Risk Analysis最新文献

The rise of globalization has led to a sharp increase in international trade with high volumes of containers, goods, and items moving across the world. Unfortunately, these trade pathways also facilitate the movement of unwanted pests, weeds, diseases, and pathogens. Each item could contain biosecurity risk material, but it is impractical to inspect every item. Instead, inspection efforts typically focus on high-risk items. However, low risk does not imply no risk. It is crucial to monitor the low-risk pathways to ensure that they are and remain low risk. To do so, many approaches would seek to estimate the risk to some precision, but increasingly lower risks require more samples. On a low-risk pathway that can be afforded only limited inspection resources, it makes more sense to assign fewer samples to the lower risk activities. We approach the problem by introducing two thresholds. Our method focuses on letting us know whether the risk is below certain thresholds, rather than estimating the risk precisely. This method also allows us to detect a significant change in risk. Our approach typically requires less sampling than previous methods, while still providing evidence to regulators to help them efficiently and effectively allocate inspection effort.

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300-m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance-based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300-m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300-m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation.

In 1977, the Soviet Union (Union of Soviet Socialist Republics [USSR]) notified the World Health Organization (WHO) about an outbreak of H1N1 influenza, which later spread to many countries. The H1N1 strain of 1977 reappeared after being absent from the world for over 20 years. This pandemic simultaneously spread to several cities in the USSR and China. Many theories have been postulated to account for the emergence of this pandemic, including natural and unnatural origins. The purpose of this study was to use the modified Grunow-Finke risk assessment tool (modified Grunow-Finke tool [mGFT]) to investigate the origin of the 1977 H1N1 pandemic. Data was collected from WHO archives and published documents. The assessment of the pandemic's origin involved the utilization of a modified version of the original Grunow-Finke risk assessment tool (GFT). Using the mGFT, the final score was 37 out of 60 points (probability: 62%), indicating a high likelihood that the Russian influenza pandemic of 1977 was of unnatural origin. Several variables supported this finding, including the sudden re-emergence of a previously extinct strain, a genetic signature of laboratory modification for vaccine development, and unusual epidemiology. Inter-rater reliability was moderate to high. By applying the mGFT to the 1977 Russian influenza pandemic, we established a high probability that this pandemic was of unnatural origin. Although this is not definitive, it is consistent with the possibility that it originated from an incompletely attenuated live influenza vaccine. The mGFT is a useful risk analysis tool to evaluate the origin of epidemics.

Product safety professionals must assess the risks to consumers associated with the foreseeable uses and misuses of products. In this study, we investigate the utility of generative artificial intelligence (AI), specifically large language models (LLMs) such as ChatGPT, across a number of tasks involved in the product risk assessment process. For a set of six consumer products, prompts were developed related to failure mode identification, the construction and population of a failure mode and effects analysis (FMEA) table, risk mitigation identification, and guidance to product designers, users, and regulators. These prompts were input into ChatGPT and the outputs were recorded. A survey was administered to product safety professionals to ascertain the quality of the outputs. We found that ChatGPT generally performed better at divergent thinking tasks such as brainstorming potential failure modes and risk mitigations. However, there were errors and inconsistencies in some of the results, and the guidance provided was perceived as overly generic, occasionally outlandish, and not reflective of the depth of knowledge held by a subject matter expert. When tested against a sample of other LLMs, similar patterns in strengths and weaknesses were demonstrated. Despite these challenges, a role for LLMs may still exist in product risk assessment to assist in ideation, while experts may shift their focus to critical review of AI-generated content.

In this paper, we develop a generic framework for systemically encoding causal knowledge manifested in the form of hierarchical causality structure and qualitative (or quantitative) causal relationships into neural networks to facilitate sound risk analytics and decision support via causally-aware intervention reasoning. The proposed methodology for establishing causality-informed neural network (CINN) follows a four-step procedure. In the first step, we explicate how causal knowledge in the form of directed acyclic graph (DAG) can be discovered from observation data or elicited from domain experts. Next, we categorize nodes in the constructed DAG representing causal relationships among observed variables into several groups (e.g., root nodes, intermediate nodes, and leaf nodes), and align the architecture of CINN with causal relationships specified in the DAG while preserving the orientation of each existing causal relationship. In addition to a dedicated architecture design, CINN also gets embodied in the design of loss function, where both intermediate and leaf nodes are treated as target outputs to be predicted by CINN. In the third step, we propose to incorporate domain knowledge on stable causal relationships into CINN, and the injected constraints on causal relationships act as guardrails to prevent unexpected behaviors of CINN. Finally, the trained CINN is exploited to perform intervention reasoning with emphasis on estimating the effect that policies and actions can have on the system behavior, thus facilitating risk-informed decision making through comprehensive "what-if" analysis. Two case studies are used to demonstrate the substantial benefits enabled by CINN in risk analytics and decision support.

Pandemics place a new type of demand from patients affected by the pandemic, imposing significant strain on hospital departments, particularly the intensive care unit. A crucial challenge during pandemics is the imbalance in addressing the needs of both pandemic patients and general patients. Often, the community's focus shifts toward the pandemic patients, causing an imbalance that can result in severe issues. Simultaneously considering both demands, pandemic-related and general healthcare needs, has been largely overlooked. In this article, we propose a bi-objective mathematical model for locating temporary hospitals and allocating patients to existing and temporary hospitals, considering both demand types during pandemics. Hospital departments, such as emergency beds, serve both demand types, but due to infection risks, accommodating a pandemic patient and a general patient in the same department is not feasible. The first objective function is to minimize the bed shortages considering both types of demands, whereas the second objective is cost minimization, which includes the fixed and variable costs of temporary facilities, the penalty cost of changing the allocation of existing facilities (between general and pandemic demand), the cost of adding expandable beds to existing facilities, and the service cost for different services and beds. To show the applicability of the model, a real case study has been conducted on the COVID-19 pandemic in the city of Qom, Iran. Comparing the model results with real data reveals that using the proposed model can increase demand coverage by 16%.

Decisions in many disciplines are based on understanding and evidence. More evidence is better than less when it enhances the decision-maker's understanding. This is achieved by reducing uncertainty confronting the decision-maker and reducing the potential for misunderstanding and failure. However, some evidence may actually augment uncertainty by revealing prior error or ignorance. True evidence that augments uncertainty is important because it identifies inadequacies of current understanding and may suggest directions for rectifying this. True evidence that reduces uncertainty may simply reconfirm or strengthen prior understanding. Uncertainty-augmenting evidence, when it is true, can support the expansion of one's previously incomplete understanding. A dilemma arises because both reduction and enhancement of uncertainty can be beneficial, and both are not simultaneously possible on the same issue. That is, uncertainty can be either pernicious or propitious. Info-gap theory provides a response. The info-gap robustness function enables protection against pernicious uncertainty by inhibiting failure. The info-gap opportuneness function enables exploitation of propitious uncertainty by facilitating wonderful windfall outcomes. The dilemma of uncertainty-augmenting evidence is that robustness and opportuneness are in conflict; a decision that enhances one, worsens the other. This antagonism between robustness and opportuneness-between protecting against pernicious uncertainty and exploiting propitious uncertainty-is characterized in a generic proposition and corollary. These results are illustrated in an example of allocation of limited resources.

The burgeoning interconnectedness of global trade in the digital age not only presents enticing opportunities but also harbors potent vulnerabilities of artificial intelligence (AI)-driven cyberattacks. This study explores the cascading impacts of these disruptive threats on economies, supply chains, and trade, utilizing the intricate lens of Computable General Equilibrium modeling. Through meticulously designed simulation scenarios, we illuminate the potential economic ramifications of cyberattacks, with a focus on regions heavily reliant on digital technologies and interwoven supply chains. The analysis reveals significant declines in real GDP, trade prices and volumes, and trade route disruptions across regions. Notably, economies like China, the United States, the United Kingdom, and the EU, due to their deep integration in global networks, face pronounced vulnerabilities. However, amidst this bleak landscape, hope emerges in the form of cyber resilience. The study showcases the effectiveness of proactive measures like adaptable production systems, diversified trade partners, and robust cybersecurity infrastructure in mitigating the adverse impacts of cyberattacks. Incorporating cyber resilience significantly dampens the reported negative consequences, highlighting the critical role of preparedness in combating digital warfare. This study underscores the urgent need for a global paradigm shift toward cyber resilience. Collective efforts to bolster cybersecurity infrastructures, foster international cooperation in threat intelligence, and establish open and resilient trade frameworks are crucial in navigating the treacherous labyrinth of AI-driven cyberattacks. By embracing resilience strategies and fostering global collaboration, we can pave the way for a more secure and prosperous digital future, where interconnectedness becomes a tool for progress, not a vulnerability to be exploited.

Uncertainties have grown around the world during the last few decades. Pandemic uncertainty has a substantial impact on economic activities, which may have a big influence on energy consumption. The goal of this investigation is to appraise the asymmetric influence of pandemic uncertainty on nonrenewable and renewable energy consumption in the top 10 energy consumer economies of the European Union (Germany, Poland, Spain, Netherlands, France, Italy, Belgium, Sweden, Czech Republic, and Finland). Previously, panel data approaches were utilized to obtain reliable outcomes on the pandemic-energy consumption nexus, regardless of the fact that various nations did not autonomously exhibit similar relationship. This investigation, on the other hand, implements a special technique "Quantile-on-Quantile" that supports us to appraise time-series interdependence in each economy by providing international yet nation-specific perceptions of the connection among the variables. Estimates show that pandemic uncertainty reduces both nonrenewable and renewable energy consumption in most selected nations at stated quantiles of the data distribution. Nonrenewable energy consumption is much more influenced by pandemic uncertainty than renewable energy consumption. Furthermore, the rank of asymmetries across our variables differentiates by the economy, emphasizing the need for decisionmakers to pay much attention to pandemics-related uncertainty and the energy sector.

Dynamic processes in various fields exhibit risk coupling phenomena, but existing risk analysis studies tend to ignore the risk coupling effects of dynamic scenarios. Considering the principles of digitization, objective quantification, and the full process that should be adopted in the risk coupling analysis, an integrated risk coupling analysis framework is proposed. Specifically, the weighted Eclat algorithm is used to mine the risk association rules, then the key risk factors are extracted by social network analysis, and the stochastic Petri net is used to complete the construction, simulation, and evolution of accident scenarios. This universal framework can analyze the risk phenomena of accident scenario evolution in a process-oriented manner and decouple risks based on key risk factors and disconnect the chain of the accident scenario evolution process. Finally, the proposed framework is applied to the coupled analysis of fire risk in Chinese urban communities to verify its feasibility and scientific validity.