Search Results (1,676)

Information is the primary driver of progress in today’s world, especially given the vast amounts of data available for extracting meaningful knowledge. The motivation for addressing the problem of forensic analysis—specifically the validity of decision making in multi-criteria contexts—stems from its limited coverage in the existing literature. Methodologically, machine learning and ensemble models represent key trends in this domain. Datasets used for such purposes can be either real or synthetic, with synthetic data becoming particularly valuable when real data is unavailable, in line with the growing use of publicly available Internet data. The integration of these two premises forms the central challenge addressed in this paper. The proposed solution is a three-layer ensemble model: the first layer employs multi-criteria decision-making methods; the second layer implements multiple machine learning algorithms through an optimized asymmetric procedure; and the third layer applies a voting mechanism for final decision making. The model is applied and evaluated through a case study analyzing the U.S. Army’s decision to replace the Colt 1911 pistol with the Beretta 92. The results demonstrate superior performance compared to state-of-the-art models, offering a promising approach to forensic decision analysis, especially in data-scarce environments. Full article

(1) Background: This article aims to understand the forms and elements through which the inhabitants of the city of Medellin have configured their subjectivity in the context of the application of neoliberal policies in the last two decades. In this way, we can approach the frameworks of understanding that constitute a fundamental part of the individuation processes in which the incorporation of their subjectivities is evidenced in neoliberal contexts that, in the historical process, have been converging with authoritarian, antidemocratic and neoconservative elements. (2) Method: A qualitative approach with a hermeneutic-interpretative paradigm was used. In-depth semi-structured interviews were conducted with 41 inhabitants of Medellín who were politically identified with right-wing or center-right positions. Data analysis included thematic coding to identify patterns of thought and points of view. (3) Results: Participants associate success with individual effort and see state intervention as an obstacle to development. They reject redistributive policies, arguing that they generate dependency. In addition, they justify authoritarian models of government in the name of security and progress, from a moral superiority, which is related to a negative and stigmatizing perception of progressive sectors and a negative view of the social rule of law and public policies with social sense. (4) Conclusions: The naturalization of merit as a guiding principle, the perception of themselves as morally superior based on religious values that grant a subjective place of certainty and goodness; the criminalization of expressions of political leftism, mobilizations and redistributive reforms and support for policies that establish authoritarianism and perpetuate exclusion and structural inequalities, closes roads to a participatory democracy that enables social and economic transformations. Full article

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39x${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

This paper presents an ethnographically informed analysis of research in Cairo and Sharm El-Sheikh (Egypt) surrounding the 2022 United Nations Framework Convention on Climate Change (UNFCCC) Conference of Parties (COP27) summit. I discuss the geopolitics and geopolitical disruptions of researching activism and activist lives in politically sensitive environments. As shown here, developing new methodological interventions plays a crucial role in understanding contextual methodological limitations, dealing with logistical challenges, and building authentic relationships with research participants. Here, I introduce counter-interviews as a methodological strategy to build trust and invest in researcher–participant relationships. This article draws on participant observation, conversations with environmental and climate activists and non-activists in Cairo prior to and after COP27 and in Sharm El-Sheikh during the second week of the summit, reflective field notes, and 20 semi-structured interviews conducted online between February and August 2023. Here, I use the term “environmental non-activism” to draw attention to the sensitivity, complexity, and fragility of political or apolitical environmental and climate action in an authoritarian context where any form of collective action is highly monitored, regulated, and sometimes criminalized by the state. The main argument of this paper is that examining interlocking power dynamics that shape and reshape the activist space in relation to the state is a requirement for understanding and researching the complexities and specificities of climate activism and non-activism in authoritarian contexts. Along with this argument, this paper invites climate education researchers to reevaluate what non-movements and non-activists in the Global South offer to their analyses of possible alternatives, socio-political change, and politics of hope (and to the broader field of activism in educational research, where commitment to disruption, refusal, and subversion play a key role. Full article

Though previous studies have demonstrated the protective benefits of sports participation against illicit drug use for a general population, how these findings apply to LGBTQ youth remains unknown. This study specifically looks at the relationship between sports participation and prescription drug misuse among sexual and gender minority youth. Using secondary data from the 2019 YRBS, we analyze associations among sports participation, sexual orientation, gender identity, and prescription drug misuse among a representative sample of U.S. high school students in Florida. Our results show that sexual and gender minority youth are at increased risk for prescription drug misuse compared to their heterosexual and cisgender peers. Moreover, sports participation is associated with higher rates of prescription drug misuse among all students, and the nuances of these trends are discussed with particular attention paid to sexual and gender minority youth. These results challenge conventional wisdom about sports participation. Without the addition of new demographic survey questions and LGBTQ youth participation in the YRBS, common myths about sports might have persisted. Our findings point to the meaningful presence of LGBTQ youth in sports, call for research and programming on LGBTQ athletes’ unique needs regarding substance misuse risk, and encourage LGBTQ-inclusive policies and practices within schools and sports programs in particular. Full article

In 1961, Los Angeles experienced the disastrous Bel Air fire, which swept through an affluent neighborhood situated in a hilly, WUI (wildland–urban interface) location. In January 2025, the city was devastated again by a nearly-simultaneous series of wildfires, the most severe of which took place close to the 1961 fire location. Disastrous WUI fires are, unfortunately, an anticipatable occurrence in many U.S. cities. A number of issues identified earlier remained the same. Some were largely solved, while other new ones have emerged. The paper examines the Palisades Fire of January, 2025 in this context. In the intervening decades, the population of the city grew substantially. But firefighting resources did not keep pace. Very likely, the single-most-important factor in causing the 2025 disasters is that the Los Angeles Fire Department operational vehicle count shrank to 1/5 of what it was in 1961 (per capita). This is likely why critical delays were experienced in the initial attack on the Palisades Fire, leading to a runaway conflagration. Two other crucial issues were the management of vegetation and the adequacy of water supplies. On both these issues, the Palisades Fire revealed serious problems. A problem which arose after 1961 involves the unintended consequences of environmental legislation. Communities will continue to be devastated by wildfires unless adequate vegetation management is accomplished. Yet, environmental regulations are focused on maintaining the status quo, often making vegetation management difficult or ineffective. House survival during a wildfire is strongly affected by whether good vegetation management practices and good building practices (“ignition-resistant” construction features) have been implemented. The latter have not been mandatory for housing built prior to 2008, and the vast majority of houses in the area predated such building code requirements. California has also suffered from a highly counterproductive stance on insurance regulation. This has resulted in some residents not having property insurance, due to the inhospitable operating conditions for insurance firms in the state. Because of the historical precedent, the details in this paper focus on the Palisades Fire; however, many of the lessons learned apply to managing fires in all WUI areas. Policy recommendations are offered, which could help to reduce the potential for future conflagrations. Full article

The manuscript examines the cause-and-effect relationships of fires in the Republic of Serbia over a fifteen-year period, primarily from the aspect of human safety. For this purpose, numerical variables describing the number of injuries and deaths in fires were introduced, on which various analysis and modeling techniques were implemented, which can be viewed in the context of data mining (DM). First, for both observed variables, stochastic modeling of their temporal dynamics was analyzed, and subsequently, cluster analysis of the values of these variables was performed using two different methods. Finally, by interpreting these variables as outputs (objectives) for the classification problem, several decision trees were formed that describe the influence and relationship of different fire causes on situations in which injuries or human casualties occur or not. In that way, several different types of fires have been identified, including rare but deadly incidents that require urgent preventive measures. Key risk factors such as fire cause, location, season, etc., have been found to significantly influence human casualties. These findings provide practical insights for improving fire protection policies and emergency response. Through such a comprehensive analysis, it is believed that some important results have been obtained that precisely describe the specific relationships between the causes and consequences of fires occurring in the Republic of Serbia. Full article

Halictidae, as a major pollinator family in bees, has significant ecological value. However, the insufficient molecular data for this group has limited our understanding of the evolutionary history of this group. Herein, we newly sequenced and assembled four mitogenomes of Halictidae, including three species of Nomiinae and one species of Rophitinae. We analyzed the characters of the newly obtained mitogenomes, including nucleotide composition, sequence length, and gene rearrangements. The length of the newly sequenced mitogenomes ranged from 16,492 to 21,192 bp, and all newly obtained mitogenomes contained 22 tRNAs, 13 protein-coding genes, two rRNAs, and one control region. Their AT content (%) ranged from 82.55 to 86.44. Relative synonymous codon usage analysis showed that UUU, UUA, and AUU were the preferred codons. The relative synonymous codon usage > 2 of mostly newly sequenced species was as follows: UUA > UCA > CGA. All newly obtained mitogenomes show gene rearrangement; we found five gene rearrangement patterns in total. Notably, ND4-trnP-ND4L-trnT was the first reported gene rearrangement pattern in bees. In addition, we reconstructed the phylogenetic relationships of Halictidae based on 10 species (eight ingroups and two outgroups), using Bayesian Inference and Maximum Likelihood approaches. Phylogenetic analysis showed that Rophitinae was the basal group within Halictidae. Full article

Smokeless powders are a commonly used low explosive within the ammunition industry. Their ease of purchase has allowed criminals to use these products to build improvised explosive devices. Canines have become a vital tool in locating such improvised devices. With differing fabrication processes, one of the most difficult challenges for canine handlers is the optimal selection of training aids to choose as odor targets to allow for broad generalization. Several studies have been underway to understand the chemical odor characterization of smokeless powders, which can help provide canine teams with essential information to understand odor signatures from powder varieties. In this study, a SPME method optimization was conducted using unburned smokeless powders to provide a chemical odor profile assessment. Concurrently, statistical analysis using PCA and Spearman’s rank correlations was performed to explore whether odor volatile composition depicted associations between and within powder brands. The results showed that a longer extraction time (24 h) was optimal across all powders, as this yielded higher compound abundance and number of extracted odor volatiles. The optimal SPME fiber varied per powder, depicting the complexity of powder composition. There were 66 highly frequent compounds among the 18 powders, including 2-ethyl-1-hexanol, diphenylamine (DPA), and dibutyl phthalate. Principal component analysis (PCA) showed that while powders may be of the same type (single/double base), they can still portray clustering differences across and within brands. The Spearman’s rank correlation within powder type suggested that the double-base powders had a slightly higher similarity index when compared with the single-base powder types. Understanding the volatile odor profiles of various smokeless powders can enhance canine training by informing the selection of effective training aids and supporting odor generalization. Full article

Environmental crime has been increasingly recognised as transnational organised crime, but efforts to build a coherent and effective international response are still in development and under threat from shifts in the funding landscape. This mixed methods study addresses the role of one significant group of actors in environmental crime enforcement, which are non-governmental organisations (NGOs) who gather intelligence that can be shared with law enforcement and regulatory agencies. The study compares their intelligence practices to findings from traditional intelligence sectors, with a focus upon criminal justice and policing. The research generated quantitative and qualitative data from NGO practitioners, which is integrated to discern three overarching themes inherent in these NGOs’ intelligence practices: the implementation of formal intelligence practices is still underway in the sector; there remains a need to improve cooperation to break down silos between agencies and NGOs, which requires an improvement in trust between these entities; the operating environment provides both opportunities and challenges to the abilities of the NGOs to deliver impact. The study concludes by positing that the characteristics of NGOs mean that this situation constitutes ‘green intelligence’, contextualising intelligence theory and highlighting areas in which agencies can further combat environmental crime. Full article

Ensuring sustainable food systems is an urgent global priority as populations grow and environmental pressures mount. Technological innovations such as genetic engineering (GE) and nanotechnology (nano) have been promoted as promising pathways for achieving greater sustainability in agriculture and food production. Yet, the sustainability of these technologies is not defined by technical performance alone; it hinges on how they are perceived by key stakeholders and how well they align with broader societal values. This study addresses the critical question of how expert stakeholders evaluate the sustainability of GE and nano-based food and agriculture (agrifood) products. Using a multi-method online platform, we engaged 42 experts across academia, government, industry, and NGOs in the United States to assess six real-world case studies—three using GE and three using nano—across ten different dimensions of sustainability. We show that nano-based products were consistently rated more favorably than their GE counterparts in terms of environmental, economic, and social sustainability, as well as across ethical and societal dimensions. Like prior studies, our results reveal that stakeholders see meaningful distinctions between nanotechnology and biotechnology, likely due to underlying value-based concerns about animal welfare, perceived naturalness, or corporate control of agrifood systems. The fruit coating and flu vaccine—both nano-enabled—received the most positive ratings, while GE mustard greens and salmon were the most polarizing. These results underscore the importance of incorporating stakeholder perspectives in technology assessment and innovation governance. These results also suggest that responsible innovation efforts in agrifood systems should prioritize communication, addressing meaningful societal needs, and the contextual understanding of societal values to build trust and legitimacy. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

Substance use is a critical public health issue in the U.S., with Black communities, particularly those with criminal justice contact, disproportionately affected. Chronic exposure to stressors can lead to substance use as a coping strategy. This study used data from 1476 Black adults with criminal justice involvement from the National Survey of American Life to examine how psychosocial stress and sleep disturbances relate to lifetime substance use and to determine if there are any sex differences. Sex-separate generalized linear models for a Poisson distribution with a log-link function estimated prevalence ratios and adjusted prevalence ratios (APRs) for lifetime alcohol abuse, lifetime cigarette, and marijuana use. Independent variables include stressors (family, person, neighborhood, financial, and work-related) and sleep problems, with covariates such as age, SES, and marital status. Lifetime alcohol abuse was associated with family stressors (APR = 2.72) and sleep problems (APR = 3.36) for males, and financial stressors (APR = 2.75) and sleep problems (APR = 2.24) for females. Cigarette use was linked to family stressors (APR = 1.73) for males and work stressors (APR = 1.78) for females. Marijuana use was associated with family stressors (APR = 2.31) and sleep problems (APR = 2.07) for males, and neighborhood stressors (APR = 1.72) for females. Lifetime alcohol abuse, as well as lifetime cigarette and marijuana use, was uniquely associated with various psychosocial stressors among Black adult males and females with criminal justice contact. These findings highlight the role of structural inequities in shaping substance use and support using a Social Determinants of Health framework to address addiction in this population. Full article

This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios. Full article