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��ࡱ�>�� u �r�rebjbj�n�n2��a��ad\ ��""��8�tm$��pl��epgpgpgpgpgpgp$jr� u<�p-��p��4�p��ep��ep��p�&�:h��(�qp�p0�p�\u�f\u��0\u��l��p�p ��p��\u��"qs: risk analysis volume 43, issue 3, march 2023 1. title: cases of real-life policies related to risk: how can they enhance risk analysis and risk science? authors: terje aven abstract: policies on risk constitute a core topic of risk analysis and risk science, and it is common at risk conferences to present real-life cases of such policies, for example related to the handling of climate change and pandemics. although these are of broad interest, showing how important issues in society are dealt with, it can be questioned to what extent and how these cases contribute to enhancing risk analysis and risk science. the present paper addresses this concern. it is argued that, in order to learn from the cases, they need in general to be more thoroughly followed up with discussions of concepts, principles, approaches, and methods for assessing, characterizing, communicating and handling risk. describing a governmental policy on, for example, the handling of covid-19 is a point of departure for interesting discussions concerning its justification and performance, in particular in relation to risk and the most updated knowledge from the risk analysis field. such discussions are, however, often lacking. the paper points to some key obstacles and challenges for the learning process, including the difficulty of distinguishing between policies, policy analysis, and politics. 2. title: development of a novel machine learning-based weighted modeling approach to incorporate salmonella enterica heterogeneity on a genetic scale in a dose�response modeling framework authors: shraddha karanth, abani k. pradhan abstract: estimating microbial dose�response is an important aspect of a food safety risk assessment. in recent years, there has been considerable interest to advance these models with potential incorporation of gene expression data. the aim of this study was to develop a novel machine learning model that considers the weights of expression of salmonella genes that could be associated with illness, given exposure, in hosts. here, an elastic net-based weighted poisson regression method was proposed to identify salmonella enterica genes that could be significantly associated with the illness response, irrespective of serovar. the best-fit elastic net model was obtained by 10-fold cross-validation. the best-fit elastic net model identified 33 gene expression�dose interaction terms that added to the predictability of the model. of these, nine genes associated with salmonella metabolism and virulence were found to be significant by the best-fit poisson regression model (p < 0.05). this method could improve or redefine dose�response relationships for illness from relative proportions of significant genes from a microbial genetic dataset, which would help in refining endpoint and risk estimations. 3. title: improving fire risk communication between authorities and micro-entrepreneurs: a mental models study of ghanaian central market fires authors: frank nyame-asiamah, bismark yeboah boasu, peter kawalek, daniel buor abstract: this study conceptualizes how fire management authorities can empower nonexpert public to participate in fire risk communication processes and increase their own responsibilities for managing fire preventive, protective and recovery processes effectively. drawing narratives from 10 disaster management experts working at government institutions and nine micro-entrepreneurs operating self-sustaining businesses in different merchandized lines in ghana, we analyzed the data thematically and explored new insights on mental models to generate a two-way fire risk communication model. the findings suggest that fire management authorities planned fire disasters at the strategic level, collaborated with multiple stakeholders, disseminated information through many risk communication methods, and utilized their capabilities to manage fire at the various stages of fire risk communication, but the outcomes were poor. the micro-entrepreneurs sought to improve fire management outcomes through attitude change, law enforcement actions, strengthened security and better public trust building. the study has implications for policymakers, governments, and risk communication authorities of developing countries to strengthen their fire disaster policies to minimize commercial fire incidents and address the damaging effects of fire on people's livelihoods, businesses, properties, and environments. our proposed two-way fire risk communication model is a new theoretical lens for experts and the nonexpert public to assess each other's beliefs about risk information and manage fire risk communication effectively at all stages. 4. title: risk analysis of fast spreading species in a kashmir himalayan national park (dachigam) for better monitoring and management authors: saba yaqoob, iqra jan, zafar a reshi, irfan rashid, manzoor a. shah abstract: huge economic costs and ecological impacts of invasive alien species (ias) in the protected areas (pas) worldwide make their timely prediction and potential risk assessment of central importance for effective management. while the preborder weed risk assessment framework has been extensively evaluated and implemented, the postborder species risk assessment framework has not been subjected to the same degree of scrutiny. here we used a rather more realistic modified version of the australian weed risk framework (awrm) for dachigam national park (dnp) in kashmir himalaya against 84 plant species, including 55 alien species and 29 fast spreading native species, for risk analysis. we found two very high-risk species, three high-risk species, 10 medium-risk species, 29 low-risk species, and 40 negligible-risk species in the dnp. the containment scores accordingly ranged from 14.4 to 293.5 comprising of 27 species that can be contained with very high feasibility, 23 species with high feasibility, 14 species with medium feasibility, and 12 species which cannot be contained easily thereby having low feasibility of containment (foc) score. however, eight species which have a negligible foc score are difficult to contain within their infestation sites. our results demonstrate the merit of the awrm with a caution that the necessary region-specific modifications may help in its better implementation. overall, these results provide quite a promising tool in the hands of protected area managers to timely and effectively deal with the problem of plant invasions. 5. title: a dynamic bayesian network-based emergency decision-making framework highlighting emergency propagations: illustrated using the fukushima nuclear accidents and the covid-19 pandemic authors: yinan cai, michael w. golay abstract: when facing public emergencies, human societies need to make decisions rapidly in order to mitigate the problems. however, this process can be difficult due to complexity of the emergency scenarios and lack of systematic methods for analyzing them. in the work reported here, we develop a framework based upon dynamic bayesian networks in order to simulate emergency scenarios and support corresponding decisions. in this framework, we highlight the importance of emergency propagation, which is a critical factor often ignored by decisionmakers. we illustrate that failure of considering emergency propagation can lead to suboptimal mitigation strategies. by incorporating this critical factor, our framework enables decisionmakers to identify optimal response strategies minimizing emergency impacts. scenarios developed from two public emergencies: the 2011 fukushima nuclear power plant accidents and the covid-19 pandemic, are utilized to illustrate the framework in this paper. capabilities of the framework in supporting decision making in both events illustrate its generality and adaptability when dealing with complex real-world situations. our analysis results reveal many similarities between these two seemingly distinct events. this indicates that seemingly unrelated emergencies can share many common features beyond their idiosyncratic characteristics. valuable mitigation insights can be obtained by analyzing a broad range of past emergencies systematically. 6. title: a value of information framework for assessing the trade-offs associated with uncertainty, duration, and cost of chemical toxicity testing authors: shintaro hagiwara, greg m. paoli, paul s. price, maureen r. gwinn, annette guiseppi-elie, patrick j. farrell, bryan j. hubbell, daniel krewski, russell s. thomas abstract: a number of investigators have explored the use of value of information (voi) analysis to evaluate alternative information collection procedures in diverse decision-making contexts. this paper presents an analytic framework for determining the value of toxicity information used in risk-based decision making. the framework is specifically designed to explore the trade-offs between cost, timeliness, and uncertainty reduction associated with different toxicity-testing methodologies. the use of the proposed framework is demonstrated by two illustrative applications which, although based on simplified assumptions, show the insights that can be obtained through the use of voi analysis. specifically, these results suggest that timeliness of information collection has a significant impact on estimates of the voi of chemical toxicity tests, even in the presence of smaller reductions in uncertainty. the framework introduces the concept of the expected value of delayed sample information, as an extension to the usual expected value of sample information, to accommodate the reductions in value resulting from delayed decision making. our analysis also suggests that lower cost and higher throughput testing also may be beneficial in terms of public health benefits by increasing the number of substances that can be evaluated within a given budget. when the relative value is expressed in terms of return-on-investment per testing strategy, the differences can be substantial. 7. title: subjective machines: probabilistic risk assessment based on deep learning of soft information authors: mario p. brito, matthew stevenson, cristi�n bravo abstract: for several years machine learning methods have been proposed for risk classification. while machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used for probabilistic risk assessment. probabilistic risk assessment is a subjective process. the problem of how well machine learning methods can emulate expert judgments is challenging. expert judgments are based on mental shortcuts, heuristics, which are susceptible to biases. this paper presents a process for developing natural language-based probabilistic risk assessment models, applying deep learning algorithms to emulate experts� quantified risk estimates. this allows the risk analyst to obtain an a priori risk assessment when there is limited information in the form of text and numeric data. universal sentence embedding (use) with gradient boosting regression (gbr) trees trained over limited structured data presented the most promising results. when we apply these models� outputs to generate survival distributions for autonomous systems� likelihood of loss with distance, we observe that for open water and ice shelf operating environments, the differences between the survival distributions generated by the machine learning algorithm and those generated by the experts are not statistically significant. 8. title: an integrated environmental risk assessment framework for coal-fired power plants: a fuzzy logic approach authors: ayfer bartan, serhat kucukali, irfan ar, kemal baris abstract: this study quantifies the environmental risk of a coal-fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. the following criteria are assessed: emissions (co, so2, nox, pm10), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. a survey was conducted with multiple stakeholders to determine the relative importance of risk factors. the survey results showed that the most concerned risks are so2 and nox emissions. the proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (eri). accordingly, we assessed the catalagzi coal-fired power plant, which has been in operation on the black sea coast in northwestern turkey. for this case study, the eri resulted in a value of 0.78 (on a scale of 0�1), showing high environmental risk to the facility. moreover, the applicability of the proposed framework was tested in several existing coal-fired power plants using simultaneous measurements. all studied coal-fired power plants in turkey have unacceptable pollutants (pm10, so2, and nox) concentration levels indicating high health risk potential. the application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in turkey to specify more strict emission limits and to monitor co2, fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake. 9. title: individual differences in numerical representations of risk in health decision making: a fuzzy-trace theory approach authors: priscila g. brust-renck, valerie f. reyna abstract: fuzzy-trace theory predicts that decisionmakers process numerical information about risk at multiple levels in parallel: the simplest level, nominal (categorical some-none) gist, and at more fine-grained levels, involving relative comparison (ordinal less-more gist) and exact quantities (verbatim representations). however, little is known about how individual differences in these numerical representations relate to judgments and decisions, especially involving health tradeoffs and relative risks. to investigate these differences, we administered measures of categorical and ordinal gist representations of number, objective numeracy, and intelligence in two studies (ns = 978 and 956). in both studies, categorical and ordinal gist representations of number predicted risk judgments and decisions beyond objective numeracy and intelligence. participants with higher scores in categorical gist were more likely to choose options to avoid cancer recurrence risks; those who were higher in ordinal gist of numbers were more likely to discriminate relative risk of skin cancer; and those with higher scores in objective numeracy were more likely to choose options that were numerically superior overall in terms of relative risk of skin cancer and of genetic risks of breast cancer (e.g., lower numerical probability of cancer). results support parallel-processing models that assume multiple representations of numerical information about risk, which vary in precision, and illustrate how individual differences in numerical representations are relevant to tradeoffs and risk comparisons in health decisions. these representations cannot be reduced to one another and explain psychological variations in risk processing that go beyond low versus high levels of objective numeracy. 10. title: language modality influences risk perception: innovations read well but sound even better authors: janet geipel, constantinos hadjichristidis, lucia savadori, boaz keysar abstract: psychological theories implicitly assume that the modality in which information is conveyed�spoken or written�leaves judgment and choice unaltered. modality is rarely considered in textbooks on judgment and decision making, and the selection of modality in research is often based on convenience. we challenge this theoretical assumption. three experiments (n = 984) show that the modality in which novel technologies are described systematically influences their perceived risk and benefit. participants either read or heard advantages and disadvantages of novel technologies and then assessed their risk and benefit. in study 1, spoken descriptions prompted more positive evaluations toward the technologies in terms of overall risks and benefits than written descriptions. studies 2 and 3 replicated this modality effect and demonstrated that affect partially explains it, as spoken descriptions induced more positive feelings toward the new technologies than written descriptions. study 3 (preregistered) showed that the influence of modality is unique to novel technologies and does not extend to familiar ones. these findings contribute theoretically to the understanding of the relationship between language and thought, and carry implications for survey research and the use of voice assistant technology. 11. title: application of data mining to minimize fire-induced domino effect risks authors: long ding, faisal khan, jie ji abstract: data mining (dm) has been applied in many advanced science and technology fields, but it has still not been used for domino effect risk management to explore minimum risk scenarios. this work investigates the feasibility of dm in minimizing the risk of fire-induced domino effects in chemical processing facilities. based on dm, an evidential failure mode and effects analysis (e-fmea), which could bridge chemical facilities� operational reliability and domino effect risk, is combined with fault tree analysis (fta) for the occurrence risk modeling of loss of containment (loc) event of chemical facilities, which is often the triggering point of fire-induced domino effects. industry specific data such as reliability data, inspection records, and maintenance records are of great value to model the potential occurrence criticality of loc. the data are used to characterize the loc risk priority number (rpn) of chemical facilities through fta and e-fmea, search and statistics rules are proposed to mine inspection records to assess loc risk factors. according to the rpn scores of facilities, inherent safety strategies to minimize risk via inventory control are proposed, and their effectiveness is tested using a well-known probit model. in this way, the approach proposes a unit-specific evidence-based risk minimization strategy for fire-induced domino effects. a case study demonstrates the capability of dm in the risk minimization of fire-induced domino effects. 12. title: risk assessment based on a stpa�fmea method: a case study of a sweeping robot authors: yanxi zhang, tiezhong liu abstract: despite rapid developments in the quality and safety of consumer products, the rise of intelligent household appliances, such as sweeping robots, has introduced new safety concerns. considering �person�product�environment� elements and the complex systems of emerging consumer products, this study presents a new method of risk assessment for consumer products: systems theoretic process analysis (stpa)�failure mode and effects analysis (fmea). as a case study, this method is applied to the safety control of a sweeping robot. the results suggest that this method can identify all the possible failure modes and injury scenarios among the product components, and the safety constraints in the hierarchical control structure of the interactive system. moreover, the stpa�fmea method combines user and environmental factors with the value of product risk events, based on the risk priority number (rpn). this provides an accurate and orderly system to reduce or eliminate the root causes of accidents and injuries. finally, analysis of unsafe control behavior and its causes can be used to suggest improved safety constraints, which can effectively reduce the risk of some injury scenarios. this paper presents a new method of risk assessment for consumer products and a general five-level complex index system. 13. title: murphy scale: a locational equivalent intensity scale for hazard events authors: yi victor wang, antonia sebastian abstract: empirical cross-hazard analysis and prediction of disaster vulnerability, resilience, and risk requires a common metric of hazard strengths across hazard types. in this paper, the authors propose an equivalent intensity scale for cross-hazard evaluation of hazard strengths of events for entire durations at locations. the proposed scale is called the murphy scale, after professor colleen murphy. a systematic review and typology of hazard strength metrics is presented to facilitate the delineation of the defining dimensions of the proposed scale. an empirical methodology is introduced to derive equivalent intensities of hazard events on a murphy scale. using historical data on impacts and hazard strength indicators of events from 2013 to 2017, the authors demonstrate the utility of the proposed methodology for computing the equivalent intensities for earthquakes and tropical cyclones. as part of a new area of research called hazard equivalency, the proposed murphy scale paves the way toward creating multi-hazard hazard maps. the proposed scale can also be leveraged to facilitate hazard communication regarding past and future local experiences of hazard events for enhancing multi-hazard preparedness, mitigation, and emergency response. 14. title: new methods for evaluating energy infrastructure development risks authors: michael j. ford, ahmed abdulla abstract: many energy technologies that can provide reliable, low-carbon electricity generation are confined to nations that have access to robust technical and economic capabilities, either on their own or through geopolitical alliances. equally important, these nations maintain a degree of institutional capacity that could lower the risks associated with deploying emergent energy technologies such as advanced nuclear or carbon capture and storage. the complexity, expense, and scrutiny that come with building these facilities make them infeasible choices for most nations. this paradigm is slowly changing, as the pressing need for low-carbon electricity generation and ongoing efforts to develop modular nuclear and carbon capture technologies have opened the door for potentially wider markets, including in nations without substantial institutional capacity. here, using advanced nuclear technologies as our testbed, we develop new methods to evaluate national readiness for deploying complex energy infrastructure. specifically, we use data envelopment analysis�a method that eliminates the need for expert judgment�to benchmark performance across nations. we find that approximately 80% of new nuclear deployment occurs in nations that are in the top two quartiles of institutional and economic performance. however, 85% of potential low-carbon electricity demand growth is in nations that are in the bottom two quartiles of performance. we offer iconic paradigms for deploying nuclear power in each of these clusters of nations if the goal is to mitigate risk. our research helps redouble efforts by industry, regulators, and international development agencies to focus on areas where readiness is low and risk correspondingly higher. �nn/ffnċ� 15. title: review of atlas of global change risk of population and economic systems by peijun shi (ed). springer nature singapore pte. ltd., gateway east, singapore 189721, singapore, pp. xx 278. isbn: 978-981-16-6690-2 (hbk), 978-981-16-6691-9 (ebk) authors: feng kong abstract: the article reviews the book �atlas of global change risk of population and economic systems� by peijun shi. 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