Risk Analysis: An International Journal
Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic
With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under‐ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time‐dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.
Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission
Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.
Adversarial Risk Analysis to Allocate Optimal Defense Resources for Protecting Cyber–Physical Systems from Cyber Attacks
Defenders have to enforce defense strategies by taking decisions on allocation of resources to protect the integrity and survivability of cyber–physical systems (CPSs) from intentional and malicious cyber attacks. In this work, we propose an adversarial risk analysis approach to provide a novel one‐sided prescriptive support strategy for the defender to optimize the defensive resource allocation, based on a subjective expected utility model, in which the decisions of the adversaries are uncertain. This increases confidence in cyber security through robustness of CPS protection actions against uncertain malicious threats compared with prescriptions provided by a classical defend–attack game‐theoretical approach. We present the approach and the results of its application to a nuclear CPS, specifically the digital instrumentation and control system of the advanced lead‐cooled fast reactor European demonstrator.
Many real‐world systems use mission aborts to enhance their survivability. Specifically, a mission can be aborted when a certain malfunction condition is met and a risk of a system loss in the case of a mission continuation becomes too high. Usually, the rescue or recovery procedure is initiated upon the mission abort. Previous works have discussed a setting when only one attempt to complete a mission is allowed and this attempt can be aborted. However, missions with a possibility of multiple attempts can occur in different real‐world settings when accomplishing a mission is really important and the cost‐related and the time‐wise restrictions for this are not very severe. The probabilistic model for the multiattempt case is suggested and the tradeoff between the overall mission success probability (MSP) and a system loss probability is discussed. The corresponding optimization problems are formulated. For the considered illustrative example, a detailed sensitivity analysis is performed that shows specifically that even when the system's survival is not so important, mission aborting can be used to maximize the multiattempt MSP.
This mixed‐methods study investigated consumers’ knowledge of chemicals in terms of basic principles of toxicology and then related this knowledge, in addition to other factors, to their fear of chemical substances (i.e., chemophobia). Both qualitative interviews and a large‐scale online survey were conducted in the German‐speaking part of Switzerland. A Mokken scale was developed to measure laypeople's toxicological knowledge. The results indicate that most laypeople are unaware of the similarities between natural and synthetic chemicals in terms of certain toxicological principles. Furthermore, their associations with the term “chemical substances” and the self‐reported affect prompted by these associations are mostly negative. The results also suggest that knowledge of basic principles of toxicology, self‐reported affect evoked by the term “chemical substances,” risk‐benefit perceptions concerning synthetic chemicals, and trust in regulation processes are all negatively associated with chemophobia, while general health concerns are positively related to chemophobia. Thus, to enhance informed consumer decisionmaking, it might be necessary to tackle the stigmatization of the term “chemical substances” as well as address and clarify prevalent misconceptions.
The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.
Mediating and Moderating Roles of Trust in Government in Effective Risk Rumor Management: A Test Case of Radiation‐Contaminated Seafood in South Korea
This study has two aims: to identify effective strategies for managing false rumors about risks and to investigate the roles that basic and situational trust in government play in that process. Online experiment data were collected nationwide from 915 adults in South Korea. They were exposed to a false rumor about radiation‐contaminated seafood and were randomly assigned to one of three rumor response conditions (refutation, denial, attack the attacker). One‐way ANOVA indicated that the refutation response yielded the highest level of situational trust in government response (TGR). Results of moderated mediation models using the PROCESS Macro indicated the following. (1) The refutation response had a positive effect on TGR, and the attack response had a negative effect. (2) There were significant interaction effects between the attack response and preexisting basic trust in government (BTG) in that the attack response had a negative effect on TGR only when BTG was low. (3) TGR significantly mediated the relationship between each of the three rumor responses and two dependent variables (intentions for rumor dissemination and for unwarranted actions), but in dramatically different ways across the responses. This study provides evidence for the superior effectiveness of the refutation rumor response and identifies specific roles of trust in government in the risk rumor management process.
Photoresist materials are indispensable in photolithography, a process used in semiconductor fabrication. The work process and potential hazards in semiconductor production have raised concerns as to adverse health effects. We therefore performed a health risk assessment of occupational exposure to positive photoresists in a single optoelectronic semiconductor factory in Taiwan. Positive photoresists are widely used in the optoelectronic semiconductor industry for photolithography. Occupational exposure was estimated using the Stoffenmanager® model. Bayesian modeling incorporated available personal air sampling data. We examined the composition and by‐products of the photoresists according to descriptions published in the literature and patents; the main compositions assessed were propylene glycol methyl ether acetate (PGMEA), novolac resin, photoactive compound, phenol, cresol, benzene, toluene, and xylene. Reference concentrations for each compound were reassessed and updated if necessary. Calculated hazard quotients were greater than 1 for benzene, phenol, xylene, and PGMEA, indicating that they have the potential for exposures that exceed reference levels. The information from our health risk assessment suggests that benzene and phenol have a higher level of risk than is currently acknowledged. Undertaking our form of risk assessment in the workplace design phase could identify compounds of major concern, allow for the early implementation of control measures and monitoring strategies, and thereby reduce the level of exposure to health risks that workers face throughout their career.
Americans’ Views of Voluntary Protective Actions Against Zika Infection: Conceptual and Measurement Issues
Understanding factors affecting decisions by people to protect themselves, or not, is critical to designing supportive communications. Here, threat, protective‐action, and stakeholder perceptions were evaluated for effects on mainland Americans’ behavioral intentions regarding Zika in April 2017, as postulated by the Protective Action Decision Model. Although all three perception types (including a novel resource sufficiency measure) affected intentions, these relationships varied widely depending upon the method used to measure adoption of actions (e.g., total count of all behaviors adopted vs. behavior‐specific analyses), the behaviors involved, and whether analysis focused on the full sample or only on people who had a reasonable opportunity to enact the behavior or who believed it relevant to their lives. There was a general contrast between mosquito control actions (removal of mosquito breeding areas and pesticide spraying) and travel‐related behaviors (avoiding travel to areas of local transmission of the virus, protecting oneself from mosquito bites after potential exposure, and practicing safe sex after potential exposure). Reported action or inaction during the 2016 mosquito season, and stages of behavior change, were both elicited in January–February 2017; both drove intentions in April 2017 for the upcoming season, although direct and indirect effects varied widely. Collectively these findings present theoretical, measurement, and practical implications for understanding, tracking, and promoting voluntary protective actions against hazards.
Whether a loss or gain frame has a persuasive advantage in communicating health risks is a matter of ongoing debate. Findings reported in the literature are mixed, suggesting that framing effects are likely complex and may be influenced by a combination of factors. This study examined reactance as a mediator and dose as a moderator of loss/gain framing effects. Adults (N = 1,039) read framed messages about the health consequences of physical (in)activity in varying message doses (i.e., number of framed statements). Compared to loss frames, gain frames generated more threat to freedom and reactance. Dosage exerted significant influence at the extremes; the one‐dose messages invoked less intentions to exercise compared to the four‐dose messages. Planned contrasts revealed significant frame × dose interactions. Notably, the one‐dose gain‐framed messages triggered significantly more freedom threat and less intentions to engage in physical activity—a situation that changed when the information was loss‐framed or when the dosage was increased.
Letter concerning: Burns AM, Barlow CA, Banducci AM, Unice KM, Sahmel J. Potential Airborne Asbestos Exposure and Risk Associated with the Historical Use of Cosmetic Talcum Powder Products
People, Pipelines, and Probabilities: Clarifying Significance and Uncertainty in Environmental Impact Assessments
Determinations of significance play a pivotal role in environmental impact assessments because they point decision makers to the predicted effects of an action most deserving of attention and further study. Impact predictions are always subject to uncertainty because they rely on estimates of future consequences. Yet uncertainty is often neglected or treated in a perfunctory manner as part of the characterization, evaluation, and communication of anticipated consequences and their significance. Proposals to construct fossil fuel pipelines in North America provide a highly visible example; casual treatment of how uncertainty affects significance determinations has resulted in poorly informed stakeholders, frustrated industry proponents, and inconsistent choices on the part of public decision makers. Using environmental assessments for recent pipeline proposals as examples, we highlight five ways in which uncertainty is often neglected when determining impact significance and suggest that a mix of known methods, new guidelines, and appropriate oversight could greatly improve current practices.
Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text‐mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNNclassify (risk or no risk), DNNreg1 (loss time), DNNreg2 (body injury), DNNreg3 (plant and fleet), DNNreg4 (equipment), and DNNreg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R 2 value for the five regression models was 0.92, with mean absolute error between 0.91 and 0.94. The presented results, in addition to the developed user‐interface module, will help practitioners obtain a better understanding of H&S challenges, minimize project costs (such as third‐party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.
Roles of Vegetable Surface Properties and Sanitizer Type on Annual Disease Burden of Rotavirus Illness by Consumption of Rotavirus‐Contaminated Fresh Vegetables: A Quantitative Microbial Risk Assessment
Enteric viruses are often detected in water used for crop irrigation. One concern is foodborne viral disease via the consumption of fresh produce irrigated with virus‐contaminated water. Although the food industry routinely uses chemical sanitizers to disinfect post‐harvest fresh produce, it remains unknown how sanitizer and fresh produce properties affect the risk of viral illness through fresh produce consumption. A quantitative microbial risk assessment model was conducted to estimate (i) the health risks associated with consumption of rotavirus (RV)‐contaminated fresh produce with different surface properties (endive and kale) and (ii) how risks changed when using peracetic acid (PAA) or a surfactant‐based sanitizer. The modeling results showed that the annual disease burden depended on the combination of sanitizer and vegetable type when vegetables were irrigated with RV‐contaminated water. Global sensitivity analyses revealed that the most influential factors in the disease burden were RV concentration in irrigation water and postharvest disinfection efficacy. A postharvest disinfection efficacy of higher than 99% (2‐log10) was needed to decrease the disease burden below the World Health Organization (WHO) threshold, even in scenarios with low RV concentrations in irrigation water (i.e., river water). All scenarios tested here with at least 99.9% (3‐log10) disinfection efficacy had a disease burden lower than the WHO threshold, except for the endive treated with PAA. The disinfection efficacy for the endive treated with PAA was only about 80%, leading to a disease burden 100 times higher than the WHO threshold. These findings should be considered and incorporated into future models for estimating foodborne viral illness risks.
Flood risk is a function of both climate and human behavior, including individual and societal actions. For this reason, there is a need to incorporate both human and climatic components in models of flood risk. This study simulates behavioral influences on the evolution of community flood risk under different future climate scenarios using an agent‐based model (ABM). The objective is to understand better the ways, sometimes unexpected, that human behavior, stochastic floods, and community interventions interact to influence the evolution of flood risk. One historic climate scenario and three future climate scenarios are simulated using a case study location in Fargo, North Dakota. Individual agents can mitigate flood risk via household mitigation or by moving, based on decision rules that consider risk perception and coping perception. The community can mitigate or disseminate information to reduce flood risk. Results show that agent behavior and community action have a significant impact on the evolution of flood risk under different climate scenarios. In all scenarios, individual and community action generally result in a decline in damages over time. In a lower flood risk scenario, the decline is primarily due to agent mitigation, while in a high flood risk scenario, community mitigation and agent relocation are primary drivers of the decline. Adaptive behaviors offset some of the increase in flood risk associated with climate change, and under an extreme climate scenario, our model indicates that many agents relocate.
Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)
Adverse outcome pathway Bayesian networks (AOPBNs) are a promising avenue for developing predictive toxicology and risk assessment tools based on adverse outcome pathways (AOPs). Here, we describe a process for developing AOPBNs. AOPBNs use causal networks and Bayesian statistics to integrate evidence across key events. In this article, we use our AOPBN to predict the occurrence of steatosis under different chemical exposures. Since it is an expert‐driven model, we use external data (i.e., data not used for modeling) from the literature to validate predictions of the AOPBN model. The AOPBN accurately predicts steatosis for the chemicals from our external data. In addition, we demonstrate how end users can utilize the model to simulate the confidence (based on posterior probability) associated with predicting steatosis. We demonstrate how the network topology impacts predictions across the AOPBN, and how the AOPBN helps us identify the most informative key events that should be monitored for predicting steatosis. We close with a discussion of how the model can be used to predict potential effects of mixtures and how to model susceptible populations (e.g., where a mutation or stressor may change the conditional probability tables in the AOPBN). Using this approach for developing expert AOPBNs will facilitate the prediction of chemical toxicity, facilitate the identification of assay batteries, and greatly improve chemical hazard screening strategies.