Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.

Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

Factors affecting farmers'choice to adopt risk management strategies:The application of multivariate and multinomial probit models

This study investigates the factors that impact farmers'adoption of risk management strategies(RMS)in Pakistan during times of uncertainty.The study examines farmers'adoption of RMS using both multinomial probit(MNP)and multivariate probit(MVP).Data were collected from 382 farmers sampled from four districts in KhyberPakhtunkhwa(KP)province of Pakistan via a multistage sampling technique.This study utilizes the MNP model,considering the assumption of Independence of Irrelevant Alternatives(IIA)and incorporating correlated error terms.The objective is to understand farmers'behavior in risky situations and determine if there is heterogeneity.Results are compared with the MVP model to assess robustness and gain deeper understanding of farmers'decisionmaking processes.The research findings reveal that our results are robust,and farmers behave homogeneously in various RMS scenarios.Farmers adopt RMS individually or in combination to mitigate the adverse effects of natural calamities on their livelihood.The risk-averse farmers,who perceive weather-related risks as a threat,access credits and information,and have farms close to a river are more likely to adopt RMS,irrespective of the format of the strategies available.Moreover,the predicted probabilities and correlation of the RMS and RM categories have strengthened our model estimation.These findings provide insights into the behavior of farmers in adopting RMS which are helpful for policymakers and stakeholders in developing strategies to mitigate the impacts of natural calamities on farmers.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

An Assessment of Procurement Risks Management Systems in the Public Sector

Risk management in public procurement is a critical aspect that needs to be addressed in the public sector.Several studies have been conducted to understand the challenges and factors influencing risk management in public procurement.These studies have explored the importance of risk management principles,the role of political influence,and the need for effective risk assessment and anticipation.The research has also highlighted the need for specific risk management mechanisms and tools to be implemented in public procurement processes.Risk management reforms in the public sector are essential but often circumvented due to assorted reasons,such as political influence and the emergence of new risks.The research investigation employs a quantitative research design.A total of 380 questionnaires were recovered from respondents.The study showed that the public sector has a procurement risk management system that is effective,but there may be some areas for improvement in the prequalification process,onboarding process,and support provided to newly onboarded suppliers.Additionally,the public sector used some strategies to mitigate and control contract risks during the procurement process,but there were some areas for improvement in the review and lessons learned process,risk mitigation measures,contract monitoring and performance evaluation mechanisms,and communication and documentation process.Finally,the results suggest that there were constraints placed on the risk management strategies currently utilized by professionals working in the public sector.These constraints include insufficient support and buy-in from senior management and stakeholders,bureaucratic or administrative hurdles,inadequate policies and regulations,insufficient training and skill development opportunities,and insufficient resources.The study highlights the significance of tackling risk management in the realm of public procurement and offers valuable perspectives on avenues for enhancement,obstacles encountered by practitioners,and the necessity of thorough evaluation and revisions.Through the adoption of the suggestions originating from this study,governmental entities can improve their procurement risk management frameworks and guarantee improved adherence to risk management principles.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Conceptual Strategy for Mitigating the Risk of Hydrogen as an Internal Hazard in Case of Severe Accidents at Nuclear Power Plant Considering Existing Risks and Uncertainties Associated with the Use of Traditional Strategies

Hydrogen challenge mitigation stands as one of the main objectives in the management of severe accidents at Nuclear Power Plants (NPPs). Key strategies for hydrogen control include atmospheric inertization and hydrogen removal with Passive Autocatalytic Recombiners (PARs) being a commonly accepted approach. However, an examination of PAR operation specificity reveals potential inefficiencies and reliability issues in certain severe accident scenarios. Moreover, during the in-vessel stage of severe accident development, in some severe accident scenarios PARs can unexpectedly become a source of hydrogen detonation. The effectiveness of hydrogen removal systems depends on various factors, including the chosen strategies, severe accident scenarios, reactor building design, and other influencing factors. Consequently, a comprehensive hydrogen mitigation strategy must effectively incorporate a combination of strategies rather than be based on one strategy, taking into consideration the probabilistic risks and uncertainties associated with the implementation of PARs or other traditional methods. In response to these considerations, within the framework of this research it has been suggested a conceptual strategy to mitigate the hydrogen challenge during the in-vessel stage of severe accident development.

Application of Multi-Criteria Decision Analysis in Enterprise Risk Management

Enterprise risk management has become increasingly crucial in today’s complex and volatile business environment. This study explores the application of Multi-Criteria Decision Analysis (MCDA) in enterprise risk management. MCDA provides a systematic approach to handling multidimensional risk assessment issues. The research begins by analyzing various types of risks faced by enterprises, including financial, operational, and strategic risks. It then examines the specific applications of major MCDA methods, such as the Analytic Hierarchy Process (AHP) and TOPSIS, in risk identification, assessment, and response. The study finds that MCDA can effectively integrate qualitative and quantitative risk information, enhancing the scientific nature of risk decision-making. However, MCDA also faces challenges in practice, such as the subjectivity in determining indicator weights. To address this issue, the research proposes improved methods combining fuzzy theory and group decision-making. Finally, case analyses illustrate the effectiveness of MCDA applications in risk management across different industries. This study provides theoretical guidance for enterprises to build more comprehensive and dynamic risk management systems.

Predictive Analytics for Project Risk Management Using Machine Learning

Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.

Nursing Risk Management in the Prevention of Post-ERCP Pancreatitis

Objective:To study the role of nursing risk management in the prevention of post-ERCP pancreatitis.Methods:80 patients who underwent ERCP in our hospital from December 2023 to April 2024 were selected and randomly divided into an observation group and a control group using the random number table method,with 40 cases in each group.The observation group was given nursing risk management interventions and patients in the control group were given routine nursing interventions,and the patients in the two groups were compared in the incidence rate of pancreatitis and the satisfaction of nursing care.Results:The incidence of postoperative pancreatitis in the observation group was significantly lower than in the control group(P<0.05).The patient satisfaction of the observation group was significantly higher than that of the control group(P<0.05).Conclusion:Nursing risk management after ERCP can reduce the incidence of postoperative pancreatitis and improve patient satisfaction.

Electronic Commerce Security and Risk Management: An Important Part of College Curriculum

With the rapid expansion of e-commerce,its security and risk management problems become increasingly prominent.In the current business environment,the ability to understand and apply e-commerce security and risk management has become an important criterion to measure a good person.Therefore,the importance of e-commerce security and risk management courses in college education is self-evident.This course can not only help students master the basic knowledge of e-commerce,but also enable them to understand how to deal with various risks in practical work and ensure the safe operation of e-commerce.At the same time,through the study of e-commerce security and risk management,students can better understand the operation mode and law of e-commerce,and lay a solid foundation for their future career.In general,e-commerce security and risk management occupy an important position in the curriculum of colleges and universities,and play a crucial role in cultivating e-commerce professionals with practical operation ability and innovative thinking.

Analytic Hierarchy Process for Technological Risks in the Process of Innovative Drug Development in China

Objective To identify the critical risks in the process of innovative drug research and development,and to provide reference for improving the efficiency of innovative drug development and risk control in China.Methods Expert investigation and analytic hierarchy process were used to determine the weights of different risks.Results and Conclusion The research and analysis results showed that the risks at different stages of development had different effects on the success rate of drug development,among which the risk at the drug discovery stage influenced the most.In the drug discovery stage,inappropriate target selection had the greatest impact on the success rate of drug development.The lack of appropriate cell tissue or animal models had the greatest impact on the success rate of drug development from the discovery of a compound to the application for clinical trials.The difference in changes between nonclinical and clinical studies had the greatest impact on the success rate of drug development from early clinical studies to pivotal clinical studies.Incorrect dose selection had the greatest impact on the success rate of drug development from pivotal clinical studies to marketing authorization applications.The biggest impact from the marketing authorization application to the approval stage was inadequate communication with regulators.After investigating the weight of risk factors in the process of innovative drug development based on scientific methods,a new perspective for the risk control of new drug development and improving the research and development efficiency is provided.

Risk Analysis of the Development of Sustainable Dongtan

Dongtan is set to be developed as a sustainable urban-rural integration,aiming to attract a wide range of commercial and leisure investments.The Shanghai Industrial Investment Corporation(SIIC),the largest international investment group owned by the Shanghai municipal government,is leading the Dongtan project in partnership with Arup.The project’s risks are categorized into eight major groups:(1)Force majeure,(2)people-related risks,(3)financial and economic risks,(4)political and country risks,(5)environmental risks,(6)completion-related risks,(7)design-related risks,and(8)technology risks.Among these,political risk is particularly notable for its high probability and significant impact.Effective project risk management is essential to foresee and address uncertainties that could jeopardize the project’s objectives and timelines.Appropriate strategies must be implemented to manage and mitigate these risks.

Identifying and Managing Technical Risks in the Process of Innovative Drugs Development in China

Objective To identify technical risks in the process of innovative drug development,and to provide reference for technical risk management so as to reduce the uncertainties and improve the efficiency of research and development.Methods The initial risk index was investigated by literature research.Then,the Likert scale was used to design a questionnaire,and the experts’opinion was used to analyze the risk factors affecting the different stages of the development of innovative drugs in China.Results and Conclusion Based on the analysis of questionnaire,31 risk indicators of five key stages in the development of innovative drugs from drug discovery to marketing authorization were established.The key risk indicators constructed in this study can provide reference for technology-related risk management in the process of innovative drug development.

Research on Financial Risk Management in the S Water Conservancy Project and Its Implications for Similar Projects

As a vital infrastructure initiative with significant impacts on national development and public welfare,water conservancy projects play an essential role in advancing agricultural development and mitigating losses caused by flooding.However,these projects often require substantial investments and extended construction periods,leading to the inevitable emergence of various financial risks during implementation.This paper aims to explore the current challenges in financial risk management of the S water conservancy project,offering practical management strategies and solutions to address these financial difficulties.Through a detailed assessment and analysis of the S water conservancy project,the study seeks to provide valuable references and insights for similar projects to better manage financial risks,ensuring their smooth progression and successful completion.The proposed control measures are designed not only to effectively mitigate the financial risks specific to the S water conservancy project but also to offer actionable lessons for other similar projects.By promoting and applying these measures,the overall financial management capabilities of the industry can be enhanced,providing robust support for the sustainable development of water conservancy projects.

Evaluation of supply chain default risk based on fuzzy influence diagram

After introducing the supply chain default risk and its causes,based on the literature review of the evaluation methods of supply chain risks,a new evaluation method called the fuzzy influence diagram which combines fuzzy sets with influence diagram theory and considers the interaction among risk factors is proposed.Furthermore,an evaluation model of the supply chain default risk is established based on the research of default risk evaluation and the fuzzy influence diagram.First,the model takes the loss of risk as a valuable node,risk factors as random nodes,drawing a risk analysis influence diagram.Then,three kinds of fuzzy sets are defined,including state fuzzy sets,probabilistic fuzzy sets and a relation fuzzy matrix.Finally,by using the fuzzy algorithm to evaluate nodes,the probability of risk occurrence and the degrees of risk loss are obtained.On the basis of the model,an instance application is used to prove its utility and effectiveness.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT) model and the K-means cluster algorithm to produce a regional landslide susceptibility map. Yanchang County, a typical landslide-prone area located in northwestern China, was taken as the area of interest to introduce the proposed application procedure. A landslide inventory containing 82 landslides was prepared and subsequently randomly partitioned into two subsets: training data(70% landslide pixels) and validation data(30% landslide pixels). Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means cluster algorithm. The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC) curve) of the proposed model was the highest, reaching 0.88,compared with traditional models(support vector machine(SVM) = 0.85, Bayesian network(BN) = 0.81,frequency ratio(FR) = 0.75, weight of evidence(WOE) = 0.76). The landslide frequency ratio and frequency density of the high susceptibility zones were 6.76/km^(2) and 0.88/km^(2), respectively, which were much higher than those of the low susceptibility zones. The top 20% interval of landslide occurrence probability contained 89% of the historical landslides but only accounted for 10.3% of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without containing more " stable" pixels. Therefore, the obtained susceptibility map is suitable for application to landslide risk management practices.

Safety risk management of underground engineering in China：Progress, challenges and strategies

Underground construction in China is featured by large scale, high speed, long construction period,complex operation and frustrating situations regarding project safety. Various accidents have been reported from time to time, resulting in serious social impact and huge economic loss. This paper presents the main progress in the safety risk management of underground engineering in China over the last decade, i.e.（1） establishment of laws and regulations for safety risk management of underground engineering,（2） implementation of the safety risk management plan,（3） establishment of decision support system for risk management and early-warning based on information technology, and（4） strengthening the study on safety risk management, prediction and prevention. Based on the analysis of the typical accidents in China in the last decade, the new challenges in the safety risk management for underground engineering are identified as follows：（1） control of unsafe human behaviors;（2） technological innovation in safety risk management; and（3） design of safety risk management regulations. Finally, the strategies for safety risk management of underground engineering in China are proposed in six aspects, i.e. the safety risk management system and policy, law, administration, economy, education and technology.

Engineering complex systems applied to risk management in the mining industry

Related to complexity, there is a wide diversity of concepts, ranging from ‘‘systemic" to ‘‘complex", implying a need for a unified terminology. Per different authors, the main drivers of complexity can be found in human behaviour and uncertainty. This complexity, structural or dynamic can be organizational, technological, or nested in their relationship. ISO international standard 31000:2009 definition of risk management ‘‘coordinated activities to direct and control an organization with regard to risk", when applied to economic sectors, industry, services, project, or activity, it requires the use of models or theories as guidelines. Therefore, as its basic elements comprehend human behaviour and/or uncertainty, risk management to be effective and adapted as much as possible to reality, must be operational within complex systems, as already demonstrated in different R&D environments. Risk management faces demanding challenges when approaching specific and endogenous needs, such as the mining sector. This paper presents a multivariable function analysis methodology approach based on complex system modelling and through real data corresponding to a risk management tool in the mining sector.

Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems,which integrate the supply chain’s manufacturing operations,information technology,and telecommunication processes.Although supply chain 4.0 aims to improve supply chains’production systems and profitability,it is subject to different operational and disruptive risks.Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems.This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group(SCDG)algorithm.Exploration and exploitation mechanisms of the basic Sine Cosine Algorithm(CSA)are adjusted and controlled by two groups of agents that can be changed dynamically during the iterations.External and internal features were collected and analyzed from different data sources of service level agreements and transaction data from various KSA firms to validate the proposed algorithm’s efficiency.A balanced accuracy of 0.989 and a Mean Square Error(MSE)of 0.0476 were achieved compared with other optimization-based classifier techniques.A one-way analysis of variance(ANOVA)and Wilcoxon rank-sum tests were performed to show the superiority of the proposed SCDG algorithm.Thus,the experimental results indicate the effectiveness of the proposed SCDG algorithm-based voting classifier.