Risk Analysis最新文献

Landslides have become increasingly frequent and destructive in Uttarakhand, leading to substantial loss of life and significant damage to infrastructure. This research focuses on generating a detailed landslide susceptibility map for a selected area in Chamoli district, Uttarakhand, by integrating remote sensing and geographical information system (GIS) techniques. Twelve critical factors influencing landslide occurrence, such as slope, aspect, vegetation cover, proximity to geological structures, distance from roads, elevation, curvature, topographic wetness index (TWI), stream power index (SPI), drainage proximity, and lithology, were considered. The Statistical Information Value Model (SIVM) was used to assess the contribution (weight) of each factor class toward landslide occurrence. These derived weights were then integrated using a weighted overlay method to produce the final landslide susceptibility map. The predictive accuracy of the model was validated through receiver operating characteristic (ROC) analysis, achieving an area under the curve (AUC) value of 0.72. The results demonstrate that the SIVM-based weighted overlay approach provides a reliable tool for identifying landslide-prone zones, offering valuable insights for land use planning and disaster mitigation.

As global carbon neutrality ambitions intensify, cross-regional embodied carbon transfers via inter-city trade increasingly pose complex governance risks and crises. Employing an environmentally extended multi-regional input-output (EE-MRIO) framework integrated with evolutionary game theory and multi-agent network analysis, this study critically investigates strategic governance responses to these risks within hierarchical administrative contexts. We introduce a refined carbon accounting approach that explicitly merges production-based and consumption-based emissions, significantly enhancing the precision and fairness of accountability mechanisms. Using multiyear data on 313 Chinese cities, we identify critical thresholds in carbon pricing that decisively shape cooperative and non-cooperative behavior in carbon mitigation. Furthermore, network structure profoundly affects governance outcomes-small-world topologies rapidly diffuse cooperative norms, whereas scale-free networks exacerbate vulnerabilities to strategic defection and systemic risk. This research offers robust theoretical advancements by clarifying the roles of strategic interactions, network topologies, and administrative incentives in shaping embodied carbon governance. Practically, we provide actionable policy interventions for mitigating systemic inefficiencies and resolving equity challenges linked to carbon leakage, trade-induced risks, and regional crises. By combining theoretical rigor with policy-oriented insights, our integrated methodological approach sets a precedent for effective and equitable climate risk governance, broadly adaptable beyond China's specific context.

Autonomous navigation in dynamic high-consequence environments, such as search and rescue (SAR) missions, often relies on multiagent robotic systems that need to learn and adapt to changing conditions. Adversarial risks can introduce further challenges in such a setting where an autonomous agent may exhibit deviations in their learned actions from training to testing. Moreover, the uncertain environment itself may also evolve with additional obstacles that can emerge during testing compared to conditions when algorithmic training of autonomous agents was performed. In this paper, we first focus on mathematically formulating the autonomous SAR problem via a risk-aware multiagent reinforcement learning approach. Thereafter, we design and implement numerical experiments to evaluate our approach under diverse hazard scenarios with a centralized training and decentralized testing paradigm. Finally, we discuss our results and steps for further research.

Future pandemics could arise from several sources, notably, emerging infectious diseases (EID); and lab leaks from high containment biological laboratories (HCBL). Recent advances in infectious disease, information technology, and biotechnology provide building blocks to reduce pandemic risk if deployed intelligently. However, the global nature of infectious diseases, distribution of HCBLs, and increasing complexity of transmission dynamics due to travel networks make it difficult to determine how to best deploy mitigation efforts. Increasing understanding of the risk landscape posed by EID and HCBL lab leaks could improve risk reduction efforts. The presented paper develops a country-level spatial network susceptible-infected-removed model based on global travel network data and relative risk measures of potential origin sources, EID, and lab leaks from biological safety level 3+ and 4 labs, to explore expected infections over the first 30 days of a pandemic. Model outputs indicate that EID and lab leaks in India, the USA, and China are most impacted at day 30. For EID, expected infections shift from high EID origin potential countries at day 10 to the USA, India, and China, while for lab leaks, the USA and India start with high lab leak potential. With respect to model uncertainties and limitations, results indicate several large, wealthy countries are influential to pandemic risk from both EID and lab leaks, indicating high leverage points for mitigation efforts.

School security remains a critical concern due to the increasing frequency of violent incidents, requiring a strategic balance between physical security measures and mental health programs. This study develops a game-theoretic framework to model the interaction between a school as the defender and a potential attacker, aiming to identify optimal investment decisions across two complementary layers of defense. Numerical illustrations calibrated with data from U.S. school shootings provide empirical support for the analysis. Through one-way and two-way sensitivity analyses, robustness tests, and scenario-based indifference curve analysis, we explore how attacker and defender valuations, intervention effectiveness, and defensive costs influence equilibrium strategies. The findings show that physical security measures have the strongest deterrent effect, but lasting protection depends on balanced investment in both security and mental health once their effectiveness exceeds critical thresholds. While physical security offers immediate deterrence, mental health interventions are essential for addressing underlying risk factors, emphasizing the complementary nature of the two approaches. The framework contributes to evidence-based decision-making for educational institutions and suggests future extensions to include external threats, incomplete information, and dynamic investment strategies.

We offer an alternative approach to toxicological risk assessment of new chemicals. We combine Operations Research techniques with those from Machine Learning to tackle the decision-making process. More specifically, we use Markov decision processes and Bayesian networks to derive the optimal cost-sensitive time-efficient Integrated Testing Strategies for chemical hazard classification under minimal expected cost in a mathematically rigorous fashion. We develop Bayesian networks which outperform state-of-the-art mechanistic causal models previously reported. More specifically, these models exhibit accuracy of 90% and sensitivity and specificity of 93% and 84%, respectively. Moreover, the inferred Bayesian networks are of considerably simpler structure as they comprise only the permeation coefficient, octanol/water coefficient, and TIMES software compared to their counterparts already in print, which comprise 15 descriptors. We use these simplified causal models to study the effect of varying misclassification costs on the nature of the optimal policy by means of sensitivity analysis. We note such analysis was previously computationally infeasible due to the fact that the variables which comprised the mechanistic model were categorical assuming a large number of possible values. We find that a variety of optimal policies can emerge subject to different misclassification costs assumed. Theoretical modeling framework developed is illustrated on the concrete example of hazard classification of skin allergens of previously unknown toxicological characteristics via integrating data obtained from in silico assays alone thus contributing to the literature of toxicological decision making based on nonanimal tests.

In recent years, frequent extreme disasters have challenged supply chain operations while smart risk warning systems are developed to facilitate firms' emergency order shifting to a new manufacturer. It is noted that reliable manufacturers are usually located in countries/regions levying carbon tax to achieve high ESG scores, so we consider a cross-border supply chain consisting of a global brand, a local brand, an overseas manufacturer and a local manufacturer to investigate the main tradeoffs for the global brand to emergently shift orders from the overseas manufacturer facing disruptions to a stable local manufacturer subject to carbon tax cost. The global brand has the option to wait for the recovery of overseas production but if it chooses emergent order shifting, it has to invest in carbon emission reduction due to ESG requirements. We intriguingly find that even though emergency order shifting helps avert delays caused by production disruptions, a more resilient supply chain does not necessarily lead to a higher profit for the global brand, depending on factors such as the relative market size, carbon tax cost, and the efficiency of carbon reduction investment. We also find that the global brand's emergency order shifting enables Pareto improvement of economic and environmental sustainability, but the win-win opportunities for both the global and local brand only appear under the recovery waiting strategy. So it is generally hard to coordinate the stakeholders' incentives to jointly optimize the ESG scores.

The use of AI technologies is being integrated into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety-critical domains. Despite the existing knowledge about uncertainty in risk analysis, no previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with one case study. We discuss the generalizability of our approach and its limitations and present recommendations for evaluation policies concerning AI systems. Future work includes extending the approach by relaxing the remaining assumptions and by experimenting with a real system.

We propose a novel dynamic generalized Pareto distribution (GPD) framework for modeling the time-dependent behavior of the peak over threshold (POT) in extreme smog (PM_2.5) time series. First, unlike static GPD, three dynamic autoregressive conditional generalized Pareto (ACP) models are introduced. Specifically, in these three dynamic models, the exceedances of air pollutant concentration are modeled by a GPD with time-dependent scale and shape parameters conditioned on past PM_2.5 and other air quality factors (SO₂, NO₂, CO) and weather factors (daily average temperature, average relative humidity, average wind speed). Second, unlike the recent studies of ACP models, we impose a logistic function autoregressive structure on the scale and shape parameters of the ACP models, which has simple calculation and flexible modeling for the scale and shape parameters, since the logistic function is used to mean that the changes in the long memory parameter occur in a continuous manner and often applied in time series models. Third, the model averaging method is applied to improve predictive performance using AIC and BIC criteria to select combined weights of the three ACP models. In addition, based on goodness-of-fit tests, the thresholds of the three ACP models are chosen by eight automatic threshold selection procedures to avoid subjectively assigning a certain value as the threshold. Maximum likelihood estimation (MLE) is employed to estimate parameters of the ACP models and its statistical properties are investigated. Various simulation studies and an example of real data in PM_2.5 time series demonstrate the superiority of the proposed ACP models and the stability of the MLE.

In an era of increasing digitization, nuclear command systems and power plants are becoming vulnerable to cyberattacks that can disrupt operations and undermine deterrence. This article examines the evolving threat landscape, drawing on literature and documented incidents of cyber intrusions into nuclear systems to identify critical technical and policy vulnerabilities. It argues that such intrusions risk eroding second-strike credibility and may create incentives for preemptive action, thereby destabilizing strategic balances. Although regulatory bodies and international organizations have issued cybersecurity guidelines for nuclear facilities, implementation remains inconsistent. To address these challenges, the study proposes a set of resilience measures encompassing advanced technical safeguards, specialized workforce training, the establishment of international norms, and enhanced crisis communication protocols. Strengthening the cyber resilience of both civilian and military nuclear assets is presented as an urgent imperative for maintaining global security and strategic stability in the digital age.