Search Results (102)

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

Gender-based violence (GBV) is a profound and pervasive societal issue, disproportionately affecting women across diverse settings, including homes, workplaces, and public spaces. Despite its prevalence, significant challenges impede research on GBV, particularly regarding data collection, analysis, and ethical handling. This study investigates the complexities inherent in GBV research, focusing on the obstacles posed by under-reporting, ethical considerations, data quality, and the need for cross-comparative standards. Using a combination of police records, web scraping, news reports, and survey data from USAID’s Demographic and Health Surveys (DHS), our study examines strategies to work with sensitive GBV datasets, while maintaining data integrity. Our study advocates for improved demographic surveying and data integration methodologies that can enhance data accuracy and comparability. The findings suggest that while technological advancements, particularly generative AI and machine learning approaches, offer promising avenues for automating survey processes, reducing costs, and enhancing data collection efficiency, they present the limitations of secondary datasets, a lack of data disaggregation, and discrepancies in data coding systems, which highlight the necessity of refining global data standards. Full article

Farmer professional cooperatives are the focus objects of agricultural carbon emission reduction; with the use of the advantages of scale economy and technology, one can promote the development of low-carbon agriculture. In order to study the influencing factors of agricultural carbon emission reduction on farmer professional cooperatives, we explore the interaction effects of carbon emission reduction behavior between farmer professional cooperatives and farmers under government interventions. This paper introduces a carbon transaction mechanism as well as reward and punishment polices into a tripartite evolutionary game model between farmer professional cooperatives, governments, and farmers. Based on the model, we identify a stable evolution strategy and perform simulation analysis. The results indicate that the carbon transaction mechanism can effectively suppress the negative effect of increased costs through higher revenues of the carbon transaction, and carbon prices above 60 CNY/ton enable cooperatives to reduce regional emissions. Higher revenues can promote positive carbon emission reduction behaviors of farmer professional cooperatives and farmers. The sharing ratio increases from 20% to 80%, and farmers gain additional benefits by cooperating in the farmer professional cooperative practices to reduce emissions. Rational regulation of carbon transaction price and quota can promote the participation of farmer professional cooperatives in carbon emission reduction practices and promote the farmers’ inclusion into farmer professional cooperatives. Full article

In the rapidly evolving landscape of biometric technologies, integrating artificial intelligence (AI) and predictive analytics offers promising opportunities and significant challenges for law enforcement and violence prevention. This paper examines the current state of biometric surveillance systems, emphasizing the application of new sensor technologies and machine learning algorithms and their impact on crime prevention strategies. While advancements in facial recognition and predictive policing models have shown varying degrees of accuracy in determining violence, their efficiency and ethical concerns regarding privacy, bias, and civil liberties remain critically important. By analyzing the effectiveness of these technologies within public safety contexts, this study aims to highlight the potential of biometric systems to improve identification processes while addressing the urgent need for strong frameworks that ensure improvements in violent crime prevention while providing moral accountability and equitable implementation in diverse communities. Ultimately, this research contributes to ongoing discussions about the future of biometric sensing technologies and their role in creating safer communities. Full article

This study examines the role of creative resistance, or “artivism”, in Nigeria’s #EndSARS movement, a youth-led campaign against police brutality that peaked in October 2020. Drawing on Robert Entman’s Framing Theory, it analyzes how different art forms reframed public perceptions of the Special Anti-Robbery Squad (SARS) and countered government efforts to delegitimize the protests. Using a qualitative approach, the research employs purposive sampling of Twitter-sourced art forms to explore how these pieces exposed systemic injustice, amplified protester voices, and mobilized local and global support. Findings reveal that artivists personalized SARS brutality, dismantled narratives portraying protesters as criminals, and invoked moral urgency through evocative symbolism, leveraging social media’s virality to sustain the movement’s momentum. The study highlights SARS’ paradoxical role as a state-sanctioned yet reviled entity, demonstrating how creative expressions clarified this ambiguity into a clarion call for reform. By situating #EndSARS within Nigeria’s protest legacy, this analysis underscores art’s transformative power in digital-age activism, offering a blueprint for resistance against oppression. It contributes to scholarship on social movements by illustrating how art and technology intersect to challenge power, preserve collective memory, and demand accountability, with implications for future struggles in Nigeria and beyond. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

This article addresses the impact of generative artificial intelligence on the creation of composite sketches for police investigations. The automation of this task, traditionally performed through artistic methods or image composition, has become a challenge that can be tackled with generative neural networks. In this context, technologies such as Generative Adversarial Networks, Variational Autoencoders, and Diffusion Models are analyzed. The study also focuses on the use of advanced tools like DALL-E, Midjourney, and primarily Stable Diffusion, which enable the generation of highly detailed and realistic facial images from textual descriptions or sketches and allow for rapid and precise morphofacial modifications. Additionally, the study explores the capacity of these tools to interpret user-provided facial feature descriptions and adjust the generated results accordingly. The article concludes that these technologies have the potential to automate the composite sketch creation process. Therefore, their integration could not only expedite this process but also enhance its accuracy and utility in the identification of suspects or missing persons, representing a groundbreaking advancement in the field of criminal investigation. Full article

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article

This perspective paper analyzes selected policy strategies for transforming transport. It identifies four primary objectives: enhance economic efficiency, increase equity, reduce negative externalities, and improve the user experience (4Es). It then develops the framework of persuasion, police, purse, and platform powers (4Ps), which are available to governments to implement change and pursue their objectives. In a series of cases, it illustrates those powers, particularly the underappreciated platform powers, the formation and promulgation of standards, which are themselves the key technology for connecting institutions, showing how the establishment of technical standards transforms existing transport and lays the groundwork for new opportunities. Full article

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment. Full article

This study seeks to determine how multicultural management affects the promotion of a healthy and efficient working climate in hotels in Athens. Specifically, it explores employees’ attitudes toward diversity and multiculturalism, the effects of multicultural communication on cooperation and interpersonal dynamics, as well as the overall effects on organizational outcomes. This study uses both quantitative and qualitative research methods through structured questionnaires on 242 employees from the units of the hotel. Descriptive statistics, ANOVA, and regression analysis were used to analyze the relationship between multicultural management practices and workplace efficiency. The findings show that positive employee experiences and attitudes toward multiculturalism improve the working environment. Multicultural communication enhances collaborative and conflict-solving skills, and efficient multicultural management enhances teamwork and organizational outcomes. Personal experiences and observations of multiculturalism also provide a lot of input to a positive work climate. Multicultural people management practices are imperative for enhancing productive organizational relations in the hospitality industry. Therefore, the issues of appreciating cultural differences, the ongoing diversity training, and helping the workers to overcome language barriers should be emphasized. Hotels should also ensure that diversity training, dialog, and polices are ongoing and clear. Further research should investigate the time-dependency of multicultural management on employee turnover and guest satisfaction, cultural differences in various regions and industries, and the impact of technology and remote work on multicultural team dynamics. Full article

The Pact for Life program was one of Brazil’s most successful initiatives in coping with an elevated incidence of deliberate lethal violent crimes (CVLI) within the jurisdiction of Pernambuco. It delineated the state into 26 Integrated Security Areas (AIS) and applied strategies to combine investigative and ostensive policing. Nevertheless, the pandemic shifted the production possibility of public security in directions that justify empirical investigations, not sufficiently covered in the current literature. This study employs variable returns to scale data envelopment analysis (DEA) and Malmquist productivity index (MPI) models to measure police efficiency and technology changes from 2019 to 2020. The proposed framework can be particularly suitable to capture changes in the production frontier resulting from technological advancements or regressions, which might otherwise be overlooked. Through a quantitative analysis, this research offers a comprehensive assessment of AISs and the operational performance of the Civil Police, emphasizing efficiency metrics and avenues for enhancement within a production-oriented context. Full article

Regional integration policies play a crucial role in promoting coordinated regional development. However, it remains unclear whether the polices simultaneously take into account urban–rural integration to achieve a dynamic balance between efficiency and equity. Based on socioeconomic data from 250 cities in China between 2003 and 2019, we used a staggered difference-in-difference method to investigate the impact of the strategy for the integrated development of the Yangtze River Delta (YD integrated development) on integrated urban–rural development. Our results indicate that the YD integrated development effectively promotes integrated urban–rural development and this conclusion holds after conducting various robustness tests and heterogeneity analyses. Additionally, the YD integrated development can facilitate integrated urban–rural development through the following three main pathways: promoting economic growth, improving road transport links, and advancing technological progress. This paper offers new insights for advancing integrated urban–rural development. The next step could involve the further exploration of the connections between external regional integration policies and internal rural reforms, which will contribute to expediting the establishment of an integrated urban–rural pattern. Full article

With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10–50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the “organization” element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome. Full article

To overcome the multiple challenges of water scarcity, agricultural land conversion, food security, and carbon emissions, an optimal collaborative management scheme for food production is urgently needed, especially in high food-production and food-consumption countries such as China. The water–land–food–carbon (WLFC) nexus provides a new perspective, but its interactions are complex, dynamic, and spatially heterogeneous; the coupling mechanism is not fully understood; and the driving forces and regulation strategies remain uncertain. Therefore, in this study, the WLFC nexus centered on low-carbon and high-quality agricultural development was systematically reviewed. The main contributions are as follows: (1) A framework of the regional agricultural WLFC nexus was proposed based on bibliographic analysis. (2) The main internal and external factors influencing the WLFC nexus in agriculture were identified by reevaluating meta-analysis review studies. The results showed that changes in the amount and type of irrigation water, the amount and planting activities of agricultural land, and climate change (temperature, precipitation, and CO₂ concentration) affected food (rice, wheat, and maize) yields and carbon emissions to varying degrees. Moreover, population, technological innovation, trade, and polices were important external factors impacting food production and carbon emissions. (3) The common methods and tools for assessing, simulating, and optimizing the WLFC nexus in agriculture were summarized from the perspectives of its status, physical links, and embodied links. Integrated indices, complex system thinking, and process-based and data-driven methods were applied in the studies of the WLFC nexus. (4) Strategies and programs for collaborative WLFC management in agriculture within 10 global river basins were compiled. These findings could help us better understand the WLFC nexus in agriculture and identify the optimal cooperative management scheme, thereby realizing low-carbon and high-quality agricultural development. Full article