The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience.

This study aims to enhance economic growth forecasting in the United Arab Emirates (UAE) by implementing a Seq2Seq deep learning model with an attention-driven Long Short-Term Memory (LSTM) network. Traditional statistical models often fail to capture the complex temporal dependencies and nonlinear trends inherent in economic time series data. To address these limitations, this research employs a structured methodology, beginning with data collection from the World Bank, including macroeconomic indicators such as GDP growth, inflation, trade balance, investment flows, and employment rates. Preprocessing steps involve handling missing values, normalization, and feature engineering. The proposed Seq2Seq LSTM model utilizes an encoder-decoder structure with an attention mechanism to assign dynamic weights to critical time points, improving forecasting accuracy. The model is trained using the Adam optimizer and evaluated using RMSE, MAE, and MAPE metrics. Results demonstrate superior predictive performance compared to traditional approaches, with improved generalization on unseen data. Findings suggest that attention-enhanced deep learning models provide more reliable economic forecasts, aiding policymakers in decision-making. Future work should explore hybrid models, incorporate external economic shocks, and optimize hyperparameter tuning for further accuracy improvements.

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants extensively used in industrial and consumer applications. Their accumulation in European agricultural soils through industrial discharges, biosolid applications, and contaminated irrigation water poses an unprecedented threat to food security, soil health, and water quality. Despite extensive laboratory research, no full-scale, long-term validated PFAS soil remediation study exists, leaving critical gaps in mitigation strategies. Existing approaches–including mobilization, immobilization, and degradation techniques–have demonstrated effectiveness in controlled environments but lack real-world validation in dynamic agricultural settings. This study proposes an artificial intelligence (AI)-driven remediation framework that integrates real-time detection tools, predictive modeling, and adaptive remediation technologies to overcome these challenges. Unlike static remediation strategies, the proposed AI-assisted system dynamically optimizes remediation interventions based on contamination patterns, soil composition, and environmental conditions. Machine learning algorithms and statistical models enable precise contamination tracking, predictive PFAS migration modeling, and automated remediation decision-making, offering a scalable and responsive solution for sustainable agricultural management. This study underscores the urgent need for large-scale, policy-backed field trials to validate AI-driven PFAS remediation technologies, bridging the gap between scientific advancements and real-world implementation. By transitioning AI-assisted mitigation from theory to an adaptive, field-deployable framework, this research ensures scalable solutions for sustainable food security, environmental resilience, and long-term public health protection.

The COVID-19 outbreak has shown how urgently good models are needed to understand and project the dissemination of infectious diseases. Graph theory's excellent basis for presenting and analyzing complex networks helps one to grasp COVID-19 transmission dynamics. This work investigates the simulation and evaluation of the COVID-19 spread using multiple graph theory approaches. We utilize algorithms, including centrality assessments, community detection, and epidemic spreading models, to identify significant transmission channels and viable intervention sites; we also study the usage of network-building strategies to show relationships between individuals and communities. By incorporating real-world data with graph-based models, we demonstrate how these approaches could increase the accuracy of infection estimates and direct public health strategies. Moreover, the paper discusses the advantages and limitations of graph theory approaches within the framework of pandemic modeling, therefore leading the next direction of investigation to improve disease outbreak reactions. This work highlights the important contribution of graph theory to increase our understanding of COVID-19 dissemination and offers a scientific foundation for handling associated problems in upcoming public health-catastrophes.

Climate change has significantly impacted various sectors, including public health, and information has become an essential aspect of daily life. This research study aims to explore the informational and organizational behavior of employees in the healthcare sector, focusing on their perception of information literacy in a constantly changing environment. The study was conducted within the department of psychiatrics at the General Hospital of Katerini focusing on its workforce. The results showed that employees demonstrated a strong sense of duty and pleasure in their work and exhibited commendable information literacy. Primary sources of information used by employees included the internet, media outlets, colleagues’ insights, printed materials, and online search engines. Job satisfaction and information literacy are notably high, with individuals over 36 showing stronger commitment to their roles. Education plays a significant role, with secondary education graduates valuing the resources available in the work environment in enhancing employee engagement and performance.

Background: The integration of digital devices in health education has transformed learning experiences, offering instant access to information and enhancing collaboration. However, their misuse for non-academic purposes, such as social media and gaming, has led to significant classroom distractions, negatively impacting student focus and academic performance (AP). This study investigates the prevalence of digital distractions in classrooms and explores strategies to enhance student engagement while minimizing disruptions. Methods: A quantitative cross-sectional study was conducted using a Google Forms survey distributed to students in a selected academic institution. A convenience sampling method was employed, and data were analyzed using descriptive statistics. Results: The 51.2% of students reported using digital devices 1 to 3 times per class, primarily for non-academic activities such as texting (21.9%), social networking (14.1%), and checking the time (41.2%). The primary benefits of using digital devices were staying connected (29.2%), entertainment (19.8%), and alleviating boredom (20.4%). The most cited negative effects were reduced attention (68.6%), distraction of peers (38.5%), and missed instructions (30.3%). 66.3% of participants supported banning digital devices in classrooms, while 59.7% favored restricting non-academic use. Conclusion: Digital devices play a dual role in health education–they enhance learning while also contributing to distractions. To address this challenge, clear policies, digital literacy programs, and self-regulation strategies are essential for promoting responsible device use and improving classroom focus. Future research should explore adaptive strategies to balance digital engagement with AP.