Browse Articles

Participant-level anomaly detection for generation and load data using dual-side LSTMs

Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning

The impacts of new quality productive forces, industrial agglomeration and energy intensity on carbon emission reduction

Abstract This study investigates the impact of new quality productive forces (NQPF) on carbon emission reduction in China, with a particular focus on the mediating roles of industrial agglomeration effect (IAE) and energy intensity (EI). Using panel data covering 30 provincial-level regions in China over the period 2003–2023, we control for key confounding factors including urbanization, industrialization, industrial structure upgrading, human capital, and foreign direct investment to empirically examine the relationship between NQPF and carbon emission reduction as well as its underlying transmission mechanisms. The results indicate that NQPF exerts a significantly negative effect on carbon emissions, with this carbon reduction impact being particularly prominent in economically developed regions and eastern coastal areas of China. Further mediation analysis reveals that IAE and EI function as two critical pathways through which NQPF influences carbon emission reduction. Specifically, the enhancement of IAE reinforces the carbon reduction effect of NQPF, whereas an increase in EI weakens this effect. Overall, our findings highlight that the development of NQPF can serve as a long-term driving mechanism for effectively achieving carbon emission reduction targets, thereby providing valuable policy implications for advancing low-carbon transition and green sustainable development in China’s economy and society.

Knowledge distillation and pseudo-labeling for lightweight YOLOv11-based structural crack detection

Development and validation of a self-management ability scale for patients with head and neck cancer undergoing radiotherapy and chemotherapy (SMA-HNC)

A detailed investigation of a-IGZO thin film-based MSM UV photodetector

An extended refutation text and funny videos reduce notorious p-value misconceptions

Abstract For many decades, the vast majority of psychology students and their supervisors alike have been prone to misunderstanding the p -value, such as confusing it with a hypothesis’ probability. The present paper offers three innovative contributions to address this problem: (1) A novel yet valid methodological approach to assess p- value misconceptions that combines correctness and subjective certainty. (2) An effective short-term digital intervention built around an extended refutation text to reduce those misconceptions in about 10 min. (3) An unorthodox humor intervention with funny videos for extra effectiveness (and chuckles). A sample of 157 undergraduate psychology students ( M age = 23.36) took part in an online experiment featuring a 2×2-factorial design (funny vs. neutral videos and intervention vs. control condition. Planned contrast analyses revealed a large positive effect of the digital intervention on the participants’ conceptual change score. Furthermore, the prior presentation of funny videos did indeed yield an additional positive effect. Overall, the findings demonstrate the potential of extended refutation texts (enhanced with funny videos) for a short and convenient digital refresher aimed at psychology students who had already taken an introductory course but needed further clarification to tackle p- value misconceptions.

Synergistic enhancement of recycled aggregate concrete using cement slurry-treated aggregates and graphite nano/micro platelets

An experimental and computational intelligence simulation for predicting the strength accuracy of self-compacting concrete using Linz–Donawitz Slag as industrial waste derivatives

Genetic diversity of Iranian Acanthophyllum species collection using SCoT and SRAP markers

An improved analytic workflow for serum mtDNA DAMP abundance, fragmentation and heteroplasmic variants: a retrospective analysis of acute respiratory failure patients

Resolving freeze-thaw surface energy exchange in soils through phase-dependent thermal measurements

Stability analysis and numerical investigation of fractional SIR model for childhood disease transmission with vaccination

Spaced practice and reactive inhibition have limited or no effects on motor sequence learning

Abstract Spaced practice in declarative memory tasks consistently yields greater learning than massed practice, but spacing effects are less consistently observed for motor skills. This study evaluates factors that may determine spacing effects on motor skill learning, including: (1) extant theories of declarative spacing effects, (2) reactive inhibition, which transiently impairs performance and may also impair learning, and (3) the micro-consolidation hypothesis, which posits that motor skill learning takes place exclusively during brief performance breaks. Across two experiments, we varied the number of correct sequences per trial and the length of breaks during training, while keeping the total correct sequence count constant, using a widely studied motor sequence task. A pronounced performance advantage was observed for the spaced groups by the end of training. However, on a later test in which the task conditions were equated, group performance was statistically indistinguishable. Hence, spaced practice yielded no or minimal learning advantage and the large reactive inhibition effect in the massed group appears to be a transient performance phenomenon without consequence for learning. Furthermore, we found no evidence for the most straightforward micro-consolidation account, which predicts greater learning with more breaks. Our results are consistent with a simple account advanced by Gupta and Rickard 1,2 , according to which learning occurs entirely online (i.e., concurrently with performance) and is independent of spacing and reactive inhibition. Finally, our findings indicate that proposed mechanisms for declarative spacing effects, such as memory reactivation and contextual variability, do not generalize to motor learning, highlighting fundamental differences between the two learning systems.

Effects of transcutaneous electrical nerve stimulation in postoperative total knee arthroplasty pain and intravenous analgesic requirement

Abstract Total Knee Arthroplasty (TKA) is a widely used procedure to relieve disability associated with advanced knee osteoarthritis. The management of postoperative TKA is crucial for the success of surgery and patient satisfaction. This study aims to determine the effects of integrating transcutaneous electrical nerve stimulation into standard postoperative TKA care on acute resting pain scores and intravenous analgesic requirements during the first three postoperative days. This randomised controlled trial was conducted from July to December 2022 at two hospitals in Lahore, Punjab, Pakistan. A total of 60 participants with TKA, aged 41 to 85 years, were recruited through purposive sampling. The control group received standard intravenous analgesics and postoperative rehabilitation, and the experimental group received transcutaneous electrical nerve stimulation (TENS) additionally. TENS was applied at a frequency of 85 Hz, pulse width of 120 µs, and at low intensity as tolerated. Pain was measured through the numeric pain rating scale, and analgesic requirements were monitored through the prescription chart. The results showed significant between-group differences in pain reduction ( p  < 0.001), whereas no statistically significant difference was observed in drug dose ( p  = 0.06) and frequency ( p  = 0.032/ corrected p  = 0.0167). TENS was a useful integrated modality to conservative pharmacological management of pain after total knee arthroplasty. Trial registration: This trial was prospectively registered at ClinicalTrials.gov (Trial ID NCT05470244) on 13th July, 2022. https//clinicaltrials.gov/study/NCT05470244.

A well-perceived, blind image quality assessment algorithm using an enhanced noise feature criterion

Abstract Many deep learning-based blind image quality assessment (BIQA) methods achieve high accuracy but rely heavily on complex network architectures and large datasets, which limit their applicability. This study proposes an enhanced perception-based no-reference (NR) BIQA method that incorporates a revised noise feature criterion for immediate and practical use. This approach was motivated by observations that conventional noise feature analysis becomes unstable in images with strong horizontal structures, such as fence-like patterns. To address this limitation, improved noise weighting and decision criteria were introduced. The method was evaluated on four publicly available databases (LIVE, CSIQ, TID2013, and KADID-10k), demonstrating higher or comparable prediction performance relative to the baseline algorithm, as measured by Spearman rank order correlation coefficient (SROCC) and Pearson linear correlation coefficient (PLCC). A detailed comparative analysis of quality estimation performance was conducted between the reference algorithm and the proposed algorithm. The estimated image quality scores were presented side by side, demonstrating that the proposed algorithm achieved more accurate estimations for the 24 perfect and distortion-free images in the TID2013 dataset. The results showed that the proposed algorithm placed all images closer to the ‘Excellent’ quality region according to Matlab help center description, aligning more closely with the expected evaluation goals than the reference algorithm.

Exploring the mechanism of stigmasterol against androgenetic alopecia using geometry optimization, network pharmacology, molecular docking, and molecular dynamics studies

CEAM-DETR: An NMS-free lightweight transformer for weed detection in soybean fields under complex conditions

Stabilization of Niaouli essential oil-loaded gelatin nanofibers: characterization and in vitro evaluation

Nutrient-wide associations of asthma, atopic dermatitis, and allergic rhinitis in Korean adults: a cross-sectional analysis of KNHANES 2016–2023

Abstract Diet may influence allergic disease risk through oxidative stress, inflammatory signaling, and immune pathways. However, evidence for specific nutrients remains inconsistent, particularly in Asian adult populations. We examined nutrient-wide associations with asthma, allergic rhinitis, and atopic dermatitis in a nationally representative cohort of Korean adults to systematically characterize nutrient-allergy relationships at the population level. We conducted a cross-sectional analysis of 37,808 adults (≥ 18 years) from the Korea National Health and Nutrition Examination Survey (KNHANES) 2016–2023. Physician-diagnosed asthma, allergic rhinitis, and atopic dermatitis were identified using standardized questionnaires. Dietary exposure was assessed using a 24-hour dietary recall, from which absolute daily intakes of 28 nutrients were derived. Nutrient variables were transformed as appropriate and standardized to 1-standard-deviation (SD) increments. For each outcome, we fitted survey-weighted logistic regression models with sequential adjustment for demographic and lifestyle covariates. Multiple testing was addressed using false discovery rate (FDR) control ( q values < 0.05). In fully adjusted models, greater intakes of total dietary fiber, potassium, and magnesium were associated with lower odds of asthma and remained significant under FDR correction ( q  < 0.05), with odds ratios (OR) per 1-SD increase ranging from 0.80 to 0.90. In contrast, allergic rhinitis showed positive associations with higher intakes of total fat, vitamin E, riboflavin, and potassium, which also met the FDR criterion (ORs per 1-SD increase approximately 1.05–1.15). For atopic dermatitis, no nutrient achieved FDR significance. In this nutrient-wide evaluation among Korean adults, only a limited subset of nutrients showed robust associations with asthma or allergic rhinitis, and the direction of association varied by allergic diseases. These findings underscore heterogeneity in diet-allergy relationships and support the value of nutrient-wide approaches for prioritizing targets for future prospective research.