Level Of Threat Research Articles

Symbolic representation in policing: How officer characteristics shape police-suspect interactions

ABSTRACT Although extensive literature has focused on officer behaviour in police killings, little research has examined suspect behaviour during these incidents. Building on the theory of symbolic representation, this study investigates how officer characteristics influence suspect behaviour, specifically the use of threats. Analysing all confirmed police killing cases in the United States from 2014 to 2017, we find that Black officers’ presence is associated with reduced threat levels from Black suspects. However, this effect is absent in the interactions between White officers and White suspects. This article has implications for officer diversity in law enforcement and its impact on suspect cooperation.

Sustained Fishing Threatens Globally Protected Species: Insights from Digital Catch and Genomic Data

Small-scale fisheries capture a wide marine biodiversity including highly vulnerable and endangered species. Despite global conservation efforts, at regional scale, strategies to conserve threatened species are often constrained by poor biological information and a paucity of fisheries data to understand the level of threat posed to their populations. In Myanmar, devil and manta rays (mobulids) are caught for meat consumption and export of gill plates. The impact of this exploitation is not fully understood as mobulid rays, especially in this region, have been poorly studied, catch levels are under-reported and the fishery is unregulated. An accurate knowledge of the species composition and catch levels is an important first step to inform potential management and protection strategies for these species. This study is the first to combine digital catch recording alongside genomic tools to analyze mobulid rays caught in the Tanintharyi Region of south Myanmar. Our catch data analyses indicate a sustained catch and decrease in total landings of mobulids from 2016 to 2018 in Tanintharyi. We identified five species of mobulid rays, including the first record of M. tarapacana for Myanmar. Our analyses of population genomics provided evidence of structured populations of M. thurstoni and partial admixture in M. mobular. Our findings are highly valuable 1) contributing to fill the knowledge gap for these threaten species in the region, and 2) as the first documentation of sustained fishing pressure these rays are exposed to, and the species under potential fishing threat in the Tanintharyi Region, a high priority biodiversity hotspot.

Prioritizing future evidence needs for marine and freshwater mammal conservation action

AbstractMarine and freshwater mammals are increasingly threatened due to human activity. To improve conservation practice, decisions should be informed by the available evidence on the effectiveness of conservation actions. Using a systematically collated database of studies that test the effectiveness of actions to conserve marine and freshwater mammals, we investigated the gaps and biases in the available scientific evidence base. While there is a growing evidence base covering actions to address key threats (e.g. fisheries and bycatch) to marine and freshwater mammal populations, we identified large geographic and taxonomic biases. There was no relationship between the number of studies and marine mammal species per ecoregion and we found biases towards coastal areas of the Global North, with many regions and species having little or no evidence available. The number of studies per species did not correlate with (1) the threat level, (2) evolutionary distinctiveness or (3) the public ‘popularity’ of the study species. We also found a mismatch between actions tested and the actions suggested as needed in the International Union for the Conservation of Nature (IUCN) Red List. Several of these gaps and biases likely reflect the feasibility of researching marine mammal populations; many species can be difficult to access, with limited baseline information on populations and threats, and testing actions can require costly long‐term monitoring. Prioritizing the most cost‐effective conservation strategies for marine and freshwater mammal species will require a comprehensive evidence base on the effects of actions. Continuing to build the necessary baseline data, and focusing future research and funding towards the priority gaps identified in this study will be important to deliver this target.

Investigation of Military Spouse Suicide.

Recent statistics released by the Department of Defense have revealed increasing numbers of military spouse suicide. Although past qualitative research has suggested possible reasons for this phenomenon, there is a lack of wide-scale quantitative research regarding suicide within the military spouse population. To fill this gap, we aimed to examine military spouses' perceptions of suicide and their ability to access mental health care. We also aimed to explore the perceptions of sucide within various subgroups of military spouses. We recruited military spouse participants (officer and enlisted) through social media to complete a 12-item Likert survey focusing on their perception of suicide in the military spouse community, including the threat level of suicide, their own mental health status, barriers and stigmas to accessing mental health care, the view from the spouse's leadership, and the leading causes for military spouse suicide. A total of 141 military spouses participated in our study. Spouses' perception of the main threats to their mental health and suicides in their community were an ongoing sense of loss of control (27.7%), loss of identity (25.5%), difficulty accessing mental health services (19.1%), and fear of seeking mental health services (10.6%). Additionally, 28.3% of participants were moderately concerned, quite concerned, or extremely concerned about their mental health. The participants also believed that the threat of suicides in their community was moderately prevalent (35.5%), quite prevalent (17.7%), and extremely prevalent (5%). There was no difference between spouses of officers or enlisted service members, spouses of service members in the Army, Navy, or Air Force on perceptions of suicide risk prevalence, stigma, participants' concern for themselves, confidence in their ability to access resources, confidence in their ability to help another spouse, or the perceived importance military leaders place on preventing suicide. Spousal tenure was similarly not significantly related to any of the variables of interest. Our military spouse particpants reported elevated perceptions about the prevalence of suicide threats, concern for themselves, and stigma toward accessing mental health resources, as well as low perceived importance placed on spouse suicide prevention by military leadership. The spouses reported moderate confidence in their ability to help another spouse or access suicide prevention resources. Our participants also reported challenges in accessing mental health services and perceived a stigma associated with receiving counseling services. Continued focus and advocacy is needed to ensure military spouses receive the mental health support needed to prevent suicide within this population.

The Role of Data Science in Enhancing Web Security

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores the role of data science in enhancing web security by leveraging machine learning algorithms and advanced analytics to predict and identify potential attacks in real-time. The main objective is to demonstrate how data-driven techniques, including predictive analytics, anomaly detection, and behavioral analysis, can be integrated into existing security frameworks to reduce vulnerabilities and strengthen defenses against cyber threats. The research gap addressed by this study lies in the insufficient application of comprehensive, data-driven methodologies for threat detection and classification in web security. The problem gap is the absence of integrated frameworks that combine feature engineering, classification models, and anomaly detection for both known and unknown threats. This study bridges these gaps by employing a structured dataset of web interactions to model, detect, and predict security threats using advanced data science techniques. Using a dataset of simulated web traffic and previous attack records, this research applies data preprocessing, feature engineering, and machine learning classification models, such as decision trees and random forests, to predict threat levels and identify anomalies. Results show that machine learning models can effectively classify threat levels, with a threat classification accuracy of 80 percent. This study contributes to the field by demonstrating how data science can improve web security practices, offering a proactive approach to detecting and mitigating cyber-attacks.

MI5, the Security State and Communist Political Refugees from Nazism in Second World War-Era Britain: The Case of Gustav Beuer, 1938–1946

Abstract This article examines the British Security Service’s monitoring of Gustav Beuer, a Sudeten German communist and former member of the Czechoslovak parliament who arrived in London as a refugee in 1938 and set up home there until 1946. Although from a friendly country, Beuer was placed under continuous surveillance and was interned, together with five of his compatriots, in 1940–41. The article uses this case to challenge previous interpretations of MI5’s role in spying on communist refugees from Central Europe. It does so by taking a model developed by the German political scientist Matthias Lemke for analysing the widely varying levels of threat to democracy posed by exceptional security measures taken by liberal states in closely defined time-frames and applying this to Second-World-War Britain. Far from pursuing a linear strategy based on anti-communist prejudice alone, MI5 was obliged to muddle along in its policy towards Beuer and other communist refugees, assimilating unexpected triggers, shifting legal-bureaucratic frameworks, changes in wartime alliances and public opinion, and instances of ministerial action and inaction. The paradoxical result of this was to inject a certain amount of (unforeseen) pragmatism into MI5’s handling of ‘suspect’ refugees, while at the same time undermining its belief that covert fact-finding alone was enough to establish who was and who was not a threat. The article concludes that the self-doubts and unease within the British security state which are often attributed to intelligence failings and spy scandals in the early Cold War period had deeper roots in the Second World War era.

Metagenomics insights into bacterial diversity and antibiotic resistome of the sewage in the city of Belém, Pará, Brazil.

The advancement of antimicrobial resistance is a significant public health issue today. With the spread of resistant bacterial strains in water resources, especially in urban sewage, metagenomic studies enable the investigation of the microbial composition and resistance genes present in these locations. This study characterized the bacterial community and antibiotic resistance genes in a sewage system that receives effluents from various sources through metagenomics. One liter of surface water was collected at four points of a sewage channel, and after filtration, the total DNA was extracted and then sequenced on an NGS platform (Illumina® NextSeq). The sequenced data were trimmed, and the microbiome was predicted using the Kraken software, while the resistome was analyzed on the CARD webserver. All ecological and statistical analyses were performed using the. RStudio tool. The complete metagenome results showed a community with high diversity at the beginning and more restricted diversity at the end of the sampling, with a predominance of the phyla Bacteroidetes, Actinobacteria, Firmicutes, and Proteobacteria. Most species were considered pathogenic, with an emphasis on those belonging to the Enterobacteriaceae family. It was possible to identify bacterial groups of different threat levels to human health according to a report by the U.S. Centers for Disease Control and Prevention. The resistome analysis predominantly revealed genes that confer resistance to multiple drugs, followed by aminoglycosides and macrolides, with efflux pumps and drug inactivation being the most prevalent resistance mechanisms. This work was pioneering in characterizing resistance in a sanitary environment in the Amazon region and reinforces that sanitation measures for urban sewage are necessary to prevent the advancement of antibiotic resistance and the contamination of water resources, as evidenced by the process of eutrophication.

Assessing Geopolitical Risks and Their Economic Impact on the USA Using Data Analytics

Understanding geopolitical risks is a paramount aspect of examining the stability and resilience of national economies, specifically in today’s rapidly evolving global surroundings. Advanced analytics in the big data era open unparalleled avenues toward the quantification and comprehension of geopolitical risks on the performance of the economy of the United States. The prime objective of this study was to analyze the impact of geopolitical events on the U.S. economy, to identify key risk factors and their economic implications as well as propose strategies for mitigating adverse effects. Datasets used in this exploration were collected from different reliable sources to assess sources of geopolitical risk data and their economic impact on the U.S. First, data on geopolitical risk were collated from a combination of real-time news reports, government databases, and international organizations involved in monitoring geopolitical events. Key sources for this included GDELT news and sentiment data, official reports from U.S. government agencies such as the Department of State and the Department of Defense about foreign policy, conflict, and security, while major financial news outlets like Bloomberg and Reuters provided moment-by-moment coverage of events in the geopolitical sphere. We applied the Geo-Risk-Regressor model, a form of multimodal design to predict geopolitical threats arising from economic indicators, real-time news sentiment, and government reports on geopolitical events. The Geo-Risk-Regression Model is an integrated set of machine learning algorithms, from time-series and NLP to econometric regression, on structured and unstructured data comprising economic indicators, real-time news sentiment, and government reports on geopolitical events. A rigorous structured procedure was followed in implementing the Geo-Risk-Regressor to analyze the economic impact of geopolitical risks in the U.S. To assess and evaluate the performance of the algorithms, two key performance evaluation metrics were utilized MSE & R-squared. Among all the models, the best performance was that of XG-Boost; it had the lowest MSE and highest R². Thus, XG-Boost is the best model fitted for the prediction of GPRD_THREAT, probably because of its robust optimization and also its capability to capture a lot of complicated patterns in data. The geopolitical threat level perceived using the proposed models will enable business organizations in the USA to identify and manage risks that may affect the operations of the business organizations. Companies can, therefore, understand factors that contribute to risk and develop contingency plans, enabling them to take proactive measures to mitigate negative impacts from geopolitical events. Predictive models will help businesses in America estimate the potential risks to their supply chains and create strategies for mitigating any disruptions that might come through geopolitical events.

Experimental biomechanical investigation of surrogate headforms equipped with Patka helmet against high-velocity ballistic impact

High-velocity bullet impact (velocity: 700 ± 15 m/s) is a significant concern during asymmetric warfare. Ballistic helmets for high-velocity bullet impact and associated biomechanical responses of the headforms are actively sought. Further, data on the performance of ballistic helmets against high-velocity impact are not available in the literature. Toward this end, we have evaluated the performance of Patka (helmet) against high-velocity bullet impact. Patka was mounted on various headforms to assess the perforation of the Patka by the bullet and to measure various biomechanical parameters such as back face deformation (BFD), head kinematics, brain simulant pressures, and neck loads. Bullet impact in front and oblique (30° right to the sagittal axis) orientations were considered. Our results suggest that the Patka was effective in mitigating the high-velocity bullet impact. Perforation of the Patka by the bullet was not observed. BFD in the witness material was negligible (<1 mm). Measured head kinematics and brain simulant pressures were comparable to the quantities typically reported during low- and medium-velocity bullet impacts. Further, most of the biomechanical responses, except angular acceleration and neck force, were below 50% injury risk for mild traumatic brain injury (mTBI). Despite these encouraging trends, the shattering of bullets into fragments was also observed. These fragments traveled a considerable distance at reasonable velocities. This work provides unique insights regarding the performance of Patka and associated biomechanical responses of the headforms against high-velocity bullet impact, with implications in the design and evaluation of futuristic ballistic helmets. TERMINOLOGIES Ballistic threat level: Ballistic threat levels are defined based on the impact velocity of the bullet. Different types of bullets are proposed to achieve the desired impact velocities corresponding to different ballistic threat levels. Ballistic helmet: Ballistic helmet is a type of protective headgear designed to protect the wearer head from potentially fatal impacts during combat operations. Ballistic performance: Ballistic performance of the helmet is evaluated based on the perforation resistance capacity of the helmet and back face deformation (BFD) of the helmet. Back face deformation (BFD): Back face deformation (BFD) of the helmet refers to the localized dent or bulge in the inner layer of the helmet due to ballistic impact. BFD is measured as a depth of indent in the witness material (e.g., Roma Plastilina #1 clay) of the cruciform cavity-based headform. Headform: Headform is a model of the human head replicating a few of the anatomical and material aspects. Headforms can vary in design and complexity based on the intended applications. Patka: Patka is a protective headgear designed to provide ballistic protection to the head against high-velocity threat (impact velocity: 700 ± 15 m/s). Patka is made up of hardened (armour) steel material. Head kinematics: Head kinematics refers to the motion of the head typically measured in terms of linear acceleration and angular velocity. Neck loads: Neck loads refer to the forces and moments experienced by the neck during a static or dynamic event. Brain simulant pressure: Brain simulant pressure is a hydrodynamic pressure generated within the brain simulant.

Population density estimates and conservation status of Aegla jacutinga Marçal &amp; Teixeira, 2020 (Decapoda: Anomura: Aeglidae) from the type locality in Brazil

Abstract Anomuran crabs of the genus AeglaLeach, 1821 are endemic to temperate and subtropical regions of South America and are commonly found in streams and rivers, where they are generally restricted to healthy environments because they are sensitive to environmental changes. The genus presents worrying data regarding its conservation, like the many Brazilian freshwater species classified as being at some level of threat of extinction. We inventoried unexplored watersheds, estimated the population density of Aegla jacutinga Marçal &amp; Teixeira, 2020 in its type locality, updated information on the distribution of the species, and assessed its conservation status using IUCN criteria.

An Improved Alert System for Emerging Infectious Diseases

This Viewpoint investigates the lack of a comprehensive infectious diseases alert system in the US and proposes solutions for such a system, including linking changing threat levels to specific response actions.

Population age structure and conservation status of Ferula sinkiangensis (Apiaceae) in Xinjiang, China

Abstract Ferula sinkiangensis K.M. Shen is a threatened medicinal plant endemic to Xinjiang, China, with a small population size and a narrow distribution range. We assessed the status of this species with respect to its population age structure, the level of threat and extinction risk. Only one population remains, in Yining County, Xinjiang. We conducted field surveys of the population in 2022 and 2023, counting 2,033 and 1,515 individuals, respectively, in 144 sample quadrats. We assessed the age structure of the population by counting the number of basal leaves of each individual. The frequency distribution had an inverted J-shape, indicative of a relatively stable age structure. However, the number of mature individuals was small, raising concerns about the risk of genetic drift and inbreeding. This species is also threatened by habitat destruction and inappropriate collection practices. We recommend that F. sinkiangensis is categorized as Critically Endangered on the IUCN Red List on the basis of criteria B2ab(iii), C2a(i) and D.

Sentiment analysis in infodemic management: leveraging the eppm Risk communication model

Infodemics are the rapid spread of false or misleading information related to public health emergencies, often through digital platforms. They can cause confusion, fear, and even harm to public health. This study investigates the application of sentiment analysis for infodemic management during disease outbreaks. Leveraging the Extended Parallel Process Model (EPPM) of risk communication, the research aims to categorize rumors based on their perceived threat level (high, medium, or low). Machine learning is employed to analyze infodemic text data collected from two Nigerian states (Oyo and Bauchi) to assess threat appraisal according to the EPPM model. The findings can inform targeted interventions for effective infodemic management before, during, and after outbreak of diseases.

Natural fiber reinforced polymer composites for ballistic protection: Design, performance, and challenges

The increasing demand for sustainable and lightweight materials in defense and security applications has driven the development of natural fiber-reinforced polymer composites (NFRPCs) for ballistic protection. This paper explores the potential of NFRPCs in ballistic armor, focusing on material design, performance evaluation, and the challenges encountered in their application. Natural fibers, such as jute, flax, and hemp, offer significant environmental and weight advantages over traditional synthetic fibers while providing acceptable impact resistance when embedded in polymer matrices. The design considerations for NFRPCs-based ballistic protection systems are examined, highlighting the interplay between fiber type, matrix selection, and composite architecture. The ballistic impact behavior of these composites is assessed through various testing methodologies, emphasizing their energy absorption, deformation mechanisms, and multi-hit performance under different threat levels. Furthermore, this study presents an analysis of the key challenges—ranging from fiber variability and moisture absorption to the optimization of mechanical properties—that hinder widespread adoption in high-performance armor applications. The results underline the potential of NFRPCs as a cost-effective, eco-friendly alternative for protective armor while emphasizing the need for further research to overcome performance limitations. Ultimately, this work aims to provide a foundation for advancing sustainable ballistic protection materials through innovative design and testing strategies.

Securing UAV Communications with Homomorphic Encryption and Key Regeneration: A Model Checking Approach

Unmanned Aerial Vehicles (UAVs) are at the centre of multiple applications across various fields, including delivery, disaster response, and surveillance. However, its use of wireless communications exposes them to security risks such as information distortion and interception. The paper presents a security model based on Homomorphic Encryption (HE) and Key Regeneration (KR) with the incorporation of Model Checking to enhance UAV communication security. Our system employs homomorphic encryption that resolves the data security problem by ensuring data security at the transmission stage, where information is processed at the receiver end without decryption, thus maintaining privacy and the data processing policy at the UAV end. Furthermore, the key regeneration protocol updates the encryption keys regularly, thus reducing the risk denial window and impact in case of key compromise. The security of the protocols is proven through model checking that establishes the system status operation through a systematic study, as per the provided security requirements. The simulation results indicate certain trade-offs in the system with performance degraded in encryption time and computations, unlike the existing system. The system enhancement outweighs the performance, as indicated by the minimized level of threat and integrity violation. Therefore, integrating HE and KR can secure UAV communication in the presence of a smart and capable adversary, making the entire system robust and dependable in the line of operation.

БЕЗПЕЧНИЙ ДОСТУП ДО СЕРВЕРІВ ІНФОРМАЦІЙНИХ СИСТЕМ, ЗАБЕЗПЕЧЕНИЙ ML-МОДЕЛЛЮ ДЛЯ БЛОКУВАННЯ ШКІДЛИВИХ ЗАПИТІВ

The article presents a detailed exploration of using the k-Nearest Neighbors (KNN) algorithm to classify and identify various types of cyberattacks, particularly SQL Injection (SQLi) attacks, within SCADA (Supervisory Control and Data Acquisition) systems. This work addresses the need to enhance server infrastructure security in SCADA systems by mitigating the risks posed by harmful requests, such as SQL injections. SCADA systems are crucial in managing industrial processes, making their servers prime cyberattack targets. Attackers often exploit vulnerabilities in server applications by injecting malicious requests through input fields or URLs, potentially gaining access to sensitive data or disrupting system operations. To address this issue, the study proposes a machine learning-based approach using the k-nearest neighbors (KNN) algorithm to detect and block harmful SQL requests. The KNN algorithm is employed to classify and identify different types of cyberattacks by comparing new attack attempts with previously observed attack patterns. By analyzing specific attributes related to each attack, the KNN method evaluates the level of threat based on proximity metrics. The proposed approach helps classify SQL injection attempts, which involve manipulating SQL code to bypass authentication or extract unauthorized data. The study demonstrates how KNN can effectively distinguish harmful SQL requests from benign ones by calculating the Euclidean distance between the new attack and historical cases. Furthermore, the article emphasizes the importance of implementing rapid and accurate detection methods for protecting server infrastructure in industrial environments. The KNN algorithm, in this context, offers a flexible and efficient solution as it adapts to various attack scenarios, improving the overall resilience of SCADA systems to cyber threats. The study’s findings contribute to the ongoing efforts in cybersecurity, focusing on integrating machine learning models to strengthen the protection of critical assets in industrial control systems. This work aims to develop protection tools for server-based industrial control systems used in SCADA systems against dangerous requests based on SQL injections, using an ML-trained model for blocking harmful requests through the k-nearest neighbors method.

Molecular evidence reveals fish-pathogenic Aphanomyces spp. (Oomycetes: Saprolegniales) on new host of the threatened seasonal cyprinodont fish Nothobranchius spp. (Teleostei: Cyprinodontiformes) in south-eastern Democratic Republic of Congo

The Aphanomyces species pose an important global threat and cause damage to aquaculture and agriculture by infecting aquatic animals and plants. Several subpopulations of the seasonal cyprinodont fish genus Nothobranchius with lesions suggestive of infection with Aphanomyces invadans, were observed in ephemeral wetland habitats of the upper Congo drainage in the south-eastern Democratic Republic of Congo. Molecular analyses on samples collected in 2023 revealed evidence of fish-pathogenic oomycetes Aphanomyces invadans and A. laevis, for the first time in the host of fish genus Nothobranchius spp. All Nothobranchius fishes in the upper Congo drainage are subject to a high level of threat and belong to one of the threatened Red List categories, due to habitat degradation of seasonal wetlands. These fishes complete their seasonal life cycle in ephemeral natural habitats. The latter makes them highly vulnerable, as such wetland habitats are often degraded by the cumulative effect of multiple human stressors which include: the cultivation of wetlands for agriculture, the abstraction of water, the expansion of urban areas and the pollution load. Evidence of infection by oomycete pathogens documented in this study, represents a critical additional threat to the unique seasonal aquatic biodiversity of the region.

The impact of violence prevention behaviour on threats and violence among child welfare social workers

ABSTRACT The aim of this study was to determine whether Violence Prevention Behaviour (VPB) at coworker-, supervisor-, and top-management levels is related to risk of exposure to threats and violence among child welfare social workers. The study design was a two-wave prospective questionnaire survey data of child welfare workers in Norway with a time-lag of six months between baseline (N = 678) and follow-up (N = 424), Overall cohort response rate was 34 per cent. Mean exposure to threats and violence was 10.08 (SD = 14.83) events at baseline and 8.75 (SD = 13.61) events at follow-up. A zero-inflated negative binomial model showed that Top management level VPB was associated with a significantly lower risk of exposure to threats and violence (β = −.22, p < 0.01) at baseline, and follow-up (β = −.19, p < 0.01). VPB at the coworker level was associated with higher levels of threats and violence at baseline (β = .19, p < 0.01), but not at follow-up. Supervisor VPB was not associated with levels of threats or violence. Efforts from top management to prioritise safety in decisions, and clearly communicating these safety prioritisations to staff, will strengthen VPB and thereby be beneficial for reducing the occurrence of threats and violence among child welfare social workers.

Communicating State Repression to the International Community: A Case Study of How China Frames Its Policies in Xinjiang Online

Abstract Regimes and their proxies seek to legitimize overt state repression abroad to avoid economic and reputational costs. Yet, few scholars have studied the international dimension of repression image management. I examine how countries communicate their repressive actions to the international community depending on the audience. Framing repression as a legitimate response to a credible threat (threat strategy) is more likely when communicating with countries facing higher levels of domestic threat. But due to in-group favoritism, when addressing in-group audiences of the repressed, governments are more likely to frame repression as necessary to protect the repressed (benevolent rule strategy). To test these claims, I collect 82,011 tweets about activities in Xinjiang published by eighty-eight Chinese diplomatic accounts from 2014 to 2020. The results suggest that regimes change their repression image management strategies depending on the audience. Chinese government accounts in countries with higher levels of domestic conflict are more likely to use the threat strategy than those in countries with lower levels of conflict, while those in countries with a similar in-group to Xinjiang (Muslim countries) are more likely to use the benevolent rule strategy than those in out-group states. This expands our understanding of the communication strategies of human rights-abusing regimes.

The impact of pandemic threats on intertemporal choices in Chinese and Americans

Intertemporal decisions are crucial to survival, especially when humans are exposed to ecological threats. However, it remains unclear whether and how pandemic threats impact intertemporal choices across cultures. We conducted two studies in two cultural contexts (N = 1180). Study 1a found that Chinese who perceived more pandemic threats showed higher temporal discounting rates (i.e., preferred immediate smaller over delayed larger rewards), and this relationship was mediated by negative emotions. Study 1b, using threat priming, revealed that Chinese participants primed with a high-threat condition (depicting a pandemic threat) showed higher temporal discounting rates compared to those primed with a low-threat condition (describing the flu). Differently, Study 2a revealed that perceived pandemic threat levels of Americans did not directly predict temporal discounting rates. Using similar threat priming, Study 2b further confirmed that Americans in the high-threat priming condition showed no significant differences in temporal discounting rates compared to those in the low-threat priming condition. The current research deepens the understanding of the cultural difference in the impact of pandemic threats on intertemporal decision-making and further informs the development of interventions that promote individual future-oriented thinking in the face of pandemics.