Search Results (1,209)

Background/Objectives: Out-of-hospital cardiac arrest (OHCA) outcomes remain poor and vary widely across communities with socioeconomic deprivation. This study examines whether delays in emergency medical services (EMS) activation, the earliest link in the Chain of Survival, mediate the association between regional deprivation and OHCA outcomes, and whether this effect is modified by bystander cardiopulmonary resuscitation (CPR) status. Methods: We analyzed adult patients (aged 18–80 years) with witnessed, EMS-treated OHCA of presumed cardiac etiology from the Korean nationwide OHCA registry (2015–2022). Regional deprivation was defined by the Regional Deprivation Index and dichotomized into deprived (top 20%) vs. non-deprived areas. Timely EMS activation, defined as collapse to EMS activation, was measured as an awareness time interval (ATI) <5 min. Outcomes were good neurological recovery (CPC 1–2) and survival to discharge. Causal mediation analysis within the counterfactual framework quantified the proportion of the association mediated by timely EMS activation, with stratification by bystander CPR status. Results: Among 43,032 patients, 6.1% resided in deprived areas. Deprived areas had lower bystander CPR (22.6% vs. 36.3%) and timely EMS activation (67.8% vs. 75.6%) (p < 0.05 for all). Regional deprivation was associated with poorer outcomes (good neurological prognosis: aOR 0.46, 95% CI 0.39–0.55; survival: aOR 0.65, 95% CI 0.57–0.73). Mediation analysis showed that ATI <5 min accounted for 3.7% of the total deprivation effect on good neurological outcome and 7.9% on survival, with stronger mediation among patients receiving bystander CPR (7.9% and 14.7%, respectively). Conclusions: Regional deprivation is significantly associated with poorer OHCA outcomes, partly mediated by delays in EMS activation, particularly among patients who received bystander CPR. Interventions to enhance early recognition, rapid EMS activation, and bystander CPR in deprived communities are critical to improving survival equity after OHCA. Full article

This paper addresses nonlinear time-varying cascade systems governed by differential equations with finite delay. Several sufficient conditions for asymptotic stability are derived, based on differing assumptions regarding the isolated subsystems and their interconnection. The cascade structure enables the treatment of a broad class of systems while simplifying stability analysis compared to conventional approaches. Moreover, it allows the stabilization problem to be decoupled: under suitable conditions, the asymptotic stability of the overall cascade system follows from the stability properties of its individual subsystems. These properties are typically verified using the direct Lyapunov method. In contrast to existing results, the theorems presented herein apply to an extended class of systems and impose relaxed conditions on the Lyapunov functions employed to establish uniform asymptotic stability. Additionally, new results are provided on semiglobal exponential stability and (non-uniform) asymptotic stability for time-varying cascade systems with delay. Collectively, these contributions broaden the applicability of the direct Lyapunov method to delayed cascade systems. Full article

Urbanization is intensifying congestion, emissions, and unequal mobility access in cities. This study aims to operationalize sustainability objectives—efficiency, environmental externalities, and service equity—in network-wide traffic system control. We propose SERL-H, a sustainability-aware hierarchical multi-agent reinforcement learning (MARL) controller. SERL-H separates fast intersection-level actuation from slower region-level coordination under a centralized-training decentralized-execution paradigm, and employs adaptive graph attention to capture time-varying interdependencies with bounded neighborhood communication. The learning reward explicitly balances delay/throughput, emissions/fuel, and an equity regularizer based on service dispersion across user groups. In a SUMO-based city-scale simulation with 100 signalized intersections, SERL-H reduces average delay from 45 s to 29 s and average travel time from 120 s to 88 s relative to fixed-time control, while increasing throughput and lowering total emissions (4800 kg to 3950 kg). A socio-economic assessment suggests higher annualized cost savings (e.g., $50.27 M/year to $65.91 M/year) and improved environmental quality indices. We also report, as supporting evidence, an optional sustainability-enhanced spatio-temporal graph predictor (SUT-GNN) that provides reliable short-horizon forecasts during peak-hour volatility. Full article

This study investigates volatility spillovers from the stock markets of the United States, Germany, China, and Japan to the UK stock market using daily data from major benchmark indices (FTSE 100, S&P 500, DAX, Shanghai Composite, and Nikkei 225) and Brent crude oil prices. Using a novel two-stage bootstrap framework, we first model time-varying conditional volatilities with GARCH-family models and compare them with long-memory FIGARCH specifications to account for persistent volatility dynamics. These volatilities are then incorporated into a VAR-X model, treating Brent crude oil price volatility as an endogenous or exogenous variable in robustness checks. To overcome limitations of traditional VARs, bootstrap-corrected GIRFs are employed to trace dynamic, order-invariant impacts across key sub-periods: the global financial crisis, Brexit, COVID-19, and the Ukraine war. We also benchmark our results against the Diebold–Yilmaz connectedness index and conduct rigorous out-of-sample forecasting and Value-at-Risk backtesting. Results reveal heterogeneous spillovers: US and German shocks trigger strong, immediate, and persistent UK market volatility, reflecting deep integration; Chinese shocks are delayed and gradual, while Japanese shocks are muted or short-lived. Spillover intensity is time-varying, peaking during global crises. Our model outperforms standard benchmarks in out-of-sample volatility forecasting and risk management applications. The study offers critical insights for investors seeking international diversification and for policymakers aiming to manage systemic risk in an interconnected global financial system. Full article

Coordinated control across multiple signalized intersections is essential for mitigating congestion propagation in urban road networks. However, existing DQN-based approaches often suffer from unstable action switching, limited interpretability, and insufficient capability to model spatial spillback between adjacent intersections. To address these limitations, this study proposes a Policy-Regulated and Aligned Deep Q-Network (PRA-DQN) for cooperative multi-intersection signal control. A differentiable policy function is introduced and explicitly trained to align with the optimal Q-value-derived target distribution, yielding more stable and interpretable policy behavior. In addition, a cooperative reward structure integrating local delay, movement pressure, and upstream–downstream interactions enables agents to simultaneously optimize local efficiency and regional coordination. A parameter-sharing multi-agent framework further enhances scalability and learning consistency across intersections. Simulation experiments conducted on a 2 × 2 SUMO grid show that PRA-DQN consistently outperforms fixed-time, classical DQN, distributed DQN, and pressure/wave-based baselines. Compared with fixed-time control, PRA-DQN reduces maximum queue length by 21.17%, average queue length by 18.75%, and average waiting time by 17.71%. Moreover, relative to classical DQN coordination, PRA-DQN achieves an additional 7.53% reduction in average waiting time. These results confirm the effectiveness and superiority of the proposed method in suppressing congestion propagation and improving network-level traffic performance. The proposed PRA-DQN provides a practical and scalable basis for real-time deployment of coordinated signal control and can be readily extended to larger networks and time-varying demand conditions. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

The increasing penetration of distributed generation in active distribution networks (ADNs) introduces significant voltage regulation challenges due to the intermittent nature of renewable energy sources. Electric springs (ESs) have emerged as a cost-effective alternative to conventional FACTS devices for voltage regulation, requiring minimal energy storage while providing fast, flexible reactive power compensation. This paper proposes a deep reinforcement learning (DRL)-based approach for voltage regulation in balanced active distribution networks with distributed generation. Electric springs are deployed at selected buses in series with noncritical loads to provide flexible voltage support. The main contributions of this work are: (1) a novel region-based penalized reward function that effectively guides the DRL agent to minimize voltage deviations; (2) a coordinated control strategy for multiple ESs using the Deep Deterministic Policy Gradient (DDPG) algorithm, representing the first application of DRL to ES-based voltage regulation; (3) a systematic hyperparameter tuning methodology that significantly improves controller performance; and (4) comprehensive validation demonstrating an approximately 40% reduction in mean voltage deviation relative to the no-control baseline. Three well-known continuous-control DRL algorithms, Twin Delayed Deep Deterministic Policy Gradient (TD3), Proximal Policy Optimization (PPO), and DDPG, are first evaluated using the default hyperparameter configurations provided by MATLAB R2022b.Based on this baseline comparison, a dedicated hyperparameter-tuning procedure is then applied to DDPG to improve the robustness and performance of the resulting controller. The proposed approach is evaluated through simulation studies on the IEEE 33-bus and IEEE 69-bus test systems with time-varying load profiles and fluctuating renewable generation scenarios. Full article

Digital chaotic systems suffer severe dynamical degradation under finite computational precision, compromising their randomness and unpredictability in security-critical applications. To address this challenge, we introduce the High-Dimensional Delayed Cyclic-Coupled Chaotic Model (HD-DCCCM), a unified framework that integrates high-dimensional coupling, delayed feedback, and time-varying parameter control. In this synergistic design, dynamic perturbations from delays and time-varying signals continuously excite the high-dimensional structure, effectively preventing the collapse into short periodic orbits typical of low-precision environments. Systematic numerical analyses confirm that the HD-DCCCM generates stable hyperchaos with significantly extended periods, consistently outperforming classical maps and representative anti-degradation methods. Moreover, the framework demonstrates strong robustness and flexibility across both homogeneous (identical maps) and heterogeneous (hybrid maps) configurations. These results position the HD-DCCCM as a general and powerful paradigm for constructing degradation-resilient chaotic systems, with broad potential for next-generation secure communications and cryptographic applications. Full article

Background: Children with special healthcare needs (CSHCNs) face persistent barriers to timely immunization in China, but comparative evidence across disease groups and vaccines, and data on immune function, are limited. Methods: We conducted a retrospective cohort study linking electronic medical records, vaccination records, and a structured telephone and questionnaire follow-up. We estimated timely vaccination by National Immunization Program (NIP) dose definitions, assessed catch-up completion at follow-up, and compared cellular/humoral/complement immune indices with published pediatric reference ranges. Group differences used ANOVA/Kruskal–Wallis and chi-square (χ²)/Fisher’s exact tests with Bonferroni correction. Results: Timely vaccination was lower than the national healthy child benchmarks for all NIP vaccines (all p < 0.001); the Japanese encephalitis virus (JE; 24.0%) and measles-containing vaccine (MCV; 25.9%) had the lowest timely completion. A subset of CSHCNs did not receive recommended catch-up vaccinations, primarily due to persistent caregivers’ concern and point of vaccination (POV) staff’s hesitancy. Delays clustered in neonatal/perinatal disorders for Bacillus Calmette–Guérin (BCG) and hepatitis B vaccine, dose 1 (HepB1). Catch-up completion was highest for hepatitis B vaccine, dose 3 (HepB3) (86.3%) and BCG (81.8%), and lowest for the diphtheria and tetanus vaccine (DT) (49.4%); MCV2 completion was particularly low in hematological diseases. Immunoglobulin A (IgA) and immunoglobulin G (IgG) concentrations were significantly lower in neonatal/perinatal disorders and infectious disease groups versus neurological and immune disorder groups (p < 0.05). No severe adverse events were reported after catch-up. Conclusions: CSHCNs in China face substantial barriers to timely NIP immunization. Timeliness and catch-up vary substantially by vaccine and underlying condition; neonatal/perinatal disorders contribute disproportionately to early-life delays. Disease-specific guidance, strengthened POV–specialist clinic coordination, immunological monitoring, and supportive policies could improve the vaccination coverage and effectiveness in this vulnerable population. Full article

This paper addresses the finite-horizon state estimation problem for multiplex networks (MNs) subject to random delays and sensor saturations under the constraint of only partial node measurements. The random time-varying delays are modeled via Bernoulli-distributed variables, while a Markovian random access protocol dynamically governs the data transmission at each time step. To tackle this problem, we design a set of robust state estimators based on partial measurements, ensuring the prescribed finite-horizon $H_{\infty }$ performance. Sufficient conditions for the existence of these estimators are established. Subsequently, the estimator gains are derived by solving the matrix inequalities inherent in these conditions. Finally, convincing numerical simulations demonstrate the effectiveness and practical applicability of the proposed algorithm. Full article

Background/Objectives: Accurate sleep stage classification is essential for evaluating sleep quality and diagnosing sleep disorders. Despite recent advances in deep learning, existing models inadequately represent complex brain dynamics, particularly the time-lag effects inherent in neural signal propagation and regional variations in cortical activation patterns. Methods: We propose the MFST-GCN, a graph-based deep learning framework that models these neurobiological phenomena through three complementary modules. The Dynamic Dual-Scale Functional Connectivity Modeling (DDFCM) module constructs time-varying adjacency matrices using Pearson correlation across 1 s and 5 s windows, capturing both transient signal transmission and sustained connectivity states. This dual-scale approach reflects the biological reality that neural information propagates with measurable delays across brain regions. The Multi-Scale Morphological Feature Extraction Network (MMFEN) employs parallel convolutional branches with varying kernel sizes to extract frequency-specific features corresponding to different EEG rhythms, addressing regional heterogeneity in neural activation. The Adaptive Spatio-Temporal Graph Convolutional Network (ASTGCN) integrates spatial and temporal features through Chebyshev graph convolutions with attention mechanisms, encoding evolving functional dependencies across sleep stages. Results: Evaluation on ISRUC-S1 and ISRUC-S3 datasets demonstrates F1-scores of 0.823 and 0.835, respectively, outperforming state-of-the-art methods. Conclusions: Ablation studies confirm that explicit time-lag modeling contributes substantially to performance gains, particularly in discriminating transitional sleep stages. Full article

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

This study introduces a predictive model based on artificial neural networks (ANNs) for estimating the dynamic performance of commercially available sensible thermal energy storage (STES) systems. The model was trained and validated using high-resolution experimental data measured from two vertical cylindrical tanks (0.3 m³ each) including internal heat exchangers and operating under both heating and cooling modes. A comprehensive sensitivity analysis was conducted on 28 ANN architectures by varying the number of hidden neurons and input delays. The optimal configuration, designated as ANN5 (12 neurons, delay = 1), demonstrated superior accuracy in predicting temperature profiles and energy exchange. Validation against an independent dataset confirmed the model’s robustness, achieving normalized root mean square errors (NRMSEs) between 0.0022 and 0.0061 for the hot tank and between 0.0057 and 0.0283 for the cold tank. Energy prediction errors were within −3.87% for charging and 0.09% for discharging in heating mode, and 7.08% for charging and 0.13% discharging in cooling mode, respectively. These results highlight the potential of ANN-based approaches for real-time control, forecasting, and digital twin applications in STES systems. Full article

Quaternion-valued neural networks (QVNNs) that have multiple types of delays (leakage, time-varying, distributed, and neutral) and defined on time scales are discussed in this paper. Quaternions form a 4D normed division algebra and allow for a better representation of 3D and 4D data. QVNNs have been proposed and applications have appeared lately. Time-scale calculus was developed to allow the joint treatment of systems, or any hybrid mixing of them, and was also applied with success to the analysis of dynamic properties for neural networks (NNs). Because of its generality, encompassing the common properties of discrete-time (DT) and continuous-time (CT) NNs, time-scale NNs dynamics research does not benefit from a fully-developed Lyapunov theory. So, Halanay-type inequalities have to be used instead. To this end, we provide a novel generalization of inequalities of Halanay-type on time scales specifically suited for neutral systems, i.e., systems with neutral delays. Then, this new lemma is employed to obtain sufficient conditions presented both as linear matrix inequalities (LMIs) and as algebraic inequalities for the exponential stability and exponential synchronization of QVNNs on time scales with the mentioned delay types. The model put forward in this paper has a generality which is appealing for practical applications, in which both DT and CT dynamics are interesting, and all the discussed types of delays appear. For both the DT and CT scenarios, four numerical applications are used to illustrate the four theorems put forward in this research. Full article

This paper investigates the robust fault-tolerant control (FTC) problem for interval type-2 fuzzy systems (IT2FS) with simultaneous time-varying input and state delays. In order to more comprehensively capture system uncertainties, an Interval Type-2 (IT2) fuzzy model is constructed, which, compared to the conventional Interval Type-1 model, better captures the uncertainty information of the system. A premise-mismatched fault-tolerant controller is designed to ensure system stability in the presence of actuator faults, while providing greater flexibility in the selection of membership functions. In the stability analysis, a novel Lyapunov–Krasovskii functional is formulated, incorporating membership-dependent matrices and delay-product terms, leading to sufficient conditions for closed-loop stability based on linear matrix inequalities (LMIs). A numerical simulation and a practical physical model are used, respectively, to illustrate the effectiveness of the proposed method. Comparative experiments further reveal the impact of input delays and actuator faults on closed-loop performance, verifying the effectiveness and robustness of the designed controller, as well as the superiority of interval type-2 over interval type-1. Full article