Risk Analysis最新文献

Risk factors at different stages of COVID-19 may interact with each other, forming a risk network. Identifying the key risk factors within this network and their interrelationships is crucial for reducing the overall risk of COVID-19. We constructed three Bayesian Belief Network (BBN) models by combining data-driven approaches with expert validation. Using the Tree-Augmented Naive Bayes (TAN) algorithm, we developed the INFORM COVID-19 Risk BBN model and the COVID-19 Regional Safety Assessment BBN model. The joint BBN model was established using the Greedy Thick Thinning (GTT) algorithm. Parameter learning was performed through maximum likelihood estimation. Expert validation, 10-fold cross-validation, and model performance metrics were employed to comprehensively assess the overall performance of the models. Additionally, mutual information analysis and sensitivity analysis were used to explore the importance of risk factors at each stage and their interdependencies. "INFORM Vulnerability" and "INFORM Lack of Coping Capacity" were identified as the two key risk factors influencing the risk of early outbreak. In the mid-to-late stages of the pandemic, "Emergency Preparedness" and "Monitoring and Detection" had the greatest impact on regional safety and control measures. Furthermore, the joint BBN model indicated that the most important risk factors affecting the overall COVID-19 risk were "Lack of Coping Capacity," "Government Risk Management Efficiency," and "Regional Resiliency," while the influence of other variables was relatively minor. The main contribution of this study lies in identifying the key risk factors at different stages of the pandemic and their interdependencies, providing policymakers with valuable insights for the rational allocation of limited health resources and the formulation of appropriate and effective prevention and control policies.

Recent government and commercial initiatives have pushed for the use of the automated, artificial intelligence (AI)-based, analysis of cyber threat intelligence. The potential bias that might be present when evaluating threat intelligence coming from human and AI sources has to be better understood before deploying automated solutions to production. We present a controlled experiment with $n=57$ master students who had a mix of experience in security and machine learning to measure the bias introduced by the source of intelligence (human vs. AI). Each participant analyzed eight threat intelligence reports from the Dutch National Cyber Security Center where the source of the final recommendation was manipulated as for coming from a human expert or an AI algorithm. Our findings revealed that participants tended to disagree with the recommendation when it was coming from AI. While expertise on ML did not have any impact, we found that participants with more security expertise tended to agree with the recommendation. In contrast, we found that the perceives bias was statistically equivalent (TOST) whether the recommendation was coming from a human or from an AI. The only (expected) factor which had an impact on perceived bias was when participants disagreed with the recommendation (irrespective whether it was human or AI). These results provide insight on the possible impact of introduction on AI on rank-and-file Tier 1 SOC analysts. The generalization of our results to professional practice requires more experiments with experienced security professionals.

The deployment of radiological dispersal devices (RDDs) via drones presents a novel security challenge, necessitating advanced tools for consequence assessment and response planning. We developed an integrated framework combining physics-based dispersion modeling, constrained optimization, and machine learning to evaluate such threats. Using a Monte Carlo approach, 2000 synthetic scenarios were generated incorporating five radionuclides (Cs-137, I-131, Co-60, Sr-90, and Am-241), meteorological variability, and geospatial risk zones. A constrained optimization routine based on the Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm with bound constraints (L-BFGS-B) identified adversarial scenarios that maximize contaminated area (>10 km²) while minimizing energy use and detection risk, revealing nonlinear trade-offs between dispersal effectiveness and operational stealth. Consequence modeling with Health Physics Code (HotSpot) and Java-based Real-time Online Decision Support system (JRODOS) showed systematic differences, with HotSpot predicting higher total effective dose (TED) and time-integrated air concentration (TIAC). I-131 posed the greatest acute thyroid risk, whereas Am-241 dominated long-term exposure. Protective action analysis demonstrated that reinforced sheltering reduces cumulative dose by up to two orders of magnitude compared to outdoor exposure. Finally, the machine learning framework achieved accurate and rapid predictions (R² = 0.975), with distance as the dominant predictor. These findings provide actionable guidance for emergency preparedness against drone-based RDD threats.

Construction vehicles and equipment are a vital resource for all construction projects, with its demand expected to increase alongside technological advancements. While the use of such equipment reduces manual labor, it also introduces new risks, potentially leading to accidents. This study quantitatively analyzes the likelihood of accidents by examining utilization rate, subcontractor types, and construction costs. A regression-based prediction model for accidents involving construction equipment is proposed, utilizing data augmentation techniques with multivariate normal and Poisson distributions to improve prediction accuracy. The study is structured around three main steps: (i) Data collection and classification, (ii) calculation of hourly operating costs (HOC) and construction costs, and (iii) data augmentation and regression analysis. Regression analysis showed high R² values exceeding 0.6 for seven types of equipment, with loaders, bulldozers, and air compressors as exceptions. Although dump trucks had the highest frequency of fatalities, the prediction model identified excavators as having the highest predicted fatality count in the case study. The proposed model emphasizes safety management by categorizing risk groups based on operating costs and construction costs. It also offers a practical process for field application, providing a valuable tool for developing regulations and making investment decisions related to safety management in construction equipment.

Floods remain one of the most devastating climate-related disasters worldwide, and their increasing frequency in South Asia has posed severe challenges for community resilience and disaster management. In Pakistan's Indus River plains, recurrent flooding continues to displace millions, underscoring the urgent need to understand psychosocial and digital dimensions of disaster preparedness. This study examines how flood-prone individuals utilize risk awareness, social support, and social media to enhance their coping appraisal and engage in collective action. Grounded in protection motivation theory (PMT), we have built a three-way interaction research model to examine how social media apps influence social support to affect the relationship between risk awareness and coping appraisal in times of flood. We collected data from perennial flood-prone inhabitants of the Indus River plains. AMOS 24 and SPSS 23 were used to analyze the collected data. Results revealed that risk awareness significantly enhances coping appraisal, which in turn strengthens collective action tendencies. This study found that social support moderates the relationship between risk awareness and coping appraisal, with stronger effects at higher social support levels. The three-way interaction analysis revealed that social media information sharing amplifies the impact of social support on the relationship between risk awareness and coping appraisal, demonstrating the fostering role of digital communication in disaster resilience. These findings underscore the synergistic impact of social support and digital platforms in fostering adaptive behaviors, offering crucial insights for disaster risk management practitioners, policymakers, and humanitarian agencies working in flood-prone regions. Ultimately, this study provides a framework for integrating social resources and digital tools into localized flood risk reduction strategies.

This study examines how question order influences responses in multidimensional risk perception measurement. Through a randomized between-subjects experiment (N = 1352) manipulating the sequence of risk perception dimensions, we identified systematic question order effects. When a general risk question followed specific dimensional questions, responses showed significant assimilation effects (i.e., general risk aligned more closely with preceding specific dimension ratings). Consequence dimension responses (severity, affect) showed assimilation effects when preceded by probability dimensions (exposure, susceptibility), while probability dimensions remained stable regardless of ordering. Within subdimensions, severity ratings were influenced by preceding affect questions, and susceptibility ratings were influenced by preceding exposure questions, both displaying assimilation patterns. Testing how individual differences in cognitive sophistication moderate susceptibility to order effects, contrary to our predictions, we found that individuals higher in analytical thinking style demonstrated stronger order effects for general risk questions than those lower in analytical thinking. These findings reveal an asymmetrical pattern where judgments requiring more analytic specificity tend to anchor evaluations that are relatively global, affective, or self-focused.

There is a lack of rigorous studies addressing the theory life cycle model in disaster management. Thus, this study aimed to review the theory life cycle to improve disaster management practices. The study employed a systematic literature review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A reductionist model was proposed, including (1) theory inception, (2) theory scrutiny, and (3) theory termination (X) or establishment (O). This model was applied to four theories: suicide rate (X1), risk perception (X2), redundancy (O1), and all hazards (O2). In pursuing the reductionist model, the field must consider disaster characteristics, the advantages and disadvantages of various theories, the changing environment, a hybridization perspective, emergency education and training, and continuous improvement. This study emphasizes the question of adaptive relevance more than previous research.

Some outcomes in medical and public health research, as well as clinical practice, must rely on patient reports and may be influenced by the prior knowledge of the patient. During the early months of the SARS-CoV-2 epidemic, changes in the sense of smell and taste were widely reported as a distinctive aspect of the new respiratory contagion. Using a Rubin Model of causal inference and data from a California Department of Public Health-sponsored survey of California farmworker health, we estimate that approximately half (56.5%) of infected patients reporting olfactory changes after a diagnosis of Covid-19 would not have reported olfactory changes if not made aware of their Covid-19 infection. The observations support a similar conclusion with respect to Covid-19-related changes in the sense of taste.

This paper examines the rationality of elite bunker building as a response to anticipated societal collapse. Indeed, the phenomenon of "prepping" for "the Event" can be framed as self-insurance and relies on a transactional view of humanity, if one is to ensure the control of a compound and fight off potential assailants. We draw on economic decision modeling to analyze how the necessity of internal control by the leader, resentment, or the perception of potential loot by outsiders interact with fortification strategies. We introduce a "Machiavelli index" to represent hostility and show that excessive investment in defense can be counterproductive and provoke attack. Maximum bunkerization may not be optimal compared to a degree of cooperation, redistribution, and efforts to reduce perceived inequality. Survival in the end times may depend less on walls and more on legitimacy, reciprocity, and strategic restraint.

Stripe graphs have emerged as a popular format for the visual communication of environmental risks. The apparent appeal of the format has been attributed to its capacity to summarize complex data in an eye-catching way that can be understood quickly and intuitively by diverse audiences. Despite the growing use of stripe graphs among academics and organizations (e.g., Intergovernmental Panel on Climate Change [IPCC]) to communicate with both lay and expert audiences, there has been no reported empirical assessment of the format. Hence, it is not clear to what extent stripe graphs facilitate data comprehension and influence risk perceptions and the willingness to engage in mitigation actions. To address these knowledge gaps, we conducted two studies in which lay participants saw "climate warming" stripe graphs that varied in color and design. We found no evidence that traditional stripe graphs (i.e., unlabeled axes), irrespective of the stripe colors, improved the accuracy of estimates of past or predicted global temperature changes. Nor did the traditional stripe graph influence risk perceptions, affective reactions, or environmental decision-making. Contrary to expectations, we found that viewing (cf., not viewing) a traditional stripe graph led to a lower willingness to engage in mitigation behaviors. Notably, we found that a stripe graph with date and temperature labels (cf., without labels): (i) helped participants develop more accurate estimates of past and predicted temperature changes and (ii) was rated more likable and helpful. We discuss how these and other findings can be utilized to help improve the effectiveness of stripe graphs as a risk communication format.