مطالب

Objective : This paper introduces a groundbreaking optimization-simulation model, a novel approach that promises to revolutionize credit scoring and loan optimization for banks. Methods : The proposed approach follows a three-stage framework: data preparation, credit scoring, and optimization simulation. In the data preparation stage, corporate client data, including bank loan information and financial statements, has been collected and processed to define and calculate relevant features. The credit scoring stage involved meticulous feature selection using the correlation method, followed by the rigorous training and testing of five classification methods: logistic regression (LR), K-nearest neighbors (KNN), artificial neural network (ANN), adaptive boosting (AdaBoost), and random forest (RF). Model performance has been evaluated using accuracy, F1-score, and area under the curve (AUC) to identify the most effective classifier. In the optimization-simulation stage, the Memetic Algorithm (MA) has been utilized to optimize loan characteristics, including loan size, interest rate, and repayment period, while minimizing the rate of loan defaults. Additionally, this stage incorporated the pre-trained credit scoring model to estimate the impact of loan characteristics on default probabilities.  Results : A case study was conducted using data from 1,000 corporate clients of Bank Tejarat. The optimization-simulation approach has successfully reduced the loan default rate from 33% to below 5%, a significant achievement that underscores its potential to mitigate banks' credit risk. This shows the effectiveness of the proposed method in reducing credit risk for banks. Additionally, the AdaBoost technique achieved the best performance among the credit assessment models. Conclusion : The optimization-simulation approach combines determining the optimal loan specifications with the credit assessment process. This approach considers the impact of loan characteristics on the likelihood of customer default and utilizes this information to reduce banks' credit risk

Objective : In the Fourth Industrial Revolution, advanced technologies are revolutionizing supply chains by enhancing data collection, processing, and analysis across material, financial, and information flows. This shift enables businesses to adopt intelligent supply chain processes with unprecedented efficiency. The integration of intelligent strategies and process-oriented approaches, supported by tools like Intelligent Business Process Management Systems (iBPMS), holds transformative potential for supply chain management, paving the way for Intelligent Supply Chain Management (iSCM) models. This study aims to identify the key dimensions and sub-dimensions of intelligent supply chain processes within the context of Industry 4.0 technologies. Methods : The research employs a meta-synthesis methodology, systematically reviewing peer-reviewed literature and international publications from 2016 to 2025. Following strict meta-synthesis protocols, the study involved keyword screening, thematic evaluation, and iterative refinement, resulting in a curated selection of 62 high-impact journal articles and 4 seminal books. These sources underwent rigorous validation to ensure scholarly relevance before analysis.  Results : The findings identified 117 open codes related to intelligent supply chain processes, which were consolidated into 18 core codes and further classified into five key dimensions: (1) Intelligent Supply Chain Management (covering SCM and intelligent procurement); (2) Process Intelligent Automation (including automation approaches, intelligent processes, and equipment); (3) Process Management (focused on process-oriented approaches, systems, and modeling); (4) Technological Infrastructure (encompassing emerging technologies, ICT infrastructure, software maturity, and robotics); and (5) Macro & Structural Dimensions (addressing managerial, industrial, e-business, market, and organizational factors). Conclusion : The study concludes that Industry 4.0 technologies—such as IoT, AI, blockchain, robotics, and big data analytics—facilitate advanced data-driven supply chain management. When integrated with iBPMS, these innovations enhance efficiency, agility, and end-to-end visibility, establishing a foundation for next-generation intelligent supply chains

The development of sports infrastructure is crucial for promoting sports culture in society; however, this process is challenged by a lack of financial resources. Donors can complement government resources by financing and accelerating sports projects, but utilizing their capacity faces obstacles. The present study aimed to identify barriers to utilizing donors' capacity in developing sports infrastructure in Iran. This study employed a qualitative content analysis method using the thematic analysis technique, which is exploratory in nature. The research population included sports experts, managers, and individuals active in the field of sports philanthropy. Sampling was conducted through purposive and snowball methods, reaching theoretical saturation after 17 interviews. Data were collected via semi-structured interviews conducted through direct communication and telephone calls. To assess validity and reliability, acceptability, transferability, confirmability, and test-retest methods were used. Data analysis yielded 52 open codes, categorized into 17 sub-themes and 6 main themes: organizational barriers, planning and management barriers, legal barriers, cultural barriers, economic barriers, and awareness and educational barriers. The present findings can serve as a crucial guide for sports organizations and the government in identifying barriers to utilize donors' capacity in developing sports infrastructure, thereby facilitating improved actual performance in this domain.

In recent years, the integration of artificial intelligence (AI) across various industries has significantly transformed traditional practices, particularly in marketing. As sports marketing increasingly turns to AI-generated content, understanding the factors that influence consumer attitudes and purchase intentions becomes crucial for the effective utilization of this innovative technology. This article examines the impact of two key factors— observability and compatibility—of AI-generated sports marketing content on consumer attitudes and their purchase intentions. In today’s competitive world, understanding how this novel technology affects consumer behavior and how they interact with marketing content is of particular importance. Data was collected through a questionnaire using simple random sampling from sports consumers (n=300). A quantitative research method and structural equation modeling were employed, and the data were analyzed using SEM-Smart PLS TM3 software. The results indicated that observability and compatibility significantly influence consumer attitudes, which in turn affect purchase intentions. The robustness of the research model was confirmed through R2 and β values, emphasizing the importance of using innovative technologies in sports marketing. This article is one of the few studies focusing on the effects of AI-generated content in the sports industry and offers suggestions for improving sports marketing strategies.

The evaluation of students' writings and the allocation of scores are traditionally time-intensive and inherently subjective, often resulting in inconsistencies among human raters. Automated essay scoring systems were introduced to address these issues; however, their development has historically been resource-intensive, restricting their application to standardized tests such as TOEFL and IELTS. Consequently, these systems were not readily accessible to educators and learners. Recent advancements in Artificial Intelligence (AI) have expanded the potential of automated scoring systems, enabling them to analyze written texts and assign scores with increased efficiency and versatility. This study aimed to compare the efficacy of an AI-based scoring system, DeepAI, with human evaluators. A quantitative approach, grounded in Corder's (1974) Error Analysis framework, was used to analyze approximately 200 essays written by Persian-speaking EFL learners. Paired sample t-tests and Pearson correlation coefficients were employed to assess the congruence between errors identified and scores assigned by the two methods. The findings revealed a moderate correlation between human and AI scores, with AI diagnosing a greater number of errors than human raters. These results underscore the potential of AI in augmenting writing assessment practices while highlighting its pedagogical implications for language instructors and learners, particularly in evaluating the essays of EFL students.