Search Results (12)

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

Improved surveillance systems provide early warnings and improve public safety. Such systems are desperately needed in light of the rising number of armed robberies in private and public places. A YOLOv8-based system specifically intended for CCTV-based armed robbery detection was developed to meet this demand in this study. The system identified weapons such as handguns, assault weapons, shotguns, and others in real-time, utilizing a custom-trained model. The system demonstrated a strong performance with an overall anomaly detection accuracy of 87.50%. The confidence level was 1.2 m (58.79) and 2 m (59.74) in determining the optimal height and distance considering the positioning of the CCTV camera. The low confidence level was attributed to the mixture of images from a general database from the Internet along with self-captured images that resulted in the overfitting of the datasets. Although improvements are needed to increase the confidence level by using real guns in training the model and reducing false negatives, the potential of YOLOv8 to enhance public safety has been confirmed by providing early warnings of armed robberies. Full article

In an era where security concerns are ever-increasing, the need for advanced technology to detect visible and concealed weapons has become critical. This paper introduces a novel two-stage method for concealed handgun detection, leveraging thermal imaging and deep learning, offering a potential real-world solution for law enforcement and surveillance applications. The approach first detects potential firearms at the frame level and subsequently verifies their association with a detected person, significantly reducing false positives and false negatives. Alarms are triggered only under specific conditions to ensure accurate and reliable detection, with precautionary alerts raised if no person is detected but a firearm is identified. Key contributions include a lightweight algorithm optimized for low-end embedded devices, making it suitable for wearable and mobile applications, and the creation of a tailored thermal dataset for controlled concealment scenarios. The system is implemented on a chest-worn Android smartphone with a miniature thermal camera, enabling hands-free operation. Experimental results validate the method’s effectiveness, achieving an mAP@50-95 of 64.52% on our dataset, improving state-of-the-art methods. By reducing false negatives and improving reliability, this study offers a scalable, practical solution for security applications. Full article

Defense wounds generally occur when a victim attempts to protect vital organs and blood vessels during an assault, and are therefore typically located on the forearms or hands. Gunshot-induced defense wounds are less frequent compared to stab wounds, which have been extensively described in the literature. We present three cases where victims unsuccessfully attempted to defend themselves from gunshots, and where the detailed description of injuries played a key role in the reconstruction of the crime scenes. In the first case, a man was shot with a smooth-bore shotgun, presenting a “through-and-through” gunshot wound on the lateral surface of the left forearm and a large gaping wound on the posterior surface of the same forearm. The second and third cases involved two men who were shot with handguns. The first displayed an entrance wound on the dorsal surface of the right hand, with burned edges and smoke soiling, and an exit wound on the palmar surface. The second case involved two wounds on the left hand: one on the dorsum and the other on the palm. To the best of our knowledge, few studies in the literature emphasize the role of gunshot defense wounds in aiding the reconstruction of crime dynamics. The cases presented in this study highlight the importance of precisely defining the pathological and morphological features of the wounds, as well as the bullet trajectories, to accurately identify defense wounds. These findings are valuable for reconstructing the dynamics of the assault and providing critical information to the public prosecutor. Full article

Ensuring military and police personnel protection is vital for urban security. However, the impact response mechanism of the UHMWPE laminate used in ballistic helmets and vests remains unclear, making it hard to effectively protect the head, chest, and abdomen. This study utilized 3D-DIC technology to analyze UHMWPE laminate’s response to 9 mm lead-core pistol bullets traveling at 334.93 m/s. Damage mode and response characteristics were revealed, and an effective numerical calculation method was established that could reveal the energy conversion process. The bullet penetrated by 1.03 mm, causing noticeable fiber traction, resulting in cross-shaped failure due to fiber compression and aggregation. Bulge transitioned from circular to square, initially increasing rapidly, then slowing. Maximum in-plane shear strain occurred at ±45°, with values of 0.0904 and −0.0928. Model accuracy was confirmed by comparing strain distributions. The investigation focused on bullet-laminate interaction and energy conversion. Bullet’s kinetic energy is converted into laminate’s kinetic and internal energy, with the majority of erosion energy occurring in the first four equivalent sublaminates and the primary energy change in the system occurring at 75 μs in the fourth equivalent sublayer. The results show the damage mode and energy conversion of the laminate, providing theoretical support for understanding the impact response mechanism and improving the efficiency of protective energy absorption. Full article

Light and noise are important factors affecting shooting performance, and shooters can exhibit physiological processes that differ from normal shooting when they are subjected to disturbed visual and auditory conditions. The purpose of this study was to explore the neural mechanism of shooting preparation in skilled shooters with visual and auditory limitations. We designed an experiment and recorded the electroencephalograph (EEG) and shooting performance indexes of 40 individuals skilled in marksmanship during the shooting preparation stage under three conditions: low light, noise interference, and a normal environment. EEG relative band power features and event-related desynchronization/synchronization (ERD/ERS) features were extracted and analyzed. The results showed that (1) the average score of the shooters was 8.55 under normal conditions, 7.71 under visually restricted conditions, and 8.50 under auditorily restricted conditions; (2) the relative EEG band power in the frontal lobe (Fp1, Fp2), frontal lobe (F4, F8), left temporal region (T7), central lobe (CP2), and parietal lobe (P3, PO3) in the theta band was significantly lower than in the other two environments (p < 0.05), and there was no significant difference between the power intensity of the shooter in the noisy environment and that in the normal environment; and (3) in the low-light environment, a significant negative correlation was found between the central region, the left and right temporal regions, and the parietal lobe (p < 0.05). These findings provide a basis for further understanding neural mechanisms in the brain during the shooting preparation phase under visually and auditorily restricted conditions. Full article

Shooting may impact the frequency of neuropathies in the upper extremity nerves or of cervical disc–root conflicts. This study was undertaken to assess whether shooting sports trained with a handgun by civilians may influence the risk factor for carpal tunnel syndrome (CTS) and other neuropathies of the brachial plexus nerve fibers. Neurophysiological studies using surface electromyography (rEMG at rest and mcEMG during maximal contraction), electroneurography (ENG), and motor-evoked potential recordings (MEPs) were performed in a select population of nine shooters, which were rigorously screened as positive through a clinical examination for carpal tunnel syndrome and other brachial plexus neuropathies among a population of forty-two subjects, to confirm the existence of pathologies in the upper extremities. Increased muscle tension in rEMG and a simultaneous decrease in motor unit activity in mcEMG were recorded both in the proximal and distal muscles of the upper extremities more frequently in the shooters than in the healthy controls—volunteers. An ENG examination confirmed CTS in the shooting hand of four subjects (4/42; 9.5%), additionally revealing a significantly decreased F-wave at the C6–C7 levels in the dominant extremities of the shooting group in comparison to the control population (p = 0.05). All the examined subjects had revealed brachial plexus pathologies on both sides according to the results of the MEP recordings upon stimulation at the C4–C8 levels (various significant differences between the shooters and control group were found), and two had ulnar neuropathy in the wrist on the shooting side. It was concluded that shooting sports are a moderate risk factor for carpal tunnel syndrome and that they significantly influence the development of other brachial plexus neuropathies. Full article

Autonomous vehicles (AVs) are rapidly transforming the automotive industry due to rising consumer interest in these vehicles worldwide. However, few studies have compared different countries in terms of public acceptance of AVs. This study compares public acceptance of AVs as a function of risk perceptions in two countries leading the AV industry—Japan and Israel. We set our study within the risk-as-feelings framework. In contrast to “risk as analysis,” which invokes factual reasoning to bear on risk assessment and decision making, “risk as feelings” takes affective cues such as the sense of dread and unfamiliarity into judgments of risk. To this end, we conducted two web-based surveys in Japan in 2017 and Israel in 2021. In a between-subjects design, we manipulated introductory video information to portray various combinations of risk factors commonly associated with AVs: system errors, external interferences with car controls (e.g., hacking), and the inability of the AV to cope with unexpected events. Next, participants were surveyed about how they perceive the risks of AVs and other well-known technologies and activities. Results showed that acceptable risk, perceived risk, and perceived benefit of AVs were all generally higher in Israel than in Japan. The opposite pattern was found for a “risk adjustment factor,” suggesting that the Japanese seek more safety before acceptance than Israelis. Furthermore, we conducted a factor analysis on seven risk dimensions, resulting in a two-factor model of dread and unfamiliarity. Cognitive mapping of AVs and other technologies and activities in the two-factor plane revealed that the AV technologies we studied (i.e., AV-car levels 3 and 4; AV-bus levels 3 and 4) have high unfamiliarity risk but moderate dread risk compared to technologies and activities such as smoking, flying, and handguns. After exposure to video-based educational content, unfamiliarity risk was less influential but dread risk—in particular, related to human-made risks—became more influential. The results indicated that manufacturers and policymakers should emphasize mitigating human-made risks instead of focusing on improving public familiarity with AVs to garner trust and improve public acceptance of the technology. Full article

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera. Full article

Study Design: The following retrospective cohort study was competed using data from the Nationwide Inpatient Sample a database from the Healthcare Cost and Utilization Project (HCUP). Objective: The objective of this retrospective cohort study is to compare the hospitalization outcomes of managing maxillofacial trauma attempted suicide among handguns, shotguns, and hunting rifles. Methods: The primary predictor variablewas the type of firearm. The outcome variableswere the hospital charges (U.S. dollars) and length of stay (days). We used SPSS version 25 for Mac (IBM Corp., Armonk, NY, USA) to conduct all statistical analyses. Results: A final sample of 223 patients was statistically analyzed. Relative to patients within the Q2 median household income quartile, patients in the Q4 median household income quartile added +$ 172,609 (p < 0.05) in hospital charges. Relative to patients living in “central” counties of metro areas, patients in micropolitan counties added +13.18 days (p < 0.05) to the length of stay. Relative to patients in the Q2 median household income quartile, patients in Q3 added +9.54 days (p < 0.05) while patients in Q4 added +11.49 days (p < 0.05) to the length of stay. Conclusions: Being within the highest income quartile was associated with increased hospital charges. Patients living in micropolitan counties have prolonged hospitalization relative to patients in metropolitan counties. Relative to the second income quartile, length of stay was higher in the third income quartile and highest in the fourth income quartile. Increase income grants access to deadlier firearms. Full article

Background: Self-inflicted gunshot wounds (SIGSWs) produce devastating facial defects that are challenging to reconstruct, but are rarely reported in large cohorts in the literature. This study sought to characterize these injuries, and identify parameters influencing complications and outcomes among survivors following facial reconstruction. Methods: A retrospective cohort study was performed identifying 22 patients with SIGSWs to the face reconstructed at our center from 2009 to 2019. Charts were reviewed for patient, injury, and reconstructive details and course. Outcomes were statistically compared to various parameters. Results: The most common firearm, orientation, and injured structure were the handgun (40.9%), submental (59.1%), and mandible (68.2%), respectively. Patients averaged a 21.7-day length of stay (LOS), 17.4 h to debridement, 2.6 days to bony fixation, 5.4 reconstructive surgeries, and 7 (31.8%) patients received at least one free flap. Fifteen (68.2%) patients had at least one major complication, although functional outcomes were ultimately relatively good overall. Notable outcome associations included submental orientation with a longer LOS (p = 0.027), external fixation with a longer LOS (p = 0.014), financial stressors with a shorter LOS (p = 0.031), and severe soft tissue injury with an increased total number of reconstructive surgeries (p = 0.039) and incomplete reconstruction (p = 0.031). There were no cases of suicidal recidivism. Conclusions: Reconstruction following facial SIGSW is challenging for both patient and surgeon, and carries a high rate of complications. However, patients can regain substantial function following reconstruction and the achievement of satisfactory outcomes. Full article

There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training. Full article