A VISCOSITY CONSTITUTIVE EQUATION FOR PMMA—MMA SOLUTIONS

A viscosity constitutive equation has been developed to describe PMMA—MMA solutions over a wide range of concentration and temperature. The approach used here combines both free-volume and coil-coil interaction considerations. The final expression successfully correlates not only our experimental data on several solutions but also literature melt data. This equation provides an essential step in modeling tubular polymerization reactors, where the residence time distribution depends strongly on the velocity profile development in the tube, and thus the rheological properties of the reacting PMMA — MMA mixture at different conversion levels and temperatures.     

Modeling zero-inflated explanatory variables in hybrid Bayesian network classifiers for species occurrence prediction

Datasets with an excessive number of zeros are fairly common in several disciplines. The aim of this paper is to improve the predictive power of hybrid Bayesian network classifiers when some of the explanatory variables show a high concentration of values at zero. We develop a new hybrid Bayesian network classifier called zero-inflated tree augmented naive Bayes (Zi-TAN) and compare it with the already known tree augmented naive bayes (TAN) model. The comparison is carried out through a case study involving the prediction of the probability of presence of two species, the fire salamander (Salamandra salamandra) and the Spanish Imperial Eagle (Aquila adalberti), in Andalusia, Spain. The experimental results suggest that modeling the explanatory variables containing many zeros following our proposal boosts the performance of the classifier, as far as species distribution modeling is concerned.     

An Introduction to Generalized Linear Models

Particle size is commonly used to determine quality and predict performance of particle systems. We consider particle size distributions inferred from a material sample using a fixed number of sieves with progressively smaller size openings, where the weight of the particles in each size interval is measured. In this article, we propose Bayes analyses for data from particle sieving studies based on parsimoniously parameterized multivariate normal approximate models for vectors of log weight fraction ratios. Additionally, we observe that the basic approach extends directly to modeling mixture contexts, which provides model flexibility and is a very natural extension when physical mixtures of materials with fundamentally different particle sizes are encountered. We also consider hierarchical modeling, where a single process produces lots of particles and the data available are (replicated) weight fraction vectors from different lots. Supplementary materials for this article are available online.     

Self-heating activation energy and specific heat capacity of sulphide mixtures at low temperature

Energy of activation (E_a) and specific heat capacity (C_p) for mixtures of sulphide minerals that on their own do not self-heat (SH), sphalerite/pyrite, pyrite/galena, chalcopyrite/galena and sphalerite/galena, were determined using a self-heating apparatus at temperatures below 100 °C in the presence of moisture. The mixtures all gave E_a ranging from 22.0 to 27.8 kJ mol⁻¹, similar to the range reported for Ni- and Cu-concentrates. The E_a is close to that for partial oxidation of H₂S which adds to the contention that the partial oxidation of H₂S contributes to SH of sulphides at low temperature. The C_p values ranged from 0.152 to 1.071 JK⁻¹ g⁻¹ as temperature rose from 50 °C to 80 °C, similar to the reported findings on Ni- and Cu-concentrates. The role of galvanic interaction in promoting SH is tested by examining correlations with the rest potential difference of the sulphides in the mixture.     

多组分稀混合物临界压力的计算

给出了一种较简单的计算稀混合物临界压力的新方法．采用Ｃｈｅｎ提出的计算正常沸点下汽化热的方法反算稀混合物的临界压力．通过近５０组稀混合物实验数据对本文方法进行校验，表明本文方法计算精度高于文献方法；且本文方法计算过程比文献方法简单得多。     

Exploring Concrete Slump Model Using Artificial Neural Networks

Fly ash and slag concrete (FSC) is a highly complex material whose behavior is difficult to model. This paper describes a method of modeling slump of FSC using artificial neural networks. The slump is a function of the content of all concrete ingredients, including cement, fly ash, blast furnace slag, water, superplasticizer, and coarse and fine aggregate. The model built was examined with response trace plots to explore the slump behavior of FSC. This study led to the conclusion that response trace plots can be used to explore the complex nonlinear relationship between concrete components and concrete slump.     

Discontinuity, decision and conflict

Summary The motivation for this paper arises out of the authors experiences in modelling real decision makers where the decisions show not only a continuous response to a continuously changing environment but also sudden or discontinuous changes. The theoretical basis involves a parametric characterisation of the environment, a decision makers perception of it in terms of a twice differentiable Distribution Function and a bounded Loss Function. Under a specified, minimizing dynamic, the resultant Expected Loss Function satisfies the conditions for a potential function and Thoms Catastrophe Classification Theorem may be used to assess the singularity points and the thresholds at which jump decisions are taken. The paper describes the theory, summarises some results on unimodal distributions illustrated by jump decisions and population polarisation. Mixture distributions are then examined and the E^* models defined. These are then briefly illustrated by reference to models which have been constructed in relation to Prison Riots, Agricultural and Economic modelling.     

Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification

Mixture of experts (ME) models comprise a family of modular neural network architectures aiming at distilling complex problems into simple subtasks. This is done by deploying a separate gating module for softly dividing the input space into overlapping regions to be each assigned to one or more expert networks. Conversely, support vector machines (SVMs) refer to kernel-based methods, neural-network-alike models that constitute an approximate implementation of the structural risk minimization principle. Such learning machines follow the simple, but powerful idea of nonlinearly mapping input data into high-dimensional feature spaces wherein a linear decision surface discriminating different regions is properly designed. In this work, we formally characterize and empirically evaluate a novel approach, named as Mixture of Support Vector Machine Experts (MSVME), whose main purpose is to combine the complementary properties of both SVM and ME models. In the formal characterization, an algorithm based on a maximum likelihood criterion is considered for the MSVME training, and we demonstrate that it is possible to train each expert based on an SVM perspective. Regarding the empirical evaluation, simulation results involving nonlinear dynamic system identification problems are reported, contrasting the performance shown by the MSVME approach with that exhibited by conventional SVM and ME models.     

Prediction of Creep Stiffness of Asphalt Mixture with Micromechanical Finite-Element and Discrete-Element Models

This study presents micromechanical finite-element (FE) and discrete-element (DE) models for the prediction of viscoelastic creep stiffness of asphalt mixture. Asphalt mixture is composed of graded aggregates bound with mastic (asphalt mixed with fines and fine aggregates) and air voids. The two-dimensional (2D) microstructure of asphalt mixture was obtained by optically scanning the smoothly sawn surface of superpave gyratory compacted asphalt mixture specimens. For the FE method, the micromechanical model of asphalt mixture uses an equivalent lattice network structure whereby interparticle load transfer is simulated through an effective asphalt mastic zone. The ABAQUS FE model integrates a user material subroutine that combines continuum elements with viscoelastic properties for the effective asphalt mastic and rigid body elements for each aggregate. An incremental FE algorithm was employed in an ABAQUS user material model for the asphalt mastic to predict global viscoelastic behavior of asphalt mixture. In regard to the DE model, the outlines of aggregates were converted into polygons based on a 2D scanned mixture microstructure. The polygons were then mapped onto a sheet of uniformly sized disks, and the intrinsic and interface properties of the aggregates and mastic were assigned for the simulation. An experimental program was developed to measure the properties of sand mastic for simulation inputs. The laboratory measurements of the mixture creep stiffness were compared with FE and DE model predictions over a reduced time. The results indicated both methods were applicable for mixture creep stiffness prediction.