This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden node...This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.展开更多
The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has signifi...The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.展开更多
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves...A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.展开更多
The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic ...The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life.展开更多
A machine learning approach based on Bayesian neural networks was developed to predict the complete fusion cross-sections of weakly bound nuclei.This method was trained and validated using 475 experimental data points...A machine learning approach based on Bayesian neural networks was developed to predict the complete fusion cross-sections of weakly bound nuclei.This method was trained and validated using 475 experimental data points from 39 reaction systems induced by ^(6,7)Li,^(9)Be,and ^(10)B.The constructed Bayesian neural network demonstrated a high degree of accuracy in evaluating complete fusion cross-sections.By comparing the predicted cross-sections with those obtained from a single-barrier penetration model,the suppression effect of ^(6,7)Li and ^(9)Be with a stable nucleus was systematically analyzed.In the cases of ^(6)Li and ^(7)Li,less suppression was predicted for relatively light-mass targets than for heavy-mass targets,and a notably distinct dependence relationship was identified,suggesting that the predominant breakup mechanisms might change in different mass target regions.In addition,minimum suppression factors were predicted to occur near target nuclei with neutron-closed shell.展开更多
Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air qual...Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow<1200 vehicles per hour.This study also classified rainfall events into four types depending on its characteristic.Wet deposition from varied rainfall types significantly affects air quality,with TypeⅠrainfall events(long-duration heavy rain)having the most pronounced impact.An LSTM model incorporating GAM and Bayesian network outcomes yields excellent performance,achieving correlation R^(2)>0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO_(2),and NO_(x);and O_(3),PM_(10),and PM_(2.5),respectively.展开更多
Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Gastrointestinal cancers,including esophageal,gastric,colorectal,liver,gallbladder,cholangiocarcinoma,and pancreatic cancers,pose a significant global health challenge due to their high mortality rates and poor progno...Gastrointestinal cancers,including esophageal,gastric,colorectal,liver,gallbladder,cholangiocarcinoma,and pancreatic cancers,pose a significant global health challenge due to their high mortality rates and poor prognosis,particularly when diagnosed at advanced stages.These malignancies,characterized by diverse clinical presentations and etiologies,require innovative approaches for improved management.Bayesian networks(BN)have emerged as a powerful tool in this field,offering the ability to manage uncertainty,integrate heterogeneous data sources,and support clinical decision-making.This review explores the application of BN in addressing critical challenges in gastrointestinal cancers,including the identification of risk factors,early detection,treatment optimization,and prognosis prediction.By integrating genetic predispositions,lifestyle factors,and clinical data,BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies.Despite their promise,the widespread adoption of BN is hindered by challenges such as data quality limitations,computational complexities,and the need for greater clinical acceptance.The review concludes with future research directions,emphasizing the development of advanced BN algorithms,the integration of multi-omics data,and strategies to ensure clinical applicability,aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.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
为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务...为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务质量评价指标体系。运用双向推理诊断模型评价机场服务质量,对机场满意度正向推理以及影响指标的反向敏感性诊断。对旅客机场服务质量满意度以及影响因素进行深入研究,并以某大型运输机场为例验证方法的可行性。研究结果表明,旅客对该机场的服务质量满意概率为0.67,一般满意概率为0.21。反向诊断得到行李提取系统、进出机场综合交通与城市连接的便利性、安检服务效率等影响因素敏感性较高。为机场科学制定提升服务质量措施提供理论基础。展开更多
基金supported by National key research and development program(No.2022YFA1602404)the National Natural Science Foundation of China(Nos.12388102,12275338,12005280)the Key Laboratory of Nuclear Data foundation(No.JCKY2022201C152)。
文摘This study investigates photonuclear reaction(γ,n)cross-sections using Bayesian neural network(BNN)analysis.After determining the optimal network architecture,which features two hidden layers,each with 50 hidden nodes,training was conducted for 30,000 iterations to ensure comprehensive data capture.By analyzing the distribution of absolute errors positively correlated with the cross-section for the isotope 159Tb,as well as the relative errors unrelated to the cross-section,we confirmed that the network effectively captured the data features without overfitting.Comparison with the TENDL-2021 Database demonstrated the BNN's reliability in fitting photonuclear cross-sections with lower average errors.The predictions for nuclei with single and double giant dipole resonance peak cross-sections,the accurate determination of the photoneutron reaction threshold in the low-energy region,and the precise description of trends in the high-energy cross-sections further demonstrate the network's generalization ability on the validation set.This can be attributed to the consistency of the training data.By using consistent training sets from different laboratories,Bayesian neural networks can predict nearby unknown cross-sections based on existing laboratory data,thereby estimating the potential differences between other laboratories'existing data and their own measurement results.Experimental measurements of photonuclear reactions on the newly constructed SLEGS beamline will contribute to clarifying the differences in cross-sections within the existing data.
基金Saudi Arabia for funding this work through Small Research Group Project under Grant Number RGP.1/316/45.
文摘The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients.Today,the mass disease that needs attention in this context is cataracts.Although deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty quantification.Therefore,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the predictions.It prepares convolutional neural network(CNN)and BCNN models.The proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN model.This study then trains them on a dataset of cataract images filtered from the ocular disease fundus images fromKaggle.The deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye conditions.When compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.
基金supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC)the National Natural Science Foundation of China(No.62107008).
文摘A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
基金financially supported by the National Natural Science Foundation of China(Grant No.52071337)the Research Initiation Funds of Zhejiang University of Science and Technology(Grant No.F701102N06)+2 种基金the High-tech Ship Research Projects Sponsored by MIIT(Grant No.CBG2N21-4-2-5)the National Key Research and Development Program of China(Grant No.2022YFC2806300)the Marine Economy Development(Six Marine Industries)Special Foundation of the Department of Natural Resources of Guangdong Province(Grant No.GDNRC[2023]50).
文摘The deepwater subsea wellhead(SW)system is the foundation for the construction of oil and gas wells and the crucial channel for operation.During riser connection operation,the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system,and continuously accumulated fatigue damage leads to fatigue failure of the SW system,rupture,and even blowout accidents.This paper proposes a hybrid Bayesian network(HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life.In the proposed approach,the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted,only considering normal conditions.The HBN model,which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system,is subsequently developed.When predictive and diagnostic analysis techniques are adopted,the dynamic reliability of the SW system is achieved,and the most influential factors are determined.Finally,corresponding safety control measures are proposed to improve the reliability of the SW system effectively.The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94%within the design life.
基金supported by National Natural Science Foundation of China(Nos.12105080 and 12375123)China Postdoctoral Science Foundation(No.2023M731015)Natural Science Foundation of Henan Province(No.242300422048).
文摘A machine learning approach based on Bayesian neural networks was developed to predict the complete fusion cross-sections of weakly bound nuclei.This method was trained and validated using 475 experimental data points from 39 reaction systems induced by ^(6,7)Li,^(9)Be,and ^(10)B.The constructed Bayesian neural network demonstrated a high degree of accuracy in evaluating complete fusion cross-sections.By comparing the predicted cross-sections with those obtained from a single-barrier penetration model,the suppression effect of ^(6,7)Li and ^(9)Be with a stable nucleus was systematically analyzed.In the cases of ^(6)Li and ^(7)Li,less suppression was predicted for relatively light-mass targets than for heavy-mass targets,and a notably distinct dependence relationship was identified,suggesting that the predominant breakup mechanisms might change in different mass target regions.In addition,minimum suppression factors were predicted to occur near target nuclei with neutron-closed shell.
基金supported by the Ministry of Environment(Environmental Protection Administration),Taiwan(Projects EPA-106-L103-02-A022,EPA-106-L102-02-A142)the"National"Science and Technology Council(Ministry of Science and Technology),Taiwan(Nos.108-2625-M-008-002,108-2119-M-008-003,108-2636-E-008-004,109-2636-E-008-008,110-2636-E-008-006,111-2636-E-008-014,and 112-2636-E-008-005(Young Scholar Fellowship Program),112-2119-M-008-010,and 108-2638-E-008-001-MY2(Shackleton Program Grant)).
文摘Traffic emissions have become the major air pollution source in urban areas.Therefore,understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models.Using real-world air pollutant data from Taipei City,this study integrates diverse factors,including traffic flow,speed,rainfall patterns,andmeteorological factors.We constructed a Bayesian network probabilitymodel based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations.Generalized Additive Model(GAM)verified non-linear relationships between traffic factors and air pollutants.Consequently,we propose a long short term memory(LSTM)model to predict airborne pollutant concentrations.This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns.Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow<1200 vehicles per hour.This study also classified rainfall events into four types depending on its characteristic.Wet deposition from varied rainfall types significantly affects air quality,with TypeⅠrainfall events(long-duration heavy rain)having the most pronounced impact.An LSTM model incorporating GAM and Bayesian network outcomes yields excellent performance,achieving correlation R^(2)>0.9 and 0.8 for first and second order air pollutants,i.e.,CO,NO,NO_(2),and NO_(x);and O_(3),PM_(10),and PM_(2.5),respectively.
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金Supported by Open Funds for Shaanxi Provincial Key Laboratory of Infection and Immune Diseases,No.2023-KFMS-1.
文摘Gastrointestinal cancers,including esophageal,gastric,colorectal,liver,gallbladder,cholangiocarcinoma,and pancreatic cancers,pose a significant global health challenge due to their high mortality rates and poor prognosis,particularly when diagnosed at advanced stages.These malignancies,characterized by diverse clinical presentations and etiologies,require innovative approaches for improved management.Bayesian networks(BN)have emerged as a powerful tool in this field,offering the ability to manage uncertainty,integrate heterogeneous data sources,and support clinical decision-making.This review explores the application of BN in addressing critical challenges in gastrointestinal cancers,including the identification of risk factors,early detection,treatment optimization,and prognosis prediction.By integrating genetic predispositions,lifestyle factors,and clinical data,BN hold the potential to enhance survival rates and improve quality of life through personalized treatment strategies.Despite their promise,the widespread adoption of BN is hindered by challenges such as data quality limitations,computational complexities,and the need for greater clinical acceptance.The review concludes with future research directions,emphasizing the development of advanced BN algorithms,the integration of multi-omics data,and strategies to ensure clinical applicability,aiming to fully realize the potential of BN in personalized medicine for gastrointestinal cancers.
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.
文摘为明确机场服务质量的影响因素及旅客对机场服务质量满意程度,提高机场服务质量,运用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)与贝叶斯网络结合的评估模型,建立大型运输机场服务质量评价指标体系。运用双向推理诊断模型评价机场服务质量,对机场满意度正向推理以及影响指标的反向敏感性诊断。对旅客机场服务质量满意度以及影响因素进行深入研究,并以某大型运输机场为例验证方法的可行性。研究结果表明,旅客对该机场的服务质量满意概率为0.67,一般满意概率为0.21。反向诊断得到行李提取系统、进出机场综合交通与城市连接的便利性、安检服务效率等影响因素敏感性较高。为机场科学制定提升服务质量措施提供理论基础。
