Search Results (626)

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Postoperative delirium (POD) is a frequent and serious complication among elderly surgical patients. Despite its clinical relevance, reliable biomarkers for early identification and pathophysiological insight remain limited. Recent evidence implicates systemic immune activation and complements dysregulation as contributors to cognitive decline after surgery. This study investigated the association between perioperative levels of selected complement pathway proteins and both the incidence and severity of POD. Methods: We performed a secondary analysis of 22 patients aged ≥ 60 years from the prospective CONFESS cohort undergoing elective spine surgery. Complement proteins (C1q, C2, C4), mannose-binding lectin (MBL), Factor D [FD], Factor B [FB], Factor I [FI] were quantified from blood samples collected at baseline, preoperatively, and on postoperative days 1 and 2. POD was assessed using the Nursing Delirium Screening Scale (Nu-DESC) and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Delirium severity was rated with the Confusion Assessment Method–Severity (CAM-S) scale. Associations were tested using univariate and multivariate regression analyses. Preoperative levels of FD and C2 were significantly elevated in patients who developed POD (FD: p = 0.023; C2: p = 0.044), while C4 levels trended lower. FD remained an independent predictor of POD in multivariate regression (p = 0.049), although cognitive performance was the only significant predictor when adjusted for surgery duration. Delirium severity was associated with perioperative reductions in C1q, FI, and FB and with increased MBL levels, explaining up to 43% of CAM-S score variance. These findings highlight the role of complement activation—particularly FD, C2, MBL—in the development and clinical expression of POD. Complement profiling may offer a novel approach for risk stratification and therapeutic targeting in perioperative neurocognitive disorders. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article

While striped catfish (Pangasianodon hypophthalmus) is an economically important aquaculture species, its genomic resources remain limited. To date, linkage maps, QTL (quantitative trait loci) analyses, and the identification of candidate genes associated with disease resistance traits are very limited. Therefore, the present study aimed to construct a high-density linkage map and identify candidate genes for this species. Our analysis was conducted on a pedigree population consisting of 560 individuals (490 offspring and 70 parents for 40 families), whose genomes were analyzed using a genotyping-by-sequencing platform. After stringent filtering, 9882 high-quality SNPs were retained for linkage analysis. Linkage analysis placed 8786 markers onto 30 linkage groups (LGs), with an average density of 0.43 SNPs per cM. Recombination rates varied across the 30 linkage groups (LGs), averaging of 3.6 cM/Mb in males, 6.7 cM/Mb in females, and 5.1 cM/Mb when sex-averaged. Using the linkage map, our QTL analysis identified three significant QTLs for disease resistance to Edwardsiella ictaluri, the causative agent of Bacillary Necrosis of Pangasius (BNP). The QTLs were located on LG1, LG9 and LG29, and their peak markers explained 17.03% of the phenotypic variance. An LD-based interval of approximately ±25 kb surrounding the QTL peak was identified as the putative candidate region. However, subsequent genome-wide association analysis did not identify significant SNP effects within these regions, suggesting that the QTLs may represent polygenic or small-effect loci that are detectable only in linkage-based analyses. In summary, this study presents the first high-density SNP-based linkage map for striped catfish and reports significant QTL and associated candidate genes related to disease resistance and growth traits. These findings provide valuable insights into the genetic architecture of economically important traits in P. hypophthalmus. Nevertheless, further validation in independent populations is required before incorporating these markers into selective breeding programs. Full article

Cross-modality person re-identification faces challenges such as illumination discrepancies, local occlusions, and inconsistent modality structures, leading to misalignment and sensitivity issues. We propose GLCN, a framework that addresses these problems by enhancing representation learning through locality enhancement, cross-modality structural alignment, and intra-modality compactness. Key components include the Locality-Preserved Cross-branch Fusion (LPCF) module, which combines Local–Positional–Channel Gating (LPCG) for local region and positional sensitivity; Cross-branch Context Interpolated Attention (CCIA) for stable cross-branch consistency; and Graph-Enhanced Center Geometry Alignment (GE-CGA), which aligns class-center similarity structures across modalities to preserve category-level relationships. We also introduce Intra-Modal Prototype Discrepancy Mining Loss (IPDM-Loss) to reduce intra-class variance and improve inter-class separation, thereby creating more compact identity structures in both RGB and IR spaces. Extensive experiments on SYSU-MM01, RegDB, and other benchmarks demonstrate the effectiveness of our approach. Full article

To address the computational redundancy, inadequate multi-scale feature capture, and poor noise robustness of traditional deep networks used for bearing vibration and acoustic signal feature extraction, this paper proposes a fault diagnosis method based on Depthwise Separable Atrous Convolution (DSAC) and Acoustic Spatial Pyramid Pooling (ASPP). First, the Continuous Wavelet Transform (CWT) is applied to the vibration and acoustic signals to convert them into time–frequency representations. The vibration CWT is then fed into a multi-scale feature extraction module to obtain preliminary vibration features, whereas the acoustic CWT is processed by a Deep Residual Shrinkage Network (DRSN). The two feature streams are concatenated in a feature fusion module and subsequently fed into the DSAC and ASPP modules, which together expand the effective receptive field and aggregate multi-scale contextual information. Finally, global pooling followed by a classifier outputs the bearing fault category, enabling high-precision bearing fault identification. Experimental results show that, under both clean data and multiple low signal-to-noise ratio (SNR) noise conditions, the proposed DSAC-ASPP method achieves higher accuracy and lower variance than baselines such as ResNet, VGG, and MobileNet, while requiring fewer parameters and FLOPs and exhibiting superior robustness and deployability. Full article

Participation in running events has expanded worldwide, consolidating itself as a form of active leisure and a driver of social and tourism engagement. Although runners’ motivations have been extensively studied, perceived inclusivity, understood as motivation derived from the event’s promotion of equitable participation across gender, age and functional ability, has rarely been examined as a distinct motivational dimension within structural models. This study analyses the motivational structure of participants in the Elche Half Marathon (Spain) and assesses the incremental contribution of inclusivity to traditional motivational frameworks. Based on a sample of 1053 valid responses, a two-stage psychometric and segmentation approach was applied. Exploratory and confirmatory factor analyses (EFA and CFA) were conducted to compare a four-factor model (sport-related hedonism, competition, socialization and digital socialization) with an extended five-factor model incorporating inclusivity. Subsequently, cluster analyses were performed using factor scores derived from each model. The results show that the inclusion of inclusivity improves model fit and increases explained variance, while also generating a more differentiated segmentation structure. The extended model revealed six motivational profiles, some of which displayed continuity with the classical solution, while others were reconfigured when inclusivity was introduced. Overall, the findings indicate that inclusivity functions as a complementary and context-dependent motivational dimension that refines the understanding of participation heterogeneity in running events. Rather than replacing traditional motives, inclusivity contributes incremental explanatory value and enhances the identification of motivational profiles, offering relevant insights for the design and management of mass-participation sporting events. Full article

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

Humulus lupulus L. (hops) is essential in brewing due to its contributions to bitterness, flavor, and aroma. This study compared the volatile profiles of five commercially important hop varieties—Amarillo, Ariana, Cascade, Centennial, and El Dorado—grown in their main regions of origin (United States for Amarillo, Cascade, and El Dorado; Germany for Ariana; and Brazil for Centennial). Headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS) enabled the identification of 312 volatile compounds, including monoterpenes (e.g., myrcene, linalool, geraniol), sesquiterpenes (e.g., humulene, caryophyllene), esters, alcohols, aldehydes, and ketones. Amarillo showed the highest myrcene content (22.61% of the total volatile area), while Centennial was distinguished by elevated γ-muurolene (20.59%), and El Dorado by the highest level of undecan-2-one (10.47%), highlighting marked varietal differences in key aroma-active constituents. Multivariate, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), clearly discriminated the five varieties: PC1 (41.04% of the variance) separated samples enriched in fruity/floral monoterpenes and esters from those dominated by woody/resinous sesquiterpenes, whereas PC2 (25.93% of the variance) reflected variation in medium-chain esters, ketones, and waxy compounds. These chemometric patterns demonstrate that both genetic background and growing region terroir strongly shape hop volatile composition and, consequently, aroma potential, providing brewers with objective criteria for selecting hop varieties to achieve specific sensory profiles in beer. Full article

In natural canyons and urban occluded environments, signal anomalies induced by the satellite diffraction effect are a critical error source affecting the positioning accuracy of deformation monitoring. This paper proposes a deep learning-based method for diffraction signal identification and mitigation. The method utilizes a LSTM network to deeply mine the time-series characteristics of GNSS observation data. We systematically analyze the impact of azimuth, elevation, SNR, and multi-feature combinations on model recognition performance, demonstrating that single features suffer from incomplete information or poor discrimination. Experimental results show that the multi-dimensional feature scheme of “SNR + Elevation + Azimuth” effectively characterizes both signal strength and spatial geometric information, achieving complementary feature advantages. The overall recognition accuracy of the proposed method reaches 84.2%, with an accuracy of 88.0% for anomalous satellites that severely impact positioning precision. Furthermore, we propose an Adaptive Weighting Method for Diffraction Mitigation Based on Uncertainty Quantification. This method constructs a variance inflation model using the probability vector output from the LSTM Softmax layer and introduces Information Entropy to quantify prediction uncertainty, ensuring that the weighting model possesses protection capability when the model fails or is uncertain. In processing a set of GNSS data collected in a highly-occluded environment, the proposed method significantly outperforms traditional cut-off elevation and SNR mask strategies, improving the AFR to 99.9%, and enhancing the positioning accuracy in the horizontal and vertical directions by an average of 80.1% and 76.4%, respectively, thereby effectively boosting the positioning accuracy and reliability in occluded environments. Full article

Urban tunnel portals constitute critical safety zones for autonomous vehicles, where abrupt luminance transitions, shortened sight distances, and densely distributed structural and traffic elements pose considerable challenges to perception reliability. Existing driving scenario datasets are rarely tailored to tunnel environments and have not quantitatively evaluated how specific infrastructure components influence perception latency in autonomous systems. This study develops a requirement-driven framework for the identification and sensitivity ranking of information perception elements within urban tunnel portals. Based on expert evaluations and a combined function–safety scoring system, nine key elements—including road surfaces, tunnel portals, lane markings, and vehicles—were identified as perception-critical. A “mandatory–optional” combination rule was then applied to generate 48 logical scene types, and 376 images after brightness (30–220 px), blur (Laplacian variance ≥ 100), and occlusion filtering (≤0.5% pixel error) were obtained after luminance and occlusion screening. A ResNet50–PSPNet convolutional neural network was trained to perform pixel-level segmentation, with inference rate adopted as a quantitative proxy for perceptual sensitivity. Field experiments across ten urban tunnels in China indicate that the model consistently recognized road surfaces, lane markings, cars, and motorcycles with the shortest inference times (<6.5 ms), whereas portal structures and vegetation required longer recognition times (>7.5 ms). This sensitivity ranking is statistically stable under clear, daytime conditions (p < 0.01). The findings provide engineering insights for optimizing tunnel lighting design, signage placement, and V2X configuration, and offers a pilot dataset to support perception-oriented design and evaluation of urban tunnel portals in semi-enclosed environments. Unlike generic segmentation datasets, this study quantifies element-specific CNN latency at tunnel portals for the first time. Full article

Background/Objectives: Behavioral inhibition (BI) has been extensively studied as an early-appearing risk factor for adverse developmental outcomes. One pathway through which BI may confer risk is via reduced competence to interact effectively with peers. Research demonstrating concurrent relations between BI and social competence supports this pathway, yet not all inhibited children experience social difficulties. This study adopted a person-centered approach to examine heterogeneity of temperament traits within a highly inhibited preschool sample and to identify how broader temperament traits contribute to variability in social functioning. Methods: Parents of preschoolers (N = 254) who met criteria for BI (≥85th percentile on the Behavioral Inhibition Questionnaire) completed measures of their child’s temperament (Children’s Behavior Questionnaire) and social competence (Social Skills Improvement System). Latent Profile Analysis was conducted using six temperament traits reflecting regulation and reactivity (anger, attentional focusing, inhibitory control, high-intensity pleasure, perceptual sensitivity, and approach). Profile differences in social competence were examined using multivariate analyses controlling for age and gender. Results: A three-profile solution emerged: Regulated, Unregulated and Angry, and Typical BI. Profile membership accounted for almost 37% of the variance in social skills scores. The Regulated group, marked by high attentional and inhibitory control and low anger, demonstrated the strongest social skills and lowest internalizing and externalizing problems. The Unregulated and Angry group, characterized by high anger and poor regulation, exhibited the greatest social difficulties. BI level itself did not significantly differentiate profiles or predict social competence. Conclusions: Findings underscore that BI is not a uniform risk factor but joins with other temperamental traits to shape social outcomes. Level of BI did not differentiate profiles or relate to social functioning, highlighting the importance of considering co-occurring regulatory and reactive traits to explain variability in outcomes among inhibited children. Identifying specific temperamental constellations may enhance early identification and inform targeted interventions for socially at-risk inhibited children. Full article

Background: This paper presents an analysis and a structured framework for improving inventory accuracy in an automotive factory, considering the current context of global disruptions. In 2023, the company recorded 20,340 inventory adjustments (1695 per month) and a 0.24% monthly net value discrepancy (EUR 256,594 YTD), with a baseline absolute discrepancy of 2.21% of sales. The project aimed to reduce adjustments to below 700 per month and the net value discrepancy to 0.1%. Methods: The research followed the Six Sigma methodology’s Define, Measure, Analyze, Improve and Control (DMAIC) phases, integrating Root Cause Analysis (RCA) and Failure Mode and Effects Analysis (FMEA) to enhance inventory accuracy in manufacturing operations. Results: Implementation significantly improved inventory accuracy: monthly adjustments decreased from 1695 to 971, the highest RPN was reduced from 576 to 144, and the absolute discrepancy-to-sales ratio stabilized at 0.98% (a 56% improvement). Financial variance was reduced to EUR 1948.10 in Q4 2024, while organizational discipline, role clarity and process control also increased. Conclusions: The integrated DMAIC–RCA–FMEA framework proved effective and replicable, enabling systematic identification of root causes, targeted corrective actions and sustainable KPI-driven improvements. The results demonstrate a scalable approach to inventory optimization that supports operational resilience and supply chain performance. Full article