Health misinformation across digital platforms has emerged as a critical fast-growing challenge to global public health, undermining trust in science and contributing to vaccine hesitancy, treatment refusal and heightened health risks. In response, this study introduces Impact, a novel simulation framework that integrates agent-based modeling (ABM) with large language model (LLM) integration and retrieval-augmented generation (RAG) to evaluate and optimize health communication strategies in complex online environments. By modeling virtual populations characterized by demographic, psychosocial, and emotional attributes, embedded within network structures that replicate the dynamics of digital platforms, the framework captures how individuals perceive, interpret and propagate both factual and misleading health messages. Messages are enriched with evidence from authoritative medical sources and iteratively refined through sentiment analysis and comparative testing, allowing the proactive pre-evaluation of diverse communication framings. Results demonstrate that misinformation spreads more rapidly than factual content, but that corrective strategies, particularly empathetic and context-sensitive messages delivered through trusted peers, can mitigate polarization, enhance institutional trust and sustain long-term acceptance of evidence-based information. These findings underscore the importance of adaptive, data-driven approaches to health communication and highlight the potential of simulation-based methods to inform scalable interventions capable of strengthening resilience against misinformation in digitally connected societies.

This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems.