درخت حوزه‌های تخصصی

Objectives: This study aims to examine the effects of financial anxiety and financial optimism on life satisfaction among accounting students. Given the increasing financial pressures faced by university students, understanding the psychological dimensions of financial behavior is essential for improving their overall well-being. Methodology/Design/Approach: The statistical population comprised undergraduate accounting students, from which 113 participants were selected through a simple random sampling method. Data were collected using standardized questionnaires and analyzed through Pearson correlation and simultaneous linear regression to test the hypothesized relationships. Findings: The results indicate a significant negative relationship between financial anxiety and life satisfaction, and a significant positive relationship between financial optimism and life satisfaction. Furthermore, both financial anxiety and financial optimism jointly and significantly predict students’ life satisfaction. Innovation: This study contributes to the literature by integrating psychological and financial perspectives to explain students’ life satisfaction. The findings emphasize the need for educational institutions to incorporate financial management training and psychological counseling into academic programs to promote financial well-being and emotional resilience among students.

This study aims to present a novel generative AI-driven system for hyper-personalized health monitoring. Dynamic data processing, predictive modeling, and flexible learning improve real-time health evaluations. By combining weighted feature aggregation, iterative least squares estimation, and selective feature extraction, the suggested strategy makes predictions that are more accurate while using less computer power. Abnormality detection methods like adaptive thresholding and Kalman filtering provide accurate health monitoring. Attention, gradient-based optimization, and sequence learning improve health trend forecasts as the model improves. Generative AI-driven wearables outperform conventional and AI-based alternatives in many key performance tests. These evaluations include prediction accuracy (94%), real-time monitoring efficiency (93%), adaptability (92%), data integration quality (95%), and system reaction time (90 ms). These devices are safer (96%), have longer battery life (32 hours), and are simpler, more comfortable, and scalable. The results suggest that creative AI can transform personal healthcare into something more adaptable, safe, and affordable. Generative AI-powered smart gadgets are the most sophisticated means to monitor health in real time and deliver individualized, data-driven medical treatment. Future research will concentrate on improving prediction models and developing AI-driven modification approaches to make them more effective in additional healthcare scenarios.

Objective : This research aims to propose a multi-criteria decision-making model for ranking hospital departments. The primary purpose of this model is to assist managers in the optimal allocation of limited resources, thereby reducing costs while increasing patient satisfaction. The ranking results help managers in decision-making processes such as equipment development, staff training, and addressing patient complaints. Methods : This study evaluated the performance of five hospital departments (Emergency, Ophthalmology, Cardiovascular, Infectious, and Neurology) in Shiraz, Iran, using the fuzzy DEMATEL-MARCOS multi-criteria decision-making method. Firstly, criteria were prioritized using the fuzzy DEMATEL method, after which hospital departments were ranked using the fuzzy MARCOS method. A sensitivity analysis was conducted to validate the results. Results : Performance metrics for the hospital departments were identified based on the Lean, Agile, Resilient, Green, and Sustainable (LARGS) paradigm. The results revealed that patient satisfaction and job satisfaction had the most substantial influence on performance, while reducing excess transportation and over-processing had the least impact. Utilizing the fuzzy MARCOS method, the hospital departments were ranked according to their overall desirability. The sensitivity of these rankings was assessed by adjusting the weights of the criteria. A comparative analysis with four other fuzzy methods (ARAS, COCOSO, EDAS, and WASPAS) confirmed that the fuzzy MARCOS method was the most effective tool for prioritizing hospital departments. Conclusion : The fuzzy MARCOS results indicated that the “Infectious Department” performed well, while the “Ophthalmology Department” required improvement. Enhancing the “Infectious Department” hinged on better staff training, cost reduction, and safe waste management. This research introduces a novel approach using the fuzzy DEMATEL-MARCOS model, enabling hospitals to assess performance through modern methodologies, such as Lean, Agile, Resilient, Green, and Sustainable, even in uncertain conditions