期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到10,665篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Machine learning and deep learning in predicting the risk of diabetic kidney disease:A systematic review and meta-analysis 认领 引用
1
作者 Qing Chen Hua-Wei Peng +2 位作者 Chen-Xiao Fu Kai-Kai Meng Jun-Bei Zhang 《World Journal of Diabetes》 SCIE 2026年第3期207-218,共12页
BACKGROUND Machine learning(ML)and deep learning(DL)algorithms have been utilised to predict the risk of diabetic kidney disease(DKD)in individuals with type 2 diabetes mellitus(T2DM).Despite promising results,concern... BACKGROUND Machine learning(ML)and deep learning(DL)algorithms have been utilised to predict the risk of diabetic kidney disease(DKD)in individuals with type 2 diabetes mellitus(T2DM).Despite promising results,concerns regarding the clinical applicability and performance of these artificial intelligence models remain.AIM To evaluates the performance of ML-based and DL-based models in predicting DKD risk among T2DM patients.METHODS A systematic search was conducted across five databases.The risk of bias was assessed using the prediction model risk of bias assessment tool.After data extraction,summary point estimates of the area under the receiver operating characteristic curve(AUC)were aggregated.Heterogeneity was evaluated with the I2 statistic and Cochrane Q test,and subgroup analyses were performed to identify potential sources of heterogeneity.RESULTS Twelve eligible studies were included.The pooled AUC for the top-performing artificial intelligence models was 0.858[95%confidence interval(CI):0.779-0.912],with a prediction interval of 0.480-0.975.Significant heterogeneity was detected(I2=99.7%).Studies employing cross-validation methods demonstrated significantly higher diagnostic accuracy(pooled AUC=0.88;95%CI:0.79-0.94)compared to those using simple holdout validation(pooled AUC=0.77;95%CI:0.59-0.89,P=0.0231).Predictive factors most frequently used for DKD prediction included age,body mass index,estimated glomerular filtration rate,serum creatinine,urinary albumin,glycated hemoglobin,systolic blood pressure,low-density lipoprotein cholesterol,high-density lipoprotein cholesterol,and triglycerides.CONCLUSION ML and DL algorithms exhibit strong performance in predicting DKD in patients with T2DM.However,future research should focus on standardizing model development and validation processes. 展开更多
关键词 Diabetic kidney disease Type 2 diabetes mellitus Predicting Deep learning Machine learning Artificial intelligence
暂未订购 下载PDF
Adaptive weight strategy for frequency-decomposed seismic attribute fusion in predicting of complex sand body distributions 认领 引用 被引量：1
2
作者 Hong-Li Wu Sheng-He Wu +7 位作者 Zhen-Hua Xu Ming-Cheng Liu Bo Yang De-GangWu Zi-Shi Xie Yu Tang Xiao-Long Wan Xin-Ping Zhou 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2367-2389,共23页
High-precision sand prediction is fundamental to improving the efficiency of oil and gas exploration and development.To address the limitations of traditional fixed-weight fusion strategies,particularly under conditio... High-precision sand prediction is fundamental to improving the efficiency of oil and gas exploration and development.To address the limitations of traditional fixed-weight fusion strategies,particularly under conditions of significant lateral variation in sand body distribution,this study proposesa dynamic weightingdeep neural network(DW-DNN)for adaptive frequency-decomposed attribute fusion.The approach integrates physical constraints with deep learning and introduces two innovations:(i)a priori weight matrices derived from the amplitudefrequency and tuning thickness relationship(amplitude variation with frequency,AvF)are embeddedintothe attention mechanism to adaptivelyallocate multiband seismic attributes,emphasizing high-frequency features for thin sands and low-frequency features for thick sands;and(ii)a deep neural network with a composite loss function combining mean squared error(MSE)and AVF-based constraints is designed to jointly optimize weight allocation and prediction accuracy.The method was applied to the Xi 233 area of theQingcheng Oilfield in the Ordos Basin and compared with conventional approaches.DW-DNN achieved high accuracy and generalizability,with an R2 of 0.92 in the 30%blind-well test,24.3%higher than conventional methods.In addition,91%of well-point errors were within 03 m,while prediction accuracies for thin(≤3 m)and thick(>3 m)sands reached 88%and 91%,respectively.The model also maintained stable performance under low well-control conditions(training-test ratio 5:5).Predicted sand distributions exhibited improved continuity and geologically plausible geometries,clearly delineating channels,lobes,and estuary bars.The results demonstrate that DW-DNN enhances frequency-decomposed attribute fusion through adaptive weight allocation,providing a robust tool for predicting sand body distributions in complex reservoirs. 展开更多
关键词 Reservoir heterogeneity Sand thickness prediction Frequency-decomposed attribute intelligent fusion Adaptive weighting Deep neural network
暂未订购 下载PDF
Predicting the temperature of CRTS Ⅲ ballastless tracks in cold regions based on a TCN-Track model 认领 引用
3
作者 Jie LIANG Shijie DENG +4 位作者 Juanjuan REN Wenlong YE Kaiyao ZHANG Dacheng LI Ronghe ZHANG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第1期43-57,共15页
The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primar... The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)Ⅲ ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures. 展开更多
关键词 Cold regions CRTSⅢballastless tracks Temperature prediction Meteorological variables Time prediction model
暂未订购 下载PDF
A new interpretable dynamic ensemble learning model with sample augmentation for predicting seismic responses of vehicle-track-bridge systems 认领 引用
4
作者 WANG Zi-yi LAI Zhi-peng JIANG Li-zhong 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2886-2902,共17页
Ensuring the operational safety of high-speed trains during earthquakes is a core challenge for China's extensive high-speed rail network.While machine learning(ML)-based seismic response assessment has become a m... Ensuring the operational safety of high-speed trains during earthquakes is a core challenge for China's extensive high-speed rail network.While machine learning(ML)-based seismic response assessment has become a mainstream approach,conventional ML methods suffer from limitations such as heavy training data demands,poor interpretability,and over-reliance on deterministic predictions.This study proposes an interpretable dynamic ensemble learning model integrated with sample augmentation to predict extreme seismic responses of vehicle-track-bridge(VTB)systems.The framework combines generative adversarial networks(GAN)for data generation,the Kepler optimization algorithm(KOA)—chosen for its superior convergence speed and optimization performance over classical algorithms—for hyperparameter tuning,and a dynamically weighted ensemble of long short-term memory(LSTM)-attention and support vector machine(SVM).A 3D nonlinear VTB model under bidirectional seismic excitation serves as the physical basis,with GAN-based augmentation mitigating data imbalance.Comprehensive validation against traditional ML models confirms significant accuracy gains,marked by reduced mean absolute error(MAE)and coefficient of determination(R2)values consistently exceeding 0.97.Shapley additive explanation(SHAP)analysis identifies key input features affecting wheel-rail interaction parameters,and Gaussian probabilistic interval prediction quantifies predictive uncertainty with adaptive confidence bounds.The findings offer references for seismic prediction and safety risk assessment of high-speed railways. 展开更多
关键词 ensemble learning model generative adversarial networks seismic response prediction SHAP analysis probabilistic interval prediction vehicle-track-bridge system
暂未订购 下载PDF
A hierarchical Bayesian–MCMC model for predicting the geometric dimensions of corrosion defects in wet gas gathering pipelines 认领 引用
5
作者 Si-Jia Chen Min Qin +2 位作者 Ke-Xi Liao Yong-Chun Mu Xiao-Dong Hao 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1487-1504,共18页
Internal corrosion is a major threat to the safety of natural gas pipelines,with defect geometry—depth,length,and width—playing a critical role in structural integrity assessments.While corrosion depth prediction ha... Internal corrosion is a major threat to the safety of natural gas pipelines,with defect geometry—depth,length,and width—playing a critical role in structural integrity assessments.While corrosion depth prediction has been widely studied,systematic probabilistic modeling of defect length and width remains limited.This study develops a hierarchical Bayesian-Markov Chain Monte Carlo(HB-MCMC)framework to jointly predict corrosion defect dimensions from in-line inspection(ILI)data.The framework integrates non-centered parameterization and adaptive sampling to improve inference efficiency and employs a hierarchical dynamic thresholding procedure for robust data preprocessing and outlier filtering.Field data from two transmission pipelines in Southwest China,comprising 1845 defect records,are analyzed.Results demonstrate that defect length and width both increase with depth,with width exhibiting stronger sensitivity.Model diagnostics confirm convergence and reliable uncertainty quantification.To further explore underlying mechanisms,OLGA multiphase flow simulations are combined with statistical predictions,providing flow-parameter profiles along the pipelines and enabling correlation analysis between local hydrodynamics and defect geometry.The proposed framework not only enhances predictive capability for defect length and width but also provides new insights into flow-corrosion interactions under real operating conditions,offering a reproducible and data-driven tool for corrosion assessment. 展开更多
关键词 Corrosion defects Defect dimensions Hierarchical Bayesian Markov Chain Monte Carlo Corrosion prediction
暂未订购 下载PDF
A statistical model for predicting rock avalanche runout using source area width from 63 representative cases 认领 引用
6
作者 Rui-chen Chen Jian Chen +2 位作者 Sheng Ma Zi-ang Yan Qian Shi 《China Geology》 CAS CSCD 2026年第2期366-382,共17页
Developing predictive models for rock avalanche runout is crucial for hazard risk mitigation and management,while also deepening the understanding of rock avalanche dynamics.In this study,a database of 63 representati... Developing predictive models for rock avalanche runout is crucial for hazard risk mitigation and management,while also deepening the understanding of rock avalanche dynamics.In this study,a database of 63 representative rock avalanches was compiled,and the geometric characteristics of these events were analyzed.Traditional regression methods were compared with neural network regression(NNR)approaches to find the optimal model.Model parameters,loss functions,and evaluation metrics were examined to determine optimal configurations.Based on correlation analyses and model performance,this study recommends the use of source area width as an alternative to failure volume in runout prediction models,effectively addressing the common challenge of estimating avalanche volume.Ultimately,an NNR-based model,utilizing mean squared logarithmic error as the loss function and incorporating source area width and fall height as input parameters,was identified as the optimal approach.This model achieved prediction errors within a−50% to 50% range with 95.2% probability,yielding a mean absolute percentage error of 23.22% and an R2 of 0.85.These findings enhance rock avalanche prediction methodologies,providing more accurate tools for assessing the potential impact zones of destructive geological events. 展开更多
关键词 Runout prediction model Rock avalanche Traditional regression Neural network regression Source area width Fall height Loss functions Prediction errors Geological hazard prevention and control engineering
暂未订购 下载PDF
Multi-scale simplified residual convolutional neural network model for predicting compositions of binary magnesium alloys 认领 引用
7
作者 Xu Qin Qinghang Wang +6 位作者 Xinqian Zhao Shouxin Xia Li Wang Jiabao Long Yuhui Zhang Yanfu Chai Daolun Chen 《Journal of Magnesium and Alloys》 SCIE EI CAS CSCD 2026年第1期117-123,共7页
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data... This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R2 compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems. 展开更多
关键词 Magnesium alloys Composition prediction Scanning electron microscope images Multi-scale simplified residual convolutional neural network
暂未订购 下载PDF
Predicting and minimizing twin-propeller noise:Hessian matrix and Fourier-Frobenius matrix analysis in improved propeller signatures theory 认领 引用
8
作者 Liantan LUO Xianghua HUANG Tianhong ZHANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期234-262,共29页
Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strateg... Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method. 展开更多
关键词 Acoustic simulation Analytical solution Frobenius matrix Hessian matrix Noise Noise prediction Propellers
暂未订购 下载PDF
An Interpretable AI Framework for Predicting Groundwater Contamination under Atmospheric and Industrial Pollution Using Metaheuristic-Optimized Deep Learning 认领 引用
9
作者 Md.Mottahir Alam Mohammed K.Al Mesfer +3 位作者 Haroonhaider Sidhwa Mohd Danish Asif Irshad Khan Tauheed Khan Mohd 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期750-783,共34页
Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially... Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios. 展开更多
关键词 Groundwater quality prediction interpretable artificial intelligence industrial and atmospheric pollution spatial-temporal-assisted Deep Belief Network Tiki-Taka Algorithm Addax Optimization Algorithm Whale Optimization Algorithm
暂未订购 下载PDF
A Data Science Framework for Predicting the Creep Rupture Life of 1.25Cr- 0.5Mo Steel for Elevated Temperature Applications 认领 引用
10
作者 Muhammad Ishtiaq Yeonwoo Kim +1 位作者 Sung-Gyu Kang Nagireddy Gari Subba Reddy 《Computers, Materials & Continua》 SCIE EI 2026年第6期919-934,共16页
The long-term reliability of 1.25Cr-0.5Mo steels in high-temperature service critically depends on their creep rupture behavior,which is strongly influenced by alloy composition,microstructural characteristics,and tes... The long-term reliability of 1.25Cr-0.5Mo steels in high-temperature service critically depends on their creep rupture behavior,which is strongly influenced by alloy composition,microstructural characteristics,and testing conditions.In this study,an advanced Artificial Neural Network(ANN)model was developed to accurately predict the creep-rupture life of 1.25Cr-0.5Mo steels,offering a data-driven framework for alloy design and service-life assessment.The model incorporated eleven compositional variables(C,Si,Mn,P,S,Ni,Cr,Mo,Cu,Al,N),average grain size,non-metallic inclusions(NMI),steel properties including hardness measured on the Rockwell B scale(HRB)yield strength(MPa),ultimate tensile strength(MPa),elongation(%),reduction in area(%),and test conditions including temperature(℃)and stress(MPa)as input features,with rupture time as the output.A total of 276 experimental datasets were compiled,of which 219 were used for training and 57 for testing.To optimize predictive performance,a systematic hyperparameter evaluation was performed.Network architectures with one to three hidden layers and 10-30 neurons per layer were examined.The optimal configuration—three hidden layers with 21 neurons—achieved outstanding predictive accuracy,yielding an RMSE of 0.00007,an adjusted R2 of 0.9930,a Pearson’s r of 0.9965,and a minimum MAE of 0.0504.Further optimization of training parameters showed that a momentum coefficient of 0.6 and a learning rate of 0.7 provided the most stable convergence behavior,while 9000 training iterations produced the lowest RMSE(0.000021).Five-fold cross-validation was employed to further assess the model’s predictive reliability and generalization.The predictive performance of the developed ANN model was further compared with multiple established machine-learning approaches to demonstrate its relative accuracy and generalization capability.The optimized ANN model was deployed in a user-friendly graphical interface(GUI)to facilitate practical implementation.Sensitivity analyses using both single-variable and two-variable approaches revealed the dominant role of key alloying elements,as well as the strong effects of test temperature and rupture stress on rupture life.The developed data science framework provides a powerful and reliable tool for predicting creep-rupture life across a broad compositional and testing window,enabling accelerated design and optimization of 1.25Cr-0.5Mo steels for high-temperature applications. 展开更多
关键词 1.25Cr-0.5Mo steel creep rupture life chemical composition temperature prediction
暂未订购 下载PDF
Clinical implications of a dynamic nomogram for predicting sepsis in acute liver failure 认领 引用
11
作者 Shi-Yan Zhang Jin-Bao Shi 《World Journal of Gastroenterology》 SCIE CAS 2026年第4期117-120,共4页
This letter comments on a web-enabled,dynamic nomogram developed for early sepsis-risk estimation in adults with acute liver failure(ALF)admitted to the intensive care unit.The study successfully established and valid... This letter comments on a web-enabled,dynamic nomogram developed for early sepsis-risk estimation in adults with acute liver failure(ALF)admitted to the intensive care unit.The study successfully established and validated the sepsis in ALF model using five routinely available variables:Age,total bilirubin,lactate dehydrogenase,albumin,and mechanical ventilation.Across cohorts,the model demonstrated strong discrimination and outperformed traditional scores.We commend the inclusion of both Western and Chinese intensive care unit cohorts,which enhances the cross-population generalizability of the findings.This letter highlights the strengths of the model,including its web-based dynamic calculator and effective risk stratification,while also acknowledging limitations such as reliance on baseline admission data,restriction to intensive care unit populations,and the absence of infection-related biomarkers.We encourage further prospective,multicenter investigations to refine the sepsis in ALF model and expand its clinical utility. 展开更多
关键词 Acute liver failure Risk prediction Predictive model Intensive care Biomarkers Dynamic nomogram
暂未订购 下载PDF
Interpretative machine learning for predicting 60-day mortality in burn patients with suspected infection 认领 引用
12
作者 Haitao Ren Yong’an Xu 《World Journal of Emergency Medicine》 SCIE CAS CSCD 2026年第3期244-249,共6页
BACKGROUND:Traditional burn severity scores have limited accuracy in predicting mortality in burn patients with infection.This study aimed to develop an interpretative machine learning(ML)model to predict 60-day morta... BACKGROUND:Traditional burn severity scores have limited accuracy in predicting mortality in burn patients with infection.This study aimed to develop an interpretative machine learning(ML)model to predict 60-day mortality in burn patients with suspected infection.METHODS:Data on burn patients with suspected infection were extracted from the Dryad database and divided into a training cohort(70%)and a test cohort(30%).Feature selection was conducted by combining the Boruta algorithm and least absolute shrinkage and selection operator(LASSO)regression.Twelve ML models were developed to predict 60-day mortality.Model robustness was evaluated in the training cohort,and the discrimination capacity was assessed in the test cohort.DeLong’s test was performed to compare the area under the curve(AUC)between the optimal model and the traditional scores(abbreviated burn severity index[ABSI]and revised Baux[rBaux]).SHapley Additive exPlanations(SHAP)analysis was used for model interpretation.RESULTS:A total of 1,391 adult burn patients with suspected infections were included:training cohort(n=973),test cohort(n=418).The overall mortality was 23.7%(n=329).The percentage of total body surface area(%TBSA),Acute Physiology and Chronic Health Evaluation IV(APACHE IV)score,and age were identified as significant predictors of 60-day mortality among burn patients with suspected infections.CatBoost achieved a well-balanced performance and better ability than the ABSI and rBaux did.CONCLUSION:The ML model incorporating the APACHE IV score improved the predicting performance of 60-day mortality in burn patients with infection.Its high interpretability may facilitates its clinical application for In the future. 展开更多
关键词 Burns Machine learning Mortality prediction Feature selection
暂未订购 下载PDF
Predicting BDS-3's short-term clock bias using the RIME-WNN model 认领 引用
13
作者 Xu Wang Chang Wang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期238-248,共11页
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr... Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites. 展开更多
关键词 Satellite clock bias Wavelet neural network Rime optimization algorithm Prediction
暂未订购 下载PDF
Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer:Limitations and suggestions 认领 引用
14
作者 Zong-Xian Zhao 《World Journal of Gastrointestinal Oncology》 SCIE 2026年第2期1-7,共7页
A core challenge in the diagnosis and treatment of esophageal cancer(EC)lies in accurately identifying patients who will benefit from neoadjuvant therapy(NAT).Yang et al reported a predictive model for NAT response in... A core challenge in the diagnosis and treatment of esophageal cancer(EC)lies in accurately identifying patients who will benefit from neoadjuvant therapy(NAT).Yang et al reported a predictive model for NAT response in EC,constructed using radiomics from T2-weighted magnetic resonance imaging(MRI)and machine learning.The model achieved an area under the curve of 0.932 in the training cohort and 0.900 in the validation cohort.While encouragingly,we urge caution with limitation.First,the study’s single-center,retrospective design with an insufficient sample size limits the model’s generalizability and significantly increases the risk of overfitting.Second,the study only extracted features from the T2-weighted MRI sequence,failing to integrate data from other functional MRI sequences such as diffusion-weighted imaging and dynamic contrast-enhanced MRI.Third,the model suffers from a"black box"issue regarding its extracted features—its low interpretability hinders clinicians’trust in and acceptance of the model.This editorial reviews the study by Yang et al,identifies its limitations,and puts forward in-depth suggestions to further optimize the model. 展开更多
关键词 Esophageal cancer Neoadjuvant therapy Magnetic resonance imaging Radiomics Predictive model Limitations
暂未订购 下载PDF
Application of modified Charlson comorbidity index for predicting outcomes following adult living donor liver transplantation 认领 引用 被引量：1
15
作者 Gowtham Rajan Amal Francis Sam +2 位作者 Akila Rajakumar Dinesh Jothimani Mohamed Rela 《World Journal of Hepatology》 2026年第1期113-123,共11页
BACKGROUND The model for end-stage liver disease(MELD)score helps assess the severity of liver disease and can predict survival after liver transplant.The Charlson comorbidity index(CCI)is frequently employed to forec... BACKGROUND The model for end-stage liver disease(MELD)score helps assess the severity of liver disease and can predict survival after liver transplant.The Charlson comorbidity index(CCI)is frequently employed to forecast the 10-year survival probability of patients with multiple health conditions.We employed the CCI to evaluate the impact of comorbid health conditions on patients and assess its predictive capability regarding health complications and mortality following living donor liver transplantation(LDLT).AIM To understand the prevalence of extrahepatic comorbidities in our cohort of LDLT patients with modified CCI(mCCI)and to analyze the utility of mCCI as a predictor of morbidity and mortality following LDLT.METHODS After obtaining institutional ethics committee approval,a retrospective analysis was conducted on 497 adult patients who underwent LDLT at our institute between January 2021 and December 2023.RESULTS Our analysis revealed that the area under the curve(AUC)of the original CCI for predicting 90-day mortality decreased when malignancy was assigned a score of 2 in patients with hepatocellular carcinoma undergoing transplantation.Therefore,we used a mCCI.Both MELD and mCCI scores demonstrated predictive value for 90-day mortality,with AUCs of 0.60 and 0.62,respectively.Using regression coefficients,we developed a composite score defined as:Combined score=[mCCI+(MELD/10)].This composite metric improved predictive accuracy,yielding an AUC of 0.70 for 90-day mortality prediction.Patients with a CCI>3 and a MELD>21 had a significantly higher 90-day mortality rate than others(12.5%vs 5.7%;P=0.02).CONCLUSION The mCCI was independent of decompensation and overall disease severity.Combining MELD and CCI scores enhanced the discriminatory power for predicting morbidity and 90-day mortality. 展开更多
关键词 Charlson comorbidity index Post living donor liver transplantation outcomes Composite outcome scores in living donor liver transplantation Charlson comorbidity index vs model for end-stage liver disease Modified Charlson comorbidity index in liver transplantation Outcome prediction in living donor liver transplantation
暂未订购 下载PDF
An Approach Integrating Data-Driven and Mechanistic Models for Predicting and Optimizing Heating Flue Temperature of Coke Ovens 认领 引用
16
作者 Yuan Shan Hongxin Dong +2 位作者 Zhongyang Han Jun Zhao Hong Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期955-965,共11页
As one of the most important parameters for coke oven of steel industry,heating flue temperature plays a pivotal role in obtaining quality-guaranteed final product.While the complexity such as nonlinearity,time-delay,... As one of the most important parameters for coke oven of steel industry,heating flue temperature plays a pivotal role in obtaining quality-guaranteed final product.While the complexity such as nonlinearity,time-delay,and coupling relationship with its heating fuel,in particular,blast furnace gas(BFG),brings about challenges for heating flue temperature prediction and optimization.As such,a data-mechanism combined driven systematic approach considering both internal and external influencing factors of coke oven is proposed in this study.To provide a solid dataset,a density-based spatial clustering of applications with noise(DBSCAN)based outlier detection algorithm is designed at first for preprocessing,which accommodates the data characteristics in practice.Then,taking full consideration of the periodic and trend features of flue temperature data,a neural network(NN)based multi-channel prediction model is constructed for temperature forecasting.In order to establish dynamic rather than static constraints for the following temperature optimization,a mechanism based controllable region assessment method is proposed.Finally,the flue temperature is optimized via a well-designed fuzzy-based approach along with swarm and evolutionary algorithms for parameter determination.Based on the real data,the simulation results demonstrate the superiority of the proposed systematic approach compared with other partially applied methods,so as to manifest its benefits for the operational optimization of coke oven in steel industry. 展开更多
关键词 Coke oven fuzzy control heating flue temperature prediction systematic approach
暂未订购 下载PDF
Interpretable logistic regression for predicting 1/1 kink-driven regular complete sawtooth phenomenon in EAST tokamak plasmas 认领 引用
17
作者 Haipeng Quan Liqing Xu +3 位作者 Chaowei Mai Yan Chao Yuhang Wang EAST Team 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第4期513-520,共8页
Sawtooth phenomena are a central topic in tokamak fusion research;nevertheless,different sawtooth classes differ markedly in underlying physics,statistical abundance and diagnostic definition.This paper focuses exclus... Sawtooth phenomena are a central topic in tokamak fusion research;nevertheless,different sawtooth classes differ markedly in underlying physics,statistical abundance and diagnostic definition.This paper focuses exclusively on regular complete sawtooth—ideal,1/1 internal-kink-driven events with full magnetic reconnection—which are the most frequent,unambiguously identifiable and theoretically best characterized(hereafter,all references to“sawtooth”denote this specific class).Compound crashes,partial-reconnection events and fast-ion-induced giant sawtooth are deliberately excluded because of their limited data volume and still-contested classification criteria,the inclusion of which would introduce intolerable label noise and theoretical ambiguity.To this aim,we propose a machine-learning-based temporal binary-classification framework that converts multi-diagnostic,high-resolution signals into precise labels distinguishing“oscillation”from“quiet”windows for this specific category,thereby supplying critical timing information for active control.A large-scale database covering 124 discharges and hundreds of millions of samples was constructed,and three representative algorithms—logistic regression,decision tree and random forest—were trained and compared.Among them,logistic regression achieved the best and most robust performance,reaching 95% accuracy on an independent test set and significantly outperforming the other models.Furthermore,shapley additive explanations(SHAP)was innovatively employed to quantify the contribution magnitude and direction of key physical features to the onset of regular 1/1 sawtooth,substantially enhancing model interpretability and physical fidelity.The study provides an efficient and robust predictor for the active intervals of ordinary 1/1 sawtooth;the uncovered correlations between physical drivers and sawtooth behavior lay a solid foundation for deepening the understanding of regular sawtooth evolution and for optimizing control strategies,thereby holding significant promise for improving the operational stability of fusion plasmas. 展开更多
关键词 sawtooth prediction logistic regression SHAP interpretative model
暂未订购 下载PDF
Innovative substructure synthesis method for predicting focal plane response in the survey module of the Chinese Survey Space Telescope 认领 引用
18
作者 Tonglei Jiang Lihua Wen +2 位作者 Wenzhong Shi Renkui Jiang Yongchao Yang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第7期481-494,共14页
The survey module,as a key scientific payload of the China Space Station Telescope,suffers from a loss in observa-tion accuracy due to micro-vibrations.High-precision full-system dynamic testing is typically conducted... The survey module,as a key scientific payload of the China Space Station Telescope,suffers from a loss in observa-tion accuracy due to micro-vibrations.High-precision full-system dynamic testing is typically conducted in the later stages of development,preventing timely feedback and design optimization during the early design phases.This constraint not only incurs significant costs but also substantially reduces efficiency.To address this,we propose a transfer path analysis(TPA)method based on substructure synthesis to predict the focal plane response.This method enhances and optimizes substructure synthesis theory through integration with TPA.It relies solely on independent substructure testing,enabling real-time updates of focal plane responses following individual substructure design modifications.The method’s accuracy was validated both numerically and experimentally.Furthermore,this study systematically analyzes focal plane vibration at various shutter speeds.It identifies dominant excitation mechanisms and key influencing factors within the assembly.Based on these findings,validated strategies are proposed to mitigate vibration transmission pathways.Results indicate a maximum micro-vibration response of 21μg at the focal plane when the shutter operates at working speed.This approach effectively characterizes the micro-vibration response of highly sensitive optical instruments under multi-source excitation at any development stage,providing a theoretical foundation and efficient analytical method for the development of the survey module. 展开更多
关键词 Vibration response prediction Dynamic-model Space Telescope
暂未订购 下载PDF
Explainable machine learning for predicting the mechanical strength of eco-friendly geopolymer concrete 认领 引用
19
作者 Veerabhadrappa ALGUR Poornima HULIPALLED +3 位作者 Shivakumar KAPPE Srishaila JAGALUR MAHALINGASHARMA Sharvani VITTA Zameer Ahamed Haneef MOHAMED 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第2期337-363,共27页
This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete(GPC)made from Ground Granulated Blast Furnace Slag(GGBS)and Suga... This study presents a clear machine learning framework aimed at forecasting the mechanical properties of environmentally sustainable geopolymer concrete(GPC)made from Ground Granulated Blast Furnace Slag(GGBS)and Sugarcane Bagasse Ash(SCBA).Four ensemble machine learning models:Random Forest(RF),AdaBoost,Gradient Boosting(GB)and XGBoost(XGB)were employed to estimate the Compressive Strength(CS),Split Tensile Strength(STS)and Flexural Strength(FS).Particle Swarm Optimization(PSO)and Bat Optimization Algorithm(BAT)algorithms were employed to optimize the hyperparameter of the model.The best test predictive accuracy with R2values for CS,STS and FS are 0.983(GB-BAT),0.991(RF-BAT)and 0.985(XGB-PSO)respectively with lower error metrics.To improve the model’s interpretability,we used SHapley Additive exPlanations and sensitivity analysis.The findings indicated that the anticipated results were significantly influenced by the GGBS content,curing duration and molarity.The study emphasizes a synergistic effect between GGBS replacement and curing age in enhancing strength development.Integrating explainable Artificial Intelligence(AI)with predictive modeling enhances clarity and provides a reliable way to get results without having lot of laboratory work.This framework is a useful tool for designing mixes based on data and encourages eco-friendly methods of building with cement-free concrete. 展开更多
关键词 GPC machine learning strength prediction GGBS SCBA explainable AI
暂未订购 下载PDF
Predicting the synthesizability of inorganic crystals by bridging crystal graphs and phonon dynamics 认领 引用
20
作者 Mei Ma Wei Ma +2 位作者 Le Gao Zong-Guo Wang Hao Liu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第1期35-44,共10页
Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experiment... Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency. 展开更多
关键词 crystal synthesizability prediction deep learning graph learning AI for science
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈