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.

Recent literature has examined the role of misinformation, biases, and other factors in contributing to the integrity of a risk study. These types of social and cognitive dynamics-referred to as narratives-comprise concern and value in a risk study. These narratives may appear to undermine aspects of objectivity in a scientific sense, but they may also shed light on aspects of a risk study that involve perceived scientific truths, related risk concerns, and values. The narratives can inform overall risk perception and the perception of quality for the risk study. As a result, understanding and classifying those narratives provides additional evidence that can potentially inform decisions for the design and implementation of a risk study. In this article, we develop a classification system that can be used to understand and address narratives that can influence a risk study and how various stakeholders perceive the risk study. This article will be of interest to risk analysts, policymakers, and risk communicators.

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.

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.

Non‑communicable diseases (NCDs) in animals, including obesity, diabetes, metabolic syndrome, cardiovascular disorders, cancers, and degenerative joint disease, are rising across companion, livestock, wildlife, and aquaculture sectors. Recent surveys document that 50%-60% of domestic cats and dogs are overweight, driving a 0.8% per year increase in feline diabetes from 0.4% in 2005 to 1.6% in 2020. In dairy herds, subclinical ketosis affects 30%-40% of cows during the transition period, reducing 305‑day milk yield by 6%, while osteoarthritis impacts 20% of intensively reared pigs. Wildlife exposed to industrial pollutants show liver tumor rates up to 25% in fish and marine mammals. Therefore, there is a need for integrative research focused on predictive risk modeling and management frameworks. Using an integrated One Health/EcoHealth framework, this mini‑review (i) quantifies NCD prevalence across taxa; (ii) dissects mechanisms linking risk factors to NCD emergence; (iii) proposes an evidence-based risk assessment model to assist future surveillance and mitigation strategies; and (iv) outlines tiered mitigation strategies spanning individual, herd, ecosystem, and policy levels.

Critical infrastructure is typically identified at the national level. However, disruption to certain infrastructure systems, facilities, and assets can have negative consequences for global societies. Such globally critical infrastructure entails a distinct risk profile for both countries dependent on the infrastructure, and countries that have such infrastructure in their territory. The goal of the article is to provide an initial framing and definition of "globally critical infrastructure" as a concept worthy of attention and explore the unique risk analysis and management challenges to support future, more rigorous examinations. For dependent countries, globally critical infrastructure exists outside of their border (or possibly outside any country's border), under sometimes drastically different economic, political, governance, and threat environments. Risk management entails unique challenges, because countries dependent on that infrastructure may have no legal or regulatory authority to shape risk management practices at facilities in other countries. Consequently, risk management may extend beyond the domains of the typical homeland or internal affairs agencies to include capabilities and responsibilities of ministries of foreign affairs, trade and commerce, and defense. However, those challenges also imply new risk management demands and options, such as new avenues for international cooperation on infrastructure protection and resilience, international funding, and enhanced monitoring. Having a globally critical infrastructure system in its borders changes the risk dynamics for a nation state, creating potential leverage over dependent nations and new avenues to garner international support, but also creates new risks to national sovereignty. Recognizing these common dependencies can better enable the global community to engage stakeholders to develop and implement systemic risk management approaches worldwide.

This paper presents the design and implementation of an expert system for the domain of functional safety of machinery, featuring a novel multilingual chatbot interface developed using the Rasa framework. Unlike traditional expert systems, this approach aims to make the complex topic of functional safety more accessible to users with limited experience by assisting with tasks such as hazard identification, risk assessment, risk reduction, and safety function recommendation. The knowledge base of the system can be populated by functional safety experts through a graphical user interface, ensuring the system's utility and accuracy. This work demonstrates that the chatbot-based expert system retains many advantages of traditional expert systems while offering a more engaging user experience. An experimental evaluation of the presented expert system using hazard scenarios from real-life projects highlights the benefits of advanced machine learning techniques and pretrained embeddings, showing improvements in system performance. Continuous updates to the training dataset are essential for maintaining effectiveness in diverse environments. Compared to general-purpose chatbots like ChatGPT, this system provides reliable, standards-based insights. The system can be utilized by inexperienced machinery design personnel, such as mechanical and mechatronic engineers, before consulting with safety experts.

Identifying critical risk factors is essential for controlling risk propagation and improving the safety management of carbon capture and storage (CCS) projects. Existing research has primarily focused on risk occurrence probability and potential consequences, with relatively less attention given to risk factor analysis, particularly their interactions within complex systems. To address this gap, 36 risk factors and 6 common accidents were identified through the literature review, analysis of accident reports, and expert interviews. We then established the CCS risk factor interaction network and identified critical structural nodes by topological analysis. To further examine the actual impact of these identified nodes and different parameters on risk propagation, we conducted a systematic simulation based on a susceptible-infected-recovered model. The results show that incomplete safety systems, inadequate safety supervision, and inadequate safety training serve as critical driving nodes, with a high potential to initiate widespread risk propagation, whereas equipment overload, adverse weather, and improper emergency handling act as critical bridge nodes whose intervention effectively suppresses risk propagation. Furthermore, the risk intervention step, propagation rate, and recovery rate affect the scale and duration of risk diffusion. This study aims to enhance system resilience by providing valuable insights for safety management in CCS projects.

Fugitive dust from concentrated animal feeding operations (CAFOs) is a potential source of produce contamination with human pathogens. Our objective was to develop a general framework and methodology for predicting preharvest produce contamination with human pathogens arising from fugitive dust derived from a nearby CAFO. We applied this framework to a case study of lettuce grown in proximity to a bovine CAFO. We implemented the EPA's AERMOD dispersion model at two locations, assessing dust dispersion and deposition over a 30-day period across 100 km surrounding a 10,000-animal CAFO. E. coli O157:H7 contaminated lettuce servings grown on fields within the 100 km were predicted using a risk assessment approach, integrating data about dust deposition, pathogen contamination in cattle manure, and pathogen survival on crops. To contextualize the results, infectious servings were predicted based on the average number of E. coli O157:H7 per serving and the E. coli O157:H7 dose-response relationship. Dust from CAFOs has the potential to deposit across at least 100 km. E. coli O157:H7 dispersion and deposition are impacted by wind direction and velocity, emission factor, and prevalence and concentration in dust. Mean E. coli O157:H7 concentrations on preharvest lettuce were predicted across the 100 km and declined considerably with distance from the CAFO. Surviving E. coli O157:H7 on preharvest lettuce arise primarily from dust deposited in the 2 weeks before harvest. Our modeling approach provides a flexible framework that can be adapted to any location, providing quantitative information to inform foodborne outbreak investigations and the development of prevention strategies.

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.

This study investigates how local context-specifically urban versus rural environments and socioeconomic conditions-influences individual crisis preparedness and resilience in Sweden. Using multilevel survey data from 12,574 respondents, we analyze both proactive preparedness actions and perceived resilience. Results show that rural residents report higher levels of preparedness and resilience than their urban counterparts. However, these differences in preparedness attenuate when controlling for individual risk perception, suggesting a mediating role. Socioeconomic context, on the other hand, does not show an independent effect beyond individual characteristics, indicating compositional rather than contextual influences. The findings highlight the importance of tailoring crisis preparedness strategies to both individual and local characteristics and stress the need for authorities to consider spatial disparities in vulnerability when planning for future crises.

An increasing number of countries have begun to utilize electric ambulances (EAs) in emergency medical transport (EMT) to meet net-zero emission targets. However, the extended battery-recovery time and limited battery capacity of EAs pose significant risks to time-sensitive and efficiency-critical EMT. On the basis of this, we aim to examine the effect of battery recovery on the performance of the EMT system with EAs and explore the carbon-reduction benefits in deploying EAs compared to fuel-powered ones. We develop a queuing model to characterize the EAs using the EMT system with two battery-recovery strategies (plug-in charging and battery swapping) and derive its key performance indicators for risk assessment. The results illustrate that when the ambulance fleet is small and most of them are EAs, the throughput time for EMT increases significantly. However, with a larger ambulance fleet, incorporating EAs can deliver a level of transportation service comparable to that of the fuel-powered ambulances, especially when the battery-swapping strategy is employed. While the use of EAs raises the input costs, achieving a critical scale of EAs enables the reduced energy cost and the social cost of carbon to quickly offset the initial investment. Finally, this study proposes policy recommendations on the construction of battery-recovery infrastructure and the deployment scale and timing of vehicles, providing optimized solutions to balance the risks of using EAs with the safety of EMT.

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.

Medical waste surged during the initial response to COVID-19, greatly increasing the risk of medical waste pollution. Medical waste management involves numerous stakeholders, including environmental, healthcare, agricultural, and laboratory regulatory authorities, organizations generating medical waste, and transportation and disposal companies. The traditional regulatory system suffers from inter-departmental obstacles and opportunistic behavior by stakeholders. All of these issues have drawn widespread attention from the government and the public in the wake of the pandemic. To address these issues, Shanghai has taken the lead in implementing digital transformation to enhance the effectiveness of medical waste management. This study constructs an innovative analytical framework encompassing function, structure, and institution to comprehensively examine digital transformation. This framework reveals how functional enhancements through digital technologies first improve operations via real-time information transmission, process reengineering, and increased efficiency, and then subsequently reshape inter-organizational relationships while requiring calibrated institutional adaptation for effective implementation. It also indicates a critical role of technological-institutional alignment in determining transformation outcomes. Based on this framework, a quadratic assignment procedure and social network analysis are used to compare the planned, traditional, and digital networks formed before and after the digital transformation of medical waste management in Shanghai. Through systematic analysis of these networks, this study investigates how digital transformation can enhance medical waste management effectiveness and provides a nuanced analysis that goes beyond mere issues of technological implementation to reveal the complex interplay between digital capabilities and institutional adaptation, and to uncover the challenges involved in achieving comprehensive collaboration.

The rapid and nationwide expansion of fifth-generation (5G) wireless cellular technology infrastructure in China has prompted serious public concerns, predominantly due to the potential adverse health effects of electromagnetic field (EMF) exposure from 5G base stations. The literature offers mixed results regarding the effectiveness of risk communication on public concerns about EMF exposure from base stations. An online survey experiment with 815 adults in Shanghai examined how different strategies of risk communication could enhance public acceptance. We manipulated the framing of intervention materials (loss- vs. gain-framed) and their information source (government, industry, or civil society). Our analysis revealed that government and industry sources, compared to civil society, were more effective in increasing public support. Additionally, gain frames generated more acceptance than loss frames. Furthermore, participants held higher levels of competence-based trust in government and industry, but no significant difference in care-based trust was detected between government and the other two sources. Both dimensions of trust were critical for public acceptance. These results suggest that the Chinese government, along with professional private sectors, could leverage emerging media platforms to foster support. These results also highlight the need for the Chinese government to address the lack of public care-based trust, especially in the context of centralized 5G deployment.

In today's algorithm-driven era, individuals not only actively seek health information through search engines or health websites but also passively encounter health-related content while browsing social media feeds. These two distinct behaviors (i.e., intentional information seeking and incidental information scanning) may each contribute to individuals' perceptions of health risks. A substantial body of work has examined the relationship between online health information behaviors (e.g., seeking) and risk perceptions across various contexts. However, the findings on the directionality of these relationships remain equivocal. Drawing on the literature on health information acquisition, this study investigates the longitudinal associations among online health information seeking, scanning, and risk perceptions. Data from a three-wave panel survey with 654 participants indicate that health information scanning and seeking exhibit a stable, reciprocal relationship over time. Moreover, information seeking is positively associated with risk perceptions across waves, whereas information scanning does not exert a direct effect. These findings contribute to theoretical developments in digitally mediated risk communication by highlighting the temporal dynamics and interplay of online information behaviors. They also offer practical guidance for designing more targeted and psychologically informed digital health communication strategies.

The transportation of liquid hydrogen (LH) via road tankers could prove to be the most cost-effective short-term option for long-distance delivery. However, there are significant risks, particularly in confined spaces like road tunnels. An accidental release of LH in these structures is likely to create a flammable hydrogen cloud, the explosion of which generates overpressures whose magnitude depends on several mutually dependent variables, including geometry, traffic, and ventilation. Nevertheless, the combined effect of the above-mentioned variables on user safety in the event of an accidental leakage and explosion of LH from a road tanker in a tunnel has yet to be investigated in detail. This study develops 3D CFD models of both the release and explosion of LH to address this issue, along with a comprehensive parametric analysis that considers different tunnel lengths, negative and positive longitudinal slopes, traffic volumes, and ventilation types (i.e., natural or longitudinal mechanical). The CFD code used was preliminarily calibrated against experimental literature tests. Subsequently, a risk analysis was carried out using the CFD results in terms of overpressures, which, combined with a probit function, made it possible to estimate the number of potential fatalities. Consequently, a probability matrix of the risk of having a given number (N) of fatalities was built as a function of the tunnel length, ventilation type (i.e., natural or mechanical), longitudinal slope, and traffic volume. The results revealed the benefits of positive gradients as well as of implementing a longitudinal mechanical ventilation system. In contrast, longer tunnels increase the probability of having a given number of fatalities. This study might serve as a reference for tunnel operators in the choice of mitigation measures and/or traffic control strategies to limit the negative consequences of the release of liquid hydrogen in road tunnels.

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.

The efficiency of emergency response is crucial, yet traditional top-down systems are often overwhelmed. Digital spontaneous volunteers (DSVs) offer a vital bottom-up force, but their effectiveness is frequently constrained by a dual dilemma of external integration and internal coordination. This study explores how to optimize DSV crowdsourcing by investigating the role of sustained trust from formal organizations and the logic of adaptive crowdsourcing based on complex adaptive systems theory. Using an agent-based model calibrated with data from the "life-saving document" case during China's 2021 Henan rainstorm, we conducted counterfactual experiments. The results demonstrate that sustained trust from formal organizations is fundamental; its erosion leads to a collapse in rescue efficiency, even with highly accessible information channels. Furthermore, the study reveals a counterintuitive finding: Adaptive crowdsourcing significantly improves efficiency not by maximizing volunteer numbers, but by restraining their generation based on real-time demand gaps. This research highlights that the effectiveness of DSV crowdsourcing hinges on dynamic trust-building and controlled, adaptive coordination, offering a conceptual shift from viewing self-organization as an uncontrollable force to a system that can be optimized through design.

Understanding community disaster resilience is critical to mitigating the disproportionate impacts of climate change and natural disasters on socially vulnerable populations. However, despite extensive discussion on disaster resilience, a systematic analysis of the extent of social inequity across climate scenarios, geographic locations, spatial scales, and sociodemographic groups remains underexplored. Our study introduces a human-centric framework to investigate social inequities in community disaster resilience related to human well-being. We combined flood hazard maps under both historical and future SSP scenarios with a compound multilayer urban spatial network model consisting of roads, communities, and essential services to evaluate the residents' service resilience during flood events. Then, we utilized the Gini coefficient and Lorenz curve to quantify the degree of inequities in resilience among different sub-populations. With Central Chongqing as a case study, our analysis reveals a significant increase in both the number of affected communities and their vulnerability under future climate conditions. We further observed a striking spatial polarization in community resilience due to the islanding effect, whereby communities are increasingly divided into those with severely limited service availability and those with sufficient resources. In addition, we found that the extent of social inequity in resilience is highly spatial and scale-specific, with moderate levels of inequity at the city level, but the degree of inequity varies greatly across sociodemographic groups at a localized level. This widening socio-spatial differentiation may trigger widespread dissatisfaction in disadvantaged communities, hindering the collective disaster response actions and engagements to enhance community resilience. Our research highlights the importance of embedding future climate variabilities, human well-being, and social equity in inclusive disaster response policies, processes, and practices.

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.