An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

System Reliability Analysis Method Based on T-S FTA and HE-BN

For high-reliability systems in military,aerospace,and railway fields,the challenges of reliability analysis lie in dealing with unclear failure mechanisms,complex fault relationships,lack of fault data,and uncertainty of fault states.To overcome these problems,this paper proposes a reliability analysismethod based on T-S fault tree analysis(T-S FTA)and Hyper-ellipsoidal Bayesian network(HE-BN).The method describes the connection between the various systemfault events by T-S fuzzy gates and translates them into a Bayesian network(BN)model.Combining the advantages of T-S fault tree modeling with the advantages of Bayesian network computation,a reliability modeling method is proposed that can fully reflect the fault characteristics of complex systems.Experts describe the degree of failure of the event in the form of interval numbers.The knowledge and experience of experts are fused with the D-S evidence theory to obtain the initial failure probability interval of the BN root node.Then,the Hyper-ellipsoidal model(HM)constrains the initial failure probability interval and constructs a HE-BN for the system.A reliability analysismethod is proposed to solve the problem of insufficient failure data and uncertainty in the degree of failure.The failure probability of the system is further calculated and the key components that affect the system’s reliability are identified.The proposedmethod accounts for the uncertainty and incompleteness of the failure data in complex multi-state systems and establishes an easily computable reliability model that fully reflects the characteristics of complex faults and accurately identifies system weaknesses.The feasibility and accuracy of the method are further verified by conducting case studies.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines

Background:The European Society for Medical Oncology(ESMO)guidelines are among the most comprehensive and widely used clinical practice guidelines(CPGs)globally.However,the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated.This study assessed ESMO CPG levels of evidence(LOE)and grades of recommendations(GOR),as well as their trends over time across various cancer settings.Methods:We manually extracted every recommendation with the Infectious Diseases Society of America(IDSA)classification from each CPG.We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.Results:Among the 1,823 recommendations in the current CPG,30%were classified as LOEⅠ,and 43%were classified as GOR A.Overall,there was a slight decrease in LOEⅠ(−2%)and an increase in the proportion of GOR A(+1%)in the current CPG compared to previous versions.The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials(LOEⅠ–Ⅱ)shows a decrease(71%vs.63%,p=0.009)while recommendations based on lower levels of evidence(LOEⅢ–Ⅴ)show an increase(29%vs.37%,p=0.01)between previous and current version.In the current versions,the highest proportion of LOEⅠ(42%)was found in recommendations related to pharmacotherapy,while the highest proportion of GOR A recommendations was found in the areas of pathology(50%)and diagnostic(50%)recommendations.Significant variability in LOEⅠand GOR A recommendations and their changes over time was observed across different cancer types.Conclusion:One-third of the current ESMO CPG recommendations are supported by the highest level of evidence.More well-designed randomized clinical trials are needed to increase the proportion of LOEⅠand GOR A recommendations,ultimately leading to improved outcomes for cancer patients.

Real-World Evidence in Localized Pancreatic: Coping with Uncertainty in Unselected Populations

Background: Localized pancreatic cancer, including resectable (R), borderline resectable (BR) and locally advanced unresectable disease (LAU), is considered in clinical guidelines for diverse treatment options based on clinical trials in selected populations. Hence, exploring with real world evidence (RWE) clinicians’ preferences for treatment options and their results seems pertinent. Methods: In a set of consecutive patients with localized pancreatic cancer assisted in a third level hospital from January 2013 to December 2022, medical records, symptoms, diagnostic process, distribution between subtypes, and treatment plans, with safety and efficacy results, were assessed. Results: A total of 152 patients with localized disease were included (43.4% R, 21.0% BR, 33.6% LAU). The population characteristics exemplified differences between daily practice and clinical trials. Tumor location and symptoms were as expected. Treatment plan was conditioned by PS or comorbidities in 23.0% of patients. In patients with R disease, surgery followed by different adjuvant chemotherapy (CT) regimes was the antineoplastic treatment of choice (64.8%) with efficacy results (OS 37.5 months;95% CI 18.4 - 56.7), in the range of contemporary standards. The common use of neoadjuvant CT for BR disease (94.4%), with surgery in 50% of them, and its results (OS 30.8 months;95% CI 10.5 - 51.2) reflected current controversies of treatment recommendations and evolution in this scenario. Paliative CT with or without radiotherapy was the standard specific treatment in LAU disease (95.1%) with survival results (PFS: 10.8 months;95% CI 8.8 - 12.7. OS: 20.3 months;95% CI 13.5 - 27.2) that justify the distinct character and the specific study of this entity. Conclusion: RWE for localized pancreatic cancer aroused from the analysis of this population confirms the distinct nature of patients assisted in daily practice, as well as mirrors the complexity of decision making in clinical assumptions in which achieving stronger evidence should be paramount.

Non-alcoholic fatty liver disease in type 2 diabetes:Emerging evidence of benefit of peroxisome proliferator-activated receptors agonists and incretin-based therapies

Nonalcoholic fatty liver disease(NAFLD)is a global epidemic,affecting more than half of the people living with type 2 diabetes(T2D).The relationship between NAFLD and T2D is bidirectional and the presence of one perpetuates the other,which significantly increases the hepatic as well as extrahepatic complications.Until recently,there was no approved pharmacological treatment for NAFLD/nonalcoholic steatohepatitits(NASH).However,there is evidence that drugs used for diabetes may have beneficial effects on NAFLD.Insulin sensitizers acting through peroxisome proliferator-activated receptor(PPAR)modulation act on multiple levels of NAFLD pathogenesis.Pioglitazone(PPARγ agonist)and saroglitazar(PPARα/γagonist)are particularly beneficial and recommended by several authoritative bodies for treating NAFLD in T2D,although data on biopsyproven NASH are lacking with the latter.Initial data on elafibanor(PPARα/δ agonist)and Lanifibranor(pan PPAR agonist)are promising.On the other hand,incretin therapies based on glucagon-like peptide-1(GLP-1)receptor agonists(GLP-1RA)and dual-and triple-hormone receptor co-agonists reported impressive weight loss and may have anti-inflammatory and antifibrotic properties.GLP-1 RAs have shown beneficial effects on NAFLD/NASH and more studies on potential direct effects on liver function by dual-and triple-agonists are required.Furthermore,the long-term safety of these therapies in NAFLD needs to be established.Collaborative efforts among healthcare providers such as primary care doctors,hepatologists,and endocrinologists are warranted for selecting patients for the best possible management of NAFLD in T2D.

Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case

This paper presents an innovative approach for the fault isolation of Light Rail Vehicle (LRV) suspension system based on the Dempster-Shafer (D-S) evidence theory and its improvement application case. The considered LRV has three rolling stocks and each one equips three sensors for monitoring the suspension system. A Kalman filter is applied to generate the residuals for fault diagnosis. For the purpose of fault isolation, a fault feature database is built in advance. The Eros and the norm distance between the fault feature of the new occurred fault and the one in the feature database are applied to measure the similarity of the feature which is the basis for the basic belief assignment to the fault, respectively. After the basic belief assignments are obtained, they are fused by using the D-S evidence theory. The fusion of the basic belief assignments increases the isolation accuracy significantly. The efficiency of the proposed method is demonstrated by two case studies.

Expert system for earthquake prediction（ESEP）（2）－Knowledge representation and evidence combination

This paper deals with knowledge representation of ESEP (Expert System for Earthqauke Prediction). Attending the characteristics of the knowledge in earthquake prediction domain, production representation and procedural representation are connected in the knowledge repesentation model of ESEP named ESEP/K, and three new ways of evidence conbination are proposed for production rules besides 'AND' and 'OR'.

A Map-Matching Algorithm forGPS/DR Integrated Navigation Systems Basedon Dempster-Shafer Evidence Reasoning

GPS (Global Positioning System) has been widely used in car navigation systems. Most car navigation systems estimate the car position from GPS and DR (dead reckoning). However, the unknown GPS noise characteristic and the unbounded DR accumulation of errors over time make the position information with undesirable position errors. The map matching can improve the position accuracy and availability of the vehicular position system. In this paper, general principle of map matching is investigated according to segmentation and feature extraction, and a map matching algorithm based on D-S (Dempster-Shafer) evidence reasoning for GPS integrated navigation system is proposed, which can find the exact road on which a car moves. For the experiments, a car navigation system is developed with some sensors and the field test demonstrates the effectiveness and applicability of the algorithm for the car location and navigation.

An Approach for Designing and Implementing Evidence Management Systems

Generally speaking, the software of management systems in law field is based on the frames of events. However, we are going to study and develop the new software of management system in which the basic elements are evidence. This kind of software is often called evidence management system. Here we are supposed to present a designing plan and an implementing approach for the evidence management system in detail. Some functions such as global, dynamic and systematic managing of evidence can be implemented in this system. We attempt to provide a function of individual searching as well. Users may carry out multi-dimensional data analysis based on the information of our database of the management systems.

A novel adaptive temporal-spatial information fusion model based on Dempster-Shafer evidence theory

In the field of target recognition based on the temporal-spatial information fusion,evidence the-ory has received extensive attention.To achieve accurate and efficient target recognition by the evi-dence theory,an adaptive temporal-spatial information fusion model is proposed.Firstly,an adaptive evaluation correction mechanism is constructed by the evidence distance and Deng entropy,which realizes the credibility discrimination and adaptive correction of the spatial evidence.Secondly,the credibility decay operator is introduced to obtain the dynamic credibility of temporal evidence.Finally,the sequential combination of temporal-spatial evidences is achieved by Shafer’s discount criterion and Dempster’s combination rule.The simulation results show that the proposed method not only considers the dynamic and sequential characteristics of the temporal-spatial evidences com-bination,but also has a strong conflict information processing capability,which provides a new refer-ence for the field of temporal-spatial information fusion.

A New Prediction System Based on Self-Growth Belief Rule Base with Interpretability Constraints

Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.

Structural Interval Reliability Algorithm Based on Bernstein Polynomials and Evidence Theory

Structural reliability is an important method to measure the safety performance of structures under the influence of uncertain factors.Traditional structural reliability analysis methods often convert the limit state function to the polynomial form to measure whether the structure is invalid.The uncertain parameters mainly exist in the form of intervals.This method requires a lot of calculation and is often difficult to achieve efficiently.In order to solve this problem,this paper proposes an interval variable multivariate polynomial algorithm based on Bernstein polynomials and evidence theory to solve the structural reliability problem with cognitive uncertainty.Based on the non-probabilistic reliability index method,the extreme value of the limit state function is obtained using the properties of Bernstein polynomials,thus avoiding the need for a lot of sampling to solve the reliability analysis problem.The method is applied to numerical examples and engineering applications such as experiments,and the results show that the method has higher computational efficiency and accuracy than the traditional linear approximation method,especially for some reliability problems with higher nonlinearity.Moreover,this method can effectively improve the reliability of results and reduce the cost of calculation in practical engineering problems.

Exploring the association of paid sick leave with healthcare utilization and health outcomes in the United States:a rapid evidence review

Objective:Paid sick leave(PSL)laws mandate employers give workers paid time off when they are sick or injured.This current study aims to examine whether access to PSL is associated with healthcare utilization and health outcomes and to summarize the types of utilization and outcomes which have been reported to be associated with PSL.Methods:We conducted a rapid evidence review.Our search of seven databases,including Medline,Embase,PsycINFO,Cochrane Library,CINAHL,Scopus,and JSTOR,on September 21,2020,identified 757 studies,30 of which were retained.Results:Previous evidence is mostly provided by cross-sectional studies with survey data.In this study,evidence suggests that PSL is significantly associated with some types of healthcare utilization and health outcomes.In terms of healthcare utilization,findings indicate PSL is associated with an increase in the use of some preventive sendees and a decrease in the use of emergency care;while findings are mixed regarding associations of PSL with health provider visits and the use of mammograms and pap smears.As for health outcomes,findings suggest PSL is associated with improved mental and self-rated health,decreased incidence of influenza-like illness,and lower occupational injuries and mortality rates.Conclusion:PSL may be an effective tool in improving some healthcare utilization and health outcomes.Future research could help identify mechanisms through which PSL access works and identify what policy components lead to better outcomes.

Religion and “Zero-Dose” Vaccination Status in Children Aged 12 to 23 Months in Benin and Togo: “Evidence of Demographic and Health Survey and Multiple Indicator Cluster Surveys”

Introduction: The relationship between religion and health has been a topic of interest in the past, and in recent years it has become increasingly visible in the social, behavioural, and health sciences. This work aims to study the influence of religion on the “zero-dose” vaccination status of children aged 12 - 23 months in Benin and Togo. Methods: This was a cross-sectional study which consisted in carrying out secondary analyzes using the databases of the most recent editions of the Demographic and Health Surveys in Benin and the Multiple Indicator Cluster Surveys (MICS) in Togo. We extracted data concerning the vaccination status (“1 = zero-dose”, “0 = vaccinated”) of the children, the religious affiliation of the head of the household or the mother and other variables related to the child, the mother, the household and the environment. By means of a logistic regression, the adjusted effect of the religious affiliation of the head of the household or of the mother on the vaccination status at “zero-dose” was determined. Results: A total of 2430 and 933 children were respectively included in the study on behalf of Benin and Togo. The prevalence of “zero-dose” vaccination status in children aged 12 - 23 months was 33.91% (95% CI = 31.67 - 36.23) in Benin and this prevalence was 26.88% (95% CI = 23.50 - 30.55) in Togo. In Benin, there is no evidence in favour of the influence of the religious affiliation of the mother on the “zero-dose” vaccination status of children. In Togo, children living in households whose head was Muslim (ORa = 3.44;95% CI = 1.29 - 9.13) were more likely to be “zero-dose” than those coming from households run by individuals with no religious beliefs. Conclusion: Further research is needed to understand the basis for the excess risk of “zero-dose” vaccination status in children that appears to be associated with the Muslim religion.

New evidence-based practice:Artificial intelligence as a barrier breaker

The concept of evidence-based practice has persisted over several years and remains a cornerstone in clinical practice,representing the gold standard for optimal patient care.However,despite widespread recognition of its significance,practical application faces various challenges and barriers,including a lack of skills in interpreting studies,limited resources,time constraints,linguistic competencies,and more.Recently,we have witnessed the emergence of a groundbreaking technological revolution known as artificial intelligence.Although artificial intelligence has become increasingly integrated into our daily lives,some reluctance persists among certain segments of the public.This article explores the potential of artificial intelligence as a solution to some of the main barriers encountered in the application of evidence-based practice.It highlights how artificial intelligence can assist in staying updated with the latest evidence,enhancing clinical decision-making,addressing patient misinformation,and mitigating time constraints in clinical practice.The integration of artificial intelligence into evidence-based practice has the potential to revolutionize healthcare,leading to more precise diagnoses,personalized treatment plans,and improved doctor-patient interactions.This proposed synergy between evidencebased practice and artificial intelligence may necessitate adjustments to its core concept,heralding a new era in healthcare.

Summary of the Best Evidence for Preoperative Pre-Rehabilitation in Patients with Lung Cancer Complicated with COPD

Objective: To search, evaluate and integrate the best domestic and foreign evidence on the preoperative pre-rehabilitation of lung cancer patients with COPD, and summarize the best evidence. Methods: Cochrane Library, BMJ Best Practice, JBI Center for International Cooperation in Evidence-Based Health Care Library, and Up to were searched by computer system Date, Embase, CINAHL, PubMed, Evidence-Based Health Care Center Database (JBI), American Guidenet, Global Guidenet, CNKI, CMBM, Wanfang, and VIP databases on preoperative pre-rehabilitation of patients with lung cancer and COPD. Literature screening and quality evaluation were conducted independently by two researchers, and evidence was extracted, summarized and summarized according to the theme. Results: A total of 13 kinds of literature were included, including 4 guidelines, 4 systematic reviews, 3 evidence summaries, and 2 expert consensus articles. This paper summarizes 44 best evidence on pre-rehabilitation of lung cancer patients with COPD, including 8 aspects: pre-rehabilitation content and principle, pre-rehabilitation assessment, exercise rehabilitation, nutrition management, psychological guidance, health education, multidisciplinary cooperation, quality control and follow-up. Conclusion: The best evidence of preoperative pre-rehabilitation for patients with lung cancer complicated with COPD is rich in content, and needs to be selected according to the characteristics of the hospital and the clinical environment. Some of the evidence still needs to be confirmed by higher-quality studies. .

Summary of the Best Evidence for the Evaluation and Management of Chemotherapy-Related Nausea and Vomiting in Cancer Patients

Objective: To summarize the best evidence for the evaluation and management of chemotherapy-related nausea and vomiting in cancer patients, so as to promote the standardized management of chemotherapy-related nausea and vomiting in cancer patients. Methods: A computer search was conducted for all evidence on chemotherapy-associated nausea and vomiting interventions, including guidelines, expert consensus, best clinical practice information booklet, recommended practice, evidence summary, and systematic review. The search period is from April 30, 2022. After the literature quality evaluation, the evidence extraction and summary of the literature meeting the quality standards. Results: A total of 16 literatures were included, including 9 guidelines, 5 systematic reviews and 2 expert consensus papers. 46 pieces of best evidence on the assessment and management of chemotherapy related nausea and vomiting in cancer patients were summarized, including 6 aspects including risk assessment and management, non-drug management, drug management, multidisciplinary cooperation, education and training, and health education. Conclusion: This study summarized the current best evidence on the assessment and management of chemotherapy-related nausea and vomiting in cancer patients. Clinical staff should apply evidence according to specific clinical scenarios, professional skills and patients’ wishes in order to reduce the degree and incidence of chemotherapy-related nausea and vomiting in cancer patients and improve the quality of care.

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

Maritime radar and automatic identification systems (AIS), which are essential auxiliary equipment for navigation safety in the shipping industry, have played significant roles in maritime safety supervision. However, in practical applications, the information obtained by a single device is limited, and it is necessary to integrate the information of maritime radar and AIS messages to achieve better recognition effects. In this study, the D-S evidence theory is used to fusion the two kinds of heterogeneous information: maritime radar images and AIS messages. Firstly, the radar image and AIS message are processed to get the targets of interest in the same coordinate system. Then, the coordinate position and heading of targets are chosen as the indicators for judging target similarity. Finally, a piece of D-S evidence theory based on the information fusion method is proposed to match the radar target and the AIS target of the same ship. Particularly, the effectiveness of the proposed method has been validated and evaluated through several experiments, which proves that such a method is practical in maritime safety supervision.

Current Situation and Prospect of the Application of Real-World Evidence in Health Care

Objective To summarize the application of real-world evidence(RWE)in the medical and healthcare field of various countries,including relevant policies,application scenarios and application methods.Methods Relevant policies and application scenarios were obtained by consulting the official websites and public documents of various countries’healthcare institutions.Systematic literature retrieval was adopted to search PubMed,EMBASE,Cochrane Library,CNKI,CBM and Wanfang databases,and all papers related to real-world study and application were included.Then,these papers were classified and analyzed by country and application method.Results and Conclusion The RWE was mainly applied to supporting the preliminary approval of a new drug,expanding drug indications,accelerating approval or supporting conditional marketing authorizations and drug safety evaluation,etc.The United Kingdom,the United States,Germany,the Netherlands,Italy,Sweden,and France admitted RWE,but they treated the data obtained from RWE with caution.After systematic literature retrieval,a total of 701 articles were obtained,including relevant studies from 36 countries,among which the United States published 264 in total.The most common study was about using real-world data(RWD)to calculate treatment-related costs,which had a total of 259 studies.Secondly,158 articles were used for epidemiological analysis.Then,138 articles were about establishing risk models to analyze disease risk factors.A total of 70 articles were real-world efficacy evaluation of the drug treatment schemes,54 articles were about pharmacoeconomic evaluation with RWD as parameters.A total of 29 articles used RWD to build predictive models,and 15 articles used RWD to evaluate the health-related quality of life in patients.The application of RWE has been used widely in the medical and healthcare field of various countries.The application scenarios are gradually diversified,the application methods of RWD become mature,and the evidence quality of RWE is also improved greatly.