Exploring the needs for SMEs for mobile data technologies : the role of qualitative research techniques

The seemingly exponential nature of technological change provides SMEs with a complex and challenging operational context. The development of infrastructures capable of supporting the wireless application protocol (WAP) and associated 'wireless' applications represents the latest generation of technological innovation with potential appeals to SMEs and end-users alike. This paper aims to understand the mobile data technology needs of SMEs in a regional setting. The research was especially concerned with perceived needs across three market segments : non-adopters, partial-adopters and full-adopters of new technology. The research was exploratory in nature as the phenomenon under scrutiny is relatively new and the uses unclear, thus focus groups were conducted with each of the segments. The paper provides insights for business, industry and academics.

Mobile data technologies and small business adoption and diffusion : an empirical study of barriers and facilitators

The technological environment in which contemporary small- and medium-sized enterprises (SMEs) operate can only be described as dynamic. The exponential rate of technological change, characterised by perceived increases in the benefits associated with various technologies, shortening product life cycles and changing standards, provides for the SME a complex and challenging operational context. The primary aim of this research was to identify the needs of SMEs in regional areas for mobile data technologies (MDT). In this study a distinction was drawn between those respondents who were full-adopters of technology, those who were partial-adopters, and those who were non-adopters and these three segments articulated different needs and requirements for MDT. Overall, the needs of regional SMEs for MDT can be conceptualised into three areas where the technology will assist business practices; communication, e-commerce and security

Who needs it? An exploratory investigation into the reaction of small and medium enterprises to mobile data technologies

The technological environment in which contemporary small and medium-sized enterprises (SMEs) operate can only be described as dynamic. The seemingly exponential nature of technological change, characterised by perceived increases in the benefits associated with various technologies, shortening product life cycles and changing standards, provides for the small and medium-sized enterprise a complex and challenging operational context. The development of infrastructures capable of supporting the Wireless Application Protocol (WAP)and associated 'wireless' applications represents the latest generation of technological innovation with potential appeal to SMEs and end-users alike. The primary aim of this research was to understand the mobile data technology needs of SMEs in a regional setting. The research was especially concerned with perceived needs across three market segments; non-adopters of new technology, partial-adopters of new technology and full-adopters of new technology. Working with an industry partner, focus groups were conducted with each of these segments with the discussions focused on the use of the latest WP products and services. Some of the results are presented in this paper.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

Oxygen consumption and muscle activation patterns during constant load exercise

The collective purpose of these two studies was to determine a link between the V02 slow component and the muscle activation patterns that occur during cycling. Six, male subjects performed an incremental cycle ergometer exercise test to determine asub-TvENT (i.e. 80% of TvENT) and supra-TvENT (TvENT + 0.75*(V02 max - TvENT) work load. These two constant work loads were subsequently performed on either three or four occasions for 8 mins each, with V02 captured on a breath-by-breath basis for every test, and EMO of eight major leg muscles collected on one occasion. EMG was collected for the first 10 s of every 30 s period, except for the very first 10 s period. The V02 data was interpolated, time aligned, averaged and smoothed for both intensities. Three models were then fitted to the V02 data to determine the kinetics responses. One of these models was mono-exponential, while the other two were biexponential. A second time delay parameter was the only difference between the two bi-exponential models. An F-test was used to determine significance between the biexponential models using the residual sum of squares term for each model. EMO was integrated to obtain one value for each 10 s period, per muscle. The EMG data was analysed by a two-way repeated measures ANOV A. A correlation was also used to determine significance between V02 and IEMG. The V02 data during the sub-TvENT intensity was best described by a mono-exponential response. In contrast, during supra-TvENT exercise the two bi-exponential models best described the V02 data. The resultant F-test revealed no significant difference between the two models and therefore demonstrated that the slow component was not delayed relative to the onset of the primary component. Furthermore, only two parameters were deemed to be significantly different based upon the two models. This is in contrast to other findings. The EMG data, for most muscles, appeared to follow the same pattern as V02 during both intensities of exercise. On most occasions, the correlation coefficient demonstrated significance. Although some muscles demonstrated the same relative increase in IEMO based upon increases in intensity and duration, it cannot be assumed that these muscles increase their contribution to V02 in a similar fashion. Larger muscles with a higher percentage of type II muscle fibres would have a larger increase in V02 over the same increase in intensity.

Pathogenicity of Plesiomonas shigelloides : interactions with eukaryotic host cells in vitro

Diarrhoea is one of the leading causes of morbidity and mortality in populations in developing countries and is a significant health issue throughout the world. Despite the frequency and the severity of the diarrhoeal disease, mechanisms of pathogenesis for many of the causative agents have been poorly characterised. Although implicated in a number of intestinal and extra-intestinal infections in humans, Plesiomonas shigelloides generally has been dismissed as an enteropathogen due to the lack of clearly demonstrated virulence-associated properties such as production of cytotoxins and enterotoxins or invasive abilities. However, evidence from a number of sources has indicated that this species may be the cause of a number of clinical infections. The work described in this thesis seeks to resolve this discrepancy by investigating the pathogenic potential of P. shigelloides using in vitro cell models. The focus of this research centres on how this organism interacts with human host cells in an experimental model. Very little is known about the pathogenic potential of P. shigel/oides and its mechanisms in human infections and disease. However, disease manifestations mimic those of other related microorganisms. Chapter 2 reviews microbial pathogenesis in general, with an emphasis on understanding the mechanisms resulting from infection with bacterial pathogens and the alterations in host cell biology. In addition, this review analyses the pathogenic status of a poorly-defined enteropathogen, P. shigelloides. Key stages of pathogenicity must occur in order for a bacterial pathogen to cause disease. Such stages include bacterial adherence to host tissue, bacterial entry into host tissues (usually required), multiplication within host tissues, evasion of host defence mechanisms and the causation of damage. In this study, these key strategies in infection and disease were sought to help assess the pathogenic potential of P. shigelloides (Chapter 3). Twelve isolates of P. shigelloides, obtained from clinical cases of gastroenteritis, were used to infect monolayers of human intestinal epithelial cells in vitro. Ultrastructural analysis demonstrated that P. shigelloides was able to adhere to the microvilli at the apical surface of the epithelial cells and also to the plasma membranes of both apical and basal surfaces. Furthermore, it was demonstrated that these isolates were able to enter intestinal epithelial cells. Internalised bacteria often were confined within vacuoles surrounded by single or multiple membranes. Observation of bacteria within membranebound vacuoles suggests that uptake of P. shigelloides into intestinal epithelial cells occurs via a process morphologically comparable to phagocytosis. Bacterial cells also were observed free in the host cell cytoplasm, indicating that P. shige/loides is able to escape from the surrounding vacuolar membrane and exist within the cytosol of the host. Plesiomonas shigelloides has not only been implicated in gastrointestinal infections, but also in a range of non-intestinal infections such as cholecystitis, proctitis, septicaemia and meningitis. The mechanisms by which P. shigelloides causes these infections are not understood. Previous research was unable to ascertain the pathogenic potential of P. shigel/oides using cells of non-intestinal origin (HEp-2 cells derived from a human larynx carcinoma and Hela cells derived from a cervical carcinoma). However, with the recent findings (from this study) that P. shigelloides can adhere to and enter intestinal cells, it was hypothesised, that P. shigel/oides would be able to enter Hela and HEp-2 cells. Six clinical isolates of P. shigelloides, which previously have been shown to be invasive to intestinally derived Caco-2 cells (Chapter 3) were used to study interactions with Hela and HEp-2 cells (Chapter 4). These isolates were shown to adhere to and enter both nonintestinal host cell lines. Plesiomonas shigelloides were observed within vacuoles surrounded by single and multiple membranes, as well as free in the host cell cytosol, similar to infection by P. shigelloides of Caco-2 cells. Comparisons of the number of bacteria adhered to and present intracellularly within Hela, HEp-2 and Caco-2 cells revealed a preference of P. shigelloides for Caco-2 cells. This study conclusively showed for the first time that P. shigelloides is able to enter HEp-2 and Hela cells, demonstrating the potential ability to cause an infection and/or disease of extra-intestinal sites in humans. Further high resolution ultrastructural analysis of the mechanisms involved in P. shigelloides adherence to intestinal epithelial cells (Chapter 5) revealed numerous prominent surface features which appeared to be involved in the binding of P. shige/loides to host cells. These surface structures varied in morphology from small bumps across the bacterial cell surface to much longer filaments. Evidence that flagella might play a role in bacterial adherence also was found. The hypothesis that filamentous appendages are morphologically expressed when in contact with host cells also was tested. Observations of bacteria free in the host cell cytosol suggests that P. shigelloides is able to lyse free from the initial vacuolar compartment. The vacuoles containing P. shigel/oides within host cells have not been characterised and the point at which P. shigelloides escapes from the surrounding vacuolar compartment has not been determined. A cytochemical detection assay for acid phosphatase, an enzymatic marker for lysosomes, was used to analyse the co-localisation of bacteria-containing vacuoles and acid phosphatase activity (Chapter 6). Acid phosphatase activity was not detected in these bacteria-containing vacuoles. However, the surface of many intracellular and extracellular bacteria demonstrated high levels of acid phosphatase activity, leading to the proposal of a new virulence factor for P. shigelloides. For many pathogens, the efficiency with which they adhere to and enter host cells is dependant upon the bacterial phase of growth. Such dependency reflects the timing of expression of particular virulence factors important for bacterial pathogenesis. In previous studies (Chapter 3 to Chapter 6), an overnight culture of P. shigelloides was used to investigate a number of interactions, however, it was unknown whether this allowed expression of bacterial factors to permit efficient P. shigelloides attachment and entry into human cells. In this study (Chapter 7), a number of clinical and environmental P. shigelloides isolates were investigated to determine whether adherence and entry into host cells in vitro was more efficient during exponential-phase or stationary-phase bacterial growth. An increase in the number of adherent and intracellular bacteria was demonstrated when bacteria were inoculated into host cell cultures in exponential phase cultures. This was demonstrated clearly for 3 out of 4 isolates examined. In addition, an increase in the morphological expression of filamentous appendages, a suggested virulence factor for P. shigel/oides, was observed for bacteria in exponential growth phase. These observations suggest that virulence determinants for P. shigel/oides may be more efficiently expressed when bacteria are in exponential growth phase. This study demonstrated also, for the first time, that environmental water isolates of P. shigelloides were able to adhere to and enter human intestinal cells in vitro. These isolates were seen to enter Caco-2 host cells through a process comparable to the clinical isolates examined. These findings support the hypothesis of a water transmission route for P. shigelloides infections. The results presented in this thesis contribute significantly to our understanding of the pathogenic mechanisms involved in P. shigelloides infections and disease. Several of the factors involved in P. shigelloides pathogenesis have homologues in other pathogens of the human intestine, namely Vibrio, Aeromonas, Salmonella, Shigella species and diarrhoeaassociated strains of Escherichia coli. This study emphasises the relevance of research into Plesiomonas as a means of furthering our understanding of bacterial virulence in general. As well it provides tantalising clues on normal and pathogenic host cell mechanisms.

Managing Trust in Online Social Networks

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.

Applying social technology to Business Process Lifecycle Management

In recent years social technologies such as wikis, blogs or microblogging have seen an exponential growth in the uptake of their user base making this type of technology one of the most significant networking and knowledge sharing platforms for potentially hundreds of millions of users. However, the adoption of these technologies has been so far mostly for private purposes. First attempts have been made to embed features of social technologies in the corporate IT landscape, and Business Process Management is no exception. This paper aims to consolidate the opportunities for integrating social technologies into the different stages of the business process lifecycle. Thus, it contributes to a conceptualization of this fast growing domain, and can help to categorize academic and corporate development activities.

Digital signal processing

Signal Processing (SP) is a subject of central importance in engineering and the applied sciences. Signals are information-bearing functions, and SP deals with the analysis and processing of signals (by dedicated systems) to extract or modify information. Signal processing is necessary because signals normally contain information that is not readily usable or understandable, or which might be disturbed by unwanted sources such as noise. Although many signals are non-electrical, it is common to convert them into electrical signals for processing. Most natural signals (such as acoustic and biomedical signals) are continuous functions of time, with these signals being referred to as analog signals. Prior to the onset of digital computers, Analog Signal Processing (ASP) and analog systems were the only tool to deal with analog signals. Although ASP and analog systems are still widely used, Digital Signal Processing (DSP) and digital systems are attracting more attention, due in large part to the significant advantages of digital systems over the analog counterparts. These advantages include superiority in performance,s peed, reliability, efficiency of storage, size and cost. In addition, DSP can solve problems that cannot be solved using ASP, like the spectral analysis of multicomonent signals, adaptive filtering, and operations at very low frequencies. Following the recent developments in engineering which occurred in the 1980's and 1990's, DSP became one of the world's fastest growing industries. Since that time DSP has not only impacted on traditional areas of electrical engineering, but has had far reaching effects on other domains that deal with information such as economics, meteorology, seismology, bioengineering, oceanology, communications, astronomy, radar engineering, control engineering and various other applications. This book is based on the Lecture Notes of Associate Professor Zahir M. Hussain at RMIT University (Melbourne, 2001-2009), the research of Dr. Amin Z. Sadik (at QUT & RMIT, 2005-2008), and the Note of Professor Peter O'Shea at Queensland University of Technology. Part I of the book addresses the representation of analog and digital signals and systems in the time domain and in the frequency domain. The core topics covered are convolution, transforms (Fourier, Laplace, Z. Discrete-time Fourier, and Discrete Fourier), filters, and random signal analysis. There is also a treatment of some important applications of DSP, including signal detection in noise, radar range estimation, banking and financial applications, and audio effects production. Design and implementation of digital systems (such as integrators, differentiators, resonators and oscillators are also considered, along with the design of conventional digital filters. Part I is suitable for an elementary course in DSP. Part II (which is suitable for an advanced signal processing course), considers selected signal processing systems and techniques. Core topics covered are the Hilbert transformer, binary signal transmission, phase-locked loops, sigma-delta modulation, noise shaping, quantization, adaptive filters, and non-stationary signal analysis. Part III presents some selected advanced DSP topics. We hope that this book will contribute to the advancement of engineering education and that it will serve as a general reference book on digital signal processing.

Exponentiated gradient algorithms for large-margin structured classification

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

A new control-volume finite-element scheme for heterogeneous porous media : application to the drying of softwood

An improved mesoscopic model is presented for simulating the drying of porous media. The aim of this model is to account for two scales simultaneously: the scale of the whole product and the scale of the heterogeneities of the porous medium. The innovation of this method is the utilization of a new mass-conservative scheme based on the Control-Volume Finite-Element (CV-FE) method that partitions the moisture content field over the individual sub-control volumes surrounding each node within the mesh. Although the new formulation has potential for application across a wide range of transport processes in heterogeneous porous media, the focus here is on applying the model to the drying of small sections of softwood consisting of several growth rings. The results conclude that, when compared to a previously published scheme, only the new mass-conservative formulation correctly captures the true moisture content evolution in the earlywood and latewood components of the growth rings during drying.

Improved image quality for x-ray CT imaging of gel dosimeters

Purpose: This study provides a simple method for improving precision of x-ray computed tomography (CT) scans of irradiated polymer gel dosimetry. The noise affecting CT scans of irradiated gels has been an impediment to the use of clinical CT scanners for gel dosimetry studies. Method: In this study, it is shown that multiple scans of a single PAGAT gel dosimeter can be used to extrapolate a ‘zero-scan’ image which displays a similar level of precision to an image obtained by averaging multiple CT images, without the compromised dose measurement resulting from the exposure of the gel to radiation from the CT scanner. Results: When extrapolating the zero-scan image, it is shown that exponential and simple linear fits to the relationship between Hounsfield unit and scan number, for each pixel in the image, provides an accurate indication of gel density. Conclusions: It is expected that this work will be utilised in the analysis of three-dimensional gel volumes irradiated using complex radiotherapy treatments.

Stochastic modelling of T cell homeostasis for two competing clonotypes via the master equation

Stochastic models for competing clonotypes of T cells by multivariate, continuous-time, discrete state, Markov processes have been proposed in the literature by Stirk, Molina-París and van den Berg (2008). A stochastic modelling framework is important because of rare events associated with small populations of some critical cell types. Usually, computational methods for these problems employ a trajectory-based approach, based on Monte Carlo simulation. This is partly because the complementary, probability density function (PDF) approaches can be expensive but here we describe some efficient PDF approaches by directly solving the governing equations, known as the Master Equation. These computations are made very efficient through an approximation of the state space by the Finite State Projection and through the use of Krylov subspace methods when evolving the matrix exponential. These computational methods allow us to explore the evolution of the PDFs associated with these stochastic models, and bimodal distributions arise in some parameter regimes. Time-dependent propensities naturally arise in immunological processes due to, for example, age-dependent effects. Incorporating time-dependent propensities into the framework of the Master Equation significantly complicates the corresponding computational methods but here we describe an efficient approach via Magnus formulas. Although this contribution focuses on the example of competing clonotypes, the general principles are relevant to multivariate Markov processes and provide fundamental techniques for computational immunology.

Linear models for endocytic transformations from live cell imaging

Endocytosis is the process by which cells internalise molecules including nutrient proteins from the extracellular media. In one form, macropinocytosis, the membrane at the cell surface ruffles and folds over to give rise to an internalised vesicle. Negatively charged phospholipids within the membrane called phosphoinositides then undergo a series of transformations that are critical for the correct trafficking of the vesicle within the cell, and which are often pirated by pathogens such as Salmonella. Advanced fluorescent video microscopy imaging now allows the detailed observation and quantification of these events in live cells over time. Here we use these observations as a basis for building differential equation models of the transformations. An initial investigation of these interactions was modelled with reaction rates proportional to the sum of the concentrations of the individual constituents. A first order linear system for the concentrations results. The structure of the system enables analytical expressions to be obtained and the problem becomes one of determining the reaction rates which generate the observed data plots. We present results with reaction rates which capture the general behaviour of the reactions so that we now have a complete mathematical model of phosphoinositide transformations that fits the experimental observations. Some excellent fits are obtained with modulated exponential functions; however, these are not solutions of the linear system. The question arises as to how the model may be modified to obtain a system whose solution provides a more accurate fit.

Estimating the loading condition of a diesel engine using instantaneous angular speed analysis

Continuing monitoring of diesel engine performance is critical for early detection of fault developments in the engine before they materialize and become a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few non intrusive condition monitoring techniques that can be utilized for such tasks. In this experimental study, IAS analysis was employed to estimate the loading condition of a 4-stroke 4-cylinder diesel engine in a laboratory condition. It was shown that IAS analysis can provide useful information about engine speed variation caused by the changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectrum directly associated with the engine firing frequency (at twice the mean shaft revolution speed) can be utilized to estimate the engine loading condition regardless of whether the engine is operating at normal running conditions or in a simulated faulty injector case. The amplitude of this order component follows a clear exponential curve as the loading condition changes. A mathematical relationship was established for the estimation of the engine power output based on the amplitude of the major order component of the measured IAS spectrum.