Search Results (2,370)

Wind turbines are subjected to complex stochastic loadings generated by various environmental sources, including wind, waves, and earthquakes. Efficient mitigation of the resulting vibrations in the structural components, such as the tower and monopile, leads to more cost-effective designs and longer operational life by reducing fatigue accumulation. Conventional vibration control systems have primarily relied on tuned mass dampers. However, alternative and non-conflicting strategies that modify the connection between the tower and the foundation at the transition piece level have recently gained attention. This study investigates the hourglass transition piece (HGTP), a novel concept that utilises the Reduced Column Section approach. The hourglass geometry enables fine-tuning of the wind turbine’s fundamental period and introduces controlled rotational motion, both contributing to a reduction in structural stresses and improved dynamic performance. To assess the efficacy of the HGTP as a vibration control system, an analytical model of a simplified wind turbine is developed. The formulation employs frequency-dependent solutions of prismatic and tapered beam elements, assembled to capture the dynamic behaviour of the turbine equipped with the HGTP. Exact dynamic stiffness matrices are derived and assembled into a stochastic framework suitable for uniformly modulated non-stationary random processes. Modal and dynamic responses are evaluated for different reductions of the hourglass central section. A case study based on the IEA 15 MW Reference Wind Turbine demonstrates that the HGTP can mitigate stochastic mean peak bending moments induced by wind and earthquake excitations by up to 50%, confirming its potential as an effective vibration control solution. Full article

The accurate prediction of extreme dynamic amplification factor (DAF) values is significantly important to ensure a long-term safety assessment of bridges under stochastic vehicular loading. However, predicting extreme DAFs is challenging due to traffic randomness, road roughness variability, and nonlinear vehicle–bridge interaction (VBI) effects. This study presents an integrated framework for extreme DAF prediction for simply supported bridges by combining stochastic traffic–bridge interaction simulations with Bayesian updating and a Peaks-Over-Threshold–Generalized Pareto Distribution (POT–GPD) model. A coupled VBI model is developed, incorporating cellular automaton-based traffic flow, multi-axle nonlinear vehicle dynamics, finite-element bridge modeling, and stochastic road roughness profiles. A new DAF definition based on dynamic displacement difference is proposed to better represent dynamic effects. DAF samples obtained from VBI simulations under different road roughness levels are analyzed using the POT method, with GPD parameters estimated through maximum likelihood and Bayesian inference. Extreme DAFs corresponding to different return periods are then determined. The results indicate that extreme DAF values increase with worsening road roughness and longer return periods and that the Bayesian POT–GPD approach effectively captures tail behavior while providing reliable uncertainty quantification for extreme DAF prediction. Full article

Introduction: Resting-state EEG (rsEEG) is a scalable window onto trait-like “executive readiness,” but findings have been fragmented by task impurity on the executive-function (EF) side and heterogeneous EEG pipelines. This review synthesizes rsEEG features that reliably track EF in healthy samples across development and aging and evaluates moderators such as cognitive reserve. Materials and methods: Following PRISMA 2020, we defined PECOS-based eligibility (human participants; eyes-closed/eyes-open rsEEG; spectral, aperiodic, connectivity, topology, microstate, and LRTC features; behavioral EF outcomes) and searched MEDLINE/PubMed, Embase, PsycINFO, Web of Science, Scopus, and IEEE Xplore from inception to 30 August 2025. Two reviewers were screened/double-extracted; the risk of bias in non-randomized studies was assessed using the ROBINS-I tool. Sixty-three studies met criteria (plus citation tracking), spanning from childhood to old age. Results: Across domains, tempo, noise, and wiring jointly explained EF differences. Faster individual/peak alpha frequency (IAF/PAF) related most consistently to manipulation-heavy working may and interference control/vigilance in aging; alpha power was less informative once periodic and aperiodic components were separated. Aperiodic 1/f parameters (slope/offset) indexed domain-general efficiency (processing speed, executive composites) with education-dependent sign flips in later life. Connectivity/topology outperformed local power: efficient, small-world-like alpha networks predicted faster, more consistent decisions and higher WM accuracy, whereas globally heightened alpha/gamma synchrony—and rigid high-beta organization—were behaviorally sluggish. Within-frontal beta/gamma coherence supported span maintenance/sequencing, but excessive fronto-posterior theta coherence selectively undermined WM manipulation/updating. A higher frontal theta/beta ratio forecasts riskier, less adaptive choices and poorer reversal learning for decision policy. Age and reserve consistently moderated effects (e.g., child frontal theta supportive for WM; older-adult slow power often detrimental; stronger EO ↔ EC connectivity modulation and faster alpha with higher reserve). Boundary conditions were common: low-load tasks and homogeneous young samples usually yielded nulls. Conclusions: RsEEG does not diagnose EF independently; single-band metrics or simple ratios lack specificity and can be confounded by age/reserve. Instead, a multi-feature signature—faster alpha pace, steeper 1/f slope with appropriate offset, efficient/flexible alpha-band topology with limited global over-synchrony (especially avoiding long-range theta lock), and supportive within-frontal fast-band coherence—best captures individual differences in executive speed, interference control, stability, and WM manipulation. For reproducible applications, recordings should include ≥5–6 min eyes-closed (plus eyes-open), ≥32 channels, vigilant artifact/drowsiness control, periodic–aperiodic decomposition, lag-insensitive connectivity, and graph metrics; analyses must separate speed from accuracy and distinguish WM maintenance vs. manipulation. Clinical translation should prioritize stratification and monitoring (not diagnosis), interpreted through the lenses of development, aging, and cognitive reserve. Full article

Background/Objectives: Cognitive impairment and sleep disturbances are highly prevalent in individuals with multiple sclerosis (MS), particularly during middle age, and negatively affect functional independence and quality of life. Although physical exercise has demonstrated cognitive and sleep-related benefits in MS, tolerance to high-intensity training is often limited. Blood flow restriction (BFR) training, which combines low-load resistance exercise with partial vascular occlusion, has emerged as a feasible alternative. This study aimed to evaluate the effects of a 12-week BFR training program on performance in specific cognitive domains and sleep quality in middle-aged adults with MS. Methods: A randomized controlled trial was conducted in 65 adults with relapsing–remitting multiple sclerosis (RRMS) aged 40–65 years and an Expanded Disability Status Scale score below 7. Participants were randomly assigned to a BFR training group or a usual-care control group. The intervention consisted of supervised low-load resistance training with BFR performed twice weekly for 12 weeks. Outcomes assessed before and after the intervention included processing speed (Symbol Digit Modalities Test), executive function (Trail Making Test A and B), verbal fluency (Isaacs Set Test), and self-reported sleep quality (Pittsburgh Sleep Quality Index). Results: Compared with controls, participants in the BFR group showed significant improvements in specific cognitive domains, including processing speed, executive function, and verbal fluency. Significant reductions were also observed in self-reported global sleep disturbance and daytime dysfunction. No adverse events were reported. Conclusions: A 12-week BFR training program improved performance in key cognitive domains and self-reported sleep quality in middle-aged adults with MS, supporting its feasibility and potential clinical relevance as an exercise-based intervention. Full article

Chronic pulmonary regurgitation (PR) after the repair of tetralogy of Fallot (TOF) and other right ventricular outflow tract (RVOT) interventions leads to progressive right ventricular (RV) dilatation, altered ventricular–ventricular interaction, and an increased risk of arrhythmia and heart failure. Pulmonary valve replacement (PVR), whether surgical or transcatheter, effectively eliminates or reduces PR and is associated with short- and mid-term improvement in RV size, symptoms, and electrocardiographic markers. However, the optimal timing of intervention remains unresolved: operating late can result in irreversible myocardial damage and arrhythmogenic substrates, whereas operating early can lead to repeated reinterventions, the impact of which on hard outcomes is uncertain. This review summarizes contemporary evidence on ventricular remodelling after PVR, focusing on cardiovascular magnetic resonance (CMR) and echocardiographic markers, and critically appraises proposed criteria for timing PVR. Classic CMR-derived thresholds (RV end-diastolic volume index [RVEDVi] 150–170 mL/m², RV end-systolic volume index [RVESVi] 80–90 mL/m²) and QRS duration cut-offs are discussed alongside emerging markers of risk, including the RV mass-to-volume ratio, diffuse myocardial fibrosis (extracellular volume fraction), strain imaging, and diastolic dysfunction. Meta-analyses show consistent reverse remodelling and symptomatic benefit after PVR, but no conclusive survival benefit has been demonstrated, and data on arrhythmic outcomes remain conflicting. Key gaps include (i) the lack of prospective randomized or carefully matched comparative studies of “early” versus “deferred” PVR; (ii) limited understanding of how myocardial fibrosis, RV hypertrophy, and diastolic dysfunction interact with volume load and timing to influence long-term outcomes; (iii) under-representation of adult and older adult TOF cohorts; and (iv) insufficient integration of multiparametric risk scores and machine-learning approaches into clinical decision-making. Future research should prioritize multicentre longitudinal cohorts with standardized imaging, electrophysiological and clinical endpoints, incorporate advanced imaging techniques (e.g., strain, 3D late gadolinium enhancement, and T1 mapping), and explore precision-medicine strategies to individualize PVR timing. Full article

In South Asia, smog has become a critical environmental concern that endangers public health, ecosystems, and the regional climate. To determine the primary causes of smog formation in Lahore during peak polluted months (October and November), the current study develops a dual analytical framework that combines cutting-edge machine learning with sector- and pollutant-specific emission analysis. To assess their relationship with Air Quality Index (AQI) and create a high-accuracy predictive model, meteorological factors and emission data from key sectors are used to build Random Forest and extreme gradient boosting (XGBoost) models. The current study evaluates the joint effects of weather and emission loads on AQI variability by integrating atmospheric dynamics with comprehensive emission profiles. The XGBoost model forecasts important pollutants from the transportation, industrial, and agricultural sectors, including carbon dioxide (CO₂), oxides of nitrogen (NO_x), Volatile Organic Compounds (VOCs), and particulate matter, in the second analytical tier. Particulate matter (PM), NO_x, and transport-related pollutants are consistently identified by the models as the primary predictors of AQI, with high prediction performance. Furthermore, a 3-fold split is used for cross-validation, making sure that each fold maintained the data’s chronological order to avoid leakage. The model has modest root mean square error (RMSE) levels (4.32 and 8.14) and high coefficient of determination (R²) values (0.93–0.99). Approximately 90% of Lahore’s annual emissions resulted from the transportation sector. These results offer aid to policymakers to anticipate air quality, identify important emission sources, and execute targeted initiatives to minimize smog and promote a healthier urban environment. The current study also helps in analyzing the causes of atmospheric and sectoral pollution. While the study captures smog dynamics during peak pollution months, its temporal scope is limited, and finer spatial measurements could further improve the generalizability of the results. Full article

Short-term electricity load forecasting has become increasingly challenging due to growing demand volatility, nonlinear load patterns, and the dynamic penetration of renewable energy sources. Conventional forecasting models often suffer from sensitivity to hyperparameter settings and limited capability in capturing long-term temporal dependencies. To address these issues, this paper proposes a hybrid forecasting framework that integrates an Improved Snow Ablation Optimizer (ISAO) with a Dilated Bidirectional Gated Recurrent Unit (Dilated BiGRU). The proposed ISAO enhances the original Snow Ablation Optimizer through three key strategies to improve performance in high-dimensional optimization problems: (i) a subgroup cooperative mechanism to alleviate cross-dimensional interference, (ii) a learning-automata-based adaptive dimension assignment strategy to dynamically allocate optimization resources, and (iii) a t-distribution-based adaptive step size mechanism to balance global exploration and local exploitation. Extensive experiments on the CEC2017 benchmark suite demonstrate that ISAO achieves superior convergence speed and optimization accuracy, with average rankings of 1.60, 1.77, and 2.03 on 30-, 50-, and 100-dimensional problems, respectively, significantly outperforming the original SAO and several state-of-the-art metaheuristic algorithms. Building upon this optimization capability, ISAO is employed to automatically tune the key hyperparameters of the Dilated BiGRU model. Experiments conducted on the Kaggle electricity load dataset show that the proposed ISAO-Dilated BiGRU model achieves MAE, MAPE, and RMSE values of 20.003, 1.711%, and 25.926, respectively, corresponding to reductions of 16.6%, 15.6%, and 17.7% compared with the baseline model, along with an R² of 0.97841. Comparative results against RNN, LSTM, Random Forest, and the original Dilated BiGRU confirm the robustness and superior long-term dependency modeling capability of the proposed framework. Overall, the proposed ISAO effectively enhances hyperparameter optimization quality and significantly improves the predictive accuracy and stability of the Dilated BiGRU model, providing a reliable and practical solution for short-term electricity load forecasting in modern power systems. Full article

High-accuracy short-term electric load forecasting is essential for ensuring the security of power systems and enhancing energy efficiency. Power load sequences are characterized by strong randomness, non-stationarity, and nonlinearity over time. To improve the precision and efficiency of short-term load forecasting in microgrids, a hybrid predictive model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and a multi-strategy enhanced Whale Optimization Algorithm (WOA) with Long Short-Term Memory (LSTM) neural networks has been proposed. Initially, this study employs CEEMD to decompose the short-term electric load time series. Subsequently, a multi-strategy enhanced WOA with chaotic initialization and reverse learning is introduced to enhance the search capability of model parameters and avoid entrapment in local optima. Finally, considering the distinct characteristics of each component, the multi-strategy improved WOA is utilized to optimize the LSTM model, establishing individual predictive models for each component, and the predictions are then aggregated. The proposed method’s forecasting accuracy has been validated through multiple case studies using the UC San Diego microgrid data, demonstrating its reliability and providing a solid foundation for microgrid system planning and stable operation. Full article

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit. Full article

The vertical actuation of multi-axis seismic simulators usually requires a redundant parallel scheme for high load capacity. Due to geometric over-constraints, the internal force coupling and the nonlinear hysteresis are high; thus, waveform reproduction quality and structural fatigue may result. A displacement–force dual closed loop cooperative control mechanism can address these problems. First, a real-time kinematic model is developed to overcome the platform pose via actuator extension, and second, a dynamic force balance loop is introduced to actively redistribute the load components. In addition, a fuzzy PID controller is incorporated to optimize gain scheduling online, compensating for hydraulic nonlinearities and time-varying structural parameters. In the experiment on a 3 × 3 m 6-DOF shaking table, the presented method performs very favorably compared to traditional methods. Under broadband random excitation, the THD of acceleration waveform drops from 15.2% (single-loop control) to 3.2%, and the internal momentum oscillation amplitude is suppressed by over 70%. The results show that our proposed method eliminates internal force dependence while maintaining high precision trajectory tracking for seismic simulation. Full article

Against the backdrop of global climate change, remote subtropical mountainous power grids face severe operational challenges due to their fragile infrastructure and complex climatic conditions. However, existing research has insufficiently addressed load forecasting in data-sparse regions, particularly lacking systematic analysis of the “meteorology–load–failure” coupling mechanism. To address this gap, this study focused on 10 kV distribution lines in a typical subtropical monsoon region of southern China. Based on hourly load and meteorological data from 2016 to 2025, we propose a two-stage hybrid model combining “Random Forest (RF) feature selection + Long Short-Term Memory (LSTM) time series forecasting”. Through deep feature engineering, composite, lagged, and interactive features were constructed. Using the RF algorithm, we quantitatively identified the core drivers of load variation across different time scales: at the hourly scale, variations are dominated by historical inertia (with weights of 0.5915 and 0.3757 for 1-h and 24-h lagged loads, respectively); at the daily scale, the logic shifts to meteorological triggering and cumulative effects, where the composite feature load_lag1_hi_product emerged as the most critical driver (weight of 0.8044). Experimental results demonstrate that the hybrid model significantly improved forecasting accuracy compared to the full-feature LSTM benchmark: on a daily scale, RMSE decreased by 13.29% and MAE by 16.67%, with R² reaching 0.8654; on an hourly scale, R² reached 0.9687. Furthermore, correlation analysis with failure data revealed that most grid faults occurred during intervals of extremely low load variation (0–5%), suggesting that “chronic stress” from environmental exposure in hot and humid conditions is the primary cause, with lightning identified as the leading external threat (26.90%). The interpretable forecasting framework proposed in this study transcends regional limitations. It provides a strategic “low-cost, high-resilience” prototype applicable to power systems in humid-subtropical zones worldwide, particularly for developing regions facing the dual challenges of data sparsity and climate vulnerability. Full article

Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article

Background: Exercise-induced fatigue (EIF) impairs performance and recovery and may contribute to overreaching/overtraining and adverse health outcomes. Beyond classical explanations (substrate depletion, metabolite accumulation, oxidative stress), accumulating evidence indicates that the gut microbiota modulates fatigue-related physiology through metabolic, immune, barrier, and neurobehavioral pathways. Methods: We conducted a structured narrative review of PubMed and Web of Science covering 1 January 2015 to 30 November 2025 using predefined keywords related to EIF, gut microbiota, recovery, and nutritional interventions. Human studies, animal experiments, and mechanistic preclinical work (in vivo/in vitro) were included when they linked exercise load, microbial features (taxa/functions/metabolites), and fatigue-relevant outcomes. Results: Across models, high-intensity or prolonged exercise is consistently associated with disrupted gut homeostasis, including altered community structure, reduced abundance of beneficial taxa, increased intestinal permeability, and shifts in microbial metabolites (e.g., short-chain fatty acids). Evidence converges on four interconnected microbiota-mediated pathways relevant to EIF: (1) energy availability and metabolic by-product clearance; (2) redox balance and inflammation; (3) intestinal barrier integrity and endotoxemia risk; and (4) central fatigue and exercise motivation via microbiota–gut–brain signaling. Nutritional strategies—particularly targeted probiotics, prebiotics/plant polysaccharides, and selected bioactive compounds—show potential to improve fatigue biomarkers and endurance-related outcomes, although effects appear context-dependent (exercise modality, baseline fitness, diet, and baseline microbiota). Conclusions: Current evidence supports a mechanistic role of the gut microbiota in EIF and highlights microbiota-targeted nutrition as a promising adjunct for recovery optimization. Future work should prioritize causal validation (e.g., fecal microbiota transplantation and metabolite supplementation), athlete-focused randomized trials with standardized fatigue endpoints, and precision approaches that stratify individuals by baseline microbiome features and training load. Full article

Diabetes mellitus remains a major global health burden, and many patients do not achieve durable glycemic control despite modern pharmacotherapy. This narrative review synthesizes evidence on plant-derived strategies that may complement standard care, focusing on two clinically aligned domains: glucose-lowering medicinal plants and plant-based sugar substitutes that reduce dietary glycemic load. We summarize key mechanistic pathways, including inhibition of α-amylase/α-glucosidase, reduced intestinal glucose entry and absorption kinetics, glucose-dependent insulinotropic effects, improved insulin signaling, suppression of hepatic gluconeogenesis, and microbiota-linked effects. We critically appraise human evidence for selected botanicals (cinnamon, fenugreek, mulberry, gymnema, gynura, rosehip, and Jerusalem artichoke) and plant sweeteners (stevia and monk fruit). Overall, clinical effects are modest and heterogeneous; the most reproducible signals are observed for mulberry leaf in blunting postprandial glucose excursions, and for cinnamon, fenugreek, and gymnema, where meta-analyses suggest modest improvements in glycemic markers. Stevia and monk fruit are best supported as glycemically neutral sucrose substitutes, while inulin-type fructans show small-to-moderate benefits with sustained intake, limited by gastrointestinal tolerability at higher doses. Key gaps include a shortage of long-term randomized trials using standardized preparations and durable endpoints such as glycated hemoglobin. Plant-derived interventions are therefore best positioned as adjuncts within individualized, evidence-based glycemic management. Full article

Integrated satellite-terrestrial networks are crucial for critical communications, yet the initial access for user equipment (UE) is hampered by signal blockage and dynamic loads, challenging traditional random access (RA) mechanisms in achieving low latency and high success rates. To address this, we propose a Sensing-aware Random Access (SaRA) mechanism. SaRA introduces a lightweight sensing micro-slot before the standard RACH procedure, leveraging the sensing signal to jointly determine an optimal access decision threshold and a candidate beam set. This proactively filters users with poor channel conditions and narrows the beam search space. We formulate the resource allocation as a constrained optimization problem and propose a practical, low-complexity algorithm. Extensive simulations validate that SaRA provides substantial gains in access latency and system access capacity under high-load conditions compared with the standard 3GPP FR2 RACH baseline, while maintaining competitive first-attempt success probability with minimal additional overhead. Full article