Estimation in Random Coefficient Autoregressive Models

Abstract. We propose the quasi‐maximum likelihood method to estimate the parameters of an RCA(1) process, i.e. a random coefficient autoregressive time series of order 1. The strong consistency and the asymptotic normality of the estimators are derived under optimal conditions.     

Thickness and composition dependence of the glass transition temperature in thin random copolymer films

Glass transition temperatures (T_g) of thin poly(styrene-co-methyl methacrylate) and poly(2-vinyl pyridine-co-styrene) films coated on a native oxide surface of Si wafer (100) were measured by ellipsometry. The thickness dependence of T_g can be properly fitted by previously suggested equation developed for homopolymers, based upon a continuous multi-layer model, although one component in thin random copolymer films demonstrates a slightly favorable interaction between a substrate and thin film, and another demonstrates a strongly favorable interaction. Surface and interface have a strong influence on T_g of thin film coated on substrate: the surface has the effect of reducing T_g, whereas the interface increases the T_g according to the degree of interaction between a substrate and thin film. This degree of interaction can be quantified as an interaction parameter (k), and is dependent on the composition of random copolymers. For the estimation of k values of thin random copolymer films, we proposed a parallel type additive function (1/k_ran=w₁/k₁+w₂/k₂) where w is a weight fraction of component.     

PRAND: GPU accelerated parallel random number generation library: Using most reliable algorithms and applying parallelism of modern GPUs and CPUs

The library PRAND for pseudorandom number generation for modern CPUs and GPUs is presented. It contains both single-threaded and multi-threaded realizations of a number of modern and most reliable generators recently proposed and studied in Barash (2011), Matsumoto and Tishimura (1998), L’Ecuyer (1999,1999), Barash and Shchur (2006) and the efficient SIMD realizations proposed in Barash and Shchur (2011). One of the useful features for using PRAND in parallel simulations is the ability to initialize up to 10¹⁹${10}^{19}$ independent streams. Using massive parallelism of modern GPUs and SIMD parallelism of modern CPUs substantially improves performance of the generators.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

Interactive fuzzy stochastic multi-level 0–1 programming using tabu search and probability maximization

In this paper, we consider interactive fuzzy programming for multi-level 0–1 programming problems involving random variable coefficients both in objective functions and constraints. Following the probability maximization model together with the concept of chance constraints, the formulated stochastic multi-level 0–1 programming problems are transformed into deterministic ones. Taking into account vagueness of judgments of the decision makers, we present interactive fuzzy programming. In the proposed interactive method, after determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating satisfactory levels of the decision makers with considerations of overall satisfactory balance among all levels. For solving the transformed deterministic problems efficiently, we also introduce novel tabu search for general 0–1 programming problems. A numerical example for a three-level 0–1 programming problem is provided to illustrate the proposed method.     

R-SMA共聚树脂及其改性研究

本文介绍了无规苯乙烯—马来酸酐共聚树脂（Ｒ—ＳＭＡ）的合成工艺、特性和用途，并叙述了Ｒ—ＳＭＡ改性研究状况．     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

COMPUTER SIMULATION OF HYDRODEMETALLATION IN FIXED-BED REACTORS

An isothermal model for hydrodemetallation (HDM) of crude oils in catalytic fixed-bed reactors is proposed. This model involves a consecutive reaction mechanism, which is capable of accounting for particle deposit profiles with interior maxima. Consistent with the fact that HDM catalysts are conglomerates formed by precipitation, the porous catalyst itself is modeled as randomly overlapping spheres of equal size. The metal is deposited as growing metal sulfide crystallites on the inner surface of the catalyst. These crystallites originate from a certain number of randomly scattered nuclei and increase in size as the deposition proceeds. The random sphere model for the catalyst and the deposit provides the changes in the catalyst pore structure—local porosity and surface area.

The mass transport within the domain of the particle is due to restricted liquid diffusion, since the diameter of the metal bearing compound (porphyrin) and the intermediate are comparable to the pore size. The diffusion restrictions taken into account are the enhanced drag imposed on a molecule by adjacent pore walls and steric partitioning.

Since the deposition process is much slower than diffusion and reaction, the pseudo-steady-state assumption can be justified. The equations of conservation for mass are solved by orthogonal collocation on finite elements. Based on this solution technique a computer simulation program of HDM is designed that allows two modes of operation: constant temperature and constant conversion. The simulation program “SIMULA” is highly flexible with regard to reaction kinetics, catalyst structure, reactor design, and operating conditions. In comparison to a base case with uniform activity, the effect of intraparticle (radial) and bed (axial) activity profiles on the conversion rate is discussed. For the case investigated, a radial distribution of activity higher at the center of the particle than at the edge can increase catalyst life by 25%, but axial distribution was less successful.     

化工过程数据校正方法的探讨

简要地介绍了数据校正中的基本概念；然后从数据协调、显著误差检测、数据校正三个方面较系统地、全面地总结了数据校正方法的研究进展和成就；最后探讨了这一领域中值得进一步研究的问题和可能的发展方向。