Modeling Approach and Analysis of the Structural Parameters of an Inductively Coupled Plasma Etcher Based on a Regression Orthogonal Design

The geometry of an inductively coupled plasma （ICP） etcher is usually considered to be an important factor for determining both plasma and process uniformity over a large wafer. During the past few decades, these parameters were determined by the ＂trial and error＂ method, resulting in wastes of time and funds. In this paper, a new approach of regression orthogonal design with plasma simulation experiments is proposed to investigate the sensitivity of the structural parameters on the uniformity of plasma characteristics. The tool for simulating plasma is CFD-ACE＋, which is commercial multi-physical modeling software that has been proven to be accurate for plasma simulation. The simulated experimental results are analyzed to get a regression equation on three structural parameters. Through this equation, engineers can compute the uniformity of the electron number density rapidly without modeling by CFD-ACE＋. An optimization performed at the end produces good results.

Suggestion of advanced regression model on friction angle of fault gouge in South Korea

Although friction characteristics of fault gouge are important to understand reactivation of fault,behavior of earthquake,and mechanism of slope failure,analysis results of fault gouge have low accuracy mostly than those of soils or rocks due to its high heterogeneity and low strength.Therefore,to improve the accuracy of analysis results,we conducted simple regression analysis and structural equation model analysis and selected major influential factors of friction characteristics among many factors,and then we deduced advanced regression model with the highest coefficient of determination(R^(2)) via multiple regression analysis.Whereas most coefficients of determination in simple regression analysis are below0.3-0.4,coefficient of determination in multiple regression analysis is remarkably large as 0.657.

Application of deep autoencoder model for structural condition monitoring

Damage detection in structures is performed via vibra-tion based structural identification. Modal information, such as fre-quencies and mode shapes, are widely used for structural dama-ge detection to indicate the health conditions of civil structures.The deep learning algorithm that works on a multiple layer neuralnetwork model termed as deep autoencoder is proposed to learnthe relationship between the modal information and structural stiff-ness parameters. This is achieved via dimension reduction of themodal information feature and a non-linear regression against thestructural stiffness parameters. Numerical tests on a symmetri-cal steel frame model are conducted to generate the data for thetraining and validation, and to demonstrate the efficiency of theproposed approach for vibration based structural damage detec-tion.

Validation of general linear modeling for identifying factors associated with Quality of Life: A comparison with structural equation modeling

Purpose: General linear modeling (GLM) is usually applied to investigate factors associated with the domains of Quality of Life (QOL). A summation score in a specific sub-domain is regressed by a statistical model including factors that are associated with the sub-domain. However, using the summation score ignores the influence of individual questions. Structural equation modeling (SEM) can account for the influence of each question’s score by compositing a latent variable from each question of a sub-domain. The objective of this study is to determine whether a conventional approach such as GLM, with its use of the summation score, is valid from the standpoint of the SEM approach. Method: We used the Japanese version of the Maugeri Foundation Respiratory Failure Questionnaire, a QOL measure, on 94 patients with heart failure. The daily activity sub-domain of the questionnaire was selected together with its four accompanying factors, namely, living together, occupation, gender, and the New York Heart Association’s cardiac function scale (NYHA). The association level between individual factors and the daily activity sub-domain was estimated using SEM?and GLM, respectively. The standard partial regression coefficients of GLM and standardized path coefficients of SEM were compared. If?these coefficients were similar (absolute value of the difference -0.06 and -0.07 for the GLM and SEM. Likewise, the estimates of occupation, gender, and NYHA were -0.18 and -0.20, -0.08 and -0.08, 0.51 and 0.54, respectively. The absolute values of the difference for each factor were 0.01, 0.02, 0.00, and 0.03, respectively. All differences were less than 0.05. This means that these two approaches lead to similar conclusions. Conclusion: GLM is a valid method for exploring association factors with a domain in QOL.

Effects of Multicollinearity on Type I Error of Some Methods of Detecting Heteroscedasticity in Linear Regression Model

Heteroscedasticity and multicollinearity are serious problems when they exist in econometrics data. These problems exist as a result of violating the assumptions of equal variance between the error terms and that of independence between the explanatory variables of the model. With these assumption violations, Ordinary Least Square Estimator (OLS) will not give best linear unbiased, efficient and consistent estimator. In practice, there are several structures of heteroscedasticity and several methods of heteroscedasticity detection. For better estimation result, best heteroscedasticity detection methods must be determined for any structure of heteroscedasticity in the presence of multicollinearity between the explanatory variables of the model. In this paper we examine the effects of multicollinearity on type I error rates of some methods of heteroscedasticity detection in linear regression model in other to determine the best method of heteroscedasticity detection to use when both problems exist in the model. Nine heteroscedasticity detection methods were considered with seven heteroscedasticity structures. Simulation study was done via a Monte Carlo experiment on a multiple linear regression model with 3 explanatory variables. This experiment was conducted 1000 times with linear model parameters of β0 = 4 , β1 = 0.4 , β2= 1.5 and β3 = 3.6. Five (5) levels of mulicollinearity are with seven (7) different sample sizes. The method’s performances were compared with the aids of set confidence interval (C.I.) criterion. Results showed that whenever multicollinearity exists in the model with any forms of heteroscedasticity structures, Breusch-Godfrey (BG) test is the best method to determine the existence of heteroscedasticity at all chosen levels of significance.

Latent Structure Linear Regression

A short review is given of standard regression analysis. It is shown that the results presented by program packages are not always reliable. Here is presented a general framework for linear regression that includes most linear regression methods based on linear algebra. The H-principle of mathematical modelling is presented. It uses the analogy between the modelling task and measurement situation in quantum mechanics. The principle states that the modelling task should be carried out in steps where at each step an optimal balance should be determined between the value of the objective function, the fit, and the associated precision. H-methods are different methods to carry out the modelling task based on recommendations of the H-principle. They have been applied to different types of data. In general, they provide better predictions than linear regression methods in the literature.

Finite element model updating for large span spatial steel structure considering uncertainties

In order to establish the baseline finite element model for structural health monitoring,a new method of model updating was proposed after analyzing the uncertainties of measured data and the error of finite element model.In the new method,the finite element model was replaced by the multi-output support vector regression machine(MSVR).The interval variables of the measured frequency were sampled by Latin hypercube sampling method.The samples of frequency were regarded as the inputs of the trained MSVR.The outputs of MSVR were the target values of design parameters.The steel structure of National Aquatic Center for Beijing Olympic Games was introduced as a case for finite element model updating.The results show that the proposed method can avoid solving the problem of complicated calculation.Both the estimated values and associated uncertainties of the structure parameters can be obtained by the method.The static and dynamic characteristics of the updated finite element model are in good agreement with the measured data.

A Study on the Impact of China’s Demographic Structure on Human Capital Accumulation-Based on a Panel Data Model

With the development of the times,the role of human capital in economic growth has been highlighted.However,in the current academic world,the research on the topic of demographic structure and human capital accumulation is not closely integrated.China is now in a period of economic transformation and upgrading,and it is urgent to change the traditional economic growth model,increase human capital investment,and cultivate new growth momentum.Therefore,this paper examines the impact of China’s population structure on human capital accumulation by constructing a mathematical model based on relevant panel data of 31 provinces(municipalities and autonomous regions)from 2001 to 2020.The empirical results show that human capital accumulation in China has a certain lagging effect,while population age and urban-rural structure enhance human capital accumulation in both quantitative and qualitative terms,respectively.In addition,industrial transformation,economic growth level,and per capita income all have significant positive effects.The effect of education,on the other hand,is not significant,while the interaction between urbanization and industrial structure may hurt it.Based on this,this paper proposes targeted countermeasures and suggestions in terms of fully developing the population quality dividend,fully tapping the talent knowledge dividend,continuously improving the health dividend,and continually tapping the aging dividend.

京津冀科技创新投入对产业结构高级化的影响

京津冀科技创新对于推动京津冀地区产业结构高级化提供了有力支撑。通过明确京津冀科技创新投入对产业结构高级化影响的被解释变量、解释变量和控制变量,确定京津冀科技创新投入对产业结构高级化影响的指标。构建京津冀科技创新投入对产业结构高级化影响的静态回归模型和动态回归模型,利用2009—2020年面板数据,深入开展京津冀地区科技创新投入对产业结构高级化影响的对比研究。结果表明:京津冀整体的科技创新投入对其产业结构高级化的影响显著,科技创新投入增加能显著促进产业结构高级化;京津冀科技创新投入对产业结构高级化的影响程度存在显著的空间差异,经济发展水平显著影响地区科技创新投入对产业结构高级化的促进力度;考虑控制变量,区域对外开放水平显著抑制地区产业结构高级化,人力资本水平与政府支持力度对地区产业结构高级化影响存在显著空间差异。根据研究结果,提出京津冀科技创新推动产业结构高级化的对策建议。

数字经济发展对可持续发展的影响

在数字经济广义定义的基础上,对数字经济的12个显著特征进行量化研究。首先,综合参考文献和专家咨询法,确定评价指标;其次,通过DS证据理论合成规则,对变异系数法和熵权法的权值进行融合,对数字经济高质量可持续发展进行综合评价;最后,建立数字经济高质量可持续发展的PLS结构方程模型和回归模型。通过实证对该模型进行观测和预报,其结果与实测值吻合较好。

基于原子类型电性拓扑状态指数的最小点火能预测模型研究

针对危险化学品最小点火能的试验测量存在一定的困难且具有很大不确定性的问题,收集了61种化学品的最小点火能试验值,并仅根据其分子结构计算了原子类型电性拓扑状态指数(ETSI),最终选择了23种ETSI作为分子描述符进行建模。首先,尝试基于61种危险化学品的最小点火能值,通过多元线性回归和支持向量机进行QSPR建模,但所建模型的内部稳定性很差,不满足基本的要求。经分析可能是因为描述符太多而样本相对较少造成的。通过从数据集中删除13个最小点火能值,ETSI的种类减少到14个,并采用相同的方法进行QSPR建模。经验证,新建模型在拟合能力、内部稳定性和外部预测能力3个方面均具有优异的性能,可以在不需要购买任何昂贵的软件和硬件的前提下,方便、快捷地预测化学品的最小点火能。

消费升级视角下互联网使用对农村生活垃圾减量的影响

基于湖北省967份农户调查数据,采用内生转换模型,聚焦抑制废弃物产生源头措施,着眼农村地区和微观层面,在“反事实”分析框架下实证检验互联网使用对农村居民生活垃圾减量的影响,并进一步从消费升级视角探究其中的作用机制。研究结果表明:(1)互联网使用显著促进了农村居民生活垃圾减量,反事实结果表明,使用互联网的农户假如未使用互联网,其生活垃圾产生量将显著增加。(2)消费升级是互联网使用作用于生活垃圾减量的主要传导机制。其中,互联网购物带来的消费数量提升会增加生活垃圾产生量,但是消费结构优化又进一步抑制生活垃圾产生量。进一步的异质性分析表明,互联网对男性、老龄人群、受教育程度较低和收入水平较高的农户影响效果更为显著。提出为切实改善农村人居环境,各级政府应积极采取夯实互联网基础设施、倡导理性消费、加快生产和生活方式全面绿色转型等措施,促进农村生活垃圾减量,提高农村生活垃圾治理效率。

数字普惠金融对山东居民消费水平的影响研究——基于中介效应的实证分析

消费是拉动经济增长的重要动力之一,扩大内需,促进消费成为促进经济复苏的重要调控方法。本文在双循环发展格局下探究数字普惠金融对山东居民消费水平的影响,通过构建数字普惠金融与居民消费水平的回归模型,进行中介效应分析,发现数字普惠金融的发展有利于山东居民消费水平的提升,同时人均可支配收入在该模型中发挥中介作用,以期为促进消费,缓解内需不足的问题提供新思路。

Estimation and testing of linkages between forest structure and rainfall interception characteristics of a Robinia pseudoacacia plantation on China’s Loess Plateau

Understanding the interaction between canopy structure and the parameters of interception loss is essential in predicting the variations in partitioning rainfall and water resources as affected by changes in canopy structure and in implementing water-based management in semiarid forest plantations.In this study,seasonal variations in rainfall interception loss and canopy storage capacity as driven by canopy structure were predicted and the linkages were tested using seasonal filed measurements.The study was conducted in nine 50 m×50 m Robinia pseudoacacia plots in the semiarid region of China’s Loess Plateau.Gross rain-fall,throughfall and stemflow were measured in seasons with and without leaves in 2015 and 2016.Results show that measured average interception loss for the nine plots were 17.9% and 9.4% of gross rainfall during periods with leaves (the growing season) and without leaves, respectively. Average canopy storage capacity estimated using an indirect method was 1.3 mm in the growing season and 0.2 mm in the leafless season. Correlations of relative interception loss and canopy storage capacity to canopy variables were highest for leaf/wood area index (LAI/WAI) and canopy cover, fol-lowed by bark area, basal area, tree height and stand density. Combined canopy cover, leaf/wood area index and bark area multiple regression models of interception loss and canopy storage capacity were established for the growing season and in the leafless season in 2015. It explained 97% and 96% of the variations in relative interception loss during seasons with and without leaves, respectively. It also explained 98% and 99% of the variations in canopy storage capacity during seasons with and without leaves, respectively. The empiri-cal regression models were validated using field data col-lected in 2016. The models satisfactorily predicted relative interception loss and canopy storage capacity during seasons with and without leaves. This study provides greater under-standing about the effects of changes in tree canopy structure (e.g., dieback or mortality) on hydrological processes.

中国能源消费结构多目标优化研究

本文使用多目标优化模型对中国能源消费结构进行优化。本文首先分析了中国1999年至2021年的能源消费结构变化趋势,然后使用能源统计数据,建立线性回归模型确定能源投入成本和环境污染治理成本两个目标函数,分别根据中等发达国家和发达国家发展经验分别确定约束条件,并通过多目标优化模型对中国能源消费结构进行优化,以实现能源投入成本最小和环境污染治理成本最小。最后本文针对多目标优化结果提出了优化能源消费结构,降低环境污染治理成本的对策建议。

大保当煤不同显微组分含量与分子结构参数关系

为探究煤岩显微组分含量及其分子结构参数之间的内在联系,以侏罗纪时期神府煤田大保当煤矿低变质程度高惰质组煤样为研究对象,通过浮沉离心法分选出不同含量煤岩显微组分的7个样品,并利用傅里叶变换红外光谱测试(FTIR)、X射线衍射测试(XRD)、X射线光电子能谱测试(XPS)、^(13)C固体核磁测试(^(13)C-NMR)技术对其物理结构、化学结构进行表征和半定量计算处理,得到一系列结构参数。FTIR结果表明,镜质组较多时,其脂肪链更多、更短,支链化程度高,惰质组含量较多时,芳香度高;XRD结果表明,不同煤岩显微组分之间,芳香层间距d_(002)、延展度L_(a)、堆垛高度L_(c)及芳香层数Nc随镜质组含量增加只存在微小差异;XPS碳元素分析结果表明,随镜质组含量增大,C—C和C—H含量总和占比逐渐增大,这与镜质组中含有大量脂肪族结构有关,惰质组较高时醚碳/酚碳(C—O)质量分数较大,这是由于惰质组芳香度较高;^(13)C-NMR分析结果表明,总芳香碳质量分数f_(a)、芳香碳质量分数f′_(a)和镜质组呈较好的正相关关系,脂肪族碳的总质量分数f_(al)与镜质组含量呈较好的负相关关系。选择拟合度较高的结构参数进行回归分析,得到镜质组含量最优预测模型。这些结论是当前大保当不同含量煤岩组分分子结构的补充信息,更好地理解与其含量之间的相关关系,为预测煤岩组分含量提供了一种方法。

地方性产业政策对产业结构升级的有效性测度——基于2003—2020年中国省级面板数据

产业政策是地方政府推动产业升级的重要手段,测度地方性产业政策对产业结构升级的影响效果具有重要意义。通过系统收集我国30个省级地方政府2003—2020年颁布的涉及产业政策的法律法规和政府规章文件并统计其数量,构建产业政策指数。基于2003—2020年中国30个省级的面板数据,分别采用固定效应回归模型和面板分位数回归模型,测度地方性产业政策对产业结构的高级化、合理化和生态化的作用效果。研究表明:地方性产业政策能显著促进产业结构的高级化、合理化和生态化。此外,从总体上来看,对于产业结构升级水平较高的省份,产业政策的促进作用更加显著。因此,地方政府在积极推动市场自主发展的前提下,应适当给予相应的政策扶持和信息引导,促进地区资源的高效合理配置。

绿色信贷能推动产业结构绿色转型升级吗?——基于我国省级面板数据的实证研究

当前我国绿色信贷规模居世界首位,绿色信贷是重要的绿色金融产品,其可通过技术进步机制和挤出效应机制影响产业结构绿色转型升级。文章运用固定效应模型对2011—2020年我国省级面板数据进行回归分析,发现从全国层面来看绿色信贷对我国产业结构绿色转型升级具有显著的正向促进作用;分地区来看,绿色信贷对我国四大地区产业结构绿色转型升级的影响呈现明显的区域异质性。今后应实施差异化绿色信贷政策、创新绿色信贷产品、完善绿色信贷的激励和约束机制,从而进一步推动我国产业结构绿色转型升级。

基于物联网的粉尘浓度数据采集优化设计

针对无线传感器网络节点数据传输的耗能性和物联网数据的海量性特点,提出一种LEACH协议和线性回归模型相结合的PM2.5数据采集优化检测系统。该系统以PM2.5粉尘浓度传感器为检测节点,通过研究基于物联网感知层的数据融合算法,提出将线性回归模型与簇状网络结构相结合,构建系统函数,实现节点间相关参数的传输,减少了网络的总能耗,延长了网络生命周期。仿真结果表明:所提数据融合策略达到了节能高效、优化采集数据的目的。