Search Results (384)

Ultra-short-term photovoltaic (PV) power forecasts are vital for secure grid operation as solar penetration rises. We propose a two-stage hybrid framework, WDT–CRMABIL–Fusion. In Stage 1, we apply a three-level discrete wavelet transform to PV power and key meteorological series (shortwave radiation and panel irradiance). We then forecast the approximation and detail sub-series using specialized component predictors: a 1D-CNN with dual residual multi-head attention (feature-wise and time-wise) together with a BiLSTM. In Stage 2, a compact dense fusion network recombines the component forecasts into the final PV power trajectory. We use 5-minute data from a PV plant in Milan and evaluate 5-, 10-, and 15-minute horizons. The proposed approach outperforms strong baselines (DCC+LSTM, CNN+LSTM, CNN+BiLSTM, CRMABIL direct, and WDT+CRMABIL direct). For the 5-minute horizon, it achieves MAE = 1.60 W and RMSE = 4.21 W with $R^2$ = 0.943 and CORR = 0.973, compared with the best benchmark (MAE = 3.87 W; RMSE = 7.89 W). The gains persist across K-means++ weather clusters (rainy/sunny/cloudy) and across seasons. By combining explicit multi-scale decomposition, attention-based sequence learning, and learned fusion, WDT–CRMABIL–Fusion provides accurate and robust ultra-short-term PV forecasts suitable for storage dispatch and reserve scheduling. Full article

Background: Metabolic syndrome, a clinical condition defined by central obesity, impaired glucose regulation, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol across the lifespan, is now a major public health issue typically managed with lifestyle, behavioral, and dietary recommendations. However, “one-size-fits-all” recommendations often yield modest, heterogeneous responses and poor long-term adherence, creating a clinical need for more targeted and implementable preventive and therapeutic strategies. Objective: To synthesize evidence on how the gut microbiome can inform precision nutrition and exercise approaches for metabolic syndrome prevention and management, and to evaluate readiness for clinical translation. Key findings: The gut microbiome may influence cardiometabolic risk through microbe-derived metabolites and pathways involving short-chain fatty acids, bile acid signaling, gut barrier integrity, and low-grade systemic inflammation. Diet quality (e.g., Mediterranean-style patterns, higher fermentable fiber, or lower ultra-processed food intake) consistently relates to more favorable microbial functions, and intervention studies show that high-fiber/prebiotic strategies can improve glycemic control alongside microbiome shifts. Physical exercise can also modulate microbial diversity and metabolic outputs, although effects are typically subtle and may depend on baseline adiposity and sustained adherence. Emerging “microbiome-informed” personalization, especially algorithms predicting postprandial glycemic responses, has improved short-term glycemic outcomes compared with standard advice in controlled trials. Targeted microbiome-directed approaches (e.g., Akkermansia muciniphila-based supplementation and fecal microbiota transplantation) provide proof-of-concept signals, but durability and scalability remain key limitations. Conclusions: Microbiome-informed personalization is a promising next step beyond generic guidelines, with potential to improve adherence and durable metabolic outcomes. Clinical implementation will require standardized measurement, rigorous external validation on clinically meaningful endpoints, interpretable decision support, and equity-focused evaluation across diverse populations. Full article

Accurate ultra-short-term wind power forecasting (WPF) is essential for maintaining power grid stability and minimizing economic risks, yet the inherent volatility of wind speed poses significant modeling challenges. To address this, this study proposes an ensemble framework integrating an Improved Triangular Topology Aggregation Optimizer (ITTAO) and a high-frequency adaptive weighting strategy. Methodologically, the ITTAO incorporates multi-strategy mechanisms to overcome the premature convergence of the traditional TTAO, thereby enabling precise hyperparameter optimization for the variational mode decomposition (VMD) and BiLSTM networks. Furthermore, in the reconstruction stage, a dynamic weighting strategy is introduced to modulate the contribution of high-frequency sub-sequences, thereby enhancing the capture of rapid fluctuations. Experimental results across multi-seasonal datasets demonstrate that the proposed hybrid model consistently outperforms representative baselines. Notably, in the most volatile scenarios, the model achieves an NMAE of 1.33%, an NRMSE of 2.20%, and an $R^2$ of 98.18%. The results demonstrate that the proposed model achieves superior forecasting accuracy, enhancing the operational stability of wind farms and the secure integration of wind energy into the power grid. Full article

This study investigates amorphous anodized porous TiO₂ (a-TiO₂) as a substrate for iridium-based oxygen evolution catalysts. The substrates were prepared via anodization of Ti foil in a glycerol-based solution for 15 min @ 60 V. Nickel was subsequently electrodeposited to act both as a conductive and sacrificial layer for the galvanic deposition of iridium from an Ir(IV) chloro-complex solution. Electrochemical anodization resulted in a uniform IrO_x layer on the a-TiO₂ substrate, featuring Ir aggregates ~250 nm in size and an Ir:Ni atomic ratio of ca. 7, as determined by EDS analysis. The quantity of Ni determined by ICP-MS bulk analysis indicated that Ni resided also within the porous matrix. Varying the Ni deposition charge density (q_Ni) revealed that an intermediate loading (1463 mC cm⁻²) provided the best balance between Ir accessibility during the galvanic replacement step and electronic continuity. The optimized IrO_x/Ir-Ni/a-TiO₂ electrode achieved excellent OER performance (η = 344 mV @ 10 mA cm⁻²; 1.68 mA μg_Ir⁻¹ @ η = 300 mV) at an ultra-low Ir loading of 2.15 μg_Ir cm⁻² and demonstrated good short-term stability, with only a 20 mV potential increase over 4 h of continuous operation at 5.5 mA cm⁻². Overall, this strategy offers a scalable pathway for producing efficient OER electrodes with minimal noble metal loading. Full article

Accurate photovoltaic (PV) power forecasting is critical for the stable operation of power systems. Existing methods rely solely on historical data, which significantly decline in forecasting accuracy at 3–4 h ahead. To address this problem, a novel ultra-short-term PV power forecasting method based on temporal attention-variable parallel fusion encoder network is proposed to enhance the stability of forecasting results by incorporating Numerical Weather Prediction data to correct temporal predictions. Specifically, independent encoding modules are constructed for both historical power sequences and future NWP sequences, enabling deep feature extraction of their respective temporal characteristics. During the decoding phase, a two-stage coupled decoding strategy is employed: for 1–8 steps predictions, the model relies solely on temporal features, while for 9–16 steps horizons, it dynamically fuses encoded information from historical power data and future NWP inputs. This approach allows for accurate characterization of future trend dynamics. Experimental results demonstrate that, compared with conventional methods, the proposed model reduces the average normalized root mean square error (NRMSE) at 4th ultra-short-term forecasting by 0.50–5.20%, while it improves the $R^2$ by 0.047–0.362, validating the effectiveness of the proposed approach. Full article

Background: Branched-chain amino acids (BCAAs) are popular as sports supplements due to their ability to enhance performance and recovery. However, the full spectrum of metabolic alterations triggered by acute supplementation with BCAAs in conjunction with exercise remains incompletely understood. Methods: A randomized crossover trial was conducted in 8 healthy active young males, who received either BCAA or placebo supplementation for three consecutive days prior to a high-intensity cycling test. Plasma samples were collected pre- and post-exercise and analyzed by ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry, followed by correlation and enrichment analyses. Results: Acute BCAA supplementation was significantly associated with enhanced fat oxidation and attenuated post-exercise increases in plasma ammonia, creatine kinase, and lactate dehydrogenase, suggesting the potential improvements in energy supply and membrane stability. Metabolomics analysis identified differential metabolites primarily involved in lipid, amino acid, and glucose metabolism. Pathway enrichment revealed coordinated regulation of fatty acid oxidation (FAO) and tryptophan-related pathways. Correlation analysis further showed that changes in metabolite profiles were strongly associated with biochemical outcomes, particularly linking enhanced fat oxidation and ammonia clearance with BCAA intake. Conclusions: Short-term BCAA supplementation could enhance FAO and membrane stability via coordinated regulation of lipid and amino acid metabolism post exercise, supporting its potential role as a precision nutrition strategy. Full article

Accurate ultra-short-term wind power forecasting is vital for the secure and economic operation of power systems with high renewable penetration. Conventional models, however, struggle with multi-scale frequency feature extraction, dynamic cross-variable dependencies, and simultaneously capturing local fluctuations and global trends. This study proposes a novel hybrid framework termed VMD–ALIF–GraphBlock–MLLA–Transformer. A dual-decomposition strategy combining variational mode decomposition and adaptive local iterative filtering first extracts dominant periodic components while suppressing high-frequency noise. An adaptive GraphBlock with MixHop convolution then models structured and time-varying inter-variable dependencies. Finally, a multi-scale linear attention-enhanced Mamba-like module and Transformer encoder jointly capture short- and long-range temporal dynamics. Experiments on a real wind farm dataset with 10-min resolution demonstrate substantial superiority over State-of-the-Art baselines across 1-, 4-, and 8-step forecasting horizons. SHAP analysis further confirms excellent consistency with underlying physical mechanisms. The proposed framework provides a robust, accurate, and highly interpretable solution for intelligent wind power forecasting. Full article

Ultra-Wideband (UWB) technology enables centimeter-level indoor positioning, but it remains highly sensitive to channel dynamics, multipath and Non-Line-of-Sight (NLoS) propagation. Recent studies increasingly apply Machine Learning (ML) methods to address these issues by modeling nonlinear channel behavior and mitigating ranging bias. This paper presents a comprehensive review and provides a critical synthesis of 169 research works published between 2020 and 2024, offering an integrated overview of how ML techniques are incorporated into UWB-based Indoor Positioning Systems (IPSs). The studies are grouped according to their functional objective, learning algorithm, network architecture, evaluation metrics, dataset, and experimental setting. The results indicate that most approaches apply ML to channel classification and ranging error mitigation, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid CNN–Long Short-Term Memory (LSTM) architectures being among the most common choices due to their ability to capture spatial and temporal patterns in the Channel Impulse Response (CIR). Despite the reported accuracy improvements, scalability and cross-environment generalization remain open challenges, largely due to the scarcity of public datasets and the lack of standardized evaluation protocols. Emerging research trends highlight growing interest in transfer learning, domain adaptation, and federated learning, along with lightweight and explainable models suitable for embedded and multi-sensor systems. Overall, this review summarizes the progress made in ML-driven UWB localization, identifies current gaps, and outlines promising directions toward more robust and generalizable indoor positioning frameworks. Full article

Background/Objectives: Nutritional patterns influence the gut–brain axis and immune signaling with potential consequences for depression and anxiety. We conducted a review focused on clinically meaningful psychiatric outcomes (symptom severity/diagnosis) to synthesize recent evidence (2020–2025) on Mediterranean-style dietary interventions; ultra-processed food (UPF) exposure; and psychobiotic/prebiotic strategies, integrating mechanistic insights relevant to practice. Methods: Searches in PubMed/MEDLINE, Scopus, and Web of Science (January 2020–October 2025) combined terms for diet, Mediterranean diet (MD), UPF, microbiota, probiotics, psychobiotics, depression, and anxiety. Eligible designs were randomized/controlled trials (RCTs), prospective cohorts, and systematic reviews/meta-analyses reporting clinical psychiatric outcomes in adults. We prioritized high-quality quantitative syntheses and recent RCTs; data were extracted into a prespecified matrix and synthesized narratively. Results: Recent systematic reviews/meta-analyses support that MD interventions reduce depressive symptoms in adults with major or subthreshold depression, although large, long-term, multicenter RCTs remain a gap. Exposure to UPF is consistently associated with higher risk of common mental disorders and depressive outcomes in large prospective cohorts. Psychobiotics (specific probiotic strains and prebiotics) show small-to-moderate benefits on depressive symptoms across clinical and nonclinical samples, with heterogeneity in strains, dosing, and duration. Mechanistic reviews implicate microbiota-derived metabolites (short-chain fatty acids) and immune–inflammatory signaling (including tryptophan–kynurenine pathways) as plausible mediators. Conclusions: Clinically, emphasizing Mediterranean-style dietary patterns, reducing UPF intake, and considering targeted psychobiotics may complement standard psychiatric care for depression. Future work should prioritize adequately powered, longer RCTs with standardized dietary protocols and microbiome-informed stratification to clarify responders and mechanisms. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

Photovoltaic (PV) power generation is inherently intermittent and volatile, complicating power system operation and control. Accurate forecasting is crucial for proactive grid responses and optimal energy resource scheduling. This study proposes a novel hybrid forecasting model that achieves high-precision PV power forecasting by integrating similar-day clustering, generating extreme weather samples, and optimizing the Bidirectional Temporal Convolutional Network (BiTCN) and Bidirectional Gated Recurrent Unit (BiGRU) model via the Animated Oat Optimization (AOO) algorithm. The proposed method outperforms other models in the three evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). The innovations lie in the integration of similar-day clustering with deep learning and the application of AOO for hyperparameter optimization, which significantly enhances forecasting accuracy and robustness. Full article

Heart rate variability (HRV) is a non-invasive biomarker that reflects autonomic nervous system dynamics, providing valuable insights into physiological adaptation, stress, and recovery in athletes. Among the various HRV metrics, the root mean square of successive differences (RMSSD) has emerged as a robust and practical measure due to its strong association with parasympathetic activity, ease of calculation, and reliability in both short- and ultra-short-term recordings. This review examines the methodological considerations for using HRV to monitor training adaptations and recovery status in athletic populations. We highlight the superiority of routine, near-daily HRV measurements over isolated assessments, emphasizing the utility of weekly averages and the coefficient of variation (CV) to capture both chronic adaptations and acute homeostatic perturbations. Additionally, we discuss the selection of HRV devices, data recording procedures, and strategies to enhance athlete compliance. While RMSSD offers significant advantages for field-based monitoring, we also address its limitations, including its sole focus on parasympathetic activity and susceptibility to external confounders. Future directions include the integration of HRV data with other physiological markers and machine learning algorithms to optimize individualized training and recovery strategies. This review provides sport scientists and practitioners with evidence-based recommendations to enhance the application of HRV in both research and real-world athletic settings. Full article

Satellite clock bias exhibits complex, time-varying periodic characteristics due to environmental disturbances. Accurate modeling and prediction of periodic terms play a crucial role in improving the precision and stability of short-term predictions. Traditional models such as spectral analysis model (SAM) estimate the frequency, amplitude, and phase of periodic terms through global fitting, which limits their ability to adapt to abrupt changes at the prediction boundary. To address this limitation, this paper proposes an improved spectral analysis model (IM-SAM) based on the inaction method (IM). The model employs IM to extract the instantaneous frequency, amplitude, and phase parameters of periodic terms precisely at the data endpoint, and utilizes the parameters of periodic terms at the data endpoint for prediction, effectively suppressing periodic fluctuations in prediction errors. Experimental results based on real GPS clock bias data demonstrate that the root mean square (RMS) of IM-SAM prediction errors is reduced by 19.14%, 14.39%, and 10.48% for 3 h, 6 h, and 12 h prediction tasks, respectively, compared with SAM. Furthermore, a kinematic precise point positioning experiment was performed using IM-SAM-predicted clock products and compared with the predicted half of IGS ultra-rapid clock products. The RMS of position error was reduced by 14.3%, 12.6%, and 7.9% in the east, north, and up directions, respectively. These results demonstrate the practical effectiveness and accuracy of IM-SAM in real-time clock prediction and GPS positioning applications. Full article

High-density micro-electrocorticography (μECoG) arrays offer precise spatial resolution with minimal invasiveness. This study employed a custom ultra-thin 64-channel μECoG array to investigate cortical activity in mice under chronic caffeine exposure. While caffeine is known to enhance short-term alertness, its long-term impact on sleep microarchitecture and brain connectivity is unclear. Continuous recordings from adult mice during baseline and recovery revealed that prolonged caffeine intake significantly reduced broadband power spectral density (PSD) and spindle power but increased interregional coherence and altered spindle duration and density. In contrast, six hours of sleep deprivation elevated PSD and coherence, mainly affecting sensorimotor and retrosplenial cortices. These findings validate the μECoG array’s functionality and demonstrate that post-chronic caffeine withdrawal lowers cortical oscillatory power yet enhances network connectivity, whereas acute sleep loss boosts global synchrony. This work clarifies how sustained caffeine use and sleep deprivation distinctly disrupt sleep homeostasis through different neural mechanisms. Full article

Background/Objectives: Dietary inorganic nitrate (NO₃⁻), primarily sourced from vegetables such as beetroot, has been shown to enhance nitric oxide (NO) bioavailability, with emerging evidence suggesting its potential to modulate autonomic function. However, the effects of NO₃⁻ supplementation on cardiac autonomic recovery post-exercise in hypertensive postmenopausal women remain poorly understood. Using data from a previously conducted randomised controlled trial, this study investigated the effects of acute (800 mg) and seven-day (400 mg/day) beetroot juice NO₃⁻ supplementation on ultra-short-term post-exercise cardiac parasympathetic recovery in hypertensive older women. Methods: In a triple-blind, placebo-controlled crossover design, fourteen postmenopausal women (59 ± 4 y) with hypertension completed two intervention arms (NO₃⁻ and placebo). Ultra-short-term heart rate variability (HRV) indices (SDNN, RMSSD, HF) were assessed across 5 min post-exercise recovery using 60 s windows. Plasma NO₂⁻ and NO₃⁻ concentrations were measured via chemiluminescence. Results: Both acute and seven-day NO₃⁻ supplementation significantly increased plasma NO₂⁻ and NO₃⁻ concentrations compared to placebo (p < 0.001). Cardiac vagal recovery, assessed via SDNN and RMSSD, was significantly enhanced in both conditions, with greater and more sustained improvements observed after the seven-day protocol. HF power was significantly higher, but only after seven-day supplementation (p = 0.009). Conclusions: Inorganic NO₃⁻ supplementation enhances post-exercise cardiac parasympathetic reactivation in hypertensive postmenopausal women. Notably, the seven-day intake (400 mg/day) protocol elicited superior autonomic benefits compared to an acute high dose. These findings highlight the potential of NO₃⁻ as a non-pharmacological strategy for improving cardiovascular autonomic recovery in high-risk populations. Full article