Search Results (132)

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

Secure Digital Evidence Management Systems (DEMSs) ae crucial for law enforcement agencies, because traditional systems are prone to tampering and unauthorised access. Blockchain technology, particularly private blockchains, offers a solution by providing a centralised and tamper-proof system. This study proposes a private blockchain using Proof of Work (PoW) to securely manage digital evidence. Miners are assigned specific nonce ranges to accelerate the mining process, called collaborative mining, to enhance the scalability challenges in DEMSs. Transaction data includes digital evidence to generate a Non-Fungible Token (NFT). Miners use NFTs to solve the puzzle according to the assigned difficulty level d, so as to generate a hash using SHA-256 and add it to the ledger. Users can verify the integrity and authenticity of records by re-generating the hash and comparing it with the one stored in the ledger. Our results show that the data was verified with 100% precision. The mining time was $2.5$ s, and the nonce iterations were as high as $80\times {10}^3$ for $d=5$. This approach improves the scalability and integrity of digital evidence management by reducing the overall mining time. Full article

This paper outlines the development and delivery of a novel, collaborative, co-production approach to incorporating lived experience in the development of policy and practice in the area of domestic abuse. “SEEKERS” (Sharing Experience, Expertise and Knowledge for Effective Responses and Support) is an initiative which brings together victims and advocates, police, practitioners and researchers as equal partners. It creates opportunities for them to share their experiences, expertise, and knowledge, so that others can learn from these and use this learning in addressing domestic abuse-related issues more effectively. Throughout this paper, we discuss some of the challenges encountered in developing and delivering activities and how these were addressed. Notable benefits of the approach will be highlighted, as indicated by feedback from those involved in a range of capacities, including police and law enforcement practitioners, policy makers, councillors, service providers, support services, victim advocates and survivors of domestic abuse. It is hoped that this paper will contribute to ongoing discussions regarding the ways in which different agencies and stakeholders can work together more effectively and how we can create methods and spaces to support meaningful interaction, collaboration, and co-production with victims. Full article

Poor officer fitness can lead to decreased occupational task performance, injuries, increased absenteeism, and a variety of negative health sequalae further adding to the challenges of staffing law enforcement agencies. Optimizing the physical fitness for both serving officers and new recruits is critical as their loss is, and will increasingly be, difficult to replace. However, maintaining and recruiting a physically fit workforce faces several challenges. For serving officers, shiftwork is known to decrease motivation to exercise and negatively impact sleep and diet. Additional factors impacting their fitness includes age-related declines in fitness, increasing obesity, long periods of sedentarism, and negative COVID-19 effects. Concurrently, recruiting physically fit recruits is challenged by declining levels of fitness, reduced physical activity, and increasing obesity in community youth. Ability-based training (ABT), individualizing physical conditioning training based on the existing fitness levels of individuals within a group, offers a potential solution for delivering physical conditioning to groups of applicants, recruits, and officers with a range of physical fitness capabilities. Law enforcement agencies should consider implementing ABT during academy training and ongoing fitness maintenance to minimize injury risk and optimize task performance. Full article

The rapid rise of AI-driven cybercrime and deepfake fraud poses complex organisational challenges for US law enforcement, particularly the Federal Bureau of Investigation (FBI). Applying Maguire’s (2003) police organisation theory, this qualitative single-case study analyses the FBI’s structure, culture, technological integration, and inter-agency collaboration. Findings underscore the organisational strengths of the FBI, including a specialised Cyber Division, advanced detection tools, and partnerships with agencies such as the Cybersecurity and Infrastructure Security Agency (CISA). However, constraints, such as resource limitations, detection inaccuracies, inter-agency rivalries, and ethical concerns, including privacy risks associated with AI surveillance, hinder operational effectiveness. Fragmented global legal frameworks, diverse national capacities, and inconsistent detection of advanced deepfakes further complicate responses to this issue. This study proposes the establishment of agile task forces, public–private partnerships, international cooperation protocols, and ethical AI frameworks to counter evolving threats, offering scalable policy and technological solutions for global law enforcement. Full article

The complexity for law enforcement and child protection agencies has been exacerbated by the proliferation of child sexual exploitation channels, facilitated by digital platforms and social media. Generative AI’s ability to analyze large datasets, recognize patterns, and generate new content makes it one of the potential solutions for detecting suspicious behavior and indicators of child sexual exploitation. This paper discusses the potential of generative AI to aid in the fight against pedophilic crimes by reviewing current research, methodologies, and challenges, as well as future directions and ethical concerns. Although the potential benefits are significant, applying AI to such a sensitive area presents numerous challenges, including privacy concerns, algorithmic bias, and potential misuse, which must be addressed carefully. Full article

This study uses a public health approach to identify the comorbid risk factors and protective factors that influence the likelihood of an officer-involved shooting (OIS). Methods: We analyzed 7.5 years of hospital inpatient data obtained from the state of Texas. The OIS subjects (n = 177) were civilians who were shot during a legal intervention involving law enforcement. The control group (n = 33,539) included persons who were hospitalized due to injuries from a car accident. Logistic regression models were used to identify the predictors of an OIS incident. The data included information on chronic diseases, vulnerable population status, health insurance, mental health diagnoses, substance use disorders, and recent trauma. Results: About one-fourth (24.3%) of OIS subjects had a diagnosed mental illness, compared to 8.4% of control subjects (p < 0.001). Factors that greatly increased the risk for an OIS included the following: schizophrenia (AOR = 2.7; CI: 1.6, 4.6), methamphetamine use disorder (AOR = 3.5; CI: 2.2, 5.5), and recent family bereavement (AOR = 8.5; CI: 1.8, 39.6). Six subjects (3.4%) were persons experiencing homelessness (PEH). Protective factors that lowered the risk for an OIS included commercial health insurance (AOR = 0.27; CI: 0.17, 0.45) and Medicaid insurance (AOR = 0.61; CI: 0.11, 0.93). Conclusions: These findings underscore the preventable nature of many OIS incidents, especially those that involve untreated mental illness, homelessness, substance use disorders, and recent trauma. Addressing the root causes of these incidents will likely require interdisciplinary collaboration among law enforcement, public health agencies, and social services. Full article

Land use transformation, the longest-standing human-driven environmental alteration, is a pressing and complex issue that significantly impacts European landscapes and contributes to global environmental change. The urgency to act is reinforced by the European Environment Agency (EEA), which identifies industrial, commercial, and residential development—particularly near major urban centers—as key contributors to land take. As the EU sets a vision for achieving zero net land take by 2050, assessing the readiness and coherence of national legislation becomes critical. This comprehensive study employs a comparative legal analysis across five European countries—Italy, Greece, Poland, France, and Ukraine—examining their laws, strategies, and commitments related to land degradation neutrality. Using a review of national legislation and policy documents, the research identifies systemic patterns, barriers, and opportunities within current legal frameworks. The present study aims to provide valuable insights for policymakers, planners, and academic institutions, fostering a comprehensive understanding of existing gaps, implementation, and inconsistencies in national land use legislation. Among the results, it has become evident that a typical “pathway” between the examined states in terms of the legislative framework on land use–land take is probably a utopia for the time being. The legislations in force, in several cases, are labyrinthine and multifaceted, highlighting the urgent and immediate need for simplification and standardization. The need for this action is further underscored by the fact that, in most cases, land use frameworks are characterized by complementary legislation and ongoing amendments. Ultimately, the research underscores the critical need for harmonized governance and transparent, enforceable policies, particularly in regions where deregulated land use planning persists. The diversity in legislative layers and the decentralized role of the authorities further compounds the complexity, reinforcing the importance of cross-country dialogue and EU-wide coordination in advancing sustainable land use development. Full article

Cyberbullying is a pervasive social issue that has gained increasing attention due to its serious consequences, such as cyberbullicide, which is suicide resulting from cyberbullying. Researchers have called for various interventions and the involvement of multiple stakeholders, including psychologists and law enforcement agencies, to address the problem. Recent studies have shown that the majority of cyberbullying incidents occur on mobile devices, referred to as mobile bullying. Therefore, there is a need to focus intervention efforts on mobile bullying, which is a subset of cyberbullying. The objective of this study was to develop a framework that could guide the creation of an awareness intervention aimed at raising awareness of mobile bullying. Although several meta-analyses have been conducted over the years on intervention effectiveness. There is a gap from the lack of frameworks and requirements to guide the development of awareness interventions. As such, a PRISMA (preferred reporting items for systematic reviews and meta-analyses) systematic literature review was conducted to identify the theories that could inform the development of such an intervention. Based on the review, a theoretical framework was developed that incorporated the basic elements identified as guiding principles for the development of awareness interventions. In the same way, theory, specifically SAT was used to inform the development of an instrumental framework for designing context-sensitive awareness interventions, incorporating the basic elements that practitioners can use to develop context-sensitive awareness interventions. The findings from the study indicate that the social context, which encompasses the social issue at hand; available media content; the proximal and distal environments; and the choice of awareness tool, which is informed by the marketing mix (i.e., price, product, people, promotion, and place), affect the reach or distribution of the intervention. The theoretical framework contributes to the body of knowledge on the subject, while the instrumental framework provides a practical approach to building customizable interventions that can be tailored to specific contexts and available resources for awareness. Full article

Ship type (e.g., Cargo, Tanker and Fishing) classification is crucial for marine management, environmental protection, and maritime safety, as it enhances navigation safety and aids regulatory agencies in combating illegal activities. Traditional ship type classification methods with AIS data are often plagued by problems such as data imbalance, insufficient feature extraction, reliance on single-model approaches, or unscientific model combination methods, which reduce the accuracy of classification. In this paper, we propose an ensemble classification method based on a stacking strategy to overcome these challenges. We apply the SMOTE technique to balance the dataset by generating minority class samples. Then, a more comprehensive ship behavior model is developed by combining static and dynamic features. A stacking strategy is adopted for the classification, integrating multiple tree structure-based classifiers to improve classification performance. The experimental results show that the ensemble classification method based on the stacking strategy outperforms traditional classifiers such as CatBoost, Random Forest, Decision Tree, LightGBM, and the ensemble classification method, especially in terms of improving classification precision, recall, F1 score, ROC curve, and AUC. This method improves the accuracy of ship type recognition, and it is suitable to real-time online classification, which is helpful for applications in marine safety monitoring, law enforcement, and illegal fishing detection. Full article

The progress in automotive technology, communication protocols, and embedded systems has propelled the development of the Internet of Vehicles (IoV). In this system, each vehicle acts as a sophisticated sensing platform that collects environmental and vehicular data. These data assist drivers and infrastructure engineers in improving navigation safety, pollution control, and traffic management. Digital artefacts stored within vehicles can serve as critical evidence in road crime investigations. Given the interconnected and autonomous nature of intelligent vehicles, the effective identification of road crimes and the secure collection and preservation of evidence from these vehicles are essential for the successful implementation of the IoV ecosystem. Traditional digital forensics has primarily focused on in-vehicle investigations. This paper addresses the challenges of extending artefact identification to an IoV framework and introduces the Collaborative Forensic Platform for Electronic Artefacts (CFPEA). The CFPEA framework implements a collaborative forensic-by-design mechanism that is designed to securely collect, store, and share artefacts from the IoV environment. It enables individuals and groups to manage artefacts collected by their intelligent vehicles and store them in a non-proprietary format. This approach allows crime investigators and law enforcement agencies to gain access to real-time and highly relevant road crime artefacts that have been previously unknown to them or out of their reach, while enabling vehicle owners to monetise the use of their sensed artefacts. The CFPEA framework assists in identifying pertinent roadside units and evaluating their datasets, enabling the autonomous extraction of evidence for ongoing investigations. Leveraging CFPEA for artefact collection in road crime cases offers significant benefits for solving crimes and conducting thorough investigations. Full article

This study advances crime analysis methodologies in Maryland by leveraging sophisticated machine learning (ML) techniques designed to cater to the state’s varied urban, suburban, and rural contexts. Our research utilized an enhanced combination of machine learning models, including random forest, gradient boosting, XGBoost, extra trees, and advanced ensemble methods like stacking regressors. These models have been meticulously optimized to address the unique dynamics and demographic variations across Maryland, enhancing our capability to capture localized crime trends with high precision. Through the integration of a comprehensive dataset comprising five years of detailed police reports and multiple crime databases, we executed a rigorous spatial and temporal analysis to identify crime hotspots. The novelty of our methodology lies in its technical sophistication and contextual sensitivity, ensuring that the models are not only accurate but also highly adaptable to local variations. Our models’ performance was extensively validated across various train–test split ratios, utilizing R-squared and RMSE metrics to confirm their efficacy and reliability for practical applications. The findings from this study contribute significantly to the field by offering new insights into localized crime patterns and demonstrating how tailored, data-driven strategies can effectively enhance public safety. This research importantly bridges the gap between general analytical techniques and the bespoke solutions required for detailed crime pattern analysis, providing a crucial resource for policymakers and law enforcement agencies dedicated to developing precise, adaptive public safety strategies. Full article

In digital societies, users’ privacy decisions not only affect personal information security and application sustainability, but also profoundly influence the formulation and enforcement of relevant laws. However, existing research mainly adopts a dichotomous perspective of rationality and bounded rationality, lacking an integrated framework to explain the complexity of users’ privacy decisions. Therefore, this study integrates privacy calculus theory and Psychological Distance Theory within the Elaboration Likelihood Model (ELM) framework to explore their mediating and moderating roles in the relationship between perceived privacy relevance and disclosure intention. To achieve the research objectives, we employed the vignette method to design an online survey with a 2 (perceived relevance: low vs. high) × 2 (temporal distance: near vs. distant) between-subjects design, ultimately collecting 293 valid responses. The conclusions are as follows: (1) perceived relevance positively affects users’ privacy disclosure intention, and perceived benefits and perceived risks play a partial mediating role between them; (2) contrary to individuals’ common preference for near temporal distance (immediate rewards), distant temporal distance (delayed rewards) have a more pronounced enhancing effect on the positive relationship between perceived relevance and perceived benefits; (3) the results excluded the influence of control variables such as gender, age, and education level on privacy disclosure intention. This study not only proposes an integrated analytical framework, revealing that users’ privacy decisions are jointly influenced by both rational and irrational factors, but also provides practical insights for application developers and regulatory agencies. Finally, we also discuss the limitations of this study and directions for future research. Full article

Despite continuous advocacy for sustainable building construction (SBC), it is hardly implemented in Africa. There have been varied views on whether regulatory characteristics concerning SBC should be voluntary or mandatory. Experts recommend customising regulatory characteristics to fit the specific country setting. This paper confirms the critical characteristics of implementing SBC in South Africa (SA) effectively. Using a structured questionnaire, data were collected from professionals in the built environment in SA. The data analysis included descriptive statistics, determining group opinion differences, model tests, and fit statistics for a confirmatory factor analysis (CFA). It was revealed that mandatory enforcement of sustainable construction rules, mandatory assessment, mandatory sustainable building certifications, and mandatory incentive programs are necessary to implement SBC successfully. This study’s practical implications stem from its ability to provide stakeholders and policymakers with the necessary understanding of the key aspects to help them properly direct reform plans. Laws, regulations, and assessments that must be strictly enforced imply that all stakeholders must have a grave corporation or mandate for practical SBC to accomplish sustainable development goals. By confirming the critical regulatory characteristics, this study would drive and assist government agencies, construction companies, policymakers, and built environment stakeholders in properly guiding improvement initiatives. Full article

Lawful Interception refers to the acquisition of the contents of communications between private individuals or organizations by subjects authorized by law. It involves three actors: the network operator (NO), the Law Enforcement Agency (LEA), and the Law Enforcement Monitoring Facility (LEMF). In the literature, standards and scientific solutions are proposed for the interception procedure and the interaction between the NO and the LEMF. However, no standard has been proposed for the interaction between the LEMF and the LEA. The absence of standards for controlling LEA (or a delegated agency) access to intercepted contents stored by the LEMF is a significant gap that should be overcome. This prevents the implementation of secure, interoperable, and automated procedures, leading to inefficiencies and security risks. In this paper, we propose to cover the above gap by adopting the Self-Sovereign Identity (SSI) paradigm. The adopted research methodology follows a multi-phase approach that includes studying existing solutions, system design, and technical feasibility testing. The study first examines existing standards and identity management frameworks and their limitations. Next, an SSI-based architecture is proposed to manage the interactions between LEA (or a delegated agency) and LEMF. Finally, a proof of concept of the proposed solution written in Python and using the Hyperledger Indy blockchain has been implemented to assess whether our proposal is technically feasible. The proposed solution enhances automation, security, and interoperability in lawful interception. Indeed, it enables machine-readable authorizations, reducing errors and improving efficiency by eliminating manual operations. Additionally, verifiable credentials and decentralized identifiers strengthen security and standardize interactions across jurisdictions, ensuring privacy-preserving identity management. By standardizing interactions between LEA and LEMF, this research contributes to a more secure, privacy-preserving, and legally compliant lawful interception process. Full article