Search Results (109)

(1) Background: It seems that (i) the most frequent and critical physical tasks during police service are fence jump, victim drag, and arrest suspect, and (ii) high fitness attributes are conducive to solving police physical tasks with higher success rates. By this, this study aims (i) to evaluate the reliability for assessing police physical tasks (PPTs) and (ii) to identify the fitness attributes that best explain the performance in carrying out PPT. We hypothesize that performance in PPT presents high reliability and that fitness attributes are significant predictors of performance in PPT. (2) Methods: A total of 76 cadets from the Portuguese Police Academy completed (i) three PPT (fence jump—FJ; victim drag—VD; and arrest suspect—AS) in two distinct sessions (T1 and T2), separated by a one-week interval (test–retest design), and subsequently, separated by a one-week interval, (ii) seven fitness tests (T3; cross-sectional design). (3) Results: It was observed that (i) the mean difference in performance (T2-T1) in FJ was 0.05 s (ICC = 0.88), in VD was −0.06 s (ICC = 0.92), and in the AS was −1.21 s (ICC = 0.81); (ii) male cadets were significantly faster, more agile, stronger, and more resistant than female cadets, and they were significantly faster at FJ and VD; (iii) in females, performance in the 30 m sprint tests, sit-ups, and horizontal jump are predictors of FJ, VD, and AS, respectively; and (iv) in males, performance in the horizontal jump and the 20 m shuttle run are predictors of FJ performance, while handgrip strength is a significant predictor in the VD. (4) Conclusions: This study showed that (i) the PPT evaluation protocol presents high reliability (ICC of 0.87, SE = 0.17), suggesting that it is a reliable protocol, capable of being applied to police officers, and (ii) within the scope of attributes that predict performance in the PPTs under study, it appears that the explosive strength of the lower limbs is a relevant attribute, regardless of gender. Full article

This article develops a method for analysing sensor data to prevent cyberattacks using a modified LSTM network. This method development is based on the fact that in the context of the rapid increase in sensor devices used in critical infrastructure, it is becoming an urgent task to ensure these systems’ security from various types of attacks, such as data forgery, man-in-the-middle attacks, and denial of service. The method is based on predicting normal system behaviour using a modified LSTM network, which allows for effective prediction of sensor data because the F1 score = 0.90, as well as on analysing anomalies detected through residual values, which makes the method highly sensitive to changes in data. The main result is high accuracy of attack detection (precision = 0.92), achieved through a hybrid approach combining prediction with statistical deviation analysis. During the computational experiment, the developed method demonstrated real-time efficiency with minimal computational costs, providing accuracy up to 92% and recall up to 89%, which is confirmed by high AUC = 0.94 values. These results show that the developed method is effectively protecting critical infrastructure facilities with limited computing resources, which is especially important for cyber police. Full article

Road traffic fatalities remain a significant global issue, despite a gradual decline in recent years. Although the number of accidents has decreased—partly due to reduced mobility during the pandemic—the figures remain alarmingly high. To further reduce these numbers, it is crucial to identify regions with the highest accident rates and predict future trends. This study aims to forecast traffic accident occurrences across Poland’s provinces. Using official police data on annual accident statistics, we analyzed historical trends and applied predictive modeling in Statistica to estimate accident rates from 2022 to 2040. Several neural network models were employed to generate these projections. The findings indicate that a significant reduction in road accidents is unlikely in the near future, with rates expected to stabilize rather than decline. The accuracy of predictions was influenced by the random sampling distribution used in model training. Specifically, a 70-15-15 split (70% training, 15% testing, and 15% validation) yielded an average error of 1.75%, and an 80-10-10 split reduced the error to 0.63%, demonstrating the impact of sample allocation on predictive performance. These results highlight the importance of dataset partitioning in accident forecasting models. Full article

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated in the study, generating over 2.6 million gaze samples (IRB No. 23-095-B). The collected eye-tracking data were analyzed using statistical summarization, temporal alignment techniques (Earth Mover’s Distance and Needleman-Wunsch algorithms), and machine learning models, including K-means clustering, random forest regression, and support vector machines (SVMs). Clustering achieved a classification accuracy of 78.26%, revealing distinct visual behavior patterns across participant groups. Proficiency prediction models reached optimal performance with a random forest regression ($R^2$ = 0.7034), highlighting scan-path variability and fixation regularity as key predictive features. These findings demonstrate that eye-tracking metrics—particularly sequence-alignment-based features—can effectively capture differences linked to both experiential training and cultural context. Beyond its immediate forensic relevance, the study contributes a structured methodology for encoding visual attention strategies into analyzable formats, offering valuable insights for cognitive modeling, training systems, and human-centered design in future perceptual intelligence applications. Furthermore, our work advances the development of autonomous vehicles by modeling how humans visually interpret complex and potentially hazardous environments. By examining expert and novice gaze patterns during simulated forensic investigations, we provide insights that can inform the design of autonomous systems required to make rapid, safety-critical decisions in similarly unstructured settings. The extraction of human-like visual attention strategies not only enhances scene understanding, anomaly detection, and risk assessment in autonomous driving scenarios, but also supports accelerated learning of response patterns for rare, dangerous, or otherwise exceptional conditions—enabling autonomous driving systems to better anticipate and manage unexpected real-world challenges. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

Street crime is a critical public concern, attracting wide social and research attention. Conventional solutions to reduce street crimes are dispatching more police force in patrol and installing more cameras for street surveillance, which, however, suffer from huge manpower and financial consumption and limited performance. Inspired by the wide application of Unmanned Aerial Vehicles (UAVs) in policing and other related missions such as street surveillance, we investigate the use of UAVs in patrolling along high-risk streets to deter street crimes. UAVs significantly outperform police officers and street cameras in terms of cost reduction and deterring performance improvement. Technically, this paper proposes a data-driven framework to schedule the patrol UAVs, including an online patrol path planning module and an offline UAV station siting module. In the first module, the street-level deterring effect of the UAVs is estimated using a prediction-enhanced method, which guides the UAVs to patrol the high-risk streets more efficiently. Evolved from the path planning algorithm, the second module utilizes a data-driven method to estimate the deterring effect of the candidate UAV stations with different numbers of UAVs. Then both the location of the UAV stations and the UAVs at each station are determined. The proposed framework is comprehensively evaluated using a 6-year crime dataset of the Denver city. The results show that the proposed framework improves the deterring effect by 58.49% on average, and up to 157.32% in extreme cases compared to baselines. Full article

Background/Objectives: The COVID-19 pandemic affected frontline workers’ mental health, including healthcare workers, firefighters, and police officers, increasing the need for effective interventions. This study focuses on the pandemic’s psychological impact, perceived stress, depression/anxiety symptoms, and resilience, examining if a brief virtual reality (VR)–based relaxation session could reduce psychological symptoms. Methods: In this preliminary study with data collected in 2025 from frontline workers who had served during the acute phase of the COVID-19 pandemic, 54 frontline workers completed a baseline assessment of the perceived psychological impact of COVID-19 pandemic, general perceived well-being, perceived stress (PSS-4), anxiety/depression (PHQ-4) and resilience (RS-25). Each participant then engaged in a 10-min immersive VR relaxation session featuring a calming 360° nature environment with audio guidance, after which questionnaires were re-administered. Paired samples t-tests and repeated-measures ANOVA evaluated pre-/post-session differences, and a hierarchical multiple linear regression model tested predictors of the change in stress. Results: Pre-session results showed moderate perceived stress and resilience and low depression/anxiety. Occupation groups varied in baseline stress, mostly reporting negative pandemic psychological effects. After VR, significantly perceived well-being increased, and stress decreased, whereas depression/anxiety changes were nonsignificant. Repeated-measures ANOVA revealed a main effect of time on stress (p = 0.003) without occupation-by-time interaction (p = 0.246), indicating all occupational groups benefited similarly from the VR session. Hierarchical regression indicated baseline depression and higher perceived pandemic-related harm independently predicted greater stress reduction, whereas resilience and baseline anxiety showed no statistically significant results. Conclusions: A single VR relaxation session lowered perceived stress among frontline workers, particularly those reporting higher baseline depression or pandemic-related burden. Limitations include the absence of a control group. Results support VR-based interventions as feasible, rapidly deployable tools for high-stress settings. Future research should assess longer-term outcomes, compare VR to alternative interventions, and consider multi-session protocols. 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

Sound event detection tasks are rapidly advancing in the field of pattern recognition, and deep learning methods are particularly well suited for such tasks. One of the important directions in this field is to detect the sounds of emotional events around residential buildings in smart cities and quickly assess the situation for security purposes. This research presents a comprehensive study of an ensemble convolutional recurrent neural network (CRNN) model designed for sound event detection (SED) in residential and public safety contexts. The work focuses on extracting meaningful features from audio signals using image-based representation, such as Discrete Cosine Transform (DCT) spectrograms, Cocheagrams, and Mel spectrograms, to enhance robustness against noise and improve feature extraction. In collaboration with police officers, a two-hour dataset consisting of 112 clips related to four classes of emotional sounds, such as harassment, quarrels, screams, and breaking sounds, was prepared. In addition to the crowdsourced dataset, publicly available datasets were used to broaden the study’s applicability. Our dataset contains 5055 audio files of different lengths totaling 14.14 h and strongly labeled data. The dataset consists of 13 separate sound categories. The proposed CRNN model integrates spatial and temporal feature extraction by processing these spectrograms through convolution and bi-directional gated recurrent unit (GRU) layers. An ensemble approach combines predictions from three models, achieving F1 scores of 71.5% for segment-based metrics and 46% for event-based metrics. The results demonstrate the model’s effectiveness in detecting sound events under noisy conditions, even with a small, unbalanced dataset. This research highlights the potential of the model for real-time audio surveillance systems using mini-computers, offering cost-effective and accurate solutions for maintaining public order. Full article

Mass shootings result in significant casualties. Due to the complexity of buildings, capturing crowd dynamics during mass shooting incidents is particularly challenging. Therefore, it is necessary to study crowd dynamics and the key mechanisms of mass shooting incidents and explore optimal building design solutions to mitigate the damage caused by terrorist attacks and enhance urban safety. In this study, we focused on the Bataclan Shooting (13 November 2015) as the target case. We used an agent-based model (ABM) to model both the attacking force (shooting) and counterforce (anti-terrorism response). According to the real situation, the dynamic behavior of three types of agents (civilians, police, and shooters) during the shooting accident was modeled to explore the key mechanism of individual behavior. Taking civilian casualties, police deaths, and shooter deaths as the real target values, we obtained combinations for optimal solutions fitting the target values. Under the optimal solutions, we verified the effectiveness and robustness of the model. We also used artificial neural networks (ANNs) to detect the predictive stability of the ABM model’s parameters. In addition, we studied the counterfactual situation to explore the impact of police anti-terrorism strategies and building exits on public safety evacuation. The results show that for the real cases, the optimal anti-terrorism size was four police and the optimal response time was 40 ticks. For double-layer buildings, it was necessary to set exits on each floor, and the uniform distribution of exits was conducive to evacuation under emergencies. These findings can improve police patrol routes and the location of police stations and promote the creation of public safety structures, enhancing the urban emergency response capacity and the level of public safety governance. Full article

Sudden cardiac death (SCD) is a major cause of mortality among patients with coronary artery disease (CAD). This study aimed to identify risk factors for CAD-related SCD (SCD_CAD) through autopsy data and genetic screening with a particular emphasis on rare variants (minor allele frequency < 0.01). We included 241 SCD_CAD cases (mean age 54.6 ± 12.8 years, 74.7% male) verified by medico-legal examination and 241 silent CAD controls (mean age 53.6 ± 15.2 years, 25.3% female) who died from severe craniocerebral trauma. Information about death characteristics was obtained from questionnaires, police reports and autopsy data. Whole-exome sequencing was performed on myocardial tissue samples. Polygenic risk score (PRS) from a previously validated model was applied and rare variant pathogenicity was predicted using in silico tools. SCD_CAD victims predominantly died at night and showed higher mortality rates during summer and winter months, with more complex coronary disease. Nocturnal time (adjusted odds ratio [AOR] = 3.53, 95% CI: 2.37–5.25, p < 0.001), winter (AOR = 2.06, 95% CI: 1.33–3.20, p = 0.001), multiple vessel occlusion (AOR = 1.79, 95% CI: 1.16–2.77, p = 0.009), right coronary artery stenosis (AOR = 2.38, 95% CI: 1.54–3.68, p < 0.001) and unstable plaque (AOR = 2.17, 95% CI: 1.46–3.23, p < 0.001) were identified as risk factors of SCD_CAD. The PRS score was associated with a 60% increased risk of SCD_CAD (OR = 1.632 per SD, 95%CI: 1.631–1.633, p < 0.001). Genetic analysis identified MUC19 and CGN as being associated with SCD_CAD. We identified both hereditary and acquired risk factors that may contribute to cardiac dysfunction and precipitate SCD in CAD patients, thereby facilitating the prevention and early recognition of high-risk individuals. Full article

Background: A rapid intervention team is a broad category of special teams used by police and emergency respondents to cover various needs. It is essential to ensure the safety and well-being of people in emergencies, minimising the risk of harm and maximising the chances of survival. Objective: This study aimed (i) to identify the fitness profiles and levels of POs from the EIR of the Lisbon Metropolitan Command (COMETLIS, PSP, Portugal), considering age classes; (ii) to directly compare the observed fitness profiles to previous research and normative data; and (iii) to compare the fitness profile of POs from the EIR with cadets from the Police Academy. Methods: This cross-sectional observational study included the participation of 121 male POs from the EIR of the Lisbon Metropolitan Command (Portugal) and 92 male cadets from the Police Academy (Lisbon, Portugal). The assessment protocol sequence involved the collection of biosocial data (age classes: ≤29 years; 30–39 years; 40–49 years), a body size assessment, and a fitness assessment (horizontal jump, handgrip strength, 60 s sit-ups and 20 m shuttle run). Results: (i) In the ≤29 years age class, POs performed better in all fitness tests (highlighting that the age class had a statistically significant effect on performance in the horizontal jump, sit-ups, 20 m shuttle run, and predicted VO₂max), and they showed significantly better performance than cadets in handgrip (left, right, and sum), and significantly worse performance in sit-ups and predicted VO₂max. (ii) In the 30–39 years age class, POs had significantly worse performance than cadets in the horizontal jump, sit-ups, 20 m shuttle run, and predicted VO₂max, even after controlling for age. Conclusions: (i) The fitness performance decreased as the age class became older; (ii) the handgrip strength and cardiovascular capacity attributes were between the standard and excellent levels according to the ACSM guidelines for the general population; (iii) POs from the EIR were stronger than cadets in terms of handgrip strength but weaker in terms of lower limb power, abdominal muscular endurance, and aerobic capacity; and (iv) the differences observed between POs from the EIR and cadets in the 30–39 years age class emphasise the importance of physical training after the training period and throughout professional life. Full article

Globally, and specifically in Poland and Lithuania, the incidence of road accidents has been on a decline over the years. The overall figures remain significantly high. Thus, it is imperative to take substantial measures to further decrease these statistics. The objective of this article is to estimate the future frequency of traffic accidents in both countries. To achieve this, a comprehensive yearly analysis of traffic incidents in Poland and Lithuania was performed. Using police records, forecasts for the years from 2024 to 2030 were established. Various neural network models were employed to predict the number of accidents. The results suggest that there remains potential for stabilization in traffic accident rates. It is undeniable that the increasing volume of vehicles on the roads, along with the development of new highways and expressways, plays a crucial role in this scenario. The result obtained depends on the model parameters (testing, validation, and training phases). Sustainable development requires comprehensive solutions, which also include improving road safety. Our research contributes to this goal by creating a tool that provides insight into the number of road accidents in analyzed countries. Full article

Many individuals will dismiss the seriousness of ex-partner stalking offences, often as a result of inaccurate and problematic beliefs about the offence (stalking myths). However, to date, stalking myth acceptance measurements have only considered attitudes about stereotypical stalking (male stalking a female). The current research considered whether inaccurate and problematic perceptions of stalking were dependent on the gender and sexuality of the perpetrator, victim, and participant. Additionally, it examined whether existing stalking myth acceptance scales measuring stereotypical stalking attitudes would predict perceptions of stalking incidents that involved female stalkers and/or male victims. Participants (N = 336) completed the stalking myth acceptance scale and then responded to a series of questions measuring their perceptions towards a stalking vignette. An independent groups design was used to manipulate the gender of the stalker and victim. The need for police intervention was greatest for incidents involving a male stalker and a female victim. Female victims of male stalking were predicted as being the most fearful, whilst male victims of female stalking were rated as least likely to be fearful. Heterosexual males and participants with minority sexual orientations were also more likely to identify the perpetrator’s actions as stalking. Finally, the SMA scales predicted participants’ attitudes for stereotypical stalking cases but not for the other scenarios. The findings demonstrate that gender plays a significant role in stalking perceptions and highlights the need for more inclusive SMA measurements to consider problematic attitudes towards non-stereotypical stalking. Full article