Search Results (219)

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by inattention, hyperactivity, and impulsivity, frequently observed in juvenile offenders. This systematic review explores the interplay between ADHD and juvenile delinquency, focusing on behavioural, psychological, and social dimensions. Following the PRISMA guidelines, a systematic literature review was conducted using EBSCO Discovery Service, Science Direct, PubMed, and snowballing techniques. Studies meeting specific inclusion criteria, including juvenile offenders diagnosed with ADHD and comparisons to non-offender or non-ADHD control groups, were analysed. The methodological quality of studies was assessed using the Joanna Briggs Institute appraisal tools. A total of 21 studies were included, highlighting significant associations between ADHD and juvenile delinquency. ADHD symptoms, especially impulsivity and emotional dysregulation, were linked to an earlier onset of offending and higher rates of property crimes. Comorbidities such as conduct disorder, substance use disorder, and depression exacerbated these behaviours. Sociodemographic factors like low education levels and adverse family environments were also critical modifiers. Early intervention and tailored treatment approaches were emphasised to address these challenges. The findings underscore the need for early diagnosis, individualised treatment, and integrative rehabilitation programmes within the juvenile justice system to mitigate long-term risks and promote social inclusion. Full article

Ride-pooling, as a sustainable mode of ride-hailing services, enables different riders to share a vehicle while traveling along similar routes. The COVID-19 pandemic led to the suspension of this service, but Transportation Network Companies (TNCs) such as Uber and Lyft resumed it after a significant delay following the lockdown. This raises the question of what determinants shape ride-pooling in the post-pandemic era and how they spatially influence shared ride-hailing compared to the pre-pandemic period. To address this gap, this study employs geospatial analysis and machine learning to examine the factors affecting ride-pooling trips in pre- and post-pandemic periods. Using over 66 million trip records from 2019 and 43 million from 2023, we observe a significant decline in shared trip adoption, from 16% to 2.91%. The results of an extreme gradient boosting (XGBoost) model indicate a robust capture of non-linear relationships. The SHAP analysis reveals that the percentage of the non-white population is the dominant predictor in both years, although its influence weakened post-pandemic, with a breakpoint shift from 78% to 90%, suggesting reduced sharing in mid-range minority areas. Crime density and lower car ownership consistently correlate with higher sharing rates, while dense, transit-rich areas exhibit diminished reliance on shared trips. Our findings underscore the critical need to enhance transportation integration in underserved communities. Concurrently, they highlight the importance of encouraging shared ride adoption in well-served, high-demand areas where solo ride-hailing is prevalent. We believe these results can directly inform policies that foster more equitable, cost-effective, and sustainable shared mobility systems in the post-pandemic landscape. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Property crime is a pressing issue in maintaining social order and urban sustainability, particularly in regions marked by pronounced socioeconomic disparity. While the link between socioeconomic stress and crime is well established, regional variations in Thailand have not been fully examined. Therefore, the purpose of this research was to examine spatial patterns of property crime and identify the potential associations between property crime and socioeconomic environment across Thailand. Using nationally compiled property-crime data from official sources across all provinces of Thailand, we employed geographic information system (GIS) tools to conduct a spatial cluster analysis at the sub-national level across 76 provinces. Both global and local statistical techniques were applied to identify spatial associations between property-crime rates and neighborhood-level socioeconomic conditions. The results revealed that property-crime clusters are primarily concentrated in the south, while low-crime areas dominate parts of the north and northeast regions. To analyze the spatial dynamics of property crime, we used geospatial statistical models to investigate the influence of socioeconomic variables across provinces. We found that property-crime rates were significantly associated with monthly income, areas experiencing high levels of household debt, migrant populations, working-age populations, an uneducated labor force, and population density. Identifying associated factors and mapping geographic regions with significant spatial clusters is an effective approach for determining where issues concentrate and for deepening understanding of the underlying patterns and drivers of property crime. This study offers actionable insights for enhancing safety, resilience, and urban sustainability in Thailand’s diverse regional contexts by highlighting geographies of vulnerability. Full article

Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of surveillance by sending interference signals to suspicious receivers, which is important for crime prevention and public safety. In this paper, we investigate the joint optimization of performance of both ISAC and active surveillance. Specifically, we formulate a bilevel optimization problem where the upper-level objective aims to maximize the probability of successful eavesdropping while the lower-level objective aims to optimize the localization performance of the radar on suspicious transmitters. By employing the Rayleigh quotient, introducing a decoupling strategy, and adding penalty terms, we propose an algorithm to solve the bilevel problem where the lower-level objective is convex. With the help of the proposed algorithm, we obtain the optimal solution of the analog transmit beamforming matrix and the digital beamforming vector. Performance analysis and discussion of key insights, such as the trade-off between eavesdropping success probability and radar localization accuracy, are also provided. Finally, comprehensive simulation results validate the effectiveness of our proposed algorithm in enhancing both the eavesdropping success probability and the accuracy of radar localization. Full article

Public security is a crucial aspect of maintaining social order. Although crime rates in Western cultures may be considered socially acceptable, it is important to continually improve security measures to prevent potential risks. With the advancements in artificial intelligence methods, particularly in deep learning and computer vision, it has become possible to detect abnormal event patterns in groups of people. This paper presents a review of the deep learning techniques employed for identifying gatherings of people and detecting anomalous events to enhance public security. Some of the open research areas are identified, including the lack of works addressing multiple cases of anomalies in large concentrations of people, which leaves open an important avenue for future scientific work. Full article

This study applies the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method combined with entropy-based weighting to systematically rank Serbian municipalities regarding economic vitality, infrastructure quality, and socio-economic stability. By developing a composite municipal performance index, the research explores the extent to which stronger economic standings relate to public safety outcomes. Infrastructure factors—including road conditions, housing quality, and water supply—are assessed through correlation and t-tests to evaluate their influence on municipal economic rankings. An ordinary least squares (OLS) regression model also examines how education and health expenditures per capita contribute to broader socio-economic resilience. The findings reveal a moderately strong, though nonlinear, negative relationship between economic performance and crime rates, with road infrastructure emerging as a consistently significant driver of economic strength. Investments in education and health initially correlate with greater municipal stability but show signs of diminishing marginal impact over time. These insights contribute to understanding the complex interplay between governance, infrastructure, and safety in transitional economies, highlighting the value of integrated data-driven approaches for regional development planning. Full article

Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global–local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset. Full article

Objective: Low birth weight is a serious public health problem even in developed countries. The objective of this study was to assess the ability of machine learning to predict low birth weight rates in big cities in the USA on an ecological/population level. Study design: The study was based on publicly available data from the Big Cities Health Inventory Data Platform. The collected data related to the 35 largest, most urban cities in the United States from 2010 to 2022. The model-agnostic approach was used to assess and visualize the magnitude and direction of the most influential predictors. Results: The models showed excellent performance with R-squared values of 0.82, 0.81, 0.81, and 0.79, and residual root mean squared error values of 1.06, 0.87, 1.03, 0.99 for KNN, Best subset, Lasso, and XGBoost, respectively. It is noteworthy that the Best subset selection approach had a high RSq and the lowest residual root mean squared error, with only a four-predictor subset. Influential predictors that appeared in three/four models were rate of chlamydia infection, racial segregation, prenatal care, percentage of single-parent families, and poverty. Other important predictors were the rate of violent crimes, life expectancy, mental distress, income inequality, hazardous air quality, prevalence of hypertension, percent of foreign-born citizens, and smoking. This study was limited by the unavailability of data on gestational age. Conclusions: The machine learning algorithms showed excellent performance for the prediction of low birth weight rate in big cities. The identification of influential predictors can help local and state authorities and health policy decision makers to more effectively tackle this important health problem. Full article

The growth of the older adult population, their wealth accumulation, and vulnerabilities from aging have contributed to increasing rates of abuse, fraud, and financial exploitation. However, the current responses and services are fragmented and ineffectual. This paper develops a novel strategy for addressing the variation in response and victim service provision through the development of a problem-solving court that is informed by the principles of restorative justice. Given the unique challenges, cases, and population, a problem-solving court for crimes against older adults will provide tailored interventions, responses, and sanctions while ensuring that older adult victims and their communities are at the center of the criminal justice process and that their needs are prioritized. Research on problem-solving courts; restorative justice; and older adult abuse, fraud, and financial exploitation are integrated with data from a case study of older adult financial exploitation in a large retirement community to develop the model problem-solving court. Consistent with best practices in victim services, the model court will provide comprehensive services in a one-stop location, while simultaneously increasing accountability for offenders who prey on this vulnerable population. The paper concludes with a plan to guide the implementation and evaluation of the proposed model problem-solving court for older adult abuse, fraud, and exploitation. Full article

This paper examines the dynamic relationship between economic indicators, law enforcement mechanisms, and property-related crimes in Mongolia using a time-series econometric approach. Relying on the theoretical frameworks of Becker’s economic model of crime and Cantor and Land’s motivation–opportunity hypothesis, the study explores the effects of unemployment, detection probability, and incarceration rates on four crime categories: total crime, theft, robbery, and fraud. An error correction model (ECM) is employed to capture both short-run fluctuations and long-run equilibrium relationships over the period 1992–2022. The empirical findings reveal that detection rates exert a statistically significant deterrent effect on robbery in the short term, while incarceration rates are effective in reducing theft. Unemployment shows a positive and significant long-run effect on theft prior to 2009 but weakens thereafter due to methodological changes in labor statistics. Fraud demonstrates a distinct response pattern, exhibiting negative associations with both incarceration and unemployment, and showing no sensitivity to detection probability. Diagnostic tests support the model’s robustness, with heteroskedasticity in the theft model addressed using robust standard errors. This study contributes to the literature by providing the first country-specific empirical evidence on crime determinants in Mongolia. It highlights the heterogeneous impact of economic and institutional factors on different crime types in a transition economy. The findings underscore the need for integrated policy responses that combine improvements in law enforcement with inclusive economic and social development strategies. Full article

Background: Gun-related homicide remains a persistent public health crisis in the United States, with over 48,000 firearm-related deaths reported in 2022, including 19,651 homicides and 27,032 suicides. Despite frequent calls for tighter gun control, firearm access alone does not explain the complexity of violence. Objective: This commentary aims to unpack the socioeconomic, psychological, and biological drivers of gun-related homicide and propose integrative, evidence-based solutions that extend beyond legislative reform. Methods: We synthesized data from peer-reviewed literature, national crime and health databases (e.g., Centers for Disease Control and Prevention and Federal Bureau of Investigation), and international reports. We examined patterns related to poverty, trauma, male aggression, neurobiology, and firearm acquisition, as well as cross-national comparisons with countries like Switzerland and Mexico. Findings: Young males, particularly those aged 10–29, accounted for 50% of homicide offenders in 2022. African Americans experienced homicide rates of 23.1 per 100,000, ten times the rate among Whites. Up to 56% of incarcerated men report childhood physical trauma, and over 40% of those in prison exhibit symptoms of serious mental illness. While firearm legislation varies widely, analysis reveals that over 90% of crime guns are acquired illegally or through informal sources. International comparisons show that poverty and weak rule of law, more than gun laws alone, correlate with elevated homicide rates. Conclusions: Reducing gun violence sustainably requires a multifaceted approach. Authors advocate for investments in trauma-informed mental health care, focused deterrence programs, early childhood interventions, and improved enforcement against illegal gun trafficking. A public health strategy that integrates social reform with targeted regulation holds the greatest promise for long-term change. Full article

Background: Synthetic cathinones (SCs) are the second most representative class of New Psychoactive Substances, with more than 100 analogues identified in the illicit drug market up to 2024. According to the United Nations Office on Drugs and Crimes, N-ethylhexedrone (NEH) and N-ethyl-nor-pentedrone (NEP) were identified among the most frequently seized SCs worldwide. However, still, little is known with regard to their pharmacological effects in humans. Methods: For the first time, we conducted a naturalistic, prospective observational study in 16 participants (7 women and 9 men) with a previous history of psychostimulant recreational use. They intranasally self-administered a single dose of NEP (n = 8, 20–40 mg) or NEH (n = 8, 20–40 mg). The physiological effects (systolic and diastolic blood pressure, heart rate, and temperature) and subjective effects (visual analogue scales, Addiction Research Center Inventory questionnaire and Evaluation of Subjective Effects of Substances with Abuse Potential questionnaire) were assessed up to 4 h after the self-administration at different time points (0, 20 and 40 min and 1, 1.5, 2, 3 and 4 h). Results: Despite several differences, both NEP and NEH produced significant effects within 20 min, with a return to baseline 3–4 h after self-administration. In general, NEP showed a faster onset and a more rapid disappearance of subjective effects than NEH. Moreover, intranasal self-administration of NEH and NEP in experienced recreational drug users, within a non-controlled setting, induces a constellation of psychostimulant-like effects. Conclusion: NEH and NEP showed similar pharmacological properties after insufflation, with typical effects of SCs Full article

While the impact of trade openness on economic growth has been widely studied, its effect on township economies remains underexplored. In view of this empirical gap, this study examines the impact of trade openness proxied by export intensity on the technical efficiency of five major township economies in South Africa—Soweto, Khayelitsha, Alexandra, Tembisa, and Soshanguve—while considering the moderating role of information and communication technology (ICT). This aim speaks to the ongoing quest to unravel factors limiting the transformation of South African townships since the advent of democracy in 1994. The analysis uses an instrumental variable stochastic frontier model and annual panel data covering the 1995–2023 period. On average, the five townships were found to have operated 19% below their full potential during the sampling period, with Soweto being the least efficient. Holding constant factors peculiar to township economies, such as crime rates and informality, the main results show that ICT plays a positive moderating role in reducing trade-related technical inefficiencies of these townships. This finding underscores the importance of targeted policy interventions, such as investments in digital infrastructure and digital literacy programs, to ensure that township economies benefit from global markets and achieve their full potential. Full article

This study conducts a systematic literature review following the PRISMA framework and the guidelines of Kitchenham and Charters to analyze the application of Internet of Things (IoT) technologies and deep learning models in monitoring violent actions and criminal activities in smart cities. A total of 45 studies published between 2010 and 2024 were selected, revealing that most research, primarily from India and China, focuses on cybersecurity in IoT networks (76%), while fewer studies address the surveillance of physical violence and crime-related events (17%). Advanced neural network models, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid approaches, have demonstrated high accuracy rates, averaging over 97.44%, in detecting suspicious behaviors. These models perform well in identifying anomalies in IoT security; however, they have primarily been tested in simulation environments (91% of analyzed studies), most of which incorporate real-world data. From a legal perspective, existing proposals mainly emphasize security and privacy. This study contributes to the development of smart cities by promoting IoT-based security methodologies that enhance surveillance and crime prevention in cities in developing countries. Full article