Search Results (12,822)

Existing metaheuristic approaches often struggle to maintain an effective exploration–exploitation balance and are prone to premature convergence when addressing highly conflicting time–cost–safety–risk trade-off problems (TCSRTPs) under complex construction project constraints, which can adversely affect project productivity, safety, and the provision of decent jobs in the construction sector. To overcome these limitations, this study introduces a hybrid metaheuristic called the Q-Learning Inspired Mountain Gazelle Optimizer (QL-MGO) for solving multi-objective TCSRTPs in construction project management, supporting the delivery of resilient infrastructure and resilient building projects. QL-MGO enhances the original MGO by integrating Q-learning with an opposition-based learning strategy to improve the balance between exploration and exploitation while reducing computational effort and enhancing resource efficiency in construction scheduling. Each gazelle functions as an adaptive agent that learns effective search behaviors through a state–action–reward structure, thereby strengthening convergence stability and preserving solution diversity. A dynamic switching mechanism represents the core innovation of the proposed approach, enabling Q-learning to determine when opposition-based learning should be applied based on the performance history of the search process. The performance of QL-MGO is evaluated using 18- and 37-activity construction scheduling problems and compared with NDSII-MGO, NDSII-Jaya, NDSII-TLBO, the multi-objective genetic algorithm (MOGA), and NDSII-Rao-2. The results demonstrate that QL-MGO consistently generates superior Pareto fronts. For the 18-activity project, QL-MGO achieves the highest hypervolume (HV) value of 0.945 with a spread of 0.821, outperforming NDSII-Rao-2, MOGA, and NDSII-MGO. Similar results are observed for the 37-activity project, where QL-MGO attains the highest HV of 0.899 with a spread of 0.674, exceeding the performance of NDSII-Jaya, NDSII-TLBO, and NDSII-MGO. Overall, the integration of Q-learning significantly enhances the search capability of MGO, resulting in faster convergence, improved solution diversity, and more reliable multi-objective trade-off solutions. QL-MGO therefore serves as an effective and computationally efficient decision-support tool for construction scheduling that promotes safer, more reliable, and resource-efficient project delivery. Full article

The continued growth of international maritime trade has driven automated container terminals (ACTs) to pursue more efficient operational management strategies. In practice, the horizontal yard layout in ACTs significantly enhances transshipment efficiency. However, the more complex horizontal transporting system calls for an effective approach to enhance automated guided vehicle (AGV) scheduling. Considering AGV charging and path conflicts, this paper proposes a multi-agent reinforcement learning (MARL) approach to address the AGV dispatching and path planning (VD2P) problem under a horizontal layout. The VD2P problem is formulated as a Markov decision process model. To mitigate the challenges of high-dimensional state-action space, a multi-agent framework is developed to control the AGV dispatching and path planning separately. A mixed global–individual reward mechanism is tailored to enhance both exploration and corporation. A proximal policy optimization method is used to train the scheduling policies. Experiments indicate that the proposed MARL approach can provide high-quality solutions for a real-world-sized scenario within tens of seconds. Compared with benchmark methods, the proposed approach achieves an improvement of 8.4% to 53.8%. Moreover, sensitivity analyses are conducted to explore the impact of different AGV configurations and charging strategies on scheduling. Managerial insights are obtained to support more efficient terminal operations. Full article

The retrofit of vacant buildings into sustainable integrated medical and elderly care facilities represents an important pathway for promoting urban regeneration and addressing population aging challenges. However, conflicts of interest among key stakeholders frequently compromise the quality of retrofit and long-term operational sustainability. To address this issue, this study develops a tripartite evolutionary game model comprising investors, builders, and operators to examine the behavioral evolution and cooperative mechanisms of these stakeholders across the investment, construction, and operation phases. Simulations were conducted based on a real-world retrofit project in Lanzhou, China, and the results suggest that: (1) Policy preference or reputational incentives alone appear insufficient to maintain cooperation, whereas their integration with economic incentives can effectively enhance the stability of cooperation among the three parties. (2) Builders exhibit higher sensitivity to penalties than operators, underscoring the pivotal role of the construction phase in ensuring retrofit quality. (3) When investors shift their role from short-term compliance regulation to long-term governance, it is more conducive to promoting operators to provide high-quality services in the long run. This paper proposes several suggestions and countermeasures, to provide practical guidance for the multi-party collaborative governance and sustainable operation of integrated medical and elderly care retrofit projects in China under the background of urban renewal. Full article

As the adoption of electric vehicles increases, hybrid energy storage systems (HESS) combining batteries and supercapacitors mitigate the conflict between high energy capacity and power demand, particularly during acceleration and transient loads. However, frequent current fluctuations accelerate battery degradation, reducing long-term performance. This study presents a multi-objective Whale–Particle Swarm Optimization Algorithm (MOWPSO) for tuning the control parameters of a HESS composed of a lithium-ion battery and a supercapacitor. The proposed full-active configuration with dual bidirectional DC converters enables precise current sharing and independent regulation of energy and power flow. The optimization framework minimizes four objectives: mean battery current amplitude, cumulative aging index, final state-of-charge deviation, and an auxiliary penalty term promoting consistent battery–supercapacitor cooperation. The algorithm operates offline to identify Pareto-optimal controller settings under the Federal Test Procedure 75 cycle, while the selected compromise solution governs real-time current distribution. Robustness is assessed through multi-seed hypervolume analysis, and results demonstrate over 20% reduction in battery aging and approximately 25% increase in effective cycle life compared to battery-only, rule-based and metaheuristic algorithm strategies control. Cross-cycle validation under highway and worldwide driving profiles confirms the controller’s adaptability and stable current-sharing performance without re-tuning. Full article

Vitamin D, traditionally regarded as a nutrient, is increasingly recognized as a pharmacologically active secosteroid with pleiotropic effects extending beyond calcium homeostasis and bone integrity. Together with vitamin K2, it participates in the fine-tuning of mineral metabolism and vascular health, potentially modulating cardiometabolic risk through intertwined endocrine and paracrine pathways. Despite widespread fortification and supplementation, vitamin D deficiency remains a major global health concern, driven by limited sun exposure, obesity, and metabolic dysfunction. Observational and mechanistic studies consistently link low serum 25(OH)D concentrations with hypertension, insulin resistance, heart failure, and increased cardiovascular mortality. At the molecular level, vitamin D exerts pharmacological actions—modulating the renin–angiotensin–aldosterone system, exerting anti-inflammatory and antifibrotic effects, and influencing endothelial and cardiomyocyte signaling. While experimental and epidemiological evidence suggests potential cardiovascular benefits, large randomized controlled trials (RCTs) provide conflicting results, particularly regarding hypertension and heart failure. However, these often-neutral results do not preclude a targeted action. On the contrary, clinical efficacy is strongly dependent on baseline deficiency status and the presence of metabolic cofactors. In this context, high-dose supplementation of Vitamin D, in combination with Vitamin K2 to prevent vascular calcification, elevates the supplement to a genuine pharmacological agent, with a distinct therapeutic potential for modulating cardiometabolic risk in selected patient subgroups. Emerging evidence supports the concept that vitamin D, when appropriately dosed and combined with K2, may act more as a low-potency pharmacological modulator than a simple nutritional supplement. This review synthesizes current mechanistic, observational, and interventional evidence, aiming to clarify whether vitamin D should be reclassified—from a micronutrient to a pharmacologically relevant agent—in cardiometabolic prevention and therapy, proposing a paradigm shift toward personalized and targeted dosing strategies, characteristic of precision pharmacology. Full article

Remaining Useful Life (RUL) prediction for aeroengines represents a core challenge in Prognostics and Health Management (PHM), with significant implications for condition-based maintenance, operational cost reduction, and flight safety enhancement. Current deep learning-based approaches encounter three major limitations when handling multi-source sensor data: conventional convolution operations struggle to model heterogeneous sensor feature distributions, leading to computational redundancy; simplistic multimodal fusion strategies often induce semantic conflicts; and high model complexity hinders industrial deployment. To address these issues, this paper proposes a novel Partial Convolution Attention-enhanced CNN-LSTM Network (PALC-Net). We introduce a partial convolution mechanism that applies convolution to only half of the input channels while preserving identity mappings for the remainder. This design retains representational power while substantially lowering computational overhead. A dual-branch feature extraction architecture is developed: the temporal branch employs a PConv-CNN-LSTM architecture to capture spatio-temporal dependencies, while the statistical branch utilizes multi-scale sliding windows to extract physical degradation indicators—such as mean, standard deviation, and trend. Additionally, an adaptive fusion module based on cross-attention is designed, where heterogeneous features are projected into a unified semantic space via Query-Key-Value mappings. A sigmoid gating mechanism is incorporated to enable dynamic weight allocation, effectively mitigating inter-modal conflicts. Extensive experiments on the NASA C-MAPSS dataset demonstrate that PALC-Net achieves state-of-the-art performance across all four subsets. Notably, on the FD003 subset, it attains an MAE of 7.70 and an R² of 0.9147, significantly outperforming existing baselines. Ablation studies validate the effectiveness and synergistic contributions of the partial convolution, attention mechanism, and multimodal fusion modules. This work offers an accurate and efficient solution for aeroengine RUL prediction, achieving an effective balance between engineering practicality and algorithmic sophistication. Full article

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning. Full article

Considering the complexity and hazardous nature of construction jobsites, selecting the effective safety risk control strategies is crucial to prevent accidents, protect labor crews, and achieve project objectives related to cost, schedule, and quality in the construction project. However, the evaluation of different safety strategies involves multiple conflicting criteria and uncertain expert judgments, making it a complex multi-criteria decision-making (MCDM) problem. To address this problem, this study develops a fuzzy-integrated MCDM framework that combines two methods: Fuzzy Analytic Hierarchy Process (FAHP), which systematically captures the relative importance of safety criteria under uncertainty, and ELECTRE III, which ranks alternative strategies by modeling preferences and veto conditions, reflecting real-world “non-compensatory” safety logic. FAHP determines criterion weights based on expert judgments, while ELECTRE III evaluates and ranks alternative safety strategies. The framework is validated through a piping construction case study, where it successfully identified the optimal safety plan. A sensitivity analysis is conducted to confirm the robustness of results, and comparative tests with other MCDM methods further support its reliability. Therefore, the proposed fuzzy-integrated framework offers an effective approach for evaluating safety risk control strategies, enhancing both safety and overall project performance, and advancing systematic safety management in the construction industry. Full article

Dempster’s combination rule in Dempster–Shafer theory is a powerful and effective tool for multi-sensor data fusion. However, counterintuitive results are possible under the condition of a high conflict between pieces of evidence. This study demonstrates that existing conflict measurements cannot prevent such results and, thus, proposes a quantitative conflict measurement based on the concept of essential conflict. This work analyzes two characteristics of the essential conflict, namely belief absolutization and uncorrectable assertions. In addition, considering the desirable properties of the measurement, this study demonstrates that the measurement of essential conflict can reveal the essence of counterintuitive results in Dempster’s combination process. Finally, properties and examples are used to validate the proposed measurement. Full article

Applications of Innovative Air Mobility (IAM) place high demands on the safe coordination of multiple UAVs and UAV-tailored takeoff and landing pads to mitigate unforeseen adverse effects. However, existing modeling approaches for multi-UAV flight operation often provide neither formal correctness guarantees nor effective mechanisms for maintaining cyber–physical consistency. To address these limitations, this paper proposes a bigraph-based digital twin framework that unifies modeling, execution, and synchronization for the management of landing operations involving multiple UAVs. Leveraging Bigraphical Reactive Systems (BRS), the framework employs a bigrid-based spatial model to formally represent UAV–pad occupancy constraints and to enforce one-to-one pad assignments via reaction rules, supporting formal proofs of safety properties. The model is linked to physical execution through modular APIs and a state-machine-based control service, enabling runtime cyber–physical synchronization. The formal specification is verified through model checking, which exhaustively explores the solution space (i.e., UAV behaviors in abstracted environments) to identify bigraph-algebraic solutions that guarantee conflict-free landings across different pad configurations. The framework is instantiated on the Crazyflie platform, demonstrating its ability to bridge formal modeling and physical execution while maintaining safety, scalability, and robustness in operational scenarios involving multiple UAVs. Full article

Collaborative multi-UAV swarms are central to many missions. This review covers the most recent two years. It organizes the literature with a scenario-aligned taxonomy. The taxonomy has 12 cells (Path/Distribution/Coverage × offline/online × static/dynamic). Nine cells are well populated and analyzed. For each, representative techniques, reported limitations, and scenario-appropriate use are summarized. Cross-scenario trade-offs are made explicit. Key examples include scalability vs. energy efficiency and centralized vs. decentralized (hybrid) architectures. The review also links offline pre-planning to online execution through architecture choices, digital-twin validation, and safety-aware collision avoidance in cluttered airspace. Unlike prior algorithm-centric or bibliometric surveys, this work applies a scenario-conditioned taxonomy, ties best-suited method families to each populated cell, and surfaces reported limitations alongside trade-offs. The result is deployment-oriented guidance that maps methods to mission context. Finally, five near-term priorities are highlighted: (i) compute-aware real-time adaptivity on resource-constrained platforms; (ii) scalable multi-objective scheduling with coupled motion and cooperative control; (iii) bandwidth-aware, conflict-resilient intra-swarm communication with reliability guarantees; (iv) certifiable planning for dense urban low-altitude corridors; and (v) energy-aware, hierarchical planners that couple offline pre-planning with online replanning. Full article

Rapid integration of renewable energy sources poses a serious problem to the functionality of microgrids since they are characterized by underlying uncertainties and variability. This paper proposes a multi-stage approach to energy management to overcome these issues in a virtual power plant that combines heterogeneous microgrids. The solution is based on multi-agent deep reinforcement learning to coordinate internal energy pricing, microgrid scheduling, and virtual power plant-level energy storage system management. The proposed model autonomously learns the optimal dynamic pricing strategies based on load and generation dynamics, which is efficient in dealing with operational uncertainties and maintaining microgrid privacy due to its decentralized structure. The efficiency of the proposed solution is tested on comparative simulations based on real-world data, which prove the superiority of the framework to the traditional operation modes, which are isolated microgrids and the energy sharing scenarios. The findings prove that the suggested solution has a dual beneficial impact on both virtual power plant operators and involved microgrids, as it leads to profit enhancement and, at the same time, system stability. This process facilitates the successful balancing of conflicting interests among the stakeholders at a time when the operation is low-carbon. The study offers an overall solution to dealing with complicated multi-microgrids and brings substantial changes in the integration of renewable energy, as well as the distributed management of energy resources. The framework is a scalable model that can be used in the future perspective of power systems with high-renewable penetration to address both economic and operational issues of the contemporary energy grids. Full article

Vitamin D is well established for its skeletal effects, being a cornerstone of several endocrine disorders. In recent years, it has come under investigation as a potential disease-modifying drug in several endocrine disorders through its immune modulatory and anti-tumorigenic action, particularly in thyroid disease, gynecologic disorders, and general fertility. Vitamin D supplementation is well established in the treatment of osteoporosis, osteomalacia, hypoparathyroidism, and primary hyperparathyroidism. In autoimmune thyroid disease, there is a negative correlation between 25(OH)D3 levels and prevalence. Currently available data are inconclusive on supplementation as a disease-modifying treatment. In Hashimoto’s thyroiditis, while some found improved thyroid function, a decline in progression, and antibody titers, these findings were not consistent, and some found no improvements. Painless postpartum thyroiditis severely lacks evidence. Interventional studies failed to demonstrate benefits in Graves’ disease. The literature consistently reports lower vitamin D levels in infertility, polycystic ovarian syndrome (PCOS), and endometriosis. In PCOS, data suggest that vitamin D supplementation is beneficial; however, results in exact benefits vary and there is no consensus on dosing. Current guidelines support supplementation as part of preconception nutritional care. In general, for female infertility and endometriosis, the results are conflicting, with a lack of high-quality evidence. The literature suggests there is a possible benefit regarding sperm motility, but not in testosterone levels for males. In conclusion, while in vitro studies and animal models are promising, the available evidence is often contradictory, with high heterogeneity in study designs and populations. Our paper highlights the need for further high-quality research to resolve current controversies. Full article

Traditional recommendation systems are largely built upon the “rational-agent” assumption, representing user preferences as static numerical vectors while neglecting the pivotal role of emotions in decision-making. However, according to the dual-system theory in cognitive psychology, human decisions are jointly governed by two interacting subsystems: a rational system responsible for deliberate reasoning and an affective system driven by emotion and intuition. Conflicts between these two systems often lead to inconsistencies between users’ preferences and emotional experiences in real-world recommendation scenarios. To address this challenge, we propose an Emotion-Enhanced Dual-Agent Collaborative Framework (EDACF) that explicitly models and leverages cognitive conflicts between users’ emotional experiences and rational preferences. EDACF introduces user and item agents equipped with separate natural language memories for preference, emotion, and conflict representations, enabling cognitive-level reasoning beyond static numerical modeling. The framework features three key innovations: (1) a conflict detection mechanism that identifies users’ cognitive inconsistency states; (2) a dual-memory update strategy that maintains preference stability while capturing emotional dynamics; and (3) an adaptive reasoning mechanism that adjusts decision weights based on detected conflicts. Extensive experiments demonstrate that EDACF outperforms state-of-the-art baselines by 9.9% in NDCG@10 and 13.1% in MRR@10, with improvements exceeding 32% among user groups with high conflict. These results highlight a paradigm shift in recommendation systems from behavior prediction toward cognitive-level understanding of user decision processes. Full article

Over the past two decades, escalating climate crises, geopolitical conflicts, and pandemics have intensified the frequency and severity of disasters, exposing severe vulnerabilities in global food systems. In this pressing context, disaster nutrition emerges as a vital domain of intervention. However, existing academic literature and field practices often address this topic through fragmented, single-axis perspectives. Nutritional physiology, food technology, humanitarian logistics, and policy–ethics frameworks tend to progress in parallel yet disconnected tracks, which results in a lack of holistic models that adequately reflect field realities. The urgency of this issue is underscored by the latest global data. In 2023 alone, disasters resulted in over 86,000 deaths, a significant increase from the preceding two-decade annual average. Furthermore, the 2025 Global Report on Food Crises reveals that 295.3 million people faced high levels of acute food insecurity in 2024, marking the sixth consecutive year this number has risen. This escalating crisis highlights the inadequacy of fragmented approaches and necessitates the development of an integrated framework for disaster nutrition. To address this fragmentation, this study redefines disaster nutrition as a multi-layered, integrated food system challenge. Based on a comprehensive literature analysis, it proposes an “Integrated Disaster Food System Model” that brings these different dimensions together within a common framework. The model is built on four main components: (i) nutritional requirements and vulnerable groups (such as infants, older adults, pregnant individuals, and populations with chronic diseases requiring special diets); (ii) product design, technology, and packaging (balancing shelf life, nutritional value, cultural acceptability, and sensory attributes, including innovative components such as microalgae and fermented foods); (iii) logistics, storage, and distribution systems (centralized storage versus localized micro-warehouses, as well as the use of drones and digital traceability technologies); and (iv) policy, regulation, ethics, and sustainability (the applicability of the Sphere Standards, fair distribution, food waste, and environmental impact). By emphasizing the bidirectional and dynamic interactions among these components, the model demonstrates how decisions in one domain affect others (for example, how more durable packaging can increase both logistics costs and carbon footprint). The study highlights the risks and cultural mismatches associated with a “one-size-fits-all high-energy food” approach for vulnerable groups and argues for the necessity of localized, context-specific, and sustainable solutions. In conclusion, the article posits that the future of disaster food systems can only be shaped through a holistic approach in which interdisciplinary collaboration, technological innovation, and ethical–environmental principles are integrated into the core of policy-making. Full article