Analysis of the Interaction between Organophosphorus Acid and Tertiary Amine Extractants in the Binary Mixtures by Fourier Transform Infrared Spectroscopy (FT-IR)

In the synergistic solvent extraction of rare earth elements by binary mixtures of organophosphorus acid and tertiary amine extractants, the extraction reaction depends on the nature and strength of the interaction between the two extractants in the mixtures. In order to correlate qualitatively the interaction in different mixtures, the FT-IR spectra for organophosphorus acid (D2EHPA, PC88A, and Cyanex 272) and tertiary amine (Alamine 336 and TEHA) extractants and their mixtures have been analyzed. The primary focus was on investigating the changes in the characteristic functional groups of acidic and amine extractants in the binary mixtures as the strength and concentration of these extractants varied. IR spectroscopy of the organics confirmed the interaction in the binary mixtures, and strength of the interaction increased with the increase in the acidity of acidic extractant and the basicity of the amine extractant.     

测井资料在层序地层识别中的应用探讨

测井曲线蕴含着丰富的地质信息,且具有很高的纵向分辨率,测井曲线的形态、幅度可以反映出岩性和沉积旋回韵律等地层特征,在油气田勘探开发中起着十分重要的作用,因此如何更好地利用测井资料进行层序划分,其层序地层学响应特征如何,其优点与局限性如何等是必须回答的问题。通过调研综述国内外研究成果,得出可以利用测井资料划分层序地层单元,并介绍了常用测井曲线识别层序的方法。     

Improved asymptotic stability conditions for neural networks with discrete and distributed delays

This paper considers the asymptotic stability problem for a class of neural networks with discrete and distributed delays. Based on a new augmented Lyapunov functional and integral inequalities, the new asymptotic stability condition is established in terms of linear matrix inequality. Meanwhile, the importance of some augmented terms in the Lyapunov functional are discussed. Compared with previous methods to deal with the distributed delay, our method is less conservative due to the use of the new Lyapunov functional. Finally, numerical examples illustrate the relaxation of obtained results and our claims.     

Evaluating alternate discrete outcome frameworks for modeling crash injury severity

This paper focuses on the relevance of alternate discrete outcome frameworks for modeling driver injury severity. The study empirically compares the ordered response and unordered response models in the context of driver injury severity in traffic crashes. The alternative modeling approaches considered for the comparison exercise include: for the ordered response framework-ordered logit (OL), generalized ordered logit (GOL), mixed generalized ordered logit (MGOL) and for the unordered response framework-multinomial logit (MNL), nested logit (NL), ordered generalized extreme value logit (OGEV) and mixed multinomial logit (MMNL) model. A host of comparison metrics are computed to evaluate the performance of these alternative models. The study provides a comprehensive comparison exercise of the performance of ordered and unordered response models for examining the impact of exogenous factors on driver injury severity. The research also explores the effect of potential underreporting on alternative frameworks by artificially creating an underreported data sample from the driver injury severity sample. The empirical analysis is based on the 2010 General Estimates System (GES) data base—a nationally representative sample of road crashes collected and compiled from about 60 jurisdictions across the United States. The performance of the alternative frameworks are examined in the context of model estimation and validation (at the aggregate and disaggregate level). Further, the performance of the model frameworks in the presence of underreporting is explored, with and without corrections to the estimates. The results from these extensive analyses point toward the emergence of the GOL framework (MGOL) as a strong competitor to the MMNL model in modeling driver injury severity.     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

Adaptation of Discrete‐Type Cores for Secondary Coils of Wireless Power Transfer System for Railway

This paper examines the shapes of cores for secondary coils of the wireless power transfer (WPT) system for a railway. An infinitely long cable‐type coil is utilized as the primary coil for the WPT system because it is economical. Thus, this paper examines the ferrite core shapes for the secondary coils that are suitable for an infinitely long primary coil. The cores should be as thin as possible to reduce weight. However, bonding small cores to manufacture larger thin cores reduces the performance of the secondary coils. As a result, the cores are paradoxically required to be thicker. Therefore, this paper proposes the adaptation of thicker discrete‐type cores with the same volume as conventional flat‐type cores for the secondary coils.     

FUZZIFYING THE “PRECISE”

Based on the fuzzy-integral model, methods and algorithms are developed for identifying the “input–output” operator of continuous and stationary discrete extremal fuzzy dynamic systems (EFDS). The EFDS “input–output” operator is restored by means of experimental data with possibilistic uncertainty, the source of which is extremal fuzzy time intervals. The regularization conditions for obtaining quasi-optimal estimates are substantiated by the proved theorems. The corresponding algorithms are provided. The results obtained are illustrated by examples in the case of a finite set of EFDS states.     

Finite‐time H ∞ filtering for a class of discrete‐time Markovian jump systems with partly unknown transition probabilities

This paper is concerned with the problem of finite‐time H _∞ filtering for a class of Markovian jump systems subject to partial information on the transition probabilities. By introducing some slack matrix variables in terms of probability identity, a less conservative bounded real lemma is derived to ensure that filtering Markovian jump systems is finite‐time stable. Finally, the existence criterion of the desired filter is obtained such that the corresponding filtering error system is finite‐time bounded with a guaranteed H _∞ performance index. An example is given to illustrate the efficiency of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.