Al Maaref University

The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM-IRC, an epidemiology-aware hyper-heuristic framework developed to optimize room-patient matching by minimizing the risk of nosocomial infections, reducing travel and specialty mismatch costs, and promoting equitable nurse workload distribution. A mixed-integer linear programming model is formulated to capture infection transmission probabilities, isolation and cohorting requirements, and multi-ward capacity constraints. On top of this model, a bio-inspired hyper-heuristic adaptively selects and refines low-level heuristics, including cohort-first greedy allocation, risk-gradient swaps, and pathogen-aware local MILP refinement, on the basis of contextual epidemiological indicators and reinforcement learning. The framework was validated using a real-world dataset obtained from a tertiary hospital in Lebanon, comprising 142 anonymized patient admissions, 35 rooms, and six nursing teams. Results demonstrate that HRPM-IRC consistently reduces modeled infection risk and workload imbalance by up to forty percent compared to conventional assignment heuristics while maintaining near-real-time decision-making capabilities suitable for dynamic hospital operations. These findings underscore the effectiveness of epidemiology-aware hyper-heuristics in enhancing hospital resilience, improving infection prevention, and supporting fair resource utilization in data-limited healthcare environments typical of Lebanon and other middle-income countries.

It was recently proved that every claw-free cubic graph admits a (1, 1, 2, 2)-packing coloring-that is, its vertex set can be partitioned into two 1-packings and two 2-packings. This result was established by Bre & scaron;ar et al. (2025). In this paper, we provide a simpler and shorter proof. (c) 2026 Published by Elsevier B.V.

Achieving Net-Zero Energy Building (NZEB) performance through retrofitting requires identifying optimal measures that effectively enhance energy efficiency. Determining these optimal retrofit strategies typically involves running thousands of building energy simulations, which imposes a substantial computational burden. To address this challenge, a novel machine learning-based framework is proposed to optimize retrofit strategies for NZEBs under future climate change scenarios. A Non-Dominated Sorting Genetic Algorithm (NSGA-III) is employed to minimize both annual energy consumption and the Predicted Percentage of Dissatisfied (PPD), while simultaneously ensuring net-zero energy balance, thereby generating a Pareto front of optimal solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-front solutions and identify the most favorable retrofit scenario. The results show that the proposed framework reduces optimization time by at least a factor of two compared with simulation-only optimization. Leveraging these computational savings, the framework evaluates a suite of passive and renewable measures across multiple future timeframes to capture the influence of climate change on retrofit performance. The findings indicate that achieving NZEB under future climate conditions requires higher levels of thermal insulation and greater renewable integration than under present-day conditions. Under the Shared Socioeconomic Pathways (SSP) framework, optimal insulation levels in the fossil fuel-dependent scenario are lower than in the sustainable scenario by up to 18% in C-type (warm temperate), 12% in D-type (snow), and 13% in E-type (polar) climates. The combined retrofit measures can reduce annual energy consumption by up to 80% and lower PPD by as much as 67% compared to the base case.

Smart farming increasingly relies on wireless sensor networks for environmental monitoring; however, collecting data from distributed sensors remains challenging in large rural fields where fixed gateways are infeasible. This paper proposes CRAFT (Cooperative Relay-Aware Flight Tasking), a decentralized dynamic task reallocation framework for multi-UAV LoRaWAN-based data muling. CRAFT partitions the monitored area into relay-assisted zones and enables unmanned aerial vehicles (UAVs) to cooperatively and adaptively reassign sensing tasks during flight based on workload disparity and received signal strength indication (RSSI). A model-based performance evaluation, derived from analytical mobility and communication assumptions, indicates that CRAFT reduces mission completion time by approximately 23%, significantly improves energy balance across UAVs, and maintains high packet delivery reliability compared to static partitioning and greedy reassignment baselines. These results highlight the potential of lightweight cooperative control for scalable and energy-efficient UAV-assisted LoRaWAN networks in smart farming applications.

IntroductionCommunication infrastructures are often severely disrupted during natural disasters, armed conflicts, and large-scale humanitarian crises, hindering coordination among emergency responders and affected populations. Rapidly deployable wireless communication systems, including mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), offer a viable solution for restoring connectivity in such environments. However, these networks face significant challenges, including dynamic topology changes, limited energy resources, unreliable communication links, and fluctuating traffic demands. Therefore, adaptive and sustainable communication management mechanisms are required to ensure resilient network performance under crisis conditions.MethodsThis study proposes a hyper-heuristic-driven framework for sustainable and crisis-resilient wireless communication systems. The framework employs a high-level hyper-heuristic controller that dynamically selects appropriate low-level communication heuristics from a heterogeneous pool comprising constructive, improvement, perturbation, and reconstructive strategies. A multi-objective optimization model is formulated to simultaneously minimize energy consumption, communication delay, and packet loss while maximizing network reliability. The heuristic selection process is guided by real-time network state indicators, including residual node energy, link quality, congestion level, and node density. The proposed framework is evaluated through extensive simulations conducted in the NS-3 network simulator under realistic disaster-response scenarios.ResultsSimulation results demonstrate that the proposed hyper-heuristic framework consistently outperforms conventional routing and communication strategies. Specifically, the approach increases network lifetime by up to 15%, improves packet delivery ratio by approximately 6‐10%, and reduces end-to-end communication delay by nearly 20%. Furthermore, significant improvements in overall energy efficiency are observed, contributing to prolonged network operation in resource-constrained environments.DiscussionThe findings indicate that hyper-heuristic optimization provides an effective mechanism for adaptive network management in rapidly changing crisis environments. By dynamically selecting communication strategies according to current network conditions, the framework enhances both resilience and sustainability while maintaining high communication performance. The proposed approach offers a practical and scalable solution for emergency response networks, highlighting the potential of hyper-heuristic methodologies to support reliable and energy-efficient wireless communications during disaster recovery and humanitarian operations.