Search Results (6)

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

Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article

Background and Objectives: Radicalization, a complex and multifaceted phenomenon, has been a subject of increasing concern in recent years, particularly due to its potential connection to acts of mass violence and terrorism. This systematic review examines the intricate link between radicalization and psychotic disorders, utilizing various sources such as observational studies, case reports, and series. It aims to highlight the prevalence of schizophrenia spectrum and other psychotic disorders among radicalized individuals and to define the role of mental health professionals in dealing with this issue, contributing to the development of prevention and treatment strategies. Materials and Methods: The methodology involved an extensive literature search across PubMed, Scopus, and APA PsycINFO up to 1 February 2024, adhering to PRISMA guidelines. The study focused on radicalization and psychotic disorders as defined by DSM-5 criteria, excluding other mental disorders. A population sample of 41 radicalized individuals diagnosed with psychotic disorders was selected, among which schizophrenia was identified as the predominant condition. Results: It was observed that 24% of these individuals passed away soon after committing their crimes, leading the researchers to rely on retrospective data for their diagnoses. The use of diverse assessment tools for psychiatric diagnosis and the lack of a standardized method for diagnosing or assessing involvement in the radicalization process were also noted. Despite limitations like reliance on observational studies and case reports, which result in low evidence quality and varied methodologies, our work provides a valuable contribution to clarifying the relationship between radicalization and psychotic disorders. However, further clinical studies are needed to delve deeper into these aspects. Conclusions: In conclusion, our review points out that individuals with psychotic disorders do not have a higher crime rate than the general population and warns against associating crimes with mental illness due to the stigma it creates. The lack of uniform psychiatric diagnostic tools and radicalization assessment highlights the need for more standardized risk assessment tools and validated scales in psychiatric diagnosis to better understand the relationship between radicalization and psychotic disorders and to develop integrated protocols. Full article

Gun violence and mass shootings kill and injure people, create psychological trauma, damage properties, and cause economic loss. The loss from gun violence can be reduced if we can detect the gunshot early and notify the police as soon as possible. In this project, a novel gunshot detector device is developed that automatically detects indoor gunshot sound and sends the gunshot location to the nearby police station in real time using the Internet. The users of the device and the emergency responders also receive smartphone notifications whenever the shooting happens. This will help the emergency responders to quickly arrive at the crime scene, thus the shooter can be caught, injured people can be taken to the hospital quickly, and lives can be saved. The gunshot detector is an electronic device that can be placed in schools, shopping malls, offices, etc. The device also records the gunshot sounds for post-crime scene analysis. A deep learning model, based on a convolutional neural network (CNN), is trained to classify the gunshot sound from other sounds with 98% accuracy. A prototype of the gunshot detector device, the central server for the emergency responder’s station, and smartphone apps have been developed and tested successfully. Full article

The United States has had more mass shooting incidents than any other country. It is reported that more than 1800 incidents occurred in the US during the past three years. Mass shooters often display warning signs before committing crimes, such as childhood traumas, domestic violence, firearms access, and aggressive social media posts. With the advancement of machine learning (ML), it is more possible than ever to predict mass shootings before they occur by studying the behavior of prospective mass shooters. This paper presents an ML-based system that uses various unsupervised ML models to warn about a balanced progressive tendency of a person to commit a mass shooting. Our system used two models, namely local outlier factor and K-means clustering, to learn both the psychological factors and social media activities of previous shooters to provide a probabilistic similarity of a new observation to an existing shooter. The developed system can show the similarity between a new record for a prospective shooter and one or more records from our dataset via a GUI-friendly interface. It enables users to select some social and criminal observations about the prospective shooter. Then, the webpage creates a new record, classifies it, and displays the similarity results. Furthermore, we developed a feed-in module, which allows new observations to be added to our dataset and retrains the ML models. Finally, we evaluated our system using various performance metrics. Full article

Shooting is a high-precision sport that depends on many factors to achieve high performance levels. The main objective of this study was to analyze the differences in anthropometric, physiological, and psychological variables by sport level in women air-pistol shooters. Fifteen female pistol shooters, including seven elite national shooters of Iran and eight non-elite shooters, participated in this study. Analyzed variables were grouped into three sections: anthropometric, physiological, and psychological. Anthropometric variables included: height, weight, body mass index, length of leg, arm span, and proportions between variables. Physiological tests include resting heart rate, static and dynamic balance, flexibility, and upper body strength. Additionally, psychological questionnaires of SMS-6 sport motivation, TSCI trait sport-confidence and SSCI state sport-confidence, ACSI-28 athletic coping skills, and SAS sport anxiety scale were used. The Shapiro–Wilks test and independent t-test were used to analyze the data. Effect size and test reliability were calculated using Cohen’s d and Cronbach’s alpha, respectively. Our results showed that elite shooters have higher values of dynamic balance (Y-test), upper body strength (sit-ups), and intrinsic motivation, and lower resting heart rate than non-elite. However, no differences were found in the anthropometric variables, nor in anxiety or coping skills. We conclude that physiological and psychological workouts should be included in the shooters’ training programs to improve their performance. Full article