Search Results (2,550)

Previous research has shown that both victim intoxication and narrative framing can influence the levels of victim blame. However, far less attention has been paid to how coercive strategy and narrative framing may interact to shape victim-blaming judgements and perceptions of sexual assault. The present study addresses this gap by examining how combinations of coercive strategies (physical force versus alcohol facilitated), narrative framing (active versus passive), and rape myth acceptance (RMA) influence victim blame and the recognition of sexual assault. Participant gender and age were also assessed in relation to RMA and victim-blaming attitudes. A total of 202 participants aged 18–63 (78.7% of women, 21.3% of men, M_Age = 28.93, SD = 14.36) completed an online survey evaluating vignettes depicting a male perpetrator sexually assaulting a female victim. Age significantly predicted victim blaming, with older participants assigning greater blame to the victim. Gender predicted both RMA and victim blame, with men reporting higher RMA and greater victim blame than women. Active framing in both the physical force and alcohol-use conditions reduced participants’ recognition of the incident as sexual assault. Participants with lower RMA consistently reported lower victim blame across conditions, and were more likely to identify the incident as sexual assault in the physical force condition. These findings highlight the influence of coercive strategies and the importance of victim-centred language in policing, legal, and media contexts, where narrative framing can meaningfully shape the recognition of sexual assault. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Background/Objectives: Immune checkpoints are critical regulatory pathways that maintain peripheral tolerance and prevent autoimmunity. Among these, the programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) axis serves as a major inhibitory pathway that terminates T cell responses. While protein-based checkpoint blockade (ICB) targeting this axis has revolutionized clinical cancer therapy, its clinical efficacy is frequently limited by low response rates, immune-related adverse events (irAEs), and the emergence of adaptive resistance. To break through these bottlenecks, genetic interruption has emerged as a high-precision alternative to modulate the PD-1/PD-L1 pathway at the nucleotide level. Methods: A comprehensive systematic review of literature was performed across major databases (PubMed, Web of Science), with a focus on high quality studies published up to 2026. Results: Direct genomic disruption via CRISPR/Cas9 and post-transcriptional silencing through RNA interference can effectively neutralize inhibitory signaling at its source. Recent advances demonstrate that targeting upstream regulatory nodes—including metabolic checkpoints (e.g., lactate metabolism) and biophysical mechanisms (e.g., liquid–liquid phase separation)—provides superior transcriptional control over PD-L1. Furthermore, engineering CAR-T cells with multiplex gene editing (e.g., TCR/B2M/PD-1 knockout) or localized scFv secretion significantly enhances antitumor potency while reducing systemic toxicity. Innovations in organ-targeted lipid nanoparticles and stimuli-responsive biomimetic carriers further address the delivery barriers in solid tumors. Conclusions: Gene therapy provides a high-precision platform for PD-1/PD-L1 modulation, offering a viable strategy to overcome adaptive resistance. Future clinical application depends on the refinement of safer editing tools, such as base editing, and the standardization of intelligent delivery systems to ensure controllable and scalable cancer immunotherapy. Full article

Sex differences and lifestyle factors such as physical activity play an important role in cardiovascular autonomic regulation. Heart rate variability (HRV) is a widely used marker of cardiac autonomic modulation and cardiovascular health. However, the combined influence of sex and physical activity levels on HRV in young, healthy adults has not been sufficiently explored. Therefore, this study investigated the effects of sex and a physically active lifestyle on HRV in men and women. A cross-sectional study was conducted on a cohort of young, healthy adults aged 18–30 and categorized into four groups based on: physically active men (AM; n = 37), sedentary men (SM; n = 44), and physically active women (AW; n = 31) and sedentary women (SW; n = 40). Regarding the impact of lifestyle, the AM group exhibited 41% higher total variance (VAR-RR) and 34% higher RMSSD (a parasympathetic index) than the SM group. The AW exhibited 74% and 78% higher VAR-RR and RMSSD, respectively, compared to the SW. Furthermore, the physically active groups (AM and AW) displayed a 75% and 50% lower LF/HF ratio, respectively, compared to their sedentary counterparts. Interestingly, the LF/HF ratio was 66% higher, and the RMSSD was 20% lower in the AM group than in the AW group. HRV indices demonstrated large to very large effect sizes. In conclusion, these findings demonstrate significantly advantage in cardiac autonomic modulation among physically active individuals and women. Together, these results reinforce the roles of female sex and an active lifestyle as important protective factors for cardiovascular health. Full article

Phormia regina (Meigen, 1826) (Diptera: Calliphoridae), a necrophagous blow fly of significant forensic value, plays a crucial role in postmortem interval (PMI) estimation. The intra-puparial period of this species constitutes approximately half of its entire immature developmental duration. Therefore, precise aging of intra-puparial specimens is essential to improve the accuracy of PMI estimation. This study was performed at a constant 25 °C, examining intra-puparial morphology, histological sections, and body weight across different developmental stages. Additionally, full-length transcriptome profiling was conducted via a hybrid sequencing strategy combining PacBio single-molecule real-time (SMRT) sequencing and Illumina short-read next-generation sequencing (NGS). Morphological studies revealed the intra-puparial morphological characteristics, the reconstruction process of tissues and organs, and the continuous body weight loss during the intra-puparial period. Transcriptomic research identified a total of 425,349 full-length non-chimeric (FLNC) sequences. After redundancy removal, 84,852 transcript sequences were obtained, of which 46,325 transcripts were annotated. Using day-0 puparia (D0) as the control, differentially expressed gene (DEG) analysis was performed on samples from subsequent developmental stages (D1–D5), identifying 4242, 7964, 9509, 10,526, and 10,011 DEGs, respectively. Results from GO enrichment and KEGG pathway analyses provided reasonable explanations for the behavioral traits of different developmental stages. Eight candidate genes for intra-puparial development were screened, most of which were highly expressed at different time points during the intra-puparial period. Quantitative real-time PCR (qRT-PCR) results showed consistent gene expression trends with the RNA-seq data, confirming the reliability of the RNA-seq results. This study provides key morphological and molecular markers for P. regina in forensic PMI estimation. Full article

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice. Full article

Intimate partner violence (IPV) affects individuals of all genders and can result in adverse physical, psychological, and social outcomes. Experiences of IPV in men remain understudied when compared with those of cisgender women, leading to considerable gaps in understanding of prevalence, experiences, disclosure, and outcomes. The Male Experiences of Intimate Partner Violence Study (ME-IPV Study) was designed to explore: nature of IPV experiences, physical and psychological impacts, barriers to reporting/disclosing, experiences of disclosure, experiences of support, and support needs in a Northern Ireland (NI) context. This mixed-method study utilised data from N = 10 qualitative interview participants (quantitative results reported separately), analysed using an Interpretative Phenomenological Analysis (IPA) framework. Participants described experiencing multiple forms of IPV, with coercive control, psychological and institutional abuse being highly prevalent. Detrimental effects of their experiences included diagnoses of anxiety, depression, and PTSD, physical symptomology, the advent/exacerbation of multiple health conditions, and suicidal ideation. Barriers to care were primarily a lack of dedicated care pathway, concerns over being believed, and stigmatic barriers. Experiences of disclosure were mixed: positive with family/friends and negative with police and institutions of state. Male experiences of IPV in NI are a significant public health issue and it is evident that the impacts of IPV on men’s physical/mental health and wellbeing are profound. Full article

Dynamin-related protein 1 (Drp1) is essential for mitochondrial dynamics in skeletal muscle, particularly in regulating fission, mitophagy, and maintaining mitochondrial function. Exercise is crucial for sustaining muscle function, promoting mitochondrial adaptations that enhance energy metabolism and oxidative capacity in skeletal muscle. In this Review, we discuss the role of Drp1 in exercise-induced mitochondrial adaptations and its potential implications for skeletal muscle health. We first address the evidence that Drp1 activity must be maintained within a narrow physiological range. Both Drp1 deficiency and overabundance provoke muscle atrophy and dysfunction, establishing a Goldilocks principle for mitochondrial fission. We then examine the multi-layered post-translational modification code that governs Drp1 activity, including canonical phosphorylation, redox-sensing modifications, and the receptor selectivity model that may specify distinct fission programs. A three-stage model of exercise-induced mitochondrial adaptation is presented, describing how Drp1 activity is temporally orchestrated from acute fragmentation through short-term remodeling to long-term network optimization, and how these morphological transitions govern substrate metabolism and determine exercise performance. The pathological consequences of Drp1 dysregulation are examined in metabolic disease, where Drp1 is chronically hyperactivated, and in aging, where Drp1 activity is deficient. Finally, we analyze the ROS-Drp1 signaling axis as the mechanistic basis for the bidirectional regulation of Drp1 by exercise. Moderate exercise-induced ROS production activates Nrf2 and AMPK signaling, which suppress excessive fission in metabolic disease while restoring insufficient fission in aging, thereby moving Drp1 activity toward the physiological Goldilocks zone in both contexts. This context-dependent, bidirectional regulation distinguishes exercise from pharmacological inhibitors and identifies the ROS-Drp1 axis as a therapeutic target for conditions at opposite ends of the Drp1 activity continuum, such as sarcopenia and type 2 diabetes. Full article

Binder-free NiMn₂O₄@NiCo₂O₄ nanocomposites with NiMn₂O₄ nanoparticle (NP) surface coverage on NiCo₂O₄ nanosheets (NSs) are fabricated on nickel foam (NF) via a two-step microwave-assisted hydrothermal (MAH) method combined with annealing treatment, which can be used as a high-performance electrode material for supercapacitors. Specifically, a tulle-like NiCo₂O₄ nanosheet framework is first in situ grown on NF, followed by the growth of NiMn₂O₄ NPs on the surface of NiCo₂O₄ NSs via a secondary MAH process. To investigate the effect of the second-step holding time (HT) of MAH on material performance, a series of experiments were carried out with an HT of 15, 30, 45, and 60 min, and the microstructures and electrochemical properties of the products were analyzed. Structural characterization results confirm the successful synthesis of well-defined NiMn₂O₄-NPs@NiCo₂O₄-NSs composites. Electrochemical tests demonstrate that the product at an HT of 30 min has the best electrochemical performance with a higher specific capacitance of 441.56 F·cm⁻² at 1 A·cm⁻² and cycling stability (75% capacitance retention after 5000 cycles at 15 A·cm⁻²). The superior electrochemical properties are mainly attributed to the unique porous tulle-like NS structure with the largest specific surface area of the 30 min product. This distinctive structure affords abundant electrochemical active sites, effectively prevents structural collapse during long-term cycling, and shortens the transmission and diffusion pathways of electrons and electrolyte ions. The optimized NiMn₂O₄@NiCo₂O₄ electrode material presents extensive application prospects for high-performance supercapacitors. Full article

This study examines the effect of values-based leadership on workplace engagement and explores the moderating role of sustainable social responsibility. The proposed study is based on the social learning theory, the leader–member exchange theory, and the social exchange theory, proposing a multidimensional model of values-based leadership, leadership qualities (LQ), ethical values (EV) and balance in achieving interests (BAI). The quantitative survey design was employed in the collection of data amongst 390 employees of the Ministry of Interior, Qatar. The measurement and the structural models were tested using the partial least squares structural equation modeling (PLS-SEM) using WarpPLS V. 7 Software. The findings show that the three dimensions of values-based leadership make important and positive contributions to engagement in the workplace. The results indicated that sustainable social responsibility had no significant moderating effect on the relationship between leadership qualities and workplace engagement, or on the relationship between achieving a balance of interests and workplace engagement. However, sustainable social responsibility significantly moderated the relationship between ethical values and workplace engagement. The study adds value to the literature on leadership and workplace engagement by separating the dimensions of values-based leadership and the contextualized enhancing role of sustainable social responsibility. Full article

In recent years, artificial intelligence (AI) has been increasingly used in criminal justice systems across the world. To achieve objectives set out through Sustainable Development Goals (SDGs), adoption of technology is inevitable and undeniable. The press release dated 25 February 2025 from India’s Ministry of Law and Justice, quoting Prime Minister of India Narendra Modi to make a “justice system that will be fully future-ready”, confirmed that the Indian law enforcement agencies are integrating AI into policing and law enforcement to enhance crime detection, criminal investigation, etc. It is intended to enhance their capabilities in solving criminal cases and delivering justice speedily and more efficiently. However, the usage of AI tools in such contexts presents a double-edged sword, as evidenced by their application in a number of cases across the world like Christopher Gatlin, Nijeer Parks, the Harm Assessment Risk Tool (HART), and in India during the 2020 Delhi riots cases. As reported by the Washington Post, in Christopher Gatlin’s case it was found that the police arrested him on the basis of the facial recognition programme matching his face with the captured video footage. He spent 17 months in jail before his release by the court, observing that the police failed to conduct fair investigation. A similar incident was reported by NJ.com and CNN Business. In the investigations following the 2020 Delhi riots, Delhi Police effected over 1900 arrests in 758 riot-related cases, relying predominantly on AI-driven facial recognition matches. Subsequent court scrutiny in decided cases raised questions about reliability, leading to widespread acquittals and discharges of the accused in 82% of decided cases as of early 2025. In certain cases, AI-driven solutions have failed, leading to criminal prosecutions of innocent people based on AI-generated evidence. This study examines the reliability, validity, and ethics of AI technology in the criminal justice system in India’s unique socio-legal and political environment. The researchers analyse three interrelated axes. First, a comprehensive review of the international algorithmic policing literature to identify successes and failures. In addition, cases of AI-assisted investigations during the Delhi riots show how facial recognition systems and other AI techniques were used for inquiry. Finally, stakeholders’ perspectives, including a preliminary survey of 27 legal experts showing strong consensus on classifying AI-FRT outputs strictly as corroborative evidence and highlighting BSA insufficiencies for addressing opacity and explainability, help identify practical, procedural, and normative fault lines. Researchers noted that while AI has the potential to revolutionise resource-constrained investigative agencies, its unquestioning and uncritical adoption risks amplify pre-existing biases, undermine presumptions of innocence, and shift the burden of refuting algorithmic inference onto the accused. Independent algorithmic audits, transparent documentation of error rates and confidence thresholds, statutory guidelines on AI tool use and admissibility, and sustained capacity-building throughout the justice delivery chain are needed to integrate it into the Indian criminal justice system. Without such measures, the very tools designed and introduced to enhance accuracy threaten to undermine the fundamental norms of the criminal justice system such as fairness and due process. This fills a gap in doctrinal analysis of AI-specific evidentiary admissibility in non-Western contexts like India. This study aims to propose policy reforms, enhance judicial discourse, and promote a more circumspect trajectory for AI adoption in Indian law enforcement by mapping the potential and risks of algorithmic evidence in a non-Western legal order. Full article

The development of highly efficient, stable, and cost-effective non-precious metal electrocatalysts to replace conventional platinum-based materials holds profound significance for accelerating the commercialization of advanced energy conversion devices, such as zinc–air batteries (ZABs). Herein, we propose a facile and highly efficient strategy to prepare a defect-rich, highly active nitrogen-doped porous carbon-based electrocatalyst (denoted U-Fe-N-C, urea-assisted iron–nitrogen–carbon material), via high-temperature co-pyrolysis of heme with urea. Our results demonstrate that urea not only serves as an excellent nitrogen source during pyrolysis, introducing abundant topological defects and heteroatom doping sites, but also induces the carbon substrate to form a hierarchical sponge-like porous structure with a high specific surface area. This unique microenvironment effectively prevents the agglomeration of iron species at high temperatures, achieving enhanced dispersion of iron species stabilized within the nitrogen-rich carbon matrix. Electrochemical evaluations reveal that under the optimal synthesis conditions (a precursor mass ratio of 1:3, calcination at 900 °C), U-Fe-N-C exhibits excellent oxygen reduction reaction (ORR) catalytic performance, delivering a half-wave potential of 0.731 V vs. RHE, and shows long-term operational durability that significantly surpasses that of commercial Pt/C. Furthermore, liquid rechargeable zinc–air batteries assembled with U-Fe-N-C as the air cathode deliver remarkable cycling stability, operating for up to 270 h of charge–discharge cycling without noticeable performance degradation. This study not only provides useful insights into the mechanisms of pore formation and assistance but also offers a practical perspective for the rational design and scalable synthesis of high-performance metal–nitrogen–carbon (M-N-C) electrocatalysts. Full article

Lithium-containing ceramics were significant tritium breeders for the fusion blanket concept, for which tritium release performance and mechanical properties serve as the core indicators for evaluating their performance as tritium breeders. The Li₄Si_0.8Ti_0.2O₄ material was designed as an advanced tritium breeder and fabricated into ceramic pebbles via the freeze-drying method. The tritium release properties of the Li₄Si_0.8Ti_0.2O₄ sample pebbles were investigated via temperature-programmed desorption (TPD). The mechanical properties of the same batch of tritium breeder pebbles were analyzed comparatively, specifically examining the change in their compressive strength before and after irradiation. The sample pebbles irradiated with different neutron doses show different tritium release characteristics, and the tritium release temperature was about 293–553 °C. This was due to the H₂-tritium isotope exchange reaction, and radiation with different neutron doses will lead to different release temperatures of tritium. The mechanical properties of the Li₄Si_0.8Ti_0.2O₄ ceramic pebbles decreased significantly after irradiation. The main reason was that the accumulation of lattice defects and helium bubbles produced by high-energy neutron irradiation leads to internal cracks and helium embrittlement in the material. These results indicate that Li₄Si_0.8Ti_0.2O₄ solid solution may be considered a potential candidate for tritium breeder materials. Full article

To address the issues of insufficient real-time performance and inadequate modeling of temporal features in traffic police gesture recognition, this paper proposes a method based on skeleton keypoints and hybrid temporal modeling. First, YOLOv11m-Pose is employed to detect human skeleton keypoints in video sequences, extracting reliable two-dimensional skeleton features. Second, this study designs a temporal modeling network that integrates a bidirectional long short-term memory (BiLSTM) with a Transformer. The BiLSTM models local temporal continuity and action transition features between adjacent frames, capturing short-term dynamic changes. The Transformer, through its self-attention mechanism, models global temporal dependencies and weights critical time steps to extract long-range discriminative information. Experimental results demonstrate that the proposed method achieved 98.91% for both Accuracy and F1-Score. In terms of Accuracy, it outperformed the BiLSTM and Transformer models by 2.43% and 7.67%, respectively. It outperforms most methods based on recurrent neural networks and feature fusion. Meanwhile, the model achieves an average inference time of just 1.3299 s per gesture sequence. Consequently, this approach strikes a favorable balance between recognition accuracy and real-time performance, demonstrating significant practical value. Full article

In the realm of public safety, the automated identification of potential threats from voluminous surveillance streams is pivotal for developing intelligent security systems. Manual monitoring of such massive video feeds is highly inefficient, prone to human fatigue, and often leads to missed detections or false alarms. Leveraging deep learning for automatic anomaly detection is therefore essential to improve response efficiency and mitigate security risks. Weakly supervised video anomaly detection (WS-VAD) has emerged as a critical yet challenging task in this domain. In this study, we propose the Temporal-Enhanced and Visual-Text Adaptive Fusion (TE-VTAF) model for robust WS-VAD. Specifically, a Dynamic Local–Global Temporal Adaptive Module (DLG-TAM) is designed to capture multi-scale temporal dependencies and extract high-level video semantics. Concurrently, a Visual-Text Adaptive Fusion Module (VTAFM) is introduced to aggregate complementary cross-modal features, utilizing a competitive activation mechanism to suppress redundant information and enhance the discriminative power between normal and anomalous events. To further refine the learning process within the Multiple Instance Learning (MIL) framework, we incorporate a Top-K outer bag loss and a K-maxmin inner bag loss. These constraints effectively maximize the inter-class separability while suppressing label noise from normal instances within positive bags, thereby bolstering the detector’s robustness. Extensive experiments demonstrate that the proposed TE-VTAF consistently outperforms state-of-the-art methods on two large-scale benchmarks, achieving an AUC of 88.93% on UCF-Crime and an AP of 85.62% on XD-Violence. Full article