Allameh Tabataba'i University

An effective way for managing and controlling a large number of inventory items or stock keeping units (SKUs) is the inventory classification. Traditional ABC analysis which based on only a single criterion is commonly used for classification of SKUs. However, we should consider inventory classification as a multi-criteria problem in practice. In this study, a new method of Evaluation based on Distance from Average Solution (EDAS) is introduced for multi-criteria inventory classification (MCIC) problems. In the proposed method, we use positive and negative distances from the average solution for appraising alternatives (SKUs). To represent performance of the proposed method in MCIC problems, we use a common example with 47 SKUs. Comparing the results of the proposed method with some existing methods shows the good performance of it in ABC classification. The proposed method can also be used for multi-criteria decision-making (MCDM) problems. A comparative analysis is also made for showing the validity and stability of the proposed method in MCDM problems. We compare the proposed method with VIKOR, TOPSIS, SAW and COPRAS methods using an example. Seven sets of criteria weights and Spearman’s correlation coefficient are used for this analysis. The results show that the proposed method is stable in different weights and well consistent with the other methods.

BACKGROUND AND AIM: Social support is an important factor that can affect mental health. In recent decades, many studies have been done on the impact of social support on mental health. The purpose of the present study is to investigate the effect size of the relationship between social support and mental health in studies in Iran. METHODS: This meta-analysis was carried out in studies that were performed from 1996 through 2015. Databases included SID and Magiran, the comprehensive portal of human sciences, Noor specialized magazine databases, IRANDOC, Proquest, PubMed, Scopus, ERIC, Iranmedex and Google Scholar. The keywords used to search these websites included "mental health or general health," and "Iran" and "social support." In total, 64 studies had inclusion criteria meta-analysis. In order to collect data used from a meta-analysis worksheet that was made by the researcher and for data analysis software, CMA-2 was used. RESULTS: The mean of effect size of the 64 studies in the fixed-effect model and random-effect model was obtained respectively as 0.356 and 0.330, which indicated the moderate effect size of social support on mental health. The studies did not have publication bias, and enjoyed a heterogeneous effect size. The target population and social support questionnaire were moderator variables, but sex, sampling method, and mental health questionnaire were not moderator variables. CONCLUSION: Regarding relatively high effect size of the correlation between social support and mental health, it is necessary to predispose higher social support, especially for women, the elderly, patients, workers, and students.

The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

The current paper examines the instructional implications of Vygotsky's (1978) seminal notion of Zone of Proximal Development, originally developed to account for the learning potential of children, and investigates ZPD applications to the concept of teacher professional development. Specific attempt has been made to see how a number of assets at the teacher's disposal namely diary writing, peer and mentor collaboration, action research, practicum and TESOL discourse can serve as scaffolders to affect the progression of ZPD in language teachers. The contributions of ZPD to the concepts of scaffolding and dynamic assessment (DA) are explored extensively and the controversial issues are addressed. There is a consensus that the notion of the zone of proximal development and socio-cultural theory of mind based on Vygotsky’s ideas are at the heart of the notion of scaffolding .This study highlights the limitations of the metaphor of scaffolding in interpreting the zone of proximal development. The concept of ZPD, as seen through the approach of DA, offers an operational view of the learners’ actual level of development and a measure of emerging and imminent development. Utilizing the concept of ZPD, DA unites traditional assessment, instruction, intervention, and remediation. Though the concept of ZPD provides an attractive metaphor for designing instruction and analyzing learning, it poses a real challenge when put into practice. The present research highlights a procedure to provide a more tangible account of ZPD, but research on this area is scanty and further explorations and investigations are needed to reflect the implications of ZPD in instructional context.

E-learning has a significant role in instruction of students in higher education, so the objective of this study is investigating the strength of the relationship between e-learning and students’ motivation among students participating in the research. This research was conducted in Tehran Alzahra University. Overall, the outcomes of this study have confirmed that e-learning is an element which affects students’ motivation. – A questionnaire was applied to collect data from students of Tehran Alzahra University; and the statistical method of Pearson's correlation coefficient, was used for data analysis. Research limitations/implications – The analysis is executed in an only country therefore, attention must be paid in generalization of the outcomes. Practical implications – The outcomes of this research will be helpful in developing countries for educational thinkers to better comprehend effects of e-learning on students’ motivation.

In the real-world problems, we are likely confronted with some alternatives that eed to be evaluated with respect to multiple conflicting criteria. Multi-criteria ecision-making (MCDM) refers to making decisions in such a situation. There are any methods and techniques available for solving MCDM problems. The evaluation ased on distance from average solution (EDAS) method is an efficient multi-criteria ecision-making method. Because the uncertainty is usually an inevitable part of he MCDM problems, fuzzy MCDM methods can be very useful for dealing with the eal-world decision-making problems. In this study, we extend the EDAS method o handle the MCDM problems in the fuzzy environment. A case study of supplier election is used to show the procedure of the proposed method and applicability of t. Also, we perform a sensitivity analysis by using simulated weights for criteria to xamine the stability and validity of the results of the proposed method. The results f this study show that the extended fuzzy EDAS method is efficient and has good tability for solving MCDM problems.

Evaluation based on Distance from Average Solution (EDAS) is a new multicriteria decision making (MCDM) method, which is based on the distances of alternatives from the average scores of attributes. Classical EDAS has been already extended by using ordinary fuzzy sets in case of vague and incomplete data. In this paper, we propose an interval-valued intuitionistic fuzzy EDAS method, which is based on the data belonging to membership, nonmembership, and hesitance degrees. A sensitivity analysis is also given to show how robust decisions are obtained through the proposed intuitionistic fuzzy EDAS. The proposed intuitionistic fuzzy EDAS method is applied to the evaluation of solid waste disposal site selection alternatives. The comparative and sensitivity analyses are also included.

طبقه ­بندی اختلال­ های روانی از دیرباز در میان متخصصان سلامت روان، موضوعی مورد مناقشه بوده است. با وجود گسترش قابل ملاحظه­ ی دانش ما در این حوزه در نیم قرن گذشته، فهم اجزا و فرایندهای آن، همچنان ابتدایی است. در این مقاله سه نظام طبقه­ بندی اختلال­ های روانی توصیف می­ شود که هدف هر یک از آن­ ها برای درک و طبقه­ بندی اختلال­ ها با یکدیگر متفاوت است: دو کتابچه راهنمای عمده تشخیصی– راهنمای تشخیصی و آماری اختلالات روانی (DSM)، طبقه­ بندی بین­ المللی بیماری­ ها (ICD) و پروژه معیارهای دامنه تحقیق (RDoC) موسسه­ ی ملی سلامت روان ایالات متحده که با تاکید بر یکپارچگی تحقیقات علوم رفتاری و علوم اعصاب چهارچوبی برای درک عمیق اختلالات روانی ارائه کرده است. برای مقایسه این سه طبقه ­بندی از چهار موضوع کلیدی بهره گرفته شده است: سبب­شناسی، شامل علیت­ های متعدد اختلال روانی؛ طبقه­ بندی یا ابعاد، آیا پدیده ­های مربوطه طبقاتی مجزا هستند یا ابعادی؟؛ آستانه­ ها، که مرز بین اختلال و عدم اختلال را تعیین می ­کنند؛ و هم ­ابتلایی، که دربردارنده­ ی این واقعیت است که افراد مبتلا به بیماری روانی اغلب دارای نیازهای تشخیصی برای شرایط مختلف هستند. اگر چه این نظام­ ها دارای درجه­ های مختلف همپوشانی و ویژگی­ های متمایز هستند، هدف مشترک هر سه آن­ ها کاهش بار رنج ناشی از اختلالات روانی است به وسیله­ ی درک بهتر و طبقه­ب ندی مناسب آن­ ها است.

Abstract Fourteen randomly assigned Iranian girls ages 12–13 years who had been sexually abused received up to 12 sessions of CBT or EMDR treatment. Assessment of post‐traumatic stress symptoms and problem behaviours was completed at pre‐treatment and 2 weeks post‐treatment. Both treatments showed large effect sizes on the post‐traumatic symptom outcomes, and a medium effect size on the behaviour outcome, all statistically significant. A non‐significant trend on self‐reported post‐traumatic stress symptoms favoured EMDR over CBT. Treatment efficiency was calculated by dividing change scores by number of sessions; EMDR was significantly more efficient, with large effect sizes on each outcome. Limitations include small N , single therapist for each treatment condition, no independent verification of treatment fidelity, and no long‐term follow‐up. These findings suggest that both CBT and EMDR can help girls to recover from the effects of sexual abuse, and that structured trauma treatments can be applied to children in Iran. Copyright © 2004 John Wiley & Sons, Ltd

One of the important activities of a company that can increase its competitiveness is market segment evaluation and selection (mse/mss). We can usually consider mse/mss as a multi-criteria decision-making (mcdm) problem, and so we need to use an mcdm method to handle it. Uncertainty is one of the important factors that can affect the process of decision-making. Fuzzy mcdm approached have been designed to deal with the uncertainty of decision-making problems. In this study, a fuzzy extension of the codas (combinative distance-based assessment) method is proposed to solve multi-criteria group decision-making problems. We use linguistic variables and trapezoidal fuzzy numbers to extend the codas method. The proposed fuzzy codas method is applied to an example of market segment evaluation and selection problem under uncertainty. To validate the results, a comparison is performed between the fuzzy codas and two other mcdm methods (fuzzy edas and fuzzy topsis). A sensitivity analysis is also carried out to demonstrate the stability of the results of the fuzz codas. For this aim, ten sets of criteria weights are randomly generated and the example is solved using each set separately. The results of the comparison and the sensitivity analysis show that the proposed fuzzy codas method gives valid and stable results.

Violence against children affects a significant portion of youth around the world. Emergencies and natural disasters escalate the risk due to weakened child protection systems and disruption of preventative mechanisms. In this systematic review, 692 related papers were searched in various databases in the initial search. After review, 11 papers were finally selected for full review. These papers were selected based on publication date, relevance to emergencies, their geographical area type of violence, age of subjects, and their gender. Most families affected by natural disasters, especially those in lower socioeconomic status, face greater social and economic pressures. The families that are more vulnerable to loss of food and shelter commit violence against children more frequently. On the other hand, while the rate of violence increases in emergencies, the reported rate of violence is less than the actual rate due to lack of required infrastructure and reporting mechanisms. The emergency housing increased risk of some types of child abuse. The history of exposure to violence, parental substance abuse, poverty, and child labor were predictors of increased violence against children in emergency situations. Sexual violence against girls after conflicts and physical violence against boys after emergencies are common forms of violence. Poverty as another predictor exposes children to more violence due to limited family economic resources and support. Given the identified predictors of violence, humanitarian organizations can come closer to providing appropriate plans to reduce the risk during and postdisaster.

The construction industry is an important industry because of its effects on different aspects of human life experiences and circumstances. Environmental concerns have been considered in designing and planning processes of construction supply chains in the recent past. One of the most crucial problems in managing supply chains is the process of evaluation and selection of green suppliers. This process can be categorized as a multi-criteria decision-making (MCDM) problem. The aim of this study is to propose a novel and efficient methodology for evaluation of green construction suppliers with uncertain information. The framework of the proposed methodology is based on weighted aggregated sum product assessment (WASPAS) and the simple multi-attribute rating technique (SMART), and Fermatean fuzzy sets (FFSs) are used to deal with uncertainty of information. The methodology was applied to a green supplier evaluation and selection in the construction industry. Fifteen suppliers were chosen to be evaluated with respect to seven criteria including “estimated cost”, “delivery efficiency”, “product flexibility”, “reputation and management level”, “eco-design”, and “green image pollution”. Sensitivity and comparative analyses were also conducted to assess the efficiency and validity of the proposed methodology. The analyses showed that the results of the proposed methodology were stable and also congruent with those of some existing methods.

Purpose – Third-party logistics (3PL) plays a main role in supply chain management and, as a result, has experienced remarkable growth. The demand for 3PL providers has become a main approach for companies to offer better customer service, reduce costs, and gain competitive advantage. This paper identifies important criteria for 3PL provider selection and evaluation, and the purpose of this paper is to select 3PL providers from the viewpoint of firms which were already outsourcing their logistics services. Design/methodology/approach – This study utilized the grey decision-making trial and evaluation laboratory (DEMATEL) method to develop 3PL provider selection criteria. Because human judgments are vague and complicated to depict by accurate numerical values, the grey system theory is used to handle this problem. Findings – The findings revealed the structure and interrelationships between criteria and identified the main criteria for 3PL provider selection. The most important criteria for 3PL provider selection are on time delivery performance, technological capability, financial stability, human resource policies, service quality, and customer service, respectively. Practical implications – The paper’s results help managers of automotive industries, particularly in developing countries, to outsource logistics activities to 3PL providers effectively and to create a significant competitive advantage. Originality/value – The main contributions of this paper are twofold. First, this paper proposes an integrated grey DEMATEL method to consider interdependent relationships among the 3PL provider selection criteria. Second, this study is one of the first studies to consider 3PL provider selection in a developing country like Iran.

BACKGROUND: Laughter Yoga founded by M. Kataria is a combination of unconditioned laughter and yogic breathing. Its effect on mental and physical aspects of healthy individuals was shown to be beneficial. OBJECTIVE: The objective of this study was to compare the effectiveness of Kataria's Laughter Yoga and group exercise therapy in decreasing depression and increasing life satisfaction in older adult women of a cultural community of Tehran, Iran. METHODS: Seventy depressed old women who were members of a cultural community of Tehran were chosen by Geriatric depression scale (score>10). After completion of Life Satisfaction Scale pre-test and demographic questionnaire, subjects were randomized into three groups of laughter therapy, exercise therapy, and control. Subsequently, depression post-test and life satisfaction post-test were done for all three groups. The data were analyzed using analysis of covariance and Bonferroni's correction. RESULTS: Sixty subjects completed the study. The analysis revealed a significant difference in decrease in depression scores of both Laughter Yoga and exercise therapy group in comparison to control group (p<0.001 and p<0.01, respectively). There was no significant difference between Laughter Yoga and exercise therapy groups. The increase in life satisfaction of Laughter Yoga group showed a significant difference in comparison with control group (p<0.001). No significant difference was found between exercise therapy and either control or Laughter Yoga group. CONCLUSION: Our findings showed that Laughter Yoga is at least as effective as group exercise program in improvement of depression and life satisfaction of elderly depressed women.

In the last decades, universities and higher education institutes have widely employed a learning management system (LMS) to monitor and manage electronic learning and teaching. Contrary to the significant role of LMS in educational settings, most research has focused on initial acceptance, and few attempts have been made to investigate factors influencing students’ continuance intention to use LMS. The present study is an effort towards this research direction by proposing an integrated model of expectation-conformation model (ECM), technology acceptance model (TAM), social influence (subjective norm), and perceived enjoyment (hedonic value). The proposed model is tested using statistical data from 153 university students from Mehralborz University (MAU), Tehran, Iran. To verify the proposed theoretical model, we ran partial least squares (PLS)/ structured equation modeling (SEM). The findings of this study reveal that the perceived usefulness is the strongest predictor of students’ continuance intention. Surprisingly, our results also indicate that students’ attitudes toward LMS and their satisfaction level exert no significant influence on continuance intention. The implications of this study and its limitations are also discussed.

Purpose Restaurants with limited promotion budgets depend mainly on word of mouth (WOM) among customers. WOM seems particularly important to the marketing of services. This is because services are experiential in nature and difficult to assess before purchase. In the restaurants context there is little research on WOM. The purpose of this paper is to examine the factors that may influence tourists’ WOM about restaurants implying on the critical role of relationship quality. Design/methodology/approach A comprehensive literature review is conducted to identify the major factors influencing WOM in the context of restaurant industry. The study utilizes self-administered questionnaire survey and the target population are the customers who have referred to the restaurants of Tehran, Iran. A convenience sampling approach was utilized to collect a sample of 326 customers. A structural equation modeling procedure is applied to the examination of the antecedents of WOM. Findings The paper found that food quality, personal interaction quality, physical environment quality, and perceived value influence WOM behavior of customer in an indirect way through relationship quality. Practical implications This research conjectured that an understanding of factors that influence the tourist to talk each other about a given restaurant are worthy of additional research. Consequently, the study helps to understand how these factors can provide alternative sources of marketing to attract the long-term economic sustainability of restaurant industry in Iran. Originality/value To the authors’ knowledge, this research will be the first attempt to explore influential factors on WOM in restaurant industry focusing on the critical role of relationship quality. It is expected that researchers will find this research a contribution to the WOM literature, particularly in restaurant industry.

Purpose The purpose of this study is to empirically study the relationship between intellectual capital (IC) components (human, structural, and physical capitals) with the traditional measures of performance of the firm (profitability, productivity and market valuation) within the pharmaceutical sector of Iran. Design/methodology/approach The empirical data were drawn from pharma companies listed in the Iranian Stock Exchange (ISE), over the six‐year period of 2004 to 2009. The analysis of correlation, simple linear multiple regression and artificial neural networks (ANNs) were applied for analyzing any existing relationship between variables in the present study. Findings The analysis indicates that the relationships between the performance of a company's IC and conventional performance indicators are varied. The findings suggest that the performance of a company's IC can explain profitability but not productivity and market valuation in Iran. Also the empirical analysis found that physical capital (VACA) was the one which was seen to have the major impact on the profitability of the firms over the period of study, in addition the result of ANN method also confirmed findings of multiple regression. Practical implications There is an immediate need for policy makers and corporate managers wake up to the need to start disclosure of the IC of firms. IC measurement is of primary interest for top executives of pharmaceutical firms in Iran. Originality/value This is an initial and pioneering study to evaluate the IC and its relationship with the traditional measures of corporate performance in the Iranian pharmaceutical industry. The present study provides a new aspect of performance measurement for research‐based industries in emerging economies and would be a good topic for further research.

well as non-statisticians.In this book, Stephen Senn explored the critical role of statistics in making decisions related to medical care, such as resource allocation, drug licensing, and causality in disease in a stimulating way without using any formula, equations, or statistical jargon, which usually non-statisticians do not like.Sharing the stories, paradoxes, and puzzles to the readers, he covered important topics such as clinical trials, life tables, vaccines, smoking and lung cancer, and even the effectiveness of prayer.Given the prominence of medical statistics and public health data during the COVID-19 pandemic, understanding statistical concepts is crucial for interpreting and applying this information.But many people do not have a statistical background.Senn's second edition of the book, which is equally interesting as the first edition, provides a comprehensive and updated overview of medical statistics and their relevance to rational decision-making in an unambiguous and easily understandable language.The second edition has been updated to cover the developments in the last two decades.It includes new material on the challenges of COVID-19 and infectious disease modelling.While statistical methods have been contentious in the past, Senn presents an engaging and insightful account of the importance of statistics in medical care.Just to make it interesting, Senn has told interesting stories about famous statisticians like Karl Pearson, Thomas Bayes, Ronald Fisher and Francis Galton, etc.There are quite a few jokes and interesting stories to illustrate difficult concepts in the book.There are quite a few interesting quotes, stories, and anecdotes in each chapter which are relevant to that chapter and make it fascinating.There are 12 chapters in the second edition and there are notes to each chapter after that.Chapters are more or less disjoint and cover a wide range of topics like significance test, clinical trials, life tables, and survival analysis, mathematics of MMR and topics like the application of statistics in law and summarising of evidence.Since COVID-19 is still prevalent, author has suggested to start reading the book from Chapter 12, followed by Chapter 11 which covers the story of MMR.By the end of the book, readers will understand how probability reasoning is crucial in making informed medical decisions that impact their health and life.I will strongly recommend this book to statisticians as well as non-statisticians who are working in the area of public health or otherwise.

Abstract Artificial intelligence (AI) is influencing different aspects of human life. An AI‐powered technology, which has been recently released, is ChatGPT. It is a cutting‐edge technology that influences second/foreign language (L2) education. Although there is increasing research on the benefits and misfits of this chatbot in different disciplines, L2 education lacks a thorough investigation. To fill this lacuna, this phenomenographic study examined the perceptions of research‐active English as a Foreign Language (EFL) teachers regarding the potentials and pitfalls of ChatGPT for L2 learning, teaching, assessment, and research. To this end, a semistructured interview was held with 30 Iranian EFL teachers with varying educational backgrounds and AI integration experiences. The results of content and thematic analysis indicated that ChatGPT is a double‐edged sword that can both benefit and hurt these areas of L2 education. The most notable potentials were augmenting learner autonomy, providing personalized learning, reducing teachers’ teaching workload, designing assessment rubrics, and summarizing lengthy papers and theses to save L2 researchers’ time and energy. Concerning pitfalls, it was reported that ChatGPT might kill creativity and academic integrity, encourage cheating in online exams, spread fake and misinformation into the world of research, and cherish high‐tech plagiarism. Some practical suggestions are made to empower L2 educators and researchers to survive in the world of AI.