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A Bayesian modelling framework with model comparison for epidemics with super-spreading 认领 引用
1
作者 Hannah Craddock Simon E.F.Spencer Xavier Didelot 《Infectious Disease Modelling》 CSCD 2025年第4期1418-1432,共15页
The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried o... The transmission dynamics of an epidemic are rarely homogeneous.Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility.Inference of super-spreading is commonly carried out on secondary case data,the expected distribution of which is known as the offspring distribution.However,this data is seldom available.Here we introduce a multi-model framework fit to incidence time-series,data that is much more readily available.The framework consists of five discrete-time,stochastic,branching-process models of epidemics spread through a susceptible population.The framework includes a baseline model of homogeneous transmission,a unimodal and a bimodal model for super-spreading events,as well as a unimodal and a bimodal model for super-spreading individuals.Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo methods.Model comparison is conducted by computing Bayes factors,with importance sampling used to estimate the marginal likelihood of each model.This estimator is selected for its consistency and lower variance compared to alternatives.Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters,such as the basic reproduction number.We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic caused by SARS-CoV-2.Model selection consistently identifies the same model and mechanism for a given disease,even when using different time series.Our estimates are consistent with previous studies based on secondary case data.Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control.Our modelling framework is disease-agnostic and implemented as an R package,with potential to be a valuable tool for public health. 展开更多
关键词 Infectious disease epidemiology Bayesian modelling Model comparison Super-spreading Transmission heterogenity
Modelling water use in Nepal's highlands:a multidisciplinary and probabilistic framework 认领 引用
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作者 Megan KLAAR Duncan QUINCEY +4 位作者 C.Scott WATSON Lee E.BROWN Bishnu PARIYAR Arjan GOSAL Jon LOVETT 《Journal of Mountain Science》 SCIE CSCD 2026年第2期489-504,共16页
Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study pres... Mountain communities in Nepal are increasingly exposed to climate-induced shifts in water availability,driven by glacial retreat,altered precipitation/snowmelt regimes,and declining groundwater sources.This study presents an integrated framework combining hydrological source analysis with socio-demographic survey data to evaluate seasonal water contributions and communitylevel water use patterns in the Upper Marsyangdi catchment,Manang District,Nepal.Isotopic(δ18O)and geochemical(silica)tracers were used in a Bayesian mixing model to quantify the seasonal contributions of glacial melt,snow,rain,and groundwater to river flow.Findings indicate that groundwater dominates pre-monsoon flow(60%-70%)while post-monsoon discharge reflects more balanced inputs from all sources.In parallel,120 household surveys were analysed using Latent Class Analysis to characterise water use across domestic,agricultural,energy,and tourism sectors.Results reveal spatial and demographic gradients in water source dependency,including gender and occupation as important predictors of water use.Respondents reported perceived increases in spring flow,alongside reductions in the availability of snow for household and tourism use and deteriorating river water quality and quantity,particularly affecting hydropower operations.Adaptation strategies include increased reliance on water storage infrastructure and source switching.The study highlights the value of applying probabilistic methods to hydrological and sociocultural data to identify vulnerable populations and inform targeted,context-sensitive adaptation strategies.The proposed framework is transferable to other high-altitude regions,offering a robust approach for assessing climate resilience through the synthesis of scientific and local knowledge systems. 展开更多
关键词 Water source attribution High mountain hydrology MixSIAR Bayesian mixing model Annapurna Himalaya
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Global ROTI forecasting with a Bayesian model based in long‑tail distributions 认领 引用
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作者 Enric Monte‑Moreno Heng Yang Manuel Hernández‑Pajares 《Satellite Navigation》 SCIE EI CSCD 2026年第1期367-393,I0008,共27页
This study introduces a Bayesian probabilistic model for forecasting the fluctuations in the Rate of TEC Index(ROTI),which indicate the presence of ionospheric disturbances that can impact Global Navigation Satellite ... This study introduces a Bayesian probabilistic model for forecasting the fluctuations in the Rate of TEC Index(ROTI),which indicate the presence of ionospheric disturbances that can impact Global Navigation Satellite Systems(GNSS)and communication networks.The forecast method divides the Earth into a grid of 2.5◦latitude by 5◦longitude cells to predict when ROTI will exceed thresholds of 0.1,0.25 and 0.5 TECU/min,with time horizons ranging from 30 min to 6 h.The method is based on the burstiness property of long‑tailed distributions and provides as a forecast the median value of activity at each range,of both ROTI amplitude and forecast horizon.Previous proposed ROTI forecasting methods may degrade when faced with missing data points and the irregular,heavy‑tailed characteris tics of ROTI.In contrast,our model,based on the power‑law dynamics observed in the persistent and bursty nature of long‑tail distributions,allows for gaps in the measurements and provides a global forecast for regions covered by the network of GNSS stations.The performance of the model has been validated against historical GNSS data across various ionospheric conditions,demonstrating its robustness.The proposed Bayesian probabilistic model demonstrates robust forecasting capabilities,validated across diverse ionospheric conditions using historical GNSS data.It achieves strong performance metrics,with Weighted Kappa values exceeding 40%for prediction horizons up to 120 min and maintaining Mean Precision above 65%across all tested horizons from 30 min to 6 h.By forecast ing the probability of ROTI exceeding specific levels,this method helps to identify geographical regions where GNSS reliability may be compromised,thereby aiding in the mitigation of adverse space weather effects on critical naviga tion and communication systems. 展开更多
关键词 Ionosphere ROTI Forecast GNSS Long‑tail distributions Bayesian model Space weather
Spatial–temporal risk of Opisthorchis felineus infection in Western Siberia and the Ural Region of Russian Federation:a joint Bayesian modelling study based on survey and surveillance data 认领 引用
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作者 Wen‑Long Zhang Yuan‑Hong Zeng Ying‑Si Lai 《Infectious Diseases of Poverty》 SCIE CSCD 2025年第5期35-47,共13页
Background Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federat... Background Opisthorchiasis infected by Opisthorchis felineus has represented a significant but understudied public health issue for the population residing in Western Siberia and the Ural Region of the Russian Federation.This study aimed to produce high-resolution spatial–temporal disease risk maps for guiding prevention strategy in the above region.Methods Data on prevalence and surveillance data reflecting reported annual incidence rate of O.felineus infection in the study region were collected through systematic review and the annual reports of the Ministry of Health of the Russian Federation.Environmental,socioeconomic and demographic data were downloaded from different open-access data sources.An advanced multivariate Bayesian geostatistical modeling approach was developed to estimate the O.felineus infection risk at high-resolution spatial–temporal by joint analysis of survey and surveillance data,incorporating potential influencing factors and spatial–temporal random effects.The annual spatial–temporal risk maps of O.felineus infection at a resolution of 5×5 km2 were produced.Results The final dataset included 76 locations of survey data and 303 locations of surveillance data on O.felineus infection.The infection risk was high(>25%)in most part of central and eastern regions,and relatively low(<25%)in most part of western region,while temporal variations were observed across the sub-regions in recent decades.Particularly,in the densely populated eastern region,there was an increased trend of infection risk from 30.46%(95%Bayesian credible intervals,BCI 10.78–53.45%)in 1980 to 53.39%(95%BCI 13.77–91.93%)in 2019 and gradually transformed into high-risk.In the study region(excluding the western region due to data sparsity),the population-adjusted estimated prevalence was 46.61%(95%BCI 15.09–76.50%)in 2019,corresponding to approximately 7.91 million(95%BCI 2.56–12.98 million)people infected.Conclusions The high-resolution risk maps of O.felineus in Western Siberia and the Ural Region of the Russian Federation have effectively captured the risk profiles,suggesting the infection risk remains high in recent years and providing substantial evidence for spatial-target control and preventive strategies. 展开更多
关键词 Opisthorchis felineus Western Siberia and the Ural Region Bayesian geostatistical modeling Joint analysis Survey and surveillance data High resolution risk mapping
Prediction and analysis of AI talent demand in Fujian Province’s ordinary highway industry based on bayesian regression principle 认领 引用
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作者 Yu Liang Yanqun Yang +2 位作者 Shoujie Huang Zhandong Zhu Qingxiong Wu 《Journal of Highway and Transportation Research and Development(English Edition)》 2026年第2期75-81,共7页
The supply of high-quality talent plays a decisive role in the sustainable development of the highway industry.To accurately capture trends in AI-highway engineering interdisciplinary talent and to inform talent culti... The supply of high-quality talent plays a decisive role in the sustainable development of the highway industry.To accurately capture trends in AI-highway engineering interdisciplinary talent and to inform talent cultivation and policy formulation in higher education,this study analyzes data from Fujian Province spanning 2019-2024,including the total number of highway engineering professionals,total industry investment,talent attrition,and the supply of college graduates.A Bayesian regression model is employed for predictive analysis.The results indicate that parameter estimates are consistent with prior assumptions,the sampling process is stable,inter-chain convergence is satisfactory,and parameter estimates are reliable.The posterior distributions are approximately normal,and the Markov chain Monte Carlo(MCMC)trajectories exhibit random behavior without discernible trends,indicating good chain mixing and robust posterior estimation.Overall,demand for AI interdisciplinary talent in the highway engineering sector of Fujian Province shows a significant upward trend.Under the baseline scenario,the total industry workforce is projected to reach 5,351 by 2029,including 1,605 AI interdisciplinary professionals,representing a cumulative increase of 687 over five years.In the optimistic scenario,driven by increased investment,reduced drain,and growth in graduate supply,the workforce is expected to expand to 6,508 by 2029,with 1,952 AI interdisciplinary professionals,a cumulative increase of 999 over five years.Even in the pessimistic scenario,despite adverse conditions such as reduced investment and higher turnover leading to a workforce decline to 4,743 by 2029,the number of AI interdisciplinary professionals is projected to continue growing,reaching 1,423,with a cumulative increase of 522 over five years.These results demonstrate that the Bayesian regression model effectively quantifies the dynamic effects of total highway industry investment,brain drain,and graduate supply on workforce scale,and that the forecasts are consistent with practical industry constraints. 展开更多
关键词 transportation highway engineering AI interdisciplinary talent prediction demand forecasting bayesian regression model
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Continuous Bayesian probability estimator in predictions of nuclear charge radii 认领 引用 被引量：2
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作者 Jian Liu Kai-Zhong Tan +4 位作者 Lei Wang Wan-Qing Gao Tian-Shuai Shang Jian Li Chang Xu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第11期283-293,共11页
Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator ... Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties. 展开更多
关键词 Machine learning Nuclear charge radii Continuous Bayesian probability estimator Bayesian model averaging
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An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis:Rationale and study design 认领 引用 被引量：1
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作者 Cheng Zhang Yi-sen Nie +8 位作者 Chuan-tao Zhang Hong-jing Yang Hao-ran Zhang Wei Xiao Guang-fu Cui Jia Li Shuang-jing Li Qing-song Huang Shi-yan Yan 《Journal of Integrative Medicine》 SCIE CAS CSCD 2025年第2期138-144,共7页
patients with PPF.TCM treatments are typically diverse and individualized,requiring urgent development of efficient and precise design strategies to identify effective treatment options.We designed an innovative Bayes... patients with PPF.TCM treatments are typically diverse and individualized,requiring urgent development of efficient and precise design strategies to identify effective treatment options.We designed an innovative Bayesian adaptive two-stage trial,hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF.An open-label,two-stage,adaptive Bayesian randomized controlled trial will be conducted in China.Based on Bayesian methods,the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial.The adaptive Bayesian trial design will employ a Bayesian hierarchical model with“stopping”and“continuation”criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached.The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented.The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score,reflecting an improvement in cough-specific quality of life.The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF,and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases.However,due to the complexity of the trial implementation,sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response.Moreover,detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. 展开更多
关键词 Progressive pulmonary fibrosis Traditional Chinese medicine Adaptive trial design Bayesian model
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A possibility evaluation model for road transportation of hazardous chemicals based on Bow-tie theory and Bayesian model 认领 引用 被引量：1
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作者 Shuo Wang Mingguang Zhang +1 位作者 Mengchen Liu Jian Zhao 《Emergency Management Science and Technology》 2025年第1期171-180,共10页
The complexity and diversity of road transport environments and the unique nature of hazardous chemicals pose significant challenges in quantifying the risk of accidents in the transport of hazardous chemicals,and fur... The complexity and diversity of road transport environments and the unique nature of hazardous chemicals pose significant challenges in quantifying the risk of accidents in the transport of hazardous chemicals,and further research is still needed to assess the possibility of real-time accidents.In this study,1,115 Chinese hazardous chemical road transport accidents are analysed,and 120 accident chains are constructed to reveal the development law of the accidents.Based on the Bow-tie theory,a qualitative inference Bow-tie diagram for multi-hazard coupled accidents in road transport of hazardous chemicals was constructed to show the evolution of the accidents.The mapping relationship between the Bow-tie diagram and the Bayesian network is established,and the Bayesian network structure is constructed to achieve the dynamic real-time accident possibility assessment combining quantitative and qualitative.In the example application,data collection every 30 s,and real-time accident probability calculations are realised,which verifies the feasibility and reasonableness of the model and method in this study and has important theoretical significance and application value for the prevention of road transport accidents of hazardous chemicals. 展开更多
关键词 road transport road transportation bow tie theory hazardous chemicalsand hazardous chemicals accident chains bayesian model
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Machine learning-driven reliability assessment of liquefaction probability based on state parameter analysis 认领 引用
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作者 Kishan Kumar Pijush Samui +1 位作者 S.S.Choudhary Hong-Hu Zhu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第4期2960-2979,共20页
The state parameter(ψ)utilising the concept of critical state soil mechanics integrates the effect of relative density and effective stress and offers notable advantages for liquefaction assessment.This study present... The state parameter(ψ)utilising the concept of critical state soil mechanics integrates the effect of relative density and effective stress and offers notable advantages for liquefaction assessment.This study presents aψ-based liquefaction analysis using the first-order reliability method(FORM)and second-order reliability method(SORM)to evaluate the liquefaction probability of failure(PL)considering the parametric uncertainty.Four machine learning(ML)models,which include long short-term memory(LSTM),bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and Bayesian non-parametric general regression(BNGR),were developed to predict PL.The reliability analysis confirmed the robustness of the results,with PL values consistent with those obtained by established methodologies.A mapping function relating the safety factor(SF)to the PL was developed using the cone penetration test(CPT)database.Performance evaluation using 15 statistical indices,alongside sensitivity and uncertainty analysis,demonstrates the relative significance of input parameters and prediction reliability.The GRU model outperformed other ML models in terms of overall performance.The BiLSTM model achieved the highest R² values of 0.976 in training,and the GRU model(0.961)in testing,with comparable root mean square error(RMSE)values of 0.046 and 0.065,respectively.Additionally,the BNGR model showed promising results in accuracy and model complexity with the lowest Akaike information criterion(AIC)value of 11.62.Williams plots were used to illustrate the models’applicability domain,while sensitivity analysis underscores the significance of input parameters,with ψ emerging as the most influential parameter.This research provides a comprehensive framework for enhancing the accuracy and reliability of liquefaction evaluation in engineering and infrastructure design. 展开更多
关键词 Liquefaction State parameter Machine learning(ML) Reliability analysis Deep learning Bayesian modelling
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Integrating process-based and deep learning models for flood simulation in karst basins 认领 引用
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作者 Bin-quan Li Yi-jie Xia +3 位作者 Si-ji Tao Yun-yao Chen Jian-fei Zhao Zhong-min Liang 《Water Science and Engineering》 EI CAS CSCD 2026年第1期23-34,共12页
Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms,often resulting in low accuracy.This study investigated two typical karst basins(the Maiweng and Li... Flood process simulation in karst basins is challenging due to complex runoff generation and concentration mechanisms,often resulting in low accuracy.This study investigated two typical karst basins(the Maiweng and Liudong river basins)in Guizhou Province,China,and developed two hydrological models for flood simulation:the karst-Xin'anjiang(Karst-XAJ)model,a modified Xin'anjiang(XAJ)hydrological model adapted for karst runoff characteristics,and the long short-term memory(LSTM)deep learning model.Their performances were compared,and their results were integrated using Bayesian model averaging(BMA).The Karst-XAJ model accurately simulated flood peak time and runoff depth but showed limited peak flow accuracy.The LSTM model performed well within a 2-h computational window,with accuracy declining for longer computational windows(3-4 h)yet maintaining a Nash-Sutcliffe model efficiency coefficient above 0.7.The BMA approach further enhanced simulation accuracy beyond individual models.Overall,both models effectively captured flood dynamics in karst basins,with the LSTM model achieving superior precision.This study offers a novel framework for simulating flood processes in karst regions with complex runoff processes. 展开更多
关键词 Karst basin Karst-Xin'anjiang model Long short-term memory neural network Bayesian model averaging Flood simulation
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Seasonal machine learning fusion for improved satellite precipitation estimates:A case study in the upper Ganjiang River,China 认领 引用
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作者 CHEN Yunyao LI Binquan +4 位作者 XIAO Yang ZHANG Huiming XU Dong ZHANG Taotao WU Zhijun 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1062-1078,共17页
Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation m... Rainfall input errors are a major source of uncertainty in flood forecasting,and merging multi-source precipitation data is essential for improving accuracy.Traditional merging methods often prioritize precipitation magnitude enhancements while overlooking event detection and false alarms.To address these limitations,this study developed a precipitation integration framework that combines machine learning classification-plus-regression models with Bayesian model averaging(BMA).Three machine learning algorithms-categorical boosting(CatBoost),light gradient boosting machine(LightGBM),and random forest(RF)-were used to improve precipitation event detection.The framework includes spatial unification of raw satellite products using bilinear interpolation,bias correction through classification-plus-regression models,and final merging via a seasonal-scale BMA model.The method integrated GSMaP,IMERG,and PERSIANN satellite precipitation products,with ground observations used for model training(2001-2014)and independent validation(2015-2020)in the Upper Ganjiang River Basin,China.Results showed that the framework significantly enhanced precipitation estimation accuracy and detection capability.LightGBM-based integration exhibited superior detection performance(FAR=0.08,CSI=0.86),while RF-based integration achieved the highest overall accuracy(RMSE=4.67,CC=0.92).Seasonal variations in BMA weights underscored the need to account for seasonal characteristics of precipitation products.Additionally,accuracy improvements were observed across all rainfall categories,especially for heavy rainstorms.The seasonal-scale BMA fusion has combined the strengths of individual corrections and further enhanced precipitation estimation.This research offers a robust method for generating accurate rainfall inputs,providing valuable support for hydrological modeling and flood forecasting applications. 展开更多
关键词 Multi-source precipitation fusion Rain classification Machine learning Bayesian model averaging Upper Ganjiang River
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Improving multibreed genomic prediction for breeds with small populations by modeling heterogeneous genetic(co)variance blockwise accounting for linkage disequilibrium 认领 引用
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作者 Weining Li Siyu Li +7 位作者 Heng Du Qianqian Huang Yue Zhuo Lei Zhou Jinhua Cheng Wanying Li Jicai Jiang Jianfeng Liu 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2026年第1期147-158,共12页
Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitionin... Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction. 展开更多
关键词 Heterogeneous genetic(co)variance Linkage disequilibrium Multibreed genomic prediction Multitrait Bayesian model Small-population breed
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Predicting the Ranking of Engineering Mechanics Students Using the Bayesian Model 认领 引用
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作者 Kuahai Yu Xiangqian Sheng +1 位作者 Sibo Dang Ruhuan Yu 《Journal of Contemporary Educational Research》 2025年第12期239-246,共8页
GPA plays an important role in the entire learning process of students.The value of GPA not only reflects students’current grades but also affects their future progress,motivation,and opportunities.It is worth noting... GPA plays an important role in the entire learning process of students.The value of GPA not only reflects students’current grades but also affects their future progress,motivation,and opportunities.It is worth noting that the grades of specific courses also have an impact on GPA.Therefore,it is necessary to predict students’performance in future courses based on their current grades.In this paper,a Bayesian model is employed to classify course grades and estimate the probability of these grades being affected by other factors in the first semester,enabling the prediction of subsequent performance.The Bayesian approach integrates prior knowledge of grade distributions through four key steps:establishing a prior probability distribution,using a likelihood function to relate grades to academic ability,combining prior and new evidence to compute posterior probabilities,and forecasting next-semester results.These predictions support timely academic interventions and adjustments to teaching strategies.By utilizing data such as assignment and exam scores,a Bayesian classification model can analyze and predict outcomes.The actual grades of students in the second semester are used to validate the predictive accuracy of the model. 展开更多
关键词 Bayesian model Predictive data Academic performance forecasting Data mining Model evaluation
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Analysis method for evaluating uncertainty in the machining process of thin-walled parts 认领 引用
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作者 Xiaoyue Li Zhaoze Sun +2 位作者 Hao Qi Yue Guo Shuowei Bai 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期136-147,共12页
The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of ... The machining process of thin-walled components is full of many uncertainties,resulting in problems such as high batch inconsistency and low pass rate.In this paper,the Bayesian network uncertainty inference model of machining process is constructed.The influence mechanism of input variables on machining distortion un-certainty is clarified.Initially,the uncertain variables are collected and the inference model of machining dis-tortion is constructed based on root-branch-leaf Bayesian network structure.The weight of the influence of each input variable on machining distortion uncertainty and maximum influence path are obtained.Next,an inverse Bayesian network is established,with the uncertainty inference results used as prior information to carry out inverse inference of machining distortion uncertainty.The influence possibility of the related factors of the main influence variables on the uncertainty of machining distortion is obtained.Aviation aluminum alloy T-shaped part was taken as an example,and the influence mechanism of initial residual stress,surface residual stress,cutting force and their related factors on the machining distortion uncertainty was investigated.The results indicated that the influential weights of initial residual stress,surface residual stress and cutting force on the machining distortion uncertainty was 0.33(maximum),0.23 and 0.04 respectively.The factors related to initial residual stress,surface residual stress,and cutting force had influence weights of 0.099(maximum),0.009,and 0.001 on the machining distortion uncertainty.The probabilities of the effects were 0.920,0.075 and 0.005,respectively.Finally,the paper compares the proposed model with MC-GBRT and BiLSTM-UP,showing average improvements of 46.7%and 63.1%in stability and 35.7%and 20.6%in computational speed,respectively.This paper proposes a Bayesian network-based machining distortion uncertainty inference model that effectively reveals the mapping mechanism between initial residual stress,surface residual stress,cutting force,and the machining distortion uncertainty in thin-walled components. 展开更多
关键词 Thin-walled components Machining distortion Uncertainty Bayesian network inference model Inverse Bayesian inference
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Impact of dam-induced hydrological fluctuations on the behavior of the endangered scaly-sided Merganser(Mergus squamatus):implications for river regulation strategies and conservation 认领 引用
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作者 Peizhong Liu Yangsirui Zhang +5 位作者 Ying He Zijian Wang Cai Lu Li Wen Qing Zeng Guangchun Lei 《Current Zoology》 SCIE CAS CSCD 2026年第1期127-137,共11页
Since the mid-20th century,the global expansion of dam and reservoir construction has profoundly altered river ecosystems,with cascading effects on channel morphology,flow regimes,and freshwater biodiversity.Among the... Since the mid-20th century,the global expansion of dam and reservoir construction has profoundly altered river ecosystems,with cascading effects on channel morphology,flow regimes,and freshwater biodiversity.Among these impacts,the disruption of natural flow variability remains a critical but under-analyzed factor,particularly regarding its influence on riparian waterbirds.To address this gap,we investigated the behavioral responses of the endangered Scaly-sided Merganser(Mergus squamatus),a wintering diving duck,to dam-induced hydrological fluctuations downstream of the Taoyuan Dam in Hunan Province,China.Using scan sampling and Multivariate Bayesian Generalized Additive Models(GAMs),we assessed the relationships between bird activity patterns and water level dynamics.Our results reveal that increased water level and rate of change are associated with heightened foraging activity and a concurrent reduction in resting and preening.These shifts likely reflect changes in prey accessibility driven by fluctuating flow conditions.Based on these findings,we recommend modulating dam outflows to 3000 m³/s during peak foraging hours and reducing discharge to 2000 m³/s during midday and night.Our study underscores the potential of incorporating waterbird behavior into dam operation strategies for balancing ecological integrity with energy production. 展开更多
关键词 river regulation foraging behavior hydrological regime diving duck Bayesian generalized additive model
Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用 被引量：1
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作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
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Modeling eccentric growth explicitly to investigate intra-annual drivers of xylem cell production using xylogenetic data 认领 引用
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作者 Lucie Nina Barbier Marc-Andre Lemay +2 位作者 Etienne Boucher Sergio Rossi Fabio Gennaretti 《Forest Ecosystems》 SCIE CAS CSCD 2026年第1期254-264,共11页
Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers infl... Xylogenesis,the process through which wood cells are formed,results in the long-term storage of carbon in woody biomass,making it a key component of the global carbon cycle.Understanding how environmental drivers influence xylogenesis during the growing season is therefore of great interest.However,studying shortterm drivers of wood production using xylogenetic data is complicated by the usual sampling scheme and the influence of eccentric growth,i.e.,heterogeneous growth around the stem.In this study,we improve xylogenesis research by introducing a statistical approach that explicitly considers seasonal phenology,short-term growth rates,and growth eccentricity.To this end,we developed Bayesian models of xylogenesis and compared them with a conventional method based on the use of Gompertz functions.Our results show that eccentricity generated high temporal autocorrelation between successive samples,and that explicitly taking it into account improved both the representativeness of phenology and intra-ring variability.We observed consistent short-term patterns in the model residuals,suggesting the influence of an unaccounted-for environmental variable on cell production.The proposed models offer several advantages over traditional methods,including robust confidence intervals around predictions,consistency with phenology,and reduced sensitivity to extreme observations at the end of the growing season,often linked to eccentric growth.These models also provide a benchmark for mechanistic testing of short-term drivers of wood formation. 展开更多
关键词 Xylogenesis Cell production Sampling biases Bayesian model Gompertz function
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Predicting nitrogen surplus in agricultural lands of China using a hybrid machine learning approach with smaller datasets and fewer features 认领 引用
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作者 Hao Wang Gaofei Yin +8 位作者 Hongda Wen Feng Wang Ziwei Yang Xueying Sun Xulin Zhang Huiqing Jiao Mengyu Zhai Wenchao Li Hongbin Liu 《Journal of Environmental Sciences》 SCIE EI CAS CSCD 2026年第4期743-753,共11页
Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen... Global nitrogen pollution in agricultural lands poses a major environmental challenge,complicating both assessment and mitigation of nitrogen surplus.Nitrogen surplus(NS)is a critical indicator for evaluating nitrogen use efficiency.However,traditional NS estimation methods often require extensive data,which are difficult to obtain in data-scarce regions.In this study,a BN-ML NS prediction model was developed by coupling Bayesian Networks(BN)and Machine Learning(ML)using long-term monitoring data(including climate,soil,and crops)from 2000 to 2019 in China.The key findings are as follows:Nitrogen fertilizer application rate(NR)is the dominant factor influencing NS across the seven regions;however,due to differences in climate,cropping patterns,and soil types,the impact of NR exhibits spatial heterogeneity;The BN-ML model demonstrates strong predictive performance,with R2values exceeding 0.9;compared to traditional NS prediction models,the BN-ML model maintains high accuracy using only half the number of features(e.g.,NR,Yield)and fewer than 120 data points;when validated at the small watershed scale,the model achieved over 80%prediction accuracy.By enabling reliable NS prediction with limited data,the proposed model supports more targeted and sustainable nitrogen management.It can assist national and regional authorities in identifying high-risk areas,optimizing fertilizer use,and formulating region-specific agricultural strategies. 展开更多
关键词 Bayesian network model Extreme gradient boosting Nitrogen management
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Comparative study of stock status and sustainable management of Pomadasys olivaceus fishery along Pakistan coast 认领 引用
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作者 Aidah BALOCH Muhsan Ali KALHORO +7 位作者 Aamir Mahmood MEMON Hasnain RAZA Shaikh SANAULLAH Suman BARUA Xu CHEN Yihong MA Ashraf MEHREEN Qun LIU 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第3期1307-1321,共15页
Fish stock assessment is essential for ensuring the sustainable utilization of marine resources.We evaluated the stock status of the Pomadasys olivaceus along both the Balochistan and Sindh coasts of Pakistan using Ca... Fish stock assessment is essential for ensuring the sustainable utilization of marine resources.We evaluated the stock status of the Pomadasys olivaceus along both the Balochistan and Sindh coasts of Pakistan using Catch-based Monte Carlo Maximum sustainable yield(CMSY),Bayesian Schaefer model(BSM),and a stock production model incorporating covariates(ASPIC)models based on catch and effort data from 2000 to 2022.Results from all models indicate the B/BMSY(relative biomass)values were below1.0 and F/FMSY(fishery exploitation)values>1,indicating that the stock is severely overfished in both regions.The estimated maximum sustainable yield(MSY)from the CMSY and BSM methods ranged between 2440–2670 metric tons(mt)for Balochistan and 2430–2650 mt for Sindh.The ASPIC model(Fox and Logistic)also indicated overexploitation,with MSY estimates of 1585 mt(Fox)and 1379 mt(Logistic)for Balochistan,showing critical stock depletion.In contrast,MSY estimates from Sindh were3260 mt and 3024 mt,suggesting stock condition was not over fished.These findings offer a scientific basis for the formulation of targeted management and conservation strategies by the government,particularly emphasizing urgent intervention for the Balochistan coast to ensure the long-term sustainability of the P.olivaceus fishery. 展开更多
关键词 maximum sustainable yield Pomadasys olivaceus Sindh Balochistan Catch-based Monte Carlo Maximum sustainable yield(CMSY) Bayesian Schaefer model(BSM) ASPIC
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Nitrogen cycling mechanisms in aquatic systems of arid areas on the Qinghai-Xizang Plateau,China 认领 引用
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作者 ZHAO Yongjia WAN Yuyu +5 位作者 SU Xiaosi ZHANG Qixing TANG Wangchun TAN Liwei YI Xiaokun DAI Yadi 《Journal of Arid Land》 SCIE CAS CSCD 2026年第5期811-832,共22页
Arid areas account for approximately one-quarter of the global land surface.Therefore,a comprehensive understanding of nitrogen cycling in arid watershed systems is essential for water environment protection and land ... Arid areas account for approximately one-quarter of the global land surface.Therefore,a comprehensive understanding of nitrogen cycling in arid watershed systems is essential for water environment protection and land use planning in dryland ecosystems.Using the Gasikule Lake Basin on the Qinghai-Xizang Plateau,China,as a representative study area,this study examined the sources,transport,and transformation mechanisms of nitrogen within a hierarchically nested hydrological system,including river water(S1R),groundwater(S2G),spring-fed river(S3R),and lake water(S4H).Dual nitrate isotopes(δ15N-NO3- and δ18O-NO3-)were integrated with a Bayesian mixing models in R(MixSIAR)to quantify external nitrate sources.In addition,δ15N-NH4+isotopes combined with microbial techniques were applied to trace nitrogen transformation processes in water bodies,where nitrogen inputs were dominated by nitrate.The results indicated that nitrate was the primary form of nitrogen input across the study area,although overall nitrate loading remained relatively low.Atmospheric deposition and soil organic nitrogen were the dominant sources of exogenous nitrate input.Microbial genera associated with nitrate reduction generally showed low relative abundance in groundwater,whereas facultatively aerobic genera were predominant in surface water.In surface water,nitrogen transformation was mainly driven by organic nitrogen ammoniation and subsequent nitrification.In contrast,groundwater systems were characterized by stronger hydrological confinement and oxygen limitation,resulting in suppressed nitrification,incomplete denitrification,and a tendency toward ammonium accumulation.Collectively,these findings define a"low-input,low-transformation,and low-loss"nitrogen regime in the Gasikule Lake Basin.This sluggish nitrogen cycling reflects a limited ecological self-remediation capacity,highlighting the inherent biogeochemical fragility of alpine desert ecosystems.This study provides a critical theoretical basis for understanding nitrogen budgets in global dryland systems and offers scientific support for water environment protection and ecological sustainability in high-altitude areas. 展开更多
关键词 nitrogen cycle endorheic basin nitrogen and oxygen isotopes Bayesian mixing models in R(MixSIAR) microbial community
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