Risk Analysis最新文献

Nowadays, factories located in COVID-19 infected countries/regions are facing random outbreaks. If blockchain is adopted, then the outbreaks can be known immediately and emergency production shifting can be enabled, although high crash cost will be incurred. Otherwise, production delay will become inevitable. We therefore formulate the tradeoffs among the high crash cost, the benefit from quick production, and the efficiency loss because of supply chain decentralization in a global brand's blockchain adoption decisions. We show that, in the presence of supply chain competition, the global brand will be benefited from blockchain adoption when the competition intensity degree is high, the crash cost is low, and the probability of COVID-19 outbreak is high. We then verify the robustness of the main findings by studying the impact of the global brand's risk attitude, its overestimation of production delay, and the unexpected production delay in the low-risk areas. In addition, we examine the social welfare and find it can also benefit from the global brand's blockchain adoption but the consumer surplus cannot.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatment outcomes.

In light of recent events related to national elections in the United States, safeguarding the security and integrity of forthcoming elections stands as a critical national priority. Elections equipment in the United States constitutes critical national infrastructure, and its operation relies on poll workers, who are trusted insiders. However, those insiders may pose risks if they make mistakes with detrimental consequences or act with malice. This research analyzes a large dataset of 2213 responses obtained from a survey of poll workers and potential poll workers in 13 states. The survey includes the Security Behavior Intentions Scale, which has been previously established and validated in the security literature. We use the responses to assess poll workers' intentions of complying with established security-related practices. We develop a novel model using information theory to examine potential weaknesses in security behaviors and identify poll worker security practices to improve to ensure the integrity of our elections. We also recommend action items and countermeasures for states and localities based upon this empirical analysis.

Over the last years, infectious diseases have been traveling across international borders faster than ever before, resulting in major public health crises such as the Covid-19 pandemic. Given the rapid changes and unknown risks that mark such events, risk communication faces the challenge to raise awareness and concern among the public without creating panic. Drawing on the social amplification of risk framework-a concept that theorizes how and why risks are amplified or attenuated during the (1) transfer of risk information (by, for instance, news media) and (2) audiences' interpretation and perception of these information-we were interested in the portrayal of risk information and its impact on audiences' risk perception over the first wave of the Covid-19 pandemic in Germany. We therefore conducted a quantitative content analysis of a major public and private television (TV) newscast (N = 321) and combined it with survey data (two-wave panel survey, t1: N = 1378 and t2: N = 1061). Our results indicate that TV news (as a major information source at that time) were characterized by both risk-attenuating and risk-amplifying characteristics, although risk-amplifying attributes were particularly pronounced by the private TV newscast. Notably, those who only used private TV news between both waves showed the highest perceived severity at time 2. However, the interaction effect of time and use of public and/or private TV news was only significant for perceived susceptibility. Overall, more research is needed to examine the effects of different types of media and changes in risk perceptions over time.

In this work, we introduce JointLIME, a novel interpretation method for explaining black-box survival (BBS) models with endogenous time-varying covariates (TVCs). Existing interpretation methods, like SurvLIME, are limited to BBS models only with time-invariant covariates. To fill this gap, JointLIME leverages the Local Interpretable Model-agnostic Explanations (LIME) framework to apply the joint model to approximate the survival functions predicted by the BBS model in a local area around a new individual. To achieve this, JointLIME minimizes the distances between survival functions predicted by the black-box survival model and those derived from the joint model. The outputs of this minimization problem are the coefficient values of each covariate in the joint model, serving as explanations to quantify their impact on survival predictions. JointLIME uniquely incorporates endogenous TVCs using a spline-based model coupled with the Monte Carlo method for precise estimations within any specified prediction period. These estimations are then integrated to formulate the joint model in the optimization problem. We illustrate the explanation results of JointLIME using a US mortgage data set and compare them with those of SurvLIME.

As synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists' research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM-based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

This study examined the paths through which the news frames of particulate matter (PM) influence support for governmental policies aiming to address PM. It also explored the mediating effects of anxiety and risk perception in the relationship between news frames and policy support, as well as the moderating effects of media exposure and psychological distance on the PM news framing effect. Based on an experimental design (N = 676), two groups of news frames were prepared for comparison: a narrative frame group and a numerical frame group. The results showed no significant differences in anxiety or risk perception between the two groups. Further, no significant mediating effects of anxiety or risk perception were found in the process through which PM news frames influence support for governmental policies. However, media exposure significantly moderated the effect of the narrative frame: With high (low) media exposure, the narrative frame positively (negatively) influenced policy support through risk perception. Moreover, when the level of psychological distance was low, the narrative frame positively influenced policy support through risk perception. This study contributes to the literature on news framing of PM by integrating cognitive and emotional mechanisms in forming policy attitudes.

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

The communication of extreme weather forecasts (e.g., heatwaves and extreme precipitation) is a challenge for weather forecasters and emergency managers who are tasked with keeping residents safe during often unprecedented situations. Weather models have inherent uncertainty, and the ability for potentially life-saving information to get to the people who need it most (e.g., those who need to evacuate) remains a challenge despite the proliferation of digital access to information and social media sites like Twitter. It is also unclear the role that community-based organizations and super-local governmental entities play or may play during weather events in transmitting weather information and providing assistance. In New York City, there remains robust inequality, with communities that are historically disadvantaged often suffering the highest number of deaths and level of destruction following weather events. Results from interviewing 26 New York City community leaders suggest that local organizations often act as intermediaries, passing on official weather information to members of their audience, regardless of the mission statement of their organization. Common challenges for communities in responding to extreme weather include lack of access to information, language barriers, and insufficient resources. Considerations for future weather communication strategies are discussed.

The proliferation of inaccurate and misleading information about COVID-19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID-19 protective behaviors in an armed-conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021. Structural equation modeling tested hypotheses generated by the PADM. The findings indicate that the infodemic reduces the likelihood of individuals adopting protective measures against COVID-19. Surprisingly, official announcements by accountable authorities do not moderate the relationship between the infodemic and protective responses. These results highlight the need for further research on resilience in armed-conflict countries. This study contributes to understanding armed-conflict countries' unique challenges in combating health crises. Addressing the infodemic and promoting accurate information is crucial in enhancing protective behaviors and mitigating the negative impact of misinformation. Policymakers and public health authorities can utilize these insights to develop targeted interventions and communication strategies that ensure accurate information dissemination and encourage the adoption of adequate protective measures.