Browse Articles

Modified resampling strategy for extreme values in imbalanced air pollution data using moving block bootstrapping approach with relevance weighting (MBB-RW)

Predictive value of internal jugular vein combining with inferior vena cava diameters by ultrasound in central venous pressure of ICU patients

Compound coastal marine–terrestrial heatwaves associated with humid-heat stress in Europe

Abstract European coastal regions - home to dense populations, and climate-sensitive ecosystems - are vulnerable to the compounding effects of marine–terrestrial heatwaves, defined here as concurrent extreme heat events over land and adjacent ocean areas. Using satellite and station-based observations, we find a nonlinear and accelerating increase in exposure to these compound events across European coastlines over the past two decades, peaking at 78 days in the Mediterranean in 2022. Attribution analysis reveals that greenhouse gas (GHG) forcing accounts for 95% of the risk of the 2022 event, and the CESM1-LE simulations indicate that such an event would be virtually absent without GHG forcing, emphasizing the nonlinear escalation of risk as GHG emissions continue. We further demonstrate that simultaneous marine heatwaves in the adjacent ocean can amplify coastal terrestrial heatwave exposure by up to 3.5 times, transforming short-lived terrestrial heatwaves into prolonged episodes of extreme heat and humidity. During compound events, coastal regions exhibit a pronounced shift toward humid heatwave regimes, characterized by wet-bulb temperatures $$> 25.5\, ^\circ$$ C and elevated specific humidity. Our finding reveals an amplifying effect of marine heatwaves on the persistence of coastal terrestrial heat extremes through enhanced moisture and heat coupling at the land–sea interface, underscoring the growing climate vulnerability of coastal populations.

Upper limb function and quality of life in Duchenne muscular dystrophy: a cross-sectional study in Chile

Differential effects of top-down crossmodal attention on subjective timing of semantic and syntactic violations

Leveraging TRPV1 for intracellular delivery of membrane impermeant compounds in the brain

Sleep quality in Spanish adults: an age- and sex-stratified cross-sectional study

Strength enhancement of calcium carbide residue-stabilized clay with perlite powder and water treatment sludge

A meta-analysis of the persuasive power of large language models

Abstract Large language models (LLMs) are increasingly used for persuasion, such as in political communication and marketing, where they affect how people think, choose, and act. Yet, empirical findings on the effectiveness of LLMs in persuasion compared to humans remain inconsistent. The aim of this study was to systematically review and meta-analytically assess whether LLMs differ from humans in persuasive effectiveness, and under which contextual conditions LLMs are particularly effective. We identified 7 studies with 17,422 participants primarily recruited from English-speaking countries and 12 effect size estimates. Egger’s test indicated potential small-study effects ( $$p =.018$$ ), but the trim-and-fill analysis did not impute any missing studies, suggesting a low risk of publication bias. We then compute the standardized effect sizes based on Hedges’ g . The results show no significant overall difference in persuasive performance between LLMs and humans ( $$g = 0.02$$ , $$p =.530$$ ). However, we observe substantial heterogeneity across studies ( $$I^2 = 75.97\%$$ ), suggesting that persuasiveness strongly depends on contextual factors. In separate exploratory moderator analyses, no individual factor (e.g., LLM model, conversation design, or domain) reached statistical significance, which may be due to the limited number of studies. When considered jointly in a combined model, these factors explained a large proportion of the between-study variance ( $$R^2 = 81.93\%$$ ), and residual heterogeneity is low ( $$I^2 = 35.51\%$$ ). Although based on a small number of studies, this suggests that differences in LLM model, conversation design, and domain are important contextual factors in shaping persuasive performance, and that single-factor tests may understate their influence. Our results highlight that LLMs can match human performance in persuasion, but their success depends strongly on how they are implemented and embedded in communication contexts.

Prevalence and factors associated with poor hand hygiene practices among adult carers of children under five in Mbale district Uganda

SVTR-MG: an optical character recognition network for food packaging spray codes

Abstract Spray codes on product packaging play a critical role in food traceability, quality control, and anti-counterfeiting verification. However, accurate recognition of spray codes in industrial environments remains a significant challenge due to factors such as small character regions, fluctuating print quality, reflective packaging materials, and character deformation. To address these issues, this paper proposes a lightweight improved network named SVTR-MG. The model incorporates a Multi-scale Dilated Feature Aggregation (MDFA) module, which leverages convolutions with varying dilation rates to expand the receptive field and effectively integrate global and local features, thereby enhancing the perception of characters under multi-scale and complex background conditions. Additionally, a Global Context Self-Attention (GCSA) module is introduced, which combines channel and spatial attention mechanisms to model long-range dependencies between characters, improving the network’s robustness to uneven illumination and structural distortions. Furthermore, a dynamic dictionary mapping mechanism is proposed to optimize output alignment during the decoding phase. Experimental results demonstrate that SVTR-MG achieves a recognition accuracy of 93.2% at an inference speed of 142 FPS in complex industrial scenarios, outperforming mainstream OCR methods by approximately 5%, and meeting the real-time and accuracy requirements for deployment in production environments.

Urine color tool for coronary heart disease phenotyping

Pharm D student’s Knowledge, perception, and practice of CHAT-GPT in clinical training: a web-based cross-sectional survey in India

Evaluating the swimming technique analysis: a multi-criteria decision approach using hesitant bipolar fuzzy with a GRA approach

Agri-Photovoltaic technology allows dual use of land for tomato production and electricity generation

An empirical evaluation of dimensionality reduction and class balancing for medical text classification

Design and biological evaluation of mesalamine–NSAID hybrids targeting the NLRP3 inflammasome: a multi-target strategy for ulcerative colitis therapy

Integrating chemical modification pathways and machine learning for optimization of nitrate removal by rapeseed (Brassica napus L.) biochar

Hierarchical human activity recognition with fusion of audio and multiple inertial sensor modalities

Correlation between spectroscopic and stoichiometric protein to lipid ratios in erythrocyte-derived extracellular vesicles and nanoerythrosomes