Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degr...Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.展开更多
In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boo...In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.展开更多
Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application ...Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.展开更多
Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is o...Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.展开更多
In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncerta...In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.展开更多
Since the 12th Five-Year Program,China textile industry has maintained a sustained,steady and sound development with the expanding scale of industrial volume,effective implementation of science and technology,brands,s...Since the 12th Five-Year Program,China textile industry has maintained a sustained,steady and sound development with the expanding scale of industrial volume,effective implementation of science and technology,brands,sustainable development,qualified personnel and other important strategies as well as the promotion of constructing the powerful textile country strategy.However,as the great changes took place in external macro situation and internal development pattern,the economic growth展开更多
Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI s...Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.展开更多
This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of co...This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of complex mechanical systems. The EPN could take epistemic uncertainty such as interval information, subjective information into account by applying D-S evidence quantification theory. A dynamic representation model is also proposed based on the dynamic operation rules of the EPN model, and an improved artificial bee colony (ABC) algorithm is employed to proceed optimization calculation during the complex systems' learning process. The improved ABC algorithm and D-S evidence theory overcome the disadvantage of extremely subjective in traditional knowledge inference efficiently and thus could improve the accuracy of the EPN learning model. Through a simple numerical case and a satellite driving system analysis, this paper proves the superiority of the EPN and the dynamic knowledge representation method in reliability analysis of complex systems.展开更多
Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, t...Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.展开更多
To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal...To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.展开更多
Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and st...Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and standardized discharge preparation intervention programs in clinical practice.Methods:Following the“5S”evidence model,literature such as guidelines,expert consensuses,evidence summaries and randomized controlled trials related to discharge preparation services for total knee arthroplasty patients were retrieved from relevant websites and databases,both domestic and international,from database inception to August 31,2025.Two researchers independently screened the literature,conducted quality appraisals,and extracted and synthesized the evidence.Results:A total of 15 articles were included,comprising 3 guidelines,4 expert consensuses,3 evidence summaries,3 systematic reviews and 2 randomized controlled trials.Ultimately,23 pieces of evidence were summarized across five aspects.Conclusion:This study synthesizes the evidence on discharge preparation services for patients undergoing total knee arthroplasty.It is recommended that healthcare professionals apply this evidence in clinical practice,considering specific circumstances and patient needs.展开更多
Molecular hydrogen(H2)demonstrates selective antioxidant and anti-inflammatory properties with therapeutic potential across musculoskeletal conditions including osteoarthritis,rheumatoid arthritis,exercise-induced mus...Molecular hydrogen(H2)demonstrates selective antioxidant and anti-inflammatory properties with therapeutic potential across musculoskeletal conditions including osteoarthritis,rheumatoid arthritis,exercise-induced muscle damage,chronic pain syndromes,tendinopathies,and muscle atrophy.This review critically evaluates preclinical and clinical evidence for H2 therapy and identifies research gaps.A comprehensive search of PubMed,EMBASE,and Cochrane Library(up to April 2025)yielded 45 eligible studies:25 preclinical and 20 clinical trials.Preclinical models consistently showed reductions in reactive oxygen species,inflammatory cytokines,and improved cell viability.Clinical trials reported symptomatic relief in osteoarthritis,decreased Disease Activity Score 28 in rheumatoid arthritis,and accelerated clearance of muscle damage markers.Delivery methods varied-hydrogen-rich water,gas inhalation,and saline infusion-hindering direct comparison.Mechanistic biomarkers were inconsistently reported,limiting understanding of target engagement.Common limitations included small sample sizes,short durations,and protocol heterogeneity.Despite these constraints,findings suggest H2 may serve as a promising adjunctive therapy via antioxidant,anti-inflammatory,and cytoprotective mechanisms.Future research should prioritize standardized delivery protocols,robust mechanistic endpoints,and longer-term randomized trials to validate clinical efficacy and optimize therapeutic strategies.展开更多
1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;...1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).展开更多
Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance pati...Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance patients remains limited.This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients,with additional subgroup analyses by age and performance status.Methods:We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2−breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025.The primary endpoint was progression-free survival(PFS).Secondary endpoints included overall response rate(ORR),disease control rate(DCR),and safety.Subgroup analyses were performed by age(<70 vs.≥70 years)and performance status(PS;0–1 vs.2–3).Results:The median PFS was 26.6 months(range,1.4–69.5).Stratified by age,median PFS was 26.9 months in patients<70 years and 26.2 months in those≥70 years(p=0.760).By PS,PFS was 26.9 months for PS 0–1 and 17.8 months for PS 2–3(p=0.099).ORR was 60.9%and DCR 93.5%;notably,all PS 2–3 patients achieved disease control.Hematologic toxicities were common,with neutropenia(80.4%)and leukopenia(86.7%)predominating,but grade≥3 anemia was rare(2.2%).Elderly patients experienced anemia more frequently,while overall toxicity remained manageable.Dose reductions occurred in 47.8%without loss of efficacy.Conclusions:In routine Japanese practice,palbociclib plus ET provided prolonged PFS and high disease control consistent with pivotal trials and international real-world evidence.Importantly,elderly patients tolerated treatment well,and selected PS 2–3 patients also derived clinical benefit.These findings indicate that neither age nor PS alone should preclude the use of palbociclib in carefully monitored real-world patients.展开更多
Financial expenses Of China’s textile machinery in fiscal year 2008 rose 28.81%due to higher indebtedness(60.57%of indebtedness rate).Analysts anticipated animminent weakening of momentum for China’s textile machine...Financial expenses Of China’s textile machinery in fiscal year 2008 rose 28.81%due to higher indebtedness(60.57%of indebtedness rate).Analysts anticipated animminent weakening of momentum for China’s textile machinery markets owing toweaker consumer spending and easing export growth.展开更多
According to the latest statistics released by China TextileMachinery & Accessories Association(CTMA),the export value offoreign-funded enterprises from Jan.to Oct.2008,reached 0.502billion US dollars,a year-on-ye...According to the latest statistics released by China TextileMachinery & Accessories Association(CTMA),the export value offoreign-funded enterprises from Jan.to Oct.2008,reached 0.502billion US dollars,a year-on-year increase of 0.08 percent.展开更多
User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient s...User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services.The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources,in different formats and often associated with uncertainty.In this paper,we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation.The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach.A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted.展开更多
In this study, the evidential belief functions （EBFs） were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers （e.g., geological, geochemical, and geophysi...In this study, the evidential belief functions （EBFs） were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers （e.g., geological, geochemical, and geophysical） related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps （belief map, disbelief map, uncertainty map, and plausibility map） are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic （ROC） curves and the area under the curves （AUC） for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties asso- ciated with evidential layers.展开更多
Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important fa...Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important factors such as interactivity and transitivity between multiple nodes.To identify the vital nodes more accurately,a high-order structure,named as the motif,is introduced in this paper as the basic unit to evaluate the similarity among the node in the complex network.It proposes a notion of high-order degree of nodes in complex network and fused the effect of the high-order structure and the lower-order structure of nodes,using evidence theory to determine the vital nodes more efficiently and accurately.The algorithm was evaluated from the function of network structure.And the SIR model was adopted to examine the spreading influence of the nodes ranked.The results of experiments in different datasets demonstrate that the algorithm designed can identify vital nodes in the social network accurately.展开更多
基金supported by the National Natural Science Foundation of China(61174022)the National High Technology Research and Development Program of China(863 Program)(2013AA013801)+2 种基金the Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems,Beihang University(BUAA-VR-14KF-02)the General Research Program of the Science Supported by Sichuan Provincial Department of Education(14ZB0322)the Fundamental Research Funds for the Central Universities(XDJK2014D008)
文摘Identifying influential nodes in complex networks is still an open issue. In this paper, a new comprehensive centrality mea- sure is proposed based on the Dempster-Shafer evidence theory. The existing measures of degree centrality, betweenness centra- lity and closeness centrality are taken into consideration in the proposed method. Numerical examples are used to illustrate the effectiveness of the proposed method.
基金supported by National Natural Science Foundation of China(Nos.71131002,71071045,71231004 and 71201042)
文摘In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.
基金Under the auspices of National Natural Science Foundation of China (No.40871188)Knowledge Innovation Programs of Chinese Academy of Sciences (No.INFO-115-C01-SDB4-05)
文摘Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.
基金co-supported by the National Natural Science Foundation of China (No. 61833016)the Shaanxi Outstanding Youth Science Foundation,China (No. 2020JC-34)the Shaanxi Science and Technology Innovation Team,China(No. 2022TD-24)
文摘Evidential Reasoning(ER)rule,which can combine multiple pieces of independent evidence conjunctively,is widely applied in multiple attribute decision analysis.However,the assumption of independence among evidence is often not satisfied,resulting in ER rule inapplicable.In this paper,an Evidential Reasoning rule for Dependent Evidence combination(ERr-DE)is developed.Firstly,the aggregation sequence of multiple pieces of evidence is determined according to evidence reliability.On this basis,a calculation method of evidence Relative Total Dependence Coefficient(RTDC)is proposed using the distance correlation method.Secondly,as a discounting factor,RTDC is introduced into the ER rule framework,and the ERr-DE model is formulated.The aggregation process of two pieces of dependent evidence by ERr-DE is investigated,which is then generalized to aggregate multiple pieces of non-independent evidence.Thirdly,sensitivity analysis is carried out to investigate the relationship between the model output and the RTDC.The properties of sensitivity coefficient are explored and mathematically proofed.The conjunctive probabilistic reasoning process of ERr-DE and the properties of sensitivity coefficient are verified by two numerical examples respectively.Finally,the practical application of the ERr-DE is validated by a case study on the performance assessment of satellite turntable system.
基金Project(71201170)supported by the National Natural Science Foundation of China
文摘In order to overcome the limitations of traditional methods in uncertainty analysis, a modified Bayesian network(BN), which is called evidence network(EN), was proposed with evidence theory to handle epistemic uncertainty in probabilistic risk assessment(PRA). Fault trees(FTs) and event trees(ETs) were transformed into an EN which is used as a uniform framework to represent accident scenarios. Epistemic uncertainties of basic events in PRA were presented in evidence theory form and propagated through the network. A case study of a highway tunnel risk analysis was discussed to demonstrate the proposed approach. Frequencies of end states are obtained and expressed by belief and plausibility measures. The proposed approach addresses the uncertainties in experts' knowledge and can be easily applied to uncertainty analysis of FTs/ETs that have dependent events.
文摘Since the 12th Five-Year Program,China textile industry has maintained a sustained,steady and sound development with the expanding scale of industrial volume,effective implementation of science and technology,brands,sustainable development,qualified personnel and other important strategies as well as the promotion of constructing the powerful textile country strategy.However,as the great changes took place in external macro situation and internal development pattern,the economic growth
基金supported by the National Natural Science Foundation of China(6167138461703338)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(2016JM6018)the Project of Science and Technology Foundationthe Fundamental Research Funds for the Central Universities(3102017OQD020)
文摘Dempster-Shafer evidence theory(DS theory) is widely used in brain magnetic resonance imaging(MRI) segmentation,due to its efficient combination of the evidence from different sources. In this paper, an improved MRI segmentation method,which is based on fuzzy c-means(FCM) and DS theory, is proposed. Firstly, the average fusion method is used to reduce the uncertainty and the conflict information in the pictures. Then, the neighborhood information and the different influences of spatial location of neighborhood pixels are taken into consideration to handle the spatial information. Finally, the segmentation and the sensor data fusion are achieved by using the DS theory. The simulated images and the MRI images illustrate that our proposed method is more effective in image segmentation.
基金supported by the National Basic Research Program of China(2013CB733002)
文摘This paper proposes a novel dynamic Petri net (PN) model based on Dempster-Shafer (D-S) evidence theory, and this improved evidential Petri net (EPN) model is used in knowledge inference and reliability analysis of complex mechanical systems. The EPN could take epistemic uncertainty such as interval information, subjective information into account by applying D-S evidence quantification theory. A dynamic representation model is also proposed based on the dynamic operation rules of the EPN model, and an improved artificial bee colony (ABC) algorithm is employed to proceed optimization calculation during the complex systems' learning process. The improved ABC algorithm and D-S evidence theory overcome the disadvantage of extremely subjective in traditional knowledge inference efficiently and thus could improve the accuracy of the EPN learning model. Through a simple numerical case and a satellite driving system analysis, this paper proves the superiority of the EPN and the dynamic knowledge representation method in reliability analysis of complex systems.
基金supported in part by NUS startup grantthe National Natural Science Foundation of China (52076037)。
文摘Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.
基金supported by the Key Research and Development Program of Shaanxi Province(2023-GHZD-33)the Open Project of the State Key Laboratory of Intelligent Game(ZBKF-23-05)the National Nature Science Foundation of China(62003267)。
文摘To address the issue of incorrect fusion results caused by conflicting evidence due to inaccurate evidence and incomplete recognition frameworks in radar airborne target tactical intention recognition,a spatiotemporal evidence fusion algorithm is proposed.To resolve the conflict evidence fusion problem caused by inaccurate evidence,the algorithm performs discounting of evidence from both spatial and temporal dimensions.Spatial discounting is influenced by both inter-evidence inconsistency and intra-evidence inconsistency,while temporal discounting is determined by time intervals and information entropy.For the problem of conflicting evidence fusion due to an incomplete recognition framework,an open recognition architecture based on dynamic composite focal elements is proposed.This approach allocates some conflicting information to temporary composite focal elements,avoiding excessive basic probability assignment(BPA)of the empty set after fusion,which can lead to deviations from the actual fusion results.Simulation experiments comparing various methods indicate that the proposed method can effectively improve target intention recognition accuracy and demonstrates good stability.
文摘Objective:To systematically summarize and evaluate the evidence on discharge preparation services for patients undergoing total knee arthroplasty,providing an evidence-based foundation for developing scientific and standardized discharge preparation intervention programs in clinical practice.Methods:Following the“5S”evidence model,literature such as guidelines,expert consensuses,evidence summaries and randomized controlled trials related to discharge preparation services for total knee arthroplasty patients were retrieved from relevant websites and databases,both domestic and international,from database inception to August 31,2025.Two researchers independently screened the literature,conducted quality appraisals,and extracted and synthesized the evidence.Results:A total of 15 articles were included,comprising 3 guidelines,4 expert consensuses,3 evidence summaries,3 systematic reviews and 2 randomized controlled trials.Ultimately,23 pieces of evidence were summarized across five aspects.Conclusion:This study synthesizes the evidence on discharge preparation services for patients undergoing total knee arthroplasty.It is recommended that healthcare professionals apply this evidence in clinical practice,considering specific circumstances and patient needs.
文摘Molecular hydrogen(H2)demonstrates selective antioxidant and anti-inflammatory properties with therapeutic potential across musculoskeletal conditions including osteoarthritis,rheumatoid arthritis,exercise-induced muscle damage,chronic pain syndromes,tendinopathies,and muscle atrophy.This review critically evaluates preclinical and clinical evidence for H2 therapy and identifies research gaps.A comprehensive search of PubMed,EMBASE,and Cochrane Library(up to April 2025)yielded 45 eligible studies:25 preclinical and 20 clinical trials.Preclinical models consistently showed reductions in reactive oxygen species,inflammatory cytokines,and improved cell viability.Clinical trials reported symptomatic relief in osteoarthritis,decreased Disease Activity Score 28 in rheumatoid arthritis,and accelerated clearance of muscle damage markers.Delivery methods varied-hydrogen-rich water,gas inhalation,and saline infusion-hindering direct comparison.Mechanistic biomarkers were inconsistently reported,limiting understanding of target engagement.Common limitations included small sample sizes,short durations,and protocol heterogeneity.Despite these constraints,findings suggest H2 may serve as a promising adjunctive therapy via antioxidant,anti-inflammatory,and cytoprotective mechanisms.Future research should prioritize standardized delivery protocols,robust mechanistic endpoints,and longer-term randomized trials to validate clinical efficacy and optimize therapeutic strategies.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20240937)the Natural Science Foundation of Shandong Province(Grant No.ZR2021QE187)+2 种基金the Shandong Higher Education“Young Entrepreneurship Talents Introduction and Cultivation Program”Project(Grant No.ZXQT20221228001)the Natural Science Foundation of China(Grant No.42502273)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4028).
文摘1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).
文摘Background:Cyclin-dependent kinase 4/6(CDK4/6)inhibitors have transformed the management of hormone receptor–positive/HER2–negative(HR+/HER2–)advanced breast cancer,yet evidence for elderly or poor-performance patients remains limited.This study examined real-world outcomes of palbociclib plus endocrine therapy in Asian patients,with additional subgroup analyses by age and performance status.Methods:We retrospectively analyzed 46 consecutive Asian patients with recurrent or de novo HR+/HER2−breast cancer treated with first-line palbociclib plus ET between April 2021 and March 2025.The primary endpoint was progression-free survival(PFS).Secondary endpoints included overall response rate(ORR),disease control rate(DCR),and safety.Subgroup analyses were performed by age(<70 vs.≥70 years)and performance status(PS;0–1 vs.2–3).Results:The median PFS was 26.6 months(range,1.4–69.5).Stratified by age,median PFS was 26.9 months in patients<70 years and 26.2 months in those≥70 years(p=0.760).By PS,PFS was 26.9 months for PS 0–1 and 17.8 months for PS 2–3(p=0.099).ORR was 60.9%and DCR 93.5%;notably,all PS 2–3 patients achieved disease control.Hematologic toxicities were common,with neutropenia(80.4%)and leukopenia(86.7%)predominating,but grade≥3 anemia was rare(2.2%).Elderly patients experienced anemia more frequently,while overall toxicity remained manageable.Dose reductions occurred in 47.8%without loss of efficacy.Conclusions:In routine Japanese practice,palbociclib plus ET provided prolonged PFS and high disease control consistent with pivotal trials and international real-world evidence.Importantly,elderly patients tolerated treatment well,and selected PS 2–3 patients also derived clinical benefit.These findings indicate that neither age nor PS alone should preclude the use of palbociclib in carefully monitored real-world patients.
文摘Financial expenses Of China’s textile machinery in fiscal year 2008 rose 28.81%due to higher indebtedness(60.57%of indebtedness rate).Analysts anticipated animminent weakening of momentum for China’s textile machinery markets owing toweaker consumer spending and easing export growth.
文摘According to the latest statistics released by China TextileMachinery & Accessories Association(CTMA),the export value offoreign-funded enterprises from Jan.to Oct.2008,reached 0.502billion US dollars,a year-on-year increase of 0.08 percent.
基金This work is supported by the University of Information Technology-Vietnam National University Ho Chi Minh City under grant No.D1-2023-10.
文摘User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services.The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources,in different formats and often associated with uncertainty.In this paper,we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation.The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach.A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted.
文摘In this study, the evidential belief functions （EBFs） were applied for mapping tungsten polymetallic potential in the Nanling belt, South China. Seven evidential layers （e.g., geological, geochemical, and geophysical） related to tungsten polymetallic deposits were extracted from a multi-source geospatial database. The relationships between evidential layers and the target deposits were quantified using EBFs model. Four EBF maps （belief map, disbelief map, uncertainty map, and plausibility map） are generated by integrating seven evidential layers which provide meaningful interpretations for tungsten polymetallic potential. On the final predictive map, the study area was divided into three target zones of high potential, moderate potential, and low potential areas, among which high potential and moderate potential areas accounted for 17.8% of the total area, containing 81% of the total deposits. To evaluate the success rate accuracy, the receiver operating characteristic （ROC） curves and the area under the curves （AUC） for the belief map were calculated. The area under the curve is 0.81 which indicates that the capability for correctly classifying the areas with existing mineral deposits is satisfactory. The results of this study indicate that the EBFs were effectively used for mapping mineral potential and for managing uncertainties asso- ciated with evidential layers.
基金the Natural Science Foundation of China(No.61662066,61163010).
文摘Identifying vital nodes is a basic problem in social network research.The existing theoretical framework mainly focuses on the lowerorder structure of node-based and edge-based relations and often ignores important factors such as interactivity and transitivity between multiple nodes.To identify the vital nodes more accurately,a high-order structure,named as the motif,is introduced in this paper as the basic unit to evaluate the similarity among the node in the complex network.It proposes a notion of high-order degree of nodes in complex network and fused the effect of the high-order structure and the lower-order structure of nodes,using evidence theory to determine the vital nodes more efficiently and accurately.The algorithm was evaluated from the function of network structure.And the SIR model was adopted to examine the spreading influence of the nodes ranked.The results of experiments in different datasets demonstrate that the algorithm designed can identify vital nodes in the social network accurately.
