Browse Articles
Discover research articles across all indexed journals
Real-world and computational identification of herbal candidates associated with adverse event patterns in glucagon-like peptide-1 therapy for obesity
Retrospective case-control study of pre-diagnosis observational and prescription data in Parkinson’s disease
Correction: Protective effects of liraglutide on hypercholesterolemia-associated atherosclerosis involve attenuation of endothelial-monocyte adhesion through down-regulating the LOX-1/NF-κB signaling pathway
Integrated analysis of nutritional composition, phytochemical, and antioxidant activity in Cotoneaster microphyllus (Wall. ex Lindl.)
Ungulate geophagy maintains termite mound habitat heterogeneity and increases internal mound temperature
Gender-specific spKt/V thresholds associated with mortality in maintenance hemodialysis patients: a nationwide Korean cohort study
Hybrid electricity management system for residential power block applications
Development, psychometric evaluation, and application of an eco-driving questionnaire based on the Health Belief Model
Victim framing shapes attitudes across diverse contexts
A person accused of victimizing others may be described as the “real” victim by their defenders to garner empathy and mitigate blame. Recent research shows that this rhetorical strategy, known as “victim framing,” can increase support for a man accused of sexually assaulting a woman. Little is known, however, about its effects in other contexts. Across five experiments ( N = 2,941), we investigated whether victim framing generalizes beyond prototypical sexual assault cases. Participants read fictionalized news reports where one party was labeled the victim (or neither was) and expressed support for the individuals involved. We found significant framing effects across diverse scenarios: (a) a man accused of sexual assault who self-described as the victim; (b) a woman accused of sexually assaulting a man; (c) same-sex assault allegations involving men or women; (d) a celebrity or stranger accused of physically assaulting his girlfriend; and (e) a police officer who shot an unarmed civilian. As in prior work, only participants who explicitly cited the victim-related language as influencing their evaluations showed robust and reliable framing effects. Multiple observer characteristics (e.g., gender, political ideology) predicted attitudes in expected ways, yet victim framing effects persisted when controlling for these individual differences. Taken together, these findings are consistent with a social-pragmatic account of victim framing: many people treat a victim label as communicating relevant information and adjust their evaluations accordingly, while others either do not draw this inference or weigh other information more strongly. Our findings highlight the power and limits of explicit forms of linguistic framing.
Accurate surgery time prediction (ASTP) strategy based on artificial intelligence techniques
Abstract Accurate timing prediction of surgery is essential for efficient operating room scheduling and ensuring patient care. This study proposes a two-layered Accurate Surgical Time Prediction (ASTP) framework. The first layer combines feature importance and advanced machine learning models to estimate surgical time. After preprocessing, two complementary interpretable AI methods were used: Long Short Term Memory with SHapley Additive exPlanations (SHAP) values and Random Forest permutation importance, to determine the importance of features. In the second layer, subsets of features (TOP-K) were progressively evaluated using HGBR and compared with multiple models: artificial neural networks (ANNs) and recurrent models (Long Short Term Memory, Gated Recurrent Unit, and hybrid Long Short-Term Memory + Gated Recurrent Unit). The proposed framework was evaluated on two datasets: a real-world dataset from an operating room at Nile Hospital and a public dataset from the Medical Informatics Operating Room Vitals and Events Repository (MOVER). In the Nile Hospital dataset, the results show that the Histogram Gradient Boosting Regression (HGBR) approach achieves the best balance, with a Mean Absolute Error of 8.89 min, Root Mean Square Error of 19.5 min, and R-squared of 0.26 using only four features, outperforming other methods. On the MOVER dataset, HGBR also demonstrated the strongest overall predictive behavior, achieving its best numerical result at TOP-12 and best subset at TOP-7, which preserved near-optimal performance with reduced input complexity. The proposed ASTP framework provides an interpretable and resource-efficient solution for surgical time prediction, supporting more intelligent operating room scheduling and facilitating future integration into hospital decision-making.
Implementation bottlenecks of near point of care HIV viral load monitoring for children and young people in Tanzania: A Qualitative Study
Background Near point-of-care (n)POC Human Immunodeficiency Virus (HIV) viral load (VL) monitoring, consisting of VL testing in laboratories close to HIV treatment facilities, improves turnaround time from sampling to result. Other benefits of using nPOC monitoring are reduced laboratory workload, and limited loss of results, all leading to improved clinic retention and treatment adherence. However, the specific implementation bottlenecks are still unclear. This study aims to investigate the bottlenecks in the implementation of nPOC HIV VL monitoring among children and young people (ages 0–24 years) living with HIV, as experienced by healthcare workers (HCWs) in Tanzania. Methods We conducted observations in clinics and in-depth interviews with HCWs from January 2023 to January 2024 at Tanzanian intervention sites within the East Africa Point-of-Care Viral Load Monitoring (EAPOC-VL) study. The EAPOC-VL study was conducted in four countries in East Africa (Tanzania, Kenya, Rwanda, Uganda). It was a cluster-randomised controlled trial, in which participants were tested at three time points (months 0, 6, and 12). We purposively selected 25 HCWs involved in implementing nPOC at the intervention sites for in-depth interviews. We interviewed HCWs at baseline (month 0) and after the initiation of nPOC HIV VL monitoring (months 1 and 6). We conducted deductive thematic framework analysis using the Measurement Instrument for Determinants of Innovations (MIDI), to which an inductive approach was added to identify facilitators and barriers across intervention, provider, organisational, social and political contexts. We used NVivo 12 to organise the data. Results A total of 75 interviews were conducted among 43 HCWs across 3 time points: 33 at baseline (T0), 25 at month 1 (T1; 19 participants from T0 and 6 new participants) and 17 at month 6 follow up (T2;6 participants from T1, 7 returning participants from T0 and 4 new participants). Observations and interviews showed that nPOC HIV VL testing improved result turnaround time and enabled same-day counselling, which motivated both HCWs and clients. This data showed that knowledge, confidence, and adherence to procedures after training. Near POC, HIV VL was supported by compatibility with existing practices, strong teamwork, and management commitment. However, challenges included clients waiting at the clinic for over two hours to receive their results, the scarcity of resources, such as rooms and electricity, and staff shortages. Finally, delays were observed when samples had to be transported to nearby laboratories. Conclusion Near POC HIV VL monitoring shortens turnaround times and enables immediate counselling. To maximise these benefits, there is a need to prioritise investment in staff training, infrastructure, improving sample handling/turnaround time and guideline alignment. Developing these areas will enhance service delivery and allow for improved outcomes among children and young people living with HIV.
Automated error localisation and correction techniques for deep-learning-based segmentation of 3D MRI sequences based on feature-derived-region aggregation
Abstract Automatic segmentation using convolutional neural networks (CNNs) has become a key tool in musculoskeletal imaging, offering substantial reductions in processing time. However, concerns about reliability often necessitate manual inspection and correction. We present a method that leverages network-derived uncertainty to automatically identify and localise segmentation errors, reducing the need for exhaustive manual review. A 3D nnU-Net was trained on delayed gadolinium-enhanced MRI of hip cartilage. Voxel-wise uncertainty scores, computed from the SoftMax outputs of ensembled sub-networks, were aggregated over feature-based supervoxels. Each region was then evaluated for its potential impact on clinically relevant metrics, generating sensitivity scores. A logistic model combined these with uncertainty data to assign risk scores, guiding attention to areas most likely to affect clinical metrics during the initial correction steps. Using these risk scores, guided supervoxel correction of just 50 supervoxels reduced the mean absolute relative error by 2.1-fold. Guided manual correction within these regions achieved a 3.5-fold reduction, an approximate 62% supervoxel correction efficiency. Correcting the top 10 regions yielded up to 88% efficiency. This approach serves as a proof-of-concept for targeted correction in hip MRI, enhancing the clinical utility of CNN-based segmentation by demonstrating that 3D feature-derived uncertainty aggregation has the potential to reduce correction burden compared to traditional 2D methods.
Mandatory lane-changing decision and control method based on game theory
This paper proposes a lane-changing decision and control framework for mandatory lane-changing scenarios based on stage game decision-making. The lane-changing process is discretized, and payoff functions are constructed for the autonomous vehicle and the following vehicles in the target lane, enabling adaptive adjustment of driving strategies and planned trajectories according to surrounding vehicle interactions. The optimal lane-changing decision is dynamically updated by incorporating environmental information and interactive feedback from surrounding vehicles. For trajectory tracking, a composite error combining lateral displacement and heading angle deviations is defined and constrained using a preset performance boundary. The constrained error is transformed into an equivalent unconstrained form, and a sliding mode controller is designed to ensure robust tracking performance. A joint simulation platform integrating traffic simulation and driver-in-the-loop experiments is established to evaluate the proposed framework. A preliminary study involving three human drivers with different driving tendencies is conducted to analyze interaction behaviors in mandatory lane-changing scenarios. The experimental results provide initial insights into the effectiveness of the proposed approach under representative conditions.
Effect of waist drum axial position on energy separation performance of Maxwell structured tubes
Abstract Combining numerical simulations and experimental validation, this study systematically investigates the influence of the axial position of the waist drum on the three-dimensional flow field structure and energy separation performance within Maxwell structured tubes. It is found that the waist drum position is a critical geometric parameter modulating internal momentum transfer and temperature stratification, exhibiting a significant non-monotonic relationship with energy separation efficiency. The cooling and heating effects of the system simultaneously reach their peaks when the waist drum is located in the middle region of the tube. Mechanism analysis reveals that this optimal position achieves the best synergy between flow field stability and secondary flow enhancement: it avoids the vortex core breakdown near the inlet caused by an upstream position, while overcoming the insufficient effective separation length associated with a downstream position. This research elucidates the kinetic-thermal energy conversion mechanism within variable cross-section vortex tubes, providing a theoretical basis for the topological optimization of Maxwell structured tubes.
HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning
Android malware detection remains a critical challenge due to the rapid increase in malware variants and the growing sophistication of obfuscation techniques. To address these issues, this paper introduces HybFusion, a holistic Android malware detection framework that integrates advanced feature fusion with ensemble learning to enhance detection effectiveness and reduce false positives. HybFusion combines two complementary feature types to overcome the limitations of existing methods in capturing a comprehensive representation of malware behaviors and semantics: (1) behavioral features extracted from the function call graph and embedded using the Graph Isomorphism Network, and (2) semantic permission features obtained from the AndroidManifest.xml file by applying a normalization process to convert permission identifiers into a permission sequence, which is then embedded using a lightweight pre-trained Transformer-based language model. This strategy enables better leveraging of semantic relationships among permissions while maintaining low computational cost. In addition, HybFusion adopts a stacking-based ensemble learning strategy that leverages the strengths of multiple classifiers to further improve detection robustness. Extensive experimental results demonstrate that HybFusion outperforms existing approaches across all evaluation metrics, achieving a recall of 99.24% and an F1-score of 99.27%.
Unified multi-task learning for hydrological processes using a shared transformer framework
Is asymmetric upper trapezius muscle activation during work associated with neck pain? A cross-sectional and longitudinal analysis
Objectives Previous studies have linked activity in the upper trapezius muscle with neck pain. However, no studies have examined whether asymmetric activation of these muscles during the working day is associated with neck pain. This study aimed to investigate this relationship. Methods Seven research institutes provided data on bilateral upper trapezius muscle activity on one working day, along with corresponding questionnaire data on cross-sectional (n = 530) and longitudinal (n = 256) neck pain intensity. The asymmetry, defined as the activity difference between the two upper trapezius muscles, was calculated as an average across the entire workday and within various intensity levels in relation to maximum voluntary isometric contraction (MVIC). Unadjusted and adjusted linear regression analyses were executed to examine the association between asymmetric muscle activation and neck pain intensity. Results In cross-sectional analyses, asymmetry in the levels 0–0.05 and 0.05–2%MVIC was significantly positively associated with neck pain intensity in both unadjusted and adjusted analyses. Asymmetry in the levels of 4–6, 6–8 and 8–10%MVIC was significantly negatively associated with neck pain in unadjusted analyses. In longitudinal analyses, significant positive associations were found for asymmetry in level 0–0.05%MVIC and negative associations for asymmetry in levels > 20%MVIC. Conclusion While asymmetry in the very low levels of muscle activity may be associated with higher neck pain intensity, asymmetry in the higher levels of muscle activity was negatively associated with neck pain intensity. However, the explained variance of the models was small, and the results should therefore be interpreted with caution. The findings suggest that work conditions facilitating simultaneous relaxation during breaks and balanced activation of both muscles during static activities may be relevant for neck pain prevention, though further research is needed to establish causality.
Simulation of long-term spatio-temporal environmental dynamics using a unified benchmark of neighbor augmenting, LSTM and graph attention models
Study on the adaptability of multilayer subway network under sudden large passenger flow disturbances
This paper proposes a multilayer network-based subway network adaptability assessment framework.Adaptability is defined as the ratio of post-disturbance cumulative network performance to its nominal value, represented as 0≤ F * ≤1. In this framework, each line is treated as a separate layer, with the introduction of composite edge weight and platform congestion factor, and the establishment a performance response function. By integrating the “physical accessibility-perceived impedance” flow diversion model and bond percolation theory, the framework characterizes failure propagation, and is verified using the Shanghai Metro regional network, with fair comparisons made against the weighted single-layer network (WSN) and the multigraph model (MLG). The results show that the critical threshold of the multilayer network is 12% and 11% higher than that of WSN and MLG, respectively; under severe attack, the network performance exceeds F * by 23%−33%, and ΔK is 22%−26% lower. Monte Carlo variance analysis further indicates a significant interaction (p < 0.01) between passenger flow arrival and route choice.