Anisotropy of X-ray structural parameters of the three phase model in cellulose I

Anisotropy of X-ray structural parameters of the three phase model proposed by Mitra and Mukherjee (ref.2), has been investigated in the case of cellulose fibres of ramie, hemp and jute. The structural parameters have been determined separately from each of the observable equatorial reflections. It has been found that the parameter degree of crystallinity as defined in the model is in fact anisotropic at least in the samples studied. The parameter degree of paracrystallinity introduced by Mitra and Mukherjee was also found to be anisotropic. Several important correlations between the structural parameters have been drawn. It has been observed, in agreement with the recent findings of Hosemann and Balta Calleja (ref.10) that the greater the paracrystalline distortion, the smaller the ‘paracrystallite size’.     

Pore network model of deactivation of immobilized glucose isomerase in packed-bed reactors II: three-dimensional simulation at the particle level

Immobilized glucose isomerase is widely used for converting glucose to fructose by enzymatic isomerization. The process takes place in a packed-bed reactor consisting of mesoporous particles with distributed pore sizes and interconnectivities. Its efficiency is, however, significantly affected by deactivation of the mesoporous particles. In this paper, we study deactivation of the mesoporous particles using a three-dimensional pore network model of the pore space with distributed pore sizes and interconnectivities, and investigate several plausible mechanisms of deactivation of the porous particles. The results of the present study, which will be used as the input for simulation of the phenomenon at the reactor level, demonstrates the strong effect of the particles’ morphology on the deactivation process.     

Influence of a mixed Al2O3+Cr2O3 superficial coating on the non-isothermal oxidation behavior of Fe and Fe-Cr alloys

The non-isothermal oxidation behavior of electrolytic-grade iron and Fe-Cr alloys in dry air has been studied using linear heating rates of 6 K/min, 10 K/ min, and 15 K/min up to a final temperature of 1273–1473 K. Some of the iron and iron-chromium alloy samples were given a surface treatment by dipping them in an aqueous solution containing both Cr and Al ions before their oxidation studies. This pretreatment has resulted in improved oxidation resistance and scale adherence as depicted by no scale rupture even after a second thermal cycle. Mass changes were recorded gravimetrically, and scales have been characterized by SEM, EPMA, and x-ray diffraction analyses.     

Lindex: a lattice-based index for graph databases

Subgraph querying has wide applications in various fields such as cheminformatics and bioinformatics. Given a query graph, q, a subgraph-querying algorithm retrieves all graphs, D(q), which have q as a subgraph, from a graph database, D. Subgraph querying is costly because it uses subgraph isomorphism tests, which are NP-complete. Graph indices are commonly used to improve the performance of subgraph querying in graph databases. Subgraph-querying algorithms first construct a candidate answer set by filtering out a set of false answers and then verify each candidate graph using subgraph isomorphism tests. To build graph indices, various kinds of substructure (subgraph, subtree, or path) features have been proposed with the goal of maximizing the filtering rate. Each of them works with a specifically designed index structure, for example, discriminative and frequent subgraph features work with gIndex, δ-TCFG features work with FG-index, etc. We propose Lindex, a graph index, which indexes subgraphs contained in database graphs. Nodes in Lindex represent key-value pairs where the key is a subgraph in a database and the value is a list of database graphs containing the key. We propose two heuristics that are used in the construction of Lindex that allows us to determine answers to subgraph queries conducting less subgraph isomorphism tests. Consequently, Lindex improves subgraph-querying efficiency. In addition, Lindex is compatible with any choice of features. Empirically, we demonstrate that Lindex used in conjunction with subgraph indexing features proposed in previous works outperforms other specifically designed index structures. As a novel index structure, Lindex (1) is effective in filtering false graphs (2) provides fast index lookups, (3) is fast with respect to index construction and maintenance, and (4) can be constructed using any set of substructure index features. These four properties result in a fast and scalable subgraph-querying infrastructure. We substantiate the benefits of Lindex and its disk-resident variation Lindex+ theoretically and empirically.     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

A comprehensive survey of industry practice in real-time systems

Real-Time Systems - This paper presents results and observations from a survey of 120 industry practitioners in the field of real-time embedded systems. The survey provides insights into the...     

Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors

In this paper, we consider the fundamental problem of frequency estimation of multiple sinusoidal signals with stationary errors. We propose genetic algorithm and outlier-insensitive criterion function based technique for the frequency estimation problem. In the simulation studies and real life data analysis, it is observed that the proposed genetic algorithm based robust frequency estimators are able to resolve frequencies of the sinusoidal model with high degree of accuracy. Among the proposed methods, the genetic algorithm based least squares estimator, in the no-outlier scenario, provides efficient estimates, in the sense that their mean square errors attain the corresponding Cramér-Rao lower bounds. In the presence of outliers, the proposed robust methods perform quite well and seem to have a fairly high breakdown point with respect to level of outlier contamination. The proposed methods significantly do not depend on the initial guess values required for other iterative frequency estimation methods.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

Nadir compromise programming: A model for optimization of multi-objective portfolio problem

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes.