محمد غلامی

Pilgrimage denotes a profound spiritual journey that cultivates personal transformation, resilience, and psychological well-being. Previous studies have explored the psychological effects of pilgrimage. However, the specific mechanisms through which these spiritual experiences foster enduring happiness still remain underexplored. This study aims to investigate how spiritual well-being enhances the joy of pilgrimage through the lens of eudaimonic well-being, drawing on the self-determination theory (SDT) and the principles of eudaimonic psychology. The study argues that spiritual well-being acts as a fundamental resource, enabling pilgrims to navigate various challenges on their journey ( e.g. , physical hardship and introspection), achieve self-actualisation, and ultimately experience deep existential satisfaction. Structural equation modelling (SEM) was employed to survey 152 Arbaeen pilgrims. The results indicated that spiritual well-being significantly influenced eudaimonic well-being, which in turn enhanced the joy derived from pilgrimage. The findings suggested that fulfilment associated with pilgrimage was not merely a fleeting emotional response but a deeply rooted psychological state arising from personal transformation, meaning-making, and spiritual elevation. This study contributes to the literature on religious tourism, positive psychology, and spirituality by highlighting the transformative power of pilgrimage as a pathway to human flourishing. It also provides both theoretical and practical frameworks for researchers, policymakers, and mental health practitioners seeking to understand and promote the psychological benefits of spiritual experiences.

Objective : This study tackles the reviewer assignment problem by proposing a model that optimizes reviewer-proposal matching based on thematic coverage, fairness, and expertise, while considering workload balance and team size constraints. The model incorporates practical constraints such as limits on the number of proposals each reviewer can handle and team composition requirements. This approach is especially relevant to institutions like academic conferences, journals, and funding organizations, aiming to enhance the integrity and efficiency of the review process. Methods : This study is classified as descriptive research with a practical orientation and relies on data collection through applied methods. The approach is grounded in mathematical modeling. Initially, the selected articles are grouped into clusters. Reviewers are then assigned to these clusters using a multi-objective binary integer programming model that incorporates all relevant criteria and constraints. To implement this model, 150 articles were selected through purposive sampling. The model was optimized using Python, employing both the branch-and-bound algorithm and a genetic metaheuristic algorithm to maximize the degree of reviewer-proposal matching within the proposed framework.  Results : The proposed model demonstrates strong practical relevance by closely reflecting real-world reviewer assignment challenges. By simultaneously optimizing thematic coverage, evaluation fairness, and reviewer expertise, the model captures the complexity of actual allocation scenarios. To validate its effectiveness, the model was solved using both the branch-and-bound algorithm and a genetic algorithm. The branch-and-bound method yielded an objective value of 177.349 in approximately one hour, while the genetic algorithm reached 120.35 in just seven minutes. Although branch-and-bound guarantees optimality, its longer runtime makes it less practical for larger datasets. Given the similarity of results, the genetic approach is a reliable and scalable alternative. Conclusion : This study introduces a new allocation strategy and mathematical model for reviewer assignment, addressing often-overlooked factors such as reviewer expertise, grouping, and conflicts of interest. By integrating these elements, the proposed model better reflects real-world conditions. Future work is encouraged to expand on these findings with new frameworks and methods.

FinTech is the solution that strengthens the way of financial communication and if we apply this technology, it will provide us with new methods of performing smarter, more agile and wider financial services. The purpose of this study is to investigate and analyze the factors affecting the implementation of FinTech in Iran's banking industry using structural equation modeling to provide comprehensive solutions of removing barriers and development of this industry in the Iranian banking industry by fully understanding the current situation. The present article is of descriptive-analytical type and data collection has been done through library studies and questionnaires. The statistical sample of the study was 12147 personnel working in Iran's banks in 1400. Due to the size of the population, Cochran’s method and random sampling method were used to determine the sample size. To analyze and classify the extracted data, a questionnaire and a test of the main hypothesis and sub-hypotheses and structural equation modeling were used using smart PLS statistical software. The obtained results confirm the main hypothesis and the sub-hypotheses of the research. The results indicated that in the banking sector, there is a need for legalization and harmonization with upstream laws, as well as the need for infrastructure and tools that are very important in adopting strategies to implement FinTech in Iran's banking industry, in order to make transparency, reduce costs, provide high-speed services, and move towards a smart economy.