Risk Decision Research Articles

Analysis of EU RASFF notifications on genetically modified food and feed from 2002 to 2023

This study examines 827 notifications about genetically modified (GM) food (724) and feed (103) in the EU's Rapid Alert System for Food and Feed (RASFF) from 2002 to 2023. It identifies the most frequently notified GM products, their origin countries, notification year, type, risk level, and subsequent actions taken by authorities. Alert notifications represented 19.20% of food and 3.88% of feed cases. Border rejections were more common for food (25.55%) than feed (18.45%). China was the primary source of food notifications (35.08%), while the US led in feed notifications (35.92%). Cereals and bakery products were the most reported food category (59.94%), followed by fruits and vegetables (11.19%). Rice was the product most often involved in GM food notifications, constituting 57.73% of the total, followed by linseed (14.64%), papaya (10.77%), and food supplements (5.11%). Undecided risk decisions were more prevalent in food (90.61%) than feed notifications (66.02%). Conversely, the 'not serious' category was higher for feed (32.04%) than food (5.25%). Serious and potential risk notifications were low for both categories. Market withdrawals accounted for 30.25% of food and 23.30% of feed notifications. This overview provides insights for policymakers and stakeholders in the agri-food sector, highlighting trends and areas of concern.

Decision model of public opinion risk in campus social network based on hybrid dynamic deletion and shortest path algorithm

Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 KB/s, which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.

Unveiling housing preferences amidst flood risks: an empirical study in an emerging market

Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations.

The dementia–nature–inclusivity nexus and the needs of people living with dementia

Abstract Understanding how to improve the physical and cognitive accessibility of visitor economy businesses and organisations wanting to offer nature-based outdoor pursuits for people with dementia is key to supporting their inclusion and agency. The aim of this qualitative study was to understand the experiences, needs and preferences of people with dementia participating in nature-based outdoor pursuits in their leisure time. Semi-structured interviews were conducted with 15 people with dementia and 15 family members and subjected to thematic analysis. Four themes related to inclusion for people with dementia and their family members reflected diversity in individual needs and preferences for engaging with nature-based outdoor pursuits, their own adaptations to maintain access including accommodating risk, how cognitive and physical accessibility can be supported by businesses, and which practical and psychosocial barriers prevent inclusion. Learning from people with dementia and their family members has helped bridge the gap to their inclusion in nature-based outdoor pursuits. Their insights will inform the development of such pursuits by businesses and organisations as well as future work into risk decision-making.

Risk Factors for Multidrug Resistance in Patients Infected with Carbapenem-Resistant Klebsiella pneumoniae: A Nomogram.

Our aim was to determine the risk factors for multidrug resistance in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP). The information of 196 patients with Klebsiella pneumoniae infection was collected. The patients were subsequently assigned to the carbapenem-resistant, multidrug-resistant, and non-multidrug-resistant groups. The risk factors for multidrug resistance in CRKP patients were assessed via least absolute shrinkage and selection operator and logistic regression analyses. Moreover, a nomogram was constructed dependent on the identified risk factors, and calibration and decision curves were plotted to detect its accuracy. Length of stay (LOS) [odds ratio (OR) and 95% confidence interval (CI): 4.558 (1.157-17.961), P = 0.030], intensive care unit (ICU) stay within 30 days [OR and 95% CI: 12.643 (3.780-42.293), P < 0.001], Glasgow Coma Scale (GCS) score [OR and 95% CI: 13.569 (2.738-67.236), P = 0.001], fungal infection [OR and 95% CI: 6.398 (1.969-20.785), P = 0.002], and cardiovascular disease (CVD) [OR and 95% CI: 3.871 (1.293-11.592), P = 0.016] were identified as risk factors for multidrug resistance in CRKP patients. The concordance index (C-index) of the constructed nomogram was 0.950 (95% CI: 0.945-0.955). Moreover, decision curve analysis elucidated the nomogram utilization across a wide range of probability thresholds, ranging from 1% to 100%. Finally, internal validation using random data validated the robustness of the predictive model, yielding a C-index of 0.937. The LOS, ICU stay within 30 days, GCS score, fungal infection, and CVD were recognized as risk factors for multidrug resistance in CRKP patients. The constructed nomogram could accurately predict multidrug-resistant CRKP infections in patients.

Forces at play: A qualitative study of risk aversion, policy and decision making for children's physically active play in schools

This study investigated (i) the social and political factors driving risk averse decision making for children's physically active play in schools, and (ii) the policies shaping these decisions in Victoria, Australia. A theory-informed multi-method case study design combined policy document mapping, semi-structured interviews, and photo elicitation with 30 education policy actors. Data were analysed using reflexive thematic analysis. Findings showed the policy landscape was heavily skewed towards managing risk and safety (84%; n = 42), with fewer, mostly optional, policies for promoting physical activity, and a lack of policy to promote or protect play. Thematic findings indicated an intersecting range of factors in this setting (including occupational health and safety, knowledge and skill gaps, parent factors, children's physical capabilities, an undervaluing of play) can deter schools from providing an environment that enables children's freely chosen, physically active play. Multi-faceted, systems-based approaches that go beyond interventions aimed at individual behaviour change are needed.

Predicting High-Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation.

To develop and validate a prediction model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF). AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non-invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction. Multicentre observational study. This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)-based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models. The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis. This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF. Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC. TRIPOD checklist for prediction model studies was followed in this study. Patients were involved in the sample of the study.

Can an Expert Opinion Mitigate Racially Biased Diversion Decisions? An Empirical Examination in the Context of Re-Offense Risk Assessment

Using a vignette-based experimental design, this study tested whether the racial or ethnic identity of a justice-impacted person influences jury-eligible individuals’ perceptions of post-conviction risk, placement (incarceration vs. community supervision), and need for mandated treatment. Furthermore, we examined whether the inclusion of data from a reoffense risk tool could mitigate racial bias. Participants recruited through Amazon’s MTurk ( N = 448) were randomly assigned to conditions varying by race/ethnicity of an examinee (Black, Latino, White) and if/how risk assessment data were included (no risk data, risk data only, and risk data reported by a qualified examiner). There was no statistically significant effect of the examinee’s racial/ethnic identity on ratings of reoffense risk or placement decisions; however, the Black examinee was more likely to be mandated with treatment. Regardless of examinee identity, participants exposed to risk information proffered by an expert more often calibrated their risk decisions to the examinee’s actual risk level.

Fats and oils contaminants: an analysis of EU RASFF notifications from 1999 to 2023

Between 1999 and 2023, an extensive analysis of 713 notifications from the EU Rapid Alert System for Food and Feed (RASFF) related to fats and oils was conducted. The analysis covered the most frequently reported products, associated hazards, countries of origin, notification classifications, risk decisions, and actions taken in response. The data demonstrate considerable variability in RASFF notifications throughout the studied period, with significant spikes and a recent upward trend. Of all notifications, 44.32 % were categorized as alerts, and 23.42 % as border rejections. In terms of risk decisions, 34.08 % were deemed serious and 60.59 % remained undecided. Palm oil emerged as the most frequently notified product, representing 37.87 % of all notifications, followed by olive oil at 15.71 % and sunflower oil at 11.36 %. The Sudan 4 dye was identified as the most common hazard, constituting 30.01 % of all fats and oils notifications. Other notable hazards included benzo(a)pyrene (26.65 %), glycidyl esters (6.03 %), and chlorpyrifos (3.37 %). The top three countries of origin for RASFF notifications on fats and oils were Ghana (22.30 %), Ukraine (13.18 %), and Italy (9.96 %). These findings could benefit further research and influence decision-making, thereby enhancing food safety protocols in the global production, processing, and distribution of fats and oils.

Clinical and demographic factors determining patient fracture risk decision point (FRDP): The improving risk communication in osteoporosis (RICO) project.

This part of the Improving Risk Communication in Osteoporosis (RICO) study aims to understand patients' willingness to initiate osteoporosis treatment given a hypothetical fracture probability-derived from the FRAX® Risk Assessment Tool-and how age, fracture history, and numeric literacy may influence this. In 2022-2023, 332 postmenopausal women at risk of fracture were interviewed from nine countries to determine participants' Fracture Risk Decision Point (FRDP), the lowest probability of major osteoporotic fracture at which they would accept an osteoporosis medication. Participants' FRDP was evaluated given eight hypothetical 10-year FRAX scores. In countries with FRAX-based treatment thresholds, over half of the participants per country reported an FRDP that was below the threshold. Collectively, 79.2% demonstrated FRDPs at or below their respective threshold. Age and fracture history did not have a strong influence on FRDP; however, those who demonstrated higher levels of numeric literacy reported a significantly higher median FRDP (10%) compared to those who showed lower levels (5%, p < 0.001). Most patients were willing to accept an osteoporosis medication prescription at a hypothetical FRAX probability that was even lower than that of their nationally recommended treatment threshold. Literacy scores had a significant influence on FRDP whereas age and fracture history did not.

A multimodal physiological-psychological data-driven study on differentiation in miners' job burnout and risk preferences.

Existing research indicates that the personality traits of miners influence their emotional regulation strategies, and these strategies in turn affect their performance in work fatigue. However, whether there is an intermediary or moderating role among these factors remains unclear. Additionally, while some studies suggest an increased likelihood of unsafe behavior among miners following fatigue, physiological data concerning their cognition remains incomplete and requires further exploration. This study aims to explore the mediating and moderating effects of emotional regulation strategies among miners regarding their personality traits and work fatigue, and to expand understanding of the cognitive and physiological data related to miners' risk decision-making following fatigue. Fifty adult miners (Mage = 25, aged 18-40, 100% male) were selected as participants. Emotional regulation tendency, significant levels of personality traits based on the Big Five Personality Traits questionnaire, and the three-dimensional levels of work fatigue were measured using emotional regulation strategy scale, Big Five Personality Traits questionnaire, and work fatigue scale, respectively. The eye-brain consistency hypothesis posits that eye movement trajectories and fixation points reflect the brain's cognitive processes and focus. Therefore, combining eye-tracking experiments, miners' preferences in risk decision-making were further measured. Expressive suppression strategies mediated between conscientiousness and depersonalization; expressive suppression strategies moderated between agreeableness and emotional exhaustion. In eye-tracking physiological experiments, significant differences were found in eye movement data among miners with varying levels of emotional exhaustion. Preferences in emotional regulation strategies play mediating and moderating roles between miners' Big Five Personality Traits and work fatigue. The levels and dimensions of work fatigue are influenced not only by personality traits but also by individual tendencies in emotional regulation strategies, which significantly affect performance in risk decision-making. The findings of this study can further enrich theories related to work fatigue among miners and provide insights for personalized safety management in mining.

Assessment and forecasting of water ecological security and obstacle factor diagnosis in the Hexi Corridor of Northwest China

The water ecological security pattern is a core factor. A scientific, accurate, and practical evaluation of water ecological security provides a theoretical basis for regional water ecological management. Using water resource data from five cities in the Hexi Corridor of Gansu Province (Jiuquan (JQ), Jiayuguan (JYG), Zhangye (ZY), Jinchang (JC), and Wuwei (WW)) from 2006 to 2021, a water ecological security evaluation index system based on the PSR (pressure-state-response) framework was constructed, covering 27 factors related to water resources, socio-economics, and the ecological environment. The main obstacle factors of water ecological security were identified using the obstacle degree model, and the grey GM(1,1) model was employed to predict water ecological security. Results indicated that the comprehensive assessment index of water ecology in the Hexi Corridor increased from 2006 to 2021, showing a transition from relatively unsafe (0.319) to basic safety and then to relatively safe (0.672). The pressure and response systems were the main limiting factors affecting water ecological security in the Hexi Corridor. After a slight decline in 2008, the overall spatial distribution continued to rise, with WW City and ZY City leading since 2016. ZY had a higher safety grade proportion (25%) compared to other areas in the Hexi region. The pressure system was the most significant obstacle to water ecological security after 2006. Prediction results indicated that the comprehensive evaluation index of water ecological security would continue to rise annually from 2022 to 2031, reaching a very safe level by 2025. The evaluation results provide a scientific basis for ecological security and risk decision-making in the study area.

A case study of highly pathogenic avian influenza (HPAI) H5N1 at Bird Island, South Georgia: the first documented outbreak in the subantarctic region

ABSTRACT Capsule HPAI H5N1 was documented for the first time in the subantarctic region on Bird Island, South Georgia, resulting in the mortality of Brown Skuas Stercorarius antarcticus, Gentoo Penguins Pygoscelis papua, Snowy Albatrosses Diomedea exulans, and Antarctic Fur Seals Arctocephalus gazella. Aims The spread of the HPAI H5N1 subtype has had dramatic impacts on numerous populations of wild birds and mammals. We describe a case study that can inform the management of HPAI for conservation practitioners and researchers globally. Methods We documented the detection, monitoring, and impact of the first known outbreak of H5N1 HPAI in the subantarctic region, at Bird Island in South Georgia (−54.3582, −36.5112) during 2023–2024. Deaths from HPAI were first suspected in September 2023 and later confirmed by genetic analysis. Results In total, 77 Brown Skuas, 38 Gentoo penguins, and 58 Snowy Albatrosses were suspected to have died from HPAI infection, and HPAI was confirmed in 5 dead Antarctic Fur Seals. Total mortality was unknown for all species, as other individuals will have been scavenged before discovery, or died at sea. Conclusion This case study provides lessons for the management, risk, safety considerations, and ethical decisions regarding animal welfare that may help guide research and management responses to HPAI outbreaks elsewhere, particularly in remote areas or in species of conservation concern.

Industrial robots and corporate risk-taking value

This paper empirically examines the impact of industrial robot application on corporate risk-taking value using data from Chinese A-share listed manufacturing companies, measured by the Sharpe ratio. The findings indicate that industrial robots significantly enhance corporate risk-taking value through information effects, innovation, and production optimisation. Heterogeneity tests reveal that firms with higher capital density, non-state ownership, lower financing constraints, and more government subsidies experience a more substantial increase in their risk-taking value. These insights contribute to understanding the evolution of enterprise risk decisions in industrial intelligence and provide theoretical references for policies promoting industrial development.

Resilience Assessment of Deep Excavation Engineering Systems Based on Bayesian Network

As a key component of infrastructure, the construction safety of deep excavation is particularly important. In order to improve the safety and stability of Deep excavation construction, research on the risk resistance and rapid recovery capabilities of Deep excavation projects is carried out. By introducing the concept of resilience, we analyze the key factors that affect the safety and resilience of Deep excavation projects, use Bayesian networks to establish a resilience model that expresses the causal relationship between factors, and learn the parameters of the network model through data samples collected through surveys; by adjusting the network Analyze the relationship between each factor and system resilience at each node, and clarify the measures that should be taken and key control objects, thereby improving the system resilience and ensuring that normal construction can be carried out safely and orderly. Research shows that this model can be used to quantify the safety toughness value of deep excavation and reason and analyze the impact of various factors on the toughness value, thereby improving risk management and decision support capabilities.

The role of gene–environment interaction in the formation of risk attitudes

Understanding the formation of risk preferences is crucial for elucidating the roots of economic, social, and health inequalities. However, this area remains inadequately explored. This study employs a risk preference measure directly linked to the labor market to examine whether previous experiences with high unemployment rates influence current risk decision-making among the elderly, and whether this impact varies by genotype. The findings indicate that individuals with low genetic predispositions for risk tolerance are more significantly influenced by historical fluctuations in unemployment rates than those with high genetic predispositions for risk tolerance. Consequently, this paper identifies genetic endowment as a crucial moderating factor that shapes how past experiences impact current decision-making processes. This disparity in how past experiences shape risk preferences based on genetic predisposition may further amplify inequalities in health, wealth, income, and other outcomes associated with risk preferences.

Integrated risk management and maintenance planning in Oil and Gas Supply Chain operations under market uncertainty

The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained.

A New Application with Conditional Risk Matrix and Decision Rules: An Assessment of the Sustainability and Performance Electric Power Company

Abstract The 2030 Agenda for Sustainable Development of the United Nations General Assembly focuses on economic development, poverty cessation, environmental sustainability, and good governance at all levels, including peace and safety. For electric power distribution concessionary companies in Brazil, it is necessary to evaluate their sustainability and performance according to the parameters laid by the National Electric Power Agency (ANEEL). For that purpose, sixty-two indicators were assembled that were directly and indirectly associated with sustainability for 38 companies in the period–2013-2021. This study proposes a new application with a conditional risk matrix and decision rules aimed at the integrated assessment of sustainability and performance of electric power companies using the conditional probability of indicators in the ascent or descent of the companies’ classification. A set of “if..., then...” decision rules were inferred by the algorithm VC-DomLEM using concepts from the Rough Sets Theory and the Dominance principle, Dominance-based Rough Set Approach (DRSA).

An Integrated Quantitative Method of Risk Analysis and Decision Making for Safety Manipulation of a Forest Firefighting Aircraft by Using Physical Models of Multi-Hazard Factors

Although firefighting aircrafts are effective for fighting forest fires, they are affected by a variety of risks related to forest fires during flight operations such as thermal radiation, temperature rise, decrease in air density, and changes in updraft aerodynamics. However, quantifying these influences poses a challenge. Our research reveals that safety standards predominantly hinge on technical indicators like flight height, speed, and angle of attack, which makes it possible to quantify the above effects. A general method framework for analysing firefighting flight safety criteria was constructed in this thesis. Using relevant physical models, such as heat radiation, temperature, air density, and updraft in a forest fire environment, safety criteria with an analytical form were established. The corresponding safety constraints of each criterion were calculated quantitatively, and the most stringent constraint was determined based on multiple factors to obtain a comprehensive safety constraint boundary composed of the critical altitude, stall speed, and critical angle of attack. Finally, the effectiveness of the method is verified in a simulation case. The research results show that the constrained boundary calculation method proposed in this study can provide a scientific basis for the formulation of technical specifications for forest aviation fire safety and aviation fire task planning.

Financing Risks and Preventive Measures for E-Commerce Corporates

The e-commerce industry has achieved tremendous development in the past decade. However, in this rapid development process, there are some problems that have caused huge hidden dangers, and financing is the most important one among them. Therefore, the topic of this article is the dangers and prevention methods of e-commerce corporations under different financing methods. The method of this paper is as follows: first, segment the financing models of e-commerce corporates, then combine the financing situation of several well-known e-commerce corporates in China, analyze relevant data, and discuss the possible problems under these financing models separately. The research shows that the short-term debt payment risk, financial leverage risk and investment decision risk are the major risks that have influences on the firms’ financing, and the first two have a greater impact. To resolve these dangers, e-commerce firms can adjust debt structure, diversify financing channels and establish a sound risk prevention mechanism.