Discourse Analysis of the Belt and Road Initiative From the Perspective of Central Asian Countries: A Case Study of the Republic of Kazakhstan

This study employs Norman Fairclough’s Critical Discourse Analysis(CDA)three-dimensional model,using the Republic of Kazakhstan as a case study,to delve into the discourse construction of China’s Belt and Road Initiative(BRI)in Central Asian countries.Through detailed analysis of policy documents,media reports,and public discussions in Central Asian countries,this paper reveals how the BRI constructs specific social practices,discourse events,and textual meanings within these nations.The findings indicate that through this global development strategy,China has not only strengthened its economic ties with Central Asian countries but has also exerted profound influences on political,cultural,and social levels.

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.

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.

Comparative Study of Volatility Forecasting Models： The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average （EWMA）, Autoregressive Integrated Moving Average （ARIMA） and Generalized Auto-Regressive Conditional Heteroscedastic （GARCH）, in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1 st January 1998 to 31 st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH （1, l） model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH （1, 1） found supports again what is found in Minkah （2007）.

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.

Modeling the Impact of Mine and Country Variations on the Cost and Country-Benefit of Gold Mining

This paper dwells on regression models of cash-cost and country-benefit developed to enable accounting for the cumulative impact of the determinant parameters in the prediction of cash-costs and country-benefits of gold mining opportunities in the justification of taxation regimes and selection of investment targets worldwide. The data used in the generation of regression models include the total cash-cost and country-benefit per ounce vs the parameters of rock-mass (type of ore body, its dip angle, strike length and thickness), mine-design (rate of gold production, type of mine, depth of mine, gold price and age of mine) and country parameters (the Fraser Institute parameters: taxation regime, infrastructure, environmental regime, political stability, labor regulations and security) were generated from 160 gold mines in the top 20 gold rich countries for a period of 7 years from 2002 to 2008. The regression models show that the determinants account for 71% and 55% of the determinants of cash-cost and country-benefit respectively. Depending on the availability of data, the regression models generated in this study could be enhanced by adding into the parameters used in the regression analysis, the unaccounted for mine and country parameters. Also, Depending on the availability of data, the Regression models generated in this study could be enhanced further by replacing the parameters of Fraser Institute ranking used in the regression analysis with the actual parameters of country effect on cash-cost and country-benefit of the gold produced. Nevertheless, the regression models generated in this study could be used to predict the cash-costs and country-benefits of gold mining opportunities in the justification of taxation regimes and selection of investment targets worldwide.

Investigation on maternal mortality in Southeast Asia,Europe and Africa using three delays model

Background:Maternal mortality is a prevalent issue in healthcare provision worldwide.It is particularly common in developing and underdeveloped countries,where maternal deaths during childbirth or pregnancy occur frequently.Various internal and external factors contribute to the high maternal mortality rate in specific regions.One model,known as the three delays model approach,examines three distinct causes that contribute to this problem.The first delay is the lack of awareness in seeking timely healthcare,the second delay involves obstacles in reaching healthcare facilities on time,and the third delay relates to poor or inadequate healthcare provision in tertiary care facilities.These delays are responsible for the elevated maternal mortality rates,with the prevalence of each delay varying across regions.Objective:The objective of this literature review is to examine and critically evaluate existing literature on perceptions and investigations regarding maternal mortality in Southeast Asia,Europe and Africa,utilizing the three delays model approach as a categorization framework.Method:This literature review followed BEME guide No.3.A total of 18 articles were included in the sample after conducting a thorough search of various databases and search engines.A Prisma flowchart was created,and the articles were critically appraised.Results:A total of 18 articles focusing on different regions were analyzed.The findings revealed that in countries of Southeast Asia,the primary cause of maternal mortality is the first delay,which refers to the lack of awareness in seeking medical care.On the other hand,in Africa and other European countries,the second and third delays are more prominently associated with maternal mortality.Conclusion:Inadequate care is one of the major causes of maternal mortality in majority of regions acrossthe globe.Multiple factors can hinder access to appropriate healthcare.The three delays model plays a significant role in the higher maternal mortality rate.By raising awareness among women and their families about the importance of seeking healthcare,the risk of fatality can be reduced.Similarly,in developing regions,it is crucial to ensure that healthcare facilities are easily accessible and provide high-quality emergency obstetric care to meet the needs of pregnant women in critical situations.

The Control Research Progress of Liberobacter asiaticum in Taizhou City

After decades of researches and discussions on occurrence regularity and control technology of citrus psyl id （Diaphorina citri Kuwayana） and Liberobacter asi-aticum, the occurrence regularity and sampling technique of citrus psyl id in orange forests in Taizhou was defined and the connections between the infection rate and carrying rate of citrus psyl id and Liberobacter asiaticum as wel as the correlations between the Liberobacter asiaticum and citrus yield loss were discussed. This paper discussed the warnings of Liberobacter asiaticum and citrus psylid control index, the economic life span of orange forests was then predicted by building the diseases spreading models with different management styles. At last, the paper put forward the comprehensive prevention and control technology and the concept that Liber-obacter asiaticum was preventable and control able.

An Approach of Reducing Overall Level of Export Fluctuations of the Export-oriented Countries

Overall level of export fluctuations of the export-oriented countries with rising export volume partly stem from the market failure caused by free choice of export enterprises,some government intervention thus may be necessary.To reduce the level of fluctuations of the export growth rates in these countries,this paper,taking the significant differences of the exports among various markets into account and thus using a new index named relative variance to measure the export volatility risks,proposes a model of merchandise market portfolio,a modified version of Markowitz model,available to provide explicit guidelines for the firms,the industries and even the whole country to optimize the structure of their export markets.An application of this model to the case of China’s apple is then discussed.The results show that the market share of China’s apple in 7 sub-markets should be redistributed drastically.

A case study of the consistency problem in the inverse estimation

Inverse technique is a widely used method in oceanography, but it has a problem that the retrieved solutions often violate model prior assumptions. To tune the model has consistent solutions, an iteration approach, which successively utilizes the posterior statistics for next round inverse estimation, is introduced and tested from a real case study. It is found that the consistency may become elusive as the determinants of solution and noise covariance matrices become zero in the iteration process. However, after several steps of such operation, the difference between posterior statistics and the model prior ones can be gradually reduced.

Thermodynamic analysis of β → α(α_2) phase transformation temperature in a Ti-Al-H alloy system

The T0 face equation of a Ti-Al-H alloy system was set up by the regular solution model,and the relationship between the β phase stabilizing parameter of hydrogen and the equilibrium phase compositions was attained.According to the T0 face equation and the thermodynamic parameters from literature,the effect of hydrogen on the β→α（α2） transformation temperature was evaluated.The calculated results were in a better consistence with the measured ones.

Spatial Pressure Distribution of Cultivated Land and the Analysis of Food Safety in Kunming City,China

The social and economical condition,cultivated land resources and general situation of grain production of research area are introduced.According to relevant data of cultivated land of Kunming City in 2006,taking the minimum per capita cultivated land and index model of cultivated land pressure,and combining with two conditions of per capita grain demand which are 300 kg and 400 kg,the comparative research method was used to calculate and analyze the minimum per capita cultivated land and index model of cultivated land pressure.The results show that when per capita grain demand-Gr is 300 kg,the per capita cultivated land in Kunming City has regional differences,reflects different regular patterns of spatial distribution,and presents obvious and typical radial distribution.Urban area of Kunming has the lowest point and be taken as the center,per capita cultivated land decreases progressively from suburban to the periphery;when per capita grain demand-Gr is 400 kg,under the same consumption level,cultivated land pressures of each county in Kunming City are different,and so are the driven factors;when per capita grain demand-Gr is 300 kg or 400 kg,the indexes of cultivated land pressure in Kunming are all greater than 1,cultivated land pressure is enormous,the indexes of cultivated land pressure of each county(district) have two conditions-greater than 1 and less than 2,and the reasons differ from one another.Combining with the spatial distribution characteristics of the cultivated land and food and the situation of industrial development,the countermeasures and suggestions are put forwarded to make full use of back-up cultivated land,to plan the distribution of industrial structure in each county(district) of Kunming City as a whole,to dispose cultivated land resources reasonably and so on.

The Development Model and Countermeasures of China's Next Generation AI Industry

In the new round of IT revolution, the AI industry has become a new engine driving economic growth. In such a context, major developed countries actively prepare and plan for this industry, and China also attaches great importance to next-generation AI development. At present, the China's nextgeneration AI industry has distinctive shortcomings in the development of core chips, basic algorithms and corresponding talents. China should make full use of its enormous advantages and favorable conditions in technology and sensibly choose from a development model of business innovation, fast iteration, application layer driving base layer, and self-developed technology standards. By drawing on the experience of developed countries, China can accelerate its building and improvement of government supported mechanisms, upgrade its system of venture capital and services, enhance the advancement of basic research in chips and algorithms, and invest more in the cultivation and introduction of next-generation AI talents in a bid to quickly strengthen the core competitiveness of the China's next-generation AI industry.

Reconstruction of incidence reporting rate for SARS-CoV-2 Delta variant of COVID-19 pandemic in the US

In recent years,advanced regularization techniques have emerged as a powerful tool aimed at stable estimation of infectious disease parameters that are crucial for future projections,prevention,and control.Unlike other system parameters,i.e.,incubation and recovery rates,the case reporting rate,Ψ,and the time-dependent effective reproduction number,R_(e)t,are directly influenced by a large number of factors making it impossible to pre-estimate these parameters in any meaningful way.In this study,we propose a novel iteratively-regularized trust-region optimization algorithm,combined with SuSvIuIvRD compartmental model,for stable reconstruction ofΨand R_(e)t from reported epidemic data on vaccination percentages,incidence cases,and daily deaths.The innovative regularization procedure exploits(and takes full advantage of)a unique structure of the Jacobian and Hessian approximation for the nonlinear observation operator.The proposed inversion method is thoroughly tested with synthetic and real SARS-CoV-2 Delta variant data for different regions in the United States of America from July 9,2021,to November 25,2021.Our study shows that case reporting rate during the Delta wave of COVID-19 pandemic in the US is between 12%and 37%,with most states being in the range from 15%to 25%.This confirms earlier accounts on considerable under-reporting of COVID-19 cases due to the impact of”silent spreaders”and the limitations of testing.

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.

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.

Numerical Prediction of Added Resistance and Vertical Ship Motions in Regular Head Waves

The numerical prediction of added resistance and vertical ship motions of one ITTC （Intemational Towing Tank Conference） S-175 containership in regular head waves by our own in-house unsteady RANS solver naoe-FOAM-SJTU is presented in this paper. The development of the solver naoe-FOAM-SJTU is based on the open source CFD tool, OpenFOAM. Numerical analysis is focused on the added resistance and vertical ship motions （heave and pitch motions） with four very different wavelengths （ 0.8Lpp 〈 2 〈 1.5L ） in regular head waves. Once the wavelength is near the length of the ship model, the responses of the resistance and ship motions become strongly influenced by nonlinear factors, as a result difficulties within simulations occur. In the paper, a comparison of the experimental results and the nonlinear strip theory was reviewed and based on the findings, the RANS simulations by the solver naoe-FOAM-SJTU were considered competent with the prediction of added resistance and vertical ship motions in a wide range of wave lengths.

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.

The Efficient Finite Element Methods for Time-Fractional Oldroyd-B Fluid Model Involving Two Caputo Derivatives

In this paper,we consider the numerical schemes for a timefractionalOldroyd-B fluidmodel involving the Caputo derivative.We propose two efficient finite element methods by applying the convolution quadrature in time generated by the backward Euler and the second-order backward difference methods.Error estimates in terms of data regularity are established for both the semidiscrete and fully discrete schemes.Numerical examples for two-dimensional problems further confirmthe robustness of the schemes with first-and second-order accurate in time.

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.