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.

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.

Subspace Minimization Conjugate Gradient Method Based on Cubic Regularization Model for Unconstrained Optimization

Many methods have been put forward to solve unconstrained optimization problems,among which conjugate gradient method(CG)is very important.With the increasing emergence of large⁃scale problems,the subspace technology has become particularly important and widely used in the field of optimization.In this study,a new CG method was put forward,which combined subspace technology and a cubic regularization model.Besides,a special scaled norm in a cubic regularization model was analyzed.Under certain conditions,some significant characteristics of the search direction were given and the convergence of the algorithm was built.Numerical comparisons show that for the 145 test functions under the CUTEr library,the proposed method is better than two classical CG methods and two new subspaces conjugate gradient methods.

Occurrence Regularity and Prediction Model of Underground Pest Adults in Hangzhou District of China

To improve forecasting and sustained control level of underground pests, trapping quantity of underground pests （black cutworm,mole cricket and scar-ab） by lamps and their field dynamics in Hangzhou district from 2005 to 2011 were investigated in the paper. The results showed that different pests had obvious differences in population dynamic. The black cutworm （Agrotis ypsilon） had several damage peaks （late May, late June and late July） and the moth amount in early period was relatively high. The mole cricket （ Gryllotalpa africana） had two damage peaks （late May to early July, early September to mid and late October）. The scarab （Anomala corpulenta） had one damage peak （late May to late June）. There were periodic changes in total quantity of underground pests among years, and the peak period appeared in the year of 2005, 2007 to 2009 and 2011, respectively. On this basis, temperature, humidity, rainfall and light were used as forecas- ting factors, using the method of stepwise regression, 19 factors with significant correlation were screened out and prediction models for occurrence quantity and oc- currence period of the three pests were established. By using accuracy degree judge model for verification, the score values of prediction model for occurrence quan-tity and occurrence period of the three underground pests were more than 58 and 70, which indicated that the historical coincident rate and prediction accuracy of estabhshed prediction models were good.

Some Properties of a Recursive Procedure for High Dimensional Parameter Estimation in Linear Model with Regularization

Theoretical results related to properties of a regularized recursive algorithm for estimation of a high dimensional vector of parameters are presented and proved. The recursive character of the procedure is proposed to overcome the difficulties with high dimension of the observation vector in computation of a statistical regularized estimator. As to deal with high dimension of the vector of unknown parameters, the regularization is introduced by specifying a priori non-negative covariance structure for the vector of estimated parameters. Numerical example with Monte-Carlo simulation for a low-dimensional system as well as the state/parameter estimation in a very high dimensional oceanic model is presented to demonstrate the efficiency of the proposed approach.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Iterative regularization method for image denoising with adaptive scale parameter

In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.

THE BASIC CONSIDERATIONS OF SELF-SReM AND SELF-BoSS MODELS

Model Investigation is the only feasible way to solve the problem about the component activities in concentrated multicomponent alloys and molten slags at present. The basic characteristic of SELF-SReM model is briefly introduced in this paper. It intends to give out the systematical value of component activities in the whole homogeneous region of a concentrated multicomponent melt, then to provide a reliable database for the description of the equilibrium conditions associated with metallurgy processes. For molten slags, the key issue is to distinguish the accuracy of thermodynamic properties in binary systems. The fundamental approach for this task is to link the microscopic bond structure and macroscopic activity based on both of the measurement of high tem- perature Raman spectroscopy and the corresponding computation simulation according to molecular dynamics and quantum chemistry.

Evaluation of Component Activities in C-Mn-Fe-Si with Model Self-SReM4

A sub-regular solution model SELFSReM4 used to evaluate activities of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Lab of Ferrometallurgy. This paper introduces the application of SELFSReM4 in evaluating activities of the components in C-Mn-Fe-Si system without SiC precipitation.

Evaluation of Component Activity in Molten MnO-SiO_(2)-Al_(2)O_(3)-CaO System with Model SELF-SReM4

A sub-regular solution model SELF-SReM4 used to evaluate activity of the components in a homogeneous region of a quaternary system has been developed in Shanghai Enhanced Laboratory of Ferrometallurgy.The application of SELF-SReM4 in C-Mn-Fe-Si system without the SiC formation has been introduced in previous paper.It’s application for molten slag of MnO-SiO2-Al2O3-CaO was introduced in this paper.They provide a basis for the prediction of the metal-slag equilibrium conditions.

Two-dimensional regularized inversion of AMT data based on rotation invariant of Central impedance tensor

Considering the uncertainty of the electrical axis for two-dimensional audo-magnetotelluric(AMT) data processing, an AMT inversion method with the Central impedance tensor was presented. First, we present a calculation expression of the Central impedance tensor in AMT, which can be considered as the arithmetic mean of TE-polarization mode and TM-polarization mode in the twodimensional geo-electrical model. Second, a least-squares iterative inversion algorithm is established, based on a smoothnessconstrained model, and an improved L-curve method is adopted to determine the best regularization parameters. We then test the above inversion method with synthetic data and field data. The test results show that this two-dimensional AMT inversion scheme for the responses of Central impedance is effective and can reconstruct reasonable two-dimensional subsurface resistivity structures. We conclude that the Central impedance tensor is a useful tool for two-dimensional inversion of AMT data.

Inversion of dispersion coeffcient in water quality model using optimal perturbation algorithm

As a primary parameter in the water quality model for shallow bays, the dispersion coefficient is traditionally determined with a trial-and-error method, which is time-consuming and requires much experience. In this paper, based on the measured data of chemical oxygen demand （COD）, the dispersion coefficient is calculated using an inversion method. In the process, the regularization method is applied to treat the ill-posedness, and an operator identity perturbation method is used to obtain the solu- tion. Using the model with an inverted dispersion coefficient, the distributions of COD, inorganic nitrogen （IN）, and inorganic phosphorus （IP） in Bohai Bay are predicted and compared with the measured data. The results indicate that the method is feasible and the inverted dispersion coefficient can be used to predict other pollutant distribution. This method may also be further extended to the inversion of other parameters in the water quality model.

Epidemic Regularity and Control Effect of Huanglongbing (HLB) in Different Management Approaches

In order to reveal the epidemic regularity of Huanglongbing （HLB） in different management approaches, different citrus production areas were selected between 2002 and 2012 to compare epidemic regularity of different types and control effects of different management approaches with plant incidence rate. All survey data in 11 years were used to build a mathematical model, and epidemic evolution and control effects were quantitatively analyzed. The results indicated that diffusion and prevalence of HLB generally increased linearly. In naturally growing citrus orchards without artificial control, the annual diseased plant rate was 11.11%, and the epidemic diffusion model was y1 = 12. 24x - 1.382 8 （ n =9, r =0. 976 9 ＊ ＊ ）. Under general prevention and control conditions, the annual diseased plant rate was 4.69%, the epidemic diffusion model was Y2 = 5. 449 8x - 1.603 5 （ n = 11, r =0. 974 9 ＊ ＊ ）, and the control effect was 43.93% （22.93% - 55.04% ）. In citrus orchards with integrated prevention and control, the epidemic diffusion model was Y3 = 0. 366 3x - 0. 342 2 （ n = 11, r = 0. 989 8 ＊ ＊ ）, the control effect was 96.15% （94.95% -97.40% ）, and the annual diseased plant rate was 0.31%. Thus, HLB is preventable and controllable as long as integrated prevention and control work is implemented well.

Liver Segmentation in CT Images Based on DRLSE Model

Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.

Asymptotic Behavior and Stability of Stochastic SIR Model with Variable Diffusion Rates

In this paper, we propose random fluctuation on contact and recovery rates in deterministic SIR model with disease deaths in nonparametric manner and derive a new stochastic SIR model with distributed time delay and general diffusion coefficients. By analysis of the introduced model, we obtain the sufficient conditions for the regularity, existence and uniqueness of a global solution by means of Lyapunov function. Moreover, we also investigate the stochastic asymptotic stability of disease free equilibria and endemic equilibria of this model. Finally, we illustrate our general results by applications.

Applying Finite Difference Method to Simulate the Performance of a Perforated Breakwater Under Regular Waves

Using a discretized finite difference method, a numerical model was developed to study the interaction of regular waves with a perforated breakwater. Considering a non-viscous, non-rotational fluid, the governing equations of Laplacian velocity potential were developed, and specific conditions for every single boundary were defined. The final developed model was evaluated based on an existing experimental result. The evaluated model was used to simulate the condition for various wave periods from 0.6 to 2 s. The reflection coefficient and transmission coefficient of waves were examined with different breakwater porosities, wave steepnesses, and angular frequencies. The results show that the developed model can suitably present the effect of the structural and hydraulic parameters on the reflection and transmission coefficients. It was also found that with the increase in wave steepness, the reflection coefficient increased logarithmically, while the transmission coefficient decreased logarithmically.

Regularity and prediction of ground pressure in Haigou Gold Mine

Previous mining excavation in upper sublevels left several mined-out areas in Haigou gold mine. To ensure safety of the main and auxiliary shafts and mining production in deeper sublevels, systematical studies on regularity, prediction, and control of ground pressure in the mine were carried out. Through 3D-numerical modeling and in-situ monitoring of acoustic emission, pressure and displacement, the ground pressure activity and the stability status of surrounding rock masses and the two shafts were assessed. Based on in-situ monitoring practice in Haigou mine,4 modes to judge rock stability according to the monitoring information of acoustic emission,pressure,and displacement were presented.

Linear Inferential Modeling: Theoretical Perspectives, Extensions, and Comparative Analysis

Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold: to present a theoretical review of some of the well known linear inferential modeling techniques, to enhance the predictive ability of the regularized canonical correlation analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities. The inferential modeling techniques considered in this study include full rank modeling techniques, such as ordinary least square (OLS) regression and ridge regression (RR), and latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS) regression, and regularized canonical correlation analysis (RCCA). The theoretical analysis shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. Also, for the RCCA method, we show that by optimizing the regularization parameter, an improvement in prediction accuracy can be achieved over other modeling techniques. To illustrate the performances of all inferential modeling techniques, a comparative analysis was performed through two simulated examples, one using synthetic data and the other using simulated distillation column data. All techniques are optimized and compared by computing the cross validation mean square error using unseen testing data. The results of this comparative analysis show that scaling the data helps improve the performances of all modeling techniques, and that the LVR techniques outperform the full rank ones. One reason for this advantage is that the LVR techniques improve the conditioning of the model by discarding the latent variables (or principal components) with small eigenvalues, which also reduce the effect of the noise on the model prediction. The results also show that PCR and PLS have comparable performances, and that RCCA can provide an advantage by optimizing its regularization parameter.

A Gradient Regularization Method in Crosswell Seismic Tomography

Crosswell seismic tomography can be used to study the lateral variation of reservoirs, reservoir properties and the dynamic movement of fluids. In view of the instability of crosswell seismic tomography, the gradient method was improved by introducing regularization, and a gradient regularization method is presented in this paper. This method was verified by processing numerical simulation data and physical model data.

空间结构模型优化的弹性参数FFT-MA随机建模

快速傅里叶变换滑动平均(FFT-MA)法是一种灵活高效的随机建模方法,在地下介质高分辨率建模、复杂介质非平稳建模和不确定性评价等方面具有重要的应用价值。准确构建空间结构模型是利用FFT-MA方法生成合理的随机模型的关键。然而,以往对FFT-MA方法的研究并未提出准确构建空间结构模型的有效方法。为此,文中提出了一种有效的空间结构模型估计方法。该方法基于反演思想,通过最小化随机模型与测井数据和地震数据的空间结构差异,分别估计空间结构模型的纵向自相关长度和横向自相关长度。同时,为了优化空间结构模型的估计效果,在纵向自相关长度的反演过程中引入边界保护正则化,以提高反演的稳定性。此外,将地震约束引入模型优选以提高随机模型的稳定性。实验结果表明:该方法能够稳定估计地下介质的非平稳空间结构模型,从而建立准确描述复杂储层非平稳空间相关特征的高分辨率随机模型。与基于序贯高斯协模拟的随机建模方法相比,使用空间结构模型优化的FFT-MA随机建模方法能够有效刻画多种复杂地质构造从而实现复杂储层建模。