Normalized glandular dose coefficients for digital breast tomosynthesis using detailed Chinese breast models

The rise in breast cancer diagnoses among Chinese women has necessitated the use of X-ray breast screening,which carries a radiation risk.This study aimed to provide a dosimetry protocol for the Chinese female population to replace the traditional standard that utilizes simplified breast models,for the accurate estimation of the mean glandular dose of a patient undergoing digital breast tomosynthesis(DBT).The first set of detailed Chinese female breast models and representative breast parameters was constructed.Considering backscatter radiation and computational efficiency,we improved the combination of these models and the Chinese reference adult female whole-body voxel phantom.Image acquisition for four commercial DBT systems that are widely employed in China was simulated using the Monte Carlo method to obtain the normalized glandular dose coefficients of DBT(D_(gN)^(DBT))and the glandular depth dose(D_(g)^(dep)(z))for different breast characteristics and X-ray spectra.We calculated a series of D_(gN)^(DBT) values for breasts with different percentage mass glandularities(5%,25%,50%,75%,and 100%)and compressed breast thicknesses(2,3,4,5,6,and 7 cm)at various tube potentials(25,28,30,32,35,and 49 kV)and target/filter combinations(W/Rh,W/Al,Mo/Mo,Rh/Rh,and Rh/Ag).The parameter dependence of the breast characteristics and beam conditions on D_(gN)^(DBT) in detailed breast models was investigated.The D_(gN)^(DBT) results were 14.6-51.0%lower than those of the traditional dosimetry standard in China.The difference in D_(gN)^(DBT) was mainly due to a decrease in the depth of the main energy deposition area caused by the glandular distribution along the depth direction.The results obtained in this study may be used to improve breast dosimetry in China and provide more detailed information on risk assessment during DBT.

Quantifying the Role of the Eddy Transfer Coefficient in Simulating the Response of the Southern Ocean Meridional Overturning Circulation to Enhanced Westerlies in a Coarse-resolution Model

This study assesses the capability of a coarse-resolution ocean model to replicate the response of the Southern Ocean Meridional Overturning Circulation(MOC) to intensified westerlies,focusing on the role of the eddy transfer coefficient(κ).κ is a parameter commonly used to represent the velocities induced by unresolved eddies.Our findings reveal that a stratification-dependent κ,incorporating spatiotemporal variability,leads to the most robust eddy-induced MOC response,capturing 82% of the reference eddy-resolving simulation.Decomposing the eddy-induced velocity into its vertical variation(VV) and spatial structure(SS) components unveils that the enhanced eddy compensation response primarily stems from an augmented SS term,while the introduced VV term weakens the response.Furthermore,the temporal variability of the stratification-dependent κ emerges as a key factor in enhancing the eddy compensation response to intensified westerlies.The experiment with stratification-dependent κ exhibits a more potent eddy compensation response compared to the constant κ,attributed to the structure of κ and the vertical variation of the density slope.These results underscore the critical role of accurately representing κ in capturing the response of the Southern Ocean MOC and emphasize the significance of the isopycnal slope in modulating the eddy compensation mechanism.

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.

Molecular Dynamics, Physical Properties, Diffusion Coefficients and Activation Energy of the Lithium Oxide (Li-O) and Sodium Oxide (Na-O) Electrolyte (Cathode)

This work is a simulation model with the LAMMPS calculation code of an electrode based on alkali metal oxides (lithium, sodium and potassium) using the Lennard Jones potential. For a multiplicity of 8*8*8, we studied a gap-free model using molecular dynamics. Physical quantities such as volume and pressure of the Na-O and Li-O systems exhibit similar behaviors around the thermodynamic ensembles NPT and NVE. However, for the Na2O system, at a minimum temperature value, we observe a range of total energy values;in contrast, for the Li2O system, a minimum energy corresponds to a range of temperatures. Finally, for physicochemical properties, we studied the diffusion coefficient and activation energy of lithium and potassium oxides around their melting temperatures. The order of magnitude of the diffusion coefficients is given by the relation Dli-O >DNa-O for the multiplicity 8*8*8, while for the activation energy, the order is well reversed EaNa-O > EaLi-O.

Identification of the Diffusion Coefficients of Pollutants ( N O 3 - and Mn2+) through the Walls of Concrete Tank and Vulnerability of the Stored Water Quality

The populations of urban centers in Congo-Brazzaville have decided to develop various methods of water storage (concrete or masonry underground tanks) for domestic use, due to shortages in the distribution of water through the public network. However, questions remain as to the physico-chemical quality of the water stored in these tanks, when these structures are built in wet and relatively polluted areas. This paper presents a model of pollutant diffusion through the cementitious matrix (concrete) of tank walls simulated at a buried reservoir. The results of the experimental and numerical simulations show that certain concrete parameters, such as porosity, permeability and diffusivity, have a significant influence on the transfer of pollutants through the concrete walls, thus altering the physico-chemical quality of the stored water. The numerical models (1D) used to predict pollutant transfer and the quality of the stored water are consistent with those of the optimal control for identifying the diffusion coefficient. Major ion concentrations appear to be correlated with system porosity and diffusion coefficient. Nevertheless, the identification of the diffusion coefficient from the optimal control method, based on an explicit numerical resolution of a finite volume PDE for the approximation of the experiment, is not consistent with that of the optimal control method.

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.

Determination of Natural Logarithm of Diffusion Coefficient and Activation Energy of Thin Layer Drying Process of Ginger Rhizome Slices

This study is an extension of the previous work done with ARS-680 Environmental Chamber. Drying is a complex operation that demands much energy and time. Drying is essentially important for preservation of ginger rhizome. Drying of ginger was modeled, and then the effective diffusion coefficient and activation energy were determined. For this purpose, the experiments were done at six levels of varied temperatures: 10°C, 20°C, 30°C, 40°C, 50°C and 60°C. The values of effective diffusion coefficients obtained in this work for the variously treated ginger rhizomes closely agreed with the average effective diffusion coefficients of other notable authors who determined the drying kinetics and convective heat transfer coefficients of ginger slices.

Impacts of Surface Exchange Coefficients on Simulations of Super Typhoon Megi(2010)Using a Coupled Ocean-Atmosphere-Wave Model

In this study,the effects of surface exchange coefficients on simulations of Super Typhoon Megi(2010)are investigated using a fully coupled ocean-atmosphere-wave model.Several experiments are conducted using different parameterization schemes for the drag(C_(D))and enthalpy exchange(C_(K))coefficients.For the selected case,considering only the leveling-off of C_(D)at high wind speeds does not effectively improve the simulated typhoon track,intensity,or size.We found that increasing C_(K)monotonically with wind speed(Komori et al.,2018)yields stronger winds and deeper pressures by enhancing latent and sensible heat fluxes,but typhoon intensity remains underestimated.We propose a new higher C_(K)than that from Komori et al.(2018)based on the theory of Emanuel(1995).This approach produces a greater modeled typhoon intensity that is in good agreement with the best track data and effectively improves the track error for the simulation.Improved accuracy for modeled typhoon intensity is achieved with the new coefficient because C_(K)/C_(D)reaches the threshold of about 0.75 predicted by Emanuel(1995).The new proposed C_(K)also results in a reasonably accurate modeled sea surface temperature.However,typhoon size and surface wave height are overestimated.This finding implies that more numerical tests for tropical cyclones of different nature(such as strong,weak,dissipating,rapidly intensifying,or weakening tropical cyclones)should be studied,and more physical processes should be explored in future coupled models.

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-high-dimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

Generalized Likelihood Ratio Tests for Varying-Coefficient Models with Censored Data

In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a distribution, with the scale constant and the number of degree of freedom being independent of nuisance parameters or functions, which is called the wilks phenomenon. Both simulated and real data examples are given to illustrate the performance of the testing approach.

Statistical Diagnostic for Varying-Coefficient Single-Index Models Based on Empirical Likelihood Method

Varying-coefficient single-index model( VCSIM) avoids the so-called "curse of dimensionality " and is flexible enough to include several important statistical models. This paper considers statistical diagnosis for VCSIM. First,the parametric estimation equation is established based on empirical likelihood. Then,some diagnosis statistics are defined. At last, an example is given to illustrate all the results.

STRONG CONVERGENCE RATES OF SEVERAL ESTIMATORS IN SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS

This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.

覆土浅埋滴灌玉米分阶段亏水条件下AquaCrop与Dual Crop Coefficient模型精度对比研究

近年来,西辽河流域地下水超采问题日益突出,改进灌溉技术降低农业用水可有效解决这一问题,但该技术缺乏科学的水分管理方法及适宜的理论模型。基于单阶段及多阶段亏水情形试验,对比同类农田水分模型精度,优选阶段性亏水管理辅助模型。研究设置7个玉米分阶段亏水调控处理,于2018、2019年进行田间小区试验,取得2个同类模型的本地化参数并比较其模拟精度。结果表明,AquaCrop和双作物系数模型可相近表达玉米冠层发育到最大而未开始衰减期间土壤水分的消耗过程,而对快速生长期与后期1m土层贮水量的模拟差异大。AquaCrop模型在土壤贮水量偏低时高估其实测值,其他情形正负偏差分布相对均匀,双作物系数模型多数情形低估其实测值。AquaCrop模型描述玉米各阶段蒸散量因亏水情形而变化能力优于双作物系数模型。AquaCrop模型和双作物系数模型的标准均方根误差平均值在模拟1 m土层贮水量时分别为4.494%~8.443%、6.017%~8.626%,在模拟蒸散量时分别为8.158%~9.510%、5.980%~15.022%。综合来看,AquaCrop模型表现更好,推荐作为适宜西辽河流域覆土浅埋滴灌玉米水分管理模型。研究可为该区域覆土浅埋滴灌种植玉米选出适宜的水分管理模型,理解新技术条件下玉米分阶段亏水调控机制及田间灌溉管理优化提供参考。

MODEL SELECTION METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

The traditional model selection criterions try to make a balance between fitted error and model complexity. Assumptions on the distribution of the response or the noise, which may be misspecified, should be made before using the traditional ones. In this ar- ticle, we give a new model selection criterion, based on the assumption that noise term in the model is independent with explanatory variables, of minimizing the association strength between regression residuals and the response, with fewer assumptions. Maximal Information Coe^cient （MIC）, a recently proposed dependence measure, captures a wide range of associ- ations, and gives almost the same score to different type of relationships with equal noise, so MIC is used to measure the association strength. Furthermore, partial maximal information coefficient （PMIC） is introduced to capture the association between two variables removing a third controlling random variable. In addition, the definition of general partial relationship is given.

Entropy weight coefficient model and its application in evaluation of groundwater vulnerability of the Sanjiang Plain

The research of groundwater vulnerability is the basic work to protect the groundwater. For utilizing groundwater resource continuably, groundwater vulnerability evaluation is necessary. Useful reference to protect, exploit and utilize on groundwater resource are provided rationally. According to the real condition of Sanjiang Plain, the indexes system is established based on the traditional DRASTIC model. The new system includes the following seven indexes： Depth of Water, Net Recharge, Aquifer Media, Soil Media, Conductivity of the Aquifer, Land Utilizing Ratio and Populace Density. The related analysis appears that the system is rather reasonable. Because traditional methods, such as analytic hierarchy process and fuzzy mathematics theory, can＇t be avoided human interference in selection of weights, they could lead to an imprecise result. In order to evaluate the groundwater vulnerability reasonably, entropy weight coefficient method is applied for the first time, which provides a new way to groundwater vulnerability evaluation. The method is a model whose weights are insured by the calculation process, so the artificial disturb can be avoided. It has been used to evaluate the groundwater vulnerability in Sanjiang Plain. The satisfied result is acquired. Comparably, the same result is acquired by the other method named projection pursuit evaluation based on real-coded accelerating genetic algorithm. It shows that entropy weight coefficient method is applicable on groundwater vulnerability evaluation. The evaluation result can provide reference on the decision-making departments.

A criterion for flow mechanisms through vertical sharp-edged orifice and model for the orifice discharge coefficient

According to the experimental data of the orifice discharge coefficient for the flow through a vertical sharp-edged orifice obtained in the previous study of this work,a theoretical criterion for flow mechanisms of small orifice（viz.thick-walled orifice and nozzle） and large orifice（viz.thin-walled orifice） was proposed based on the ratio of orifice diameter to plate thickness.It can help explain the dissipation of the mechanical energy loss in the flow process for the two flow mechanisms under different operating regimes.The main parameters such as orifice diameter,plate thickness and liquid head were correlated,and a semi-empirical model for orifice coefficient and an empirical model with high precision at the stable region were developed.

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.

Modeling of softsphere normal collisions with characteristic of coefficient of restitution dependent on impact velocity

This letter presents a theoretical model of the normal （head-on） collisions between two soft spheres for predicting the experimental characteristic of the coefficient of restitution dependent on impact velocity. After the contact force law between the contacted spheres during a collision is phenomenologically formulated in terms of the compression or overlap displacement under considera- tion of an elastic-plastic loading and a plastic unloading subprocesses, the coefficient of restitution is gained by the dynamic equation of the contact process once an initial impact velocity is input. It is found that the theoretical predictions of the coefficient of restitution varying with the impact velocity are well in agreement with the existing experimental characteristics which are fitted by the explicit formula.

Establishment and Application of UFC-ACC Heat Transfer Coefficient Model

Based on medium plate runout table ultra-fast cooling( UFC)-accelerated cooling equipment( ACC) system,a heat transfer coefficient model was constructed. Firstly,according to the measured data,heat transfer coefficients under different roll speed and water volume were calculated by using an inverse heat conduction method. Secondly,a monofactorial heat transfer coefficient calculation formula was obtained. Finally,the heat transfer coefficient model based on medium plate runout table UFC-ACC system was constructed by intercept function,slope function,interaction influence function and linear or nonlinear influencing factors. The precision of these models was validated by comparing model prediction value with measured data,and the results were in good agreement with practical needs,and the average deviation was less than 5%.

Calculation of Activity Coefficient from Immiscible Binary Alloy Phase Diagram by Means of Modified Sub-regular Solution Model

The modified sub regular solution model was used for a calculation of the activity coefficient of immiscible binary alloy systems. The parameters needed for the calculation are the interaction parameters, λ 1 and λ 2, which are represented as a linear function of temperature, T . The molar excess Gibbs free energy, G m E, can be written in the form G m E= x A x B[( λ 11 + λ 12 T )+( λ 21 + λ 22 T ) x B ] The calculation is carried out numerically for three immiscible binary alloy systems, Al Pb, Cu Tl and In V. The agreement between the calculated and experimentally determined values of activity coefficient is excellent.