Search Results (938)

Problem: Controlling nonlinearly parameterized systems with unknown disturbances remains challenging because classical adaptive approaches rely on separation-of-variables and reparameterization techniques, leading to increased parameter dimensions, conservative stability bounds, and implementation complexity. Objective: This paper develops a data-driven predictive fuzzy adaptive control (DD-PFAC) framework that eliminates the need for separation techniques while achieving superior tracking performance and formally certified stability. Novelty: The key innovation is a two-layer architecture. Layer 1 provides direct fuzzy approximation of composite nonlinear functions (system dynamics plus disturbance bound) without parameter reparameterization, reducing parameter complexity from $O\left(qn\right)$ to $O\left(nN\right)$. Layer 2 employs Hankel matrix-based predictive optimization to adaptively tune both control gains $c_i\left(k\right)$ and adaptation rates ${\gamma }_i\left(k\right)$ online using 80–150 recent input–output samples. Methodology: A Lyapunov function augmented with a prediction-error term is used to prove uniform ultimate boundedness of all closed-loop signals. A projection-based recursive least-squares algorithm updates the gain parameters online while guaranteeing $c_i\left(k\right)\ge c_{min}>0$ at all times. Results: Comparative simulations demonstrate 31.4% reduction in integral square error, 27.8% reduction in mean absolute error, and 37.4% reduction in steady-state error versus traditional adaptive fuzzy control. A four-group ablation study confirms that adaptive gain scheduling contributes 27.7% and predictive compensation contributes 6.5% to the total MAE improvement. Robustness tests validate consistent 28–32% performance advantage across sinusoidal, pulse, step, and large-disturbance scenarios. Full article

With the explosive growth of the Internet of Things (IoT) and cloud-computing traffic, Intrusion Detection Systems (IDSs) have become a cornerstone of network security. However, modern traffic data often exhibits extreme class imbalance and long-tailed distributions, leading to persistently high miss rates for minority attack categories in Machine Learning (ML)-based IDSs. Conventional oversampling may introduce decision noise, whereas standard Generative Adversarial Networks (GANs) can suffer from training instability and mode collapse when modeling high-dimensional tabular traffic features. To address these challenges, we propose a high-fidelity traffic augmentation framework based on an Adaptive Hybrid-loss Conditional GAN (AH-CGAN). Specifically, AH-CGAN introduces an iteration-dependent adaptive gradient penalty (AGP) schedule to enforce the Lipschitz continuity constraint more effectively during training and incorporates a feature-matching objective to align intermediate critic representations between real and synthetic traffic. Experiments on the CIC-IDS2017 benchmark show that AH-CGAN generates distribution-consistent synthetic samples and that augmentation improves downstream detection across multiple classifiers. In particular, the weighted F1-score of Logistic Regression increases from 0.8237 to 0.8697 (Δ = +0.0460, i.e., +4.6%). Overall, the proposed approach enhances minority coverage in the feature space and can improve class separability, providing a practical solution for long-tailed IDS. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tuning, which tends to be inefficient and susceptible to subjective factors. To address this issue and improve prediction accuracy, this study proposes a hybrid modeling framework, integrating Bayesian Optimization (BO), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. In this approach, Automatic Identification System (AIS) data is leveraged to predict the total voyage duration before departure, thereby deriving the vessel’s ETA. The model, referred to as BO-CNN-LSTM, utilizes BO for automatic hyperparameter tuning, employs CNN for extracting local features, and applies LSTM network to capture temporal dependencies. The model is developed using a dataset of 32,972 distinct voyage records, among which 23,947 are retained as valid samples after data cleaning. Pearson correlation analysis is conducted to select key input variables, including navigation speed, ship type, sailing distance, and deadweight tonnage. Additionally, sailing distance is processed using the Ramer–Douglas–Peucker algorithm. Experimental evaluation indicates that the BO-CNN-LSTM model achieves a coefficient of determination of 0.987, along with a mean absolute error and root mean square error of 6.078 and 8.730, respectively. These results significantly outperform comparison models such as CNN, LSTM, CNN-LSTM, random forest, AdaBoost, and Elman neural networks. Overall, this study validates the effectiveness and superiority of the proposed BO-CNN-LSTM model in ship ETA prediction, providing an efficient and effective prediction solution for intelligent maritime transportation systems. Full article

As the global energy transition accelerates, clean energy development has surged. However, accurately modeling correlations and uncertainties of hydro, wind, and photovoltaic energy remains challenging in long-term scheduling for energy complementarity. This study employs Latin hypercube sampling and Cholesky decomposition to capture the temporal correlations of water runoff, wind, and photovoltaic resources. It generates numerous scenarios for uncertainty simulation. The scenario set is reduced based on probability distance while maintaining a high-fidelity approximation. A stochastic dual-objective model is proposed for long-term multi-energy complementary system scheduling (LMCS), aiming to maximize expected revenue considering carbon emission costs while ensuring minimum power output guarantees. An evolutionary algorithm—namely, an orthogonal multi-population evolutionary (OMPE) algorithm based on orthogonal design and a multi-population search framework—is introduced, along with constraint-handling strategies. Three annual-regulation hydropower stations in the Hongshui River Basin serve as a case study. The experimental results indicate that generated scenarios capture temporal characteristics with high accuracy. The proposed algorithm efficiently solves the LMCS problem, achieving average increases of 5.46% and 3.89% in revenue and minimal output compared to benchmarks. The validation results demonstrate that orthogonalization-based initialization, recombination operators, and dominance rules significantly enhance OMPE performance. Sensitivity analysis indicates that economic efficiency and risk trade-offs can be adjusted by varying scenario numbers. Full article

This study examined temporal scalp electroencephalography (EEG) absolute power and brief self-reported psychological state measures before and after participation in a Life Story Program (LSP) in older adults. Five older women participated in the study. For each participant, pre- and post-assessments were scheduled at approximately the same time of day and included a brief four-item questionnaire and biosignal acquisition in a controlled seated environment. EEG was recorded at 500 Hz from T5 and T6 during an eyes-closed resting condition. For EEG analysis, only non-speaking segments were used; the initial 3–5 min stabilization period was excluded, and the subsequent 10 min of data were analyzed. One participant was excluded after outlier screening, resulting in a final EEG sample of four participants. EEG preprocessing included linear detrending, 60 Hz notch filtering, 0.5–50 Hz band-pass filtering, artifact rejection, and Welch-based estimation of absolute power in the delta, theta, alpha, beta, and gamma bands. Given the small sample size, all analyses were treated as exploratory. Questionnaire responses remained generally stable across assessments. No statistically significant pre–post differences were observed after false discovery rate correction, although small reductions, particularly in the gamma band, were observed. These findings should be interpreted as preliminary observations requiring confirmation in larger controlled studies with broader multichannel EEG coverage and more robust recording configurations. Full article

To address the problem of renewable energy curtailment and the need for operational economic optimization in integrated energy systems with high penetration of wind and solar power, a coordinated optimization method integrating thermoelectric decoupling, ammonia-blended combustion technology, and energy storage capacity leasing is proposed. First, a chaotic-improved Latin Hypercube Sampling (C-LHS) method, combined with an improved K-means clustering algorithm, is employed to generate representative wind–solar–load scenarios. This approach improves the efficiency of uncertainty scenario generation while reducing computational burden and maintaining solution accuracy. Secondly, by coordinating the operation of thermal energy storage and electric boilers, the “heat-led power generation” constraint is relaxed, and, in combination with ammonia-blended combustion in combined heat and power (CHP) units, the system’s flexibility and renewable energy accommodation capability are enhanced. Finally, with the objective of minimizing total operating cost, a day-ahead scheduling model incorporating electrical energy storage (EES) leasing optimization is established. For EES, under a shared energy storage market mechanism, the golden section search (GSS) algorithm is employed to optimize the day-ahead leasing capacity. The simulation results demonstrate that the proposed method improves renewable energy accommodation while maintaining economic performance, and effectively reduces the overall operating cost of the system. These findings confirm the effectiveness of the proposed strategy in enhancing both system flexibility and economic performance. Full article

Caring for children with physical disabilities can be a daunting responsibility, often placing significant financial, psychological, social and health-related strains on the primary caregivers. This qualitative study explored the experiences of caregivers caring for children with physical disabilities in the Hardap region of Namibia. Using purposive sampling, twenty caregivers were selected as participants in the study. Data was collected using semi-structured interview schedules. Following the interviews, the data were manually analysed and categorised into distinctive themes and sub-themes and summarised in the final report as verbatim quotations. Study findings reveal that caregivers are motivated and determined to provide optimum care for children with physical disabilities under their care by acquiring assistive devices for them and assisting the children with activities of daily living. However, poverty and the general shortage of assistive devices, mostly wheelchairs, provide adverse conditions that are inimical to the development of children’s functional independence in daily living tasks. The burden of carrying the children was noted to be potentially deleterious to the caregivers’ physical health. The study concluded that providing assistive equipment for the children will ease the caregivers’ burden of care while equalising socioeconomic opportunities for both children with physical disabilities and their caregivers. The study only covered a small sample size in a small geographical area of Namibia. Therefore, interpretation and generalisation of the findings need to account for the specific context in the Hardap region of Namibia. Therefore, there remains scope for conducting further research with a larger sample size and one that is more geographically representative of Namibia. Full article

Background: Sleep problems are prevalent among student populations worldwide. Medical students, facing heavy academic workloads and intense pressure, are particularly susceptible to sleep disorders. While sleep quality among Chinese university students has been consistently declining, research focusing on medical students in tropical island provinces like Hainan remains insufficient. This study aims to address this geographical gap by analyzing the sleep quality status and influencing factors among medical students in Hainan Province. Objective: To investigate the current status of sleep quality and its associated factors among medical students in Hainan Province, providing a scientific basis for developing targeted interventions. Methods: A cross-sectional survey was conducted in April 2024 using purposive sampling to recruit undergraduate students from a medical university in Hainan. The Self-Rating Scale of Sleep (SRSS) developed by Li Jianming was administered, and 551 valid questionnaires were collected anonymously. Data were analyzed using univariate analysis and pairwise comparisons to assess sleep quality and associated factors, with demographic variables as independent variables. Results: Among participants, 40.1% reported sleep problems (31.2% mild, 8.2% moderate, 0.7% severe). The mean total SRSS score was 21.78 ± 5.73. Compared to the national norm, medical students showed significantly higher scores in sleep quality, insufficient arousal, and post-insomnia responses (p < 0.05). Academic major was identified as a significant influencing factor (p = 0.012), with clinical medicine students demonstrating significantly poorer sleep quality than health management majors (p = 0.010). No significant differences were found for gender or academic year. Conclusions: Sleep problems are prominent among medical students in Hainan, with clinical medicine students at higher risk due to academic and professional pressures. Recommendations include optimizing curriculum schedules, strengthening psychological support systems, and developing targeted interventions for clinical majors. Full article

Despite the widespread adoption of BIM, information exchange across disciplines remains hindered by heterogeneous structures at the tabular data level, particularly when integrating data across multiple discipline-specific models. Manual mapping, rigid templates, or one-off programming scripts are labor-intensive and difficult to scale, limiting automated querying, cross-model aggregation, and schedule-level analytics. This study proposes a lightweight, workflow-driven approach for semantic normalization and cross-model integration of BIM schedule data, with optional script-supported workflow configuration used only to assist the configuration of deterministic, rule-guided mapping logic, rather than serving as a core analytical method. By introducing a customizable subcategory layer, the workflow enables fine-grained semantic alignment and efficient normalization across diverse schedule datasets, implemented through lightweight Python scripting and rule-guided semantic matching used solely as a supporting mechanism for deterministic field mapping. Using structural, architectural, and HVAC models, we demonstrate a stepwise process including data cleaning, hierarchical classification, consistency checking, batch analytics, and automated computation of cross-model metrics such as opening-to-wall ratios. Sample-based validation confirms the workflow’s reliability, achieving semantic mapping agreement rates above 95% and reducing manual processing time by more than 85%. The workflow is readily extensible to other disciplines and modeling conventions, supporting high-throughput data integration for tasks such as design coordination, semantic alignment, RFI reduction, accelerated design reviews, and data-driven decision making. Overall, rather than introducing a new algorithm, the contribution of this work lies in formalizing a reusable, schedule-level workflow abstraction that enables consistent semantic alignment and automated cross-model aggregation without relying on rigid ontologies or training-intensive learning-based models. Any optional tooling used during workflow configuration is auxiliary and does not constitute a standalone learning-based method requiring model training or performance benchmarking. This provides a reusable methodological foundation for scalable, schedule-level BIM data integration and cross-model analytics. Full article

Background/Objectives: Chronic traumatic spinal cord injury (SCI) causes persistent neurological deficits for which no clinically effective regenerative therapy is currently available. Mesenchymal stromal/stem cells (MSCs), particularly Wharton’s jelly-derived MSCs (WJ-MSCs), demonstrate immunomodulatory and neurotrophic potential. This phase I/II study evaluated the safety and efficacy of intrathecal allogeneic WJ-MSC administration in individuals with chronic incomplete cervical SCI. Methods: In this multicentre, randomised, double-blind, placebo-controlled trial (NCT05054803, EudraCT 2021-000346-18), 18 participants with chronic (1–5 years post-injury) incomplete cervical SCI (AIS B–D) received two intrathecal injections of WJ-MSCs (0.7–1.3 × 10⁶ viable cells/kg) or a placebo at baseline and 3 months. Seventeen participants completed the 12-month follow-up. Primary outcomes assessed safety, and secondary endpoints included International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) motor and sensory scores, spasticity, neuropathic pain, functional independence, neurophysiological measures, and quality of life. Results: Intrathecal WJ-MSC administration was safe and well tolerated. Eighty adverse events occurred (placebo: 26; WJ-MSC: 54), predominantly mild or moderate; four severe events were unrelated to treatment. Both groups demonstrated significant within-group improvements in total motor scores at 12 months, with no between-group difference. No treatment effects were observed for sensory scores, electrophysiological measures, functional independence, spasticity, pain, or patient-reported outcomes. Conclusions: In this first randomised, placebo-controlled trial evaluating intrathecal WJ-MSCs in chronic incomplete cervical SCI, WJ-MSC administration demonstrated a favourable safety profile; however, no significant between-group differences were detected relative to the placebo. Given the limited sample size and early-phase design, the efficacy findings should be interpreted cautiously. Future research should explore enhanced cell products, intensified dosing schedules, optimised delivery strategies, early intervention, and multimodal therapeutic combinations. Full article

Background: Resistant major depression (RMD) is characterized by persistent depressive symptoms despite adequate pharmacological treatment, leading to functional impairment and increased physical comorbidity. Lifestyle interventions, particularly physical activity, are promising adjuncts, yet factors influencing engagement remain poorly understood. Methods: A purposive sampling approach and thematic analysis informed by a socioecological framework were employed to explore participants’ lived experiences after completing a 12-week supervised combined exercise program. Semi-structured interviews were thematically analyzed. Results: Engagement was influenced by three main themes: intrapersonal (symptoms, lifestyle, medication, program expectations), interpersonal (family, peers, healthcare professionals), and environmental (program location, schedule, session design) factors. Motivation was shaped by emotional, physical, and social goals, while barriers included fatigue, anhedonia, and side effects of medication. Conclusions: Engagement in exercise interventions for RMD is shaped by the interaction of personal, social, and environmental factors. Understanding lived experiences can inform the design of person-centered, sustainable interventions. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

Background: Congested tournament schedules impose substantial physiological stress in team sports; however, the integrated endocrine and inflammatory responses to real competitive match load in female handball players remain insufficiently characterized. Objective: This study aimed to characterize the acute biochemical responses, including hormonal, inflammatory, muscle damage, and bone metabolism markers, elicited by competitive tournament load in female handball players and to provide practical insights for optimizing recovery strategies and load management during short-term competitive periods. Methods: In a pre–post study design, venous blood samples were collected from competitive female athletes (n = 8; age 20.83 ± 2.93 years) before the first match and after the fourth consecutive match of an official university qualification tournament. Biochemical analyses included cortisol, insulin, IL-6, creatine kinase (CK), IGF-1, irisin, lactate dehydrogenase (LDH), osteocalcin, and testosterone. Pre-to-post changes were assessed using paired t-tests and effect sizes. Results: Tournament load induced substantial multisystem physiological perturbations. Significant increases were observed in cortisol (p < 0.001), insulin (p = 0.044), IL-6 (p < 0.001), CK (p < 0.001), and osteocalcin (p = 0.005), indicating activation of the hypothalamic–pituitary–adrenal axis, systemic inflammation, muscle membrane disruption, and enhanced bone turnover. Conversely, IGF-1 (p < 0.001) and testosterone (p = 0.004) significantly decreased, reflecting suppression of anabolic signaling and a shift toward a catabolic hormonal environment under cumulative match stress. LDH significantly decreased (p = 0.002), while irisin showed no significant change (p > 0.05). Conclusions: These findings demonstrate that congested tournament schedules provoke an integrated endocrine–inflammatory stress response in female handball players. Importantly, the observed anabolic–catabolic imbalance highlights the need for individualized recovery strategies, optimized load management, and adequate recovery periods to mitigate maladaptation and reduce injury risk during short-term competitive tournaments. Full article

High-rise elevator group control systems operate under pronounced nonstationarity during commuting peaks, post-event surges, and capacity degradation, where the waiting time distribution becomes right-tail heavy and stresses service-level agreements (SLAs) defined by coverage and high-quantile targets. At the same time, the time-of-use tariffs and carbon constraints sharpen the tension between peak-power control, energy savings, and service capacity. This paper proposes a two-layer resilience scheduling framework that integrates queueing-based planning with safe reinforcement learning (RL) fine-tuning. In the planning layer, parsimonious queueing approximations and scenario-based evaluation construct a finite set of implementable mode cards and emergency switching cards; Sample Average Approximation (SAA) combined with Conditional Value-at-Risk (CVaR) constraints filter candidates to enforce tail-risk-aware service limits while keeping power demand within a prescribed envelope. In the execution layer, online dispatch is formulated as a constrained Markov decision process; within the planning layer limits, action masking and Lagrangian safe RL learn small adaptive adjustments to suppress tail-waiting risk and improve recovery dynamics without increasing peak-power commitments. The experiments under morning peaks and post-event surges confirm tail risk reduction and accelerated recovery. For partial outages, the framework prioritizes SLA coverage and recovery speed, accepting a bounded increase in tail risk as a manageable trade-off. Throughout all tests, peak power remains within the prescribed limits. Improvements persist across random seeds and demand fluctuations, indicating distributional robustness and cross-scenario generalization. Ablation studies further reveal complementary roles: removing the planning layer CVaR screening worsens tail performance, while removing the execution layer action masking increases constraint violations and destabilizes recovery. Full article