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Stress wave propagation and crushing mechanism of soft-hard composite coal under water-jet impact load
Counterspeech encouraging users to adopt the perspective of minority groups reduces hate speech and its amplification on social media
Abstract Online intergroup hostility is a pervasive and troubling issue, yet experimental evidence on how to curb it remains scarce. This study examines counterspeech as a user-driven strategy to reduce hate speech. Drawing on theories that suggest adopting the perspective of minority groups can reduce prejudice, we randomized four counterspeech strategies across the senders of 2102 xenophobic Twitter messages. Compared to a passive control group, we find that the pooled effect of the three perspective-centered strategies—traditional perspective-taking, analogical perspective-taking, and perspective getting—increased the likelihood that the sender deleted their xenophobic message by +0.14 SD ( $$p=0.003$$ ), decreased the number of likes the xenophobic message received by others (− 0.133 SD, $$p=0.040$$ ), but yielded a limited and not statistically significant estimate for the share of xenophobic messages the sender posted over the following four weeks (− 0.084 SD, $$p=0.178$$ ). Differences between the three perspective-centered strategies were generally small and not statistically significant, though analogical perspective-taking—encouraging senders to compare their own experiences of being attacked online with their discriminatory behavior toward outgroups—appears to have slightly larger effects across multiple outcomes. Disapproval messages without a perspective shift produced smaller and non-significant estimates. These findings advance our theoretical understanding of how counterspeech works and provide actionable insights for how users can contribute to reducing intergroup hostility and its amplification online—especially at a time when many platforms are scaling back content moderation.
High intensity exercise before sleep boosts memory encoding the next morning
Abstract The importance of sleep for memory consolidation has been extensively studied, but its role for memory encoding remains less well characterized. At the molecular and cellular level, the renormalization of synaptic weights during sleep has received substantial support, which is thought to free capacity to encode new information at the behavioral level. However, at the systems level and behaviorally, support for this process playing a major role for memory function remains scarce. In the current study, we investigated the utility of moderate- and high-intensity evening exercise as a low-cost low-tech intervention to modulate sleep and its influence on subsequent encoding in the morning. Our findings indicate that high-intensity interval training (HIIT) improved post-sleep memory performance with effects lasting up to 24 h after initial encoding. In addition, we show that especially the early parts of the encoding task were affected by the HIIT intervention. Intriguingly, participants with lower encoding abilities seemed to benefit more from the HIIT intervention suggesting it not only as a tool for basic research but also as a candidate for applications to boost memory performance in mental disorders or in the elderly. These results provide first evidence that acute exercise can affect learning processes even hours after it occurs.
Parkinson’s disease tremor explained by reflex loop changes
Topological isomers of a potent wound healing peptide: Structural insights and implications for bioactivity
Performance management and development system in South Africa, a necessary evil: Qualitative study
Performance management focused on development enhances individual and organisational performance and enables improved services. Achieving performance management objectives is vital for addressing healthcare worker shortages and ensuring equitable, quality healthcare. A weakened South African district-based primary health care system links to inadequate leadership and governance. This study aimed to explore how doctors in primary health care perceive performance management and development systems. The objectives examined what medical officers understand about it and their experiences. Emerging themes may provide insights into enhancing implementation. This study used a qualitative, interpretive, and phenomenological research design. Stratified purposive sampling based on PMDS completion and employment duration led to four focus group discussions with 17 participants. A thematic analysis was performed. The overarching theme was the Performance Management and Development System as a necessary evil, with benefits and challenges. The subthemes included understanding components, comprehending clinic systems to improve outcomes, nurturing employee-supervisor interactions, fostering performance and learning culture, and facilitating personal and professional growth. Additional subthemes included ambiguity in fairness, lack of management capacity, and need for a bottom-up approach and realistic implementation. Effective implementation of a Performance Management and Development System requires managers and supervisors to drive this process strategically. Those responsible for clinical governance should invest in personal development to understand the process and consider appropriate implementation tools.
Land use and land cover classification and terrestrial ecosystem carbon storage changes in Vietnam based on Sentinel images
Bimetallic organic framework-derived porous Co3O4/Fe2O3 nanosheets for acetone sensing
Blind source separation and unmanned aerial vehicle classification using CNN with hybrid cross-channel and spatial attention module
Automated classification of chondroid tumor using 3D U-Net and radiomics with deep features
The association between BMI and healthcare burden, stratified by race and healthcare utilization among middle-aged patients in the US
SLC10A3 regulates ferroptosis of glioblastoma through the STAT3/GPX4 pathway
Machine learning to improve predictive performance of prehospital early warning scores
Abstract Early warning scores are used to assess acute patients’ risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however they tend to identify a considerable number of false positive cases, especially among prehospital patients. We investigated the development and validation of predictive scores based on machine learning models among patients (aged ≥ 18 years) who used ambulances in the North Denmark Region from July 1, 2016, to December 31, 2020. The machine learning models were compared to standard early warning scores (NEWS2 and DEPT), on 7- and 30-day mortality and intensive care admission. The cohort of 219,323 patients was split into development (n = 175,458 (80%)) and validation (n = 43,865 (20%)) datasets to respectively develop and test the machine learning models. These models were logistic regression, random forest, Bayesian networks, and gradient boosting. The machine learning models outperformed NEWS2 and DEPT, with fewer false positives, reducing the number of patients needed to screen by nearly half, for 7 day mortality. This has the potential to reduce both under- and over-triage, improving the precision of the triage among prehospital patients.
Differential modulation of low- and high-frequency mu oscillations during the observation of manual, facial, and non-biological movements
Structural basis for sirtuin 2 activity and modulation: Current state and opportunities
Data augmentation of time-series data in human movement biomechanics: A scoping review
Background: The integration of machine learning and deep learning methodologies has transformed data analytics in biomechanics. However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted participant access that hinder the development of robust algorithms. Additional issues include variability in sensor placement, soft tissue artifacts, and low diversity in movement patterns. These challenges make it difficult to train models that perform reliably across individuals, tasks, and settings. Data augmentation can help address these limitations, but its use in biomechanical time-series data remains insufficiently evaluated. Objective: This scoping review on data augmentation for biomechanical time-series data focuses on examining current techniques, evaluating their effectiveness, and offering recommendations for their application. Design: Four online databases (PubMed, IEEE Xplore, Scopus, and Web of Science) were used to find studies published between 2013 and 2024. Following PRISMA-ScR guidelines, two screening processes were conducted to identify relevant publications. Results: 21 publications were identified as relevant. There is no universal best practice for augmenting biomechanical time-series data; instead, methods vary based on study aims. A key issue identified is the absence of soft tissue artifacts in synthetic data, leading to discrepancies and emphasizing the need for realistic techniques. Furthermore, many studies lack proper evaluation of augmentation methods, making it difficult to understand the effects of different techniques. This understanding is crucial for assessing the impact of the augmented data set on downstream models and evaluating the quality of the data augmentation process. Conclusion: This review highlights the importance of data augmentation in addressing limited data availability and improving model generalization in biomechanics. Tailoring augmentation to data characteristics can enhance the performance and relevance of predictive models. However, understanding how different augmentation techniques impact data quality and downstream performance remains essential for developing better methods.