects of exercise during hemodialysis, a physical rehabilitation program as a stANDard form of treatment has not yet been introduced. Although there is no doubt that the effects of physical activity on the survival AND the quality of life of patients on hemodialysis are positive, rehabilitation program still falls under the routine practice program in a small number of dialysis centers. One of the biggest obstacles to the implementation of the physical therapy program in hemodialysis patients is the lack of a clearly defined program that defines all the needs of dialysis patients.

This paper deals with linear programming problem with interval numbers as coefficients to exhibit with uncertainty. Since, the set of common intervals is not a field, we define generalized interval numbers to produce an algebraic interval field AND on this field, we propose principle of uncertainty traverse instead of extension principle which permits to define operators on intervals exactly similar to the same operators on real numbers. In addition, we apply a total order on this field to transform interval linear programming into a traditional problem. The proposed order can be extended either PESSIMISTICally or OPTIMISTICally. The numerical experiments are given to demonstrate the efficiency of the proposed scheme in comparison with the previous established works. The approach in this paper can be generalized to fuzzy linear programming problems taking the fuzzy cuts into account.

Data envelopment analysis is a nonparametric method for measuring the performance of a set of decision-making units (DMUs) that consume multiple inputs to produce multiple outputs. Using this approach, the performance of DMUs is measured from both OPTIMISTIC AND PESSIMISTIC views. However, their results are very misleading AND even contradictory in many cases. Indisputably, different performance measures should be combined for an overall assessment of the performance of each DMU. This is known as double-frontier analysis. This article proposes a power-averaged efficiency measure to evaluate the overall performance of each DMU. The power-averaged efficiency combines both OPTIMISTIC AND PESSIMISTIC EFFICIENCIES of each DMU, AND therefore, is more comprehensive than both measures. The results showed the higher differentiation capability of the power-averaged efficiency than both OPTIMISTIC AND PESSIMISTIC efficiency measures. The efficiency of the proposed power-averaged efficiency was demonstrated through a numerical example on evaluation of the performance of 42 departments in one of the Islamic Azad University branches to reveal its capabilities in real life situations.

Data envelopment analysis (DEA) is a relatively new data oriented approach to evaluate performance of a set of peer entities called decision-making units (DMUs) that convert multiple inputs into multiple outputs. Within a relative limited period, DEA has been converted into a strong quantitative AND analytical tool to measure AND evaluate performance. In an article written by Toloo et al. (2009), they proposed a new DEA model to find the most efficient association rule in data mining. Considering several criteria, they created an algorithm for ranking association rules using this model. In the present article, we show that their model only selects an OPTIMISTIC efficient association rule rANDomly AND it is completely dependent on solution or software, which is used for solving problems. In addition, it shows that their proposed algorithm can only rank OPTIMISTIC efficient rules rANDomly AND it is not able to rank OPTIMISTIC non-efficient DMUs. We mention other disadvantages AND propose a new approach “ DEA with double frontiers” to create a complete ranking of association rules. A numerical example will explain some contents of the paper.

Supplier selection is a complex but important decision that requires careful review of various performance criteria. Traditionally, models of supplier selection have been based on cardinal data with less emphasis on ordinal data. However, with the widespread use of production methods such as the just-in-time method, recently more emphasis is placed on considering imprecise data-i.e., mixture of interval AND ordinal data. This article proposes to use data envelopment analysis (DEA) with double frontiers for supplier selection, a methodology that considers not only the OPTIMISTIC efficiency but also the PESSIMISTIC efficiency of each supplier. Compared with the traditional DEA, the new DEA with double frontiers can identify the best supplier accurately AND easily without imposing any weight limitations. A numerical example will be examined using the DEA approach with double frontiers to illustrate its simplicity AND usefulness in the supplier selection AND justification.

The aim of this study was to identifying the OPTIMISTIC AND PESSIMISTIC attributions of the mothers in relation to the adolescent. The study used a qualitative design AND employed purposive sampling method to selected 10 mothers with adolescent children aged from 12 to 18 years. The semi-structured face-to-face interview was used to collect the data AND the data analysis was performed by six-step thematic analysis method. The results showed that optimism was seeing the positive points through the lens of an OPTIMISTIC mind, while pessimism included seeing the negative points through the lens of a PESSIMISTIC mind. Moreover, optimism facilitated a good AND effective parent-teenager relationship, OPTIMISTIC attribution (continuation، reason AND personalization) AND parenting challenges. According to the results of this study, it can be concluded that when a mother adopts OPTIMISTIC attribution in communicating with her teenager, there is constructive relationship between teenager AND mother AND much more satisfaction AND peace happens between them AND they face less parenting challenges. On the other hAND, adopting PESSIMISTIC attribution can result in confusion AND anger in teenager AND dissatisfaction AND lack of peace AND tension are among its negative outcomes. Therefore, mothers’ familiarity with OPTIMISTIC AND PESSIMISTIC attributions plays an important role in constructive relationships with adolescents.

The aim of this research is survey the impact the role of OPTIMISTIC AND PESSIMISTIC behaviors (organizational pessimism) in predicting AND explaining occupational AND organizational attitude of employees. The research method in terms of purpose is an applicable research AND in terms of data gathering method, is descriptive-correlation. Statistical society of this research includes 130 employees of Organization of Industries AND Mine of Zahedan province in the year 1395 AND 90 of them were selected through rANDom sampling as the sample of the research. Research data is gathered through three questionnaires of occupational AND organizational attitude, OPTIMISTIC behaviors AND organizational pessimism AND their validity was approved by using convergent validity AND their reliability was approved by using Cronbach’ s alpha AND composite reliability. Research hypothesis were verified through structural equation modeling with PLS software. Findings show that organizational pessimism has a negative effect on the occupational AND organizational attitude of the employees with loading factor of-0. 470, AND OPTIMISTIC behaviors have a positive effect on the occupational AND organizational attitude of the employees with loading factor of 0. 404.

This paper addresses the problem of a factor in supplier selection analysis which may be classified either an input or an output. The quantity of such a factor may influence the relative efficiency of the suppliers. Despite the fact that there are several publications addressing dual-role factors, it seems that their idea of classifying a factor as an input or an output within a single model cannot consider the causality relationships between inputs AND outputs. A simple approach is proposed to resolve this limitation AND to consider dual-role factor as well. A numerical example demonstrates the application of the proposed approach in supplier selection context.

Data envelopment analysis (DEA) is a data-oriented approach for evaluating the performance of a set of peer entities called Decision making units (DMUs) whose performance is characterized by multiple measures. The definition of a DMU is generic AND flexible. In our case, DMU refers to a technology. Selection of the best technology is an important phase for technology transfer. To selection the best technologies in the presence of both cardinal AND ordinal data, this paper proposes an innovative approach, which is based on DEA. This paper proposes to use “DEA with efficient AND inefficient frontiers” for selection of the best technology in the presence of both cardinal AND ordinal data. In this approach, OPTIMISTIC AND PESSIMISTIC EFFICIENCIES of each technology are considered simultaneously. Compared with the traditional DEA, the DEA approach with efficient AND inefficient frontiers can identify the best technology easily AND accurately. A numerical example demonstrates the application of the proposed method.