Search Results (46)

The increasing threat of terrorism involving Radiological Dispersal Devices (RDDs) necessitates comprehensive evaluation and preparedness strategies, especially in densely populated public areas. This study aims to assess the potential consequences of RDD detonation, focusing on the effective doses received by individuals and the ground deposition of radioactive materials in a hypothetical urban environment. Utilizing the HotSpot code, simulations were performed to model the dispersion patterns of ¹³⁷Cs and ²⁴¹Am under varying meteorological conditions, mirroring the complexities of real-world scenarios as outlined in recent literature. The results demonstrate that ¹³⁷Cs dispersal produces a wider contamination footprint, with effective doses exceeding the public exposure limit of 1 mSv at distances up to 1 km, necessitating broad protective actions. In contrast, ²⁴¹Am generates higher localized contamination, with deposition levels surpassing cleanup thresholds near the release point, creating long-term remediation challenges. Dose estimates for first responders highlight the importance of adhering to operational dose limits, with scenarios approaching 100 mSv under urgent rescue conditions. Overall, the findings underscore the need for rapid dose assessment, early shelter-in-place orders, and targeted decontamination to reduce population exposure. These insights provide actionable guidance for emergency planners and first responders, enhancing preparedness protocols for RDD incidents in major urban centers. Full article

Heart transplantation represents a pivotal intervention for end-stage heart failure, extending survival. However, it imposes profound physical, psychological, and social challenges that often undermine recipients’ quality of life (QoL). These challenges are especially pronounced in collectivist cultural contexts like China, where familial obligations and stigma surrounding chronic illness intensify existential burdens. Grounded in theoretical frameworks including Coping Theory, Self-Determination Theory, Socioemotional Selectivity Theory, and Terror Management Theory, this cross-sectional study explored the interplay between social support and QoL among Chinese heart transplant recipients, elucidating the mediating roles of self-esteem and death anxiety, as well as their sequential chain-mediating pathway. Employing validated psychometric instruments, including the Social Support Rating Scale (SSRS), Rosenberg Self-Esteem Scale (RSES), Templer Death Anxiety Scale (T-DAS) and SF-36 Health Survey, along with chain-mediation modeling, the analysis revealed that social support exerts a direct positive influence on QoL, supplemented by indirect effects through enhanced self-esteem, reduced death anxiety, and a chained cognitive-existential mechanism linking these factors. These insights highlight the complex psychosocial dynamics of post-transplant adaptation, advocating for targeted and culturally attuned interventions. These interventions include family-based support programs, self-esteem enhancement strategies, and death anxiety counseling. The aim is to promote holistic rehabilitation and sustained well-being among heart transplant recipients in China’s context. Full article

Background/Objectives: Fear of terrorism can impact psychological functioning and behavior even without direct exposure. Little is known about how anticipatory terrorism fears manifest among nursing students in European contexts. This study assessed terrorism catastrophizing among Croatian nursing students and examined sociodemographic predictors. Methods: A cross-sectional correlational study was conducted between October and December 2024 among 348 nursing students, using the validated Terrorism Catastrophizing Scale (TCS). Behavioral and habitual changes related to the terrorism threat were also measured. Non-parametric tests and bootstrapped regression analyses (1000 resamples) explored associations with sociodemographic variables. Results: Mean TCS score was 38.4 ± 8.0, indicating moderate catastrophizing, with subscale means of 16.8 (Helplessness), 11.7 (Rumination), and 9.8 (Magnification). Female students scored higher across all TCS measures (p < 0.001). Employment was associated with greater catastrophizing and behavioral changes, while urban residence was linked to fewer habitual and overall behavioral modifications. Higher income was associated with lower magnification. TCS scores correlated moderately with behavioral changes (rs = 0.27, p < 0.001). Non-parametric tests (Mann–Whitney U, Kruskal–Wallis, Spearman correlation) were applied due to non-normal distributions. Conclusions: Terrorism catastrophizing in this population is moderate and influenced by gender, employment, and residential context. Findings suggest targeted mental health support and tailored risk communication strategies may benefit nursing students in similar low-risk settings. 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

Energy supply is currently considered a key area that is essential for the functioning of the entire society, remaining one of the most fundamental sectors of critical infrastructure worldwide. However, the functionality of energy systems is threatened by a number of threats from various areas, such as natural influences, technological threats, terrorism, and even state-supported organized attacks. For this reason, there is an active effort by all interested parties to achieve a sufficient resilience and sustainability level of these systems. Currently, various tools are used for this purpose, the essence of which is to ensure the preparedness of energy systems. Primarily, basic dependable systems aspects are applied according to the planning documentation and according to the N-1 principle from the transmission system code. These tools are functional and very proven in practice. However, the sprawling threat landscape and the COVID-19 pandemic have shown that the use of individual, separate tools may not comprehensively cover the entire area of preparedness, especially for unexpected events or expected events of unexpected dimensions. To address this challenge, the article takes up the professional abstract recommendation of ensuring the preparedness of the entire system comprehensively, i.e., by involving all possible tools, knowledge, and resources that the critical entity has. It proposes and tailors a Business Continuity Management System (BCMS) for the energy domain. The approach covers the entire management system of the organization, in which it establishes, implements, operates, monitors, reviews, maintains, and improves the continuity of activities in terms of key energy system functions. The aim is to ensure the sustainability of the functionality of the given systems within acceptable ranges. The article presents the targeted BCMS targets, building blocks, and representative implementation methods and tools. It is argued that the proposal is ready for application in the specific area of energy critical entities and systems by providing examples of partial implementation. Full article

Making cities resilient and secure remains a central goal in urban policy strategies, where established methods, technologies, and best experiences are applied or replicated when the knowledge of a threat is already well established. The scientific community and specialized bodies are invited to comprehend and evaluate disastrous events that are still not well explored to broaden the concept of resilient cities. Among these, terrorism in the European-built environment remains an underexplored topic, despite various studies assessing its economic, social, and political dimensions, exploring the radicalist matrix, or examining the post-effects of high-impact disastrous events. Within this framework, this work presents an algorithm for the risk assessment of historic urban open areas (uOAs) in Europe, combining theories of the terrorism phenomenon, the normative experiences, and the phenomenological results of violent acts in uOAs. Specifically, the algorithm is determined by studying physical qualities/properties and elements that usually feature the uOAs, using a limited set of descriptors. The descriptors and their formulation are set starting from their qualification, in compliance with the risk determinant (Hazard, Vulnerability, and Exposure), and discussed starting from participatory methods (Delphi and AHP). The algorithm is finally applied to Italian historic squares, testing the mathematical approach, verifying theories of the phenomenon, and setting up a comprehensive three-dimensional risk matrix for both soft and hard targets. This latest constitutes an operative tool to assess the investigated built environment exposed to terrorist threats aimed at developing more detailed mitigative strategies. Full article

With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network’s attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images. Full article

Sustainable Development Goal 13 echoes the fact that all countries must make urgent and stringent efforts to mitigate against and adapt to climate change and its associated impacts. Climate financing is one of the key mechanisms used to enable countries to remain resilient to the hastening effects of climate change. In this paper, we empirically assess the effect of institutional governance indicators on the amount of climate finance received by 21 nations for which progress towards the internationally agreed-upon target of reducing global warming to 1.5 °C is tracked. We use the fixed-effects ordinary least squares (OLS) and the feasible generalized least squares (FGLS) estimators, drawing on the Climate Action Tracker panel data from 2002 to 2020. Empirical results reveal that perceived political stability significantly enhanced climate finance inflows among countries that strongly increased their NDC targets, while perceived deterioration in corruption control negatively impacted the amount of climate finance received by the same group of countries. Therefore, governments should reduce corruption tendencies while striving to avoid practices and alliances that lead to any form of violence, including terrorism and civil war. Low developing countries (LDCs) in particular need to improve the standard of public services provided to the populace while maintaining a respectable level of autonomy from political influences. Above all, as countries work towards strengthening institutional governance, there is an urgent need for developed economies to assist developing economies in overcoming debt stress since the likelihood of future resilience and prosperity is being undermined by the debt crisis, with developing countries spending almost five times as much annually on repayment of debt as they allocate to climate adaptation. Full article

Dried urine spot (DUS) is a micro-sample collection technique, known for its advantages in handling, storage and shipping. It also uses only a small volume of urine, an essential consideration in working with small animals, or in acute medical situations. Alkyl-phosphonic acids are the direct and indicative metabolites of organophosphorus chemical warfare agents (OP-CWAs) and are present in blood and urine shortly after exposure. They are therefore crucially important for monitoring casualties in war and terror scenarios. We report here a new approach for the determination of the metabolites of five CWAs in urine using DUS. The method is based on a simple and rapid sample preparation, using only 50 µL of urine, spotted and dried on DBS paper, extracted using 300 µL methanol/water and analyzed via targeted LC-MS/MS. The detection limits for the five CWAs, sarin (GB), soman (GD), cyclosarin (GF), VX and RVX in human urine were from 0.5 to 5 ng/mL. Recoveries of (40–80%) were obtained in the range of 10–300 ng/mL, with a linear response (R² > 0.964, R > 0.982). The method is highly stable, even with DUS samples stored up to 5 months at room temperature before analysis. It was implemented in a sarin in vivo exposure experiment on mice, applied for the time course determination of isopropyl methylphosphonic acid (IMPA, sarin hydrolysis product) in mice urine. IMPA was detectable even with samples drawn 60 h after the mice’s (IN) exposure to 1 LD₅₀ sarin. This method was also evaluated in a non-targeted screening for multiple potential CWA analogs (LC-Orbitrap HRMS analysis followed by automatic peak detection and library searches). The method developed here is applicable for rapid CWA casualty monitoring. Full article

Raman spectroscopy is a type of inelastic scattering that provides rich information about a substance based on the coupling of the energy levels of their vibrational and rotational modes with an incident light. It has been applied extensively in many fields. As there is an increasing need for the remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact, easily transportable, and fully automated remote Raman detection system for trace detection and identification of information, with high-level confidence about the target’s composition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system provides good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a convolutional neural network (CNN)-based algorithm is implemented and packaged into the recognition software to achieve faster and more accurate detection and identification. This prototype offers a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth. Full article

Sanctions are a recurrent issue on the international scene that has gained relevance in recent decades. This article intends to approach this matter in an innovative way by analyzing the relative importance of sanctions’ types and objectives, besides target countries’ characteristics, on sanctions outcomes. Unlike most previous studies, we use more comprehensive data and a competing risk discrete-time hazard model to analyze the differences between sanctions termination by target compliance and sender capitulation. Our results show that the determinants for the two outcomes differ and that there are differences in the efficacy of sanctions according to their type and objective. We conclude that while higher levels of political volatility, democracy, and equality in target countries increase the probability of compliance, higher levels of democracy and globalization increase the probability of sender capitulation. Smart sanctions seem to be more effective at targeting compliance, as the likelihood of compliance is higher for financial and military sanctions than for trade. The likelihood of compliance also increases if the objective is to promote democracy and decreases if the objectives are policy, regime change, or terrorism. Instead, the probability of sender capitulation is higher for travel and trade sanctions and if the objective is to promote human rights. Full article

Malaysia is no exception when it comes to the Daesh threat. Several vulnerable Muslim populations have been previously targeted by Daesh via specific modus operandi to fulfil the terrorism agenda. Based on a persistent concern about Daesh-related issues and their consequences, this article critically explores the role of the security agency, the Counter-Terrorism Division within the Royal Malaysia Police (RMP), in addressing Daesh radicalisation in Malaysia. It examines the process and effectiveness of the top down and ‘soft’ approaches undertaken by the RMP via the rehabilitation and deradicalisation of former Daesh detainees before they rejoin society. The research is qualitative, and is based on a focus group discussion and in-depth interviews with representatives from the Counter Terrorism Division, terrorism experts, government officials and former detainees. The findings show that the RMP’s efforts to curb Daesh intimidation have been effective in terms of decreasing the number of new terrorism incidents, militant recruitment and the establishment of networks and cells. The introduction of ‘Module 30′, which involves theological and psychological improvement, and civil order, along with vocational training and ‘lifelong-monitoring’, has significantly contributed to rehabilitating and deradicalising the majority of former Daesh convicts in Malaysia, such that they embrace peace and renounce violence and religious extremism. Full article

Objectives: Pregnancy is often associated with reduced sleep quality and an increase in sleep disorders, such as restless leg syndrome, obstructive sleep apnea, and insomnia. There are few studies investigating the prevalence of parasomnias in pregnancy, although they may be expected to be a significant problem, as disturbed sleep in this time period in addition to these sleep disorders may trigger parasomnia episodes. Methods: We conducted a survey using an online questionnaire focusing on a comparison of the prevalence of parasomnias in three time periods: 3 months before pregnancy, during pregnancy, and 3 months after delivery. We also inquired about psychiatric and neurological comorbidities, current anxiety and depression symptoms, and pregnancy complications. Results: A total of 325 women (mean age 30.3 ± 5.3 years) participated in the online survey. The overall number of reported parasomnias increased during pregnancy compared to the 3 months before pregnancy (p < 0.001) and decreased after childbirth (p < 0.001). Specifically, we found a significant increase in sleepwalking (p = 0.02) and night terrors (p < 0.001), as well as in vivid dreams (p < 0.001) and nightmares (p < 0.001) during pregnancy. A similar significant increase during pregnancy was reported for head explosion (p < 0.011). In contrast, the number of episodes of sleep paralysis increased after delivery (p = 0.008). At the individual level, an increase in the severity/frequency of individual parasomnia episodes was also observed during pregnancy. Participants whose vivid dreams/nightmares persisted after delivery had higher BDI-II and STAI-T scores. Our data also suggest a significant impact of migraines and other chronic pain, as well as complications during pregnancy, on the presence of parasomnia episodes in our cohort. Conclusions: We have shown that the prevalence of parasomnias increases during pregnancy and needs to be targeted, especially by non-pharmacological approaches. At the same time, it is necessary to inquire about psychiatric and neurological comorbidities and keep in mind that more sleep disorders may be experienced by mothers who have medical complications during pregnancy. Full article

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content. Full article

Counter terrorism is a huge challenge for public spaces. Therefore, it is essential to support early detection of threats, such as weapons or explosives. An integrated fusion engine was developed for the management of a plurality of sensors to detect threats without disrupting the flow of commuters. The system improves security of soft targets (such as airports, undergrounds and railway stations) by providing security operators with real-time information of the threat combined with image and position data of each person passing the monitored area. This paper describes the results of the fusion engine in a public-space trial in a metro station in Rome. The system consists of 2D-video tracking, person re-identification, 3D-video tracking, and command and control (C&C) formulating two co-existing data pipelines: one for visualization on smart glasses and another for hand-over to another sensor. Over multiple days, 586 commuters participated in the trial. The results of the trial show overall accuracy scores of 97.4% and 97.6% for the visualization and hand-over pipelines, respectively, and each component reached high accuracy values (2D Video = 98.0%, Re-identification = 100.0%, 3D Video = 99.7% and C&C = 99.5%). Full article