Search Results (130)

Distinguishing peri-mortem trauma from heat-induced trauma is often a challenging aspect of forensic anthropology casework where fire has been used as a means of concealing evidence. This paper aims to explore the extent to which peri-mortem ballistic trauma characteristics are still present after burning and whether they can be distinguished from heat-induced fractures. This research used Sus domesticus femora and ribs that had been manually defleshed and shot with 7.92 × 57 mm Mauser ammunition at a shooting distance of 3 m, 10 m and 20 m. This type of firearm and ammunition were commonly used in a number of conflicts, such as the Spanish Civil War (1936–1939). The fracture patterns as a result of the ballistic trauma were analysed prior to placing the samples in an electric furnace, where they were heated at a peak temperature of 850 °C for 30 min. Post-burning, each fragment was analysed for ballistic and heat-induced trauma. Following reconstruction, entry and exit wound morphology and radiating fractures remained, with entry wounds being more clearly defined than exit wounds. Ballistic trauma characteristics such as bevelling were still apparent after burning. The results of this study reveal that pre-existing ballistic trauma is still identifiable after bones have been exposed to heat and it is possible to reconstruct the bones to gain a better interpretation. Full article

The necessity for intelligent monitoring has grown more urgent as the number of crimes involving firearms and knives in homes and public areas has increased. Traditional CCTV systems require human operators, whose attentiveness and the impracticability of monitoring multiple video feeds simultaneously limit their effectiveness. Artificial intelligence (AI)-based vision systems can automatically detect firearms and enhance public safety, thereby overcoming this constraint. In accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) criteria, a systematic evaluation of AI-based weapon detection for security monitoring is conducted. The paper summarizes research works on AI, machine learning, and deep learning techniques for identifying weapons in surveillance footage from 2016 to 2025, encompassing 101 research papers. The reported precision ranged from 78% to 99.5%, recall ranged from 83% to 97%, and mean average precision (mAP) ranged from approximately 70% to 99%. While AI-based monitoring significantly enhances detection accuracy, issues with inconsistent evaluation criteria, limited real-world validation, and dataset variability persist. The research study emphasizes the need for uniform benchmarking, robust privacy protections, and standardized datasets to ensure the ethical and reliable implementation of AI-driven weapon-detection systems. Full article

Veteran suicide remains a critical public health crisis in the United States, with rates nearly twice those of the general population. Addressing this challenge requires multiple evidence-based interventions across settings. This paper presents a system dynamics model developed within the Face the Fight™ veteran suicide prevention initiative to evaluate and optimize strategies from 2022 to 2032. The model integrates peer-reviewed evidence on intervention effectiveness, subject-matter expert calibration, and annual updates from Veterans Affairs and grantee data to estimate the potential population-level impact of suicide prevention. The model organizes veterans by levels of suicide distress and estimates the impact of interventions in an initial three target areas aligned with a public health approach to suicide prevention: creating protective environments (e.g., secure firearm storage), strengthening access and delivery of suicide care (e.g., suicide-specific clinical programs), and identifying and supporting people at risk (e.g., suicide screening). Model results indicate that focusing solely on high-distress veterans is insufficient to reduce suicide rates to those of the general population, while balanced portfolios combining clinical, community, and firearm-safety approaches yield the greatest projected benefit. Sensitivity analyses demonstrate the model’s responsiveness to population distress distributions and intervention capacities, underscoring the need for a balanced, scalable strategy. Evaluating suicide-prevention impact is inherently challenging, but the model provides a dynamic and transparent framework for assessing investment effectiveness, refining strategies, and forecasting long-term outcomes. Its adaptability ensures ongoing insights to guide funding priorities, informs data-driven policy, and extends to other populations and public health challenges where multiple interventions interact to influence outcomes. Full article

Gunshot injuries are challenging conditions because of the unique characteristics of the wounding agents producing soft tissue damage that may be compounded by the formation of an expanding temporary cavity (cavitation). Variations in ballistic performance leading to higher energy transfer by the projectile, including bullet tumbling, deformation, and fragmentation, cause increased soft tissue injury and may also lead to more extensive bone comminution compromising local blood supply. Once life-threatening injuries have been excluded or properly addressed, the emergency management of localized trauma from bullets and shotgun pellets may be complicated due to progressive tissue necrosis within the zone of injury. Additionally, the risk of infection should be tackled, especially in high energy bone injuries. War experience suggests a baseline separation between wounds with limited tissue destruction which can routinely be managed as simple penetrating injuries and those resulting from high energy transfer to the tissues involving a substantial amount of necrotic elements surrounding the wound channel which call for a more aggressive surgical approach. A further justification for such a distinction is the need for antibiotic therapy, which varies according to most studies depending on the wounding mechanism, the nature of the wound, and the extent of tissue injury. The emergency physician should also be aware of the possibility of “bizarre” bullet paths resulting in occult injuries of important anatomic structures. Full article

Background/Objectives: Toolmark analysis on cartridge cases offers critical insights in forensic ballistics, as the impressions left on cartridge cases by firearm components—such as the firing pin, breech face, and annulus—carry distinctive patterns and act as unique identifiers that can be used for firearm linkage. This study aims to develop a systematic and interpretable feature extraction pipeline for these regions to support future automation and comparison studies in forensic cartridge case analysis. Methods: A dataset of 20 high-resolution cartridge case images was prepared, and each region of interest (firing pin impression, breech face, and annulus) was manually annotated using the LabelMe tool. ImageJ and Python-based scripts were employed for feature extraction, capturing geometric descriptors (area, perimeter, circularity, and eccentricity) and texture-based features (Local Binary Patterns and Haralick statistics). In total, 61 quantitative features were derived from the annotated regions. Similarity between cartridge cases was evaluated using Euclidean distance metrics after normalization. Results: The extracted and calibrated region-wise geometric and texture features demonstrated distinct variation patterns across firing pin, breech face, and annulus regions. Pairwise similarity analysis revealed measurable intra-class differences, indicating the discriminative potential of the extracted features even within cartridges likely fired from the same firearm. Conclusions: This study provides a foundational, region-wise quantitative framework for analysing cartridge case impressions. The extracted dataset and similarity outcomes establish a baseline for subsequent research on firearm identification and model-based classification in forensic ballistics. Full article

Orbital floor fractures are primarily caused by blunt trauma to the area around the eyes. These injuries most commonly affect the orbital floor and medial wall due to the fragility of these structures. The mechanism typically involves transmission of force through the orbital rim or an acute increase in intraorbital pressure caused by globe displacement. Blowout fractures often occur alongside additional maxillofacial fractures and periorbital soft tissue injuries. The reported causes mirror those of general maxillofacial trauma and include motor vehicle collisions, interpersonal violence, falls, sports-related injuries, incidents involving firearms, and occupational accidents. Here, we present the case of a 56-year-old male patient who sustained an exceptionally rare injury pattern characterized by a complete orbital floor fracture with globe dislocation into the maxillary sinus. Such extensive fractures are associated with significant functional impairments, including diplopia, enophthalmos, and restricted extraocular muscle movement, as well as marked aesthetic deformity. Comprehensive diagnostic imaging, comprising coronal, sagittal, and three-dimensional CT reconstructions, was crucial for accurately assessing the extent of bony disruption and soft tissue involvement. Particular emphasis should be placed on imaging that clearly delineates the extraocular muscles and the optic nerve, as precise evaluation of these structures is essential for surgical planning and prognosis. Surgical management involved repositioning of the globe and the orbital contents, followed by reconstruction of the orbital floor using a titanium mesh anchored to the infraorbital rim. This case highlights the technical challenges of total orbital floor reconstruction, emphasizing the importance of meticulous anatomical restoration for achieving optimal functional and aesthetic outcomes. Full article

In this work, the influence of glass fibers on the performance of the injection molding process for a PA6-based AR15/M4 grip was investigated numerically. The process was realistically modeled using Autodesk Moldflow Insight for different glass fiber percentages (0 wt%, 15 wt%, 30 wt%, 45 wt%). The simulation results were evaluated, including the temperature distribution, flow time, pressure drop, pumping power, volumetric shrinkage and warpage displacement. The findings indicate that, with 15 wt% glass fibers, the material exhibits the shortest fill period (0.62 s) and the lowest pressure drop (0.0061 MPa) and power consumption (0.000433 kW), indicating maximum flow efficiency. On the other hand, a 30 wt% GF setup exhibited the largest volumetric shrinkage (17.76% at most) and warpage (Y: 1.213 mm), even though it had better thermal conductivity. The 45 wt% GF material exhibited the lowest amount of shrinkage and distortion but led to a greater energy consumption compared to 30 wt% GF. Overall, the 15 wt% GF grade provided the highest average process efficiency and dimensional accuracy; therefore, it is the most appropriate grade for precision molded firearm components. Full article

In this study we evaluated over a 1-year period, the ability of Epipremnum aureum leaves to collect particulate matter (PM)-bound Pb from an indoor environment. Using Illumina MiSeq, we investigated the changes in the phylloplane microbiome connected with the accumulation of this pollutant. Plants were placed in a shooting room, where PM release from each shot was recorded, along with PM_2.5 and PM₁₀ sequestration and leaf element enrichment by ICP. Additionally, black carbon (BC) sequestration was determined, and SEM-EDX was performed on leaves after 12 months of exposure. Our results indicated that ambient air pollution shapes microbial leaf communities by affecting their diversity. At the order level, Pseudomonadales, along with Micrococcales, appeared (at a low relative abundance) after exposure to indoor PM-bound Pb air pollution. This study provides a unique comparison of Epipremnum aureum air filtration performance between a standard office environment and a firearm shooting range. The air filtration approach holds promise for reducing indoor air pollution, but more knowledge about the underlying mechanisms supporting genera capable of coping with airborne pollutants is still required. Full article

The periphery of Lomami National Park in the Democratic Republic of the Congo (DR Congo) is experiencing intense and increasing hunting pressure, driven by both local subsistence needs and growing urban demand for bushmeat. This situation poses a serious challenge to sustainable natural resource management and underscores the need to realign protected-area policies with the realities faced by surrounding communities. In the absence of comprehensive ecological monitoring, this study used hunters’ perceptions to assess the current availability of mammalian wildlife around the park. From October to December 2023, surveys were conducted using a snowball sampling method with 60 hunters from nine villages bordering the park. Results show that hunting is a male-dominated activity, mainly practiced by individuals aged 30–40 years, with firearms as the primary tools. It occurs both in the park’s buffer zones and, alarmingly, within its core protected area. This practice has contributed to the local disappearance of key species such as African forest elephant (Loxodonta cyclotis), African buffalo (Syncerus caffer), and African leopard (Panthera pardus pardus), and to the marked decline of several Cephalophus species. These patterns of overexploitation reveal critical weaknesses in current conservation strategies and point to the urgent need for integrated, community-based resource management approaches. Strengthening law enforcement, improving ranger support, and enhancing participatory governance mechanisms are essential. Equally important is the promotion of sustainable alternative livelihoods—including livestock farming, aquaculture, and agroforestry—to reduce hunting dependence and build long-term resilience for both biodiversity and local communities. Full article

Background: Firearm-related injuries are the leading cause of death among children and adolescents (ages 1 to 19 years) in the United States. Access to and carrying firearms are key risk factors for violence and adolescent firearm use. This study examines the association between family conflict and adolescent gun carrying in Los Angeles County, and the extent to which household and neighborhood contexts contribute to adolescent gun carrying. Methods: We use cross-sectional multilevel data from adolescents ages 12–17 years in the Los Angeles Family and Neighborhood Study, conducted in 2002, to fit a series of generalized linear mixed models to examine the association between family conflict (scale range: 0–2) and adolescent gun carrying. Models include random effects to examine the contributions of household and neighborhood contexts on the outcome measure. Results: After controlling for demographic characteristics, gang involvement, substance use, and household and neighborhood contexts, adolescent experiences of family conflict remain positively associated with adolescent gun carrying behavior (OR = 3.45, p = 0.043). Random effects estimates indicate that a relatively large amount of variation in adolescent gun carrying is explained by household and neighborhood contexts: 23% and 24%, respectively. Conclusions: Multilevel family and community-level interventions, with an emphasis on family violence, are necessary components of prevention strategies to reduce high rates of firearm-related mortality among US adolescents. Full article

Mass shootings are forms of loosely defined violent crimes typically involving four or more casualties by firearm and have become increasingly more frequent, and organized and speedy responses from police are necessary to mitigate harm and neutralize the perpetrator. Recent, widely publicized police responses to mass shooting events have been criticized by the media, government, and public. With the advancements in artificial intelligence, specifically single-shot detection (SSD) models, computer programs can detect harmful weapons within efficient time frames. We utilized YOLO (You Only Look Once), an SSD with a Convolutional Neural Network, and used versions 5, 7, 8, 9, 10, and 11 to develop our detection system. For our data, we used a Roboflow dataset that contained almost 17,000 images of real-life handgun scenarios, designed to skew towards positive instances. We trained each model on our dataset and exchanged different hyperparameters, conducting a randomized trial. Finally, we evaluated the performance based on precision metrics. Using a Python-based design, we tested our model’s capabilities for surveillance functions. Our experimental results showed that our best-performing model was YOLOv10s, with an mAP-50 (mean average precision 50) of 98.2% on our dataset. Our model showed potential in edge computing settings. Full article

Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time–frequency spectrogram representations, including Mel, Bark, MFCC, CQT, Cochleagram, STFT, FFT, Reassigned, Chroma, Spectral Contrast, and Wavelet. The dataset consists of 2148 gunshot recordings from four firearm types, collected in a semi-controlled outdoor environment under multi-orientation conditions. To leverage advanced computer vision techniques, all spectrograms were converted into RGB images using perceptually informed colormaps. This enabled the application of image processing approaches and fine-tuning of pre-trained Convolutional Neural Networks (CNNs) originally developed for natural image classification. Six CNN architectures—ResNet18, ResNet50, ResNet101, GoogLeNet, Inception-v3, and InceptionResNetV2—were trained on these spectrogram images. Experimental results indicate that CQT, Cochleagram, and Mel spectrograms consistently achieved high classification accuracy, exceeding 94% when paired with deep CNNs such as ResNet101 and InceptionResNetV2. These findings demonstrate that transforming time–frequency features into RGB images not only facilitates the use of image-based processing but also allows deep models to capture rich spectral–temporal patterns, providing a robust framework for accurate firearm sound classification. Full article

Background/Objectives: The integration of artificial intelligence (AI) into forensic science is expanding, yet its application in firearm injury diagnostics remains underexplored. This study investigates the diagnostic capabilities of ChatGPT-4 (February 2024 update) in classifying gunshot wounds, specifically distinguishing entrance from exit wounds, and evaluates its potential, limitations, and forensic applicability. Methods: ChatGPT-4 was tested using three datasets: (1) 36 firearm injury images from an external database, (2) 40 images of intact skin from the forensic archive of the University of Catania (negative control), and (3) 40 real-case firearm injury images from the same archive. The AI’s performance was assessed before and after machine learning (ML) training, with classification accuracy evaluated through descriptive and inferential statistics. Results: ChatGPT-4 demonstrated a statistically significant improvement in identifying entrance wounds post-ML training, with enhanced descriptive accuracy of morphological features. However, its performance in classifying exit wounds remained limited, reflecting challenges noted in forensic literature. The AI showed high accuracy (95%) in distinguishing intact skin from injuries in the negative control analysis. A lack of standardized datasets and contextual forensic information contributed to misclassification, particularly for exit wounds. Conclusions: While ChatGPT-4 is not yet a substitute for specialized forensic deep learning models, its iterative learning capacity and descriptive improvements suggest potential as a supplementary diagnostic tool in forensic pathology. However, risks such as overconfident misclassifications and AI-generated hallucinations highlight the need for expert oversight and cautious integration in forensic workflows. Future research should prioritize dataset expansion, contextual data integration, and standardized validation protocols to enhance AI reliability in medico-legal diagnostics. Full article

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible. Full article

The application of artificial intelligence within forensic image analysis marks a significant step forward for the non-destructive examination of evidence, a crucial practice for maintaining the integrity of a crime scene. While non-destructive testing (NDT) methods are established, the integration of AI, particularly for analyzing ballistic evidence, requires further exploration. This preliminary study directly addresses this gap by focusing on the use of deep learning to automate the analysis of bullet holes. This work investigated the performance of two state-of-the-art convolutional neural networks (CNNs), YOLOv8 and R-CNN, for detecting ballistic markings in digital images. The approach treats digital image analysis itself as a form of non-destructive testing, thereby preserving the original evidence. The findings demonstrate the potential of AI to augment forensic investigations by providing an objective, data-driven alternative to traditional assessments and increasing the efficiency of evidence processing. This research confirms the feasibility and relevance of leveraging advanced AI models to develop powerful new tools for Forensic Science. It is expected that this study will contribute worldwide to help (1) the police indict criminals and prove innocence; (2) the justice system judges and proves people guilty of their crimes. Full article