Search Results (58,307)

Cardiovascular disease (CVD) is closely linked to metabolic disorders such as obesity, dyslipidemia, and hepatic steatosis. This study investigated the anti-thrombotic and metabolic effects of KoreaGinseng F Max (KGF), a standardized extract rich in ginsenosides, in high-fat diet (HFD)-induced obese rats, and complementary in silico analyses were used to explore putative molecular targets and pathways. The extract was standardized to contain 36.97 mg/g of ginsenosides Rg1, Rb1, and Rf. Male rats were administered KGF (50, 100, or 200 mg/kg) orally for six weeks. KGF significantly improved lipid profiles by reducing serum triglycerides, total cholesterol, and low-density lipoprotein (LDL) levels. Histological analysis revealed a dose-dependent reduction in hepatic steatosis and adipocyte size. Potential anti-thrombotic activity was evaluated using a FeCl₃-induced carotid artery thrombosis model, with aspirin (30 mg/kg) included as a positive control. KGF200 delayed thrombus formation and produced a carotid blood flow pattern comparable to that observed in the aspirin-treated group, without significant alterations in serum ALT, AST, BUN, or creatinine levels. To further generate mechanistic hypotheses, complementary in silico analyses, including target prediction, GO/KEGG enrichment, network analysis, and molecular docking, were performed using the marker compounds. Eight overlapping genes, including STAT3, PTAFR, VEGFA, FGF2, HPSE, IL2, HSP90AA1, and LGALS3, associated with thrombotic regulation were identified. Pathway analysis suggested that PI3K–Akt signaling, calcium signaling, Th17 cell differentiation, and proteoglycan/ECM-related signaling may represent putative pathway-level mechanisms underlying the observed protective effects. Molecular docking suggested possible interactions between the marker ginsenosides and several predicted hub targets. Collectively, these findings suggest that KGF may have potential for further investigation as a natural product-derived material for improving HFD-associated metabolic and thrombotic dysfunction, while the predicted multi-target and multi-pathway effects require further experimental validation. Full article

Accurate photovoltaic (PV) power forecasting is important for grid dispatch, energy-storage management, and electricity-market trading. This paper presents a PV power forecasting framework that combines multi-model ensemble learning with multi-perspective interpretability analysis. A 22-dimensional feature set was constructed from irradiance, meteorological, temporal, and lagged-power variables. Five supervised regressors, a zero-shot Chronos-Bolt-Small time-series foundation model, and six additional forecasting baselines were evaluated at two PV plants. The three ensemble schemes used only the five supervised regressors and were compared on the final 20% of the 2019 development data; the complete 2020 period was used only for final evaluation. The selected methods achieved normalized RMSE values of 0.0350 and 0.0376 at Sites 1 and 2, respectively. SHAP, PDP/ICE, LIME, and permutation importance were applied to the ensemble model at each site. Recent power-history features dominated this one-step-ahead forecasting task with a 15 min horizon, while irradiance features provided additional site-dependent information. Full article

Background: Digital behavioral phenotyping of autism spectrum disorder (ASD) offers a promising approach for developing more scalable diagnostic frameworks across diverse global contexts. Machine learning (ML) models show promise for ASD diagnosis using behavioral videos, but critical questions remain regarding whether models trained on data from one country work in another, and how the background of the raters affects the accuracy. Our work addresses these questions by testing whether ML models can accurately diagnose ASD across different populations and rater groups. Methods: This work evaluates the performance of a supervised ML framework for binary classification of ASD versus non-ASD [speech, language and communication disorders (SLC) + neurotypical (NT)] in a cohort of 227 children in Bangladesh. We first assessed the cross-domain model transferability of a clinical-instrument-trained logistic regression model (LR-9) on behavioral ratings that were based on videos of Bangladeshi children interacting with caregivers and toys at two major child development centers in Dhaka, Bangladesh. We then trained five diverse classifiers (Logistic Regression, Random Forest, XGBoost, SVM, and RuleFit) on the full annotated Bangladeshi dataset. Using SHAP-based consensus elbow feature selection, we identified a compact set of features that maintained the performance. Finally, we developed ensemble models to improve predictive stability. Results: The LR-9 model, originally trained on U.S. clinical instrument data, was evaluated on video-based behavioral ratings from 214 Bangladeshi children. When tested on Bangladeshi clinician ratings, the LR-9 model achieved a sensitivity of 86.1% (95% CI: [0.78–0.93]) and AUC of 0.79 (95% CI: [0.73–0.86]). The distinction across rater groups was between trained raters (clinicians and students) and crowd workers, who showed lower sensitivity 28.5% (95% CI: [0.21, 0.39]). When tested on the aggregated ratings from all groups, the model achieved an AUC of 0.78 (95% CI: [0.72–0.84]). Inter-rater reliability followed the same pattern: individual agreement was fair (Krippendorff’s α = 0.26), but the multi-rater consensus was reliable (ICC(1,k) = 0.84), with Bangladeshi clinicians showing the highest agreement (α = 0.34) and crowd workers the lowest (α = 0.20). We then trained new models directly on the Bangladeshi ratings. All model types achieved similar AUC values (0.86–0.89), with overlapping confidence intervals. Using just 8–11 key behaviors kept the similar performance while cutting the features by 66–75%. Combining ensembles gave similar results (e.g., Bayesian averaging: AUC 0.88 [0.78, 0.95]) but with more stable predictions. Conclusion: This study provides evidence that mobile video-based ASD diagnosis can achieve comparable performance (AUC: 0.89 [0.76, 0.96]) to models trained on clinical instrument data. This work contributes to the development of broader adaptable autism detection tools, bypassing the dependence on traditional clinical instrument data. Full article

6PPD and its ozonation product 6PPD-quinone (6PPD-Q) are ubiquitous tire-derived pollutants linked to environmental and potential human health risks. This study systematically investigated their mechanisms in lipid-metabolism-related diseases (atherosclerosis, type 2 diabetes, and nonalcoholic fatty liver disease) through network toxicology, transcriptomic validation, molecular docking, and experimental models. Targets of 6PPD and 6PPD-Q were predicted using multiple databases and intersected with disease-associated genes. Protein–protein interaction networks, hub gene screening, GO/KEGG enrichment, and GEO transcriptomic datasets identified key shared core targets, including PTGS2, MMP9, CXCL8 (for 6PPD), MAPK14, and PTGS2 (for 6PPD-Q). Molecular docking suggested potential strong binding affinities. Integrative analysis highlighted convergence on oxidative stress, inflammation, lipid dysregulation, and MAPK signaling. In vivo, 40-day exposure to 6PPD and 6PPD-Q in C57BL/6 mice induced hepatic steatosis, elevated serum TC, LDL-C, and HDL-C, upregulated inflammatory cytokines (TNF-α, IL1B, IL6, and IFNG), and core targets. In vitro, both compounds caused dose-dependent cytotoxicity, ROS accumulation, glutathione redox imbalance, and pro-inflammatory activation. These findings suggest that 6PPD and 6PPD-Q may contribute to lipid-metabolism-related toxic responses through shared and distinct processes involving oxidative stress, inflammatory activation, and lipid dysregulation. These results provide preliminary mechanistic insights into their metabolic toxicity and support further experimental evaluation for environmental health risk assessment. Full article

Accurately predicting time-difference signals is a key prerequisite for extracting effective temporal characteristics from the output of RTD fluxgate sensors and enhancing the measurement reliability of the sensors. However, the nonlinear characteristics and temporal dependencies of residence time difference (RTD)-fluxgate time-difference signals make accurate prediction difficult. In this study, an improved Transformer neural network model is proposed for RTD-fluxgate time-difference signals. By introducing positional encoding, the proposed method incorporates temporal position information into the Transformer model, enabling effective extraction of temporal features from sensor output signals. To alleviate overfitting during model training, dropout and weight decay strategies are introduced to improve the generalization capability of the prediction model. The predicted time-difference signals are analyzed and the key temporal features are extracted for sensor signal processing applications. The proposed method is compared with feedforward neural networks and long short-term memory networks. Experimental results obtained under the same constant magnetic field demonstrate that the proposed method improves cosine similarity (CS) by 37% and 4%, and reduces mean square error (MSE) by 29% and 2%, respectively. The results verify the effectiveness of the proposed approach for RTD-fluxgate time-difference signal prediction and provide a data-driven method for sensor signal analysis and performance evaluation in magnetic measurement applications. This method provides a novel approach for predicting the time-difference signals of RTD fluxgate sensors, offering a reliable reference for subsequent signal processing and improving the accuracy and consistency of magnetic field data. Full article

Epilepsy is increasingly recognized as a systemic disorder involving complex interactions between the brain and peripheral systems. Among these, the gut microbiota has emerged as a key regulator of host metabolism and immune homeostasis through the production of bioactive metabolites that mediate the communication between gut and brain. In recent years, growing evidence has linked gut dysbiosis to epilepsy, particularly in drug-resistant forms, and interventional studies targeting the gut microbiota in animal models suggest that microbiota-driven metabolic alterations may contribute to seizure generation and recurrence, as well as the associated neuropathology and cognitive deficits. In this review, we summarize current knowledge on the role of the gut microbiota–metabolome axis in acquired epilepsy, with a particular focus on short-chain fatty acids (SCFAs) and tryptophan-derived pathways. SCFAs represent major microbial products involved in energy metabolism, inflammation, blood–brain barrier integrity, neurotransmission and epigenetic mechanisms. In parallel, microbiota-dependent tryptophan metabolism represents a central hub linking intestinal microbial activity to brain function through serotonin, kynurenine, and indole pathways. Dysregulation of these pathways may influence neuronal excitability and contribute to seizures. Converging evidence supports the concept that epilepsy is associated with a coordinated alteration of gut microbial composition and host–microbiota metabolic interactions. However, further research is needed to elucidate the mutual communication between the gut and its microbiota and the metabolic flux, and their influence on brain function in neurological conditions. A better understanding of the underlying pathways and mechanisms may highlight novel therapeutic strategies and discover novel biomarkers of disease trajectory. Full article

Underwater object detection is crucial for marine ecological monitoring and resource exploration. However, low underwater contrast causes severe aliasing between foreground and background in the spatial domain, and conventional methods struggle to effectively decouple their features. Transformer architectures incur high computational overhead. Conversely, over-compressing these models severely degrades their ability to detect heavily camouflaged or small marine organisms. High-quality underwater samples are limited, and existing time-consuming generative strategies are hard to implement efficiently on edge devices. To address these challenges, this paper proposes a resource-efficient framework for underwater object detection. First, we design a Wavelet-Enhanced Feature Pyramid Network that combines a saliency-focus mechanism and a discrete wavelet transform to overcome background noise in both spatial and frequency domains, extracting features of hidden small objects. Second, a data-dependent dynamic token pruning technique removes redundant tokens, effectively mitigating the computational bottleneck without sacrificing essential semantic capacity. Finally, for extreme sample scarcity, we introduce a Feature Correction Module and a two-stage fine-tuning and feature correction strategy, using a high-precision teacher model to guide a compressed student network in adaptively compensating for optical shifts with few samples. Experiments on URPC2020 and DUO demonstrate that our method improves small object detection accuracy while reducing parameter count and computational overhead, striking a good balance between accuracy and inference efficiency. Full article

Optimal reactive power flow (ORPF) is a steady-state optimization problem used to determine reactive-power-related control settings in AC power systems while satisfying network operating constraints. In renewable-integrated transmission systems, explicitly representing the feasible reactive-power contribution of inverter-interfaced resources is essential, because wind, photovoltaic (PV), and battery energy storage system (BESS) units cannot operate as unlimited or overly flexible reactive-power sources. Their reactive capability depends on active-power output, apparent-power rating, voltage conditions, and equipment-level capability curves. This paper evaluates the impact of including these capability curves in the solution of the ORPF problem. A MATLAB-DIgSILENT PowerFactory co-simulation framework is implemented, in which MATLAB applies a hybrid particle swarm optimization-pattern search procedure and DIgSILENT PowerFactory performs repeated AC power-flow evaluations using detailed network models and predefined capability limits. The framework is tested on modified IEEE 39-bus and IEEE 118-bus systems with wind, PV, and BESS resources. The results show that neglecting capability curves can produce unrealistic reactive-power allocations for inverter-based units, whereas enforcing these limits shifts the ORPF solution toward operating points consistent with the modeled equipment capability. The study demonstrates the importance of capability-curve representation for obtaining physically meaningful steady-state ORPF results and supports clearer comparison of constrained and unconstrained dispatch cases. Full article

Biogenic volatile organic compounds released from soil (SBVOCs) are an important component of the material exchange and information transmission between terrestrial ecosystems and the atmosphere. Soil ecosystems act as both critical sources and frequently overlooked sinks of BVOCs. SBVOC emissions are mainly regulated by the temperature, moisture, and pH of the soil. Climate warming may enhance volatilization and microbial production in the short term. However, its long-term effects depend on drought, vegetation composition, substrate availability, permafrost thaw, and microbial acclimation. SBVOCs also influence microbial activity, nutrient cycling, plant–microbe interactions, plant defence, and below ground trophic interactions, although the strength of evidence differs among these functions. Ecologically, SBVOCs promote carbon cycling, modulate plant-microbe interactions, and influence atmospheric chemistry. This review further synthesizes SBVOC emission and uptake patterns across different climatic zones. Several challenges remain, particularly the scarcity of long-term quantitative measurements and difficulties in distinguishing multiple emission sources. Our understanding of rhizosphere interactions and climate-change feedback is also limited. It is essential to enhance long-term observational studies and optimize models to deepen our understanding of the role of SBVOCs in the global carbon cycle and air quality. Full article

This paper addresses human error in network orchestration systems and the high cost and resource requirements of integrating artificial intelligence (AI) for network orchestration. It proposes a framework for implementing an AI decision-maker and automation. Data are fed into the AI decision-maker to trigger designated automation robots’ tasks or notify IT specialists to gradually implement automated robots, ensuring efficient resource use, reducing costs, and enhancing productivity. We evaluated the proposed method in a simulation with genuinely uncertain outcomes, across 20 independent runs: the AI decision-maker reached 78.3% accuracy against an estimated 79.1% achievable ceiling, and the proposed framework reduced operational cost by 61.4 ± 0.7% relative to fully manual operation—the best of six operating policies in the training environment—while an explicit sensitivity guard, rather than the learned model, accounts for the absence of security incidents; under distribution shift, the framework retains 43.2 ± 0.8% savings, second only to a hand-tuned rule-based router that requires environment-specific threshold calibration. However, the proposed method requires an IT specialist to implement it properly, and the AI model’s accuracy depends on the amount of input data. In the end, we recommend that future work conduct a study focused on AI decision-makers, test the proposed method on real-world companies, and implement AI decision-makers across various departments to cover a broader range of the company’s systems. Full article

Model predictive control (MPC) has emerged as one of the most effective control strategies for autonomous vehicle path tracking owing to its capability to explicitly handle dynamic constraints and optimize future control actions. However, its tracking performance strongly depends on the appropriate tuning of prediction, control, and weighting parameters, which remains a challenging and computationally demanding task under varying driving conditions. This paper proposes an Adaptive Curvature-Aware MPC (CAMPC) framework optimized through a hybrid Bayesian Optimization–Tree-structured Parzen Estimator (BO–TPE) approach to automatically identify optimal MPC parameters while accounting for upcoming road curvature. The proposed controller incorporates future curvature information to adapt the vehicle speed profile and steering behavior, thereby improving tracking accuracy and control smoothness in complex road geometries. The framework is evaluated in the CARLA autonomous driving simulator under three challenging driving scenarios, including a roundabout, an urban environment, and a highly curved road. Experimental results demonstrate that the proposed CAMPC consistently outperforms a conventional PID controller, achieving improvements of 47%, 58%, and 85% in trajectory-tracking performance across average and maximum lateral and angular errors while reducing steering, braking, and acceleration efforts by more than 80%. Furthermore, the controller satisfies real-time execution requirements with an average computation time of 31 ms and a 95th-percentile latency of 35 ms, confirming its suitability for practical autonomous driving applications. These results demonstrate that integrating curvature-aware prediction with adaptive BO–TPE parameter optimization significantly enhances the robustness, accuracy, computational efficiency, and real-time capability of MPC for autonomous vehicle path tracking in challenging driving environments. Full article

Background and Objective: Post-Intensive Care Syndrome is a multidimensional sequela of critical illness that includes physical, cognitive, and psychological impairments after intensive care unit discharge. Systemic inflammation has been proposed as one potential mechanism contributing to selected PICS domains, but the direction, timing, and clinical relevance of this relationship remain uncertain. This study assessed whether serial concentrations of C-reactive protein, procalcitonin, and interleukin-6 were associated with PICSQ severity in ICU survivors. Materials and Methods: This prospective single-center cohort study included 267 adult ICU patients in the primary complete case analysis. CRP, PCT, and IL-6 were measured at predefined time points during hospitalization. PICS severity was assessed using the Post-Intensive Care Syndrome Questionnaire at ICU discharge and at 2 and 3 months after discharge. The primary endpoint was total PICSQ severity at 3 months. Correlation analyses, threshold-based group comparisons, and logistic and multivariable linear regression models adjusted for age, sex, reason for ICU admission, and length of hospitalization were performed. Secondary and domain-specific analyses were considered exploratory. Results: Correlation analyses did not show a consistent association between inflammatory biomarkers and total PICSQ severity at ICU discharge, 2 months, or 3 months. Weak inverse associations were observed between selected CRP, PCT, and IL-6 measurements and cognitive domain scores, indicating lower cognitive impairment scores among patients with higher biomarker values in some analyses. These findings were not consistent across time points and should be interpreted cautiously. In unadjusted threshold-based analyses at 3 months, selected associations were observed for CRP after one week, PCT on day 4, and IL-6 on days 2 and 4, mainly in the psychological domain. In fully adjusted linear regression models, none of the selected biomarker thresholds remained statistically significant at p < 0.05. In adjusted linear regression, PCT ≥ 2 ng/mL on day 4 showed a borderline association with the total PICSQ score at 3 months, with β = 0.44, 95% CI −0.07 to 0.94, p = 0.089, N = 267. IL-6 > 10 pg/mL on day 2 showed a borderline association with psychological domain severity at 3 months, with β = 0.46, 95% CI from −0.005 to 0.92, p = 0.052. In adjusted logistic regression, IL-6 > 10 pg/mL on day 2 was associated with higher odds of psychological domain score ≥ 4 at 3 months, with adjusted OR 3.61, 95% CI 1.26 to 10.37, p = 0.017, N = 266. No consistent association was observed for the physical domain. Conclusions: In this exploratory cohort, routinely available inflammatory biomarkers were not consistently associated with global PICSQ severity across all assessment time points. Selected time-dependent and domain-specific associations, particularly involving PCT and IL 6 at 3 months, may indicate a possible link between inflammatory activation and later psychological PICS burden. These findings do not establish causality or predictive performance and require external validation before CRP, PCT, or IL 6 can be used for PICS risk stratification. Full article

Accurate short-term electrical load forecasting is of paramount importance for economic dispatch, reliable grid operation, and efficient demand-side management. However, hybrid forecasting frameworks constructed with deep learning models exhibit strong sensitivity to hyperparameter settings. Moreover, swarm-intelligence optimization algorithms are prone to premature convergence when tuning the hyperparameters of forecasting models, thereby degrading prediction performance. In addition, complex load sequences contain local fluctuations and key temporal segments that are difficult to capture using a single recurrent architecture. To address these challenges, this paper proposes a short-term electrical load forecasting method based on a BiGRU-Attention network optimized by an improved multi-strategy black-winged kite algorithm (IMBKA). The BiGRU extracts bidirectional temporal dependencies from historical load windows, while the attention module assigns adaptive weights to informative time steps and suppresses redundant historical information. To improve hyperparameter optimization, IMBKA introduces Sobol sequence initialization and adaptive elite differential mutation. Sobol sequence initialization enhances population coverage, and adaptive elite differential mutation strengthens information exchange among high-quality individuals. Experimental results on electrical load datasets from Singapore, Australia, and Belgium show that IMBKA-BiGRU-Attention achieves favorable forecasting performance among the compared models. The proposed model obtains RMSE values of 70.07 MW, 159.49 MW, 231.82 MW, and 163.43 MW in the Singapore, Australian, Belgian weekday, and Belgian weekend experiments, respectively. Compared with the best-performing model among the evaluated baselines in each experiment, the RMSE is reduced by 4.65%, 16.48%, 3.34%, and 11.39%, respectively. Full article

Accurately identifying the functional characteristics of urban rail transit stations and classifying them accordingly helps uncover passenger flow patterns and optimize resource allocation, thereby enhancing the coordination efficiency of multimodal urban transportation systems. Existing studies on the delineation of station influence areas often exhibit overlapping zones, leading to insufficient characterization of regional heterogeneity. Additionally, classification methods predominantly rely on static single indicators and lack integration of multi-scale features. To address these limitations, this paper proposes a non-overlapping zoning algorithm for precisely defining station influence areas. By incorporating multidimensional indicators—including dynamic passenger flows, resident attributes, connection characteristics, and spatial distribution—a fine-grained station classification model is developed using an enhanced Partitioning Around Medoids (PAM) algorithm. Building on the classification outcomes, a dual-scenario framework (weekday vs. weekend) is established, and Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) models are applied to analyze the spatiotemporal patterns of passenger flows. A case study of Beijing rail transit stations demonstrates that the enhanced PAM algorithm significantly improves clustering performance. Four distinct station types are identified on weekdays: Peripheral Basic-Service Type, Core Commuting-Aggregation Type, Exurban Residential-Transit-Dependent Type, and Multifunctional-Complex Type. On weekends, stations are classified into three categories: Peripheral Living-Service Type, Core Leisure-Vitality Type, and Central Mixed-Use Type. Furthermore, the driving factors of passenger flows exhibit notable spatiotemporal heterogeneity: on weekdays, commuting demand dominates, with jobs–housing ratio, educational attainment ratio, and road network density serving as core positive factors; on weekends, leisure demand becomes prominent, showing strong synergistic effects among jobs–housing ratio, Points of Interest (POI) density, and road network connectivity. The research findings provide theoretical support for the functional classification and refined management of rail transit stations. Full article

Glutaredoxins (GRXs) are small oxidoreductases involved in redox regulation, but the biochemical properties of plant-specific CC-type GRXs remain poorly understood, particularly in legumes. In a previous transcriptomic analysis, two CC-type glutaredoxins from Lotus japonicus, LjGRX460 and LjGRX569, were identified as differentially expressed during symbiosis with nitrogen-fixing rhizobia. Here, both proteins were produced recombinantly and characterized by sequence analysis, structural modelling and in vitro biochemical assays. Phylogenetic and sequence studies confirmed that both proteins belong to the plant-specific CC-type GRX class but differ in active site composition and overall sequence organization. Homology modelling and molecular dynamics simulations revealed distinct conformational properties, including differences in active site accessibility, electrostatic surface distribution and putative oxidation-dependent structural rearrangements. Comparative analyses with representative class I and II GRXs supported substantial structural divergence, suggesting functional specialization. Optimized expression and purification protocols yielded soluble recombinant proteins. Both LjGRX460 and LjGRX569 displayed a strong tendency to form soluble high molecular weight aggregates, similar to the class I control GRX. Enzymatic assays showed low oxidoreductase activity towards classical disulfide substrates compared with class I GRXs, while molecular docking suggested reduced affinity for bis(2-hydroxyethyl) disulfide (HEDS) and preferential interaction with L-cystine. These results indicate that LjGRX460 and LjGRX569 are unlikely to function as classical oxidoreductases and may instead perform specialized regulatory functions. Full article