Effects of Moisture Content and Plasticity Index on Duncan-Chang Model Parameters of Hydraulic Fill Soft Soil

In order to explore the effects of moisture content and plasticity index on Duncan-Chang model parameters?K,n,?C?and?Rf,?we selected 8 groups of soft soil with water content of 69.1%?-?94.3% and plasticity index of 32.2?-?54.1 for triaxial unconsolidated undrained shear test. The results show that?Cuu,?K?and?n?values all showed a downward trend, and?Rf?variation was not obvious with the increase of moisture content. The variation rule of each parameter is not obvious with the increase of plasticity index. When moisture content is constant,?Cuu?and?n?values do not change much,?K?increases with the increase of plasticity index within the range of 70%?-?80% moisture content, and does not change much with the increase of plasticity index when moisture content is greater than 80%,?Rf?has no obvious rule.?When the plasticity index is constant,?Cuu,?Kand?n?decrease with the increase of moisture content,?Rf?has no obvious rule. The maximum value of?Cuu?is 20.18?kPa, the minimum is 3.72?kPa, and the maximum to minimum ratio is 5.42. The maximum value of?K?is 0.517, the minimum is 0.022, and the maximum to minimum ratio is 23.5. The maximum value of?n?is 1.198, the minimum is 0.150, and the maximum to minimum ratio is 7.99. The maximum value of?Rf?is 0.872, the minimum is 0.679, and the maximum to minimum ratio is 1.28.

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. .

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 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 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.

Effects of gravel on the water absorption characteristics and hydraulic parameters of stony soil

The eastern foothills of the Helan Mountains in China are a typical mountainous region of soil and gravel,where gravel could affect the water movement process in the soil.This study focused on the effects of different gravel contents on the water absorption characteristics and hydraulic parameters of stony soil.The stony soil samples were collected from the eastern foothills of the Helan Mountains in April 2023 and used as the experimental materials to conduct a one-dimensional horizontal soil column absorption experiment.Six experimental groups with gravel contents of 0%,10%,20%,30%,40%,and 50%were established to determine the saturated hydraulic conductivity(K_(s)),saturated water content(θ_(s)),initial water content(θ_(i)),and retention water content(θ_(r)),and explore the changes in the wetting front depth and cumulative absorption volume during the absorption experiment.The Philip model was used to fit the soil absorption process and determine the soil water absorption rate.Then the length of the characteristic wetting front depth,shape coefficient,empirical parameter,inverse intake suction and soil water suction were derived from the van Genuchten model.Finally,the hydraulic parameters mentioned above were used to fit the soil water characteristic curves,unsaturated hydraulic conductivity(K_(θ))and specific water capacity(C(h)).The results showed that the wetting front depth and cumulative absorption volume of each treatment gradually decreased with increasing gravel content.Compared with control check treatment with gravel content of 0%,soil water absorption rates in the treatments with gravel contents of 10%,20%,30%,40%,and 50%decreased by 11.47%,17.97%,25.24%,29.83%,and 42.45%,respectively.As the gravel content increased,inverse intake suction gradually increased,and shape coefficient,K_(s),θ_(s),andθ_(r)gradually decreased.For the same soil water content,soil water suction and K_(θ)gradually decreased with increasing gravel content.At the same soil water suction,C(h)decreased with increasing gravel content,and the water use efficiency worsened.Overall,the water holding capacity,hydraulic conductivity,and water use efficiency of stony soil in the eastern foothills of the Helan Mountains decreased with increasing gravel content.This study could provide data support for improving soil water use efficiency in the eastern foothills of the Helan Mountains and other similar rocky mountainous areas.

Modeling of Computer Virus Propagation with Fuzzy Parameters

Typically,a computer has infectivity as soon as it is infected.It is a reality that no antivirus programming can identify and eliminate all kinds of viruses,suggesting that infections would persevere on the Internet.To understand the dynamics of the virus propagation in a better way,a computer virus spread model with fuzzy parameters is presented in this work.It is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity,which depends on the quantity of virus.Considering this,the parametersβandγbeing functions of the computer virus load,are considered fuzzy numbers.Using fuzzy theory helps us understand the spread of computer viruses more realistically as these parameters have fixed values in classical models.The essential features of the model,like reproduction number and equilibrium analysis,are discussed in fuzzy senses.Moreover,with fuzziness,two numerical methods,the forward Euler technique,and a nonstandard finite difference(NSFD)scheme,respectively,are developed and analyzed.In the evidence of the numerical simulations,the proposed NSFD method preserves the main features of the dynamic system.It can be considered a reliable tool to predict such types of solutions.

Parameter sensitivity analysis for a biochemically-based photosynthesis model

A challenge for the development of Land Surface Models(LSMs) is improving transpiration of water exchange and photosynthesis of carbon exchange between terrestrial plants and the atmosphere, both of which are governed by stoma in leaves. In the photosynthesis module of these LSMs, variations of parameters arising from diversity in plant functional types(PFTs) and climate remain unclear. Identifying sensitive parameters among all photosynthetic parameters before parameter estimation can not only reduce operation cost, but also improve the usability of photosynthesis models worldwide. Here, we analyzed 13 parameters of a biochemically-based photosynthesis model(FvCB), implemented in many LSMs, using two sensitivity analysis(SA) methods(i.e., the Sobol’ method and the Morris method) for setting up the parameter ensemble. Three different model performance metrics, i.e.,Root Mean Squared Error(RMSE), Nash Sutcliffe efficiency(NSE), and Standard Deviation(STDEV) were introduced for model assessment and sensitive parameters identification. The results showed that among all photosynthetic parameters only a small portion of parameters were sensitive, and the sensitive parameters were different across plant functional types: maximum rate of Rubisco activity(Vcmax25), maximum electron transport rate(Jmax25), triose phosphate use rate(TPU) and dark respiration in light(Rd) were sensitive in broad leafevergreen trees(BET), broad leaf-deciduous trees(BDT) and needle leaf-evergreen trees(NET), while only Vcmax25and TPU are sensitive in short vegetation(SV), dwarf trees and shrubs(DTS), and agriculture and grassland(AG). The two sensitivity analysis methods suggested a strong SA coherence;in contrast, different model performance metrics led to different SA results. This misfit suggests that more accurate values of sensitive parameters, specifically, species specific and seasonal variable parameters, are required to improve the performance of the FvCB model.

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Constructing Confidence Regions for Autoregressive-Model Parameters

We discuss formulas and techniques for finding maximum-likelihood estimators of parameters of autoregressive (with particular emphasis on Markov and Yule) models, computing their asymptotic variance-covariance matrix and displaying the resulting confidence regions;Monte Carlo simulation is then used to establish the accuracy of the corresponding level of confidence. The results indicate that a direct application of the Central Limit Theorem yields errors too large to be acceptable;instead, we recommend using a technique based directly on the natural logarithm of the likelihood function, verifying its substantially higher accuracy. Our study is then extended to the case of estimating only a subset of a model’s parameters, when the remaining ones (called nuisance) are of no interest to us.

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.

Effect of Milling Parameters on DEM Modeling of a Planetary Ball Mill

The effects of the milling parameters involving shape of powder particles, rotation speed, and ball-to-powder diameter (BPDR) on DEM modeling in the planetary ball mill were investigated. BPDR was varied from 1 to 10. The results revealed that the size and shape of the powder particles do not give a significant change in simulation results when BPDR attains maximum value of 10. The increasing of BPDR leads to the increase of simulation time and size. Hence, the effect of change of the powder particle shape on the calculated data size is not significant. The results also revealed that the increasing rotation speed increases impact energy between powder particles.

Parameters of new three-water model based on nuclear magnetic experiment and optimization algorithm

Clastic rock reservoir is the main reservoir type in the oil and gas field.Archie formula or various conductive models developed on the basis of Archie’s formula are usually used to interpret this kind of reservoir,and the three-water model is widely used as well.However,there are many parameters in the threewater model,and some of them are difficult to determine.Most of the determination methods are based on the statistics of large amount of experimental data.In this study,the authors determine the value of the parameters of the new three-water model based on the nuclear magnetic data and the genetic optimization algorithm.The relative error between the resistivity calculated based on these parameters and the resistivity measured experimentally at 100%water content is 0.9024.The method studied in this paper can be easily applied without much experimental data.It can provide reference for other regions to determine the parameters of the new three-water model.

Parameter estimation of the stochastic AMR model and its application to the study of several strong earthquakes

Based on the stochastic AMR model, this paper constructs man-made earthquake catalogues to investigate the property of parameter estimation of the model. Then the stochastic AMR model is applied to the study of several strong earthquakes in China and New Zealand. Akaikes AIC criterion is used to discriminate whether an accelerating mode of earthquake activity precedes those events or not. Finally, regional accelerating seismic activity and possible prediction approach for future strong earthquakes are discussed.

Ensemble Bayesian method for parameter distribution inference:application to reactor physics

The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.

Insights into the adsorption performance and mechanism of Cr(Ⅵ)onto porous nanocomposite prepared from gossans and modified coal interface:Steric,energetic,and thermodynamic parameters interpretations

Herein,iron oxide/hydroxides deposits(gossans)were utilized,for the first time,in the fabrication of magnetite nanoparticles(MNPs)to load modified coal(MC).The as-synthesized MNPs@MC composite was characterized via different techniques and utilized for the Cr(Ⅵ)remediation.Experimental studies supported by theoretical treatment were applied to offer a new overview of the Cr(Ⅵ)adsorption geometry and mechanism at 25-45℃.Experimental results suggested that the Cr(Ⅵ)uptake was mainly governed by adsorption-reduction coupled mechanism.The Langmuir model fitted well the Cr(Ⅵ)adsorption data with maximum adsorption capacities extended from 115.24 to 129.63 mg·g^(-1).Theoretical calculations indicated that Cr(Ⅵ)ions were adsorbed on the MNPs@MC following the theory of the advanced monolayer statistical model.The number of ions removed per site ranged from 1.88 to1.23 suggesting the involvement of vertical geometry and multi-ionic mechanism at all temperatures.The increment of the active sites density and the adsorption capacity at saturation with improving temperature reflected an endothermic process.Energetically,the Cr(Ⅵ)adsorption was controlled by physical forces as the adsorption energies were less than 40 kJ·mol^(-1).The calculated free enthalpy,entropy.and internal energy explained the spontaneous nature and the viability of Cr(Ⅵ)adsorption on the MNPs@MC adsorbent.These results offer a new approach in utilizing the iron-rich deposits as gossans in the preparation of magnetic and low-cost adsorbents for wastewater remediation.

Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nio events? Results from a Theoretical Model

Within a theoretical ENSO model, the authors investigated whether or not the errors superimposed on model parameters could cause a significant ＂spring predictability barrier＂ （SPB） for El Nio events. First, sensitivity experiments were respectively performed to the air-sea coupling parameter, α and the thermocline effect coefficient μ. The results showed that the uncertainties superimposed on each of the two parameters did not exhibit an obvious season-dependent evolution; furthermore, the uncertainties caused a very small prediction error and consequently failed to yield a significant SPB. Subsequently, the conditional nonlinear optimal perturbation （CNOP） approach was used to study the effect of the optimal mode （CNOP-P） of the uncertainties of the two parameters on the SPB and to demonstrate that the CNOP-P errors neither presented a unified season-dependent evolution for different El Nio events nor caused a large prediction error, and therefore did not cause a significant SPB. The parameter errors played only a trivial role in yielding a significant SPB. To further validate this conclusion, the authors investigated the effect of the optimal combined mode （i.e. CNOP error） of initial and model errors on SPB. The results illustrated that the CNOP errors tended to have a significant season-dependent evolution, with the largest error growth rate in the spring, and yielded a large prediction error, inducing a significant SPB. The inference, therefore, is that initial errors, rather than model parameter errors, may be the dominant source of uncertainties that cause a significant SPB for El Nio events. These results indicate that the ability to forecast ENSO could be greatly increased by improving the initialization of the forecast model.

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.

An analytical model for estimating rock strength parameters from small-scale drilling data

The small-scale drilling technique can be a fast and reliable method to estimate rock strength parameters. It needs to link the operational drilling parameters and strength properties of rock. The parameters such as bit geometry, bit movement, contact frictions and crushed zone affect the estimated parameters.An analytical model considering operational drilling data and effective parameters can be used for these purposes. In this research, an analytical model was developed based on limit equilibrium of forces in a Tshaped drag bit considering the effective parameters such as bit geometry, crushed zone and contact frictions in drilling process. Based on the model, a method was used to estimate rock strength parameters such as cohesion, internal friction angle and uniaxial compressive strength of different rock types from operational drilling data. Some drilling tests were conducted by a portable and powerful drilling machine which was developed for this work. The obtained results for strength properties of different rock types from the drilling experiments based on the proposed model are in good agreement with the results of standard tests. Experimental results show that the contact friction between the cutting face and rock is close to that between bit end wearing face and rock due to the same bit material. In this case,the strength parameters, especially internal friction angle and cohesion, are estimated only by using a blunt bit drilling data and the bit bluntness does not affect the estimated results.