Risk Analysis最新文献

The participation frequency and duration of water-based activities are typically higher for training rowing athletes than for rowing tourists, resulting in great exposure risks of polluted water for training rowing athletes. Thus, evaluating the health risks of training rowing athletes is essential to ensure their safety. In this study, quantitative microbial risk assessment (QMRA) combined with disability-adjusted life years (DALYs) was used to probabilistically examine the health risks of training rowing athletes in the Dongshan River Watershed, Taiwan, and to inversely determine the critical levels of river fecal coliforms (FCs) for risk benchmarks of 10^-4, 10^-5, and 10^-6 per person per year (pppy). Monte Carlo simulation was employed to quantify the variability of QMRA and DALY parameters. The relationship between FC observations and critical FC levels was investigated to identify suitable risk benchmarks for river environmental management. The results indicated that the risk of disease burden (DB) for training rowing athletes ranged from 108.4 × 10^-6 to 267.2 × 10^-6 pppy. These risks posed potential health threats to training rowing athletes. Given the ratios of observations exceeding critical FC levels, preliminary environmental management for river water quality was suggested at a DB risk of 10^-5 pppy. The representative value of critical FC concentrations corresponding to this risk level was found to be 2603 colony-forming units/100 mL.

Given the critical importance of lifeline infrastructures in maintaining society functioning, the main objective of infrastructure restorations following disasters is to satisfy community demand in a rapid and effective manner. In existing literature, community demand on infrastructure services is often assumed to remain constant before and after disasters, which might lead to a mismatch between restored infrastructure serviceability and actual community demand. To address this gap, this study proposes an integrated demand-oriented infrastructure restoration framework. The integrated framework is designed to (1) estimate community demand using a Bayesian-based method, allowing for the integration of multiple information sources and the rapid updating of demands as new data becomes available; (2) develop a demand-oriented optimization model that prioritizes resource allocation to the infrastructure components serving communities with higher levels of demand; and (3) create a reliable solution method using an iterative process to accommodate the dynamics of disaster situations, complemented by a hybrid simulation-optimization approach to manage demand uncertainty. For illustrative purposes, the restoration of interdependent power and water infrastructure networks in Shelby County, TN, is studied. The results demonstrate that the proposed framework significantly improves the satisfaction of community demand, and meanwhile decreases the penalty costs associated with unmet demands. Beyond post-disaster restoration, the framework is capable of assisting decision-makers in simulating various disaster scenarios, enabling more community-centered resilience planning.

Urban bus accidents present major safety and operational challenges, particularly in densely populated metropolitan areas. This study develops a machine learning-based analytical framework to identify, quantify, and interpret the factors associated with severe bus accidents. The framework integrates three components: (i) a structural topic model (STM) to extract latent accident scenarios from unstructured narrative data, (ii) an extreme gradient boosting (XGBoost) classifier to predict accident severity, and (iii) SHapley Additive exPlanations (SHAP) for post hoc interpretation of model outputs at both global and local levels. Using over 15,000 bus accident records (2013-2018) from a Tier-2 city in Jiangsu Province, China, the findings show that incorporating text-derived accident patterns markedly improves both predictive accuracy and interpretability. The analysis highlights elevated risks linked to rear-end collisions involving electric scooters, sudden stops leading to passenger injuries, and left-turn maneuvers in congested areas. SHAP-based explanations yield actionable insights for drivers, transit operators, and policymakers, facilitating targeted safety interventions. Methodologically, this study advances interpretable risk modeling through the integration of structured and unstructured data, and the modular analytical framework provides a transferable foundation for applications across diverse domains of transportation and risk analysis.

This study, based on Complex Adaptive Systems theory and the "4C" framework, explores the dynamics of information sharing and collaboration networks within China's emergency management system during disasters. It rigorously explores the nuances in the connections and differences between these networks. Employing Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), the research scrutinizes the relationships of disaster information sharing and collaboration among local public departments in the aftermath of the 2016 Funing tornado in Jiangsu, China. This study is dedicated to understanding how these networks evolve within a hierarchical administrative framework. The findings underscore three pivotal trends in the evolution of information and collaboration networks: a reduction in network redundancy, localized strengthening in ties, and differential adaptations. These trends are instrumental in enhancing the broader understanding of emergency management. They spotlight the importance of efficient information dissemination and robust collaborative frameworks, particularly in the context of China's centralized and hierarchical emergency management structure.

Nowadays, many multinational firms (MNFs) still stick to overseas manufacturing for the benefits of low production costs and tax planning opportunities. However, such a strategy comes along with production shocks caused by power outages, fires, strikes, and so on. In this article, we use a Resilience Triangle framework to measure the risk of production shocks during the shock and recovery time. We explore two risk management strategies for MNFs: enhancing overseas manufacturing resilience via advanced technologies and reshoring to local manufacturing. We outline the MNF's trade-offs among overseas resilience loss, production cost, tax planning opportunity, and local manufacturing subsidy. We quantify the production-and-delivery delays caused by overseas manufacturing shocks and highlight the value of advanced production technologies in mitigating shocks and accelerating recovery. We find that the MNF's production strategy may switch from overseas manufacturing to local manufacturing and then back to overseas manufacturing when the local manufacturing subsidy is not too high and the local manufacturing cost is moderate. We show that overseas manufacturing with advanced production technologies can achieve a win-win situation regarding the MNF's resilience performance and profitability, as they enable the MNF to better balance production risks and financial gains.

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

A raft of authors argue that society is drowning in a sea of misinformation, disinformation, and fake news. Some claim we are living in a new world disorder, misinformation age, or post-truth era, fueled in part by social media, influential podcasters, and emerging AI systems. We argue that the now dominant interpretation of the risk of misinformation has been undergirded by an oversimplified understanding of broader communication processes. Thinking of misinformation as a standalone risk object has distorted conceptions of messages, messengers, and audiences, as well as how the underlying problems associated with false and misleading information could and should be addressed. Our article unpacks and then constructively critiques the dominant interpretation of misinformation by examining the tendency to (i) define misinformation in isolation from communication, (ii) neglect messengers' intentions, (iii) perceive audiences as susceptible misinformation recipients, and (iv) reduce communication to a one-way process of misinforming. We conclude by arguing that a communication-based approach, grounded in the agency of messengers and audiences, offers a more nuanced and holistic foundation for interpreting and addressing the complex challenges associated with false and misleading messages. This perspective encourages policymakers and researchers to approach communication in its full complexity, engage in multiway processes, draw on the existing interdisciplinary communication literature, and remain attentive to both the challenges and opportunities of today's evolving communication ecosystem.

Enhancing intraregional disaster preparedness and response capabilities is crucial for effectively managing noncatastrophic disasters in localized areas. This paper proposes a primary governmental strategy focused on signing disaster insurance contracts with capacity reservation, alongside two supplementary strategies: building predisaster stockpiles and spot market procurement. Among these, our focus is on developing a comprehensive disaster insurance model with capacity reservation functionality, which integrates both financial and operational elements to facilitate public-private collaboration. Using game-theoretical modeling, we analyze government-insurer interactions, with solutions derived through backward induction. The model is validated through a case study in China, focusing on the response of S Government and W Company to Typhoon Rumbia. The results offer a series of important insights. Zero-deductible contracts, though unconventional, emerge as an optimal mechanism in localized disasters by minimizing entry barriers and sustaining insurer profitability. For insurers, long-term cooperation is more attractive in low-volatility, short-duration events, as it enhances capacity amortization and operational efficiency. Meanwhile, policyholders exhibit highly context-sensitive behavior, with stockpiling decisions shaped by lead time, spot market prices, and disaster characteristics. The model uncovers distinct preparedness thresholds that support flexible, scenario-specific strategies, advancing the theory and practice of disaster readiness for regional governments.

The harmful effects of climate change have brought global warming into focus, prompting a growing body of research on its economic impact and the development of targeted climate policies aimed at mitigating these effects and promoting sustainability. Within this context, the main objective of this paper is to investigate whether the presence of transition risk drivers, in particular, the implementation of environmental policies in the United States, initiates risks or fosters green innovation and financial performance. This performance is related to the adjustment process toward a low-carbon economy, widely known as the Porter hypothesis. Using a panel threshold regression model over the period 1990-2020, our results show that market-based climate policies have a heterogeneous effect on the firm's green innovation and financial performance. Specifically, we find an inverted-U-shaped relationship between carbon price and firm outcomes including green innovation and financial performance. These findings have significant implications for practice, as they reveal the mechanism through which climate policies can optimally affect a firm's green innovation activity and financial performance.