The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spat...The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spatiotemporal evolution of ES functions,as well as the trade-offs and synergies among these functions,remain poorly understood,constraining effective watershed-scale management.To address this challenge,this study quantified four ES functions,i.e.,water yield(WY),carbon storage(CS),habitat quality(HQ),and soil conservation(SC)in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model,and proposed an innovative integration of InVEST with a Bayesian Belief Network(BBN)to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference.A trade-off and synergy index(TSI)was developed to assess the spatial interaction intensity among ES functions,while sensitivity and scenario analyses were employed to determine key driving factors,followed by spatial optimization to delineate functional zones.Results revealed distinct spatiotemporal variations:WY increased from 98.69 to 120.52 mm;SC rose to an average of 3.05×10^(4) t/hm^(2);CS remained relatively stable(about 15.50 t/km^(2));and HQ averaged 0.51 with localized declines.The BBN achieved a high accuracy of 81.9%and effectively identified strong synergies between WY and SC,as well as between CS and HQ,while clear trade-offs were observed between WY and SC versus CS and HQ.Sensitivity analysis indicated precipitation(variance reduction of 9.4%),land use(9.8%),and vegetation cover(9.1%)as key driving factors.Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin,while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins,highlighting strong spatial heterogeneity.Overall,this study advances ES research by combining process-based quantification with probabilistic modeling,offering a robust framework for studying nonlinear interactions,driving mechanisms,and optimization strategies,and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.展开更多
The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investig...The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investigates the interdependencies among SPI components and their impact on country-level sustainability performance.Using a Bayesian Belief Network(BBN)approach,the analysis explores the interdependencies among 12 SPI components(including advanced education,basic education,environmental quality,freedom and choice,health,housing,inclusive society,information and communications,nutrition and medical care,rights and voice,safety,and water and sanitation)and their collective influence on sustainability performance.Data from the Sustainable Development Report and SPI datasets,covering 162 countries(including Australia,China,United Arab Emirates,United Kingdom,United States,and so on),were used to assess the relative importance of each SPI component.The key findings indicate that advanced education,inclusive society,and freedom and choice make substantial contributions to high sustainability performance,whereas deficiencies in nutrition and medical care,water and sanitation,and freedom and choice are associated with poor sustainability performance.The results reveal that sustainability performance is shaped by a network of interlinked SPI components,with education and inclusion emerging as key levers for progress.The study emphasizes that targeted improvements in specific SPI components can significantly enhance a country’s overall sustainability performance.Rather than visualizing countries’progress through composite indicator-based heat maps,this study explores the interdependencies among SPI components and their role in sustainability performance at the global level.The study underscores the importance of a multidimensional policy approach that addresses social and environmental factors to enhance sustainability.The findings contribute to a deeper understanding of how SPI components interact and shape sustainable development.展开更多
Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a ...Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.展开更多
The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on fa...The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.展开更多
With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recogn...With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).展开更多
The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always...The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always been considerably risky;however,managing Research and Development(R&D)project risks has become even more important given today’s tight schedules and limited resources.Risk management has to be an integral part of the development process.The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project.A risk quantification model based on the Bayesian belief network is proposed,which is effective in capturing the interaction between various risk factors.The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.展开更多
Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple in...Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity.The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling.The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment.Initial analysis from well workover data,of a 5-node BBN provided insights on two different levels of severity of an accident,the’Accident’and’Incident’level,and on its respective profiles of the initiating events and the investigation-reported human causes.The initial results demonstrate that the data learnt from the database can be used to structure the BBN,give insights on how human reliability pertaining to well activities can be modelled,and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes.It is also proposed that the integrated treatment of various sources of information(database and expert judgement)through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.展开更多
Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions...Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.展开更多
The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related ...The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.展开更多
This study aims to evaluate the relative importance of the pillars and indicators within th e Energy Transition Index(ETI) and their influence on overall ETI performance.Using a dataset from the World Economic Forum&#...This study aims to evaluate the relative importance of the pillars and indicators within th e Energy Transition Index(ETI) and their influence on overall ETI performance.Using a dataset from the World Economic Forum's 2024 ETI report,which covers 120 countries,this research applies Bayesian Belief Networks(BBNs),a probabilistic graphical modeling technique suited for analyzing complex interdependencies among variables.Two models are developed:one connecting the ETI to its eight pillars,and another linking it to 22 selected indicators.Findings reveal that the finance and investment pillar has the strongest positive association with ETI scores,whereas innovation is the area with the highest concentration of low-performing countries.Additionally,strong synergies are observed across digital infrastructure readiness and education quality,highlighting opportunities for multi-dimensional policy interventions.The study provides actionable insights for policymakers,such as prioritizing financial instruments,strengthening regulatory frameworks,and enhancing educational and digital infrastructure to accelerate progress in energy transitions.展开更多
This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual fo...This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.展开更多
Pipeline corrosion analysis is considered a challenging topic due to the complexity and uncertainty of the factors involved.The uncontrolled consequences of corrosion impact the"natural ecosystem,society,and econ...Pipeline corrosion analysis is considered a challenging topic due to the complexity and uncertainty of the factors involved.The uncontrolled consequences of corrosion impact the"natural ecosystem,society,and economy."Investigating corrosion plays a crucial role in managing incidents.This paper aims to provide an effective management tool for predicting corrosion using Bayesian modeling.This study illustrates how to integrate Bayesian modeling with"incomplete data,scientific information in various formats,and expert knowledge,"utilizing it effectively.Employing Netica software,a fault tree representing important elements affecting the natural gas transmission lines corrosion is converted into a cause-and-effect diagram of the Bayesian belief network.Natural gas transmission lines corrosion the model's output response is then analyzed after defining the relationships between these corrosion-affecting variables.The correlation between failures and corrosion is considered through a bivariate normal distribution as the likelihood function in the Bayesian update,and the model was validated using OpenBUGS software.In the next step,sensitivity analysis and scenario analysis were conducted in two industrial zones located in the central regions of the country.The final findings showed that the suggested model can produce accurate results for Corrosion monitoring systems in the Natural Gas Industry,providing an efficient approach to assess safety,quantitative risk analysis,and forming the basis for decisions aimed at averting pipeline episodes.展开更多
This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category...This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.展开更多
This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in ...This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in Indonesia.28,936 PAMSIMAS projects in 33 provinces in Indonesia were analyzed.The data indicates that 85.4%of the water supply systems were fully functioning,9.1%were partially functioning,and 5.5%were not functioning.In the regression analysis,good management is positively associated with functionality and a high investment per capita is negatively associated with the functionality.The latter suggests the need for comprehen-sive economic analysis in the feasibility study in scattered housing sites and remote-undeveloped areas.We also found that high community participation at the beginning of the project was associated with the not functioning system,while women’s participation was positively associated with the functionality.Furthermore,the household connection is more likely to be functioning than communal connection.BBN analysis shows if the beneficiaries do not pay for water,the probability of not functioning systems is 20 times higher than systems with fee collec-tion.Moreover,the combination of strong management,strong financial status,and household connection rather than communal connection increases the probability of fully functioning to 98%.Improvement of data collection is also necessary to monitor the current conditions of all PAMSIMAS systems in Indonesia.This study offers a country-level perspective for better implementation of the community-based rural water supply and sanitation program in developing countries.展开更多
Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied ...Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied in grassland ecosystems.This study aims to fill this gap through a case study of Xilinhot City,Inner Mongolia Autonomous Region,China.We examined the association between grassland provisioning services and herds-men’s well-being between 1985 and 2015 through participatory observations,interviews,surveys,and Bayesian belief network modeling.Considering the uncertainties of weather and sheep prices,we developed four scenarios to examine the future well-being of herdsmen.Our results show that the most important factor for herdsmen’s well-being was income,which is highly sensitive to the market price of sheep and precipitation.Considering the uncertainties of sheep prices and precipitation,scenario analysis revealed a divergence between income and well-being.While herdsmen’s income is most likely to increase with low precipitation and increased sheep prices,their well-being is most likely to improve with abundant precipitation and increased sheep prices.Based on our find-ings,we argue that developing alternative income sources(e.g.,tourism),reducing dependence on government subsidies through commercial insurance,and branding lamb with grassland ecosystem to alleviate the impact of price fluctuations would help improve herdsmen’s well-being in all scenarios.展开更多
As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This pape...As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This paper summarized the development and recent studies of the explanations of D-S model, evidence combination algorithms, and the improvement of the conflict during evidence combination, and also compared all explanation models,algorithms, improvements, and their applicable conditions. We are trying to provide a reference for future research and applications through this summarization.展开更多
基金supported by the Science and Technology Project of Shaanxi Province Water Conservancy,China(2025slkj-10)the Natural Science Basic Research Program of Shaanxi Province,China(S2025-JC-QN-2416).
文摘The Wuding River Basin,situated in the Loess Plateau of northern China,is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service(ES)functions.However,the mechanisms driving the spatiotemporal evolution of ES functions,as well as the trade-offs and synergies among these functions,remain poorly understood,constraining effective watershed-scale management.To address this challenge,this study quantified four ES functions,i.e.,water yield(WY),carbon storage(CS),habitat quality(HQ),and soil conservation(SC)in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff(InVEST)model,and proposed an innovative integration of InVEST with a Bayesian Belief Network(BBN)to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference.A trade-off and synergy index(TSI)was developed to assess the spatial interaction intensity among ES functions,while sensitivity and scenario analyses were employed to determine key driving factors,followed by spatial optimization to delineate functional zones.Results revealed distinct spatiotemporal variations:WY increased from 98.69 to 120.52 mm;SC rose to an average of 3.05×10^(4) t/hm^(2);CS remained relatively stable(about 15.50 t/km^(2));and HQ averaged 0.51 with localized declines.The BBN achieved a high accuracy of 81.9%and effectively identified strong synergies between WY and SC,as well as between CS and HQ,while clear trade-offs were observed between WY and SC versus CS and HQ.Sensitivity analysis indicated precipitation(variance reduction of 9.4%),land use(9.8%),and vegetation cover(9.1%)as key driving factors.Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin,while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins,highlighting strong spatial heterogeneity.Overall,this study advances ES research by combining process-based quantification with probabilistic modeling,offering a robust framework for studying nonlinear interactions,driving mechanisms,and optimization strategies,and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.
基金the resources and support provided by the American University of Sharjah to conduct this research
文摘The social progress index(SPI)measures social and environmental performance beyond traditional economic indicators,providing transparent and actionable insights into the true condition of societies.This study investigates the interdependencies among SPI components and their impact on country-level sustainability performance.Using a Bayesian Belief Network(BBN)approach,the analysis explores the interdependencies among 12 SPI components(including advanced education,basic education,environmental quality,freedom and choice,health,housing,inclusive society,information and communications,nutrition and medical care,rights and voice,safety,and water and sanitation)and their collective influence on sustainability performance.Data from the Sustainable Development Report and SPI datasets,covering 162 countries(including Australia,China,United Arab Emirates,United Kingdom,United States,and so on),were used to assess the relative importance of each SPI component.The key findings indicate that advanced education,inclusive society,and freedom and choice make substantial contributions to high sustainability performance,whereas deficiencies in nutrition and medical care,water and sanitation,and freedom and choice are associated with poor sustainability performance.The results reveal that sustainability performance is shaped by a network of interlinked SPI components,with education and inclusion emerging as key levers for progress.The study emphasizes that targeted improvements in specific SPI components can significantly enhance a country’s overall sustainability performance.Rather than visualizing countries’progress through composite indicator-based heat maps,this study explores the interdependencies among SPI components and their role in sustainability performance at the global level.The study underscores the importance of a multidimensional policy approach that addresses social and environmental factors to enhance sustainability.The findings contribute to a deeper understanding of how SPI components interact and shape sustainable development.
基金Projects(2016YFE0200100,2018YFC1505300-5.3)supported by the National Key Research&Development Plan of ChinaProject(51639002)supported by the Key Program of National Natural Science Foundation of China
文摘Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.
文摘The uncertainty during the period of software project development often brings huge risks to contractors and clients. If we can find an effective method to predict the cost and quality of software projects based on facts like the project character and two-side cooperating capability at the beginning of the project,we can reduce the risk. Bayesian Belief Network(BBN) is a good tool for analyzing uncertain consequences, but it is difficult to produce precise network structure and conditional probability table.In this paper,we built up network structure by Delphi method for conditional probability table learning,and learn update probability table and nodes’confidence levels continuously according to the application cases, which made the evaluation network have learning abilities, and evaluate the software development risk of organization more accurately.This paper also introduces EM algorithm, which will enhance the ability to produce hidden nodes caused by variant software projects.
基金National Natural Science Foundation of China(No. 70971021)
文摘With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).
文摘The project success is critical to the business performance in the era of fierce competition and globalization.The basis for project success lies in the capabilities of managing risks effectively.Innovation has always been considerably risky;however,managing Research and Development(R&D)project risks has become even more important given today’s tight schedules and limited resources.Risk management has to be an integral part of the development process.The purpose of this research is to develop a model to assess and estimate the risk exposure of an R&D project.A risk quantification model based on the Bayesian belief network is proposed,which is effective in capturing the interaction between various risk factors.The aim of this model is to empower the project managers to predict the failure risk probability of R&D projects.
基金The authors would like to acknowledge the support of Lloyd’s Register Singapore,Lloyd’s Register Consulting Energy AB(Sweden),Nanyang Technological University,Singapore Institute of Technology and the Singapore Economic Development Board(EDB)under the Industrial Postgraduate Program in the undertaking of this work(RCA-15/424).
文摘Decommissioning of offshore facilities involve changing risk profiles at different decommissioning phases.Bayesian Belief Networks(BBN)are used as part of the proposed risk assessment method to capture the multiple interactions of a decommissioning activity.The BBN is structured from the data learning of an accident database and a modification of the BBN nodes to incorporate human reliability and barrier performance modelling.The analysis covers one case study of one area of decommissioning operations by extrapolating well workover data to well plugging and abandonment.Initial analysis from well workover data,of a 5-node BBN provided insights on two different levels of severity of an accident,the’Accident’and’Incident’level,and on its respective profiles of the initiating events and the investigation-reported human causes.The initial results demonstrate that the data learnt from the database can be used to structure the BBN,give insights on how human reliability pertaining to well activities can be modelled,and that the relative frequencies from the count analysis can act as initial data input for the proposed nodes.It is also proposed that the integrated treatment of various sources of information(database and expert judgement)through a BBN model can support the risk assessment of a dynamic situation such as offshore decommissioning.
基金This study was part of research work sponsored by the National Key Research&Development Plan of China(Nos.2018YFC 1505300-5.3 and 2016YFE0200100)the Key Program of the National Natural Science Foundation of China(Grant No.51639002).
文摘Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.
文摘The digital reactor protection system(RPS)is one of the most important digital instrumentation and control(I&C)systems utilized in nuclear power plants(NPPs).It ensures a safe reactor trip when the safety-related parameters violate the operational limits and conditions of the reactor.Achieving high reliability and availability of digital RPS is essential to maintaining a high degree of reactor safety and cost savings.The main objective of this study is to develop a general methodology for improving the reliability of the RPS in NPP,based on a Bayesian Belief Network(BBN)model.The structure of BBN models is based on the incorporation of failure probability and downtime of the RPS I&C components.Various architectures with dual-state nodes for the I&C components were developed for reliability-sensitive analysis and availability optimization of the RPS and to demonstrate the effect of I&C components on the failure of the entire system.A reliability framework clarified as a reliability block diagram transformed into a BBN representation was constructed for each architecture to identify which one will fit the required reliability.The results showed that the highest availability obtained using the proposed method was 0.9999998.There are 120 experiments using two common component importance measures that are applied to define the impact of I&C modules,which revealed that some modules are more risky than others and have a larger effect on the failure of the digital RPS.
文摘This study aims to evaluate the relative importance of the pillars and indicators within th e Energy Transition Index(ETI) and their influence on overall ETI performance.Using a dataset from the World Economic Forum's 2024 ETI report,which covers 120 countries,this research applies Bayesian Belief Networks(BBNs),a probabilistic graphical modeling technique suited for analyzing complex interdependencies among variables.Two models are developed:one connecting the ETI to its eight pillars,and another linking it to 22 selected indicators.Findings reveal that the finance and investment pillar has the strongest positive association with ETI scores,whereas innovation is the area with the highest concentration of low-performing countries.Additionally,strong synergies are observed across digital infrastructure readiness and education quality,highlighting opportunities for multi-dimensional policy interventions.The study provides actionable insights for policymakers,such as prioritizing financial instruments,strengthening regulatory frameworks,and enhancing educational and digital infrastructure to accelerate progress in energy transitions.
基金National Natural Science Foundation of China (60975073)Aeronautical Science Foundation of China (2008ZC13011)+1 种基金Research Foundation for Doctoral Program of Higher Education of China (20091102110006)Fundamental Research Funds for the Central Universities
文摘This article proposes a novel fuzzy virtual force （FVF） method for unmanned aerial vehicle （UAV） path planning in compli-cated environment. An integrated mathematical model of UAV path planning based on virtual force （VF） is constructed and the corresponding optimal solving method under the given indicators is presented. Specifically,a fixed step method is developed to reduce computational cost and the reachable condition of path planning is proved. The Bayesian belief network and fuzzy logic reasoning theories are applied to setting the path planning parameters adaptively,which can reflect the battlefield situation dy-namically and precisely. A new way of combining threats is proposed to solve the local minima problem completely. Simulation results prove the feasibility and usefulness of using FVF for UAV path planning. Performance comparisons between the FVF method and the A＊ search algorithm demonstrate that the proposed approach is fast enough to meet the real-time requirements of the online path planning problems.
文摘Pipeline corrosion analysis is considered a challenging topic due to the complexity and uncertainty of the factors involved.The uncontrolled consequences of corrosion impact the"natural ecosystem,society,and economy."Investigating corrosion plays a crucial role in managing incidents.This paper aims to provide an effective management tool for predicting corrosion using Bayesian modeling.This study illustrates how to integrate Bayesian modeling with"incomplete data,scientific information in various formats,and expert knowledge,"utilizing it effectively.Employing Netica software,a fault tree representing important elements affecting the natural gas transmission lines corrosion is converted into a cause-and-effect diagram of the Bayesian belief network.Natural gas transmission lines corrosion the model's output response is then analyzed after defining the relationships between these corrosion-affecting variables.The correlation between failures and corrosion is considered through a bivariate normal distribution as the likelihood function in the Bayesian update,and the model was validated using OpenBUGS software.In the next step,sensitivity analysis and scenario analysis were conducted in two industrial zones located in the central regions of the country.The final findings showed that the suggested model can produce accurate results for Corrosion monitoring systems in the Natural Gas Industry,providing an efficient approach to assess safety,quantitative risk analysis,and forming the basis for decisions aimed at averting pipeline episodes.
基金supported,in part,by the Faculty Research Grant(FRG23-E-B91)from the American University of Sharjah.
文摘This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.
文摘This study used multinomial logistic regression and Bayesian belief networks(BBN)to analyze factors influenc-ing the functionality of the community-based rural drinking water supply and sanitation program(PAMSIMAS)in Indonesia.28,936 PAMSIMAS projects in 33 provinces in Indonesia were analyzed.The data indicates that 85.4%of the water supply systems were fully functioning,9.1%were partially functioning,and 5.5%were not functioning.In the regression analysis,good management is positively associated with functionality and a high investment per capita is negatively associated with the functionality.The latter suggests the need for comprehen-sive economic analysis in the feasibility study in scattered housing sites and remote-undeveloped areas.We also found that high community participation at the beginning of the project was associated with the not functioning system,while women’s participation was positively associated with the functionality.Furthermore,the household connection is more likely to be functioning than communal connection.BBN analysis shows if the beneficiaries do not pay for water,the probability of not functioning systems is 20 times higher than systems with fee collec-tion.Moreover,the combination of strong management,strong financial status,and household connection rather than communal connection increases the probability of fully functioning to 98%.Improvement of data collection is also necessary to monitor the current conditions of all PAMSIMAS systems in Indonesia.This study offers a country-level perspective for better implementation of the community-based rural water supply and sanitation program in developing countries.
基金the National Basic Research Program of China(Grant No.2014CB954302)。
文摘Grassland ecosystems support well-being with food,shelter,income,and culture of herdsmen.While the associa-tion between ecosystem services and human well-being has been widely studied,such association is understudied in grassland ecosystems.This study aims to fill this gap through a case study of Xilinhot City,Inner Mongolia Autonomous Region,China.We examined the association between grassland provisioning services and herds-men’s well-being between 1985 and 2015 through participatory observations,interviews,surveys,and Bayesian belief network modeling.Considering the uncertainties of weather and sheep prices,we developed four scenarios to examine the future well-being of herdsmen.Our results show that the most important factor for herdsmen’s well-being was income,which is highly sensitive to the market price of sheep and precipitation.Considering the uncertainties of sheep prices and precipitation,scenario analysis revealed a divergence between income and well-being.While herdsmen’s income is most likely to increase with low precipitation and increased sheep prices,their well-being is most likely to improve with abundant precipitation and increased sheep prices.Based on our find-ings,we argue that developing alternative income sources(e.g.,tourism),reducing dependence on government subsidies through commercial insurance,and branding lamb with grassland ecosystem to alleviate the impact of price fluctuations would help improve herdsmen’s well-being in all scenarios.
基金supported by the Special Project in Humanities and Social Sciences by the Ministry of Education of China(Cultivation of Engineering and Technological Talents)under Grant No.13JDGC002
文摘As one of the most important mathematical methods, the Dempster-Shafer（D-S）evidence theory has been widely used in date fusion, risk assessment, target identification, knowledge reasoning,and other fields. This paper summarized the development and recent studies of the explanations of D-S model, evidence combination algorithms, and the improvement of the conflict during evidence combination, and also compared all explanation models,algorithms, improvements, and their applicable conditions. We are trying to provide a reference for future research and applications through this summarization.
