درخت حوزه‌های تخصصی

Objective : Changing energy consumption habits is fundamental to promoting sustainable development. This study simulates energy consumption patterns in shared spaces at the University of Tehran using a dynamic model, focusing on residents’ behaviors to reduce electricity use and improve system efficiency. Methods : To address the complex energy consumption system in student dormitories—shaped by behavioral, technical, environmental, and policy-related factors—a system dynamics approach was adopted. Experts in energy management and systems modeling contributed through collaborative meetings and interviews. Data were gathered from national institutions and questionnaires covering 2011–2021. Using Vensim DSS 6.4E, a dynamic model was built and simulated for 2021–2041. Sensitivity analysis helped identify effective energy management policies based on residents’ behavior, and the model’s outputs were analyzed to evaluate key variables.  Results : The model identified four key strategies for managing electricity consumption based on behavioral factors: management through economic investments, residents’ energy-saving behaviors, the actions and behaviors of dormitory staff and administrators, and increased investment in building infrastructure and equipment upgrades. Subsequently, each strategy's individual and combined implementation was examined and compared regarding its impact on the target variables. Combining the first and third strategies was ultimately identified as the most effective approach to electricity consumption management, considering the residents' behavioral patterns. Conclusion : The study shows that combining economic investments with behavioral strategies is the most effective way to manage electricity consumption and improve environmental and social outcomes.

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.