Search Results (1,859)

Serious youth violence is a public health issue nationally in the UK and internationally. The Youth Endowment Fund (YEF) was established in March 2019, with a £200 million endowment and a ten-year mandate, with a mission to prevent children and young people from becoming involved in violence. This article gives an overview of YEF’s successes and challenges to date, focusing specifically on the experience of evaluating tertiary interventions. After providing an overview of YEF’s approach to funding and evaluation, the article summarises YEF’s work focused on tertiary prevention, including: work to test interventions already being implemented in the UK; adapting and evaluating evidence-based interventions from other jurisdictions in the UK; innovations in a group approach to carrying out evaluations; and embedding a focus on racial equity in tertiary prevention. Next, the article discusses the design issues involved in high-quality evaluation of tertiary prevention, including the scale required and the processes for obtaining consent from young people to participate in evaluations. The article then documents the many challenges and lessons learned from implementing tertiary prevention evaluations, especially focusing on the recruitment and retention of young people. Finally, the article discusses the lessons and places them in a wider context. Full article

This paper examines how scam compounds, money mules and crypto-assets operate as interdependent elements of contemporary money-laundering chains. It assesses whether existing anti-money laundering (AML) and crypto-asset regulatory frameworks are capable of disrupting these chains holistically, rather than addressing individual components in isolation, with particular reference to scam-compound activity in Southeast Asia. The study adopts a qualitative comparative case-study methodology grounded in legal and regulatory analysis. Four empirically grounded cases are examined: two Southeast Asian scam-compound enforcement cases (Cambodia and Myanmar) and two European crypto-asset seizure cases (Ireland and Italy). Judicial decisions, enforcement actions and regulatory instruments are analysed through a chain-based analytical framework aligned with Financial Action Task Force (FATF) standards, the EU Markets in Crypto-Assets Regulation (MiCA) and the Anti-Money Laundering Authority (AMLA) framework. The analysis reveals a structural divergence in enforcement strategies: Southeast Asian responses increasingly prioritise network- and infrastructure-level disruption of scam compounds, whereas European approaches remain largely centred on post-offence crypto-asset seizure through traditional proceeds-of-crime mechanisms. Across all jurisdictions, money mules emerge as a critical yet systematically under-regulated intermediary layer enabling the resilience of crypto-laundering operations. The paper advances existing AML typologies by conceptualising scam compounds, money mules and crypto-assets as interconnected components of a single crypto-laundering chain. This chain-based perspective offers a novel analytical and regulatory lens for understanding organised crypto-enabled fraud. The study is based on a qualitative, case-based design and does not aim for statistical generalisation. However, the analytical framework developed is transferable to other jurisdictions experiencing similar scam-compound and crypto-laundering dynamics. The findings suggest that effective AML enforcement requires coordinated intervention across multiple nodes of the laundering chain, including scam compound infrastructure and money mule networks, alongside traditional asset-seizure mechanisms and CASP supervision. By highlighting the structural links between scam compounds, coercive labour and crypto-laundering mechanisms, the paper underscores the broader social harms of crypto-enabled fraud and the need for integrated regulatory responses that address both financial crime and human exploitation. Full article

This interdisciplinary study integrates criminological theory with geospatial methods to analyse large, multi-format datasets using geostatistical techniques. The aim is to predict where Child Criminal Exploitation (CCE) is likely to cluster, based on the spatial convergence of contextual risk factors. Drawing on insights from General Strain Theory (GST) and prior research on CCE, this study integrated seven open-source datasets capturing educational attainment, age demographics, violent crime, deprivation, and paramilitary-related violence. These variables were operationalised to construct a proxy measure for strain. Spatial analysis was conducted using ArcGIS Pro, including the Data Interoperability extension, to enable efficient integration and interrogation of multi-format geospatial data. Geospatial analysis demonstrated that contextual risk factors for CCE are spatially clustered. Using four search parameters, a small subset of wards with elevated risk were identified. This resulted in a reduction in ward locations by 85–99%, land area under investigation from 14.45% to 0.84%, and affected population from 17.91% to 1.41%, enabling more targeted and efficient resource allocation. As understanding of the contextual factors contributing to CCE improves, this methodological approach offers scalable and data-driven means of identifying high-risk areas. By integrating geospatial analysis with criminological theory, the model supports more effective safeguarding strategies and prioritisation of limited public resources. This study is limited by the absence of multi-agency datasets, which were beyond its scope. Future research aims to incorporate cross-sector data to validate and refine the model through ground-truthing, enhancing its predictive accuracy and practical applicability. Full article

Background/Objectives: Bloodstain pattern analysis (BPA) is widely used in crime scene investigation (CSI), yet its practical application, evidential limits, and interpretive role are often discussed in fragmented or technique-focused terms. This systematic literature review examines how BPA is used in CSI, with emphasis on its operational functions, interpretive scope, and scientific robustness. Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in Scopus using predefined Boolean strings. After screening, eligibility assessment, and manual review, 18 peer-reviewed research articles published between 1996 and 2026 were included. Data were extracted systematically and analysed using thematic synthesis. Results: The findings show that BPA is applied in CSI as an integrated evidential pathway rather than as a single analytical procedure. Its uses include bloodstain detection and documentation, geometric reconstruction through trajectory and area-of-origin analysis, differentiation of mechanisms and sources to prevent misclassification, activity-level inference based on transfer and contact phenomena, and temporal reasoning related to trace formation. The review also highlights the role of validation infrastructures, including blood substitutes, animal analogues, and computational methods, which support training, experimentation, and reproducibility under ethical and practical constraints. Across the literature, reconstruction accuracy is shown to be sensitive to documentation quality, measurement assumptions, environmental conditions, and contextual limitations. Conclusions: Overall, BPA contributes to CSI by enabling structured, context-aware interpretation of blood evidence while remaining subject to measurement assumptions, contextual influences, and cognitive factors that may affect reconstruction outcomes. Its evidential value lies not only in reconstructing events, but also in supporting transparent, testable, and defensible forensic reasoning. Full article

Background/Objectives: Children and adolescents exposed to criminal victimization are at increased risk for depression; however, the contribution of nutritional status to depressive symptom severity in this vulnerable population remains poorly understood. Therefore, we aimed to examine the associations between depression severity and nutritional parameters in child and adolescent victims of crime. Methods: This cross-sectional study included 72 children and adolescents (aged 10–16 years) referred to a forensic medicine department in Türkiye. Nutritional status was assessed using anthropometric measurements (body weight, body mass index [BMI], BMI-Z score, and body fat percentage), three-day dietary records, and the Mediterranean Diet Quality Index (KIDMED). Depression severity was evaluated using the Kutcher Adolescent Depression Scale (KADS). The associations were analyzed using Pearson’s rho correlation and forward stepwise linear regression. Potential confounding variables, including age, gender, socioeconomic status, and trauma-related characteristics, were recorded and considered during the analysis; however, due to the limited sample size and to avoid model overparameterization, they were not fully adjusted for in the final model. Results: Depression severity was positively correlated with the body weight, BMI, BMI-Z score, body fat percentage, and dietary energy, carbohydrate, protein, and fat intakes (all p < 0.05). In contrast, the vitamin C and dietary fiber intakes, breastfeeding duration, and KIDMED scores were negatively correlated with the KADS scores (p < 0.05). Regression analysis revealed that the lower KIDMED scores, higher body fat percentage, and greater body weight were significantly associated with depression severity, collectively explaining 82.2% of the variance in the KADS scores. Conclusions: Poor diet quality and adverse body composition are strongly associated with depression severity in child and adolescent victims of crime. These findings suggest that nutritional factors may be associated with depression severity in child and adolescent victims of crime; however, the results should be interpreted as preliminary and hypothesis-generating. Full article

The concept of “Healthocide,” first defined by Abi-Rached and colleagues, describes the deliberate and systematic destruction of health systems as a weapon of war. Nowhere is this phenomenon more extensively documented than in Gaza, where the collapse of healthcare infrastructure since October 2023 has been rapid, wide-ranging, and intentionally sustained. The consequence is not only immediate excess mortality, but also profound, long-term loss of population health measured in life-years, a metric that captures both premature death and reductions in expected lifespan. To address the aftermath of such destruction, we propose the framework of “Healthogenesis,” defined as a Palestinian-led, equity-driven, and rights-anchored approach to health system reconstruction in which international actors serve as enablers rather than agenda-setters. The aim of Healthogenesis is not merely to restore pre-war capacity, but to build a resilient, sovereign, and future-proof health ecosystem. Using available demographic and mortality data, we estimate that more than three million life-years have already been lost in Gaza since October 2023. Projection models suggest that an additional 1.1 to 2.2 million life-years could be lost over the coming decade unless an organized programme of reconstruction begins immediately. Quantifying harm in life-years reframes the discourse from moral outrage to measurable obligation. If Healthocide names the crime, then Healthogenesis outlines the cure: a coherent, data-anchored, ethically grounded roadmap for rebuilding a devastated health system and protecting the health futures of an entire population. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

Background/Objectives: The goal of this study was to characterize changes in autofluorescence of epithelial cells obtained from saliva stains that occur with time and investigate the potential for these changes to serve as time-since-deposition (TSD) signatures for this sample type. Methods: Saliva from 50 individuals was used to create 208 deposits that were aged between one day and nine months. Autofluorescence profiles of individual cells were obtained from each sample using imaging flow cytometry (IFC) and analyzed across nine different emission channels ranging between 435 nm and 800 nm. Results: Results showed strong evidence for linear increases in autofluorescence intensity when epithelial cells from a single donor deposit were measured over time (12 of 14 donors r ≥ 0.9). When autofluorescence profiles from all 50 donors were combined into a single time series, variation in autofluorescence intensity was observed between individual deposits with the same TSD. This inter-contributor variation decreased the overall strength of the linear relationship (r = 0.83) and yielded residual errors of ~8 days for samples that were actually 1 day old and ~82 days for samples that were over 180 days old using a linear regression model. Although this approach may not currently be amenable to estimating TSD to the day with high accuracy, clear, non-overlapping differences in autofluorescence intensity were still observed between certain time intervals, e.g., saliva deposits that were aged for 1 day compared to saliva deposits that were aged for more than 120 days. Conclusions: This suggests that cellular autofluorescence signatures have the potential to be probative when hypotheses for sample deposition involve disparate time intervals or as a screening tool for identifying which samples are most likely relevant to the crime in question based on their deposition time. Full article

Most of the Peruvian inquisitorial processes from the 17th and 18th centuries in the Americas addressed love spells, and not the crimes of heresy they were originally meant to adjudicate. Thanks to the records that have been preserved from the Court of the Peruvian Inquisition, we know that many of the women in the Andes habitually resorted to the practice of witchcraft, divination and prognostication, and that it played an important cultural and social role searching for an update in the future in loving terms. From aristocrats to the displaced, whether European immigrants, Native Americans, or enslaved Africans, witchcraft connected all these female groups in such colonial cities. What were their sorcery practices? What were they trying to achieve with their doings? What does a study of the inquisitorial processes allow us to understand about the social and cultural function of female sorcery? These are some of the questions we answer in this article. Full article

Background: While human cranial sutures are well-established indicators for age-at-death estimation in forensic anthropology, facial sutures remain an underutilized resource despite their critical role in facial growth and development. Macroscopic examination of craniofacial suture closure patterns reflects physiological aging processes and can provide valuable information at crime scenes. This study aimed to address the gap of knowledge by quantitatively evaluating the efficacy of facial suture closure patterns for age estimation. Methods: A sample consisting of 296 Thai skulls was analyzed to assess facial suture closure based on anatomical morphology. The sutures were evaluated using various established classification systems to determine the most effective method for predicting age ranges. To ensure consistency and reliability, the evaluations were conducted by three independent raters. Results: The assessment demonstrated good Intraclass Correlation (ICC = 0.755, df = 14, p < 0.05). Among the classification methods tested, the Modified Meindl and Lovejoy Scoring System yielded the highest sensitivity, ranging from 90.9% to 100% in males and 75.4% to 96.1% in females. Specifically, the zygomaticomaxillary suture showed the highest sensitivity in males, whereas the frontonasal and sphenozygomatic sutures were the most sensitive indicators in females. Utilizing the total sum score (TSS), the following sex-specific linear regression formulas for age-at-death were generated: (Males: Age-at-death = 1.7625(TSS) − 17.094. Females: Age-at-death = 1.7325(TSS) − 12.865). Conclusions: Facial sutures exhibit distinct, sex-specific closure patterns that serve as robust and reliable indicators for estimating age, with higher sensitivity generally observed in males. The utility of this novel method is heavily dependent on the scoring system employed, highlighting the critical importance of utilizing modified, sex-specific analyses. While these population-specific models tailored to the Thai demographic effectively refine age estimation outcomes, integrating this methodology with broader biological profiling remains essential for high-confidence forensic identification. Full article

Modern automotive paints have a thin undercoat and color coat layer protected by a thick clear coat layer. All too often, only the clear coat layer of the automotive paint is recovered at the crime scene of a vehicle-related fatality. Searches for motor vehicle paint databases of clear coats using commercial software typically generate large hitlists that are difficult for a forensic paint examiner to work through unless additional information is provided for the search. To address this problem, deep learning has been applied to the infrared spectra of automotive clear coats to identify patterns in their spectra indicative of the motor vehicle manufacturer. An in-house automotive paint library of 2796 clear coat infrared spectra from six automotive manufacturers and 100 assembly plants was partitioned into training, validation, and prediction sets. Each spectrum has 1880 measurements over the spectral range of 4000 cm⁻¹ to 376 cm⁻¹. Several multilayer perceptron neural network models, each with three hidden layers, were developed that achieved high classification success rates for the training, validation, and prediction sets. The addition of convolutional layers to the deep learning neural network models did not improve the performance of these models. Full article

Under rapid urbanization, street crime poses growing challenges to urban safety. Existing studies often treat gray and green spaces as independent variables, limiting the understanding of nonlinear crime patterns and spatiotemporal heterogeneity. Using day–night street crime data from Shanghai between 2010 and 2020, this study applies an interpretable machine learning framework combining CatBoost and SHAP to examine how the coupling of gray–green spatial structures influences street crime. Gray–green spatial morphology is quantified using both MSPA- and Fragstats-based indicators, which are integrated into composite coupling indices. The results indicate that gray–green structural coupling exhibits significant nonlinear and threshold-dependent effects on street crime. Compared with conventional Fragstats metrics, MSPA-based structural indicators demonstrate stronger explanatory power. Theft-specific analysis further indicates that gray-space core–edge structures exhibit higher crime risk at night, with this effect becoming more pronounced in the later period. Across both study periods and day–night contexts, green branch areas (G_BRANCH) consistently show stable inhibitory effects, with the strongest suppression occurring when G_BRANCH values range between 0 and 1.6 and interact with gray core–edge structures (B_CORE and B_EDGE). These findings provide quantitative evidence that gray–green spatial structures function through coupled, nonlinear interactions and offer targeted spatial planning implications for crime prevention in high-density cities. Full article

Financial fraud and money laundering continue to challenge financial stability and regulatory oversight, motivating the widespread adoption of machine learning models for transaction monitoring. Although ensemble models such as Random Forest and XGBoost achieve strong predictive performance, their deployment in high-stakes financial environments is constrained by limited interpretability, overconfident predictions, and the absence of principled mechanisms for expressing decision uncertainty. Emerging regulatory expectations increasingly emphasise transparency, accountability, and operational reliability, underscoring the need for evaluation frameworks that extend beyond predictive accuracy. This study proposes the Integrated Transparency and Confidence Framework (ITCF), a deployment-oriented approach that unifies model explainability, statistically valid uncertainty quantification, and operational decision support for fraud detection. ITCF combines instance-level explanations generated via Local Interpretable Model-Agnostic Explanations (LIME) with distribution-free uncertainty estimation using split conformal prediction. The framework incorporates selective explainability, abstention-based routing, and uncertainty-driven triage to support human-in-the-loop review. Using the PaySim dataset of 6,362,620 mobile-money transactions, Random Forest and XGBoost models are evaluated under extreme class imbalance using F1-score, AUC–ROC, and Matthews Correlation Coefficient (MCC). At a target coverage level of 90% ($\alpha =0.1$), both models achieve empirical coverage close to the target level, with XGBoost producing smaller prediction sets and superior recall, MCC, and latency. ITCF provides transaction-level explanations for uncertain cases and specifies an auditable workflow that is intended to support transparency, traceability, and risk-aware human review, thereby enabling defensible human decision-making in regulated environments. Overall, this study illustrates how explainability and uncertainty quantification can be combined in a deployment-oriented evaluation workflow while noting that real-world validation remains a future endeavour. Full article

Background/Objectives: The potential role of domestic animals in DNA transfer, persistence, prevalence and recovery (TPPR) warrants careful consideration in forensic contexts. This mini narrative review aims to provide an updated overview of human DNA transfer involving household dogs and cats as vectors, to clarify their forensic relevance, and to identify key considerations for the design of future experimental research. Methods: A narrative review was conducted using multiple electronic databases as search engines without restriction related to the timing of publication. Results: Experimental evidence shows that dogs and cats readily acquire human DNA following even brief contact, acting as reservoirs for primary DNA transfer. Once acquired, human DNA can be redistributed via secondary transfer to a wide range of substrates, such as gloved hands, vehicle interiors, clothing, and surfaces. Moreover, multi-step and higher-order transfer events have been documented, highlighting the complexity of DNA transfer involving household animals. Conclusions: The sampling on pets may be included in certain scenarios and may contribute to building a Bayesian network together with the experimental data. To deal with uncertainty during probability assignment, more experimental data, especially addressing the main variables impacting DNA TPPR involving pets, should be generated and are highly needed to assist in activity level evaluation. Full article

Despite near-global legal reforms to tackle voyeurism and upskirting offences (VUs), such behaviours remain prevalent in Singapore—an under-reached population for empirical research in the niche of image-based sexual abuse and one where conservative views and sex-related taboos persist. This study consists of interviews with ten young adult Singaporean nationals about their understanding of VUs, victim-survivors thereof, and how such views interact with Singaporean culture and societal norms. Reflective thematic analysis was used to delineate the two predominant themes of the (1) Unaccountability of Perpetrators, wherein VUs are minimised and excused at both societal (Technological and Institutional Affordances) and individual levels (Sexual Deviancy & Pornography), and (2) Burden of Victimisation, which explored perceptions of victim-survivors as a gendered experience (Gendered Vulnerability), where norms around modesty impacted victim-blaming (Moralised Modesty & Responsibility), resulting in harm minimisation (Harm Awareness & Minimisation). Findings have implications for how legislators, law enforcement, and educational institutions address the minimisation of gender-based violence through shifts in social narratives, awareness, and responses. Full article