To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforce...To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.展开更多
Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to ob...Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.展开更多
Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost imp...Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.展开更多
The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the ...The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.展开更多
The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the ...The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.展开更多
The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give ...The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give indexes classifying method and index normalizing formula of this type problem. By building objective function that minimizes general weighted distance from every alternative to the relatively best and worst alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, and by building another objective function that minimizes general weighted distance from the optimal membership degree of every decision-maker to every alternative to the group optimal alternative and the group inferior alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, which are both based on probability theory and fuzzy theory. Aftermost a model is established which collects group preferences. This method provides a new idea and approach for solving multiobjective decision-making problem among uncertain system, which is applicable for practical problem. Finally a case study shows a satisfactory result.展开更多
The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the...The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.展开更多
BACKGROUND The participation of caregivers,who play a crucial role in the recovery of patients with gastrointestinal tumors,in family nutrition support decisions can help tailor nutrition plans to meet the specific ne...BACKGROUND The participation of caregivers,who play a crucial role in the recovery of patients with gastrointestinal tumors,in family nutrition support decisions can help tailor nutrition plans to meet the specific needs and lifestyle habits of the patient,thereby enhancing the effectiveness of nutritional intake.AIM To assess the impact of caregiver-shared decision-making in family nutritional support with mindfulness-based behavioral therapy on the risk of malnutrition and mood states in patients with gastrointestinal tumors.METHODS Patients with gastrointestinal tumors(n=118)treated at the Jiangnan University Affiliated Hospital between December 2021 and March 2024 were assigned to the observation(n=59)and control(n=59)groups using the random number table method.In addition to the standard treatment and basic nursing measures im-plemented in the control group,the integrated approach was implemented in the observation group.The nutritional and mood state indicators,compliance,and satisfaction before and 6 months after implementing the intervention were com-pared between the groups.RESULTS The body mass index,serum albumin levels,and transferrin levels,as well as the scores for all seven dimensions of the Profile of Mood States questionnaire,in the observation were higher than those in the control group after the 6-month follow-up period(P<0.05).However,the Nutrition Risk Screening 2002 and Mindful Attention Awareness Scale scores were lower than those in the control group(P<0.05).The compliance and satisfaction rates were 94.92%and 98.31%,respectively,which were higher than those of the control group(79.66%and 88.14%,respectively;P<0.05).CONCLUSION The implementation of the integrated approach significantly reduced the risk of malnutrition and improved mood states in patients with gastrointestinal tumors.Moreover,the compliance and satisfaction rates were higher.展开更多
Renewable energy sources,including wind,solar,and biofuels,are essential for promoting sustainable economic development and mitigating environmental challenges.As China’s overseas investments in renewable energy expa...Renewable energy sources,including wind,solar,and biofuels,are essential for promoting sustainable economic development and mitigating environmental challenges.As China’s overseas investments in renewable energy expand,effective risk assessment and management have become critical.This study develops a comprehensive risk evaluation framework for China’s overseas renewable energy investments using the Fuzzy Analytic Hierarchy Process(FAHP).The framework incorporates political,economic,and project-specific risks,organized through three primary criteria,nine sub-criteria,and thirty tertiary indicators.By integrating expert judgments with fuzzy set theory,the FAHP methodology assigns accurate weights to risk factors and ensures consistency in evaluation.The findings identify political risks as the most significant,emphasizing their influence on investment strategies.These insights offer valuable guidance for policymakers and investors to enhance risk management strategies and ensure the sustainability of China’s renewable energy initiatives abroad.展开更多
To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This st...To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.展开更多
Flood is one of the major challenges facing human societies.Adapting to future flood risks involves deep uncertainty,especially when long-term projections of climate change are considered.This study proposed a Two-sta...Flood is one of the major challenges facing human societies.Adapting to future flood risks involves deep uncertainty,especially when long-term projections of climate change are considered.This study proposed a Two-stage Robust Decision Making(2S-RDM)framework to help devise flexible and robust strategies capable of addressing the inherent deep uncertainty associated with managing flood risks.Taking the Yangtze River Basin in China as a case study,we simulated flood risks across∼0.6 million scenarios until 2050.This analysis considered four types of uncertain factors,i.e.,future climate change,socio-economic growth,industrial structure transformation,and population aging.We then examined the effectiveness of four adaptation measures and their combinations,i.e.building elevation,tunnel construction,people relocation,and river basin conservation.Our projections show that without immediate adaptation,an estimated 0.9 to 27.3 million people will be impacted by floods until 2050,accompanied with$33.8 to$198.5 billion economic losses in the entire basin.When defining the goal as limiting the affected population<0.05%and ensuring economic losses<0.02%,we identified 24 global robust strategies capable of meeting this criterion in>80%of scenarios.Then,we compared the 24 global robust strategies regarding their relative costs and performances in each of the future scenario pools.The final recommended solutions are hybrid strategies that integrate engineering-based measures with‘soft’adaptation options(e.g.Elevation++,Tunnel++,and Relocation).This study provides tools to design flood adaptation strategies not only robust across diverse scenarios but also flexible for decision-makers to customize and refine their strategies based on specific needs.展开更多
The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is piv...The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.展开更多
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 ...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.展开更多
Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,...Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,and external environmental factors,scientifically predict the scale,direction,and fluctuation of cash flow within a certain period in the future.This article focuses on the application of cash flow forecasting models in enterprise investment and financing decisions,sorts out the types and core functions of the models,analyzes their specific roles in investment project screening,financing plan formulation,risk prevention and control,and fund allocation,points out the existing problems in current applications,and proposes optimization paths.Research shows that the scientific application of cash flow forecasting models can enhance the accuracy and rationality of enterprises’investment and financing decisions,and help enterprises achieve sustainable development.展开更多
This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with pa...This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with particular relevance to the autoimmune hepatitis-primary biliary cholangitis(AIH-PBC)subgroup.The findings indicate that an LOS threshold of 6 achieves high sensitivity and specificity in predicting liver-related mortality among AIH-PBC patients.Moreover,low Hb levels emerge as a significant mortality predictor across all AILDOS cases.These results contribute valuable perspectives on risk stratification in AILDOS,highlighting the promise of non-invasive prognostic tools.Future studies with larger cohorts are needed to substantiate LOS and Hb as robust markers for clinical application.展开更多
基金Supported by National Key Research and Development Program of China(Grant No.2022YFE0117100)National Science Foundation of China(Grant No.52102468,52325212)Fundamental Research Funds for the Central Universities。
文摘To solve problems of poor security guarantee and insufficient training efficiency in the conventional reinforcement learning methods for decision-making,this study proposes a hybrid framework to combine deep reinforcement learning with rule-based decision-making methods.A risk assessment model for lane-change maneuvers considering uncertain predictions of surrounding vehicles is established as a safety filter to improve learning efficiency while correcting dangerous actions for safety enhancement.On this basis,a Risk-fused DDQN is constructed utilizing the model-based risk assessment and supervision mechanism.The proposed reinforcement learning algorithm sets up a separate experience buffer for dangerous trials and punishes such actions,which is shown to improve the sampling efficiency and training outcomes.Compared with conventional DDQN methods,the proposed algorithm improves the convergence value of cumulated reward by 7.6%and 2.2%in the two constructed scenarios in the simulation study and reduces the number of training episodes by 52.2%and 66.8%respectively.The success rate of lane change is improved by 57.3%while the time headway is increased at least by 16.5%in real vehicle tests,which confirms the higher training efficiency,scenario adaptability,and security of the proposed Risk-fused DDQN.
基金supported by the National Natural Science Foundation of China (No.72071150).
文摘Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.
基金Funding for this study was received from the Ministry of Education and Deanship of Scientific Research at King Abdulaziz University,Kingdom of Saudi Arabia under Grant No.IFPHI-286-611-2020.
文摘Security is an important component in the process of developing healthcare web applications.We need to ensure security maintenance;therefore the analysis of healthcare web application’s security risk is of utmost importance.Properties must be considered to minimise the security risk.Additionally,security risk management activities are revised,prepared,implemented,tracked,and regularly set up efficiently to design the security of healthcare web applications.Managing the security risk of a healthcare web application must be considered as the key component.Security is,in specific,seen as an add-on during the development process of healthcare web applications,but not as the key problem.Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application.In this row,the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability.This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own,and not depend solely on the external security of healthcare web applications.Furthermore,in terms of healthcare web application’s security-durability,the security risk variable is measured,and vice versa.Hence,the findings of our study will also be useful in improving the durability of several web applications in healthcare.
文摘The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi-time points and multi-indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.
文摘The characteristics of the financing model are firstly analyzed when the e-commerce enterprises participate in the supply chain finance. Internet supply chain finance models are divided into three categories with the standard of whether the Electronic commerce enterprises provide funds for small and medium enterprises instead of banks. And then we further study the financing process and the functions of the e-commerce platform with specific examples. Finally, combined with the characteristics of the supply chain finance model, we set up a small and medium enterprises credit evaluation model based on the principle of variable weight with its dynamic data. At the same time, a multi time points and multi indicators decision-making method based on the principle of variable weight is proposed and a specific example is presented. In this paper, the Multi-criteria decision-making model with the principle of variable weight has been used two times. At last, a typical case has been analyzed based on this model with a higher accuracy rate of credit risk assessment.
文摘The multiobjective group decision-making problem under risk is common in reality. This paper focuses on the study about risky multiobjective group decision-making problem where the index value is not certain. We give indexes classifying method and index normalizing formula of this type problem. By building objective function that minimizes general weighted distance from every alternative to the relatively best and worst alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, and by building another objective function that minimizes general weighted distance from the optimal membership degree of every decision-maker to every alternative to the group optimal alternative and the group inferior alternative, the optimal membership degree of every decision-maker to every alternative can be obtained, which are both based on probability theory and fuzzy theory. Aftermost a model is established which collects group preferences. This method provides a new idea and approach for solving multiobjective decision-making problem among uncertain system, which is applicable for practical problem. Finally a case study shows a satisfactory result.
基金This work was supported by Project of Philosophy and Social Science Foundation of Shanghai,China(Grant No.2020BGL011).
文摘The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.
文摘BACKGROUND The participation of caregivers,who play a crucial role in the recovery of patients with gastrointestinal tumors,in family nutrition support decisions can help tailor nutrition plans to meet the specific needs and lifestyle habits of the patient,thereby enhancing the effectiveness of nutritional intake.AIM To assess the impact of caregiver-shared decision-making in family nutritional support with mindfulness-based behavioral therapy on the risk of malnutrition and mood states in patients with gastrointestinal tumors.METHODS Patients with gastrointestinal tumors(n=118)treated at the Jiangnan University Affiliated Hospital between December 2021 and March 2024 were assigned to the observation(n=59)and control(n=59)groups using the random number table method.In addition to the standard treatment and basic nursing measures im-plemented in the control group,the integrated approach was implemented in the observation group.The nutritional and mood state indicators,compliance,and satisfaction before and 6 months after implementing the intervention were com-pared between the groups.RESULTS The body mass index,serum albumin levels,and transferrin levels,as well as the scores for all seven dimensions of the Profile of Mood States questionnaire,in the observation were higher than those in the control group after the 6-month follow-up period(P<0.05).However,the Nutrition Risk Screening 2002 and Mindful Attention Awareness Scale scores were lower than those in the control group(P<0.05).The compliance and satisfaction rates were 94.92%and 98.31%,respectively,which were higher than those of the control group(79.66%and 88.14%,respectively;P<0.05).CONCLUSION The implementation of the integrated approach significantly reduced the risk of malnutrition and improved mood states in patients with gastrointestinal tumors.Moreover,the compliance and satisfaction rates were higher.
基金supported by the project VSB-TU Ostrava,SP2024/045.
文摘Renewable energy sources,including wind,solar,and biofuels,are essential for promoting sustainable economic development and mitigating environmental challenges.As China’s overseas investments in renewable energy expand,effective risk assessment and management have become critical.This study develops a comprehensive risk evaluation framework for China’s overseas renewable energy investments using the Fuzzy Analytic Hierarchy Process(FAHP).The framework incorporates political,economic,and project-specific risks,organized through three primary criteria,nine sub-criteria,and thirty tertiary indicators.By integrating expert judgments with fuzzy set theory,the FAHP methodology assigns accurate weights to risk factors and ensures consistency in evaluation.The findings identify political risks as the most significant,emphasizing their influence on investment strategies.These insights offer valuable guidance for policymakers and investors to enhance risk management strategies and ensure the sustainability of China’s renewable energy initiatives abroad.
基金supported by the National Natural Science Foundation of China(5187051675)。
文摘To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.
基金supported by The National Natural Science Foundation of China(72304136,71921003,72234003,72222012)National Postdoctoral Program for Innovative Talents(BX20230159)Jiangsu R&D Special Fund for Carbon Peaking and Carbon Neutrality(BK20220014).
文摘Flood is one of the major challenges facing human societies.Adapting to future flood risks involves deep uncertainty,especially when long-term projections of climate change are considered.This study proposed a Two-stage Robust Decision Making(2S-RDM)framework to help devise flexible and robust strategies capable of addressing the inherent deep uncertainty associated with managing flood risks.Taking the Yangtze River Basin in China as a case study,we simulated flood risks across∼0.6 million scenarios until 2050.This analysis considered four types of uncertain factors,i.e.,future climate change,socio-economic growth,industrial structure transformation,and population aging.We then examined the effectiveness of four adaptation measures and their combinations,i.e.building elevation,tunnel construction,people relocation,and river basin conservation.Our projections show that without immediate adaptation,an estimated 0.9 to 27.3 million people will be impacted by floods until 2050,accompanied with$33.8 to$198.5 billion economic losses in the entire basin.When defining the goal as limiting the affected population<0.05%and ensuring economic losses<0.02%,we identified 24 global robust strategies capable of meeting this criterion in>80%of scenarios.Then,we compared the 24 global robust strategies regarding their relative costs and performances in each of the future scenario pools.The final recommended solutions are hybrid strategies that integrate engineering-based measures with‘soft’adaptation options(e.g.Elevation++,Tunnel++,and Relocation).This study provides tools to design flood adaptation strategies not only robust across diverse scenarios but also flexible for decision-makers to customize and refine their strategies based on specific needs.
文摘The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.
文摘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.
文摘Cash flow is a core element for enterprises to maintain operations and development.Cash flow forecasting models,through systematic analysis of an enterprise’s historical cash flow data,trends in operating activities,and external environmental factors,scientifically predict the scale,direction,and fluctuation of cash flow within a certain period in the future.This article focuses on the application of cash flow forecasting models in enterprise investment and financing decisions,sorts out the types and core functions of the models,analyzes their specific roles in investment project screening,financing plan formulation,risk prevention and control,and fund allocation,points out the existing problems in current applications,and proposes optimization paths.Research shows that the scientific application of cash flow forecasting models can enhance the accuracy and rationality of enterprises’investment and financing decisions,and help enterprises achieve sustainable development.
文摘This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with particular relevance to the autoimmune hepatitis-primary biliary cholangitis(AIH-PBC)subgroup.The findings indicate that an LOS threshold of 6 achieves high sensitivity and specificity in predicting liver-related mortality among AIH-PBC patients.Moreover,low Hb levels emerge as a significant mortality predictor across all AILDOS cases.These results contribute valuable perspectives on risk stratification in AILDOS,highlighting the promise of non-invasive prognostic tools.Future studies with larger cohorts are needed to substantiate LOS and Hb as robust markers for clinical application.
