Preliminary Study on the Linear Relationship between the Internal Pressure Head of Rubber Dam Bags and Real-time Dam Height

The design operating conditions of rubber dams were analyzed,and it is found that the operating conditions are similar to the actual operating conditions of changes in the internal pressure ratio of a specific rubber dam bag in the process of filling and draining. Based on this,the linear relationship curve between the internal pressure head H0 and the real-time dam height H and its approximate analytical formula can be obtained,which can be used as a supplement and correction method for the measurement method of real-time dam height during rubber dam operation,and provides reference for rubber dam project managers.

Effect of Ionic Liquid on Retention of Solutes on C_(18) Column Based on Linear Solvation Energy Relationships

Several chromatography systems with ionic liquids and a mixture of water with the modifier as mobile phase were characterized via the linear solvation energy relationships（LSER） model. The effects of the ionic liquids and modifier（methanol） concentrations on the retention of 10 solutes（caffeine, pyridine, aniline, phenol, methylparaben, acetopenone, m-cresol, p-cresol, o-cresol, and benzene） were discussed. The LSER model demonstrated high potential to predict retention factors with high squared correlation coefficients（r^2〉 0.97）. A comparison of predictable and experimental retention factors revealed that LSER can adequately reproduce the experimental retention factors of the solutes under different investigated experimental conditions. This model is a helpful tool to evaluate the retention characteristics of ionic liquid systems and to understand the interactions of solutes and ionic liquids.

Linear Free Energy Relationships in Extraction of Rare Earths by Acidic Organophosphorus Extractants

The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extractant molecules are investigated by linear regression analysis in the extraction of rare earths by various classes and structures of monoacidic organophosphorus extractants. The results indicate that in Linear free energy relationship formula 1gK(ex) = rho Sigma sigma(Phi) + psi Sigma upsilon + h generally follows for this kind of extraction systems. Accordingly, the quantitative structure-behaviour relationships of extractants are discussed. These relationships can be preliminarily applied to predict the 1gK(ex) values of rare earth extraction with definite structures of this class of extractants, and thus can provide some directions for the design of new RE extractants.

Structural Characterization of Volatile Components of Rosa Banksiae Ait for Estimation and Prediction of Their Linear Retention Indices and Retention Times

The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.

Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods

In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Relationships between Rainy Days,Mean Daily Intensity and Seasonal Rainfall in Normal,Flood and Drought Years over India

here are limitations in using the seasonal rainfall total in studies of Monsoon rainfall climatology. A correlation analysis of the individual station seasonal rainfall with all India seasonal mean rainfall has been made. After taking the significance test (strictly up to 5% level) the stations which are significantly correlated have been considered in this study in normal, flood and drought years respectively. Analysis of seasonal rainfall data of 50 stations spread over a period of 41 years suggests that a linear relationship fits better than the logarithmic relationship when seasonal rainfall versus number of rainy days is studied. The linear relationship is also found to be better in the case of seasonal rainfall versus mean daily intensity.

Dominance hierarchy and social relationships in a group of Captive black-and-white snub-nosed monkeys (Rhinopithecus bietl)

Different types of dominance hierarchies reflect different social relationships in primates. In this study, we clarified the hierarchy and social relationships in a one-male unit of captive Rhinopithecus bieti observed between August 1998 and March 1999. Mean frequency of agonistic behaviour among adult females was 0.13 interactions per hour. Adult females exhibited a linear hierarchy with a reversal of 10.9%, indicating an unstable relationship; therefore, R. bieti appears to be a relaxed/tolerant species. The lack of a relationship between the agonistic ratio of the adult male towards adult females and their ranks indicated that males did not show increased aggression towards low-ranking females. Differentiated female affiliative relationships were loosely formed in terms of the male, and to some extent influenced by female estrus, implying that relationships between the male and females is influenced by estrus and not rank alone. A positive correlation between the agonistic ratio of adult females and their ranks showed that the degree to which one female negatively impacted others decreased with reduction in rank. Similarly, a positive correlation between the agonistic ratio of females and differences in rank suggests that a female had fewer negative effects on closely ranked individuals than distantly ranked ones. These data indicate that rank may influence relationships between females. A steeper slope of regression between the agonistic ratio and inter-female rank differences indicated that the extent of the power difference in high-ranking females exerting negative effects on low-ranking ones was larger during the mating season than the birth season, suggesting that rank may influence the mating success of females.

Quantitative Structure-biodegradability Relationship Study about the Aerobic Biodegradation of Some Aromatic Compounds

10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.

Web-based supplier relationship framework using agent systems

In order to enable both manufacturers and suppliers to be profitable on today’s highly competitive markets, manufacturers and suppliers must be quick in selecting best partners establishing strategic relationship, and collaborating with each other so that they can satisfy the changing competitive manufacturing requirements. A web-based supplier relationships (SR) framework is therfore proposed using multi-agent systems and linear programming technique to reduce supply cost, increase flexibility and shorten response time. Web-based SR approach is an ideal platform for information exchange that helps buyers and suppliers to maintain the availability of materials in the right quantity, at the right place, and at the right time, and keep the customer-supplier relationship more transparent. A multi-agent system prototype was implemented by simulation, which shows the feasibility of the proposed architecture.

Hormesis has emerged as a more common and fundamental dose-response model than the threshold or linear-no-threshold(LNT) models

在回顾传统的有阈和线性无阈的剂量-反应关系的基础上,分析此类该模型被毒理学领域认同的主要原因和其在预测低剂量效应中的缺陷,进而提出了高剂量接触呈现抑制效应,而低剂量却呈现促进或刺激作用这一全面的激效兴奋性剂量-反应关系,弥补了现有模型的不足,并充分论证这种激效性剂量-反应关系在不同种属、接触因素和生物学终点中的普遍性,并探讨了不同物质的激效兴奋作用的可能机制以及激效兴奋模型的三大优点,最后提出了激效模型谋求广泛接受的现实困难和发展趋势。

Quantitative Structure-activity Relationship Models of Monomer Reactivity

The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.

Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.

Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

山西地磁台阵受晋南线高压直流输电接地极干扰的特征分析与处理

2014年9月山西省地震局与中国地震局地球物理研究所在山西北部恒山断裂带区域共同建设了地磁台阵,布设了5台GM4型磁通门磁力仪,安装于代县、山阴、应县、繁峙和怀仁,以获取区域场地磁三分量观测资料,台阵的5个测点位于高压直流输电晋南线接地极附近。通过对比山西地磁台阵5个测点GM4型磁通门磁力仪与其他台站地磁秒数据,量算出高压直流输电线路(晋北—南京)对山西地磁台阵地磁数据干扰幅度,对其进行相关性和误差分析,求得拟合公式。结果表明,山西地磁台阵受晋南线高压直流输电干扰,Z分量变化幅度随台站位置至晋南线垂直距离增加而减小,H、D分量干扰幅度与Z分量干扰幅度线性相关,以Z分量变化量为因变量,使用参考拟合公式求取的H、D分量变化量,可应用于山西地磁台阵受晋南线高压直流输电干扰的日常处理工作。

矿山充填新型胶凝材料添加量与充填体抗压强度关系研究

工业废渣粉煤灰具有胶凝特性,基于此成功研制出一种用于矿山井下充填的新型胶凝材料。为能够在不同抗压强度要求下快速准确地计算出胶凝材料的添加量,通过进行抗压强度试验,并利用一元线性回归法拟合出不同养护龄期下充填试块抗压强度与胶凝材料添加量之间的数量关系。研究结果表明,研发的新型胶凝材料不仅有效地解决了粉煤灰排放所带来的环境污染问题,同时由于以粉煤灰为主要成分的胶凝材料成本相对低廉,降低了充填成本,使充填采矿法在矿山开采中能得到更广泛的应用。

数字经济影响农村产业融合的机理研究——基于中国省域面板数据的实证研究

基于2011—2021年中国省级面板数据,采用固定效应模型、面板门槛回归模型等实证检验数字经济对农村产业融合发展的影响及其非线性关系。研究发现:(1)数字经济显著促进了我国农村产业融合发展,该结论经过一系列稳健性检验、内生性处理后依然成立。(2)数字经济对农村产业融合发展的促进作用存在边际效应递减的非线性特征,资本积累水平、居民消费水平、高质量人力资本水平在这一促进作用中发挥门槛效应。(3)随着农村产业融合发展水平的提高,数字经济对农村产业融合发展的促进作用呈现出由弱逐渐变强再弱化的趋势,相较于西部地区,数字经济在东、中部地区对农村产业融合发展的促进作用较弱。为充分发挥数字经济促进农村产业融合发展的积极效应,需要在加快发展数字经济、推进数字乡村建设的同时,促进金融创新、缓解金融约束,推进共同富裕、提振消费信心,重视人才培养与引进、保障人才供给。

昆明泥炭土压缩指标与基本物理指标相关性研究

泥炭土作为一种对建筑物沉降有着重要影响的特殊土,其压缩指标是影响工程安全的关键指标。对昆明地区勘察数据中的压缩系数和压缩指数进行了回归分析,并建立了回归模型,开展了昆明地区4个不同场地泥炭土一维固结试验,基于试验结果和国内外类似经验公式验证了回归模型的适用性。结果表明:压缩系数α_(1-2)、压缩指数C_(c)与天然含水率ω_(0)、初始孔隙比e_(0)具有高度相关线性关系,其中压缩指数与初始孔隙比的关系为C_(c)=0.15e_(0)-0.17;回归模型与试验结果及国内外类似经验公式有较好的一致性。

制造业与生产性服务业协同集聚能否推动绿色创新效率提升?

本文在提出理论机制与测算中国30个省市产业协同集聚和绿色创新效率水平的基础上,深入探究了两者的非线性关系和多重影响机制。研究结果表明:2011-2021年间,全国绿色创新效率波动上升,产业协同集聚水平呈缓慢下降;产业协同集聚对绿色创新效率的影响呈显著“U”型特征,且这一结果在处理稳健性问题后依旧成立;长江经济带产业协同集聚对绿色创新效率的影响呈“U”型且即将跨越拐点,而非长江经济带仅具有线性关系;知识溢出及税收竞争在产业协同集聚与绿色创新效率的非线性关系中发挥了显著的中介作用;此外,知识产权保护及环境规制还在两者的关系中起到了不同方向的调节作用。

普惠金融对经济增长与城乡收入差距的影响

为考察普惠金融对经济增长与城乡收入差距的影响,文章选取2013—2020年我国31个省份的面板数据,通过纳入债券、股票等直接融资工具指标构建普惠金融指数,并利用静态面板可行广义最小二乘回归模型、动态面板广义矩回归模型,研究全国以及东、中、西部地区普惠金融发展水平对经济增长与城乡收入差距的影响。结果表明:总体来看,普惠金融与经济增长存在“U”型关系,普惠金融与城乡收入差距存在“倒U”型关系。分地区来看,普惠金融对经济增长与城乡收入差距的影响均表现出一定程度的区域异质性。