Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time data.Firstly, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

Development and application of a two-dimensional water quality model for the Daqinghe River Mouth of the Dianchi Lake

Water quality models are important tools to support the optimization of aquatic ecosystem rehabilitation programs and assess their efficiency. Basing on the flow conditions of the Daqinghe River Mouth of the Dianchi Lake, China, a two-dimensional water quality model was developed in the research. The hydrodynamics module was numerically solved by the alternating direction iteration （ADI） method. The parameters of the water quality module were obtained through the in situ experiments and the laboratory analyses that were conducted from 2006 to 2007. The model was calibrated and verified by the observation data in 2007. Among the four modelled key variables, i.e., water level, COD （in CODcr）, NH4＋-N and PO43-P the minimum value of the coefficient of determination （COD） was 0.69, indicating the model performed reasonably well. The developed model was then applied to simulate the water quality changes at a downstream cross-section assuming that the designed restoration programs were implemented. According to the simulated results, the restoration programs could cut down the loads of COD and PO43-P about 15%. Such a load reduction, unfortunately, would have very little effect on the NH4^＋-N removal. Moreover, the water quality at the outlet cross-section would be still in class V （3838-02）, indicating more measures should be taken to further reduce the loads. The study demonstrated the capability of water quality models to support aquatic ecosystem restorations.

Simulation of two-dimensional interior ballistics model of solid propellant electrothermal-chem ical launch with discharge rod plasma generator

Instead of the capillary plasma generator(CPG),a discharge rod plasma generator(DRPG)is used in the30 mm electrothermal-chemical(ETC)gun to improve the ignition uniformity of the solid propellant.An axisymmetric two-dimensional interior ballistics model of the solid propellant ETC gun(2D-IB-SPETCG)is presented to describe the process of the ETC launch.Both calculated pressure and projectile muzzle velocity accord well with the experimental results.The feasibility of the 2D-IB-SPETCG model is proved.Depending on the experimental data and initial parameters,detailed distribution of the ballistics parameters can be simulated.With the distribution of pressure and temperature of the gas phase and the propellant,the influence of plasma during the ignition process can be analyzed.Because of the radial flowing plasma,the propellant in the area of the DRPG is ignited within 0.01 ms,while all propellant in the chamber is ignited within 0.09 ms.The radial ignition delay time is much less than the axial delay time.During the ignition process,the radial pressure difference is less than 5 MPa at the place 0.025 m away from the breech.The radial ignition uniformity is proved.The temperature of the gas increases from several thousand K(conventional ignition)to several ten thousand K(plasma ignition).Compare the distribution of the density and temperature of the gas,we know that low density and high temperature gas appears near the exits of the DRPG,while high density and low temperature gas appears at the wall near the breech.The simulation of the 2D-IB-SPETCG model is an effective way to investigate the interior ballistics process of the ETC launch.The 2D-IB-SPETC model can be used for prediction and improvement of experiments.

Performance assessment of two-dimensional hydraulic models for generation of flood inundation maps in mountain river basins

Hydraulic models for the generation of flood inundation maps are not commonly applied in mountain river basins because of the difficulty in modeling the hydraulic behavior and the complex topography. This paper presents a comparative analysis of the performance of four twodimensional hydraulic models (HEC-RAS 2D, Iber 2D, Flood Modeller 2D, and PCSWMM 2D) with respect to the generation of flood inundation maps. The study area covers a 5-km reach of the Santa B-arbara River located in the Ecuadorian Andes, at 2330 masl, in Gualaceo. The model's performance was evaluated based on the water surface elevation and flood extent, in terms of the mean absolute difference and measure of fit. The analysis revealed that, for a given case, Iber 2D has the best performance in simulating the water level and inundation for flood events with 20- and 50-year return periods, respectively, followed by Flood Modeller 2D, HEC-RAS 2D, and PCSWMM 2D in terms of their performance. Grid resolution, the way in which hydraulic structures are mimicked, the model code, and the default value of the parameters are considered the main sources of prediction uncertainty.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

HIGH RESOLUTION PARAMETRIC MODELLING FOR TWO-DIMENSIONAL RADAR TARGET USING PRONY ALGORITHM

On the conditions of low-resolution radar, a parametric model for two-dimensional radar target is described here according to the theory of electromagnetic scattering and the geometrical theory of diffraction. A high resolution estimation algorithm to extract the model parameters is also developed by building the relation of the scattering model and Prony model. The analysis of Cramer-Rao bound and simulation show that the method here has better statistical performance. The simulated analysis also indicates that the accurate extraction of the diffraction coefficient of scattering center is restricted by signal to noise ratio, radar center frequency and radar bandwidth.

A Numerical Algorithm Based on Quadratic Finite Element for Two-Dimensional Nonlinear Time Fractional Thermal Diffusion Model

In this article,a high-order scheme,which is formulated by combining the quadratic finite element method in space with a second-order time discrete scheme,is developed for looking for the numerical solution of a two-dimensional nonlinear time fractional thermal diffusion model.The time Caputo fractional derivative is approximated by using the L2-1formula,the first-order derivative and nonlinear term are discretized by some second-order approximation formulas,and the quadratic finite element is used to approximate the spatial direction.The error accuracy O(h3+
t2)is obtained,which is verified by the numerical results.

Global Two-Dimensional Chemistry Model and Simulation of Atmospheric Chemical Composition

A global two-dimensional zonally averaged chemistry model is developed to study the chemi-cal composition of atmosphere. The region of the model is from 90°S to 90°N and from the ground to the altitude of 20 km with a resolution of 5° x 1 km. The wind field is residual circulation calcu-lated from diabatic rate. 34 species and 104 chemical and photochemical reactions are considered in the model. The sources of CH4, CO and NOx, which are divided into seasonal sources and non-seasonal sources, are parameterized as a function of latitude and time. The chemical composi-tion of atmosphere was simulated with emission level of CH4, CO and NOx in 1990. The results are compared with observations and other model results, showing that the model is successful to simu-late the atmospheric chemical composition and distribution of CH4. Key words Global two-dimensional chemistry model - Atmospheric composition - Emission This work was supported by the State Key Program for basic research “ Climate Dynamics and Cli-mate Prediction Theory” (Pandeng-yu-21).The authors would like to express their thanks to the National Oceanic and Atmospheric Administration (NOAA), Climate Monitoring and Diagnostics Laboratory (CMDL), Carbon Cycle Group for providing the observational data of CO and CH4.

Comprehensive two-dimensional river ice model based on boundary-fitted coordinate transformation method

River ice is a natural phenomenon in cold regions, influenced by meteorology, geomorphology, and hydraulic conditions. River ice processes involve complex interactions between hydrodynamic, mechanical, and thermal processes, and they are also influenced by weather and hydrologic conditions. Because natural rivers are serpentine, with bends, narrows, and straight reaches, the commonly-used one-dimensional river ice models and two-dimensional models based on the rectangular Cartesian coordinates are incapable of simulating the physical phenomena accurately. In order to accurately simulate the complicated river geometry and overcome the difficulties of numerical simulation resulting from both complex boundaries and differences between length and width scales, a two-dimensional river ice numerical model based on a boundary-fitted coordinate transformation method was developed. The presented model considers the influence of the frazil ice accumulation under ice cover and the shape of the leading edge of ice cover during the freezing process. The model is capable of determining the velocity field, the distribution of water temperature, the concentration distribution of frazil ice, the transport of floating ice, the progression, stability, and thawing of ice cover, and the transport, accumulation, and erosion of ice under ice cover. A MacCormack scheme was used to solve the equations numerically. The model was validated with field observations from the Hequ Reach of the Yellow River. Comparison of simulation results with field data indicates that the model is capable of simulating the river ice process with high accuracy.

Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R2 values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China

Machine learning is currently one of the research hotspots in the field of landslide prediction.To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models,Conghua District,which is the most prone to landslide disasters in Guangzhou,was selected for landslide susceptibility evaluation.The evaluation factors were selected by using correlation analysis and variance expansion factor method.Applying four machine learning methods namely Logistic Regression(LR),Random Forest(RF),Support Vector Machines(SVM),and Extreme Gradient Boosting(XGB),landslide models were constructed.Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic(ROC)curves.The results showed that LR,RF,SVM,and XGB models have good predictive performance for landslide susceptibility,with the area under curve(AUC)values of 0.752,0.965,0.996,and 0.998,respectively.XGB model had the highest predictive ability,followed by RF model,SVM model,and LR model.The frequency ratio(FR)accuracy of LR,RF,SVM,and XGB models was 0.775,0.842,0.759,and 0.822,respectively.RF and XGB models were superior to LR and SVM models,indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

DETERMINATION OF PARAMETERS IN A TWO-DIMENSIONAL MODEL FOR FIXED-BED ADSORBERS

A new method is proposed for the determination of the parameters in a two-dimensionalmodel which characterizes the properties of axial and radial mixing and mass transport in afixed-bed adsorber.Parameter estimation for the model is carried out with methane-air-5A molecularsieve in a bed under the condition of step injection of tracer from a point on the main axis of thebed by the curve fitting method in the time domain.

Two-dimensional hydrodynamic robust numerical model of soil erosion based on slopes and river basins

Erosion is an important issue in soil science and is related to many environmental problems,such as soil erosion and sediment transport.Establishing a simulation model suitable for soil erosion prediction is of great significance not only to accurately predict the process of soil separation by runoff,but also improve the physical model of soil erosion.In this study,we develop a graphic processing unit(GPU)-based numerical model that combines two-dimensional(2D)hydrodynamic and Green-Ampt(G-A)infiltration modelling to simulate soil erosion.A Godunov-type scheme on a uniform and structured square grid is then generated to solve the relevant shallow water equations(SWEs).The highlight of this study is the use of GPU-based acceleration technology to enable numerical models to simulate slope and watershed erosion in an efficient and high-resolution manner.The results show that the hydrodynamic model performs well in simulating soil erosion process.Soil erosion is studied by conducting calculation verification at the slope and basin scales.The first case involves simulating soil erosion process of a slope surface under indoor artificial rainfall conditions from 0 to 1000 s,and there is a good agreement between the simulated values and the measured values for the runoff velocity.The second case is a river basin experiment(Coquet River Basin)that involves watershed erosion.Simulations of the erosion depth change and erosion cumulative amount of the basin during a period of 1-40 h show an elevation difference of erosion at 0.5-3.0 m,especially during the period of 20-30 h.Nine cross sections in the basin are selected for simulation and the results reveal that the depth of erosion change value ranges from-0.86 to-2.79 m and the depth of deposition change value varies from 0.38 to 1.02 m.The findings indicate that the developed GPU-based hydrogeomorphological model can reproduce soil erosion processes.These results are valuable for rainfall runoff and soil erosion predictions on rilled hillslopes and river basins.

Impact of Large Fresh Water Discharges into Hakata Bay Due to Torrential Rain Using Nested Two-Dimensional Convective-Dispersive Model

In this study, numerical simulation of a two-dimensional convective-dispersive model in Hakata Bay, Japan, is performed to analyze the impact of major river discharges due to torrential rain in Fukuoka City. Tank models are applied to calculate river discharges, which are taken into consideration as river inflow in the hydrodynamic model of Hakata Bay. A two-way nesting “edge” technique is developed and applied in the model in order to consider the influence of narrow and complex geographical features. The area around “Island City” and Imazu Bay are calculated in high resolution. The resulting model has high reproducibility since the calculated river discharges, tidal current, and salinity show good agreement with observed data. To analyze the impact of large river discharges, the calculation period is set from 11 September 2002 to 21 September 2002 since there was torrential rain on September 16 in the given year in Fukuoka City (163.5 mm/d). The results show that low-salinity water covered the whole of the inner part of Hakata Bay, and water of lower salinity than outer sea water (<34.0 psu) spread out to the bay’s mouth two days after the torrential rain event. Fresh water covered the entire area of Imazu Bay and flowed out from the mouth of the Bay after the torrential rain event. The behavior of fresh water after a few days of torrential rain was remarkably different from normal discharge river flow. These results indicate that the environment in Imazu Bay can be degraded severely by torrential rain. Therefore, countermeasures to protect ecosystems in Hakata Bay must be examined immediately.

A NEW TWO-DIMENSIONAL "MAN-WCV" MATHEMATICAL MODEL OF THE HUMAN THERMOREGULATION

In the paper a new two-dimensional 'man-WCV'(water cooling vest) mathematical model is developed. This model is of practical use: it can predict transient temperature responses and body temperature distribution for a person in a nonuniform hot environment, doing various jobs and dressed in different clothes. In addition, the results calculated from the model can be used to optimize the distribution of the tube-net lined on the WCV and to evaluate an individual thermal conditioning system with cooling water. The results obtained from the model agree well with the author's experimental data.

Vertical two-dimensional non-hydrostatic pressure model with single layer

The vertical two-dimensional non-hydrostatic pressure models with multiple layers can make prediction more accurate than those obtained by the hydrostatic pres- sure assumption. However, they are time-consuming and unstable, which makes them unsuitable for wider application. In this study, an efficient model with a single layer is developed. Decomposing the pressure into the hydrostatic and dynamic components and integrating the x-momentum equation from the bottom to the free surface can yield a horizontal momentum equation, in which the terms relevant to the dynamic pressure are discretized semi-implicitly. The convective terms in the vertical momentum equation are ignored, and the rest of the equation is approximated with the Keller-box scheme. The velocities expressed as the unknown dynamic pressure are substituted into the continuity equation, resulting in a tri-diagonal linear system solved by the Thomas algorithm. The validation of solitary and sinusoidal waves indicates that the present model can provide comparable results to the models with multiple layers but at much lower computation cost.