Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

Numerical Models and Methods of Atmospheric Parameters Originating in the Formation of the Earth’s Climatic Cycle

Atmospheric models are physical equations based on the ideal gas law. Applied to the atmosphere, this law yields equations for water, vapor (gas), ice, air, humidity, dryness, fire, and heat, thus defining the model of key atmospheric parameters. The distribution of these parameters across the entire planet Earth is the origin of the formation of the climatic cycle, which is a normal climatic variation. To do this, the Earth is divided into eight (8) parts according to the number of key parameters to be defined in a physical representation of the model. Following this distribution, numerical models calculate the constants for the formation of water, vapor, ice, dryness, thermal energy (fire), heat, air, and humidity. These models vary in complexity depending on the indirect trigonometric direction and simplicity in the sum of neighboring models. Note that the constants obtained from the equations yield 275.156˚K (2.006˚C) for water, 273.1596˚K (0.00963˚C) for vapor, 273.1633˚K (0.0133˚C) for ice, 0.00365 in/s for atmospheric dryness, 1.996 in2/s for humidity, 2.993 in2/s for air, 1 J for thermal energy of fire, and 0.9963 J for heat. In summary, this study aims to define the main parameters and natural phenomena contributing to the modification of planetary climate. .

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.

Introduction of parameterized sea ice drag coefficients into ice free-drift modeling

Many interesting characteristics of sea ice drift depend on the atmospheric drag coefficient （Ca） and oceanic drag coefficient （Cw）. Parameterizations of drag coefficients rather than constant values provide us a way to look insight into the dependence of these characteristics on sea ice conditions. In the present study, the parameterized ice drag coefficients are included into a free-drift sea ice dynamic model, and the wind factor a and the deflection angle θ between sea ice drift and wind velocity as well as the ratio of Ca to Cw are studied to investigate their dependence on the impact factors such as local drag coefficients, floe and ridge geometry. The results reveal that in an idealized steady ocean, Ca/Cw increases obviously with the increasing ice concentration for small ice floes in the marginal ice zone, while it remains at a steady level （0.2-0.25） for large floes in the central ice zone. The wind factor a increases rapidly at first and approaches a steady level of 0.018 when A is greater than 20%. And the deflection angle ~ drops rapidly from an initial value of approximate 80° and decreases slowly as A is greater than 20% without a steady level like a. The values of these parameters agree well with the previously reported observations in Arctic. The ridging intensity is an important parameter to determine the dominant contribution of the ratio of skin friction drag coefficient （Cs＇/Cs） and the ratio of ridge form drag coefficient （Cr＇/Cr） to the value of Ca/Cw, a, and 8, because of the dominance of ridge form drag for large ridging intensity and skin friction for small ridging intensity among the total drag forces. Parameterization of sea ice drag coefficients has the potential to be embedded into ice dynamic models to better account for the variability of sea ice in the transient Arctic Ocean.

Transient elastography with controlled attenuation parameter for the diagnosis of colorectal polyps in patients with nonalcoholic fatty liver disease

BACKGROUND The severity of nonalcoholic fatty liver disease(NAFLD)and lipid metabolism are related to the occurrence of colorectal polyps.Liver-controlled attenuation parameters(liver-CAPs)have been established to predict the prognosis of hepatic steatosis patients.AIM To explore the risk factors associated with colorectal polyps in patients with NAFLD by analyzing liver-CAPs and establishing a diagnostic model.METHODS Patients who were diagnosed with colorectal polyps in the Department of Gastroenterology of our hospital between June 2021 and April 2022 composed the case group,and those with no important abnormalities composed the control group.The area under the receiver operating characteristic curve was used to predict the diagnostic efficiency.Differences were considered statistically significant when P<0.05.RESULTS The median triglyceride(TG)and liver-CAP in the case group were significantly greater than those in the control group(mmol/L,1.74 vs 1.05;dB/m,282 vs 254,P<0.05).TG and liver-CAP were found to be independent risk factors for colorectal polyps,with ORs of 2.338(95%CI:1.154–4.733)and 1.019(95%CI:1.006–1.033),respectively(P<0.05).And there was no difference in the diagnostic efficacy between liver-CAP and TG combined with liver-CAP(TG+CAP)(P>0.05).When the liver-CAP was greater than 291 dB/m,colorectal polyps were more likely to occur.CONCLUSION The levels of TG and liver-CAP in patients with colorectal polyps are significantly greater than those patients without polyps.Liver-CAP alone can be used to diagnose NAFLD with colorectal polyps.

A 3D attention U-Net network and its application in geological model parameterization

To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.

Introduction to Mesh Based Generated Lumped Parameter Models for Electromagnetic Problems using Triangular Elements

This paper is an introduction to mesh based generated reluctance network modeling using triangular elements.Many contributions on mesh based generated reluctance networks using rectangular shaped elements have been published,but very few on those generated from a mesh using triangular elements.The use of triangular elements is aimed at extending the application of the approach to any shape of modeled devices.Basic concepts of the approach are presented in the case of electromagnetic devices.The procedure for coding the approach in the case of a flat linear permanent magnet machine is presented.Codes developed under MATLAB environment are also included.

Parameterized thermal model of a mixed mantle convection

Simple parameterized models, either whole mantle convection or layered mantleconvection, cannot explain the tectonic characteristics of the Earth's evolution history, therefore a mixed mantle convection model has been carried out in this paper. We introduce a time-dependent parameter F, which denotes the ratio betWeen the mantle material involved in whole mantle convection and the material of the entire mantle, and introduce a local Rayleigh number Raloc as well as two critical numbers Ra1 and Ra2. These parameters are used to describe the stability of the phase boundary between the upper and lower mantle. The result shows that the mixed mantle convection model is able to simulate the episodic tectonic evolution of the Earth.

A unified Minorization-Maximization approach for estimation of general mixture models

The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Improved Diurnal Cycle of Precipitation on Land in a Global Non-Hydrostatic Model Using a Revised NSAS Deep Convective Scheme

In relatively coarse-resolution atmospheric models,cumulus parameterization helps account for the effect of subgridscale convection,which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation.In this study,the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model,i.e.,the Yin-Yang-grid Unified Model for the Atmosphere.Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of the cloud work function corresponding to the free tropospheric large-scale forcing.Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed an improvement in the diurnal precipitation variation by using a revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger.By reducing the occurrence of convection during peak solar radiation hours,the revised scheme was shown to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak.In addition,the revised scheme enhanced the simulation capability of the precipitation probability density function,such as increasing the extremely low-and high-intensity precipitation events and decreasing small and moderate rainfall events,which contributed to the reduction of precipitation bias over mid-latitude and tropical land areas.

A novel small-signal equivalent circuit model for GaN HEMTs incorporating a dual-field-plate

An accurate and novel small-signal equivalent circuit model for GaN high-electron-mobility transistors(HEMTs)is proposed,which considers a dual-field-plate(FP)made up of a gate-FP and a source-FP.The equivalent circuit of the overall model is composed of parasitic elements,intrinsic transistors,gate-FP,and source-FP networks.The equivalent circuit of the gate-FP is identical to that of the intrinsic transistor.In order to simplify the complexity of the model,a series combination of a resistor and a capacitor is employed to represent the source-FP.The analytical extraction procedure of the model parameters is presented based on the proposed equivalent circuit.The verification is carried out on a 4×250μm GaN HEMT device with a gate-FP and a source-FP in a 0.45μm technology.Compared with the classic model,the proposed novel small-signal model shows closer agreement with measured S-parameters in the range of 1.0 to 18.0 GHz.

Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

Extremely large-scale hybrid reconfigurable intelligence surface(XL-HRIS),an improved version of the RIS,can receive the incident signal and enhance communication performance.However,as the RIS size increases,the phase variations of the received signal across the whole array are nonnegligible in the near-field region,and the channel model mismatch,which will decrease the estimation accuracy,must be considered.In this paper,the lower bound(LB)of the estimated parameter is studied and the impacts of the distance and signal-tonoise ratio(SNR)on LB are then evaluated.Moreover,the impacts of the array scale on LB and spectral efficiency(SE)are also studied.Simulation results verify that even in extremely large-scale array systems with infinite SNR,channel model mismatch can still limit estimation accuracy.However,this impact decreases with increasing distance.

A coupled ice-ocean model for the Bohai Sea Ⅰ. Study on model and parameter

According to the earlier international studies on the coupled iceocean model and the hydrology, meteorology, and icefeatures in the Bohai Sea, a coupled iceocean model is developed based on the National Marine EnvironmentForecast Centers (NMEFC) numerical forecasting ice model of the Bohai Sea and the Princeton ocean model (POM).In the coupled model, the transfer of momentum and heat between ocean and ice is two-way, and the change of icethickness and concentration depends on heat budget not only at the surface and bottom of ice, but also at the surfaceof open water between ices. The dynamic and thermodynamic coupling process is expatiated emphatically. Somethermodynamic parameters are discussed as well.

Selection of the Linear Regression Model According to the Parameter Estimation

In this paper, based on the theory of parameter estimation, we give a selection method and, in a sense of a good character of the parameter estimation, we think that it is very reasonable. Moreover, we offer a calculation method of selection statistic and an applied example.

Biomass-Based Rice (Oryza sativa L.) Aboveground Architectural Parameter Models

To quantify the relationships between rice plant architecture parameters and the corresponding organ biomass, and to research on functional structural plant models of rice plant, this paper presented a biomass-based model of aboveground architectural parameters of rice （Oryza sativa L.） in the young seedling stage, designed to explain effects of cultivars and environmental conditions on rice aboveground morphogenesis at the individual leaf level. Various model variables, including biomass of blade and blade length, were parameterized for rice based on data derived from an outdoor experiment with rice cv. Liangyou 108, 86You 8, Nanjing 43, and Yangdao 6. The organ dimensions of rice aboveground were modelled taking corresponding organ biomass as an independent variable. Various variables in rice showed marked consistency in observation and simulation, suggesting possibilities for a general rice architectural model in the young seedling stage. Our descriptive model was suitable for our objective. However, they can set the stage for connection to physiological model via biomass and development of functional structural rice models （FSRM）, and start with the localized production and partitioning of assimilates as affected by abiotic growth factors. The finding of biomass-based rice architectural parameter models also can be used in morphological models of blade, sheath, and tiller of the other stages in rice life.

Parameter identification of hysteretic model of rubber-bearing based on sequential nonlinear least-square estimation

In order to evaluate the nonlinear performance and the possible damage to rubber-bearings （RBs） during their normal operation or under strong earthquakes, a simplified Bouc-Wen model is used to describe the nonlinear hysteretic behavior of RBs in this paper, which has the advantages of being smooth-varying and physically motivated. Further, based on the results from experimental tests performed by using a particular type of RB （GZN 110） under different excitation scenarios, including white noise and several earthquakes, a new system identification method, referred to as the sequential nonlinear least- square estimation （SNLSE）, is introduced to identify the model parameters. It is shown that the proposed simplified Bouc- Wen model is capable of describing the nonlinear hysteretic behavior of RBs, and that the SNLSE approach is very effective in identifying the model parameters of RBs.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.