Numerical simulation of flow in a photocatalytic reactor containing baffles

In this study a new immobilized flat plate photocatalytic reactor for wastewater treatment has been investigated using computational fluid dynamics (CFD). The reactor consists of a reactor inlet, a reactive section where the catalyst is coated, and outlet parts. For simulation, the reactive section of the reactor was modelled with an array of baffles. In order to optimize the fluid mixing and reactor design, this study attempts to investigate the influence of baffles with differing heights on the flow field of the flat plate reactor. The results obtained from the simulation of a baffled flat plate reactor hydrodynamics for differing baffle heights for certain positions are presented. Under the conditions simulated, the qualitative flow features, such as the distribution of local stream lines, velocity contours, and high shear region, boundary layers separation, vortex formation, and the underlying mechanism are examined. At low and high Re numbers, the influence of baffle heights on the distribution of species mass fraction of a model pollutant are also highlighted. The simulation of qualitative and quantitative properties of fluid dynamics in a baffled reactor provides valuable insight to fully understand the effect of baffles and their role on the flow pattern, behaviour, and features of wastewater treatment using a photocatalytic reactor.

Women and representation in local government : international case studies

Women and Representation in Local Government opens up an opportunity to critique and move beyond suppositions and labels in relation to women in local government. Presenting a wealth of new empirical material, this book brings together international experts to examine and compare the presence of women at this level and features case studies on the US, UK, France, Germany, Spain, Finland, Uganda, China, Australia and New Zealand. Divided into four main sections, each explores a key theme related to the subject of women and representation in local government and engages with contemporary gender theory and the broader literature on women and politics. The contributors explore local government as a gendered environment; critiquing strategies to address the limited number of elected female members in local government and examine the impact of significant recent changes on local government through a gender lens. Addressing key questions of how gender equality can be achieved in this sector, it will be of strong interest to students and academics working in the fields of gender studies, local government and international politics.

Gender patterns for aspirations for transitional employment and training and development in local government

Purpose – The purpose of this paper is to determine the patterns of transitional employment (TE) aspirations and training and development (T&D) needs of women within local government. Design/methodology/approach – A quantitative survey methodology was used to identify aspirations in a sample of 1,068 employees from the Australian Local Government Association. Findings – Mature-aged women were very interested in continuous learning at work despite their limited formal education. Their training preferences consisted of informal delivery face-to-face or online in the areas of management or administration. Younger women were interested in undertaking university courses, while a minority were interested in blue collar occupations. Practical implications – Through the identification of patterns of TE and T&D aspirations, long term strategies to develop and retain women in local government may be developed. Findings suggest that mature-aged women would benefit from additional T&D to facilitate entry into management and senior administration positions, as well as strategies to facilitate a shift in organizational climate. Social implications – Mature-aged women were found to be a potentially untapped resource for management and senior administrative roles owing to their interest in developing skills in these fields and pursuing TE. Younger women may also benefit from T&D to maintain their capacity during breaks from employment. Encouragement of women in non-traditional areas may also address skill shortages in the local government. Originality/value – Mature-aged women were found to be a potentially untapped resource for management and senior administrative roles owing to their interest in developing skills in these fields and pursuing TE. Younger women may also benefit from T&D to maintain their capacity during breaks from employment. Encouragement of women in non-traditional areas may also address skill shortages in the local government.

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.

The link between age, career goals, and adaptive development for work-related learning among local government employees

More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for learning at work in relation to demographics and career goals. It was found that mature age was associated with perceptions of preferential treatment of younger workers with respect to learning and development. Age was also correlated with several career goals. Findings suggest that younger workers’ learning and development options are better catered for in the workplace. Mature aged workers may compensate for unequal learning opportunities at work by studying for an educational qualification or seeking alternate job opportunities. The desire for a higher level job within the organization or educational qualification was linked to engagement in learning and development goals at work. It is suggested that an understanding of employee perceptions in the workplace in relation to goals and activities may be important in designing strategies to retain workers.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.

Wide baseline correspondence extraction beyond local features

Robust, affine covariant, feature extractors provide a means to extract correspondences between images captured by widely separated cameras. Advances in wide baseline correspondence extraction require looking beyond the robust feature extraction and matching approach. This study examines new techniques of extracting correspondences that take advantage of information contained in affine feature matches. Methods of improving the accuracy of a set of putative matches, eliminating incorrect matches and extracting large numbers of additional correspondences are explored. It is assumed that knowledge of the camera geometry is not available and not immediately recoverable. The new techniques are evaluated by means of an epipolar geometry estimation task. It is shown that these methods enable the computation of camera geometry in many cases where existing feature extractors cannot produce sufficient numbers of accurate correspondences.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Local Rademacher complexities

We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.

Development of a sewerage infrastructure buffer assessment tool for local authorities

Urban expansion continues to encroach on existing or newly implemented sewerage infrastructure. In this context, legislation and guidelines, both national and international, provide limited direction to the amenity allocation of appropriate buffering distances for land use planners and infrastructure providers. A review of published literature suggests the dominant influences include topography, wind speed and direction, temperature, humidity, existing land uses and vegetation profiles. A statistical criteria review of these factors against six years of sewerage odour complaint data was undertaken to ascertain their influence and a complaint severity hierarchy was established. These hierarchical results suggested the main criteria were: topographical location, elevation relative to the odour source and wind speed. Establishing a justifiable criterion for buffer zone allocations will assist in analytically determining a basis for buffer separations and will assist planners and infrastructure designers in assessing lower impact sewerage infrastructure locations.