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
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.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 critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a pati...The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.展开更多
Power grid construction projects are distinguished by their wide variety,high investment,long payback period,and close relation to national development and human welfare.To improve the investment accuracy in such proj...Power grid construction projects are distinguished by their wide variety,high investment,long payback period,and close relation to national development and human welfare.To improve the investment accuracy in such projects and effectively prevent investment risks,this paper proposes an investment optimization decision-making method for multiple power grid construction projects under a certain investment scale.Firstly,an in-depth analysis of the characteristics and development requirements of China’s power grid projects was performed.Thereafter,the time sequence and holographic method was adopted to conduct multi-dimensional,multi-perspective risk assessment of different parts of power grid projects,and a holographic risk assessment index system was developed.Moreover,an investment decision model considering the comprehensive risk based on combination weighting was developed according to the output and input of power grid construction projects.A new combination weighting optimization method that takes into account the investment willingness of enterprises was designed to improve the current weighting evaluation methods.Finally,the validity and applicability of the proposed evaluation method were verified by case examples.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
Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pP...Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.展开更多
Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective to...Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.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
Water-salt balance is critical for the stable coexistence of salt-affected and groundwater-fed oasis-desert ecosystems. Yet, a comprehensive investigation of how soil salinization and groundwater degradation threaten ...Water-salt balance is critical for the stable coexistence of salt-affected and groundwater-fed oasis-desert ecosystems. Yet, a comprehensive investigation of how soil salinization and groundwater degradation threaten the coexistence of oasis-desert ecosystems is still scarce, especially under the compounding effects of human activities and climatic changes. Here, we assessed the impacts of irrigated agriculture on hydrological regimes in oasisdesert systems, investigated the spatio-temporal variations of soil salinization in irrigated cropland, and evaluated the implications of the interplays of soil salinization and groundwater degradation on the coexistence of oasis-desert ecosystems in northwestern China, based on meaningful modelling approaches and comprehensive measurements over 1995–2020. The results showed that the irrigation return flow coefficient decreased sharply from 0.21 ± 0.09 in the traditional irrigation period to 0.09 ± 0.01 in the water-saving irrigation period. The continuous drop in groundwater tables and significant degradation of groundwater quality are occurring throughout this watershed. The eco-environmental flows are reaching to their limit with watershed closures(i.e.,the drainage from the oasis region into the desert region is being weakened or even eliminated), although these progressions were largely hidden by regional precipitation and streamflow variability. The process of salt migration and accumulation across different landscapes in oasis-desert system is being reshaped, and soil salinization in water-saving agricultural irrigated lands is accelerating with a regional average annual growth rate of18%. The vegetation in this watershed is degrading, and anthropogenic disturbance accelerates this trend. Our results highlight that environmental stress adaptation strategies must account for resilience maintenance to avoid accelerating catastrophic transitions in oasis-desert ecosystems. Determining the optimal oasis scales and formulating the best irrigation management plans are effective and resilient decision-making ways to maintain the coexistence relationship of oasis-desert ecosystem in drylands.展开更多
BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpfu...BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.展开更多
A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal ...A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.展开更多
Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In thi...Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.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
Objectives This study aimed to clarify the relationship between the content of proxy decision-making made by families of patients with malignant brain tumors regarding treatment policies and daily care and the cues le...Objectives This study aimed to clarify the relationship between the content of proxy decision-making made by families of patients with malignant brain tumors regarding treatment policies and daily care and the cues leading to those decisions.Methods Semi-structured personal interviews were used to collect data.Seven family members of patients with malignant brain tumors were selected to participate in the study by purposive sampling method from June to August 2022 in the Patient Family Association of Japan.Responses were content analyzed to explore the relationship between the content of decisions regarding“treatment policies”and“daily care”and the cues influencing those decisions.Semi-structured interviews were analyzed by using thematic analysis.Results The contents of proxy decisions regarding“treatment policies”included implementation,interruption,and termination of initial treatments,free medical treatments,use of respirators,and end-of-life sedation and included six cues:treatment policies suggested by the primary physician,information and knowledge about the disease and treatment obtained by the family from limited resources,perceived life threat from symptom worsening,words and reactions from the patient regarding treatment,patient’s personality and way of life inferred from their treatment preferences,family’s thoughts and values hoping for better treatment for the patient.Decisions for“daily care”included meal content and methods,excretion,mobility,maintaining cleanliness,rehabilitation,continuation or resignation from work,treatment settings(outpatient or inpatient),and ways to spend time outside and included seven cues:words and thoughts from the patient about their way of life,patient’s reactions and life history inferred from their preferred way of living,things the patient can do to maintain daily life and roles,awareness of the increasing inability to do things in daily life,family’s underlying thoughts and values about how to spend the remaining time,approval from family members regarding the care setting,advice from medical professionals on living at home.Conclusions For“treatment policies,”guidelines from medical professionals were a key cue,while for“daily care,”the small signs from the patients in their daily lives served as cues for proxy decision-making.This may be due to the lack of information available to families and the limited time available for discussion with the patient.Families of patients with malignant brain tumors repeatedly use multiple cues to make proxy decision-making under high uncertainty.Therefore,nurses supporting proxy decision-making should assess the family’s situation and provide cues that facilitate informed and confident decisions.展开更多
Group living is widespread across diverse taxa,and the mechanisms underlying collective decision-making in contexts of variable role division are critical for understanding the dynamics of group stability.While studie...Group living is widespread across diverse taxa,and the mechanisms underlying collective decision-making in contexts of variable role division are critical for understanding the dynamics of group stability.While studies on collective behavior in small animals such as fish and insects are well-established,similar research on large wild animals remains challenging due to the limited availability of sufficient and systematic field data.Here,we aimed to explore the collective decision-making pattern and its sexual difference for the dimorphic Tibetan antelopes Pantholops hodgsonii(chiru)in Xizang Autonomous Region,China,by analyzing individual leadership distribution,as well as the joining process,considering factors such as calving stages and joining ranks.The distinct correlations of decision participants’ratio with group size and decision duration underscore the trade-off between accuracy and speed in decision-making.Male antelopes display a more democratic decision-making pattern,while females exhibit more prompt responses after calving at an early stage.This study uncovers a partially shared decision-making strategy among Tibetan antelopes,suggesting flexible self-organization in group decision processes aligned with animal life cycle progression.展开更多
Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined ma...Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined mathematical models that often fail to capture equipment-specific degradation,(2)offline optimization methods that assume access to future data,and(3)the absence of component-level guidance.To address these challenges,we propose a data-driven framework for component-level decision-making.The framework leverages streaming sensor data to predict the remaining useful life(RUL)without relying on mathematical models,employs an online optimization algorithm suitable for practical settings,and,through remanufacturing simulations,provides guidance on which components should be replaced.In a case study on gas-insulated switchgear,the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models.Furthermore,by employing online optimization,it determined a remanufacturing timing close to the global optimum using only past and current data.In addition,unlike previous studies,the framework enables component-level decision-making,allowing for more detailed and actionable remanufacturing guidance in practical applications.展开更多
BACKGROUND Mesalamine is the recommended first-line treatment for inducing and maintaining remission in mild-to-moderate ulcerative colitis(UC).However,adherence in real-world settings is frequently suboptimal.Encoura...BACKGROUND Mesalamine is the recommended first-line treatment for inducing and maintaining remission in mild-to-moderate ulcerative colitis(UC).However,adherence in real-world settings is frequently suboptimal.Encouraging collaborative patient-provider relationships may foster better adherence and patient outcomes.AIM To quantify the association between patient participation in treatment decisionmaking and adherence to oral mesalamine in UC.METHODS We conducted a 12-month,prospective,non-interventional cohort study at 113 gastroenterology practices in Germany.Eligible patients were aged≥18 years,had a confirmed UC diagnosis,had no prior mesalamine treatment,and provided informed consent.At the first visit,we collected data on demographics,clinical characteristics,patient preference for mesalamine formulation(tablets or granules),and disease knowledge.Self-reported adherence and disease activity were assessed at all visits.Correlation analyses and logistic regression were used to examine associations between adherence and various factors.RESULTS Of the 605 consecutively screened patients,520 were included in the study.The median age was 41 years(range:18-91),with a male-to-female ratio of 1.1:1.0.Approximately 75%of patients reported good adherence at each study visit.In correlation analyses,patient participation in treatment decision-making was significantly associated with better adherence across all visits(P=0.04).In the regression analysis at 12 months,this association was evident among patients who both preferred and received prolonged-release mesalamine granules(odds ratio=2.73,P=0.001).Patients reporting good adherence also experienced significant improvements in disease activity over 12 months(P<0.001).CONCLUSION Facilitating patient participation in treatment decisions and accommodating medication preferences may improve adherence to mesalamine.This may require additional effort but has the potential to improve long-term management of UC.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funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
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 critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.
基金supported by the State Grid Science and Technology Project (SGTYHT/16-JS-198)
文摘Power grid construction projects are distinguished by their wide variety,high investment,long payback period,and close relation to national development and human welfare.To improve the investment accuracy in such projects and effectively prevent investment risks,this paper proposes an investment optimization decision-making method for multiple power grid construction projects under a certain investment scale.Firstly,an in-depth analysis of the characteristics and development requirements of China’s power grid projects was performed.Thereafter,the time sequence and holographic method was adopted to conduct multi-dimensional,multi-perspective risk assessment of different parts of power grid projects,and a holographic risk assessment index system was developed.Moreover,an investment decision model considering the comprehensive risk based on combination weighting was developed according to the output and input of power grid construction projects.A new combination weighting optimization method that takes into account the investment willingness of enterprises was designed to improve the current weighting evaluation methods.Finally,the validity and applicability of the proposed evaluation method were verified by case examples.
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.
基金supported by the Deanship of Graduate Studies and Scientific Research at Qassim University(QU-APC-2024-9/1).
文摘Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.
文摘Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.
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.
基金supported by the National Key R&D Program of China(2022YFF1303301)the National Natural Science Foundation of China(52179026,42101115,41901100,32301671)+1 种基金the China Postdoctoral Science Foundation Project(2022M720162)the XPCC Science and Technique Foundation(2021AB021).
文摘Water-salt balance is critical for the stable coexistence of salt-affected and groundwater-fed oasis-desert ecosystems. Yet, a comprehensive investigation of how soil salinization and groundwater degradation threaten the coexistence of oasis-desert ecosystems is still scarce, especially under the compounding effects of human activities and climatic changes. Here, we assessed the impacts of irrigated agriculture on hydrological regimes in oasisdesert systems, investigated the spatio-temporal variations of soil salinization in irrigated cropland, and evaluated the implications of the interplays of soil salinization and groundwater degradation on the coexistence of oasis-desert ecosystems in northwestern China, based on meaningful modelling approaches and comprehensive measurements over 1995–2020. The results showed that the irrigation return flow coefficient decreased sharply from 0.21 ± 0.09 in the traditional irrigation period to 0.09 ± 0.01 in the water-saving irrigation period. The continuous drop in groundwater tables and significant degradation of groundwater quality are occurring throughout this watershed. The eco-environmental flows are reaching to their limit with watershed closures(i.e.,the drainage from the oasis region into the desert region is being weakened or even eliminated), although these progressions were largely hidden by regional precipitation and streamflow variability. The process of salt migration and accumulation across different landscapes in oasis-desert system is being reshaped, and soil salinization in water-saving agricultural irrigated lands is accelerating with a regional average annual growth rate of18%. The vegetation in this watershed is degrading, and anthropogenic disturbance accelerates this trend. Our results highlight that environmental stress adaptation strategies must account for resilience maintenance to avoid accelerating catastrophic transitions in oasis-desert ecosystems. Determining the optimal oasis scales and formulating the best irrigation management plans are effective and resilient decision-making ways to maintain the coexistence relationship of oasis-desert ecosystem in drylands.
文摘BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using the χ^(2) test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.
基金Project(50809058)supported by the National Natural Science Foundation of China
文摘A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.
基金This work is supported by the National Natural Science Foundation of China(Nos.72071150,71871174).
文摘Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.
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.
文摘Objectives This study aimed to clarify the relationship between the content of proxy decision-making made by families of patients with malignant brain tumors regarding treatment policies and daily care and the cues leading to those decisions.Methods Semi-structured personal interviews were used to collect data.Seven family members of patients with malignant brain tumors were selected to participate in the study by purposive sampling method from June to August 2022 in the Patient Family Association of Japan.Responses were content analyzed to explore the relationship between the content of decisions regarding“treatment policies”and“daily care”and the cues influencing those decisions.Semi-structured interviews were analyzed by using thematic analysis.Results The contents of proxy decisions regarding“treatment policies”included implementation,interruption,and termination of initial treatments,free medical treatments,use of respirators,and end-of-life sedation and included six cues:treatment policies suggested by the primary physician,information and knowledge about the disease and treatment obtained by the family from limited resources,perceived life threat from symptom worsening,words and reactions from the patient regarding treatment,patient’s personality and way of life inferred from their treatment preferences,family’s thoughts and values hoping for better treatment for the patient.Decisions for“daily care”included meal content and methods,excretion,mobility,maintaining cleanliness,rehabilitation,continuation or resignation from work,treatment settings(outpatient or inpatient),and ways to spend time outside and included seven cues:words and thoughts from the patient about their way of life,patient’s reactions and life history inferred from their preferred way of living,things the patient can do to maintain daily life and roles,awareness of the increasing inability to do things in daily life,family’s underlying thoughts and values about how to spend the remaining time,approval from family members regarding the care setting,advice from medical professionals on living at home.Conclusions For“treatment policies,”guidelines from medical professionals were a key cue,while for“daily care,”the small signs from the patients in their daily lives served as cues for proxy decision-making.This may be due to the lack of information available to families and the limited time available for discussion with the patient.Families of patients with malignant brain tumors repeatedly use multiple cues to make proxy decision-making under high uncertainty.Therefore,nurses supporting proxy decision-making should assess the family’s situation and provide cues that facilitate informed and confident decisions.
基金supported by the National Natural Science Foundation of China(Grant no.32101237)the China Postdoctoral Science Foundation(Grant no.2021M691522)+1 种基金the National Key Research and Development Program(Grant no.2022YFC3202104)the Tibet Major Science and Technology Project(Grant no.XZ201901-GA-06).
文摘Group living is widespread across diverse taxa,and the mechanisms underlying collective decision-making in contexts of variable role division are critical for understanding the dynamics of group stability.While studies on collective behavior in small animals such as fish and insects are well-established,similar research on large wild animals remains challenging due to the limited availability of sufficient and systematic field data.Here,we aimed to explore the collective decision-making pattern and its sexual difference for the dimorphic Tibetan antelopes Pantholops hodgsonii(chiru)in Xizang Autonomous Region,China,by analyzing individual leadership distribution,as well as the joining process,considering factors such as calving stages and joining ranks.The distinct correlations of decision participants’ratio with group size and decision duration underscore the trade-off between accuracy and speed in decision-making.Male antelopes display a more democratic decision-making pattern,while females exhibit more prompt responses after calving at an early stage.This study uncovers a partially shared decision-making strategy among Tibetan antelopes,suggesting flexible self-organization in group decision processes aligned with animal life cycle progression.
基金supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government Ministry of Knowledge Economy(No.RS-2023-00244330)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF RS-2023-00219052RS-2024-00352587)。
文摘Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment.However,existing approaches face three significant limitations:(1)reliance on predefined mathematical models that often fail to capture equipment-specific degradation,(2)offline optimization methods that assume access to future data,and(3)the absence of component-level guidance.To address these challenges,we propose a data-driven framework for component-level decision-making.The framework leverages streaming sensor data to predict the remaining useful life(RUL)without relying on mathematical models,employs an online optimization algorithm suitable for practical settings,and,through remanufacturing simulations,provides guidance on which components should be replaced.In a case study on gas-insulated switchgear,the proposed framework achieved RUL prediction performance comparable to an oracle model in an online setting without relying on predefined mathematical models.Furthermore,by employing online optimization,it determined a remanufacturing timing close to the global optimum using only past and current data.In addition,unlike previous studies,the framework enables component-level decision-making,allowing for more detailed and actionable remanufacturing guidance in practical applications.
文摘BACKGROUND Mesalamine is the recommended first-line treatment for inducing and maintaining remission in mild-to-moderate ulcerative colitis(UC).However,adherence in real-world settings is frequently suboptimal.Encouraging collaborative patient-provider relationships may foster better adherence and patient outcomes.AIM To quantify the association between patient participation in treatment decisionmaking and adherence to oral mesalamine in UC.METHODS We conducted a 12-month,prospective,non-interventional cohort study at 113 gastroenterology practices in Germany.Eligible patients were aged≥18 years,had a confirmed UC diagnosis,had no prior mesalamine treatment,and provided informed consent.At the first visit,we collected data on demographics,clinical characteristics,patient preference for mesalamine formulation(tablets or granules),and disease knowledge.Self-reported adherence and disease activity were assessed at all visits.Correlation analyses and logistic regression were used to examine associations between adherence and various factors.RESULTS Of the 605 consecutively screened patients,520 were included in the study.The median age was 41 years(range:18-91),with a male-to-female ratio of 1.1:1.0.Approximately 75%of patients reported good adherence at each study visit.In correlation analyses,patient participation in treatment decision-making was significantly associated with better adherence across all visits(P=0.04).In the regression analysis at 12 months,this association was evident among patients who both preferred and received prolonged-release mesalamine granules(odds ratio=2.73,P=0.001).Patients reporting good adherence also experienced significant improvements in disease activity over 12 months(P<0.001).CONCLUSION Facilitating patient participation in treatment decisions and accommodating medication preferences may improve adherence to mesalamine.This may require additional effort but has the potential to improve long-term management of UC.
