The concept of resilience intrinsically links with both complexity and adaptive capacity. Scholars from different fields agree on this. Still, the detailed relations between resilience, complexity, and adaptive capacity need a more thorough theoretical analysis. This article analyses resilience with the help of assumptions from complex adaptive systems (CAS) theory to answer two questions in more detail: What is the relation between resilience and complexity? How can adaptive capacity contribute to resilience? By applying basic ideas from CAS theory to the resilience discourse, the article deduces that complexity of a system is a necessary condition for resilience because complex systems consist of agents that possess adaptive capacity, whereas simple systems consist of mere elements that cannot adapt to unexpected disruptions. The relation between complexity and resilience is multidimensional. Growing complexity leads to a growing need for resilience because the chances for severe, unexpected disruptions increase. The analysis of adaptive capacities revealed that systems and the agents they consist of can possess of specialized and general adaptive capacity. General adaptive capacity is the core feature of resilience because it enables systems to cope with unexpected disruptions. System design principles such as diversity within functional groups and redundancy help to increase general adaptive capacity. The same is true on the community level for social capital and on the individual level for disaster preparedness measures because they increase coping capacities independent of specific hazards.

Culture can have a major impact on how we perceive different hazards. In the Romantic period, nature was described and portrayed as mysterious and benevolent. A deep connection to nature was perceived as important. We proposed that this romantic view would be positively related to people's risk perceptions of man-made hazards and, more specifically, to concerns about climate change. Further, we hypothesized that the Romantic perception of nature leads to a biased perception of natural hazards and that the moral component of action is of particular importance above and beyond the mere efficacy of the action. We conducted an online survey in Germany (N = 531), a country where Romanticism had a very widespread influence. The study shows that individuals with a Romantic view of nature perceived greater risks associated with climate change than those without such a view. In addition, those with a Romantic view of nature were more likely to support measures to reduce the risks of climate change, even when it is said that such measures are not effective. Finally, the study found a significantly higher positive correlation between Romantic views of nature and risk perceptions of man-made versus natural hazards. The results suggest that ideas developed during the Romantic era continue to influence hazard perception in Germany.

Imported agricultural pests can cause substantial damage to agriculture, food security, and ecosystems. In the United States, the Agricultural Quarantine Inspection Monitoring (AQIM) program conducts random sampling to estimate the probabilities that cargo and passengers arriving at ports of entry carry pests. Assessing these risks accurately is critical to enable effective policies and operational procedures. This study introduces a pathway-level analysis with an objective function aligned with AQIM's goal, offering a new perspective compared to the current container-by-container approach, which relies on heuristics to set inspection rates. We formulate an optimization model that minimizes the mean squared error of the probability estimates that AQIM obtains. The central decision-making tradeoff that the model explores is whether it is preferable to sample more arriving containers (and fewer boxes per container) or more boxes per container (and fewer containers), given limited resources. We first derive an analytical solution for the optimal sampling strategy by leveraging several approximations. Then, we apply our model to a numerical case study of maritime cargo sampling at the Port of Long Beach. Across a wide range of parameter settings, the optimal strategy samples more containers (but fewer boxes per container) than the current AQIM protocol. The difference between the two strategies and the accuracy improvement with the optimal approach are larger if the pest statuses of boxes in the same container are more strongly correlated. We recommend that AQIM record box-level (beyond only container-level) inspection data, which could be used to estimate this correlation and other model parameters.

In light of recent events related to national elections in the United States, safeguarding the security and integrity of forthcoming elections stands as a critical national priority. Elections equipment in the United States constitutes critical national infrastructure, and its operation relies on poll workers, who are trusted insiders. However, those insiders may pose risks if they make mistakes with detrimental consequences or act with malice. This research analyzes a large dataset of 2213 responses obtained from a survey of poll workers and potential poll workers in 13 states. The survey includes the Security Behavior Intentions Scale, which has been previously established and validated in the security literature. We use the responses to assess poll workers' intentions of complying with established security-related practices. We develop a novel model using information theory to examine potential weaknesses in security behaviors and identify poll worker security practices to improve to ensure the integrity of our elections. We also recommend action items and countermeasures for states and localities based upon this empirical analysis.

The proliferation of inaccurate and misleading information about COVID-19 on social media poses a significant public health concern. This study examines the impact of the infodemic and beneficial information on COVID-19 protective behaviors in an armed-conflict country. Using the protective action decision model (PADM), data were collected from 1439 participants through a questionnaire in Yemen between August 2020 and April 2021. Structural equation modeling tested hypotheses generated by the PADM. The findings indicate that the infodemic reduces the likelihood of individuals adopting protective measures against COVID-19. Surprisingly, official announcements by accountable authorities do not moderate the relationship between the infodemic and protective responses. These results highlight the need for further research on resilience in armed-conflict countries. This study contributes to understanding armed-conflict countries' unique challenges in combating health crises. Addressing the infodemic and promoting accurate information is crucial in enhancing protective behaviors and mitigating the negative impact of misinformation. Policymakers and public health authorities can utilize these insights to develop targeted interventions and communication strategies that ensure accurate information dissemination and encourage the adoption of adequate protective measures.

SARS-CoV-2 Omicron and its sub-lineages have become the predominant variants globally since early 2022. As of January 2023, over 664 million confirmed cases and over 6.7 million deaths had been reported globally. Current infection models are limited by the need for large datasets or calibration to specific contexts, making them difficult to apply to different settings. This study aims to develop a generalized multinomial probabilistic model of airborne infection to assist public health decision-makers in evaluating the effectiveness of public health interventions (PHIs) across a broad spectrum of scenarios. The proposed model systematically incorporates group characteristics, epidemiology, viral loads, social activities, environmental conditions, and PHIs. Assumptions about social distance and contact duration that estimate infectivity during short-term group gatherings have been made. The study is differentiated from earlier works on probabilistic infection modeling in the following ways: (1) predicting new cases arising from more than one infectious person in a gathering, (2) incorporating additional key infection factors, and (3) evaluating the effectiveness of multiple PHIs on SARS-CoV-2 infection simultaneously. Although the results show that limiting group size has an impact on infection, improving ventilation has a much greater positive health impact. The proposed model is versatile and can flexibly accommodate other scenarios or airborne diseases by modifying the parameters allowing new factors to be added.

This study examined the paths through which the news frames of particulate matter (PM) influence support for governmental policies aiming to address PM. It also explored the mediating effects of anxiety and risk perception in the relationship between news frames and policy support, as well as the moderating effects of media exposure and psychological distance on the PM news framing effect. Based on an experimental design (N = 676), two groups of news frames were prepared for comparison: a narrative frame group and a numerical frame group. The results showed no significant differences in anxiety or risk perception between the two groups. Further, no significant mediating effects of anxiety or risk perception were found in the process through which PM news frames influence support for governmental policies. However, media exposure significantly moderated the effect of the narrative frame: With high (low) media exposure, the narrative frame positively (negatively) influenced policy support through risk perception. Moreover, when the level of psychological distance was low, the narrative frame positively influenced policy support through risk perception. This study contributes to the literature on news framing of PM by integrating cognitive and emotional mechanisms in forming policy attitudes.

The communication of extreme weather forecasts (e.g., heatwaves and extreme precipitation) is a challenge for weather forecasters and emergency managers who are tasked with keeping residents safe during often unprecedented situations. Weather models have inherent uncertainty, and the ability for potentially life-saving information to get to the people who need it most (e.g., those who need to evacuate) remains a challenge despite the proliferation of digital access to information and social media sites like Twitter. It is also unclear the role that community-based organizations and super-local governmental entities play or may play during weather events in transmitting weather information and providing assistance. In New York City, there remains robust inequality, with communities that are historically disadvantaged often suffering the highest number of deaths and level of destruction following weather events. Results from interviewing 26 New York City community leaders suggest that local organizations often act as intermediaries, passing on official weather information to members of their audience, regardless of the mission statement of their organization. Common challenges for communities in responding to extreme weather include lack of access to information, language barriers, and insufficient resources. Considerations for future weather communication strategies are discussed.

As synthetic biology is extensively applied in numerous frontier disciplines, the biosafety and biosecurity concerns with designing and constructing novel biological parts, devices, and systems have inevitably come to the forefront due to potential misuse, abuse, and environmental risks from unintended exposure or potential ecological impacts. The International Genetically Engineered Machine (iGEM) competition often serves as the inception of many synthetic biologists' research careers and plays a pivotal role in the secure progression of the entire synthetic biology field. Even with iGEM's emphasis on biosafety and biosecurity, continuous risk assessment is crucial due to the potential for unforeseen consequences and the relative inexperience of many participants. In this study, possible risk points for the iGEM projects in 2022 were extracted. An attack tree that captures potential risks and threats from experimental procedures, ethical issues, and hardware safety for each iGEM-based attack scenario is constructed. It is found that most of the attack scenarios are related to experimental procedures. The relative likelihood of each scenario is then determined by using an established assessment framework. This research expands the traditionally qualitative analysis of risk society theory, reveals the risk formation in the synthetic biology team, and provides practical implications.

In this work, we introduce JointLIME, a novel interpretation method for explaining black-box survival (BBS) models with endogenous time-varying covariates (TVCs). Existing interpretation methods, like SurvLIME, are limited to BBS models only with time-invariant covariates. To fill this gap, JointLIME leverages the Local Interpretable Model-agnostic Explanations (LIME) framework to apply the joint model to approximate the survival functions predicted by the BBS model in a local area around a new individual. To achieve this, JointLIME minimizes the distances between survival functions predicted by the black-box survival model and those derived from the joint model. The outputs of this minimization problem are the coefficient values of each covariate in the joint model, serving as explanations to quantify their impact on survival predictions. JointLIME uniquely incorporates endogenous TVCs using a spline-based model coupled with the Monte Carlo method for precise estimations within any specified prediction period. These estimations are then integrated to formulate the joint model in the optimization problem. We illustrate the explanation results of JointLIME using a US mortgage data set and compare them with those of SurvLIME.

Over the last years, infectious diseases have been traveling across international borders faster than ever before, resulting in major public health crises such as the Covid-19 pandemic. Given the rapid changes and unknown risks that mark such events, risk communication faces the challenge to raise awareness and concern among the public without creating panic. Drawing on the social amplification of risk framework-a concept that theorizes how and why risks are amplified or attenuated during the (1) transfer of risk information (by, for instance, news media) and (2) audiences' interpretation and perception of these information-we were interested in the portrayal of risk information and its impact on audiences' risk perception over the first wave of the Covid-19 pandemic in Germany. We therefore conducted a quantitative content analysis of a major public and private television (TV) newscast (N = 321) and combined it with survey data (two-wave panel survey, t1: N = 1378 and t2: N = 1061). Our results indicate that TV news (as a major information source at that time) were characterized by both risk-attenuating and risk-amplifying characteristics, although risk-amplifying attributes were particularly pronounced by the private TV newscast. Notably, those who only used private TV news between both waves showed the highest perceived severity at time 2. However, the interaction effect of time and use of public and/or private TV news was only significant for perceived susceptibility. Overall, more research is needed to examine the effects of different types of media and changes in risk perceptions over time.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Although persuasive messages are designed to motivate individuals to engage in intended behaviors, they do not always work. Often, people follow previously established values and ideologies and dismiss persuasive messages. We examine how participants react to a persuasive message related to plastic pollution and how these reactions shape their willingness to recycle and reuse. Results indicate that environmental values and political ideology are associated with message derogation in distinct ways, which, in turn, affect risk perception, self-efficacy, and intention to recycle and reuse. Further, past behavior moderates the relationship between message derogation and perceived risk, but not the relationship between message derogation and self-efficacy. These results suggest that pre-existing values and ideologies play an important role in message derogation, a hitherto under-researched phenomenon that has key implications for self-reported behavioral change. Moreover, past behavior could serve as a powerful lever in steering risk perception and behavioral intent.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatment outcomes.

The way political identity serves as a foundation for political polarization in the United States permits elites to extend conflict rapidly to new issue areas. Further, the types of cognitive mechanisms and shortcuts used in the politically polarized information environment are similar to some of those used in risk perception. Consequently, political elites may easily create partisan risk positions, largely through politically focused social amplification of risk. The COVID-19 pandemic provided a natural experiment for testing predictions about such risk politics. We asked questions about pandemic-related views, behaviors, and policies at the outset of the pandemic in April 2020 and again in September 2020 via public opinion surveys. Our data and analyses focus primarily on a single state, with some analysis extended to four states. We begin by demonstrating strong linkages between political partisan identification on the one hand and support for co-partisan elites, use of partisan information sources, and support for co-partisan policies on the other hand. We then find evidence that pandemic risk positions correspond with partisan information sources and find support for a mechanism involving partisan-tinted evaluation of elite cues. Partisan risk positions quickly became part of the larger polarized structure of political support and views. Finally, our evidence shows on the balance that partisan risk positions related to the pandemic coalesced and strengthened over time. Overall, while self-identified Democrats consistently viewed the coronavirus as the primary threat, self-identified Republicans quickly pivoted toward threats to their freedoms and to the economy.

Listeria monocytogenes is a foodborne pathogen of concern for cancer patients, who face higher morbidity and mortality rates than the general population. The neutropenic diet (ND), which excludes fresh produce, is often utilized to mitigate this risk; however, an analysis weighing the theoretical listeriosis risk reduction of produce exclusion aspects of the ND and possible negative tradeoffs has never been conducted. Consequently, this work constructed decision analytic models using disability-adjusted life years (DALYs) to compare the impacts of the ND, such as increased neutropenic enterocolitis (NEC) likelihood, with three alternative dietary practices (safe food handling [SFH], surface blanching, and refrigeration only) across five age groups, for cancer patients who consume ready-to-eat salad. Less disruptive diets had fewer negative health impacts in all scenarios, with median alternative diet DALYs per person per chemotherapy cycle having lower values in terms of negative health outcomes (0.088-0.443) than the ND (0.619-3.102). DALYs were dominated by outcomes associated with NEC, which is more common in patients following the ND than in other diets. Switchover point analysis confirmed that, because of this discrepancy, there were no feasible values of other parameters that could justify the ND. Correspondingly, the sensitivity analysis indicated that NEC mortality rate and remaining life expectancy strongly affected DALYs, further illustrating the model's strong dependence on NEC outcomes. Given these findings, and the SFH's ease of implementation and high compliance rates, the SFH diet is recommended in place of the ND.

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.

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.

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 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.

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.