Threat Assessment Research Articles

Assessment of radiological hazards in terms of gross α -β activities in groundwater in and around Beldih mine region of eastern India.

In the present study, liquid scintillation counting triple to double coincidence ratio technique is used to ascertain the gross α and β activity in groundwater samples collected from the Beldih mine region in the vicinity of the South Purulia Shear Zone (SPSZ) of Chota Nagpur Plateau in eastern India. A total of sixty samples were collected from deep tube wells located in the study area to assess the potential health threats caused by α and β emitting radionuclides present in these water samples. Average gross α activity in the region of study is 0.09 ± 0.05Bq/L, with a maximum of 3.22 ± 0.07Bq/L. On the other hand, the average gross β activity is found to be 0.13 ± 0.02Bq/L, with a maximum of 0.29 ± 0.02Bq/L. It was observed that gross α activity level in three samples exceeds the safety limit of 0.5Bq/L recommended by the World Health Organization. No significant gross β activity was observed. However, the radiological parameters for assessment of potential health threats due to ionizing radiation have been observed to be significantly high for adults. The results of this study indicate that the radiological assessment of groundwater in the Beldih mine region may be extended in future.

Explainable Machine Learning Models for Real-Time Threat Detection in Cybersecurity

In the rapidly evolving landscape of cybersecurity, traditional machine learning models often operate as "black boxes," providing high accuracy but lacking transparency in decision-making. This lack of explainability poses challenges for trust and accountability, especially in critical areas like threat detection and incident response. Explainable machine learning models aim to address this by making the model's predictions more understandable and interpretable to users. This research integrates explainable machine learning models for real-time threat detection in cybersecurity. Data from multiple sources, including network traffic, system logs, and user behavior, undergo preprocessing such as cleaning, feature extraction, and normalization. The processed data is passed through various machine learning models, including traditional approaches like SVM and decision trees, as well as deep learning models like CNN and RNN. Explainability techniques such as LIME, SHAP, and attention mechanisms provide transparency, ensuring interpretable predictions. The explanations are delivered through a user interface that generates alerts, visualizations, and reports, facilitating effective threat assessment and incident response in decision support systems. This framework enhances model performance, trust, and reliability in complex cybersecurity scenarios.

Environmental Threat Assessment Framework for Mining Activities in Guinea: An Integrated Approach for Sustainable Development.

The present study aimed to investigate the environmental consequences of mining activities in boke bauxite mining areas and the Kerouane iron mining project in Guinea using a mixed-methods approach that combines quantitative and qualitative data. A reference matrix was used to evaluate the impact of the mining activities, classifying them as negligible, moderate, or significant. Data were collected from October 2022 to January 2023 by assessing their impact on water pollution, soil, noise, air quality, vegetation, fauna, and flora. These findings indicate concerns regarding the water pH, electrical conductivity, and turbidity in both the Boke and Kerouane regions. The soil composition analysis revealed the presence of Cr, Cu, Ni, and Zn in both the Boke and Kerouane project areas. Noise levels exceeded the guideline levels and substantial amounts of particulate matter (PM) were detected, with high levels of PM10 in mining operations. The Kerouane Iron Project resulted in the direct loss of 2929 ha of natural habitats and 466 ha of modified habitats, with the extraction of approximately 1.291 billion tons of ore anticipated over a 22-year mine lifespan. This study proposes an environmental threat assessment framework that integrates technical and human activity data to evaluate the environmental impacts of mining activities comprehensively. To promote sustainable development and minimize the negative impacts of mining, an integrated index of economic and environmental performance in the mining sector is recommended, along with collaboration between researchers and policymakers to develop effective climate-change mitigation strategies.

Beyond binary: Analyzing closed-source data to compare specific roles and behaviors within violent and nonviolent terrorist involvement.

Increasingly, studies compare risk and protective factors for involvement in violent and nonviolent terrorist behaviors. This exploratory study investigates whether this distinction is sufficient, or whether it should be disaggregated further into more granular terrorist roles and behaviors. Using data on 404 referrals to a UK countering violent extremism Prevent hub specializing in mental health and associated needs, we compare violent and nonviolent referrals, and then more specific behaviors (vulnerability, proactive extremism, foreign fighting, and violence planning). Bivariate and multivariate analyses show there is value in disaggregating beyond the binary violence versus nonviolence distinction, as more (and more detailed) relationships emerged when using the disaggregated set of behaviors. While gender did not differentiate violent and nonviolent referrals, women were more likely to be referred for radicalization vulnerability or potential foreign fighting. Extreme right-wing and extreme Islamist referrals were no more or less violent overall, but Islamist referrals were disproportionately referred for both the most and least violent behaviors. Personality and developmental disorders were associated with violence, and disaggregated behaviors provided detailed insight into the drivers of these associations. These exploratory findings, while interesting, likely do not generalize beyond our specific sample. Instead, this study's value lies in demonstrating the utility for both research and, eventually, practice of disaggregating beyond violence and nonviolence. The results demonstrate clear operational implications for threat assessment in the need to include a more refined set of risk factors to aid in assessing risk of more relevant outcomes than terrorist involvement overall.

Is It Always Advisable to Promote Geodiversity and Geoheritage in a Traditional Recreational Area? A Case Study from Brno Reservoir and Its Surroundings (Czechia)

Geodiversity and geoheritage represent an important resource for geotourism and subsequent economic development both in remote natural areas and in traditional, frequently visited recreational areas. Identification, assessment, and suitable management of particular geosites may contribute to the development of sustainable forms of tourism and attract more people to the topics of geodiversity and geoheritage. However, if there is only one aim represented by geotourism development, there can be a danger of overexploitation of geoheritage, especially if the sites are promoted less adequately and not provided suitable protection. This paper is focused on the assessment of particular sites, in terms of their Earth science interest, regarding their geotourist and geoeducational potential; it is also focused on an assessment of possible risks and threats which may be related to the higher promotion and more intensive use of these sites. Based on this, proposals for sustainable management, both for geotourism and geoeducational development and nature conservation, are presented and discussed.

Similarities between copycat mass shooters and their role models: An empirical analysis with implications for threat assessment and violence prevention

BackgroundAlthough an important subset of mass shooters has admitted copying previous shooters, there has been almost no empirical research on the similarities between mass shooting role models and their copycats. Such analysis is essential for understanding who is most susceptible to the influence of high-profile mass shooters and what behaviors they are likely to copy. MethodsWe first compiled all documented instances we could find globally of public mass shooters and active shooters becoming a role model for a copycat from 1966 to 2022 (n = 205) and calculated how often their risk profiles and behaviors were similar. Next, we ran simulated matches (n = 2000) and used binary logistic regression to test whether copycats were significantly more similar to their role models than to a random shooter. FindingsCompared to a random shooter, copycat attackers were significantly closer to their role models in age and more likely to share the same sex, race, country, incident location type, and offender outcome. Nearly 80% of copycats attacked more than one year after their role model, and the average temporal gap was approximately eight years. Copycats averaged significantly fewer victims killed and wounded than their role models. ConclusionsThe risk that high-profile mass shooters influence copycat attackers persists for many years, with the most susceptible individuals sharing characteristics of the role model shooters themselves. These findings could be used to make media coverage of mass shootings safer and to inform triage and case prioritization for threat assessment and violence prevention.

Lithuanian Intelligence Services’ Strategic Communication: 2012–2022 National Threat Assessments, Activity Reports and Recruitment Videos

This study examines the communication of the State Security Department of the Republic of Lithuania and the Second Investigation Department under the Ministry of National Defence in 2012–2022. It identifies the aspects relevant to the strategic communication of the intelligence services, analyses specifics of the texts and tendencies in national threat assessments and activity reports, and examines how these institutions present themselves. The study applies the theory of securitisation and critical discourse analysis. It includes other research related to the topic, practices of foreign intelligence services in publishing annual reports and using the YouTube social media platform, media publications and other sources. The study concludes that strategic communication for intelligence agencies is essential for the long-term success of their operations. By focusing on strategic objectives, they inform and educate the public about themselves and national security and develop a cooperative relationship, contributing to deterrence. Both institutions examined in this study are becoming not only formally more open to the public, but also aim to build and maintain a high institutional reputation, connect with their audience and create an image of professionalism, modern accountability and openness to the public.

The influence of meteorological conditions of the Non-Chernozem zone on the phytosanitary condition of grain bean crops

There is a tendency to expand the area of bean cultivation in the northern direction, which is due to climatic changes and breeding achievements. There are 191 varieties of beans registered in the state register of breeding achievements approved for use, of which 31 varieties are recommended for cultivation in the belt between 50° and 60° latitude. The relevance of bean cultivation in the Non-Chernozem zone is increasing. This is due to the increased demand for beans, which are characterized by high nutritional value. Also, this crop is promising as an alternative in grain crop rotations. The study of the phytosanitary condition of bean plants in potentially new cultivation regions in order to develop a crop protection system is of interest. The promotion of crops to new territories is inevitably accompanied by phytosanitary risks, which have been little studied due to insufficient information about bean diseases. The literature provides an assessment of potential threats. However, their manifestation is closely related to the climatic and weather conditions of the growing season. The analysis of the interrelationships of the phytosanitary condition of crops with the temperature and humidity regimes of the bean growing period is relevant.

Analyzing violent attacker texts: examining ideologies through machine learning-assisted topic modeling

ABSTRACT The rise in extremist violence is a global concern, prompting advancements in threat assessment. This study applies machine learning to dissect publicly available texts from violent attackers with white supremacist, incel, and political extremist ideologies, as well as from those driven by personal grievances. Through natural language processing, the research identifies specific linguistic patterns and themes inherent to each group and explores the intersections among them. It uncovers shared narratives of disenfranchisement and aggression alongside unique ideological signifiers. Additionally, it identifies themes unique to each group, offering a clear thematic distinction between ideologies. These insights contribute to a more accurate identification of radicalization indicators, supporting the development of intervention strategies tailored to the ideological nuances of each group. Enhanced threat assessment tools informed by this research can improve the precision of prevention efforts by law enforcement and mental health professionals, reflecting the complex realities of extremist motivations and actions.

Does job gamification perception promote the job involvement of gig workers? The role of cognitive assessment and overwork—evidence from Chinese delivery platforms

PurposeThis paper takes the gig workers in Chinese delivery platform as the research object and adopts a questionnaire survey to explore the complex influence of job gamification perception on the job involvement of gig workers, via the mediating role of cognitive assessment and moderating role of overwork, in order to provide research and data support for the development of platform gamification.Design/methodology/approachThe study conducted a three-wave online questionnaire survey to obtain 300 final samples from Chinese delivery platforms. Hypotheses were tested hierarchical regression and bootstrap methods.FindingsDrawing on cognitive appraisal theory, we observed an inverted U-shaped relationship of gig workers between job gamification perception and job involvement. The mediating role of cognitive assessment and moderating role of overwork were also considered. Both challenge and threat assessment mediate the relationship between job gamification perception and job involvement. Direct effect of job gamification perception on job involvement and indirect effect of job gamification perception on cognitive assessment are moderated by overwork.Originality/valueIn the past, the research on job gamification mostly focused on the traditional forms of employment, but this study focuses on the new forms of employment and from the perspective of individual self-perception, explores the influence of job gamification perception on the job involvement of gig workers in Chinese delivery platform and investigated the dialectical role of job gamification perception. The findings enrich the literature and theoretical research on job gamification perception and job involvement and provide new references and perspectives for management practice.

Situational crime prevention as a harm mitigation policy for active shooter incidents

AbstractResearch summarySituational crime prevention (SCP) is an environmental crime control perspective with enormous practical and policy relevance due to its practitioner‐friendly theoretical approach. This study examines whether SCP interventions reduce incident casualty outcomes in active shooter incidents. We used an inductive, open‐source data set of 555 active shooter and mass public shooting sites to study the applicability of SCP to active shooter and mass public violence. Our findings suggest a harm mitigation role for SCP: active shooter sites with stronger holistic SCP had fewer casualties. We assessed perpetrator motivation to test displacement, a core critique of SCP, and found that the harm mitigation potential of SCP persists even in the presence of a highly motivated offender.Policy summarySCP could be a practical and effective method to decrease casualties in the event of an active shooting, which is a highly motivated crime type that is difficult to predict and prevent. Public locations may select a range of appropriate SCP techniques based on individual resources and needs. The totality and interactions of these techniques may contribute to public safety in general, with diffuse benefits. This policy solution is highly oriented toward practice and real‐life application, and may be used to supplement existing preventative measures like threat assessment and gun legislation.

Improvement of public management mechanisms in the direction of countering hybrid threats to national security

The article represents an attempt of conceptual comprehension of the phenomenon of hybrid threats, which, although inextricably linked with hybrid warfare, still may not include armed conflict even with participation of only irregular combatants. Rather, hybrid threats are aimed at undermining adversary’ infrastructure or gaining much control over it, and sow internal societal tension. Geopolitical components, drivers and factors of hybrid threats are analyzed. Based on analysis of literature sources and practical cases, it is concluded that anticipatory approach is the only way of effective combatting hybrid threats, but implementation of this approach could be backed by a collective change in mentality and a stronger national narrative, while the initiative should be suggested by public administration. There is a need for fresh thinking while expanding the traditional enemy-centric threat assessment and response.

Multi-UAV Cooperative Target Assignment Method Based on Reinforcement Learning

To overcome the problems of traditional distributed target allocation algorithms in terms of lack of target strategic priority, poor scalability, and robustness, this paper proposes a proximal strategy optimization algorithm that combines threat assessment and attention mechanism (TAPPO). Based on the distributed training framework, the algorithm integrates a threat assessment and dynamic attention strategy and designs a dynamic reward function based on the current hit rate of the drone and the missile benefit ratio to improve the algorithm’s exploration ability and scalability. Through an 8vs8 multi-UAV confrontation experiment in a digital twin simulation environment, the results show that the agent using the TAPPO algorithm for target allocation defeats the state machine with an 85% winning rate and is significantly better than other current mainstream target allocation algorithms, verifying the effectiveness of the algorithm.

Target Trial Emulation for Evaluating Health Policy.

Target trial emulation is an approach to designing rigorous nonexperimental studies by "emulating" key features of a clinical trial. Most commonly used outside of policy contexts, this approach is also valuable for policy evaluation as policies typically are not randomly assigned. In this article, we discuss the application of the target trial emulation framework in a policy evaluation context. The policy trial emulation framework includes 7 components: the units and eligibility criteria, definitions of the exposure and comparison conditions, assignment mechanism, baseline ("time zero") and follow-up, outcomes, causal estimand, and statistical analysis and assumptions. Policy evaluations that emulate a randomized trial across these dimensions can yield estimates of the causal effects of the policy on outcomes. Using the policy trial emulation framework to conduct and report on research design and methods supports transparent assessment of threats to causal inference in nonexperimental studies intended to assess the effect of a health policy on clinical or population health outcomes.

Анализ угроз информационной безопасности органов государственной власти с помощью нейронных сетей

<p>Ensuring the information security of public authorities requires the use of specialized tools that take into account various sources of information threats and their constant changes. This study aims to develop a methodology for analyzing these threats using neural networks. The research uses methods such as machine learning and neural network analysis to systematize the data. The authors adapted the Multi-Layer Perceptron (MLP) architecture and configured the hyperparameters of the neural network to achieve their objectives. The neural network was trained using the Python programming language, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1 score. The results of the study included the development of a method for creating a data set that encompasses assessments of threats to the information security of various public authorities and their sources. Additionally, the study evaluated the effectiveness of neural networks in solving classification problems for public authorities. Finally, the study interpreted the results of neural network analysis to determine the resistance of public authorities against information security threats.</p>

Assessing measles risk transmission in Iran: a utilization of the World Health Organization’s programmatic risk assessment tool,2022

BackgroundDespite successful efforts to eliminate measles in Iran, imported measles cases continue to be reported. Because measles is endemic in neighboring countries. This research aims to evaluate the risk of measles transmission in different regions of Iran.MethodsMeasles case-based surveillance data of the Expanded Program of Immunization containing 31 provinces and 463 districts from 2019 to 2021 were assessed. The WHO Measles Programmatic Risk Assessment tool was used to evaluate the risk of disease transmission in four domains: population immunity, surveillance quality, program delivery performance, and threat assessment. scores were categorized as low, medium, high, or very high risk.ResultsDuring 2019–2021, the average incidence of measles was 1.9 per 1 million. Chabahar and Mashhad with 76 and ./6per million reported the highest and lowest incidence respectively. All 463 districts were categorized as low risk in risk assessment. Andimeshk, Chabahar, and Bojnurd obtained the highest risk scores with 27, 24, and 25 respectively. All districts were classified as low risk for population immunity. The average coverage of (MMR1) and (MMR2) was 95% or higher. All districts received the minimum points for surveillance quality.ConclusionAll regions are placed at a low level of disease transmission risk. However, the tool is not able to assess the risk at the rural or peripheral sectors level. The indicators used in this tool are the same for all countries with different epidemiological features (elimination, endemic). Sensitivity analysis can optimize the use of this tool for countries with different disease conditions.

Multimodal vehicle trajectory prediction and integrated threat assessment algorithm based on adaptive driving intention

As autonomous driving and connected communication technologies advance swiftly, vehicle trajectory prediction has become increasingly significant. The motion of a vehicle is contingent not only on its historical trajectory but is also subject to the influence of surrounding vehicles, thereby exhibiting intricate social and temporal interdependencies. Furthermore, the inherent randomness and uncertainty in driver behavior render vehicle trajectory prediction inherently multimodal, a factor that is frequently neglected in current research. Against this backdrop, a multimodal vehicle trajectory prediction (MTP) model based on an encoder-decoder architecture is proposed to hierarchically extract historical features of vehicles. The model consists of five key components: temporal feature encoder module, spatial interaction module, spatial-temporal dependence module, driving intention fusion module and multimodal trajectory output module. Experiments on the NGSIM dataset show that the predictive performance of the model has been improved to varying degrees, especially at 3–5 s, where the improvement is more significant. Compared with state-of-the-art models, the Root Mean Square Error (RMSE) error at 5 s time horizon is 3.38 m on NGSIM dataset, which represents a 25 % improvement. To measure the safety of predicted trajectories, we propose a comprehensive threat assessment model that combines collision time (TTC), headway (TH) and time to lateral collision (TLC) metrics based on safe distance theory. This model not only evaluates the longitudinal collision threat in the following state, but also evaluates the lateral collision threat during driving maneuvers in multi lane scenarios, thereby comprehensively improving the safety of vehicle driving. This research also offers new perspectives and insights for the development of autonomous driving.

Identification of complexes formed between sulphur mustards and arsenic-containing chemical warfare agents

Large quantities of toxic chemical warfare material, such as mixtures of sulphur mustard (HD) and arsenic-containing chemical warfare agents (As-CWAs) have been submerged in the Baltic Sea region after World War II. Little is known about the possible reactivity between HD and As-CWAs. In this study, we used simple reaction mixtures and ultra-high performance liquid chromatography combined with mass spectrometric techniques to study the reactivity of HD and the half-mustard 2-chloroethyl ethyl sulphide (CEES) with As-CWAs. Altogether 22 novel As-CWA-HD- and As-CWA-CEES-complexes were identified. Eight of the As-CWA-HD-complexes were also detected in environmental samples collected from known CWA dumping sites in the Baltic Sea region. Because the As-CWA-HD- and As-CWA-CEES-complexes have structural moieties of both S-mustards and As-CWAs, they might have toxic properties of both CWA-types. Therefore, their occurrence in the environment is concerning and their potential negative effect on the wellbeing of marine biota and humans should be investigated in the future. This is the first time alkylation of As-CWAs is reported, providing new knowledge on the reactivity of S-mustards. The results increase the understanding of the environmental fate of HD and is valuable for the assessment of the environmental threat of sea-dumped CWAs.

The Japanese tits evaluate threat level based on the posture of a predator

Previous studies have shown that the threat level posed by a predator can vary with physical features. In this study, we tested whether the wing posture of a raptor could serve as a clue for bird threat assessment. We observed the behavioral response of Japanese tits Parus minor to taxidermy dummies Eurasian sparrowhawks Accipiter nisus with either spread wings and closed wings. The results showed that the response scores to sparrowhawks with spread wings were higher than those to sparrowhawks with closed wings. We suggested that Japanese tits can assess the predation risk associated with changes in wing posture of predators.

The native fish diversity with environmental influencing factors in the Daqing River basin, China

IntroductionInvestigating and assessing native fish diversity and conducting regional assessments of threats are vital for the conservation of aquatic biodiversity. For this specific study, the Daqing River basin in China was chosen as the research area. Field surveys were carried out between 2018 and 2019, supplemented by a review of pertinent literature and other records.MethodsTo evaluate fish diversity, the following parameters are selected for analysis: Relative abundance (Pi), Frequency of occurrence (Fi), Margalef richness index (D) , Shannon Wiener diversity index (H′) , and Pielou evenness index (J′).Relative abundance levels are categorized as follows: dominant species make up more than 10%, common species account for 1%–10%, and occasional species constitute less than 1%. Canoco 5.0 software was utilized to conduct Redundancy Analysis (RDA) on the fish species composition and environmental factors at 34 sampling sites.ResultsThe findings revealed that 85 fish species have been recorded in the basin, consisting of 78 freshwater species categorized into 8 orders, 17 families, and 59 genera, among which Cypriniformes comprised 64% of the total catch. The low proportion of threatened and endemic species was an important feature of the fish diversity of the Daqing River. Despite the theoretical presence of 85 species, field surveys managed to collect only 42 species, of which 33 were confirmed as naturally occurring within the basin. The average values of Margalef richness index, 22 Shannon Wiener diversity index and Pielou evenness index were 1.72, 2.04 and 0.80 respectively, signifying a low fish diversity with relatively uniform distribution across the sampled regions.DiscussionThis study found that the community structure and diversity of fish were closely related to environmental factors, particularly water quality. Among these factors, ammonia nitrogen emerged as a significant determinant of fish diversity. High concentrations of ammonia nitrogen inhibit fish growth, endanger health, and can even lead to death. Additionally, dams and other water infrastructure also impact fish community structure and diversity. This baseline study can provide a basis for the protection and ecological restoration of fish resources in the Daqing River.