Search Results (179)

Tibia morphology is a significant factor in poultry germplasm and market traits. Copy number variation (CNV) has been identified as a structural source of genetic variation for complex traits. We profiled genome-wide CNVs in Dong Tao chickens and nine other local breeds and performed a breed-based case–control CNV-GWAS (Dong Tao vs. reference breeds). We sequenced 152 chickens, including 46 Dong Tao, and annotated genes and pathways. A total of 22,972 CNVs were detected, of which 2193 were retained after filtration across 33 chromosomes, with sizes ranging from 2 kilobases to 12.8 megabases. Principal component analysis indicated an overall weakness in the breed structure and a sex-related trend within Dong Tao. A deletion on chromosome 3 at 36,529,501 to 36,539,000 was observed in Dong Tao. The exploratory screen identified 44 CNV regions at nominal significance (p < 0.05), distinguishing Dong Tao from other breeds. Thirty-seven regions contained 99 genes, including CHRM3 within the chromosome 3 deletion and CRADD overlapping two CNVs. Enrichment analysis indicated thiamine metabolism and growth hormone receptor signalling as the primary pathways of interest, with TPK1, SOCS2, and FHIT identified as potential candidates. These results provide a CNV landscape for Dong Tao and prioritize variant regions and pathways potentially relevant to its robust tibia morphology; however, no direct CNV–tibia phenotype regression was performed. Full article

Constructing reusable accident-text corpora is hindered by anonymization, heterogeneous sources, and sparse labels, which complicate cross-document event linking. We propose a spatiotemporal lattice-constrained approach that encodes administrative hierarchies and temporal granularity, defines domain-informed consistency criteria, instantiates spatial/temporal relations via a subset of RCC-8 and Allen’s interval algebra, estimates anchor weights via smoothing with monotonic projection, and fuses signals using a constrained monotonic network with explicit probability calibration. An active-learning decision rule—combining maximum probability with a probability-gap criterion—supports scalable automatic labeling, and controlled augmentation leverages instruction-tuned LLMs under lattice constraints. Experiments show competitive ranking (Hit@1 = 41.51%, Hit@5 = 77.33%) and discrimination (ROC-AUC = 87.34%), with the best F1 (62.46%). The method yields the lowest calibration errors (Brier = 0.14; ECE = 1.97%), maintains performance across sources, and exhibits the smallest F1 fluctuation across thresholds (Δ = 1.7%). In deployment-oriented analyses, it auto-labels 77.7% of cases with 97.51% accuracy among high-confidence outputs while routing 22.3% to review, where the true-positive rate is 81.46%. These findings indicate that integrating structured constraints with calibrated probabilistic fusion enables accurate, auditable, and scalable event linking for accident-corpus construction. Full article

There has been significant interest in solving robot path planning problems using fuzzy logic-based methods. Recently, the Genetic Algorithm-based Hierarchical Interval Type-2 Fuzzy (GA-HIT2F) system has been introduced as a novel planner in this domain. However, this method lacks adaptability to changes in target points, and insufficient flexibility can lead to planning failures in local minimum traps, making it difficult to apply to complex scenarios. In this paper, we identify the limitations of the original GA-HIT2F approach and propose an enhanced Q-Learning-aided Adaptive Hierarchical Interval Type-2 Fuzzy (QL-HIT2F) algorithm for path planning. The proposed planner incorporates reinforcement learning to improve a robot’s capability to avoid collisions with special obstacles. Additionally, the average obstacle orientation (AOO) is introduced to optimize the robot’s angular adjustments. Two supplementary robot parameters are integrated into the reinforcement learning action space, along with fuzzy membership parameters. The training process also introduces the concepts of meta-map and sub-training. Simulation results from a series of path planning experiments validate the feasibility and effectiveness of the proposed QL-HIT2F approach. Full article

Background: There is a critical need for feasible, non-equipment based, safe, and cost-effective exercise interventions to promote muscle strength, dynamic postural balance, and independent mobility in adolescents with cerebral palsy (CP) or spina bifida (SB). Objectives: This study aimed to examine the feasibility and preliminary response of a novel exercise program: Functionally Loaded High-Intensity Circuit Training (FUNHIT) and conventional High-Intensity Circuit Training (HIT) in adolescents with CP/SB. Methods: Enrolled participants were allocated to FUNHIT or HIT or Controls in our randomized control trial. The interventions were delivered 2×/week × 4 weeks. Feasibility was assessed through process, operational, and scientific metrics. Outcome measures included maximum walking speed, Four Square Step Test (FSST), Timed Up and Go (TUG) and its dual-task variants, Lateral Step-Up Test (LSUT), Fear of Falling (FoF) and physical activity (PA) questionnaires. Results: We tested 5 participants (1 CP, 4 SB) in our study. Recruitment and retention rates were acceptable (63% enrollment, 100% retention and adherence). FUNHIT (n = 2) participants showed improvements in maximum walking speed (8–12%), FSST (15–29%), LSUT (22–33%), and TUG (4%). The HIT participant (n = 1) demonstrated improved TUG dual-task performance (40%) and FSST (30%) only. Control participants (n = 2) had varied changes (from 0–24%) in mobility, strength, balance. No adverse events were reported. Participants successfully followed (100%) the prescribed exercise dosage over the four-week period. Conclusions: FUNHIT and HIT are feasible and safe interventions for adolescents with ambulatory CP and SB who retain motor function, showing promising preliminary improvements in muscle strength, dynamic balance, and independent mobility. Our findings need to be validated in larger samples. Full article

Reaction time and hand–eye coordination are critical neuromotor skills in school-aged children, influencing academic, cognitive, and motor development. The objective of this study was to evaluate schoolchildren’s performance on reaction time tests using Neural Trainer device sensors and wearable technology, establishing baseline metrics and identifying lateral performance asymmetries. Fifty-nine schoolchildren performed six sensor-based motor tests involving bimanual and unimanual interaction: P1 (10 timed repetitions, bimanual), P2 (10 timed repetitions, left hand), P3 (10 timed repetitions, right hand), P4 (hits, bimanual), P5 (hits, left hand), and P6 (hits, right hand). Neural Trainer devices with four light nodes were used for activity monitoring. Data was analyzed using statistical methods to assess time, accuracy, and variability. The results showed that the average times were P1 = 8.69 ± 1.44 s, P2 = 8.90 ± 1.30 s, and P3 = 8.83 ± 1.29 s. The average successes were P4 = 22.90 ± 3.10, P5 = 22.00 ± 3.40, and P6 = 24.42 ± 2.72 hits. Significant differences were found between hands in successes (p < 0.001) but not in times (p = 0.716). The ANOVA for the hit trials revealed significant differences between conditions, F(2, 174) = 9.30, p < 0.001. The conclusions indicate that sensor-based systems such as the Neural Trainer device demonstrated the potential to provide objective and consistent measurements of reaction time in schoolchildren; however, further studies comparing its performance with established clinical assessment tools are necessary to confirm its validity and diagnostic accuracy. Full article

Background/Purpose: Skilled motor performance depends on the action–observation networks (AONs), which supports the internal simulation of perceived movements. While expertise effects are well-documented in sports, neuroimaging evidence in golf is scarce, particularly on temporal dynamics across swing phases. This study examines how golf expertise modulates AON activation and functional connectivity during temporally distinct swing phases (pre-hitting vs. hitting) and assesses implications for predictive-coding models of motor skill. Methods: Fifty-seven participants (elite golfers: n = 28; controls: n = 29) underwent functional magnetic resonance imaging (fMRI) scanning while viewing golf swing videos segmented into pre-hitting and hitting phases. Data analysis employed generalized linear models (GLMs) with two-sample t-tests for group comparisons and generalized psychophysiological interaction (gPPI) to assess functional connectivity using GLM-identified activation clusters as seeds. Results: (1) Compared to controls, elite golfers showed stronger activation in right insula and posterior cingulate cortex during pre-hitting, and in right cerebellum and bilateral postcentral cortex during hitting phases. The hitting > pre-hitting contrast revealed enhanced bilateral postcentral gyrus activation in golfers. (2) gPPI analysis demonstrated significant group × phase interaction in functional connectivity between right postcentral gyrus and left precuneus. Conclusions: Elite golf expertise dynamically retunes AON across swing phases, shifting from anticipatory interoceptive processing to impact-centered sensorimotor–parietal circuitry. These findings refine predictive-coding models of motor skill and identify the postcentral–precuneus loop as a potential target for neurofeedback interventions aimed at optimizing golf performance. Full article

Background/Objectives: While morphological similarity and incomplete specimens pose a challenge to the precise identification of Coelogyne orchids, accurate species and genus assignment is essential for conservation and CITES enforcement. This study evaluated the efficacy of five DNA barcode regions—rbcL, matK, trnH-psbA, atpF-atpH, and ITS2—and their combinations for species- and genus-level discrimination within the genus Coelogyne, aiming to develop a rapid and simple diagnostic tool for use by customs officers and trade inspectors. This is the first comprehensive comparative analysis of these five barcode regions specifically within Coelogyne, a genus underrepresented in molecular identification studies, and the first to propose multi-locus combinations for potential practical use. This study identified DNA barcode regions with high resolution and reliability, providing a solid basis for practical identification kits. Such tools will enhance CITES enforcement by enabling rapid detection of Coelogyne species in trade, directly supporting their conservation and contributing to the reduction in illegal orchid trade. Methods: Using a CTAB protocol, genomic DNA was extracted from leaf samples belonging to 19 Coelogyne species. Sanger sequencing was performed after PCR amplification using published primer sets for every barcode region. Sequences were modified in BioEdit, and BLASTn (accessed 15 June 2025) was used to compare them to GenBank (NCBI Nucleotide). Amplification efficiency was calculated per locus. Species and genus identification success rates were determined by the congruence of top BLAST hits with morphologically pre-identified taxa. Multi-barcode combinations (matK + rbcL, ITS2 + matK, matK + trnH-psbA, rbcL + trnH-psbA, and matK + rbcL + trnH-psbA) were also assessed. Results: With rbcL, atpF-atpH, and ITS2 yielding ≤11%, the highest single-locus species identification rates were for trnH-psbA (21%) and matK (16%). Among single-locus barcodes, matK showed the highest performance, with 84% genus assignment. ITS2 reached 27%, but genus-level resolution remained limited for the rbcL, trnH-psbA and atpF-atpH barcodes. Multi-barcode approaches maintained species resolution: matK + rbcL + trnH-psbA, matK + rbcL, and matK + trnH-psbA correctly identified 16% of species and achieved 74–79% genus assignment. Conclusions: No single locus achieves robust species discrimination in Coelogyne, but trnH-psbA, matK and atpF-atpH provide the best single-marker performance. Using the matK locus alone, in combination with either trnH-psbA or rbcL, or all three together ensures consistent genus-level identification and significantly improves taxonomic resolution. This study introduces a novel multi-locus barcode strategy tailored to Coelogyne, offering a practical solution for identification and enforcement. While promising, this approach represents a potential application that requires further validation before routine implementation. Full article

University advising at matriculation must operate under strict information constraints, typically without any post-enrolment interaction history.We present a unified, leakage-free pipeline for predicting early dropout risk and generating cold-start programme recommendations from pre-enrolment signals alone, with an optional early-warning variant incorporating first-term academic aggregates. The approach instantiates lightweight multimodal architectures: tabular RNNs, DistilBERT encoders for compact profile sentences, and a cross-attention fusion module evaluated end-to-end on a public benchmark (UCI id 697; n = 3630 students across 17 programmes). For dropout, fusing text with numerics yields the strongest thresholded performance (Hybrid RNN–DistilBERT: f1-score ≈ 0.9161, MCC ≈ 0.7750, and simple ensembling modestly improves threshold-free discrimination (Area Under Receiver Operating Characteristic Curve (AUROC) up to ≈0.9488). A text-only branch markedly underperforms, indicating that numeric demographics and early curricular aggregates carry the dominant signal at this horizon. For programme recommendation, pre-enrolment demographics alone support actionable rankings (Demographic Multi-Layer Perceptron (MLP): Normalized Discounted Cumulative Gain @ 10 (NDCG@10) ≈ 0.5793, Top-10 ≈ 0.9380, exceeding a popularity prior by $25–27$ percentage points in NDCG@10); adding text offers marginal gains in hit rate but not in NDCG on this cohort. Methodologically, we enforce leakage guards, deterministic preprocessing, stratified splits, and comprehensive metrics, enabling reproducibility on non-proprietary data. Practically, the pipeline supports orientation-time triage (high-recall early-warning) and shortlist generation for programme selection. The results position matriculation-time advising as a joint prediction–recommendation problem solvable with carefully engineered pre-enrolment views and lightweight multimodal models, without reliance on historical interactions. Full article

Complex associations among production cost factors, multi-source cost information silos, and opaque transmission mechanisms of hidden costs in open-pit coal mining were addressed. The production process—including drilling, blasting, excavation, transportation, and dumping—was taken as the application context. A corpus of 103 open-pit coal mining standards and related research documents was constructed. Eleven entity types and twelve relationship types were defined. Dynamic word vectors were obtained through transformer (BERT) pre-training. The optimal entity tag sequence was labeled using a bidirectional long short-term memory–conditional random field (BiLSTM–CRF) 9 model. A total of 3995 entities and 6035 relationships were identified, forming a symmetry-aware knowledge graph for open-pit coal mining costs based on the BERT–BiLSTM–CRF model. The results showed that, among nine entity types, including Parameters, the F1-scores all exceeded 60%, indicating more accurate entity recognition compared to conventional methods. Knowledge embedding was performed using the TransH inference algorithm, which outperformed traditional models in all reasoning metrics, with a Hits@10 of 0.636. This verifies its strong capability in capturing complex causal paths among cost factors, making it suitable for practical cost optimization. On this basis, a symmetry-aware BERT–BiLSTM–CRF knowledge graph of open-pit coal mining costs was constructed. Knowledge embedding was then performed with the TransH inference algorithm, and latent relationships among cost factors were mined. Finally, a knowledge-graph-based cost factor identification system was developed. The system lists, for each cost item, the influencing factors and their importance ranking, analyzes variations in relevant factors, and provides decision support. Full article

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications. Full article

Chronic neck pain is a multifactorial condition involving physical, psychological, and neurological dimensions. This case report describes the clinical course of a 25-year-old female with chronic neck pain and recurrent headaches who underwent a 6-week integrative intervention consisting of manual therapy and pain neuroscience-based sensorimotor retraining, administered three times per week. Outcome measures included the Headache Impact Test-6 (HIT-6), Neck Pain and Disability Scale (NPDS), Pain Catastrophizing Scale (PCS), Fear-Avoidance Beliefs Questionnaire (FABQ), pressure pain threshold (PPT), cervical range of motion (CROM), and functional near-infrared spectroscopy (fNIRS) to assess brain activity. Following the intervention, the patient demonstrated marked reductions in pain and psychological distress: HIT-6 decreased from 63 to 24 (61.9%), NPDS from 31 to 4 (87.1%), FABQ from 24 to 0 (100%), and PCS from 19 to 2 (89.5%). Improvements in PPT and CROM were also observed. fNIRS revealed decreased dorsolateral prefrontal cortex (DLPFC) activation during pain stimulation and movement tasks, suggesting a possible reduction in central sensitization burden. These findings illustrate that an integrative approach targeting biopsychosocial pain mechanisms may be beneficial in managing chronic neck pain, improving function, and modulating cortical responses. This report provides preliminary evidence in support of the clinical relevance of combining manual therapy with neurocognitive retraining in similar patients. Full article

Within the field of remote sensing, Unmanned Aerial Vehicle (UAV) infrared object detection plays a pivotal role, especially in complex environments. However, existing methods face challenges such as insufficient accuracy or low computational efficiency, particularly in the detection of small objects. This paper proposes a lightweight and accurate UAV infrared object detection model, YOLO-UIR, for small object detection from a UAV perspective. The model is based on the YOLO architecture and mainly includes the Efficient C2f module, lightweight spatial perception (LSP) module, and bidirectional feature interaction fusion (BFIF) module. The Efficient C2f module significantly enhances feature extraction capabilities by combining local and global features through an Adaptive Dual-Stream Attention Mechanism. Compared with the existing C2f module, the introduction of Partial Convolution reduces the model’s parameter count while maintaining high detection accuracy. The BFIF module further enhances feature fusion effects through cross-level semantic interaction, thereby improving the model’s ability to fuse contextual features. Moreover, the LSP module efficiently combines features from different distances using Large Receptive Field Convolution Layers, significantly enhancing the model’s long-range information capture capability. Additionally, the use of Reparameterized Convolution and Depthwise Separable Convolution ensures the model’s lightweight nature, making it highly suitable for real-time applications. On the DroneVehicle and HIT-UAV datasets, YOLO-UIR achieves superior detection performance compared to existing methods, with an mAP of 71.1% and 90.7%, respectively. The model also demonstrates significant advantages in terms of computational efficiency and parameter count. Ablation experiments verify the effectiveness of each optimization module. Full article

Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. To tackle these difficulties, this study introduces YOLO-SRMX, a lightweight real-time object detection framework specifically designed for infrared imagery captured by UAVs. Firstly, the model utilizes ShuffleNetV2 as an efficient lightweight backbone and integrates the novel Multi-Scale Dilated Attention (MSDA) module. This strategy not only facilitates a substantial 46.4% reduction in parameter volume but also, through the flexible adaptation of receptive fields, boosts the model’s robustness and precision in multi-scale object recognition tasks. Secondly, within the neck network, multi-scale feature extraction is facilitated through the design of novel composite convolutions, ConvX and MConv, based on a “split–differentiate–concatenate” paradigm. Furthermore, the lightweight GhostConv is incorporated to reduce model complexity. By synthesizing these principles, a novel composite receptive field lightweight convolution, DRFAConvP, is proposed to further optimize multi-scale feature fusion efficiency and promote model lightweighting. Finally, the Wise-IoU loss function is adopted to replace the traditional bounding box loss. This is coupled with a dynamic non-monotonic focusing mechanism formulated using the concept of outlier degrees. This mechanism intelligently assigns elevated gradient weights to anchor boxes of moderate quality by assessing their relative outlier degree, while concurrently diminishing the gradient contributions from both high-quality and low-quality anchor boxes. Consequently, this approach enhances the model’s localization accuracy for small targets in complex scenes. Experimental evaluations on the HIT-UAV dataset corroborate that YOLO-SRMX achieves an ${\mathrm{mAP}}_{50}$ of 82.8%, representing a 7.81% improvement over the baseline YOLOv8s model; an F1 score of 80%, marking a 3.9% increase; and a substantial 65.3% reduction in computational cost (GFLOPs). YOLO-SRMX demonstrates an exceptional trade-off between detection accuracy and operational efficiency, thereby underscoring its considerable potential for efficient and precise object detection on resource-constrained UAV platforms. Full article

Growth traits are the most important economic traits in red tilapia (Oreochromis spp.) production, and are the main targets for its genetic improvement. Increasing salinity levels in the environment are affecting the growth, development, and molecular processes of aquatic animals. Red tilapia tolerates saline water to some degree. However, few credible genetic markers or potential genes are available for choosing fast-growth traits in salt-tolerant red tilapia. This work used genome-wide association study (GWAS) and RNA-sequencing (RNA-seq) to discover genes related to four growth traits in red tilapia cultured in saline water. Through genotyping, it was determined that 22 chromosomes have 12,776,921 high-quality single-nucleotide polymorphisms (SNPs). One significant SNP and eight suggestive SNPs were obtained, explaining 0.0019% to 0.3873% of phenotypic variance. A significant SNP peak associated with red tilapia growth traits was located on chr7 (chr7-47464467), and plxnb2 was identified as the candidate gene in this region. A total of 501 differentially expressed genes (DEGs) were found in the muscle of fast-growing individuals compared to those of slow-growing ones, according to a transcriptome analysis. Combining the findings of the GWAS and RNA-seq analysis, 11 candidate genes were identified, namely galnt9, esrrg, map7, mtfr2, kcnj8, fhit, dnm1, cald1, plxnb2, nuak1, and bpgm. These genes were involved in ‘other types of O-glycan biosynthesis’, ‘glycine, serine and threonine metabolism’, ‘glycolysis/gluconeogenesis’, ‘mucin-type O-glycan biosynthesis’ and ‘purine metabolism signaling’ pathways. We have developed molecular markers to genetically breed red tilapia that grow quickly in salty water. Our study lays the foundation for the future marker-assisted selection of growth traits in salt-tolerant red tilapia. Full article

Background/Objectives: Considering the extensive use of digital tools among adolescents and the effects of game addiction on physical, social, emotional, and cognitive domains, this study aimed to investigate the relationship between digital game addiction and the vestibulo-ocular reflex in high school students. Methods: In this descriptive relational study, the relationship between digital game addiction and the functional head impulse test was investigated in adolescents. Two groups of adolescents, with and without digital game addiction, were compared based on the functional head impulse test. The Digital Game Addiction Scale was administered to assess digital game addiction in adolescents aged 14 to 18 years. Results: The findings were analyzed statistically, and the results indicated a statistically significant relationship between digital game addiction and the vestibulo-ocular reflex, with digital game addiction negatively affecting the vestibulo-ocular reflex in adolescents. Conclusions: The findings indicate that digital game addiction in adolescents may impair VOR function, suggesting a potential negative impact on balance and perceptual processing. These results highlight the importance of early interventions and digital literacy programs to mitigate the adverse effects of excessive gaming during adolescence. Full article