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 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
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
Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weight...Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.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
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
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 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 3.05×104 t/hm2;CS remained relatively stable(about 15.50 t/km2);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
Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differenti...Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations(FODEs).The goal of this study is to increase the fractional model of the TB virus’s(FMTBV)accuracy.Stochastic solvers have never been used to solve FMTBV previously.The Bayesian regularized artificial(BRA)method and neural networks(NNs),often referred to as BRA-NNs,were used to solve the FMTBV model.Each scenario features five occurrences that each reflect a different order of derivatives,ranging from 0.8,0.85,0.9,0.95,and 1,as well as five potential rates for different parameters.Training data made up 90%of the data,testing data made up 5%,and validation data made up 5%of the data used to illustrate the FMTBV’s approximations.To verify that the BRA-NNs were correct,the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB.Comprehensive Simulink results in terms of mean square error,error histogram,and regression analysis investigations further highlight the competence,dependability,and accuracy of the suggested BRA-NNs.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
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent ...Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.展开更多
To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures,this study proposed a risk assessment method based on accident tree and Bayesian network for airport air...To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures,this study proposed a risk assessment method based on accident tree and Bayesian network for airport aircraft activity areas.It identified influencing factors in the aircraft activity area from the perspectives of person-vehicle-road-environment-management and analyzed their relationships.The Bayesian network was utilized to determine initial probabilities for each influencing factor.Findings indicated a relatively high overall safety level in the airport's road traffic system.Accident trees were employed to qualitatively and quantitatively analyze common human-vehicle accident patterns.The initial probabilities obtained from the Bayesian network served as basic event probabilities in the accident tree to determine the occurrence probability of the top event.Taking a 4F airport in China as an example,accident cause analysis identified five important risk sources in human-vehicle accidents,including blind spots for special vehicles,illegal driving by drivers,pedestrians violating regulations,passengers entering restricted areas,and blind spots at intersections.Corresponding safety management measures were formulated.The study concluded that the integration of Bayesian networks and accident trees effectively determines accident probabilities and offers specific solutions,thus playing a crucial role in enhancing road traffic safety management within aviation airports.展开更多
Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian ne...Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.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
Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantita...Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.展开更多
BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managi...BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.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 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 Key R&D Projects in Jiangsu Province(BE2021729)the Key Primary Research Project of Primary Strengthening Program(KYZYJKKCJC23001).
文摘Multi-agent systems often require good interoperability in the process of completing their assigned tasks.This paper first models the static structure and dynamic behavior of multiagent systems based on layered weighted scale-free community network and susceptible-infected-recovered(SIR)model.To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors,a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems.A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm.A method for evaluating system interoperability is designed based on simulation experiments,providing reference for the construction planning and optimization of organizational application of the system.Finally,the feasibility of the method is verified through case studies.
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 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 3.05×104 t/hm2;CS remained relatively stable(about 15.50 t/km2);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supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1445).
文摘Being a nonlinear operator,fractional derivatives can affect the enforcement of existence at any given time.As a result,the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations(FODEs).The goal of this study is to increase the fractional model of the TB virus’s(FMTBV)accuracy.Stochastic solvers have never been used to solve FMTBV previously.The Bayesian regularized artificial(BRA)method and neural networks(NNs),often referred to as BRA-NNs,were used to solve the FMTBV model.Each scenario features five occurrences that each reflect a different order of derivatives,ranging from 0.8,0.85,0.9,0.95,and 1,as well as five potential rates for different parameters.Training data made up 90%of the data,testing data made up 5%,and validation data made up 5%of the data used to illustrate the FMTBV’s approximations.To verify that the BRA-NNs were correct,the generated simulations were described in the following solutions using the FOLotkaVolterra approach in MATLAB.Comprehensive Simulink results in terms of mean square error,error histogram,and regression analysis investigations further highlight the competence,dependability,and accuracy of the suggested BRA-NNs.
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Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
文摘Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.
文摘To assess road traffic safety risk in civil aviation airports and develop effective accident prevention measures,this study proposed a risk assessment method based on accident tree and Bayesian network for airport aircraft activity areas.It identified influencing factors in the aircraft activity area from the perspectives of person-vehicle-road-environment-management and analyzed their relationships.The Bayesian network was utilized to determine initial probabilities for each influencing factor.Findings indicated a relatively high overall safety level in the airport's road traffic system.Accident trees were employed to qualitatively and quantitatively analyze common human-vehicle accident patterns.The initial probabilities obtained from the Bayesian network served as basic event probabilities in the accident tree to determine the occurrence probability of the top event.Taking a 4F airport in China as an example,accident cause analysis identified five important risk sources in human-vehicle accidents,including blind spots for special vehicles,illegal driving by drivers,pedestrians violating regulations,passengers entering restricted areas,and blind spots at intersections.Corresponding safety management measures were formulated.The study concluded that the integration of Bayesian networks and accident trees effectively determines accident probabilities and offers specific solutions,thus playing a crucial role in enhancing road traffic safety management within aviation airports.
基金supported by the National Natural Science Foundation of China(Grant No.41374118)the Research Fund for the Higher Education Doctoral Program of China(Grant No.20120162110015)+3 种基金the China Postdoctoral Science Foundation(Grant No.2015M580700)the Hunan Provincial Natural Science Foundation,the China(Grant No.2016JJ3086)the Hunan Provincial Science and Technology Program,China(Grant No.2015JC3067)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant No.15B138)
文摘Conventional artificial neural networks used to solve electrical resistivity imaging （ERI） inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian neural network （PBNN） nonlinear inversion method and a sample design method based on the K-medoids clustering algorithm. In the sample design method, the training samples of the neural network are designed according to the prior information provided by the K-medoids clustering results; thus, the training process of the neural network is well guided. The proposed PBNN, based on Bayesian regularization, is used to select the hidden layer structure by assessing the effect of each hidden neuron to the inversion results. Then, the hyperparameter αk, which is based on the generalized mean, is chosen to guide the pruning process according to the prior distribution of the training samples under the small-sample condition. The proposed algorithm is more efficient than other common adaptive regularization methods in geophysics. The inversion of synthetic data and field data suggests that the proposed method suppresses the noise in the neural network training stage and enhances the generalization. The inversion results with the proposed method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.
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。
基金supported by the National Natural Science Foundation of China(Nos.52279107 and 52379106)the Qingdao Guoxin Jiaozhou Bay Second Submarine Tunnel Co.,Ltd.,the Academician and Expert Workstation of Yunnan Province(No.202205AF150015)the Science and Technology Innovation Project of YCIC Group Co.,Ltd.(No.YCIC-YF-2022-15)。
文摘Rock mass quality serves as a vital index for predicting the stability and safety status of rock tunnel faces.In tunneling practice,the rock mass quality is often assessed via a combination of qualitative and quantitative parameters.However,due to the harsh on-site construction conditions,it is rather difficult to obtain some of the evaluation parameters which are essential for the rock mass quality prediction.In this study,a novel improved Swin Transformer is proposed to detect,segment,and quantify rock mass characteristic parameters such as water leakage,fractures,weak interlayers.The site experiment results demonstrate that the improved Swin Transformer achieves optimal segmentation results and achieving accuracies of 92%,81%,and 86%for water leakage,fractures,and weak interlayers,respectively.A multisource rock tunnel face characteristic(RTFC)dataset includes 11 parameters for predicting rock mass quality is established.Considering the limitations in predictive performance of incomplete evaluation parameters exist in this dataset,a novel tree-augmented naive Bayesian network(BN)is proposed to address the challenge of the incomplete dataset and achieved a prediction accuracy of 88%.In comparison with other commonly used Machine Learning models the proposed BN-based approach proved an improved performance on predicting the rock mass quality with the incomplete dataset.By utilizing the established BN,a further sensitivity analysis is conducted to quantitatively evaluate the importance of the various parameters,results indicate that the rock strength and fractures parameter exert the most significant influence on rock mass quality.
基金Supported by the Chinese Nursing Association,No.ZHKY202111Scientific Research Program of School of Nursing,Chongqing Medical University,No.20230307Chongqing Science and Health Joint Medical Research Program,No.2024MSXM063.
文摘BACKGROUND Portal hypertension(PHT),primarily induced by cirrhosis,manifests severe symptoms impacting patient survival.Although transjugular intrahepatic portosystemic shunt(TIPS)is a critical intervention for managing PHT,it carries risks like hepatic encephalopathy,thus affecting patient survival prognosis.To our knowledge,existing prognostic models for post-TIPS survival in patients with PHT fail to account for the interplay among and collective impact of various prognostic factors on outcomes.Consequently,the development of an innovative modeling approach is essential to address this limitation.AIM To develop and validate a Bayesian network(BN)-based survival prediction model for patients with cirrhosis-induced PHT having undergone TIPS.METHODS The clinical data of 393 patients with cirrhosis-induced PHT who underwent TIPS surgery at the Second Affiliated Hospital of Chongqing Medical University between January 2015 and May 2022 were retrospectively analyzed.Variables were selected using Cox and least absolute shrinkage and selection operator regression methods,and a BN-based model was established and evaluated to predict survival in patients having undergone TIPS surgery for PHT.RESULTS Variable selection revealed the following as key factors impacting survival:age,ascites,hypertension,indications for TIPS,postoperative portal vein pressure(post-PVP),aspartate aminotransferase,alkaline phosphatase,total bilirubin,prealbumin,the Child-Pugh grade,and the model for end-stage liver disease(MELD)score.Based on the above-mentioned variables,a BN-based 2-year survival prognostic prediction model was constructed,which identified the following factors to be directly linked to the survival time:age,ascites,indications for TIPS,concurrent hypertension,post-PVP,the Child-Pugh grade,and the MELD score.The Bayesian information criterion was 3589.04,and 10-fold cross-validation indicated an average log-likelihood loss of 5.55 with a standard deviation of 0.16.The model’s accuracy,precision,recall,and F1 score were 0.90,0.92,0.97,and 0.95 respectively,with the area under the receiver operating characteristic curve being 0.72.CONCLUSION This study successfully developed a BN-based survival prediction model with good predictive capabilities.It offers valuable insights for treatment strategies and prognostic evaluations in patients having undergone TIPS surgery for PHT.
