Mass transfer between a liquid and an array of discs in a cylindrical container. Part I: Pumped flow or rotation alone

This paper deals with average mass transfer between a liquid and both sides of discs arranged as baffles in a cylindrical container. The case of pumped flow alone through the stationary arrangement is first considered; the radial flow between the pairs of stationary discs is divergent and convergent successively. In other experiments, there is no pumped flow, but the discs are rotated between the stationary annular discs. The mass transfer coefficients are measured electrochemically for different geometrical and hydrodynamic conditions and the results are empirically correlated. The behaviour of corresponding surfaces involving a convergent or a divergent radial flow is discussed.     

Electrical conductivity: a new tool for the determination of high hydrostatic pressure-induced starch gelatinisation

The degree of gelatinisation and electrical conductivity of wheat starch and tapioca starch suspensions (5% w/w) were determined after a pressure treatment of up to 530 MPa. With increasing pressure at a constant treatment time the degree of gelatinisation increased resulting in a gelatinisation curve similar to that of thermal gelatinisation. A pressure increase also caused an increase in electrical conductivity. A good linear relationship between the degree of gelatinisation and the electrical conductivity for both starches investigated was found. Since electrical conductivity correlates well with the degree of gelatinisation of starches after pressure treatment it is applicable for the quick and simple determination of pressure-induced starch gelatinisation.     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

Food-web structure in low- and high-dimensional trophic niche spaces

A question central to modelling and, ultimately, managing food webs concerns the dimensionality of trophic niche space, that is, the number of independent traits relevant for determining consumer–resource links. Food-web topologies can often be interpreted by assuming resource traits to be specified by points along a line and each consumer''s diet to be given by resources contained in an interval on this line. This phenomenon, called intervality, has been known for 30 years and is widely acknowledged to indicate that trophic niche space is close to one-dimensional. We show that the degrees of intervality observed in nature can be reproduced in arbitrary-dimensional trophic niche spaces, provided that the processes of evolutionary diversification and adaptation are taken into account. Contrary to expectations, intervality is least pronounced at intermediate dimensions and steadily improves towards lower- and higher-dimensional trophic niche spaces.     

Some Power Mappings with Low Differential Uniformity

 Differentially uniform power mappings of the form f (x)=x ^d over GF(p ⁿ ) are considered. We construct an infinite family of 2-uniform mappings in the binary case. In the nonbinary case we give two large families of k-uniform mappings with low values of k. We also show how to construct families of sequences from differentially 1-uniform power mappings, which have parameters as good as the best presently known comparable families of sequences. Received: November 4, 1996; revised version: February 14, 1997     

Meta-Analytic Structural Equation Modeling: A Two-Stage Approach.

To synthesize studies that use structural equation modeling (SEM), researchers usually use Pearson correlations (univariate r), Fisher z scores (univariate z), or generalized least squares (GLS) to combine the correlation matrices. The pooled correlation matrix is then analyzed by the use of SEM. Questionable inferences may occur for these ad hoc procedures. A 2-stage structural equation modeling (TSSEM) method is proposed to incorporate meta-analytic techniques and SEM into a unified framework. Simulation results reveal that the univariate-r, univariate-z, and TSSEM methods perform well in testing the homogeneity of correlation matrices and estimating the pooled correlation matrix. When fitting SEM, only TSSEM works well. The GLS method performed poorly in small to medium samples. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Genetic studies on several bean traits of Coffea canephora coffee in Cameroon

The genetic parameters of several bean traits of coffee from the species Coffea canephora were estimated using two mating designs: a triangular diallel with 6 parents and a partial diallel with 18 parents. The traits studied were: normal bean weight, the percentage of peaberries, the length, width and thickness of the beans. These characteristics were studied along with yield. The yield data came from harvests obtained over 14 years divided into an initial cycle of 6 years, a second cycle of 5 years after cutting back and a third cycle of 3 years after topping. The general combining abilities (GCA) were the principal sources of significant variation for the traits studied. Transmission of these traits was therefore mostly additive. Yield over 14 years was the most heritable trait. The rank correlations between the GCA of the bean traits and those of yield were not significant. The genetic correlations estimated in the partial diallel did not reveal any link between bean traits and yield. Only one genetic correlation between the peaberry rate and yield was significant. The peaberry rate was linked to fertility, which thus appeared to be a genetic component of yield. Index‐based selection combining an increase in yield with improved bean traits could therefore be carried out without any particular difficulty. Copyright © 2005 Society of Chemical Industry     

Superconductivity in the two-band hubbard model

We present a study of superconducting correlations in a two-band Hubbard model with a wide band strongly hybridized with a narrow band in which an attractive on-site interaction is operating. The narrow band pairs can induce superconducting correlations in the wide band through hybridization interaction. A generalized gap function for the induced wide-band pairing is obtained and its properties in the intermediate interaction region are analysed. Relevance of the results to high-T _c superconductors is briefly discussed.     

油气藏铼-锇同位素定年的进展与挑战

铼(Re)、锇(Os)元素具有对氧化还原的敏感性和亲有机质特性,富集于烃源岩、油砂、原油、沥青等油气藏相关地质样品中,并被用于油气藏同位素定年。该方法既可通过Re-Os等时线直接获取油气成藏过程的绝对地质年龄,又可利用Os同位素初始值开展油源对比。分析近十余年来油气藏Re-Os同位素年代学研究的进展与不足,指出为推动该定年技术的发展及其在油气勘探实践中的应用,学术界和工业界应密切合作共同探索Re-Os同位素在油气系统中的分馏机理,全面评价硫酸盐热化学还原反应(TSR)、热解事件等后期地质过程对Re-Os同位素体系的潜在影响;综合运用无机和有机地球化学方法,建立判别油气来源的有效工具,以帮助合理选择满足同源特征的Re-Os同位素定年对象。这些问题的解决将有助于尽快建立油气藏Re-Os同位素定年的实用技术方案,并为业内的终端用户提供完整的使用指南。