Search Results (161)

Background: The impact of the COVID-19 pandemic on temporal trends in peer violence and bullying deserves closer scrutiny. The aim of the present study was to examine temporal trends in peer violence and bullying among school-aged children before and after the COVID-19 pandemic. Methods: Data from the Health Behaviour in School-aged Children (HBSC) surveys (2013/2014–2021/2022) were analyzed to track changes in peer violence and bullying over time. The sample encompassed over 700,000 students aged 11, 13, and 15 from more than 40 countries across Asia, Europe, and North America. Results: Traditional (school) bullying perpetration and victimization did not change significantly over time. A significant decreasing trend in engagement in physical fighting between the 2013/2014 and 2021/2022 surveys was observed among male participants aged 15. In contrast, a significant increasing trend in engagement in physical fighting was observed among female participants aged 11 and 13 years. Following the pandemic, increases in cyberbullying perpetration and victimization were observed among students aged 11 and 13, a trend not evident among 15-year-olds. Conclusion: Except for cyberbullying, the pandemic did not appear to influence trends in peer violence and bullying, which remained largely stable or reflected trajectories that had begun prior to the pandemic. Full article

Gender-based violence (GBV) in South Africa continues to be a critical issue affecting the society. It is deeply entrenched within patriarchal structures shaped by historical injustices of colonialism and apartheid. Despite several legislative and policy initiatives aimed at addressing the scourge of GBV, media representations often perpetuate traditional gender stereotypes and biased narratives. Evidence from previous studies highlights the role of media in reinforcing gender inequalities by frequently sensationalizing violence and victim-blaming, thereby marginalizing the experiences of women. This paper contributes to ongoing debates on media framing by critically examining contemporary South African media narratives on GBV. The study will employ a framing analysis approach to scrutinize the key frames utilized by South African media in reporting GBV, drawing from significant scholarly works and news articles from IOL on GBV. Three empirical questions guide this study: (i) What frames are prevalent in media coverage of GBV in post-pandemic South Africa? (ii) Are there identifiable biases within these media narratives? (iii) How do media representations either reinforce or challenge entrenched patriarchal discourses? Analysis of selected news articles reveals persistent media practices that reinforce gender-biased stereotypes and diminish women’s agency and lived realities. Despite notable examples of counter-discourses emerging through digital activism, traditional media channels often undermine progressive efforts, perpetuating the perception that GBV remains inadequately addressed. This paper underscores the urgent need for more nuanced and transformative media practices that challenge systemic gender inequalities and promote genuine societal change in South Africa’s evolving post-apartheid context. The paper explores the growing GBV matters in the post-apartheid South Africa as presented in the media and argues that GBV matters. The paper pays particular attention to the inclusion and framing of women’s perspectives in the coverage of GBV in South Africa. Full article

With most individuals in the U.S. having regular access to an internet connection and/or owning smartphones, digital communication has become an inevitable part of daily life for adults and adolescents. Consequently, forming, maintaining, and ending relationships via digital media is a widespread phenomenon; however, there is also an ongoing risk of technologically facilitated intimate partner violence (IPV) perpetration and victimization. The current paper conducts a traditional narrative review to synthesize the extant research on the nature and scope of technologically facilitated IPV among Generation Z individuals. Four hundred and fifty studies were screened, and a total of thirty-eight studies—that met the inclusion criteria—were reviewed for the study. The current paper endeavors to explore the scope and pattern of technologically facilitated IPV. It examines Generation Z individuals’ vulnerability towards technologically facilitated IPV and assesses the impact of generative artificial intelligence on IPV perpetration and mitigation. The study also investigates any scope of association between online and offline violence victimization and perpetration. Finally, the paper also discusses recommendations to enhance violence mitigation programs and support services for younger victims through technologically facilitated means. Full article

In 2024, the conservative media outlet Daily Wire produced a documentary film entitled Am I Racist? Created by political commentator and author Matt Walsh and director Justin Folk, the film became one of the highest-grossing documentaries of the last decade. Unlike traditional documentaries, Walsh employs a rhetoric of irony against anti-racist adherents to obstruct their influence and inoculate mostly conservative viewers. His method, however, is unusual and even questionable in conservative Christian circles. The film is analyzed using a Bakhtinian analysis of dialogic opposition wherein Walsh embodies three ironic characters—Rogue, Fool, and Clown—in order to expose the monologue of anti-racism. The analysis demonstrates the dialogization of the anti-racist monologue through rhetorical enactments of anacrisis and syncrisis. Through juxtapositions of anti-racist ideologists and their everyday racist opponents, Walsh obstructs the future effectiveness of the ideology. Even more, by becoming a DEI expert himself, he performatively distorts the monologue to victimize opponents and entertain viewers through the public spectacle. Ultimately, Am I Racist? demonstrates a unique modern turn and strategy in conservative and, more importantly, Christian rhetorical strategies that needs more attention in the future. Full article

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

Several approaches are currently being used by law enforcement to locate the remains of victims. Yet, traditional methods are invasive and time-consuming. Unmanned Aerial Vehicle (UAV)-based remote sensing has emerged as a potential tool to support the location of human remains and clandestine graves. While offering a non-invasive and low-cost alternative, UAV-based remote sensing needs to be tested and validated for forensic case work. To assess current knowledge, a systematic review of 19 peer-reviewed articles from four databases was conducted, focusing specifically on UAV-based remote sensing for human remains and clandestine grave location. The findings indicate that different sensors (colour, thermal, and multispectral cameras), were tested across a range of burial conditions and models (human and mammalian). While UAVs with imaging sensors can locate graves and decomposition-related anomalies, experimental designs from the reviewed studies lacked robustness in terms of replication and consistency across models. Trends also highlight the potential of automated detection of anomalies over manual inspection, potentially leading to improved predictive modelling. Overall, UAV-based remote sensing shows considerable promise for enhancing the efficiency of human remains and clandestine grave location, but methodological limitations must be addressed to ensure findings are relevant to real-world forensic cases. Full article

Operating systems such as Windows, Linux, and macOS include built-in commands that enable administrators to perform essential tasks. These same commands can be exploited by attackers for malicious purposes that may go undetected by traditional security solutions. This research identifies an unmitigated risk of misuse of a standard command to disconnect network services on victim devices. Thus, we developed a novel Proof-of-Concept (PoC) malware named DCmal-2025 and documented every step of its lifecycle, including the core idea of the malware, its development, impact, analysis, and possible countermeasures. The proposed DCmal-2025 malware can cause a Denial-of-Service (DoS) condition without exploiting any software vulnerabilities; instead, it misuses legitimate standard commands and manipulates the routing table to achieve this. We developed two types of DCmal-2025: one that triggers a DoS immediately and another that initiates it after a predefined delay before restoring connectivity. This study evaluated 72 antivirus detection rates of two malware types (DCmal-2025 Type 1 and Type 2) written in C and Rust using VirusTotal. The source code for both types was undetected by any of the antivirus engines. However, after compiling the source code into executable files, only some Windows executables were flagged by general keywords unrelated to DCmal-2024 behaviour; Linux executables remained undetected. Rust significantly reduced detection rates compared to C—from 7.04% to 1.39% for Type 1 and from 9.72% to 4.17% for Type 2. An educational institution was chosen as a case study. The institution’s network topology was simulated using the GNS3 simulator. The result of the case study reveals that both malware types could cause a successful DoS attack by disconnecting targeted devices from all network-based services. The findings underscore the need for enhanced detection methods and heightened awareness that unexplained network disconnections may be caused by undetected malware, such as DCmal-2025. Full article

With their traditional focus on financial compensation, tort law systems worldwide struggle with the adverse effects the claims resolution process can have on victims of personal injury. It has therefore been argued that tort law systems should be more emotionally intelligent and more mindful of the non-financial needs of victims. In this debate, the perspective of the wrongdoer has been largely neglected. Drawing from empirical research on the personal experiences of wrongdoers in the Dutch personal injury practice and building on theories of procedural and restorative justice, this contribution argues that, to optimize the societal value of tort law systems, attention should be paid to the wrongdoer’s perspective. A tort law system that lacks sufficient opportunity for wrongdoers to personally make amends is deficient both in terms of morality and justice, as it deprives both victims and wrongdoers of a chance at emotional and moral recovery from the injurious event. We therefore believe this represents a shared future for all of us: towards restorative tort law. Full article

The pervasive integration of technology into the daily lives of children and adolescents, coupled with the popularity and extensive use of social media by this age group, has raised significant concerns, highlighting cyberbullying victimization as a serious global public health issue that requires further investigation. The aim of the present study was to examine the impact of cyberbullying victimization above and beyond traditional forms of peer victimization on adolescents’ school-related distress using a sample of 28,883 adolescents across nine countries. This study also assessed the moderating role of teacher support on the association between cyberbullying victimization and school-related distress. The results of structural equation modeling suggested that adolescents who experienced higher levels of victimization (both traditional and cyber) scored significantly higher on school-related distress. In addition, the main effect of cyberbullying victimization above and beyond traditional forms of peer victimization on school-related distress was significant. More importantly, cyberbullied victims who perceived their teachers as supportive reported lower school-related distress compared to their peers with low teacher support. As technology continues to become more accessible in both homes and educational settings, the findings of this study underscore the need to address cyberbullying as a distinct phenomenon posing unique challenges for adolescents and requiring targeted intervention strategies. In addition, the findings contribute to understanding a more comprehensive portrait of adolescent development and how social support influences adolescents’ well-being. Full article

The landscape of cybercrime has undergone significant transformations over the past decade. Present-day threats include AI-generated attacks, deep fakes, 5G network vulnerabilities, cryptojacking, and supply chain attacks, among others. To remain resilient against contemporary threats, it is essential to examine historical data to gain insights that can inform cybersecurity strategies, policy decisions, and public awareness campaigns. This paper presents a comprehensive analysis of the evolution of cyber trends in state-sponsored attacks over the past 20 years, based on the council on foreign relations state-sponsored cyber operations (2005–present). The study explores the key trends, patterns, and demographic shifts in cybercrime victims, the evolution of complaints and losses, and the most prevalent cyber threats over the years. It also investigates the geographical distribution, the gender disparity in victimization, the temporal peaks of specific scams, and the most frequently reported internet crimes. The findings reveal a traditional cyber landscape, with cyber threats becoming more sophisticated and monetized. Finally, the article proposes areas for further exploration through a comprehensive analysis. It provides a detailed chronicle of the trajectory of cybercrimes, offering insights into its past, present, and future. Full article

Background/Objectives: Organized physical activity is frequently considered a protective factor against bullying behaviors, yet evidence within the university context remains limited. This study investigates the relationships between physical activity levels, body mass index (BMI), and involvement in traditional and digital bullying, taking into account the differences between students with and without structured sports training. Methods: A total of 2767 first-year students from Transylvania University of Brașov participated. The sports group (n = 161; 65 females, 96 males) was compared to the non-sports group (n = 2606; 1472 females, 1134 males). Instruments included the Physical Activity Questionnaire for Adolescents (PAQ-A), validated scales for traditional and cyberbullying and victimization, and BMI calculation. Statistical analyses involved t-tests (two-tailed), 2 × 2 factorial ANOVA, and sex-stratified multiple linear regressions. Results: Students with sports training reported higher physical activity (PAQ-A 4.2–4.6), lower BMI, and lower bullying involvement (traditional ≈ 14–21% vs. ≈32%; cyber ≈ 8–17% vs. ≈25%). Group differences were large for physical activity (Hedges’ g ≈ 1.5) and moderate for BMI and bullying (g ≈ 0.68–0.96; point-biserial r² ≈ 3–4%). ANOVA showed sports status main effects (partial η_p² ≈ 4–5% for bullying/BMI; ≈20% for PAQ-A). In regressions, sports status (B = −0.30 to −0.44) and physical activity (B = −0.22 to −0.32) predicted lower aggression/victimization, whereas BMI showed positive associations (B = 0.11 to 0.18) (all p < 0.001). Sex × sports interactions were significant for PAQ-A and for traditional and cyber-victimization. Conclusions: Structured physical activity contributes to reducing the risk of bullying involvement and supports better psychosocial adjustment among students. These findings underscore the educational and preventive potential of university sports programs. Full article

Maritime transport is vital to the global economy, yet the frequency of natural disasters at sea continues to rise, resulting in more persons falling overboard. Therefore, effective maritime search and rescue (SAR) hinges on accurately predicting the probable distribution of drifting victims and on rapidly devising an optimal search plan. Conventional SAR operations either rely on rigid, pre-defined patterns or employ reinforcement-learning techniques that yield non-unique solutions and incur excessive computational time. To overcome these shortcomings, we propose an adaptive SAR framework that integrates three modules: (i) the AP98 maritime-drift model, (ii) Monte Carlo particle simulation, and (iii) a mixed-integer linear programming (MILP) model. First, Monte Carlo particles are propagated through the AP98 model to generate a probability density map of the victim’s location. Subsequently, the MILP model maximizes the cumulative probability of rescue success while minimizing a composite cost index, producing optimal UAV search trajectories solved via Gurobi. Experimental results on a 10 km × 10 km scenario with five UAVs show that, compared with traditional parallel-line search, the proposed MILP approach increases cumulative success probability by 12.4% within the first twelve search steps, eliminates path overlap entirely, and converges in 9.5 s with an optimality gap of 0.79%, thereby demonstrating both efficiency and real-time viability. When MIPFocus (a solver setting in Gurobi that controls the emphasis of the Mixed Integer Programming solver) aims at the optimal solution and uses the parallel solution method at the same time, the best result is achieved. Full article

Rapid building damage assessments are vital for an effective earthquake response. In Japan, traditional Earthquake Damage Certification (EDC) surveys—followed by the issuance of Disaster Victim Certificates (DVCs)—are often inefficient. With advancements in remote sensing technologies and deep learning algorithms, their combined application has been explored for large-scale automated damage assessment. However, the scarcity of remote sensing data on damaged buildings poses significant challenges to this task. In this study, we propose an Uncertainty-Guided Fusion Module (UGFM) integrated into a standard decoder architecture, with a Pyramid Vision Transformer v2 (PVTv2) employed as the encoder. This module leverages uncertainty outputs at each stage to guide the feature fusion process, enhancing the model’s sensitivity to collapsed buildings and increasing its effectiveness under diverse conditions. A training and in-domain testing dataset was constructed using post-earthquake aerial imagery of the severely affected areas in Noto Prefecture. The model approximately achieved a recall of 79% with a precision of 68% for collapsed building extraction on this dataset. We further evaluated the model on an out-of-domain dataset comprising aerial images of Mashiki Town in Kumamoto Prefecture, where it achieved an approximate recall of 66% and a precision of 77%. In a quantitative analysis combining field survey data from Mashiki, the model attained an accuracy exceeding 87% in identifying major damaged buildings, demonstrating that the proposed method offers a reliable solution for initial assessment of major damage and its potential to accelerate DVC issuance in real-world disaster response scenarios. Full article

This paper presents the implementation of a language model–based strategy for the automatic codification of crime narratives for the production of official statistics. To address the high workload and inconsistencies associated with manual coding, we developed and evaluated three models: an XGBoost classifier with bag-of-words features and word embeddings features, an LSTM network using pretrained Spanish word embeddings as a language model, and a fine-tuned BERT language model (BETO). Deep learning models outperformed the traditional baseline, with BETO achieving the highest accuracy. The new ENUSC (Encuesta Nacional Urbana de Seguridad Ciudadana) workflow integrates the selected model into an API for automated classification, incorporating a certainty threshold to distinguish between cases suitable for automation and those requiring expert review. This hybrid strategy led to a 68.4% reduction in manual review workload while preserving high-quality standards. This study represents the first documented application of deep learning for the automated classification of victimization narratives in official statistics, demonstrating its feasibility and impact in a real-world production environment. Our results demonstrate that deep learning can significantly improve the efficiency and consistency of crime statistics coding, offering a scalable solution for other national statistical offices. Full article

Facial authentication has gained widespread adoption as a biometric authentication method, offering a convenient alternative to traditional password-based systems, particularly on mobile devices equipped with front-facing cameras. While this technology enhances usability and security by eliminating password management, it remains highly susceptible to spoofing attacks. Adversaries can exploit facial recognition systems using pre-recorded photos, videos, or even sophisticated 3D models of victims’ faces to bypass authentication mechanisms. The increasing availability of personal images on social media further amplifies this risk, making robust anti-spoofing mechanisms essential for secure facial authentication. To address these challenges, we introduce FaceCloseup, a novel liveness detection technique that strengthens facial authentication by leveraging perspective distortion inherent in close-up shots of real, 3D faces. Instead of relying on additional sensors or user-interactive gestures, FaceCloseup passively analyzes facial distortions in video frames captured by a mobile device’s camera, improving security without compromising user experience. FaceCloseup effectively distinguishes live faces from spoofed attacks by identifying perspective-based distortions across different facial regions. The system achieves a 99.48% accuracy in detecting common spoofing methods—including photo, video, and 3D model-based attacks—and demonstrates 98.44% accuracy in differentiating between individual users. By operating entirely on-device, FaceCloseup eliminates the need for cloud-based processing, reducing privacy concerns and potential latency in authentication. Its reliance on natural device movement ensures a seamless authentication experience while maintaining robust security. Full article