Symbolic computation and perfect fluids in general relativity

Focuses on two areas within the field of general relativity. Firstly, the history and implications of the long-standing conjecture that general relativistic, shear-free perfect fluids which obey a barotropic equation of state p = p(w) such that w + p = 0, are either non-expanding or non-rotating. Secondly the application of the computer algebra system Maple to the area of tetrad formalisms in general relativity.

Layered fragment architecture for situational methodology engineering

Many organizations make use of information system development methodologies to guide their staff in developing computerised information systems. This thesis contributes to methodology engineering research by introducing a number of important innovations in methodology fragment architectures.

Structural studies of ambient temperature plastic crystal ion conductors

A number of novel organic ionic compounds based on the pyrrolidinium cation are described which have been found to be ion conductors in their solid states around room temperature. The properties of the compounds are consistent with their exhibiting plastic crystal phases. In order to understand some of the molecular origins of the plastic crystal behaviour and the ion conductivity that it promotes, a number of related compounds based on the imidazolium and ammonium cations are also described which have structural elements in common with the pyrrolidinium cation, but which do not show the plastic behaviour. It is found therefore that the nature of the cation is quite critical to the development of this behaviour. The alkyl methyl pyrrolidinium cation is found to produce plastic crystal phases when the alkyl chains are short, thereby preserving the ability of the cation to rotate with minimal steric hindrance. The ammonium and imidazolium cations of comparable size and structure are less able to produce these plastic phases, in many cases because the low temperature phase proceeds to melt rather than forming a stable rotator phase.

Enhancing clouds, internet-scale distributed systems, using attributed web services

This thesis presents a new framework allowing cloud services to be stateful, cloud resource state and characteristics to be published, and brokering for easy cloud resource discovery and selection to be offered. Using the framework, new technology developed significantly simplifies the discovery, selection and use of clusters on the Internet.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Analysis of 5-hydroxyisoflavones by surface-enhanced Raman spectroscopy : genistein and methoxy derivatives

A series of 5-hydroxy-isoflavones—genistein, biochanin A, prunetin, and 4′,7-dimethoxygenistein—have been studied by surface-enhanced Raman spectroscopy (SERS). Citrate reduced silver colloids were employed as a standard technique to measure SER spectra over a range of pH and concentrations. Density functional theory calculations were used to assist in determining the mode of interaction of isoflavones with the silver nanoparticles. It is revealed that biochanin A and prunetin interact with the silver nanoparticles upon deprotonation of the 7- and the 4′-OH groups, respectively, to show SERS activity. Correlations of their spectra with SERS of genistein strongly support the presence of multiple interaction modes involving both of the OH groups in genistein, in a similar manner to daidzein. Surprisingly, however, under these conditions, the 5-OH group was found to be noninteractive as revealed by attempts to measure SERS of 4′,7-dimethoxygenistein. This was attributed partly to the low solubility and, more importantly, to the influence of steric hindrance, caused by the position of the pendant phenyl ring, which prevented interaction with the Ag colloid surface. These results complement recent work on daidzein and formononetin and provide further insight into understanding the SER spectra of isoflavones.

Automated classification of human daily activities in ambulatory environment

This paper presents a human daily activity classification approach based on the sensory data collected from a single tri-axial accelerometer worn on waist belt. The classification algorithm was realized to distinguish 6 different activities including standing, jumping, sitting-down, walking, running and falling through three major steps: wavelet transformation, Principle Component Analysis (PCA)-based dimensionality reduction and followed by implementing a radial basis function (RBF) kernel Support Vector Machine (SVM) classifier. Two trials were conducted to evaluate different aspects of the classification scheme. In the first trial, the classifier was trained and evaluated by using a dataset of 420 samples collected from seven subjects by using a k-fold cross-validation method. The parameters σ and c of the RBF kernel were optimized through automatic searching in terms of yielding the highest recognition accuracy and robustness. In the second trial, the generation capability of the classifier was also validated by using the dataset collected from six new subjects. The average classification rates of 95% and 93% are obtained in trials 1 and 2, respectively. The results in trial 2 show the system is also good at classifying activity signals of new subjects. It can be concluded that the collective effects of the usage of single accelerometer sensing, the setting of the accelerometer placement and efficient classifier would make this wearable sensing system more realistic and more comfortable to be implemented for long-term human activity monitoring and classification in ambulatory environment, therefore, more acceptable by users.

Edge evaluation using necessary components

We classify all the different kinds of errors that can occur in edge detection and then develop measures for quantifying these errors. It is shown that these sets of measures are complete and independent and form necessary components of an edge-evaluation scheme. The principle that an edge-evaluation measure should have certain qualitative properties is used to develop a method for combining these error components into a single combined measure.