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A study of Coulomb explosion in a dense plasma focus hotspot using EPOCH particle-in-cell simulation code
How to end poverty and protect Earth: inside the debate tearing up economics
Evaluating the efficacy of Bemcentinib (BGB) in modulating Axl activity in the brain of the rNLS8 inducible mouse model of ALS
Gender predicts Tai Chi motivational profiles and participation among college students
Phenology, growth and yield of Kangra tea in relation to agrometeorological indices
Abstract In this study, phenology, growth, yield characteristics, and agrometeorological indices—Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU), and Hygrothermal Units (HYTU)—in Kangra tea ( Camellia sinensis L.) were examined. Plant height, leaf count, leaf area index (LAI), and dry matter accumulation were all examined using data gathered during eight phenophases (P1–P8). During early phenophases, GDD had a positive impact on vegetative growth. The strongest positive correlations were found for LAI during P3–P4 ( r = 0.217) and number of leaves during P1–P2 ( r = 0.352). On the other hand, during P5–P6, excessive thermal accumulation had a negative effect on dry matter accumulation ( r = − 0.382) and LAI ( r = − 0.316). HTU had a negative effect on LAI during later phenophases ( r ≈ − 0.42 to − 0.48), but it had moderately positive associations with leaf number during P2–P4. During P5–P6, HYTU showed significant negative correlations with both dry matter accumulation ( r = − 0.82) and LAI ( r = − 0.78), suggesting detrimental effects of combined heat and humidity stress. While dry matter accumulation was negatively impacted during P5–P6 ( r ≈ − 0.52), PTU had a positive effect on LAI, with the strongest correlation seen during P4–P5 ( r = 0.421). Plant height ( r = 0.66), rainfall ( r = 0.91), temperature ( r = 0.97), dry matter accumulation ( r = 0.74), and LAI ( r = 0.81) all had positive correlations with yield. The findings highlight the importance of agrometeorological indices in understanding tea phenology, improving yield prediction, and developing climate-resilient management strategies for sustainable Kangra tea production.
Optical filter sorts light by its ‘quantum statistics’
Object detection algorithm based on infrared-visible dual-modality feature fusion
Development and validation of the digital competence framework and scale for pre-service physical education teachers
Abstract General teacher Digital Competence (DC) frameworks, which lack a subject-specific pedagogical perspective, are limited in their ability to effectively guide technology integration practices in physical education contexts. Using an exploratory sequential mixed-methods design, this study developed and validated a subject-specific framework and measurement instrument for the DC of Pre-service Physical Education Teachers (PSPETs). The results showed that the PSPETs’ DC framework comprises Digital Resources for Physical Education Teaching, Integration and Implementation in Physical Education Teaching, Assessment and Feedback in Physical Education Teaching, Digital Ethics and Safety in Physical Education Teaching, Professional Activities, Fostering Students’ Use of Digital Technology for Health Maintenance, and Personality Traits in Digital Technology Application. Based on this framework, a measurement instrument was developed and evaluated through item analysis and cross-validation using 1,437 valid questionnaires. The results confirmed the instrument’s satisfactory reliability and validity, as well as the good fit of its seven-factor structural model. Further survey results based on 3,128 PSPETs indicated that institutional tier, GPA Ranking, academic year, and city tier were positively associated with DC, whereas first-generation college student status and low-income family status were negatively associated with DC. By incorporating a subject-specific perspective, this study contributes a domain-specific framework that integrates technology integration with subject-based educational objectives. It also provides a reference for the design of Physical Education Teacher Education (PETE) curricula, the diagnosis of DC, and differentiated professional development.
Association of serum 25-hydroxyvitamin D levels with some cardiometabolic risk factors, thyroid, and hematological risk factors among students of Hormozgan University
Application of data-driven modeling techniques for predicting the shear strength of reinforced concrete beams
Abstract Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting the shear strength of RC beams using a systematically curated experimental database comprising 1,977 specimens collected from published literature. The database was preprocessed to remove incomplete and duplicate records, and the optimal ANN architecture was selected using the total goodness function. Model performance was evaluated using 10-fold cross-validation together with multiple statistical metrics, including the coefficient of determination (R²), mean absolute error, root mean squared error, bias, and prediction interval. The ANN achieved R² values of 0.9968 and 0.9686 for the representative training and testing datasets, respectively, and an overall R² of 0.993, with 1,668 predictions falling within the ± 30% error criterion. Comparative evaluation with the Canadian Standards Association (CSA), American Concrete Institute 318 (ACI 318), and Eurocode 2 (EC2) design-code models demonstrated that the proposed ANN consistently achieved superior predictive accuracy and reliability. Parametric analyses further confirmed that the predicted trends agree with established reinforced concrete shear mechanics, highlighting the dominant influence of the shear span-to-depth ratio, beam geometry, and reinforcement ratio on shear resistance. The proposed framework provides an accurate and robust decision-support tool that complements conventional design-code methods for predicting the shear strength of RC beams.
Isotopic evidence for a cold and distant origin of 3I/ATLAS
An automated derivative-based method for detection of motor evoked potential onset latencies in multi-muscle transcranial magnetic stimulation studies
Abstract Motor evoked potential (MEP) onset latency is a useful neurophysiological measure, but manual measurement is time-consuming in transcranial magnetic stimulation (TMS) studies with large numbers of trials. This is particularly relevant in cortical mapping studies recording from multiple muscles simultaneously, where automated methods could support more scalable analyses. Existing onset-detection methods have shown promise in more restricted datasets, but their performance in heterogeneous multi-muscle mapping data remains uncertain. In this pilot validation study, three healthy adults underwent TMS cortical mapping with simultaneous EMG recording from eight upper-limb muscles during resting and active conditions, yielding 3,840 EMG epochs. Three independent raters classified MEP presence and marked onset latency for all trials. Inter-rater agreement was assessed using Fleiss’ κ for detection and ICC(2,1) for latency. Human majority vote for MEP presence and mean latency ratings were used as the reference standard to benchmark a novel derivative-ratio algorithm against an existing method. Human raters showed moderate to strong agreement for MEP detection (Fleiss’ κ = 0.69) and high reliability for latency ratings (ICC(2,1) = 0.95), with a pooled mean absolute pairwise difference of 0.88 ms. The derivative-ratio algorithm showed strong detection performance and human-like latency estimates, outperforming a previously published algorithm. These pilot validation data suggest that the derivative-ratio method provides promising automatic MEP onset latency detection in complex multi-muscle cortical mapping data and may provide a basis for scalable latency analysis. By enabling reproducible extraction of conduction-related MEP features, this approach may support future biomarker studies in neurological disorders characterised by altered corticospinal excitability or corticospinal conduction, pending further validation in larger and clinically diverse datasets.
Explainable hybrid multi-branch CNN–ViT–GNN framework for robust hibiscus leaf disease classification
In silico analysis and validation of the cancer-associated fibroblasts-related gene CAMK4 promotes bladder cancer progression
A study on the rapid evaluation of injection-production capacity for depleted gas reservoir UGS by PI-GNN
Fractional high-Chern insulator in twisted rhombohedral graphene
Multi-cell quasi-solid-state photo-supercapacitor integrating ZnO, ZnO/g-C₃N₄, and MnO₂ for simultaneous light harvesting and energy storage
Magnetic character of the low-energy enhancement in 70Zn
Daytime sleepiness shapes health-related quality of life response to CPAP in obstructive sleep apnea
Abstract Improvements in health-related quality of life (HRQoL) after continuous positive airway pressure (CPAP) therapy vary widely among patients with obstructive sleep apnea (OSA). Excessive daytime sleepiness (EDS) may influence patient-reported outcomes beyond traditional disease severity metrics. We investigated whether baseline EDS modifies HRQoL responses to CPAP in patients with coronary artery disease (CAD) and OSA. We analyzed 1-year changes in HRQoL among CPAP-treated CAD patients with moderate-to-severe OSA (apnea–hypopnea index ≥ 15 events/h) from the RICCADSA cohort. Patients were classified as sleepy (Epworth Sleepiness Scale ≥ 10) or nonsleepy. HRQoL was assessed at baseline and after 1 year using the SF-36. Between- and within-group changes were examined, and multivariable linear regression models evaluated associations between CPAP usage and HRQoL changes. The study included 228 patients (126 sleepy, 102 nonsleepy). At baseline, patients with EDS had significantly lower physical and mental component summary scores and greater impairment across several SF-36 domains. After 1 year of CPAP therapy, improvements were observed in selected domains in both groups; however, patients with EDS continued to report lower mental and psychosocial scores. Baseline SF-36 scores were the strongest predictors of HRQoL change, whereas CPAP usage was not independently associated with improvements. HRQoL responses to CPAP therapy in patients with CAD and OSA were heterogeneous and strongly influenced by baseline daytime sleepiness. EDS identifies a subgroup with greater baseline impairment and persistent residual deficits despite treatment, highlighting the clinical relevance of symptom burden beyond apnea severity when interpreting patient-centered outcomes.