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.

Research on Hydrological Time Series Prediction Based on Combined Model

Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.

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

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.

Application of hydrological models in a snowmelt region of Aksu River Basin

This study simulated and predicted the runoff of the Aksu River Basin, a typical river basin supplied by snowmelt in an arid mountain region, with a limited data set and few hydrological and meteorological stations. Two hydrological models, the snowmelt-runoff model （SRM） and the Danish NedbФr-AfstrФmnings rainfall-runoff model （NAM）, were used to simulate daily discharge processes in the Aksu River Basin. This study used the snow-covered area from MODIS remote sensing data as the SRM input. With the help of ArcGIS software, this study successfully derived the digital drainage network and elevation zones of the basin from digital elevation data. The simulation results showed that the SRM based on MODIS data was more accurate than NAM. This demonstrates that the application of remote sensing data to hydrological snowmelt models is a feasible and effective approach to runoff simulation and prediction in arid unguaged basins where snowmelt is a major runoff factor.

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.

Predicting coastal morphological changes with empirical orthogonal function method

In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.

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.

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.

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.

山区县域洪水预测研究--以阳朔县为例

山区县域的洪水灾害是我国防洪减灾体系中的薄弱环节。为提升洪水预测的准确性,以桂林阳朔县为例,基于HEC-HMS和InfoWorksICM模拟平台,构建田家河流域的水文水动力模型。利用2017—2022年间多场洪水数据对模型进行率定和验证。在此基础上,根据田家河与漓江的洪峰遭遇,预测12种不同洪水重现期组合的情景工况,并对每种情景下的淹没耕地面积、村庄数量、旧城区淹没面积以及新城区淹没面积进行统计和分析。研究结果表明:(1)遇龙河沿岸受灾情况受其上游暴雨洪水的影响,田家河流域洪水重现期TTJH=50a时,淹没耕地面积将超过8.15km2,沿途受影响村庄超过27个;(2)旧城区主要受漓江过境洪水的影响,漓江洪水重现期TLJ=20a时,漓江水位漫堤行洪,西街将处于1~2m的水深之中;(3)新城区对田家河流域及漓江洪水都表现出较高的敏感性,当洪水等级超过TTJH=10a且TLJ=20a或者TTJH=20a且TLJ=10a时,新城区将会遭遇较严重的洪灾。研究结果可为山区县域洪水预测提供参考。

相空间重构后矿井涌水量序列地质学含义及其应用研究

目的为了确定相空间重构矿井涌水量序列的地质学含义并提高涌水量预测精度,方法以王行庄矿为例,在涌水量序列相空间重构后,对重构后相空间列向量与涌水量主控因素进行相关性分析,并在此基础上建立混沌理论与人工神经网络耦合(Chaos-ENN)的涌水量预测模型。结果结果表明:相空间的嵌入维数等于矿井涌水量主控因素个数;相空间的第1,2,4,5,6列向量分别与C_(2)tL_(7-8)含水层水位埋深、O_(2)m+Є_(3)ch含水层水位埋深、采空区面积、C_(2)tL_(1-4)含水层水位埋深、开拓长度具有较高的相关性,第3列与不易量化的其他综合因素有关;构建的Chaos-ENN涌水量预测模型在王兴庄矿的预测精度达到97.91%。结论涌水量序列重构后相空间的列向量具有明确的地质学含义。利用混沌理论可以量化涌水量预测模型中ENN输入层的个数及取值,所以仅需涌水量序列值就可以建立矿井涌水量预测的Chaos-ENN模型,该模型解决了涌水量预测中存在的主控因素难以确定和不易量化的难题,且预测精度高,具有较高的推广价值。

基于物联网和GCNN-LSTM的河流水文预测方法

针对河流水文存在预测精度不高的问题,利用物联网技术设计了分布式的降雨和水文信息自动采集系统,并提出了一种基于图卷积神经网络和长短期记忆网络模型对河流水位和径流量进行预测的方法;首先通过分析确定了影响河流水文的主要因素,将流域范围内的降雨量信息组成网格化的二维图形矩阵;然后提出了GCNN-LSTM预测模型,将含有降雨信息的二维图形矩阵作为网络模型的输入,获取该流域内降雨与水文变化的时空分布特征;最后采用所提出的GCNN-LSTM预测模型对河南省周口市段颍河的历史水文数据进行训练,再利用训练后的网络对测试集数据进行预测,得到了较高精度的径流量和水位结果,径流量预测结果的RMSE、MAPE和MAE分别仅为17.09 m^(3)/s、1.68%和8.57 m^(3)/s,水位预测结果的RMSE、MAPE和MAE分别仅为0.32 m、0.65%和0.29 m,与其他几种预测方法相比表现出了优越性,对科学合理利用水资源和防洪减灾具有重要意义。

基于TCN-BiLSTM与LSTM模型对比预测北洛河径流

【目的】本研究旨在探究TCN-BiLSTM耦合模型与传统LSTM模型在径流模拟预测中的性能,为洪水风险管理和区域水资源规划提供准确有效的径流预测模型。【方法】以北洛河流域为研究区,基于双向长短期记忆网络(BiLSTM)和时域卷积网络(TCN)建立一种新的径流预测耦合模型TCN-BiLSTM。利用相关性分析,筛选预测径流的输入因子,确定4种不同的输入方案应用于TCN-BiLSTM耦合模型和传统LSTM模型,每个模型分别预测1、2、3 d的径流量。采用平均绝对误差(MAE)、均方根误差(RMSE)和拟合优度(R^(2))来评估模型的预测性能。【结果】(1)TCN-BiLSTM耦合模型整体预测性能优于LSTM模型,TCN-BiLSTM模型R^(2)达到0.91,高于LSTM的0.89。相比于LSTM,TCN-BiLSTM对于峰值和突变点的捕捉能力更强,对于波动大的复杂数据预测效果更优;(2)在针对未来1~3 d径流量预测中,随着预见期的延长,4种方案下TCN-BiLSTM和LSTM模型的预测效果均有所下降,相较于预测1 d,预测3 d的TCNBiLSTM和LSTM模型的R^(2)分别平均下降了0.17和0.14,RMSE分别平均增大了4.59和4.40,MAE分别平均增大了1.26和1.31;(3)在4种输入方案里,日累积降水量和日径流量作为输入变量时,模型的预测效果最好。降水数据的加入使得TCN-BiLSTM和LSTM模型相较于单一日径流数据作为输入变量时,1、2、3 d径流量预测的R^(2)分别提高15%、14%、6%和18%、14%和1%。【结论】TCN-BiLSTM耦合模型和LSTM模型R^(2)均能达到0.85以上,TCN-BiLSTM模型R^(2)较LSTM提高了2%。对比来看,TCN-BiLSTM模型在拟合洪水过程中表现更为优异,对于汛期的预测性能优于非汛期。输入变量对模型的影响较大,有效且高质量的气象数据能够提高模型的预测性能。

黄河上游径流量对气候变化的响应和预测模型

全球气候变化下的黄河上游径流量变化及其预测是流域生态水文研究的热点之一。利用2008—2020年流域内48个气象站的监测数据和头道拐水文站径流数据,系统分析了黄河上游流域径流量与气候要素变化的关系,并建立径流量预测模型。结果表明,单一月份的月平均气温、月降水量和月蒸散发量多未表现出显著的年际变化趋势,但年平均气温和年降水量显著上升,而年蒸散发量也有上升趋势但统计不显著;多数单一月份的月径流量和年径流量均在年际上呈显著增加趋势。在月平均气温、月降水量、月蒸散发量均与月径流量显著相关的基础上,基于气候要素建立的黄河上游流域径流量预测模型具有良好的预测效果。

基于SWAT模型的格尔木河上游分布式水文模拟和径流预测

研究格尔木河流域水文循环过程并预测未来流域水资源的变化特征,对地区生态环境保护和下游盐湖矿产资源可持续开发利用具有重要意义。选取格尔木水文站以上区域构建SWAT(Soil and Water Assessment Tool)分布式水文模型。采用大气同化数据集为气象驱动,联合区域内纳赤台和格尔木水文站的实测月尺度径流数据进行参数的率定和验证。在率定期和验证期内,纳什效率系数、确定性系数和相对偏差系数均达到了良好的标准,表明SWAT模型在格尔木河高寒山区流域水文过程模拟中具有较好的适用性。研究表明流域降水量偏少,地表径流量、壤中流量与降水量的变化趋势具有较好的一致性,降水量年际变化中蒸散发量为主要消耗量,占40.26%。根据未来气候预测模型RegCM4.6,预测路径浓度RCP2.6、RCP4.5和RCP8.53种情景下格尔木河未来40年径流量呈增加趋势。3种情景下的年平均径流量较基准期(2006—2018年)分别增加了7.63%、11.01%、15.96%;随着温室气体排放浓度的增加,径流量呈现出增加趋势,特别是夏秋季增幅较大。短时间内径流量增大可能会引发格尔木市洪涝灾害,破坏盐湖企业生产设施;但若将洪水资源进行调控和利用,不仅防范了洪涝灾害,同时也利于解决盐湖企业日渐增大的用水需求难题。

基于CMIP6模式的叶尔羌河流域未来水文干旱风险预估

全球变暖导致干旱等极端事件频发,严重威胁生态安全和社会经济可持续发展,尤其是干旱区这一气候响应敏感区。本研究基于流域站点观测气象、水文等数据和第六次国际耦合模式比较计划(CMIP6)全球气候模式数据,应用分布式水文模型HEC-HMS模型,模拟并预估了塔里木河重要源流——叶尔羌河流域历史(1986—2014年)与未来(2015—2100年)径流变化趋势以及水文干旱风险。结果表明:(1)HEC-HMS模型在干旱区流域适用性较好,未来3个共享社会经济路径(SSPs)下叶尔羌河流域年径流和标准化径流指数(SRI)都将呈显著增加趋势(P<0.1),SRI上升速率约0.13~0.27·(10a)^(-1)。(2)构建并对比了流域历史和未来时期4个干旱特征变量的边缘分布,未来干旱持续时间、干旱强度和烈度峰值都明显大于历史时期,干旱程度可能将不断加剧。(3)相较于历史时期,叶尔羌河流域未来水文干旱发生的联合概率将持续减小,且未来干旱事件的重现期都将有不同程度的延长。研究结果可为叶尔羌河流域水资源管理与规避水文干旱风险措施的制定提供科学参考。

极端气候事件对洞庭湖水文连通性变化的影响

为揭示洞庭湖近十几年水文连通性的变化特征并对其未来变化进行预测,研究极端气候事件在洞庭湖水文连通性变化中作出的贡献,使用水文连通性指数法、ETCCDI极端气候指数、Hurst指数以及数理统计分析方法进行研究。研究结果表明:洞庭湖水文连通性整体呈现夏季>秋季>春季>冬季的特征,夏季、秋季、春季和冬季的整体连通性指数均值分别为0.95、0.88、0.81和0.63,且洞庭湖的水文连通性在近30年比较稳定;经持续性预测发现洞庭湖水文连通性Hurst指数均大于0.5,表示其在没有人类活动干扰的情况下会在未来呈延续下降的趋势;洞庭湖水文连通性指数随着水位增加逐渐增加且增速逐渐放缓,水位增加对其具有正向影响的边际递减效应;极端降水事件对洞庭湖水文连通性具有较为明显的正向影响,而极端气温事件对洞庭湖水文连通性影响作用较小。研究结果有助于充分认识洞庭湖水资源演变规律,对保障洞庭湖流域水资源安全具有重要的理论和现实意义。

基于CEEMDAN-LightGBM模型的洪水预测研究

为了应对暴雨可能引发的洪涝灾害风险,基于黄河利津水文站监测的水文等数据,以LightGBM为基准模型,运用经自适应噪声完备集合经验模态分解(CEEMDAN)算法优化后的CEEMDAN-LightGBM模型对水位进行预测,并将其与长短期记忆网络(LSTM)模型、LightGBM模型的预测效果进行对比。以2个气候条件不同的黄河水文站(利津、花园口)的水文数据为原始数据集输入CEEMDAN-LightGBM模型,验证模型的适应性和稳定性。结果表明:CEEMDAN-LightGBM模型在水位预测方面表现出优越的性能,相较于LSTM、LightGBM模型,该模型的E_(MA)分别减小了46.08%、9.95%,E_(RMS)分别减小了33.01%、43.01%,E_(MAP)分别减小了94.99%、3.82%,R^(2)分别增大了30.48%、7.58%。CEEMDAN-LightGBM模型还能预测流量这一重要水文特征,为模型预测洪水发生提供更有力的判断依据。对比CEEMDAN-LightGBM模型预测花园口水文站与利津水文站的水位和流量效果,除预测两站水位的E_(MAP)值相差23.64%外,E_(MA)值、E_(MAP)和E_(RMS)值相差均不超过10%,R^(2)相差不超过2%。