With the rise of the Internet of Medical Things (IoMT), healthcare systems increasingly rely on Wireless Body Area Networks (WBANs) for continuous, real-time patient monitoring and clinical decision-making. These applications require ultra-low latency, high reliability, and energy efficiency. Typically, they operate via mobile devices, such as smartphones, wearables, or WBAN coordinators, which collect, process, and transmit medical data. However, the limited processing capabilities and energy constraints of these devices often lead to increased delays and degraded system performance. To address these challenges, Mobile Edge Computing (MEC) has emerged as a promising solution that brings computation closer to the network edge. This paper addresses the optimization problem of task offloading and resource allocation in WBAN-MEC systems, where each task can be executed locally on the mobile device, offloaded to the MEC server, or to the cloud. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model involving offloading decisions and the allocation of communication and computational resources. Our objective is to maximize task completion subject to time constraints, minimize mobile energy consumption, and ensure efficient use of MEC resources. We propose a Collaborative Multi-Agent Task Offloading and Resource Allocation (CoMA-TORA) framework, which decomposes the complex optimization problem into two coordinated components: a decentralized offloading decision component and a centralized resource allocation component. The framework is implemented using an actor-critic reinforcement learning architecture, with a global critic that evaluates a shared reward for coordinated decision-making. Simulation results show that CoMA-TORA outperforms both traditional and DRL-based approaches in delay-sensitive healthcare environments.

Role of socioeconomic status in children's resource allocation under inequality.

Nonoperating room anesthesia (NORA) services are rapidly expanding and now represent a substantial proportion of anesthetic care. Efficient systems are critical to enable limited anesthesia resources to meet this increased demand. Traditional operating room efficiency metrics are poorly suited to these complex and heterogeneous environments. Efficiency in NORA should be viewed not merely as throughput but through the 'Triple Aim' framework of improving access, reducing cost, and enhancing quality. Recent literature highlights the importance of preprocedural optimization, safety-focused procedural management, and artificial intelligence modeling to address variability in NORA workflows. Emerging data supports the use of real-time dashboards and artificial intelligence tools to enhance scheduling accuracy and resource allocation. Improving NORA efficiency requires integrated operational, financial, and workforce strategies rather than isolated interventions. Adoption of systematic preprocedural triage, data-driven scheduling and case routing, standardized intraprocedural workflows, effective recovery pathways, and continuous performance metrics are key elements needed to achieve the Triple Aim. Future research should focus on harnessing rapidly evolving technology, including artificial intelligence tools, to create and refine NORA-specific benchmarks and scalable analytic tools to support continued improvement of distributed procedural care.

Resource allocation in software-defined networks: Current status, research challenges, and future prospects

Automatic modulation classification (AMC) is a fundamental signal-awareness mechanism in intelligent communication systems, including cognitive radio, dynamic spectrum sensing, electronic warfare, and signal intelligence, enabling adaptive transmission, resource allocation, and receiver optimization. However, under atmospheric turbulence and nonlinear channel impairments, conventional deep-learning-based AMC, particularly those based on multilayer perceptrons, suffers from limited interpretability, slow convergence, and degraded robustness. Kolmogorov-Arnold Networks (KANs) provide a lightweight and explainable alternative; nevertheless, their generic activation function libraries are not aligned with turbulence-induced statistical characteristics, leading to suboptimal gradient propagation and feature discrimination. To address this mismatch, we derive a closed-form expression for the joint atmospheric-receiver noise model and construct a distribution-aware activation function library based on its Fox-H function representation. Experimental results demonstrate that the modified KAN achieves higher classification accuracy and reduced computational complexity in atmospheric turbulence channels by tailoring the activation functions to the channel noise statistics.

Resource allocation and scheduling for flexible manufacturing systems based on timed Petri nets

Tailoring instruction to personality: The mediating role of cognitive tendencies in the effect of extraversion on higher vocational college students' self-regulated learning.

Correlation between the frailty index derived from laboratory tests and 4-week all-cause mortality in critically ill patients with pneumonia.

Atrial fibrillation (AF) is a highly prevalent comorbidity in patients with cardiomyopathy (CM), associated with worse cardiovascular outcomes. This study aims to provide a comprehensive, national-level analysis of AF and CM-related mortality in the United States. The Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was utilized, using death certificates from 1999 to 2024. The study included patients aged ≥15 years with CM and AF. Statistical analyses were conducted to calculate age-adjusted mortality rates (AAMRs) per 100,000 individuals and annual percent changes with 95% confidence intervals (CIs). Between 1999 and 2024, CM with concomitant AF accounted for 134,470 deaths among individuals aged 15 years or older. The overall AAMR rose from 1.5 per 100,000 in 1999 to 2.3 in 2024. From 1999 to 2016, the AAMR rose modestly (1.5-1.8), followed by a pronounced rise from 2016 to 2022 (1.8-2.5), and a relative decline by 2024 (2.5-2.3). Compared with 2019, mortality in 2020 demonstrated a 15% relative increase (incidence rate ratio = 1.15; 95% CI: 1.11-1.19). Males had disproportionately higher AAMRs compared to females. By race, the highest AAMRs were observed in non-Hispanic (NH) Black and White populations (1.8 each). Regionally, the West and Midwest exhibited the highest AAMRs (1.9 each). Urban-rural stratification revealed higher AAMRs among rural areas (2.2) when compared with urban (1.8) areas. Targeted public-health interventions and resource allocation to address this growing cardiovascular mortality burden, particularly in high-risk demographic groups, are needed.

Development and validation of a clinlabomics-based machine-learning model for noninvasive risk stratification of moderate-to-severe OSA.

Coordinated optimization of emergency repair for the post-disaster transportation network and the emergency resource allocation scheme

Plant tolerance is explained by resource-based plant-nematode interactions.

Psychosocial stress and chronic pain: A threshold model of biological lock-in.

Adaptive NOMA/OMA Switching and Resource Allocation Optimization for SiPM-Based VLC Systems

Prevalence, risk factors, and impact of sexual abuse among children with disabilities in Asian countries: A scoping review of three decades (1995-2025).

• Framework insights guide strategic decisions for EPRTs and safety system placement. • PN models effectively evaluate emergency maintenance impact on NPP resilience. • The study enhances understanding of human efficiency’s role in NPP resilience. The escalating frequency of extreme natural disasters due to climate change poses unprecedented risks to nuclear power plants (NPPs), underscoring the need to quantify the efficacy of manual emergency responses in enhancing resilience. However, quantifying the effectiveness of these measures remains a significant challenge. This paper develops a Petri Net-based resilience assessment framework to model multi-phase accident progression in an NPP subjected to extreme events, including loss of coolant accidents and station blackout scenarios. The PN models integrate stochastic system degradation processes, automated safety responses, manual recovery processes, and offsite resource mobilisations. The simulation results show that the developed model can successfully assess the impact of the efficiency of human responses on NPP resilience. This work provides actionable insights for optimising NPP emergency procedures and resource allocation strategies. The findings underscore the importance of timely manual interventions during emergency scenarios, offering a quantitative basis for enhancing nuclear safety management policies.

WARGM-PDPG: A dual-phase policy gradient graph mamba neural network for device placement algorithms.

Predicting length of stay in the pediatric intensive care unit at a tertiary center in Saudi Arabia using machine learning.

A complete survey on artificial intelligence based resource allocation for sixth generation mobile

4PL-Driven optimization of electric vehicle battery recycling networks: enhancing greenness, timeliness and resilience against disruptions.