Risk Analysis最新文献

Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.

Mycotoxins are secondary metabolites produced by fungi found in corn and are anticipated to increase globally due to enhanced weather extremes and climate change. Aflatoxin (AFL) is of concern due to its harmful effects on human and animal health. AFL can move through complex grain supply chains in the United States, including multiple stakeholders from farms, grain elevators, grain and ethanol processors, and feed mills, before reaching end users, putting numerous entities at risk. Since corn is an essential food and feed product, risk management of AFL must be considered. This case study aimed to (1) calculate the probabilities of pivotal events with AFL in corn at Food Safety Modernization Act-regulated entities using an event tree analysis (ETA) and (2) propose recommendations based on factors identified through the ETA for AFL risk management. The ETA was based on historical AFL prevalence data in Iowa above a 20-part per billion (ppb) threshold (2.30%). Results showed four single-point failures in feed safety systems, where countermeasures did not function as designed. Failure is defined as the type 2 error of corn being infected with AFL <20 ppb, when it is >20 ppb, and the overall system fails to detect this with contaminated corn reaching end users. The success rate is defined as detecting the corn samples correctly >20 ppb. The average success rate was 50.14%, and the failure rate was 49.86%. It was concluded that risk-informed decisions are a critical component of effective AFL monitoring in corn, with timely intervention strategies needed to minimize the overall effects on end users.

Social norms are often considered as behavioral guidelines to mitigate health and environmental risks. However, our understanding of the magnitude of their impact on risk-mitigating behaviors and how perceptions of risks affect the magnitude remains limited. Given the increasing importance of understanding factors influencing behavioral responses to health and environmental risks, this research examines whether the relationship between social norms and behavioral intention to mitigate health and environmental risks is a function of (1) risk perceptions and (2) uncertainty about risk perceptions. A cross-sectional survey involving a national sample (N = 803) across three health and environmental risks (i.e., infectious diseases, climate change, and water shortage) is conducted. The results reveal a three-way interaction between descriptive norms, uncertainty about susceptibility, and uncertainty about severity on behavioral intention to mitigate the risk. Individuals exhibit the strongest intention to engage in risk-mitigating behaviors when they perceive prevailing social norms and are uncertain about their susceptibility to the risk and the severity of the risk. Moreover, injunctive norms interact with uncertainty about susceptibility to influence behavioral intention, such that the more uncertain individuals feel about their vulnerability to a risk, the stronger the impact of injunctive norms is on behavioral intention. Neither descriptive nor injunctive norms interact with perceived risks to influence behavioral intention. This study contributes valuable insights into the interplay between social norms, uncertainty about perceived risk, and behavioral intention, and offers valuable theoretical and practical implications.

We examine the impact of climate risk on discouraged borrowers among small and medium-sized enterprises (SMEs) in the eurozone, using a unique European Central Bank dataset focusing on the demand side of credit markets. We argue that two opposing channels may exist in this relationship: Either climate risk has a negative effect stemming from increased demand for sustainable or climate-resilient projects that enhance creditworthiness, or climate risk has a positive effect arising from heightened climate uncertainty and risk aversion, leading to credit self-rationing among SMEs. Our findings reveal that heightened climate risk prompts SMEs to self-ration credit, leading to higher probabilities of discouraged borrowers. Our research deepens the understanding of the impact of climate risk on credit-related decisions, stressing the need for proactive measures to integrate climate risk assessments into regulatory frameworks and lending practices. The findings underscore the vulnerability of SMEs to climate risk, emphasizing emphasizing the importance of tailored support mechanisms for economic resilience.

Two recent trends made this project possible: (1) The recognition that near misses can be predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine learning (ML) tools that make data analytics accessible for many organizations. Increasingly, organizations are learning from prior incidents to improve safety and reduce accidents. The U.S. Coast Guard (USCG) uses a reporting system called the Marine Information for Safety and Law Enforcement (MISLE) database. Because many of the incidents that appear in this database are minor ones, this project initially focused on determining if near misses in MISLE could be predictors of future accidents. The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level. Using this finding, a predictive AI/ML model was built for each waterway type by vessel combination. Random forest decision tree AI/ML models were used to identify waterways at significant accident risk. An R-based predictive model was designed to be run monthly, using data from prior months to make future predictions. The prediction models were trained on data from 2007 to 2022 and tested on 10 months of data from 2022, where prior months were added to test the next month. The overall accuracy of the predictions was 92%-99.9%, depending on model characteristics. The predictions of the models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of the system.

International relations (IR) have great uncertainty and instability. Bad IR or conflicts will bring about heavy economic losses and widespread social unrest domestically and internationally. The accurate prediction for bilateral relations can support decision making for timely responses, which will be used to find ways to maintain development in the complex international situation. An international relations quantitative evaluation model (IRQEM) is proposed by integrating a variety of research models and methods like the interpretative structural modeling method (ISM), Bayesian network (BN) model, the Bayesian search (BS), and the expectation-maximization (EM) algorithm, which is novel for IR research. Factors from several different fields are identified as BN nodes. Each node is assigned different state values. The hierarchical structure of these BN nodes is obtained by ISM. The data collection of 192 cases is used to construct the BN model by GeNIe 4.0. The IRQEM can be used to evaluate the influence of emergencies on IR. The critical factors of IR also can be explored through our proposed model. Results show that the prediction of bilateral relations under emergencies can be realized by updating the indicator set when emergencies occur. The capability to anticipate threats of IR changes is advanced by optimizing the reporting information of IR forecasting through a combination of qualitative and quantitative methods, charts, and texts. Relevant analysis results can provide support for national security decision making.

The term "real risk" and variations of this term are commonly used in everyday speech and writing, and in the scientific literature. There are mainly two types of use: i) in statements about what the real risk related to an activity is and ii) in statements about the risk related to an activity being real. The former type of use has been extensively discussed in the literature, whereas the latter type has received less attention. In the present study, we review both types of use and analyze and discuss potential meanings of type ii) statements. We conclude that it is reasonable to interpret a statement about the risk being real as reflecting a judgement that there is some risk and that the knowledge supporting this statement is relatively strong. However, such a statement does not convey whether the risk is small or large and needs to be supplemented by a characterization of the risk.

We examined hazard and risk-related metrics of the highest- and lowest-income counties and municipalities in each U.S. state. Indicators of natural and anthropogenic hazards, health outcomes, location of locally unwanted land uses, food insecurity, and other metrics were used to measure social and environmental justice. As expected, the highest-income places have better health outcomes, access to assets that protect health, and high municipal ratings of place quality compared with their poorest counterparts. Yet, they also have higher natural hazard risks and are more likely to live near concentrations of anthropogenic hazards. That is, high-income places have a lot to lose. Although the poorest jurisdictions demonstrate cumulative disadvantages, those in rural areas are exposed to less dense motor vehicle traffic and other hazards and risks associated with urban life. Relationships between income and the geography of hazards and risks are not simple. Even the highest-income areas face challenges. We suggest improvements in databases and tools to increase the focus on and monitoring of the breadth of risks people face in all areas.

Dengue fever (DF) is a pervasive public health concern in tropical climates, with densely populated regions, such as India, disproportionately affected. Addressing this issue requires a multifaceted understanding of the environmental and sociocultural factors that contribute to the risk of dengue infection. This study aimed to identify high-risk zones for DF in Jaipur, Rajasthan, India, by integrating physical, demographic, and epidemiological data in a comprehensive risk analysis framework. We investigated environmental variables, such as soil type and plant cover, to characterize the potential habitats of Aedes aegypti, the primary dengue vector. Concurrently, demographic metrics were evaluated to assess the population's susceptibility to dengue outbreaks. High-risk areas were systematically identified through a comparative analysis that integrated population density and incidence rates per ward. The results revealed a significant correlation between high population density and an increased risk of dengue, predominantly facilitated by vertical transmission. Spatially, these high-risk zones are concentrated in the northern and southern sectors of Jaipur, with the northern and southwestern wards exhibiting the most acute risk profiles. This study underscores the importance of targeted public health interventions and vaccination campaigns in vulnerable areas. It further lays the groundwork for future research to evaluate the effectiveness of such interventions, thereby contributing to the development of robust evidence-based strategies for dengue risk mitigation.