Risk Analysis最新文献

This study introduces a new probability model for the risk priority number (RPN) in Failure Mode and Effects Analysis (FMEA), addressing limitations of the traditional RPN calculation, which assumes independence among severity, occurrence, and detection scores. Leveraging sufficient statistics within a Bayesian framework, the proposed model captures the inherent dependencies among these components, providing a more realistic and flexible representation of risk. Simulation studies validate the estimator's superior accuracy and stability, while empirical analyses on both AI risk assessment and gas refinery fire risk data sets demonstrate its effectiveness and adaptability across diverse domains and sampling strategies. Model comparisons using p-values and the Akaike information criterion (AIC) confirm the new model as the best fit for categorical risk data, aligning naturally with our theoretical approach. The results suggest that this new model enhances the reliability and interpretability of FMEA risk assessments, providing a powerful tool for decision making and risk mitigation in complex safety-critical systems.

Most nations across the globe have already embraced climate legislation to tackle the challenge of climate change. This article considers the role of country risk (i.e., economic risk, financial risk, political risk, and climate physical risk) in affecting the relationship between climate mitigation legislation (CML) on climate mitigation innovations (CMIs) using a panel of 130 countries from 1995 to 2022. The findings show that CML generally promotes CMI. However, moderating effects reveal that country risk can weaken the positive impacts of CML on CMI, underscoring the importance of integrating risk management into legislative frameworks to drive CMI. Asymmetry checks show that the direct and moderating effects are more pronounced in countries with greater CMI, suggesting that greater CMI requires stronger risk mitigation. Heterogeneity analysis reveals the moderating effect of risks on the impact of CML on CMI differs significantly between developed and developing countries, with developing countries facing a more urgent need for climate risk management.

Current assessments of port resilience primarily focus on the risks affecting its operations, often neglecting the ripple effects across different subsystems within a port. In multimodal container ports, these sub-systems include liner shipping, feeder shipping, railways, and trucking. Moreover, prevailing research predominantly addresses port resilience from a macro perspective without detailing micro-level operational concerns. This article proposes a new integrated methodology that not only considers but also quantifies the ripple effects across different multimodal sub-systems and their impact on overall port resilience. It employs real operational and accident data to assess the resilience of a multimodal container port under different disruption scenarios, hence providing valuable insights into preventing systemic failures through targeted interventions at the subsystem level. The proposed methodology comprises three principal components: a system dynamics (SD) simulation that integrates variables and factors affecting port resilience, a resilience analysis model that converts system performance into a resilience metric based on three fundamental criteria, and a comprehensive port system resilience assessment utilizing Evidential Reasoning (ER). Each step, from the detailed simulation model reflecting micro-level mechanisms to aggregating information across subsystems, builds toward determining the port's overall resilience. Multiple disruptive scenarios are designed and derived from historical failures and field investigations to validate the effectiveness of the proposed methodology. The results demonstrate that the proposed approach effectively assesses port performance under disruptions, identifies critical subsystems, and supports timely recovery strategies. Applicable to other port systems, this approach offers essential insights for improving long-term resilience in container port operations.

Workforce reductions, such as those implemented by the Department of Government Efficiency, can have far-reaching effects that extend beyond immediate job losses. This study employs a systems-based modeling approach, combining traditional Input-Output (IO) analysis with the inoperability Input-Output Model (IIM), to investigate how staffing cuts impact economic activity and erode institutional functions across interconnected sectors. The study reveals that reductions in federal staff have a significant impact on industries that rely heavily on government contracts and infrastructure, including aerospace, transportation, and high-tech services. These disruptions create ripple effects throughout supply networks and regional economies, resulting in delays, cancellations, and reduced operational capacity. Notably, the extent and pattern of losses identified here align with findings from independent reports, which highlight hidden costs such as declines in productivity, contract terminations, and maintenance backlogs that often offset the expected savings from workforce reductions. Unlike models that only focus on output, the IIM framework captures functional degradation, providing a more accurate breakdown of impacts on various economic sectors. These findings underscore the limitations of cost-cutting measures that overlook systemic interdependencies, highlighting the need for policies that strike a balance between fiscal objectives and institutional resilience. An adaptive, risk-aware approach to workforce planning can help maintain essential services while managing organizational change.

Modern slavery has become recognized as one of the world's great human rights challenges due to the high prevalence of coercive labor exploitation associated with the production and consumption of many goods and services across the globe. Yet, while its practice is commonly considered to be "unseen" and far removed from many people's everyday lives and working experiences, the micro-level bases of individual perceptions and actions taken in response to this "distal" threat remain poorly understood. In this paper, we develop and test a model linking the "psychological distance of modern slavery risk" to individual concerns, ethical organizational climate, and intentions to engage in mitigating behaviors in the workplace. Results from a survey of 511 working adults from UK businesses show that "closer" psychological distance to modern slavery is associated with higher levels of concern and greater intention to act in response to this risk. We also find that ethical climate moderates the impact of modern slavery risk concerns on intentions to engage in mitigating behaviors. Our study findings, therefore, complement existing research by pinpointing the key roles of psychological distance and ethical climate in modern slavery risk responses and highlighting the potential for micro-level interventions to help promote antislavery action.

A method for conducting Bayesian elicitation and learning in risk assessment is presented. It assumes that the risk process can be described as a fault tree. This is viewed as a belief network, for which prior distributions on primary event probabilities are elicited by means of a pairwise comparison approach. A novel and fully Bayesian updating procedure, following different observation campaigns of the events in the fault tree for the posterior probabilities assessment, is described. In particular, the goal is to handle contexts where there are limited data information (one of the challenges for elicitation), thus keeping simple the elicitation process and adequately quantifying the uncertainties in the analysis. Often, an important consideration in these contexts is the trade-off between how many of the events in the fault tree can be observed against the information that the extra data yield. How this can be addressed within this method is demonstrated. The application is illustrated through three real examples, including the motivating example of risk assessment of spacecraft explosion during controlled reentry.

Wireless emergency alerts (WEAs) are one tool to communicate imminent wildfire risk and provide guidance to at-risk people. Because WEAs must be short, messages often omit information such as the type of hazard or detailed guidance and often include jargon terms intended to provide both risk and guidance while using fewer characters (i.e., "evacuation warning"). However, we do not know how well understood these jargon terms are among the public in areas where they are used or what impact their use has on message perceptions when other key information is omitted. Furthermore, it is not clear whether omitting information or different jargon terms is differentially impactful for those with or without previous wildfire experience. To investigate, we asked participants to interpret a randomly assigned commonly used jargon term in their own words, and then we conducted a 2 × 2 × 2 experiment varying whether the hazard was identified as a wildfire, whether guidance was explained in plain language, and which jargon term was used (evacuation warning vs. evacuation order). We measured the impact of these factors on motivations for protective action moderated by whether or not participants had previous wildfire experience. Our results show a poor understanding of "evacuation warning" across experience levels. We also saw significantly elevated perceptions of understanding and believing message content and self-efficacy for messages that included evacuation orders, rather than evacuation warnings, among those without previous experience. We discuss the implications of these results for the use of jargon in wildfire messaging and recommend its omission where possible.

A Quantitative Microbial Risk Assessment (QMRA) model was developed to predict the risk of listeriosis associated with consuming ready-to-eat (RTE) products in Iran. Utilizing Monte Carlo simulation software, 79 positive cases were identified out of 2608 samples, yielding an overall prevalence of 58.5%. Prevalence varied across categories (1.01%-6.74%), with Listeria monocytogenes levels below the 100 CFU g^-1 threshold in positive samples. The 5th and 95th percentiles for annual listeriosis risk per serving were estimated for vulnerable (6.05 × 10^-8 to 1.16 × 10^-7) and general populations (1.36 × 10^-9 to 2.59 × 10^-9). The chicken Turkish kebab ranked highest in the 99th percentile for both subpopulations. Model projections included a 99th percentile estimate of 40 annual listeriosis cases, predominantly in the vulnerable population. Chicken Turkish kebab, chicken kebab, restaurant salad, traditional cheese, and RTE raw vegetables had the highest predicted cases. Sensitivity analyses emphasized the impact of serving size, prevalence, and specific product type on illness probability. The QMRA highlighted a significant listeriosis risk from contaminated RTE products, particularly for vulnerable populations. Validation through Kolmogorov-Smirnov and Anderson-Darling tests confirmed the statistical significance (p > 0.05) of the bootstrapped model's fit.

Bird strikes pose a risk to aviation. Collisions between birds and airplanes result in a threat to human lives, economic losses, and material damage. The majority of these collisions occur on airfields during takeoff and landing. Knowing what bird species are present on airfields, in what numbers, and relating that to the extent to which these birds are involved in collisions can help to direct bird control activities to specific bird species and thus reduce bird strikes. In this article, we offer a method to quantify the risk of bird strikes at airfields based on counts of bird abundance on airfields. We analyzed bird abundance in relation to bird strike risks based on a dataset from six Dutch airfields covering three decades. We used the data to define two metrics: Species Strike Impact (SSI) and Bird Strike Risk Index (BSRI), which are both independent of aspects such as bird behavior, habitat, season, or weather. These two metrics, respectively, reflect the bird strike risk per individual of a bird species on an airfield based on hazard probability and severity (SSI), and they provide quick insight in the local status of overall bird strike risks by summing all species-related risks into one overall index (BSRI). Both metrics are calculated from counts on the airfield of birds, bird strikes, and air traffic movements. This method can be readily incorporated as a leading indicator in flight safety management at airfields, enabling bird control personnel to take risk-reducing actions targeted at specific bird species on airfields.

Forest fires are integral to forest ecosystems as they influence nutrient cycling, plant regeneration, tree density, and biodiversity. However, human-induced climate change and activities have made forest fires more frequent, more intense, and more widespread, exacerbating their ecological and socioeconomic impact. Forest fires shape Tamil Nadu's diverse forest ecosystems, yet rising anthropogenic pressure and a warmer, drier climate have increased both their frequency and severity. We used a presence-only Maximum Entropy (MaxEnt) model to map the state-wide probability of fire occurrence and to guide the Tamil Nadu Forest Department (TNFD) in proactive suppression planning. Fire-occurrence points for 2020 (around 1900 ignitions) trained the model; independent ignitions from 2021 and 2022 (n = 2,906) validated it. Around nineteen topographic, climatic, and anthropogenic predictors, including Euclidean distance to cropland, rangeland, and roads, were resampled to 1 km resolution. The model showed excellent discrimination (AUC = 0.92) and achieved an overall test-set accuracy of 0.88 (Cohen's κ = 0.71). Distance to cropland (32.8 % permutation importance) and rangelands (25.8%) emerged as the strongest individual drivers, highlighting the combined influence of escaped agricultural burns and fuel condition on ignition risk. Jenks-optimized breaks split the landscape into Low (< 0.30), Medium (0.30-0.60), and High (≥ 0.60) classes, subsequently aggregated to the state's 2109 forest ranges. Although the High-risk zone comprises only 6.4 % of ranges (136/2109), it captured 54% of the 2021-22 ignitions, demonstrating substantial management leverage in the form of pre-season patrol planning and fuel-break maintenance. The resulting fire-probability map can help TNFD to prioritize patrol surges, pre-position water tankers, and refine early-warning bulletins for the 32 ranges exceeding the 0.80 "critical" threshold. Our approach provides a transferable template for data-poor tropical regions seeking to align limited suppression resources with the pockets of greatest ignition pressure. Future work should embed dynamic weather streams and near-real-time fuel-moisture indices to move from seasonal risk zoning toward operational early-warning.