Development of a new Cox model for predicting long-term survival in hepatitis cirrhosis patients underwent transjugular intrahepatic portosystemic shunts

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.

Prediction of bridge temperature field and its effect on behavior of bridge deflection based on ANN method

In recent years, the bridge safety monitoring has been paid more attention in engineering field. How- ever, the financial and material resources as well as human resources were costly for the traditional monitoring means. Besides, the traditional means of monitoring were low in accuracy. From an engineering example, based on neural network method and historical data of the bridge monitoring to construct the BP neural network model with dual hidden layer strueture, the bridge temperature field and its effect on the behavior of bridge deflection are forecasted. The fact indicates that the predicted biggest error is 3.06% of the bridge temperature field and the bridge deflection behavior under temperature field affected is 2. 17% by the method of the BP neural net-work, which fully meet the precision requirements of the construction with practical value.

The long-term prediction of the oil-contaminated water from the Sanchi collision in the East China Sea

The condensate and bunker oil leaked from the Sanchi collision would cause a persistent impact on marine ecosystems in the surrounding areas. The long-term prediction for the distribution of the oil-polluted water and the information for the most affected regions would provide valuable information for the oceanic environment protection and pollution assessment. Based on the operational forecast system developed by the First Institute of Oceanography, State Oceanic Administration, we precisely predicted the drifting path of the oil tanker Sanchi after its collision. Trajectories of virtual oil particles show that the oil leaked from the Sanchi after it sank is mainly transported to the northeastern part of the sink location, and quickly goes to the open ocean along with the Kuroshio. Risk probability analysis based on the outcomes from the operational forecast system for years 2009 to2017 shows that the most affected area is at the northeast of the sink location.

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

Lithium-ion battery degradation trajectory early prediction with synthetic dataset and deep learning

Knowing the long-term degradation trajectory of Lithium-ion(Li-ion) battery in its early usage stage is critical for the maintenance of the battery energy storage system(BESS) in reality. Previous battery health diagnosis methods focus on capacity and state of health(SOH) estimation which can receive only the short-term health status of the cell. This paper proposes a novel degradation trajectory prediction method with synthetic dataset and deep learning, which enables to grasp the characterization of the cell's health at a very early stage of Li-ion battery usage. A transferred convolutional neural network(CNN) is chosen to finalize the early prediction target, and the polynomial function based synthetic dataset generation strategy is designed to reduce the costly data collection procedure in real application. In this thread, the proposed method needs one full lifespan data to predict the overall degradation trajectories of other cells. With only the full lifespan cycling data from 4 cells and 100 cycling data from each cell in experimental validation, the proposed method shows a good prediction accuracy on a dataset with more than 100 commercial Li-ion batteries.

New scheme of anticipating synchronization for arbitrary anticipation time and its application to long-term prediction of chaotic states

How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory. Different from ＇traditional techniques （or regimes）＇ proposed in the previous literature, the present scheme guarantees that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

A Lightweight Temporal Convolutional Network for Human Motion Prediction

A lightweight multi-layer residual temporal convolutional network model(RTCN)is proposed to target the highly complex kinematics and temporal correlation of human motion.RTCN uses 1-D convolution to efficiently obtain the spatial structure information of human motion and extract the correlation in the time series of human motion.The residual structure is applied to the proposed network model to alleviate the problem of gradient disappearance in the deep network.Experiments on the Human 3.6M dataset demonstrate that the proposed method effectively reduces the errors of motion prediction compared with previous methods,especially of long-term prediction.

Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases.Proper assessment is crucial in the initial design stages.This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations.Influential parameters(i.e.,the excavation geometry,diaphragm wall thickness,pile geometry,strength and small-strain stiffness of the soil,and soft clay thickness)were considered and employed in the developed equations.It is practically unfeasible to obtain measurement data;hence,artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases,using the three-dimensional finite element method.The developed equations were established through a multiple linear regression analysis of the artificial data,using the transformation technique.In addition,the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect,using the plane strain ratio parameter.The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

National Prediction of Ambient Fine Particulates: 2000-2009

A large body of evidence links ambient fine particulates (PM2.5) to chronic disease. Efforts continue to be made to improve large scale estimation of this pollutant for within-urban environments and sparsely monitored areas. Still questions remain about modeling choices. The purpose of this study was to evaluate the performance of spatial only models in predicting national monthly exposure estimates of fine particulate matter at different time aggregations during the time period 2000-2009 for the contiguous United States. Additional goals were to evaluate the difference in prediction between federal reference monitors and non-reference monitors, assess regional differences, and compare with traditional methods. Using spatial generalized additive models (GAM), national models for fine particulate matter were developed, incorporating geographical information systems (GIS)-derived covariates and meteorological variables. Results were compared to nearest monitor and inverse distance weighting at different time aggregations and a comparison was made between the Federal Reference Method and all monitors. Cross-validation was used for model evaluation. Using all monitors, the cross-validated R2 was 0.76, 0.81, and 0.82 for monthly, 1 year, and 5-year aggregations, respectively. A small decrease in performance was observed when selecting Federal Reference monitors only (R2 = 0.73, 0.78, and 0.80 respectively). For Inverse distance weighting (IDW), there was a significantly larger decrease in R2 (0.68, 0.71, and 0.73, respectively). The spatial GAM showed the weakest performance for the northwest region. In conclusion, National exposure estimates of fine particulates at different time aggregations can be significantly improved over traditional methods by using spatial GAMs that are relatively easy to produce. Furthermore, these models are comparable in performance to other national prediction models.

Quantitative electroencephalography in predicting on outcome of awakening in long-term unconscious patients after severe traumatic brain injury

Objective To explore quantitative electroencephalography in unconscious patients after severe traumatic brain injury (TBI) to predict awakening. Methods All cases were divided into two groups(the awake group 19 cases and the unfavourable prognosis group 22 cases).Two weeks after admission the original EEGs were preformed in 41 patients suffering from severe TBI with duration of disturbance of

Long-Term Outcomes after Coronary Artery Bypass Grafting with Risk Stratification

Background: Risk stratification of long-term outcomes for patients undergoing Coronary artery bypass grafting has enormous potential clinical importance. Aim: To develop risk stratification models for predicting long-term outcomes following coronary artery bypass graft (CABG) surgery. Methods: We retrospectively revised the electronic medical records of 2330 patients who underwent adult Cardiac surgery between August 2016 and December 2022 at Madinah Cardiac Center, Saudi Arabia. Three hundred patients fulfilled the eligibility criteria of CABG operations with a complete follow-up period of at least 24 months, and data reporting. The collected data included patient demographics, comorbidities, laboratory data, pharmacotherapy, echocardiographic parameters, procedural details, postoperative data, in-hospital outcomes, and follow-up data. Our follow-up was depending on the clinical status (NYHA class), chest pain recurrence, medication dependence and echo follow-up. A univariate analysis was performed between each patient risk factor and the long-term outcome to determine the preoperative, operative, and postoperative factors significantly associated with each long-term outcome. Then a multivariable logistic regression analysis was performed with a stepwise, forward selection procedure. Significant (p < 0.05) risk factors were identified and were used as candidate variables in the development of a multivariable risk prediction model. Results: The incidence of all-cause mortality during hospital admission or follow-up period was 2.3%. Other long-term outcomes included all-cause recurrent hospitalization (9.8%), recurrent chest pain (2.4%), and the need for revascularization by using a stent in 5 (3.0%) patients. Thirteen (4.4%) patients suffered heart failure and they were on the maximum anti-failure medications. The model for predicting all-cause mortality included the preoperative EF ≤ 35% (AOR: 30.757, p = 0.061), the bypass time (AOR: 1.029, p = 0.003), and the duration of ventilation following the operation (AOR: 1.237, p = 0.021). The model for risk stratification of recurrent hospitalization comprised the preoperative EF ≤ 35% (AOR: 6.198, p p = 0.023), low postoperative cardiac output (AOR: 3.622, p = 0.007), and the development of postoperative atrial fibrillation (AOR: 2.787, p = 0.038). Low postoperative cardiac output was the only predictor that significantly contributed to recurrent chest pain (AOR: 11.66, p = 0.004). Finally, the model consisted of low postoperative cardiac output (AOR: 5.976, p < 0.001) and postoperative ventricular fibrillation (AOR: 4.216, p = 0.019) was significantly associated with an increased likelihood of the future need for revascularization using a stent. Conclusions: A risk prediction model was developed in a Saudi cohort for predicting all-cause mortality risk during both hospital admission and the follow-up period of at least 24 months after isolated CABG surgery. A set of models were also developed for predicting long-term risks of all-cause recurrent hospitalization, recurrent chest pain, heart failure, and the need for revascularization by using stents.

结构刚塑性动力解的弹性补偿

近年来,我国学者以膜力因子法和饱和分析方法相结合为理论工具,对梁、板等结构件在脉冲载荷作用下的塑性大变形行为作了全面深入的研究,为脉冲加载下结构的最终挠度提供了优于历史上各种刚塑性近似解的最佳刚塑性预测公式。然而,由于实际工程应用中金属结构弹塑性动力响应的复杂性和数值模拟的局限性,与考虑材料弹性效应的结果相比,刚塑性解对脉冲加载下结构所预测的最终挠度的误差有多大,是一个亟待解决的关键问题。对这个问题的首阶段研究成果厘清了材料弹性对脉冲加载下结构塑性动态大变形的影响,定量评估了由最佳刚塑性理论解与弹塑性数值模拟得到的最终挠度预测结果之间的差异。在此基础上,提出了补偿弹性效应的策略和方法,即:在已有的最佳刚塑性解预测的挠度基础上添加一个补偿项,将补偿项表达为脉冲载荷强度的效应与结构自身刚度的效应分离的变量函数,并尽量减少待定系数/指数的数量,以求表达式的简洁;根据这些原则在金属结构的主要工程应用领域内选定结构刚度和外载参数的变化范围,对固支梁和固支方板的案例实施拟合与补偿,最后得到了对梁和板增添补偿项后的简单而实用的最终挠度预测公式,其相对误差在3%的范围之内,很适合工程设计应用。文末列表给出了符号与公式的一览,并对梁和方板的结果作了综合和比较。

基于CEEMDAN-VMD-PSO-LSTM模型的桥梁挠度预测

针对桥梁运行阶段的健康状态监测,构建了CEEMDAN-VMD-PSO-LSTM模型对桥梁挠度进行预测。该模型主要分为二次模态分解平稳化、粒子群优化(PSO)算法和长短期记忆(LSTM)网络预测三大模块,共有5个步骤:①利用自适应噪声完备集合经验模态分解(CEEMDAN)算法对桥梁原始挠度序列进行初次模态分解,分解为若干本征模态分解函数(IMF);②使用样本熵(SampEn/SE)计算各IMF分量的复杂度,并通过K-means聚类为高频、中频和低频3个IMF分量;③通过变分模态分解(VMD)算法对高频IMF分量进行二次模态分解;④分别对各个IMF分量通过PSO算法得出LSTM最优超参数组合;⑤将各最优超参数分别代入LSTM模型进行训练,并将各预测结果融合为最终的预测结果。结果表明:该预测方法具有最高的预测精度,为智慧桥梁的安全监测监控提供了新的技术方法。

火灾下双钢板-混凝土组合墙抗冲击机理分析与挠度预测

双钢板-混凝土组合墙(steel-concrete composite wall,SC wall)常用于核电站、超高层等重要结构的承重构件,其在偶然荷载作用下的力学性能也是其推广应用的关键指标。为此,针对火灾下SC墙的抗冲击性能进行研究并给出相关设计建议。首先建立了SC墙在火灾与冲击耦合作用下的有限元模型,在验证模型可靠性基础上,开展了火灾下SC墙抗冲击机理的分析;然后研究了轴力、受火时间、材料强度、冲击能量与抗剪连接件形式等参数对SC墙在火灾下抗冲击性能的影响规律;最后给出了该类构件在耦合工况下跨中峰值挠度的预测公式。结果表明:随着受火时间的增加,SC墙受冲击变形模式由局部冲切逐渐转变为整体弯曲破坏;火灾下,混凝土为SC墙受冲击的主要耗能部件;混凝土强度、轴力与抗剪连接件形式对SC墙在高温下的抗冲击性能影响显著,钢板强度的影响则较小;建议的公式可较合理地预测火灾下SC墙受冲击后的跨中峰值挠度。

基于数字仿真的折弯机挠度补偿性能预测优化

通过对液压板料折弯机的结构分析,构建折弯机数字仿真性能预测模型,并将性能预测结果与试验实测值进行比对,关键部位预测精度达到96%以上。提出以上、下模位移差曲线的直线度作为折弯角度一致性的评价指标,开展液压挠度补偿力优化,基于折弯机数字仿真模型开展挠度补偿性能预测,对比不同液压挠度补偿力作用下的折弯机性能,发现当液压挠度补偿力为折弯压力的7/12时位移差曲线的直线度最高,直线度误差最大降低约40.9%;同步开展折弯试验,试验中当液压挠度补偿力设置为折弯压力的7/12时折弯角度一致性达到最小值,折弯角度一致性误差最大降低约55.5%,试验结果与预测结果保持一致。

基于长期监测数据的全寿命周期内桥梁温致挠度极值的预测

挠度是检验桥梁健康状态的重要参数之一,精准评估桥梁挠度在全寿命周期内是否超限具有重要工程意义。基于某桥挠度和温度场的长期监测数据,分析了桥梁上、下游挠度的时变监测规律,考察了挠度和季节温度之间的相关性监测规律,并通过研究桥梁挠度累积概率特性及其最佳累积分布函数,提出了同时考虑季节温度变化和日变化随机特性影响的桥梁挠度全寿命周期预测方法。结果表明:桥梁挠度具有良好的季节变化特征,且与温度之间具有良好的相关性,因此在预测全寿命周期挠度值时需要充分考虑季节温度的影响;每天内的挠度极大值呈现出平稳随机特征,因此在挠度预测时还需要充分考虑挠度极大值的随机特性影响;挠度极大值的平稳随机特征可采用概率统计特性描述,相较于Normal和Weibull分布函数,GEV分布函数可以较好地描述挠度极大值的概率统计特性;提出了挠度全寿命周期预测方法,挠度在全寿命周期内的预测值由两部分组成,即由季节温度引起的挠度值和极值随机特性引起的挠度值,其中季节温度占主要影响;计算出了桥梁挠度在全寿命周期内的预测值为190.14 mm。研究成果可为桥梁结构服役安全评估提供参考。

基于三点弯曲试验的混凝土叠合梁跨中挠度研究

预制装配式结构采用工厂预制与现场安装相结合的方式,可降低环境污染,减少资源浪费,其中装配式混凝土结构常采用叠合梁、叠合板,叠合结构整体性和抗震性能好。选取某构件厂成品叠合梁(YKL2a-XS-1)进行了三点弯曲试验,获得叠合梁荷载挠度曲线,分析叠合梁跨中挠度与荷载的关系;依据梁跨中挠度计算公式,计算叠合梁跨中挠度。叠合梁加载变形特点与适筋梁受弯性能相似,加载期间,叠合面无滑移,预制再生混凝土与后浇普通混凝土整体性较好;随着荷载增加,叠合梁的挠度增加,跨中挠度实测值增加幅度最大,跨中挠度未超过设计限值,叠合梁满足工程实际中构件的延性要求;依据梁跨中挠度计算公式得到的跨中挠度理论值高于真实值。