Neurocomputing strategies in structural design — on analysing weights of feedforward neural networks

The sequel of two papers explores the applicability of selected neurocomputing strategies in the optimization of structural systems. The present paper describes the use of interconnection weights of a multilayer, feedforward neural network to extract information pertinent to a design space modelled by such a network. It is shown that aweights analysis provides a technique to assess the effect of all input quantities on a given output. Such dependencies are expressed in the form of atransition matrix, and their evaluation is reduced to the inspection of elements of a matrix row. Explicit formulae are derived for networks with one and two hidden layers and can easily be generalized to networks with an arbitrary number of hidden layers. In addition to its use as a tool to partition design spaces, the weights analysis may be employed to assist in determining the size of hidden layers and an adequate number of training patterns (input-output pairs). Several numerical examples from the field of structural analysis are provided, and the paper underscores the utility of the present technique in decomposition driven optimal design; such optimization is treated in full in the companion paper.     

Catalyst instabilities during the liquid phase partial oxidation of methane

A promising catalytic system for the low temperature oxidation of methane to a methanol derivative has been investigated under both batch and semi-continuous operation in two different reactor types. The system comprises of a bimetallic palladium and copper(II) chloride catalyst contained in a trifluoroacetic acid (TFA) and an aqueous phase. Methane, oxygen and a co-reductant carbon monoxide constitute the gas phase. Typical operating conditions were a temperature of 85 °C and a pressure of 83 bar.

The yields of the methyl trifluoroacetate product observed in this present work were less than those obtained in other batch autoclave works, which employed only 4 ml of liquid phase, compared with 50 ml in this study. Furthermore, an encouraging initial product formation rate of ca. 40 mol/m³ h, quickly decreased after the first hour, and came to an apparent end after only 2 h. This observation had not been reported previously.

Work performed in a semi-continuous porous tube reactor (300 ml of re-circulating liquid phase) also showed the same reaction characteristics as in the batch reactor. Thus, the deteriorating product formation rate cannot be attributed to gaseous reactant depletion (batch operation). The results suggest problems associated with catalyst instabilities, e.g. with the previously elucidated Wacker chemistry.     

Algorithms for spatial collocation pattern mining in a limited memory environment: a summary of results

Rapid growth of spatial datasets requires methods to find (semi-)automatically spatial knowledge from these sets. Spatial collocation patterns represent subsets of spatial features whose instances are frequently located together in a spatial neighborhood. In recent years, efficient methods for collocation discovery have been developed, however, none of them assume limited size of the operational memory or limited access to memory with short access times. Such restrictions are especially important in the context of the large size of the data structures required for efficient identification of collocation instances. In this work we present and compare three algorithms for collocation pattern mining in a limited memory environment. The first algorithm is based on the well-known joinless method introduced by Shekhar and Yoo. The second and third algorithms are inspired by a tree structure (iCPI-tree) presented by Wang et al. In our experimental evaluation, we have compared the efficiency of the algorithms, both on synthetic and real world datasets.     

Long-term cyclability of nanostructured LiFePO4

Amorphous LiFePO₄ was obtained by lithiation of FePO₄ synthesized by spontaneous precipitation from equimolar aqueous solutions of Fe(NH₄)₂(SO₄)₂·6H₂O and NH₄H₂PO₄, using hydrogen peroxide as oxidizing agent. Nano-crystalline LiFePO₄ was obtained by heating amorphous nano-sized LiFePO₄ for different periods of time. The materials were characterized by TG, DTA, X-ray powder diffraction, scanning electron microscopy (SEM) and BET. All materials showed very good electrochemical performance in terms of energy and power density. Upon cycling, a capacity fading affected the materials, thus reducing the electrochemical performance. Nevertheless, the fading decreased upon cycling and after the 200th cycle the cell was able to cycle for more than 500 cycles without further fading.     

Damage localization for beams based on the wavelet correlation operator

The continuous wavelet transform (CWT) is one of the crucial damage identification tools in the vibration-based damage assessment. Because of the vanishing moment property, the CWT method is capable of featuring damage singularity in the higher scales, and separating the global trends and noise progressively. In the classical investigations about this issue, the localization property of the CWT is usually deemed as the most critical point. The abundant information provided by the scale-domain information and the corresponding effectiveness are, however, neglected to some extent. Ultimately, this neglect restricts the sufficient application of the CWT method in damage localization, especially in noisy conditions. In order to address this problem, the wavelet correlation operator is introduced into the CWT damage detection method as a post-processing. By means of the correlations among different scales, the proposed operator suppresses noise, cancels global trends, and intensifies the damage features for various mode shapes. The proposed method is demonstrated numerically with emphasis on characterizing damage in noisy environments, where the wavelet scale Teager-Kaiser energy operator is taken as the benchmark method for comparison. Experimental validations are conducted based on the benchmark data from composite beam specimens measured by a scanning laser vibrometer. Numerical and experimental validations/comparisons present that the introduction of wavelet correlation operator is effective for damage localization in noisy conditions.     

Cryo-EM image alignment based on nonuniform fast Fourier transform

In single particle analysis, two-dimensional (2-D) alignment is a fundamental step intended to put into register various particle projections of biological macromolecules collected at the electron microscope. The efficiency and quality of three-dimensional (3-D) structure reconstruction largely depends on the computational speed and alignment accuracy of this crucial step. In order to improve the performance of alignment, we introduce a new method that takes advantage of the highly accurate interpolation scheme based on the gridding method, a version of the nonuniform fast Fourier transform, and utilizes a multi-dimensional optimization algorithm for the refinement of the orientation parameters. Using simulated data, we demonstrate that by using less than half of the sample points and taking twice the runtime, our new 2-D alignment method achieves dramatically better alignment accuracy than that based on quadratic interpolation. We also apply our method to image to volume registration, the key step in the single particle EM structure refinement protocol. We find that in this case the accuracy of the method not only surpasses the accuracy of the commonly used real-space implementation, but results are achieved in much shorter time, making gridding-based alignment a perfect candidate for efficient structure determination in single particle analysis.     

Segmentation of microscope images of living cells

This paper describes a segmentation method combining a texture based technique with a contour based method. The technique is designed to enable the study of cell behaviour over time by segmenting brightfield microscope image sequences. The technique was tested on artificial images, based on images of living cells and on real sequences acquired from microscope observations of neutrophils and lymphocytes as well as on a sequence of MRI images. The results of the segmentation are compared with the results of the watershed and snake segmentation methods. The results show that the method is both effective and practical.

An empirical analysis of heuristics for solving the two-machine flow shop problem with job release times

The theoretical analysis of heuristics for solving intractable optimization problems has many well-known drawbacks. Constructed instances demonstrating an exceptionally poor worst-case performance of heuristics are typically too peculiar to occur in practice. Theoretical results on the average-case performance of most heuristics could not be established due to the difficulty with the use of probabilistic analysis. Moreover, the heuristics for which some type of asymptotic optimality has been proven are likely to perform questionably in practice. The purpose of this paper is to confront known theoretical results with our empirical results concerning heuristics for solving the strongly NP-hard problem of minimizing the makespan in a two-machine flow shop with job release times. The heuristics' performance is examined with respect to their average and maximum relative errors, as well as their optimality rate, that is, the probability of being optimal. In particular, this allows us to observe that the asymptotic optimality rate of so called “almost surely asymptotically optimal” heuristic can be zero. We also present a new heuristic with short worst-case running time and statistically prove that it outperforms all heuristics known so far. However, our empirical experiments reveal that the heuristic is on average slower that its competitors with much longer worst-case running times.     

Application of Sensitivity Analysis to Discover Potential Molecular Drug Targets

Mathematical modeling of signaling pathways and regulatory networks has been supporting experimental research for some time now. Sensitivity analysis, aimed at finding model parameters whose changes yield significantly altered cellular responses, is an important part of modeling work. However, sensitivity methods are often directly transplanted from analysis of technical systems, and thus, they may not serve the purposes of analysis of biological systems. This paper presents a novel sensitivity analysis method that is particularly suited to the task of searching for potential molecular drug targets in signaling pathways. Using two sample models of pathways, p53/Mdm2 regulatory module and IFN-β-induced JAK/STAT signaling pathway, we show that the method leads to biologically relevant conclusions, identifying processes suitable for targeted pharmacological inhibition, represented by the reduction of kinetic parameter values. That, in turn, facilitates subsequent search for active drug components.