Search Results (175)

Recent climate changes have led to an increase in flood intensity, often resulting in frequent levee overtopping, which causes significant human and property damage. High vulnerability to such breaches is expected in general, especially at river curves. This study aims to predict the occurrence of levee overtopping at these critical points and to suggest a curve, the levee overtopping risk curve, to assess overtopping probabilities. For this purpose, several dimensionless parameters, such as superelevation relative to levee height ($∆$y/H) and the channel’s Froude number, were examined. Based on dimensional analysis, a relationship was developed, and the levee overtopping curve was finally proposed. The accuracy of this curve was validated through numerical analysis using a selected levee case, which clearly distinguished between safe and risky conditions for levee overtopping. The curve is designed for immediate integration into the hydraulic design processes, providing engineers with a reliable method for optimizing levee design to mitigate overtopping risks. It also serves as a critical decision-making tool in flood risk management, particularly for urban planning and infrastructure development in areas prone to flooding. Full article

The impact of alternative distribution channels, such as bank Automated Teller Machines (ATMs), on the financial industry is growing due to technological advancements. Investing in ideal locations is critical for new ATM companies. Due to the many factors to be evaluated, this study addresses the problem of determining the best location for ATMs to be deployed in Istanbul districts by utilizing the multi-criteria decision-making framework. Furthermore, the advantages of fuzzy logic are used to convert expert opinions into mathematical expressions and incorporate them into decision-making processes. For the first time in the literature, a model has been proposed for ATM location selection, integrating clustering and the interval-valued Fermatean fuzzy analytic hierarchy process (IVFF-AHP). With the proposed methodology, the districts of Istanbul are first clustered to find the risky ones. Then, the most suitable alternative location in this district is determined using IVFF-AHP. After deciding the ATM locations with IVFF-AHP, in the last step, a Double Deep Q-Network Reinforcement Learning model is used to optimize the Cash in Transit (CIT) vehicle route. The study results reveal that the proposed approach provides stable, efficient, and adaptive routing for real-world CIT operations. Full article

Sleep deprivation (SD) is known to impair cognitive functions, and its effect on vigilance and concentration has been explored extensively. However, its effect on the decision-making ability has been researched to a lesser extent. With varying methodologies and conflicting findings in the literature, the effect of SD on decision-making remains complex and inconsistent. Given the critical implications for fields where decision-making is essential, such as medicine, understanding the impact of SD on this cognitive process is crucial. This scoping review aimed to map the existing literature on the effects of SD on decision-making, identify research trends, and highlight inconsistencies to provide implications for practice and research. The review was conducted following PRISMA-ScR guidelines. Databases searched include APA Psych, Web of Science, Scopus, Academic Search Complete, and PubMed. Inclusion criteria focused on peer-reviewed studies from 2014 onward, exploring the impact of SD on decision-making across various tasks and designs. The final selection included 25 articles, representing 2276 participants. The review may suggest that SD, whether partial or total, impairs decision-making ability, with many studies reporting increased risky decisions. The severity of impairment varied based on the type of decision-making task and the duration of SD. However, a few studies reported insignificant effects, particularly in economic decision-making tasks. Moderating factors, such as gender and the origin of sleep loss (voluntary vs. involuntary), were also identified as influential. Sleep deprivation commonly impairs the decision-making ability, with significant implications for high-stakes professions. However, the variability in findings suggests a need for further research into the moderating factors. The review underscores the importance of adequate sleep for cognitive function and the need for policies that mitigate the risks of SD in critical decision-making environments. Full article

Introduction: The COVID-19 pandemic highlighted the need for digital tools that support public health decision-making and behavior change. Dashboards became a primary method for communicating infectious disease data. However, their influence on preventive health behaviors (PHBs) is not well understood—especially among young adults. This group is less likely to adhere to PHBs, but highly familiar with online tools. Methods: Two experimental studies were conducted with young adult participants (200 in Study 1, 228 in Study 2) who viewed the same COVID-19 data in dashboards with or without actionable components. Participants were randomly assigned to different dashboard conditions to measure, on seven-point Likert scales, their PHB intentions and perceptions of behavioral control, attitudes, norms, and risk. The actionable dashboard interventions, designed using the theory of planned behavior, included dynamic behavioral guidance and risk level visualizations. Results: Actionable dashboards versus basic dashboards significantly increased PHB intentions (B = 0.84, p < 0.001, Study 1). Dynamic behavioral guidance was the key dashboard component influencing PHB intentions (B = 0.61, p = 0.005, Study 2). Parallel mediation analysis testing norms, attitudes, behavioral control, and perceived risk against one another found that only norms explained the link between the dashboard intervention and PHB intentions (B_boot = 0.18 and 0.19). Conclusions: Findings suggest that actionable dashboards can effectively promote PHB by influencing psychosocial beliefs. These dashboards provide context and guidance, making risky situations more manageable and directing individuals to appropriate preventive actions. Public health professionals should consider incorporating behavioral guidance into community health dashboards to improve their effectiveness. Full article

As the global population ages, so too does the prevalence of older people with chronic kidney disease (CKD). Helping people age well with CKD and supporting older people with end-stage kidney disease (ESKD) to make personalized decisions regarding kidney replacement therapy (KRT) or conservative care (CC) are an essential component of care. However, these factors are relatively underreported in both the fields of nephrology and geriatric medicine, and prospective, randomized evidence is lacking. This narrative review article, authored by both nephrologists and geriatricians, discusses specific geriatric issues that arise in older people with CKD and why they matter. The available evidence for KRT or CC in older people with frailty is outlined. The importance of performing a comprehensive geriatric assessment, or a modified nephrogeriatric assessment, to ensure a systematic evaluation of the person’s medical problems and life needs, goals, and values is described. We consider different models of nephrogeriatric care and how they may be implemented. Kidney supportive care—addressing an individual’s symptoms and overall well-being alongside the more traditional nephrological principles of preventing disease progression and optimizing risk—is highlighted throughout the article. We outline ways of identifying the later stages of a person’s disease journey, when transition to palliative care is indicated, and elaborate methods of preparing patients for this through multidisciplinary advance care planning. Finally, we discuss practice and systems for nephrogeriatric care in five different European countries and consider future directions, challenges, and highlights in this rapidly evolving, increasingly relevant field. Full article

This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally. Full article

Innovation has played a vital role in continuing business operations worldwide amid the challenges of the COVID-19 pandemic. Innovation is critical for the success and survival of global organizations. Due to the risky long-term nature of innovation, executives with decision-making power may act cynically. Such pessimistic actions become normal when executive compensation is based on the firm’s short-term outcomes. Therefore, the current research examines the effect of executive compensation on research and development (R&D) investment using data from the world’s top 48 innovative companies in Australia. The proposed model was tested using Smart-PLS (v.3.2.8). The findings indicate that board composition significantly and positively affects R&D investment. Likewise, the long-term composition of executives has a positive effect, whereas short-term executive compensation has a negative effect on R&D. Hence, this research suggests that to increase innovation, firms should control the myopic actions of top management by orientating their compensation toward long-term innovation. Full article

Agriculture, livestock, and forestry are crucial in mitigating hydrogeological risks, such as floods, particularly severe in the Mediterranean region. Still, the ecosystem services (ESs) provided by these activities are often undervalued. However, to assign them an economic value and ensure their effective incorporation into decision-making processes and territorial planning, they must first be recognized, appreciated, and deemed necessary by society. Despite several studies on ESs in the primary sector, research on agroecosystem flood regulation is limited, leaving key aspects unaddressed for decision-makers. No previous studies explicitly address the evaluation of ESs provided by agriculture, livestock, and forestry businesses in hydrogeological risky environments, especially in flood-prone areas. This study investigates the perception of the ESs provided by the above activities, focusing on those furnished in areas subject to hydrogeological instability. It also focuses on Sardinia (Italy), which is highly susceptible to hydrogeological instability. Through a quantitative survey of 270 residents and non-residents, the research provides evidence of society’s perception of the above ESs. Supporting ESs obtain greater appreciation in crop activities, particularly concerning the preservation of pollinating insects, soil fertility, biodiversity, and water quality. Among the regulatory Ess, appreciation is most prominent in fire risk management and flood risk regulation. Similar arguments can be made for livestock activities. Forestry activities are perceived as key players in managing flood risk, landslide risk, soil erosion, and climate change. The Multiple Correspondence Analysis indicates that appreciating one ES often leads to the recognition of others. Additionally, a set of Logit Regressions showed that while age and gender do not influence ESs perception, education level and awareness of climate change-related emergencies play a significant role. Those findings support more informed decision-making and fostering sustainable practices in areas at risk of hydrogeological disasters and lead to several important implications for practitioners, academics, and policymakers. Full article

This paper reexamines the question of gender differences in financial relative risk aversion using updated methods and data. Specifically, the paper revisits the 1998 work “Are women more risk averse?” by Jianakoplos and Bernasek, suggests refinements in their model in relation to the database used, namely the U.S. Federal Reserve Board’s Survey of Consumer Finances (SCF), and performs new tests on the latest SCF from 2022. The suggested refinements pertain first to an enhanced computation of wealth, which includes additional categories of assets such as 401(k)s or other thrift savings accounts, and second to the more subtle handling and consideration of specific demographic data of the SCF respondents. Unlike the original study, which also included married couples, the new study focuses exclusively on single-headed (never-married) households. This eliminates ambiguity about the actual financial decision maker in households, enabling a clearer assessment of individual gendered behavior. Following the refinements, the new tests reveal a continuing pattern of decreasing relative risk aversion; however, contrary to the 1998 findings, there is no significant gender difference in financial relative risk aversion in 2022. This study also documents that education levels strongly influence risk-taking: single women with higher education levels are more likely to hold risky assets, while for men, higher education correlates with less risk-taking. The paper concludes by informing policymakers and financial educators so as to further tailor their strategies for promoting gender equality in financial decision-making. Full article

In uncertain situations, individuals seek to maximize rewards while managing risks. Yet, the effects of acute stress and anxiety on decision-making in ambiguous and risky contexts are unclear. This study aims to contribute to the exploration of how acute stress influences sensitivity to immediate vs. delayed rewards, risk management strategies, and the role of anxiety in these processes. This study used the laboratory acute stress induction paradigm to analyze the direction of influence of acute stress on ambiguity decision-making and risky decision-making in males and then used moderating effect analysis to study the impact of anxiety on this process. The results show that a combination of the Socially Evaluated Cold Pressor Test and the Sing-a-Song Stress Test can successfully induce acute stress, which reduces both the proportion of the options selected that represent long-term rewards and risk-adjustment ability. Additionally, trait anxiety had a moderating effect on the influence of stress on ambiguity decision-making. Acute stress reduces focus on long-term rewards while increasing focus on short-term rewards, leading to impulsivity and impaired risk-adjustment. Additionally, to some extent, high trait anxiety scores predict better performance in making decisions under ambiguity during stress. Full article

As Arctic sea ice continues to melt and global demand for clean energy rises, Russia’s Liquefied Natural Gas (LNG) exports via the Northern Sea Route (NSR) are rapidly increasing. To ensure the operational safety of LNG carriers and safeguard the economic interests of stakeholders, including shipowners, a thorough assessment of the navigability of various ice-class LNG carriers along this route is essential. This study collected Arctic ice condition data from 2014 to 2023 and applied the Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology to calculate the Risk Index Outcome (RIO) for LNG carriers with No Ice Class, Arc4, and Arc7 ice classifications in Arctic waters. A navigability threshold of 95% RIO ≥ 0 was established to define navigable windows, and critical waters were identified where sections of the route remain in hazardous or risky conditions year-round. The results indicate that for No Ice Class vessels, Arc4 vessels, and Arc7 vessels, the navigable windows for westbound Route 1 and Route 2 under light, normal, and heavy ice conditions range from 70 to 133 days, 70 to 365 days, and 70 to 365 days, respectively, while for eastbound Route 3, the navigable windows range from 0 to 84 days, 0 to 238 days, and 7 to 365 days, respectively. The critical waters affecting the navigability of No Ice Class vessels, Arc4 vessels, and Arc7 vessels are primarily located in the Kara Sea, Laptev Sea and East Siberian Sea. This study, using the POLARIS methodology, provides valuable insights into the navigability of LNG carriers with different ice classes along the NSR, supporting the development and utilization of Arctic energy and shipping routes while offering decision-making support for stakeholders involved in Arctic maritime operations. Full article

Driving a vehicle, whether motorized or not, is a risky activity that can lead to a traffic accident and directly or indirectly affect all road users. In particular, road crashes involving pedestrians have caused the highest number of deaths and serious injuries in recent years. In order to prevent and reduce the occurrence of these types of traffic accidents and to optimize the use of the available resources of the administrations in charge of road safety, an updatable predictive model using Markov chains is proposed in this work. Markov chains are used in fields as diverse as hospital management or electronic engineering, but their application in the field of road safety is considered innovative. They are prediction and decision techniques that allow the estimation of the state of a given system by simulating its stochastic risk level. To carry out this study, the available information on traffic accidents involving pedestrians in the database of the Local Police of Badajoz (a medium-sized city in the southwest of Spain) in the period 2016 to 2023 were analyzed. These data were used to train a predictive model that was subsequently used to estimate the probability of occurrence of a traffic crash involving pedestrians in different areas of this city, information that could be used by the authorities to focus their efforts in those areas with the highest probability of a road crash occurring. This model can improve the identification of high-risk locations, and urban planners can optimize decision making in designing appropriate preventive measures and increase efficiency to reduce pedestrian crashes. Full article

Background: Pediatric and congenital heart surgery (PCHS) is highly risky. Complications associated with this surgical procedure are mainly caused by the severity of the disease or the unnecessary, late, or premature execution of the procedure, which can be fatal. In this context, prognostic models are crucial to reduce the uncertainty of the decision to perform surgery; however, these models alone are insufficient to maximize the probability of success or to reverse a future scenario of patient death. Method: A new approach is proposed to reverse the prognosis of death in PCHS through the use of (1) machine learning (ML) models to predict the outcome of surgery; (2) an explainability technique (ET) to determine the impact of main risk factors; and (3) a simulation method to design health scenarios that potentially reverse a negative prognosis. Results: Accuracy levels of 96% in the prediction of mortality and survival were achieved using a dataset of 565 patients undergoing PCHS and assessing 10 risk factors. Three case studies confirmed that the ET known as LIME provides explanations that are consistent with the observed results, and the simulation of one real case managed to reverse the initial prognosis of death to one of survival. Conclusions: An innovative method that integrates ML models, ETs, and Simulation has been developed to reverse the prognosis of death in patients undergoing PCHS. The experimental results validate the relevance of this approach in medical decision-making, demonstrating its ability to reverse negative prognoses and provide a solid basis for more informed and personalized medical decisions. Full article

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension funds. Traditional methods often face challenges in computational efficiency and applicability to different market conditions. This study aimed to innovatively transplant an algorithm from reinforcement learning that enhances the efficiency and accuracy of life cycle asset allocation. We synergized tabular methods with Monte Carlo simulations to solve the pension problem. This algorithm was designed to correspond states in reinforcement learning to key variables in the pension model: wealth, labor income, consumption level, and proportion of risky assets. Additionally, we used cleaned and modeled survey data from Chinese consumers to validate the model’s optimal decision-making in the Chinese market. Furthermore, we optimized the algorithm using parallel computing to significantly reduce computation time. The proposed algorithm demonstrated superior efficiency compared to the traditional value iteration method. Serial execution of our algorithm took 29.88 min, while parallel execution reduced this to 1.42 min, compared to the 41.15 min required by the value iteration method. These innovations suggest significant potential for improving pension fund management strategies, particularly in the context of the Chinese market. Full article

Children and adolescents face a wide variety of developmental changes and environmental challenges, and it is estimated that at least one in five children aged 3–17 will experience behavioral or mental health issues. This period of life coincides with major changes in brain structure and function that have profound long-term consequences for learning, decision-making (including risk taking), and emotional processing. For example, continued development of the prefrontal cortex in adolescence is a sensitive period during which individuals are particularly susceptible to risky behaviors, environmental stressors, and substance use. While recent advances in mental health literacy programs have paved the way for increased awareness of the benefits of mental health curricula in schools, these efforts could be greatly bolstered with support in basic neuroscience education in developmentally appropriate and area-specific content. Here, we provide a discussion on the basic structural and functional changes occurring in the brain throughout childhood, how this contributes to changes in cognitive function, and the risk factors posed by early life adversity, stress, and drug use. Finally, we provide a perspective on the benefits of integrating findings from the field of neuroscience and suggestions for tools to better equip students, teachers, administrators, and school mental health staff to provide new directions for addressing the mental health crises faced by millions of children and youth each year. Full article