几何平均连接性指数:一种修正的分子连接性指数

本文试图改进Kier和Hall提出的分子连接性指数,提供一种更加完美的分子表征和分子设计技术。根据分子连接性指数定义,本文对其进行几何平均修正,使其更具解释性和丰富的含义,并命名为几何平均连接性指数(GMCI)。新的分子连接性指数用于一组含3-8个碳原子的烷烃7种性质的QSAR／QSPR研究,统计结果表明,由几何平均连接性指数所建模型普遍比传统的分子连接性指数所建模型要好。     

一种新的基团染色指数及其倒指数的应用研究

根据分子中基团的特性和连接性,提出了一种新的基团染色指数^mG及其倒指数．研究了多环芳烃气相色谱保留指数RI与基团染色指数之间的定量关系,通过逐步回归分析,以94种多环芳烃化合物的RI实验值为基础,建立了根据基团染色指数估算RI的定量模型,属于优级模型。该方法既考虑了分子中基团的特性,又考虑了其连接性。对94种多环芳烃气相色谱保留指数的计算结果表明,估算值与实验值的一致性令人满意,平均相对误差为1．81％。与其它拓扑指数相比,该指数结构选择性和相关性好。     

拓扑距离特征指数对多氯联苯的QSPR研究

根据分子拓扑理论,提出1种新的多氯联苯(PCBs)化合物分子中,顶点原子(基团)点价值δ_i的计算方法,据以构建分子的结构参数——拓扑距离特征指数(TDEI);同时考虑分子体积以及氯原子在苯环上的位置和数目对PCBs性质的影响,引入分子体积参数(V_R)和基团定位指数(D),然后,采用多元线性回归法,分别建立了PCBs的正辛醇-水分配系数(lgK_(OW))和水溶解度(-lgS_w)的定量结构-性质相关模型,其相关系数分别达到0.9972和0.9730,均优于前人的工作。利用模型预测另外7个PCBs分子的lgK_(OW)和-lgS_W,预测值与实验值的一致性令人满意。     

有机化合物吸着系数(Koc)估算模型比较

基于592种化合物的Koc实测数据,分别采用片段常数法和分子连接性指数法建立了其估算模型.根据模型残差分析了两种模型的预测误差,并运用2种方式的jackknife检验比较了它们的稳健性.片段常数模型涉及较多变量,其应用受到有限的已知片段常数以及烦琐的片段划分过程的限制,相比之下,分子连接性指数模型变量数较少,其应用时只需了解化合物的结构式.片段常数模型的估算精度略高于分子连接性指数模型,两者的差别主要体现在logKoc<1和logKoc>4的化合物上.分子连接性指数模型的稳健性比片段常数模型高.在依次删除一类特定类别化合物时,其估算误差的变异幅度均小于片段常数模型.     

支持向量机用于酚类化合物毒性的QSAR研究

从分子结构出发,计算25个酚类化合物的分子连接性指数及分子的价连接性指数,用线性逐步回归方法建立4参数的最佳方程,以此4参数作为输入参数,将留一法(L00)应用到BP网络、径向基函数(RBF)神经网络,及新颖的机器学习方法支持向量机,建立酚类化合物预测黑呆头鱼毒性的QSAR模型.应用非线性SVM法建立的预测模型结果,优于BP网络和RBF网络,SVM、BP、RBF模型预测的相关系数分别为0.959,0.940和0.945,令人满意.     

链烷烃分子结构设计(I)分子构成基团的确定

以链烃一阶分子连接性指数及其与状态方程参数的关系为基础，利用神经元网络预测与之对应的。然后根据、确定分子结构的路径指数并将其转换为与分子结构，构成基团相关的顶点度数，从而确定构成基团的种类与个数。     

拓扑集成指数与TIBO类衍生物构效关系的研究

4H-甲基咪唑苯二氮(?)酮(TIBO)类衍生物是抗爱滋病的一种新药,分子连接性指数是经证明应用广泛、较为成功的一种指数,本文定义并计算了TIBO类衍生物原子的特征值δi,利用量子化学计算方法,建构新的拓扑集成指数G和分子连接性指数mX,基于多元回归技术建立的对TIBO类衍生物药物的油水分配系数,作出精确估算和预测的定量结构-活性相关关系,得到的多元回归方程为:logP=0.782 G-0.1430X 0.2312X-3.829,估算的平均相对误差为2.53%。为了检验模型的稳定性和预测能力,做了留一法交互校验,预测平均相对误差为3.40%。该模型相关系数高,稳定性好,预测能力强。     

链烷烃分子结构设计（I）

以链烃一阶分子连接性指数及其与状态方程参数的关系为基础，利用神经元网络预测与之对应的，然后根据，确定分子结构的路径指数并将其转换与分子结构，构成基团相关的顶点度数，从而确定的构成基团的种类与个数。     

基于基团属性的手性Am指数及应用

代替原子序数,采用有意义的属性,例如基团尺寸,来比较手性中心键连4个基团的大小,并通过各基团属性的排序判断手性中心的构型为类R/S。以所得到的新构型为基础,扩展了基于分子结构和距离矩阵的Am指数,提出了新的手性Am指数,以其与物化手性描述符相结合来表征一个分子的手性,并应用于脂化酶作为催化剂条件下,手性伯醇产物的立体选择预测以手性指数和Random Forests建立模型,通过交叉验证,能正确预测整个数据集的90%的对映异构体。     

烷基硫醇在不同固定相上的QSRR研究

计算出体系中烷基硫醇化合物的各种结构参数,以优选出的分子连接性指数和量化参数为结构描述符,首次采用反向传播算法(BP)人工神经网络、径向基函数网络(RBF)2种非线性方法建立了参数少且精度高的定量结构-色谱保留相关(QSRR)模型,预测了烷基硫醇在4种极性固定上的气相色谱保留指数(RJ)。结果表明:在4种固定相上建立的BP模型均优于RBF模型且非线性方法(BP、RBF)优于文献中多元线性回归(MLR)方法,所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力。     

Inputting molecular weights into a multilayer perceptron to estimate refractive indices of dialkylimidazolium-based ionic liquids—A purity evaluation

Dialkylimidazolium-based ionic liquids (ILs) are one of the most employed and accessible ILs. These novel chemicals possess unique physicochemical properties which, unfortunately, are greatly altered by impurities. A simple method to evaluate the purity level of ILs is proposed, as a direct relationship exists between refractive index (RI) and purity. Two multilayer perceptrons (MLPs) have been designed to estimate the RI values using the molecular weights (MWs) of the imidazolium-based ILs. The RI is defined as the single output of the created neural network models. These MLPs offered low verification prediction errors (less than 0.48% in both cases), thus leading to useful mathematical tools that are able to more than adequately estimate the RI of imidazolium-based ILs by solely relying on the MWs. Therefore, an extremely manageable mathematical tool that can accurately estimate the RIs of imidazolium-based ILs, and, in the end, their purity, has been created. Additional tests were developed with experimental data regarding two imidazolium-based ILs to evaluate the applicability of the networks, and the results were successful in terms of RI and purity estimation.     

含离子液体体系的二元液液相平衡的计算

离子液体是许多领域中逐渐推广的绿色溶剂。鉴于离子液体在双相化学和工业萃取过程中的应用,对含离子液体体系的液液相平衡的研究,将有利于科研和生产。本文以文献中提供的未进行过数据关联的含[Et3NH][Al2Cl7]、[Emim] [NTf2]、[Bmim][PF6]、[Penmim][PF6]、[Hxmim][PF6]、[Hptmim][PF6]、[Omim][PF6]等7种离子液体与苯(C6C6),三氯化磷(PCl3),正丙醇(C3H7OH),正丁醇(C4H9OH),正戊醇(C5H11OH)等组成的10个二元系的溶解度数据为基础,应用NRTL和UNIQUAC活度系数模型进行了关联计算,得到了相应的模型参数。通过比较计算值与实验值,结果令人满意,NRTL和UNIQUAC方程计算得到的10个体系的全体平均误差(AAD,Overall-average deviation)的平均值分别为3．228％和2．691％。同时,在应用NRTL和UNIQUAC活度系数模型对含离子液体的二元体系作相平衡数据的关联的时候,首次将相互作用参数由常用的对温度的线性函数关系改进为对温度的双曲线型函数关系,从而显著地提高了对上部会溶点计算的准确性。     

Editing and versioning for high performance network models in a multiuser environment

Network data models are frequently used as a mechanism to describe the connectivity between spatial features in GIS applications. Real-life network models are dynamic in nature since spatial features can be periodically modified to reflect changes in the real world objects that they model. Such updates may change the connectivity relations with the other features in the model. In order to perform analysis the connectivity must be reestablished. Existing editing frameworks are not suitable for a dynamic environment, since they require network connectivity to be reconstructed from scratch. Another requirement for GIS network models is to provide support for a multiuser environment, where users are simultaneously creating and updating large amounts of geographic information. The system must support edit sessions that typically span a number of days or weeks, the facility to undo or redo changes made to the data, and the ability to develop models and alternative application designs without affecting the published database. The row-locking mechanisms adopted by many DBMSs is prohibitively restrictive for many common workflows. To deal with long-lasting transactions, a solution based on versioning is thus preferrable. In this paper we provide a unified solution to the problems of dynamic editing and versioning of network models. We first propose an efficient algorithm that incrementally maintains connectivity within a dynamic network. Our solution is based on the notion of dirty areas and dirty objects (i.e., regions or elements containing edits that have not been reflected in the network connectivity index). The dirty areas and objects are identified and marked during the editing of the network feature data; they are then subsequently cleaned and connectivity is re-built. Furthermore, for improving performance, we propose a ‘hyperedge’ extension to the basic network model. A hyperedge drastically decreases the number of edge elements accessed during solve time on large networks; this in turn leads to faster solve operations. We show how our connectivity maintenance algorithms can support the hyperedge enhanced model. We then propose a new network model versioning scheme that utilizes the dirty areas/objects of the connectivity rebuild algorithm. Our scheme uses flexible reconciling rules that allow the definition of a resolving mechanism between conflicting edits according to user needs. Moreover, the utilization of dirty areas/objects minimizes the overhead of tracking the editing history. The unified editing and versioning solution has been implemented and tested within ESRI’s ArcGIS system.     

Comparing classical and neural regression techniques in modeling crude oil viscosity

The importance of crude oil viscosity makes its accurate determination necessary for reservoir performance calculations, evaluation of hydrocarbon reserves, planning thermal methods of enhanced oil recovery, and designing production equipment and pipelines. Viscosity data are also involved in several dimensionless parameters to calculate flow regimes, friction factors and pressure gradients in multiphase flow problems. Numerous research efforts have been directed towards the development of viscosity models that are capable of accurately predicting crude oil viscosity as a function of production data, and/or composition of well stream fluids, if available, using equation of State. Since fluid compositions are not always available, most of the efforts were focused on developing viscosity correlations using classical regression techniques.The study presents, for the first time, a comparison among several models developed using both classical regression techniques (CRT) and neural regression techniques (NRT). These models are developed in this study from viscosity data collected from different oil fields. The models have also been tested using another collection of viscosity data that was not used before in the development phase. Results show that viscosity models developed using NRT were more accurate than viscosity models developed using CRT. Based on this comparison, a viscosity model is therefore presented, which uses stock-tank oil API gravity, gas gravity, pressure(s), and temperature(s) to predict crude oil viscosity. The model was developed using General Regression Neural Network algorithm.     

Genetic programming based models for prediction of vapor-liquid equilibrium

The design, operation, and control of chemical separation processes heavily rely on the knowledge of the vapor-liquid equilibrium (VLE). Often, conducting experiments to gain an insight into the separation behavior becomes tedious and expensive. Thus, standard thermodynamic models are used in the VLE prediction. Sometimes, exclusively data-driven models are also used in VLE prediction although this method too possesses drawbacks such as a trial and error approach in specifying the data-fitting function. For overcoming these difficulties, this paper employs a machine learning (ML) formalism namely “genetic programming (GP)” possessing certain attractive features for the VLE prediction. Specifically, three case studies have been performed wherein GP-based models have been developed using experimental data, for predicting the vapor phase composition of a ternary, and a group of non–ideal binary systems. The inputs to models consists of three pure component attributes (acentric factor, critical temperature, and critical pressure), and as many intensive thermodynamic parameters (liquid phase composition, pressure, and temperature). A comparison of the VLE prediction and generalization performance of the GP-based models with the corresponding standard thermodynamic models reveals that the former class of models possess either superior or closely comparable performance vis-a-vis thermodynamic models. Noteworthy features of this study are: (i) a single GP-based model can predict VLE of a group of binary systems, and (ii) applicability of a GP-based model trained on an alcohol-acetate series data for its higher homolog. The VLE modeling approach exemplified here can be gainfully extended to other ternary and non-ideal binary systems, and for designing corresponding experiments in different pressure and temperature ranges.     

Updating reference values and predictive models of the OCRA method in the risk assessment of work-related musculoskeletal disorders of the upper limbs

A database has been established combining existing data for 23 groups of workers with different level of exposure to repetitive movements of the upper limbs. For all groups, data were available regarding an exposure index (OCcupational Repetitive Actions - OCRA index) and clinically determined UL-WMSDs outcomes (PA = Prevalence of workers affected by one or more UL-WMSDs; PC = Prevalence of single diagnosed cases of an UL-WMSDs). Using these data, new critical values of the OCRA index have been estimated for discriminating different exposure levels (green, yellow, red areas) and new forecasting models of expected PA and PC in exposed populations based on OCRA exposure indexes. The new critical values of the OCRA index were estimated by an original approach in which data for the effect variable (PA) in a reference population not exposed to the relevant risks are combined with the regression function between OCRA and PA. The best simple regression functions between OCRA exposure indexes and health outcomes variables (PA; PC) were researched to obtain forecasting models of effects starting from exposure. Discussion of the results obtained considers their intrinsic limitations, as they are based on prevalence studies, as well as providing recommendations and cautions in the use of the proposed classification system and forecasting models when the OCRA method is applied.     

Estimating ternary viscosity in terms of Moelwyn-hughes' model

In the present work, a new method for estimating the ternary viscosity based on a combination of Moelwyn-Hughes'[1] and Chou's thermodynamic geometric model [2–3] has been developed. This new method has been employed to evaluate the viscosities of AuAgCu ternary system. The good agreement between the calculated and experimental data in the ternary system indicates that this approach can be successfully used to predict the viscosities of ternary and multicomponent systems.