Risk Analysis最新文献

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%.

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.

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.

The ups and downs of climate policy uncertainty (CPU) cast a captivating shadow over the budgets allocated to renewable energy (RE) technologies, where strategic choices and risk assessment will determine the course of our green environmental revolution. The main intention of this investigation is to scrutinize the effect of CPU on the RE technology budgets (RETBs) in the top 10 countries with the highest RE research and development budgets (the USA, China, South Korea, India, Germany, the United Kingdom, France, Japan, Australia, and Italy). Although former researchers have typically employed panel data tools to contemplate the connection between CPU and RE technology, they repeatedly ignored variations in this connection throughout different economies. In contrast, our research adopts a unique approach, "quantile-on-quantile," to check this association at the country-to-country level. This approach offers a comprehensive worldwide perspective while procuring tailor-made perceptions for individual economies. The outcomes suggest that CPU significantly decreases RETBs across several data quantiles in our sample nations. In addition, the outcomes underscore that the connections between our variables differ among nations. These outcomes highlight the significance of policymakers implementing thorough appraisals and skillfully governing plans relevant to CPU and RETBs.

In today's highly competitive business environment, firms strive to maximize profitability by minimizing or eliminating disruptions and failures to maintain a competitive edge. This study focuses on evaluating risks in a hydraulic pump factory as a means to achieve sustainable growth. To accomplish this, a team of experts was formed to identify potential errors, utilizing a combination of risk priority number criteria weighted by Fuzzy Shannon's entropy and a fusion of multi-criteria decision-making and failure mode and effects analysis for evaluating and ranking failures. Moreover, the study emphasizes the importance of considering the interaction among risk assessment indicators, the inclusion of cost of failure, and modeling under fuzzy uncertainty circumstances, as they have a notable impact on the final ranking of failures to be processed for risk mitigation action planning. This research brings a new dimension to enhance the overall effectiveness of risk assessment by aggregation, as evidenced by a novel use of data classification in machine learning and correlation in statistics. The findings indicate that the aggregated ranking data series is best matched and most influenced by the weighted aggregated sum product assessment method, with the highest rate of recall and precision accomplished.

Many studies have observed a correlation between beliefs regarding nature's resilience and (political) preferences regarding the organization of society. Liberal-egalitarians, for example, generally believe nature to be much more fragile than libertarians, who believe nature to be much more resilient. Cultural theory explains this correlation by the idea that people are only able to see those risks that fit their preferred organization of society. This article offers an alternative, second explanation for the observed correlation: Both beliefs regarding nature's resilience and political preferences can be explained by the same cognitive biases toward ambiguous risk, that is, dispositions determining our expectations regarding the possible state of affairs resulting from our acts and their probabilities. This has consequences for political philosophy and the psychology of risk. In particular, there is a knowledge gap in psychology regarding the cognitive biases underlying the belief that despite ambiguity, experts can determine safe limits for human impacts on the environment.

This study tackles an integrated emergency medical supply planning problem, which incorporates supply prepositioning and dynamic in-kind donation management in healthcare coalitions. Although this problem is vital for field practice, it is not investigated in the existing emergency supply planning literature. To fill the gap, we propose a two-stage stochastic programming model, which facilitates the planning of emergency medical supply prepositioning before disasters and dynamic supply transshipment and in-kind donation solicitation and distribution after disasters. With a case study on the healthcare coalition of West China Hospital in Sichuan Province of China under the background of the COVID-19 epidemic, the proposed model and seven comparison models are optimally solved to show the effectiveness and benefits of our model. We conduct sensitivity analysis to generate managerial insights and policy suggestions for better emergency medical supply management practices in healthcare coalitions.

Climate change and natural hazard risk assessments often overlook indirect impacts, leading to a limited understanding of the full extent of risk and the disparities in its distribution across populations. This study investigates distributional justice in natural hazard impacts, exploring its critical implications for environmental justice, equity, and resilience in adaptation planning. We employ high-resolution spatial risk assessment and origin-destination routing to analyze coastal flooding and sea-level rise scenarios in Aotearoa New Zealand. This approach allows the assessment of both direct impacts (property exposure) and indirect impacts (physical isolation from key amenities) on residents. Indirect impacts, such as isolation and reduced access to resources, have significant adverse effects on well-being, social cohesion, and community resilience. Including indirect impacts in risk assessments dramatically increases the overall population burden, while revealing complex effects on existing inequalities. Our analysis reveals that including indirect impacts increases the overall population burden, but the effect on inequalities varies. These inequalities can be exacerbated or attenuated depending on scale and location, underscoring the need for decision-makers to identify these nuanced distributions and apply context-specific frameworks when determining equitable outcomes. Our findings uncover a substantial number of previously invisible at-risk residents-from 61,000 to 217,000 nationally in a present-day event-and expose a shift in impact distribution toward underserved communities. As indirect risks exacerbate disparities and impede climate adaptation efforts, adopting an inclusive approach that accounts for both direct and indirect risks and their [un]equal distribution is imperative for effective and equitable decision-making.

This article argues that frontier artificial intelligence (AI) developers need an internal audit function. First, it describes the role of internal audit in corporate governance: internal audit evaluates the adequacy and effectiveness of a company's risk management, control, and governance processes. It is organizationally independent from senior management and reports directly to the board of directors, typically its audit committee. In the Institute of Internal Auditors' Three Lines Model, internal audit serves as the third line and is responsible for providing assurance to the board, whereas the combined assurance framework highlights the need to coordinate the activities of internal and external assurance providers. Next, the article provides an overview of key governance challenges in frontier AI development: Dangerous capabilities can arise unpredictably and undetected; it is difficult to prevent a deployed model from causing harm; frontier models can proliferate rapidly; it is inherently difficult to assess frontier AI risks; and frontier AI developers do not seem to follow best practices in risk governance. Finally, the article discusses how an internal audit function could address some of these challenges: Internal audit could identify ineffective risk management practices; it could ensure that the board of directors has a more accurate understanding of the current level of risk and the adequacy of the developer's risk management practices; and it could serve as a contact point for whistleblowers. But frontier AI developers should also be aware of key limitations: Internal audit adds friction; it can be captured by senior management; and the benefits depend on the ability of individuals to identify ineffective practices. In light of rapid progress in AI research and development, frontier AI developers need to strengthen their risk governance. Instead of reinventing the wheel, they should follow existing best practices. Although this might not be sufficient, they should not skip this obvious first step.

This study aimed to adapt and validate the Disaster Resilience Scale, originally developed by Becker et al. and revised by Paton et al., for assessing disaster resilience within the Turkish school community with a focus on Community Engagement Theory. This theory emphasizes the role of community involvement in disaster resilience at various levels, including the individual, community, and societal/institutional. The study was conducted in two phases. In the first phase, data from 428 teachers were analyzed to assess the validity and reliability of the scale's Turkish version and its alignment with dimensions. In the second phase, data from 1,422 teachers were used to further verify the reliability of using the Generalizability Theory test, and confirm validity through confirmatory factor analysis. The results confirmed that the Turkish version of the scale, with its 12 factors and 52 items was valid and reliable. Cronbach's Alpha coefficients for the dimensions ranged from 0.80 to 0.91, indicating high reliability. The findings highlight the practical implications of adapting the DRS for enhancing disaster resilience in school communities and underscore the importance of community engagement in disaster preparedness and education.