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.

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.

Factors influencing pore-pressure prediction in complex carbonates based on effective medium theory

A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.

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.

The Study of Medium- and Short-term Prediction for Artux Earthquake (M_S=6.9) and Usunan Earthquake (M_S=5.8)

In this paper, the process of medium- and short-term prediction (submitted in special cards) of the Artux earthquake (MS=6.9) and the Usurian earthquake (MS=5.8) in Xinjiang area, is introduced. The imminent seismic risk regions are judged based on long- and medium-term seismic risk regions and annual seismic risk regions determined by national seismologic analysis, combined with large seismic situation analysis. We trace and analyze the seismic situation in large areas, and judge principal risk regions or belts of seismic activity in a year, by integrating the large area’s seismicity with geodetic deformation evolutional characteristics. As much as possible using information, we study synthetically observational information for long-medium- and short-term (time domain) and large-medium -small dimensions (space domain), and approach the forecast region of forthcoming earthquakes from the large to small magnitude. A better effect has been obtained. Some questions about earthquake prediction are discussed.

The idea and project of the “Medium－Scale Experiment Field for Earthquake Prediction”──Research on observations and applications of mining earthquake in Mentougou Coal Mine

A brief account of the development of the research on mining earthquakes and the general situation of the Mentougou Coal Mine medium scale experiment field for earthquake prediction and the project of monitor and prediction is given. The differences of waveforms between mining earthquakes and natural earthquakes is discussed. The magnitude frequency distribution of the 79 000 mining earthquakes of over M L1.0 from 1984 to 1995 is summarized . Finally, taking PH and PV, the principal compressive stress components of the focal mechanism of the mining earthquakes, as the criteria, analyses the stress background of the 12 large mining earthquakes.

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.

Research on TSIP method for medium－term earthquake prediction

This paper introduces the space increased probability of strong earthquakes (SIP)-a new design based on the algorithm CN of time increased probability of strong earthquake (TIP). The authors have done a prediction research passing in review of eight strong earthquakes with M>6 in the last 20 years in East China. The result shows that six of the eight strong earthquakes were in the space-time domain of the time and space probability of strong earthquake (TSIP) prediction. The prediction accuracy is 75%, the space-time domain rate of the TSIP precaution is 5%, the diagnosed value of R is 0. 70. So the TSIP as a method of medium-term earthquake prediction has good practicality, efficiency and prospects of applying.

“Small earthquake modulation window” and its applications in medium－term prediction

Based on the observations of many years, it has been found that “small earthquake modulation windows” exist inthe situation of some special geological structures, which respond sensitively to the variations of regional stressfields and the activities of earthquake swarms greater than moderate strong magnitude, and can supply some precursory information. More than two “small earthquake modulation windows” can also provide a general orientation of the first main earthquake of a earthquake cluster. Compared with “seismic window” based on frequency itis no doubt that the “modulation-window” has an unique characteristic of applicational significance to mediumterm earthquake prediction with a time scale of two or three years.

Seismicity Quantification and Its Application to Medium-term Earthquake Prediction

Bed on the analysis of each parameter describing seismicity,we think A(b)-value can betterquantitatively describe the feature of the enhancement and quietness of seismicity in this paper. Thedata of moderate or small earthquakes during 1972～1996 in North China are used in space scanningof A(b)-value. The result shows that 2～3 years before most strong earthquakes there wereObviously anomaly zones of A(b)-value with very good prediction effect. Some problems about themedium-term prediction by using A(b)-value are also discussed.

Separation density prediction of geldart A^(-)dense medium in gas-solid fluidized bed coal beneficiators

Gas-solid Fluidized Bed Coal Beneficiator(GFBCB)process is a crucial dry coal beneficiation fluidization technology.The work employs the GFBCB process alongside a novel Geldart A^(-)dense medium,consisting of Geldart A magnetite particles and Geldart C ultrafine coal,to separate small-size separated objects in the GFBCB.The effects of various operational conditions,including the volume fraction of ultrafine coal,the gas velocity,the separated objects size,and the separation time,were investigated on the GFBCB's separation performance.The results indicated that the probable error for 6∼3 mm separated objects could be controlled within 0.10 g/cm^(3).Compared to the traditional Geldart B/D dense medium,the Geldart A/A^(-)dense medium exhibited better size-dependent separation performance with an overall probable error 0.04∼0.12 g/cm^(3).Moreover,it achieved a similar separation accuracy to the Geldart B/D dense medium fluidized bed with different external energy for the small-size object beneficiation.The work furthermore validated a separation density prediction model based on theoretical derivation,available for both Geldart B/D dense medium and Geldart A/A^(-)dense medium at different operational conditions.

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.

A Comparison Study of the Methods of Conditional Nonlinear Optimal Perturbations and Singular Vectors in Ensemble Prediction

The authors apply the technique of conditional nonlinear optimal perturbations （CNOPs） as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic （QG） model in a perfect-model scenario. Ensemble forecasts for the medium range （14 days） are made from the initial states perturbed by CNOPs and singular vectors （SVs）. 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.

Earthquake prediction from China's mobile gravity data

The relation between plate tectonics and earthquake evolution is analyzed systematically on the basis of 1998-2010 absolute and relative gravity data from the Crustal Movement Observation Network of China. Most earthquakes originated in the plate boundary or within the fault zone. Tectonic deformation was most intense and exhibited discontinuity within the tectonically active fault zone because of the differential movement; the stress accumulation produced an abrupt gravity change, which was further enhanced by the earthquake. The gravity data from China's Mainland since 2000 obviously reflected five major earthquakes （Ms 〉 7）, all of which were better reflected than before 2000. Regional gravity anomalies and a gravity gradient change were observed in the area around the epicenter about 2 or 3 years before the earthquake occurred, suggesting that gravity change may be a seismic precursor. Furthermore, in this study, the medium-term predictions of the Ms7.3 Yutian, Ms8.0 Wenchuan, and Ms7.0 Lushan earthquakes are analytically pre- sented and evaluated, especially to estimate location of earthquake.

Preliminary Study on Probabilistic Prediction of Seismic Hazard in a Period of 10 Years

Many uncertainty factors need be dealt with in the prediction of seismic hazard for a 10-year period.Restricted by these uncertainties,the result of prediction is also uncertain to a certain extent,so the probabilistic analysis method of seismic hazard should be adopted.In consideration of the inhomogeneity of the time,location,and magnitude of future earthquakes and the probabilistic combination of the background of long-term seismic hazard(geology,geophysical field,etc.)and the precursors of earthquake occurrence,a model of probabilistic prediction of seismic hazard in a period of 10 years s proposed.Considering the inhomogeneity of data and earthquake precursors for different regions in China,a simplified model is also proposed in order to satisfy the needs of different regions around the country.A trial in North China is used to discuss the application of the model.The method proposed in this paper can be used in the probabilistic prediction of seismic hazard in a period of 10 years.According to

The new method of prediction on mining subsidence and deformation

A new probability function of mining overlying strata and subsidence is put forward that has a general statistical significance based on the ideal stochastic medium displacement model. It establishes a new system of prediction on horizontal mining subsidence and deformation, which gives a new method for prediction on mining subsidence and deformation.

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.

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.