Search Results (11,049)

Background: Dietary factors may influence psychosocial outcomes through inflammatory pathways, but evidence in adolescents remains limited. The aim was to examine whether dietary inflammatory potential is associated with psychosocial functioning among Spanish adolescents. Methods: This cross-sectional study is based on a subsample from the Eating Healthy and Daily Life Activities (EHDLA) project, conducted in the Region of Murcia (Spain). Dietary inflammatory potential was assessed using a dietary inflammatory index (DII)-informed Food Frequency Questionnaire (FFQ)-derived inflammatory score based on a validated 45-item food frequency questionnaire. Behavioral functioning was evaluated using the Strengths and Difficulties Questionnaire (SDQ). Associations between DII-informed FFQ-derived inflammatory scores and SDQ outcomes were examined using robust linear regression models adjusted for sociodemographic, lifestyle, and anthropometric factors. Results: The analysis included 674 adolescents (57.1% girls; age: 14.0 [13.0–15.0] years). Higher DII-informed FFQ-derived inflammatory score (more pro-inflammatory diet) was associated with greater total behavioral difficulties (unstandardized beta coefficient [B] = 0.058; 95% confidence interval [CI]: 0.005–0.111; standardized β = 0.099), conduct problems (B = 0.025; 95% CI: 0.011–0.039; β = 0.148), hyperactivity/inattention (B = 0.032; 95% CI: 0.013–0.052; β = 0.154), and externalizing problems (B = 0.056; 95% CI: 0.028–0.084; β = 0.181), and with lower prosocial behavior (B = −0.029; 95% CI: −0.045 to −0.014; β = −0.149). Multiple imputation sensitivity analyses yielded estimates in a similar direction, although the associations did not reach statistical significance. No significant associations were observed for emotional symptoms, peer relationship problems, or internalizing difficulties. Conclusions: Higher dietary inflammatory potential was associated with a less favorable psychosocial functioning profile among adolescents, particularly in externalizing domains. Longitudinal studies and anti-inflammatory dietary interventions are needed to determine whether the observed associations translate into meaningful changes in psychosocial functioning. Full article

Background/Objectives: Acquired brain injury (ABI) can disturb sexual regulation, but the literature does not support a single lesion-to-behavior relationship. This critical narrative review evaluates hypersexuality, sexual disinhibition, and rare acquired paraphilic manifestations after traumatic brain injury (TBI) and stroke, and asks how sparse, heterogeneous evidence can inform proportionate clinical care. Methods: PubMed/MEDLINE, Scopus, and Web of Science were searched up to 6 August 2026. Two reviewers independently screened records and full texts. Evidence appraisal considered methodological quality, directness, and competing explanations. Thirteen English-language case-based publications were assessed, with neurological observations distinguished from pharmacological comparators. Results: Sexual dysfunction is common after ABI, whereas dysregulated sexual behavior is less frequent and poorly quantified. TBI primarily supports a distributed vulnerability model involving diffuse axonal injury and disruption of frontal regulatory connections. Stroke can nominate strategic candidate nodes, but lesion-network effects and clinical context remain decisive. Clinical observations associate inappropriate sexual behavior with broader behavioral difficulties, without establishing a uniform syndrome. Recent guidelines support individualized sexuality care, while contemporary imaging studies strengthen network plausibility without directly establishing sexual-dysregulation mechanisms. Conclusions: A phenotype-first framework separates dysfunction from dysregulation and distinguishes direct clinical evidence from mechanistic extrapolation. Assessment should establish what changed and whether reversible contributors explain the behavior before assigning a persistent phenotype. Management should match the dominant clinical problem, preserve consensual intimacy, and use the least restrictive targeted strategy. Explicit monitoring should determine whether benefit justifies continued treatment. Case-based evidence and language-restricted selection limit generalizability; prospective studies require standardized phenotypes and repeated observation across settings. Full article

Accurate cryptocurrency price forecasting represents an important yet challenging problem in financial time-series analysis due to the highly volatile, nonlinear, and noise-sensitive nature of digital asset markets. Although transformer-based architectures have recently demonstrated strong capabilities in temporal sequence modeling, their behavior under high-frequency cryptocurrency dynamics and the role of explainability-guided feature refinement remain insufficiently explored. To address this gap, this study presents a comprehensive transformer-based forecasting framework for hourly cryptocurrency price prediction and investigates the impact of explainability-guided feature optimization on forecasting performance, robustness, and interpretability. Five transformer architectures—Vanilla Transformer, Informer, Autoformer, Reformer, and Temporal Fusion Transformer (TFT)—are systematically evaluated across five major cryptocurrency assets: Bitcoin (BTC), Ethereum (ETH), Solana (SOL), Dogecoin (DOGE), and Ripple (XRP). The experimental framework employs Open, High, Low, Close, and Volume (OHLCV) data together with a broad set of engineered technical indicators and evaluates model performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). To improve interpretability and reduce feature redundancy, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated directly into the forecasting pipeline. Based on the resulting explanations, asset-specific feature subsets are constructed, and all models are subsequently retrained using the refined feature representations. The results show that explainability-guided feature refinement provides compact, model-aware, and interpretable feature subsets with competitive forecasting performance; however, its effect on prediction accuracy is dependent on the cryptocurrency asset, transformer architecture, retained feature subset size, and market conditions. Additional robustness, sensitivity, alternative feature-selection, and statistical significance analyses indicate that the SHAP–LIME Top-15 subset should be interpreted as a conservative dimensionality-reduction strategy rather than a universally optimal feature-selection rule. The findings further reveal that transformer architectures incorporating sparse attention, decomposition mechanisms, or gating structures generally provide stronger performance than the Vanilla Transformer under highly volatile hourly market conditions. Overall, the proposed framework demonstrates that combining transformer-based forecasting with explainability-guided feature refinement can support interpretable and parsimonious high-frequency financial time-series modeling, while highlighting the importance of evaluating robustness, feature-selection sensitivity, and statistical variability alongside average forecasting errors. Full article

Finite-control-set model predictive control provides excellent torque–speed regulation for switched reluctance motor drives but requires an online combinatorial search at every control instant, making low-cost embedded implementation challenging. This article investigates whether offline reinforcement learning can distill policies from multiple classical controllers into a single feedforward policy requiring neither online optimization nor controller gain tuning. A replay buffer is populated with trajectories generated by three demonstrators—hysteresis current control, proportional–integral control with pulse-width modulation, and finite-control-set model predictive control—using a finite-element model of a four-phase 8/6 switched reluctance machine parameterized from measurements of the physical drive. An implicit Q-learning agent then learns a control policy without evaluating actions outside the offline dataset. The central finding is that demonstration diversity governs the stability of offline reinforcement learning on this problem: policies trained from a single demonstrator experience early mode collapse in all fifteen runs, whereas two- or three-demonstrator datasets converge stably in all fifteen. Behavior cloning trained on the identical buffer, split, architecture, and deployed controller provides the reference point for interpreting this result. It matches the offline RL policy on torque quality and improves on its speed regulation, exhibiting none of the seed-to-seed fragility seen at no load while requiring roughly $8\%$ more switching transitions. The stability requirement therefore appears to be a property of the advantage-weighted offline RL objective rather than the control task, and the measured benefit of that objective on this problem is confined to switching effort. We report this rather than claim a broader advantage. The characterization of the distilled controller shows that it generalizes to operating points that are not included in the training dataset, gains nothing systematic beyond approximately 60% of the replay buffer, remains insensitive to $\pm 20\%$ perturbations of all reward weights, and degrades gracefully under measurement noise while the current mask enforces the peak-current constraint throughout. A deployment analysis shows that the 18,432 multiply–accumulate policy meets a $50\mathsf{\mu }$s control period in its existing form at a measured cost of about $2\%$ in torque ripple. All the results are simulation-based on a finite-element model parameterized from a physical machine. Full article

Cross-dataset network intrusion detection faces challenges arising from distribution shifts, heterogeneous feature fields, changing attack distributions, and unreliable explanations across data sources. To address these problems, this paper proposes a fuzzy-enhanced cost-aware Low-Rank Adaptation (LoRA) inference model with multiple security experts. The model uses Qwen2.5-7B-Instruct as a shared backbone and constructs five lightweight LoRA security experts for normal traffic, volumetric attacks, code execution attacks, web application attacks, and reconnaissance behaviors. A cost-aware router selects the appropriate expert by jointly considering neural adaptability, fuzzy consistency derived from raw traffic features, inference cost, and runtime load. To improve evaluation reliability, the model further integrates a leakage-free data protocol with separate raw and standardized feature streams, as well as a traffic-semantic consistency verification module. Across four experimental corpora derived from three independently collected data sources, the proposed method achieves a Macro-F1 of 0.887 on the pooled test set. Parameters are selected on the validation set subject to a contradiction rate of no more than 0.05 and a low-confidence trigger rate of no more than 0.10, and are then held fixed for testing. The contradiction rate among test outputs meeting the verification score threshold is 0.048. This metric reflects output consistency under the selected conditions and does not constitute an independent assessment of explanation quality. Full article

This paper proposes a two-stage, reliability-driven multi-objective planning framework for fast electric vehicle charging stations (FCSs) integrated with solar photovoltaic (PV) generation and battery energy storage systems (BESS) in coupled power–transportation networks. The framework simultaneously addresses electrical network constraints, transportation-driven charging demand, and techno-economic trade-offs in high EV penetration scenarios. A benchmark IEEE 69-bus radial distribution system is co-simulated with a 25-node transportation network to realistically capture spatial and temporal interactions between EV mobility and grid operation. To quantify the combined impacts of voltage stability, service continuity, and charging uncertainty, a novel average voltage deviation reliability index (AVDRI) is introduced. Spatially and temporally varying EV charging demand is modeled using a hybrid approach that integrates queuing theory with gravity-based traffic interaction models, enabling a realistic representation of stochastic arrival patterns and route-dependent charging behavior. In the first stage, a multi-objective optimization problem is formulated to determine the optimal locations and charging capacities of FCSs, minimizing system power losses, reliability degradation, and total system cost while maximizing EV serviceability. Multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimization (MOGWO), and a proposed hybrid GWOPSO algorithm are comparatively evaluated. In the second stage, a bisection-based sizing strategy is employed to determine the optimal PV and BESS capacities required to mitigate solar intermittency and peak power generation mismatches. Results demonstrate that the proposed hybrid GWOPSO based framework achieves superior convergence characteristics and delivers significant improvements in voltage profile, reliability indices, power loss reduction, and overall techno-economic performance compared to conventional approaches. Full article

The running-in phase of hermetic reciprocating compressors is characterized by slow, nonstationary changes in operational signals and is commonly assessed using empirically defined test durations or manually defined indicators. From a data-analysis perspective, this constitutes a feature extraction problem, as informative temporal patterns might be identifiable from long and noisy time series acquired during operation. This paper investigates the use of Long Short-Term Memory (LSTM) networks as a data-driven feature extraction mechanism for running-in analysis based on electrical current and vibration measurements. LSTM-based sequence encoding is used to transform raw time-series segments into compact representations that summarize their temporal structure and enable discrimination between running-in and steady-state regimes, as well as characterization of intermediate conditions in the learned feature space using a multilayer perceptron. Model structure, input configuration, and segmentation parameters are selected through an optimization procedure under weak supervision. Experimental results show that the extracted LSTM features capture consistent temporal signatures of running-in across different compressor units, with one unit exhibiting outlier behavior in latent space analysis. The results support the use of recurrent neural networks for noninvasive running-in assessment in rotating machinery and related condition monitoring tasks. Full article

Annotated medical imaging data remain scarce, and labels are often weak and noisy: in mammography, an examination comprises several high-resolution views, yet the diagnostic outcome is recorded only at the breast level. Such problems are naturally cast as Multiple Instance Learning (MIL), where the model must infer instance-level structure from bag-level labels alone. Although contemporary foundation encoders supply strong general-purpose embeddings, augmenting MIL data in this representation space remains an open problem as established techniques act on one instance at a time and ignore the statistical dependencies binding a bag together. We address this with SetFlow, a generative model that learns the distribution of complete MIL bags directly in a frozen encoder’s embedding space. SetFlow couples flow-matching training with a Set Transformer-inspired backbone, making it invariant to instance ordering while modeling intra-bag relationships. Generation is conditioned jointly on class label and per-instance scale, yielding coherent, semantically faithful bags rather than isolated vectors. Evaluating on two large public mammography datasets and two encoders, we assess distributional fidelity, nearest-neighbor behavior, and downstream augmentation utility. We show that generated bags reproduce real-data statistics and improve classification in certain configuration, with performance gains varying on the amount of synthetic data. An architecture ablation confirms each design choice contributes to performance. Full article

Background: Insufficient physical activity has become a significant public health problem. Currently, physical education classes alone are insufficient to meet the physical activity levels recommended by the World Health Organization. Physically Active Learning (PAL), as a novel curriculum, promotes physical activity by teaching new knowledge in various school subjects. Objective: To systematically evaluate the effects of PAL interventions on primary school students’ physical fitness, physical activity, and sedentary behavior, characterize the key features of PAL interventions, identify research gaps, and provide evidence-based recommendations for practice. Methods: Following the PRISMA guidelines, four electronic databases—Web of Science, ProQuest, Ebsco, and Embase—were searched, and English-language controlled intervention studies that met the PICOS eligibility criteria were included. Two researchers independently screened the literature and extracted and cross-checked the data. Results: This study included 17 studies from 8 countries published between 2009 and 2025. These studies involved primary school students aged 6 to 12. PAL most consistently increased MVPA (moderate-to-vigorous physical activity) or number of steps during the targeted academic lesson, whereas effects on whole-school-day, daily, or weekly physical activity were inconsistent. Lesson-level sedentary behaviour generally decreased or was replaced by walking, standing, or sit-to-stand transitions, but effects on whole-day sedentary time were mixed. Physical fitness interventions yielded varied outcomes, demonstrating significant improvements in upper body strength and explosive muscle strength and endurance, while showing inconsistent effects on aerobic capacity, and no significant changes in BMI or agility indicators. Conclusions: Our findings indicate that PAL has the potential to improve students’ physical activity levels in the classroom. However, its effects on physical fitness indicators remained equivocal across evaluated outcomes. PAL should be considered part of a broader school strategy to promote physical activity rather than as a standalone approach. To maximize health benefits, stronger integration of PAL with active recess, active commuting, and structured physical education is recommended. Full article

Aim: This study aimed to determine health literacy and genital hygiene behavior levels, examine the relationship between these variables, and evaluate the factors associated with genital hygiene behaviors among women aged 18 years and older who were recruited through an online survey in Türkiye. Methods: This descriptive cross-sectional study was conducted in Türkiye between 31 March and May 2026 with 469 women aged 18 years and older who were recruited through an online survey distributed via social media. Data were collected using the Health Literacy Scale and the Genital Hygiene Behaviors Scale. Descriptive statistics, Mann–Whitney U test, Kruskal–Wallis test, Spearman correlation analysis, and multiple linear regression analysis were used. Results: The mean Health Literacy Scale score was 109.61 ± 13.77 (possible range: 25–125), while the mean Genital Hygiene Behaviors Scale score was 95.94 ± 9.91 (possible range: 23–115). A positive, statistically significant, but small correlation was found between health literacy and genital hygiene behaviors (r = 0.292, p < 0.001). In the regression analysis, health literacy was independently associated with genital hygiene behaviors (β = 0.344, p < 0.001). Receiving genital hygiene education or information (β = 0.142, p = 0.001) and consulting a physician when experiencing a health problem (β = 0.094, p = 0.019) were also independently associated with genital hygiene behaviors. Conclusions: Higher health literacy was associated with more positive genital hygiene behaviors among women recruited online in Türkiye; however, the magnitude of the association was small. Receiving genital hygiene education or information and seeking professional healthcare were also associated with more positive behaviors. These findings support further investigation of health literacy-informed educational approaches, particularly through longitudinal and intervention studies, rather than establishing that such approaches would causally improve genital hygiene behaviors. Full article

Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms show substantial developmental heterogeneity, yet less is known about how co-occurring psychopathology is associated with membership in different longitudinal symptom trajectories. This study examined developmental trajectories of parent-reported ADHD symptoms across seven annual assessment waves from late childhood through adolescence using data from the Adolescent Brain Cognitive Development (ABCD) Study. ADHD symptoms were assessed using the Child Behavior Checklist DSM-5-Oriented ADHD Problems scale and therefore represent dimensional symptom scores rather than clinical ADHD diagnoses. Growth mixture modelling was conducted in an analytic sample of 9718 participants and identified three trajectory classes: low/stable (79.87%), high/stable (10.77%), and remitting (9.36%). Baseline associations with class membership were subsequently examined using the R3STEP procedure in 9716 participants, preserving the identified class structure while accounting for classification uncertainty. Relative to the low/stable class, male sex and higher baseline affective, anxiety, conduct, and oppositional defiant problem scores were associated with greater odds of membership in both the remitting and high/stable classes, whereas age and somatic problems showed no clear associations. Comparisons between the remitting and high/stable classes were largely similar, although male sex was associated with greater odds of remitting-class membership. Overall, the findings demonstrate substantial heterogeneity in the developmental course of ADHD symptoms and suggest that baseline co-occurring psychopathology is associated primarily with differentiation between lower- and higher-symptom trajectories rather than clearly distinguishing persistent from remitting elevated-symptom pathways. Full article

University students are particularly vulnerable to mental health problems during emerging adulthood, and suicidal ideation represents a major public health concern. This study examined the relationships among attachment styles, leisure activities, social network use, and suicidal ideation in Chilean university students, considering the role of biological sex. A cross-sectional, descriptive–correlational design was employed with a sample of 1096 students aged 18–29 years. Participants completed measures of suicidal ideation, adult attachment, and leisure activities. Descriptive statistics, chi-square analyses, ANOVA, and binary logistic regression models were conducted. Results revealed a high prevalence of medium-to-high suicidal ideation (55.9%) and insecure attachment styles (63.5%). Women reported higher levels of insecure attachment, a greater risk of suicidal ideation, and lower participation in physical leisure activities than men. Insecure attachment was significantly associated with a higher likelihood of suicidal ideation, whereas participation in physical leisure activities was associated with a lower likelihood of suicidal ideation. Logistic regression analyses confirmed that both attachment style and physical leisure remained significant predictors of suicidal ideation after controlling for sex. No significant associations were found between suicidal ideation and daily internet use or most social network variables. These findings highlight the relevance of relational and behavioral factors in understanding suicidal ideation and underscore the potential value of promoting physical leisure activities and identifying insecure attachment early as areas for further research and intervention in university populations. Full article

This article addresses the problem of slope stability from two complementary perspectives. On the one hand, it considers practical issues related to improving the efficiency of construction on subsidence soils. On the other hand, since traditional experimental and theoretical methods proved insufficient, it was necessary to develop new methodological tools for solving this class of problems. A method is developed for calculating slope stability without predefining the shape of the failure surface, using the equations of deformation mechanics for a weighty soil massif under different formulations of the limit-equilibrium condition. The study extends the traditional approach by explicitly incorporating the Coulomb–Mohr limit-equilibrium condition within the deformation theory of plasticity. The finite element method is adopted as the general numerical tool for obtaining the parameters describing the behavior of the structure–soil system. Numerical analysis of the slope-retaining wall interaction shows that the most critical mechanism is the development of maximum horizontal displacements near the retaining walls, caused by shear deformation. Full article

Underwater fish detection and multi-object tracking support marine ecological monitoring, underwater robot inspection, and fish behavior analysis. In a tracking-by-detection framework, this study targets three specific problems: unstable detection inputs for small- and medium-scale elongated fish, short-term trajectory breaks when low-confidence true detections are discarded, and identity switches caused by local candidate-edge competition when fish cross or move close together. We address these problems with a joint framework that combines the FishDet-XR detector and the FishBoT-SLR-TDR tracker. FishDet-XR stabilizes detector inputs through the proposed SFP-AFPN-XR feature-fusion neck, strip-shaped directional feature modeling, the Simple Parameter-Free Attention Module (SimAM), and positive-prior sampling. FishBoT-SLR-TDR improves tracker-side association through Spatially-Gated Low-Score Recovery (SLR) and Trajectory-Direction Reranking (TDR). Experiments on BrackishMOT-onlyfish show that the framework improves detection localization and tracking continuity while maintaining real-time inference. At the detection stage, FishDet-XR improves mean average precision at an Intersection over Union threshold of 0.50 (mAP₅₀) from 74.48% to 76.19%, mean average precision averaged over Intersection over Union thresholds from 0.50 to 0.95 (mAP_50–95) from 40.72% to 43.55%, and Precision from 86.75% to 88.00% compared with YOLO11s-640, while maintaining 54.76 frames per second (FPS). Compared with the YOLO11s-640 + BoT-SORT baseline, the final detection-tracking chain increases Higher Order Tracking Accuracy (HOTA) from 40.090 to 42.497 and identity F1 score (IDF1) from 51.372 to 54.926, while reducing identity switches (IDSW) from 184 to 159 and trajectory fragmentations (Frag) from 270 to 253. Full article

The precise measurement of the vehicle pose is a key technology for achieving the intelligent inspection and understanding of vehicle behavior. This paper presents a vehicle pose measurement method utilizing a line-space dual-ellipse representation of wheel rims. First, a dual quadratic curve formulation is established to model wheel-rim projection ellipses, where the common tangent extraction is transformed into a generalized eigenvalue problem, enabling analytical vehicle rotation recovery from a single image. Second, a metric vehicle translation recovery scheme is developed by integrating the vehicle’s longitudinal wheelbase constraint. The research conducts comprehensive and systematic experimental verification, including simulation, comparative analysis, vehicle test, and the physical benchmark experiment. The experimental results demonstrate that the proposed method achieves high measurement accuracy and reliability, providing a solution for vehicle pose measurement in practical intelligent vehicle inspection. Full article