Comparison of different severity scores in correlating hemoglobin levels with the severity of hepatic decompensation: An observational study

BACKGROUND Chronic liver disease is a growing global health problem,leading to hepatic decompensation characterized by an array of clinical and biochemical complic-ations.Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score,model of end-stage liver disease(MELD)score,and MELD-Na score.Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes.Although studies have explored anemia in liver disease,few have investigated the correlation of hemoglobin level with the severity of hepatic decompensation.AIM To determine the relationship between hemoglobin levels and the severity of decompensated liver disease and comparing the strength of this correlation using the Child-Pugh,MELD,and MELD-Na scores.METHODS This cross-sectional study was conducted at a tertiary care hospital with 652 decompensated liver disease patients enrolled in the study.Data was collected on demographics,clinical history,and laboratory findings,including hemoglobin levels,bilirubin,albumin,prothrombin time(international normalized ratio),sodium,and creatinine.The Child-Pugh,MELD,and MELD-Na scores were calculated.Statistical analysis was performed using Statistical Package for the Social Sciences version 26,and correlations between hemoglobin levels and severity scores were assessed using Spearman's correlation coefficient.RESULTS The study included 405 males(62.1%)and 247 females(37.9%)with an average age of 58.8 years.Significant inverse correlations were found between hemoglobin levels and Child-Pugh,MELD,and MELD-Na scores(P<0.01),with the MELD scoring system being the strongest correlator among all.One-way analysis of variance revealed significant differences in hemoglobin levels across the severity groups of each scoring system(P=0.001).Tukey's post hoc analysis confirmed significant internal differences among each severity group.CONCLUSION Understanding the correlation between hemoglobin and liver disease severity can improve patient management by offering insights into prognosis and guiding treatment decisions.

A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage

Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.

A method to calculate design tide levels on the basis of numerical model of tidal current and its application

In order to determine the design tide levels in the areas without measured tide level data, especially in the areas where it is difficult to measure tidal levels, a calculation method based on a numerical model of tidal current is proposed. The essentials of the method are described, and its application is illustrated with an example. The results of the application show that the design tide levels calculated by the method are close to those determined by long-time measured tide level data, and its calculation precision is high, so it is feasible to use the method to determine the design tide levels in the areas.

Methodology for local correction of the heights of global geoid models to improve the accuracy of GNSS leveling

At present,one of the methods used to determine the height of points on the Earth’s surface is Global Navigation Satellite System(GNSS)leveling.It is possible to determine the orthometric or normal height by this method only if there is a geoid or quasi-geoid height model available.This paper proposes the methodology for local correction of the heights of high-order global geoid models such as EGM08,EIGEN-6C4,GECO,and XGM2019e_2159.This methodology was tested in different areas of the research field,covering various relief forms.The dependence of the change in corrected height accuracy on the input data was analyzed,and the correction was also conducted for model heights in three tidal systems:"tide free","mean tide",and"zero tide".The results show that the heights of EIGEN-6C4 model can be corrected with an accuracy of up to 1 cm for flat and foothill terrains with the dimensionality of 1°×1°,2°×2°,and 3°×3°.The EGM08 model presents an almost identical result.The EIGEN-6C4 model is best suited for mountainous relief and provides an accuracy of 1.5 cm on the 1°×1°area.The height correction accuracy of GECO and XGM2019e_2159 models is slightly poor,which has fuzziness in terms of numerical fluctuation.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Research on the Digital Economy's Impact on China's Economic Growth:Based on the Variations in Urbanization Levels Across China's Eastern,Central,and Western Regions and Sectoral Heterogeneity

This paper uses inter-provincial panel data from 2011 to 2017,a linear regression model,and a threshold model to conduct empirical analyses of the impact of the digital economy on China's overall economic growth and the three main sectors of industry.The paper then investigates the impact and effects the digital economy has had on the economic growth of the three main sectors of industry in China's eastern,central,and western regions.Finally,the paper investigates the most significant differences among the various regions and the threshold effects of urbanization levels on the relationship between the digital economy and economic growth.The findings indicate a significantly positive correlation between the digital economy and regional economic growth.Moreover,geographical factors notably influence this correlation.The digital economy exerts a positive effect on all sectors of industry.It may not substantially impact industrial development in regions with highly developed infrastructure.Regarding the other regions,the digital economy exhibits varying degrees of impact due to the differences in the specific indicators.The conclusion drawn by the threshold model is that the magnitude of the threshold effect correlates with geographic factors.No threshold effect was observed in the eastern region,while the threshold effect occurred in the central region when the urbanization levels for the provinces were below 0.6645.Similarly,the threshold effect was noted in the western region when the urbanization level was below 0.3931.Considering all of this,the study also offers policy recommendations that will help balance the regional development of digital economies,accelerate the digital transformation of traditional industries,enhance digital infrastructure construction,refine the formulation and implementation of data policy,and establish relevant incentive mechanisms.

Inclusive Multiple Models(IMM)for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.

Modeling Bridge Condition Levels in the United States

The objectives of this paper are to （I） quantify the effects of age and other key factors on bridge deterioration rates, and （2） provide bridge managers with strategic forecasting tools. A model for forecasting substructure conditionisestimated from the National Bridge Inventory that includes the effects of bridge material, design load, structural type, operating rating, average daily traffic, water, and the state where the bridge is located. Bridge age is the quantitative independent variable. The relationship between age and substructure condition is a fourth-order polynomial. Some of the key findings are： （I） a bridge substructure is expected to lose from 0.52 to 0.11 rating points per decade as it ages from 10 to 70 years; （2） levels of deterioration increase significantly as the material changes from concrete, to steel, to timber; （3） slab bridges have lower levels of deterioration than other structures; （4） bridges that span water have lower condition ratings; （5） bridges with higher operating ratingshave higher condition ratings; and （6） substructure condition ratings vary significantly among states.

Seasonal responses of monthly mean sea levels in the Bohai Sea to hydrometeorological forcing and their double screening regression models

-In this paper, the maximum entropy spectral, the cross-spectral and the frequency response analyses are madeon the basis of the data of monthly mean sea levels at coastal stations in the Bohai Sea during 1965-1986. The results show that the annual fluctuations of the monthly mean sea levels in the Bohai Sea are the results of the coupling response of seasonal variations of the marine hydrometeorological factors. Furthermore, the regression prediction equation is obtained by using the double screening stepwise regression analysis method . Through the prediction test , it is proved that the obtained results are desirable.

Storyline Extraction of Document-Level Events Using Large Language Models

This article proposes a document-level prompt learning approach using LLMs to extract the timeline-based storyline. Through verification tests on datasets such as ESCv1.2 and Timeline17, the results show that the prompt + one-shot learning proposed in this article works well. Meanwhile, our research findings indicate that although timeline-based storyline extraction has shown promising prospects in the practical applications of LLMs, it is still a complex natural language processing task that requires further research.

基于多尺度统计形状模型的Levelset分割方法

提出并建立了一种基于小波分析的多尺度统计模型,将该统计模型作为先验知识引入Mumford-Shah能量约束函数,从而指导水平集函数进行图像分割。实验表明,当对拓扑结构复杂的医学图像进行分割时,该方法具有明显的效果,同时分割速度和精度都得到了明显改善。

构建高校金融学课程教学“3-levels”模式——基于调查问卷数据的实证研究

以金融学为研究对象,基于教学问卷调查数据,对金融学专业和非金融学专业学生及金融学课程教学情况进行了统计分析,同时结合金融学的课程特点和金融人才需求,建立了适合金融学教学的知识结构扩展型、教学手段多样型、学习成果展示型的"3-levels"教学模式。

Sea level rise projection in the South China Sea from CMIP5 models

Future potential sea level change in the South China Sea （SCS） is estimated by using 24 CMIP5 models under different representative concentration pathway （RCP） scenarios. By the end of the 21st century （2081–2100 relative to 1986–2005）, the multimodel ensemble mean dynamic sea level （DSL） is projected to rise 0.9, 1.6, and 1.1 cm under RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, resulting in a total sea level rise （SLR） of 40.9, 48.6, and 64.1 cm in the SCS. It indicates that the SCS will experience a substantial SLR over the 21st century, and the rise is only marginal larger than the global mean SLR. During the same period, the steric sea level （SSL） rise is estimated to be 6.7, 10.0, and 15.3 cm under the three scenarios, respectively, which accounts only for 16%, 21% and 24% of the total SLR in this region. The changes of the SSL in the SCS are almost out of phase with those of the DSL for the three scenarios. The central deep basin has a slightly weak DSL rise, but a strong SSL rise during the 21st century, compared with the north and southwest shelves.

Greenland Ice Sheet Contribution to Future Global Sea Level Rise based on CMIP5 Models

Sea level rise （SLR） is one of the major socioeconomic risks associated with global warming. Mass losses from the Greenland ice sheet （GrIS） will be partially responsible for future SLR, although there are large uncertainties in modeled climate and ice sheet behavior. We used the ice sheet model SICOPOLIS （Simulation COde for POLythermal Ice Sheets） driven by climate projections from 20 models in the fifth phase of the Coupled Model Intercomparison Project （CMIP5） to estimate the GrlS contribution to global SLR. Based on the outputs of the 20 models, it is estimated that the GrIS will contribute 0-16 （0-27） cm to global SLR by 2100 under the Representative Concentration Pathways （RCP） 4.5 （RCP 8.5） scenarios. The projected SLR increases further to 7-22 （7-33） cm with 2~basal sliding included. In response to the results of the multimodel ensemble mean, the ice sheet model projects a global SLR of 3 cm and 7 cm （10 cm and 13 cm with 2~basal sliding） under the RCP 4.5 and RCP 8.5 scenarios, respectively. In addition, our results suggest that the uncertainty in future sea level projection caused by the large spread in climate projections could be reduced with model-evaluation and the selective use of model outputs.

New partial cooperation model for bilevel programming problems

Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.

High level architecture evolved modular federation object model

To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model （FOM）, a modular FOM is proposed by high level architecture （HLA） evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model （BOM） is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.

Model test of the influence of cyclic water level fluctuations on a landslide

Many landslides in reservoir areas continuously deform under cyclic water level fluctuations due to reservoir operations. In this paper,a landslide model, developed for a typical colluvial landslide in the Three Gorges Reservoir area, is used to study the effect of cyclic water level fluctuations on the landslide. Five cyclic water level fluctuations were implemented in the test, and the fluctuation rate in the last two fluctuations doubled over the first three fluctuations. The pore water pressure and lateral landslide profiles were obtained during the test. A measurement of the landslide soil loss was proposed to quantitatively evaluate the influence of water level fluctuations. The test results show that the first water level rising is most negative to the landslide among the five cycles. The fourth drawdown with a higher drawdown rate caused further large landslide deformation. An increase of the water level drawdown rate is much more unfavorable to the landslide than an increase of the water level rising rate. In addition, the landslide was found to have an adaptive ability to resist subsequent water level fluctuations after undergoing large deformation during a water level fluctuation. The landslide deformation and observations in the field were found to support the test results well.

Incorporation of GIS Based Program into Hydraulic Model for Water Level Modeling on River Basin

Water resources management usually requires that hydraulic, ecological, and hydrological models be linked. The Hy- drologic Engineering Center River Analysis System (HEC-RAS) hydraulic model and the Hydrologic Engineering Center Geospatial River Analysis System (HEC-GEORAS), imitates flow and water profiles in the Neka river basin’s downstream flood plain. Hydrograph phases studied during the flood seasons of 1986-1999 and from 2002-2004 were used to calibrate and verify the hydraulic model respectively. Simulations of peak flood stages and hydrographs’ evaluations are congruent with studies and observations, with the former showing mean square errors between 4.8 - 10 cm. HECRAS calculations and forecast flood water levels. Nash-Sutcliffe effectiveness (CR3) is more than 0.92 along with elevated levels of water which were created with some effectiveness (CR5) of 0.94 for the validation period. The coupled two models show good performance in the water level modeling.

Impacts of the Lowest Model Level Height on Tropical Cyclone Intensity and Structure

Variable thicknesses in the lowest half-ηmodel level （LML） are often used in atmospheric models to compute surface diagnostic fields such as surface latent and sensible heat fluxes.The effects of the LML on simulated tropical cyclone （TC）evolution were investigated in this study using the Weather Research and Forecasting （WRF） model.The results demonstrated notable influences of the LML on TC evolution when the LML was placed below 12 m.The TC intensification rate decreased progressively with a lowering of the LML,but its ultimate intensity change was relatively small.The maximum 10-m winds showed different behavior to minimum sea level pressure and azimuthally-averaged tangential winds,and thus the windpressure relationship was changed accordingly by varying the LML.The TC circulation was more contracted in association with a higher LML.Surface latent heat fluxes were enhanced greatly by elevating the LML,wherein the wind speed at the LML played a dominant role.The changes in the wind speed at the LML were dependent not only on their profile differences,but also the different heights they were taken from.Due to the enhanced surface heat fluxes,more intense latent heat release occurred in the eyewall,which boosted the storm＇s intensification.A higher LML tended to produce a stronger storm,and therefore the surface friction was reinforced,which in turn induced stronger boundary layer inflow together with increased diabatic heating.

Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

The ensemble optimal interpolation （EnOI） is applied to the regional ocean modeling system （ROMS） with the ability to assimilate the along-track sea level anomaly （TSLA）. This system is tested with an eddy-resolving system of the South China Sea （SCS）. Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation （RMSD） of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error （AGE） perform better than no AGE error （NoAGE） when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models.