Emerging water treatment technologies for decentralised systems : an overview of selected systems suited for application in towns and settlements in remote and very remote regions of Australia and vulnerable and lagging rural regions in Sri Lanka

The primary purpose of this paper is to overview a selection of advanced water treatment technology systems that are suited for application in towns and settlements in remote and very remote regions of Australia and vulnerable and lagging rural regions in Sri Lanka. This recognises that sanitation and water treatment are inextricably linked and both are needed to reduce risks to environment and population health from contaminated water sources. For both Australia and Sri Lanka only a small fraction of the settlements in rural and remote regions are connected to water treatment facilities and town water supplies. In Australia’s remote/very remote regions raw water is drawn from underground sources and rainwater capture. Most settlements in rural Sri Lanka rely on rivers, reservoirs, wells, springs or carted water. Furthermore, Sri Lanka has more than 25,000 hand pumped tube wells which saved the communities during recent droughts. Decentralised water supply systems offer the opportunity to provide safe drinking water to these remote/very remote and rural regions where centralised systems are not feasible due to socio-cultural, economic, political, technological reasons. These systems reduce health risks from contaminated water supplies. In remote areas centralized systems fail due to low population density and less affordability. Globally, a new generation of advanced water treatment technologies are positioned to make a major impact on the provision of safe potable water in remote/very remote regions in Australia and rural regions in Sri Lanka. Some of these systems were developed for higher income countries. However, with careful selection and further research they can be tailored to match local socio-economic conditions and technical capacity. As such, they can equally be used to provide decentralised water supply in communities in developed and developing countries such as Australia and Sri Lanka.

Multi-action recognition via stochastic modelling of optical flow and gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%.

Quantifying uncertainty in parameter estimates for stochastic models of collective cell spreading using approximate Bayesian computation

Wound healing and tumour growth involve collective cell spreading, which is driven by individual motility and proliferation events within a population of cells. Mathematical models are often used to interpret experimental data and to estimate the parameters so that predictions can be made. Existing methods for parameter estimation typically assume that these parameters are constants and often ignore any uncertainty in the estimated values. We use approximate Bayesian computation (ABC) to estimate the cell diffusivity, D, and the cell proliferation rate, λ, from a discrete model of collective cell spreading, and we quantify the uncertainty associated with these estimates using Bayesian inference. We use a detailed experimental data set describing the collective cell spreading of 3T3 fibroblast cells. The ABC analysis is conducted for different combinations of initial cell densities and experimental times in two separate scenarios: (i) where collective cell spreading is driven by cell motility alone, and (ii) where collective cell spreading is driven by combined cell motility and cell proliferation. We find that D can be estimated precisely, with a small coefficient of variation (CV) of 2–6%. Our results indicate that D appears to depend on the experimental time, which is a feature that has been previously overlooked. Assuming that the values of D are the same in both experimental scenarios, we use the information about D from the first experimental scenario to obtain reasonably precise estimates of λ, with a CV between 4 and 12%. Our estimates of D and λ are consistent with previously reported values; however, our method is based on a straightforward measurement of the position of the leading edge whereas previous approaches have involved expensive cell counting techniques. Additional insights gained using a fully Bayesian approach justify the computational cost, especially since it allows us to accommodate information from different experiments in a principled way.

Characterisation of the cascode gate drive of power MOSFETs in clamped inductive switching applications

This thesis proposes a novel gate drive circuit to improve the switching performance of MOSFET power switches in power electronic converters. The proposed topology exploits the cascode configuration, allowing the minimisation of switching losses in the presence of practical circuit constraints, which enables efficiency and power density improvements. Switching characteristics of the new topology are investigated and key mechanisms that control the switching process are identified. Unique analysis tools and techniques are also developed to demonstrate the application of the cascode gate drive circuit for switching performance optimisation.

The dual nature of information systems in enabling a new wave of hardware ventures: Towards a theory

Hardware ventures are emerging entrepreneurial firms that create new market offerings based on development of digital devices. These ventures are important elements in the global economy but have not yet received much attention in the literature. Our interest in examining hardware ventures is specifically in the role that information system (IS) resources play in enabling them. We ask how the role of IS resources for hardware ventures can be conceptualized and develop a framework for assessment. Our framework builds on the distinction of operand and operant resources and distinguishes between two key lifecycle stages of hardware ventures: start-up and growth. We show how this framework can be used to discuss the role, nature, and use of IS for hardware ventures and outline empirical research strategies that flow from it. Our work contributes to broadening and enriching the IS field by drawing attention to its role in significant and novel phenomena.

Determining reverse salient types and evolutionary dynamics of technology systems with performance disparities

Technological system evolution is marked by the uneven evolution of constituent sub-systems. Subsequently, system evolution is hampered by the resulting state of unevenness, or reverse salience, which results from the presence of the sub-system that delivers the lowest level of performance with respect to other sub-systems, namely, the reverse salient. In this paper, we develop absolute and proportional performance gap measures of reverse salience and, in turn, derive a typology of reverse salients that distinguishes alternative dynamics of change in the evolving system. We subsequently demonstrate the applicability of the measures and the typology through an illustrative empirical study of the PC (personal computer) technological system that functions as a gaming platform. Our empirical analysis demonstrates that patterns of temporal dynamics can be distinguished with the measurement of reverse salience, and that distinct paths of technological system evolution can be identified as different types of reverse salients emerge over time.

Quantitative genetic modeling of lateral ventricular shape and volume using multi-atlas fluid image alignment in twins

Despite substantial progress in measuring the 3D profile of anatomical variations in the human brain, their genetic and environmental causes remain enigmatic. We developed an automated system to identify and map genetic and environmental effects on brain structure in large brain MRI databases . We applied our multi-template segmentation approach ("Multi-Atlas Fluid Image Alignment") to fluidly propagate hand-labeled parameterized surface meshes into 116 scans of twins (60 identical, 56 fraternal), labeling the lateral ventricles. Mesh surfaces were averaged within subjects to minimize segmentation error. We fitted quantitative genetic models at each of 30,000 surface points to measure the proportion of shape variance attributable to (1) genetic differences among subjects, (2) environmental influences unique to each individual, and (3) shared environmental effects. Surface-based statistical maps revealed 3D heritability patterns, and their significance, with and without adjustments for global brain scale. These maps visualized detailed profiles of environmental versus genetic influences on the brain, extending genetic models to spatially detailed, automatically computed, 3D maps.

4D deformation modeling of cortical disease progression in Alzheimer's dementia

This work describes the development of a model of cerebral atrophic changes associated with the progression of Alzheimer's disease (AD). Linear registration, region-of-interest analysis, and voxel-based morphometry methods have all been employed to elucidate the changes observed at discrete intervals during a disease process. In addition to describing the nature of the changes, modeling disease-related changes via deformations can also provide information on temporal characteristics. In order to continuously model changes associated with AD, deformation maps from 21 patients were averaged across a novel z-score disease progression dimension based on Mini Mental State Examination (MMSE) scores. The resulting deformation maps are presented via three metrics: local volume loss (atrophy), volume (CSF) increase, and translation (interpreted as representing collapse of cortical structures). Inspection of the maps revealed significant perturbations in the deformation fields corresponding to the entorhinal cortex (EC) and hippocampus, orbitofrontal and parietal cortex, and regions surrounding the sulci and ventricular spaces, with earlier changes predominantly lateralized to the left hemisphere. These changes are consistent with results from post-mortem studies of AD.

Characterization of alluvial formation by stochastic modelling of paleo-fluvial processes: The concept and method

Modelling fluvial processes is an effective way to reproduce basin evolution and to recreate riverbed morphology. However, due to the complexity of alluvial environments, deterministic modelling of fluvial processes is often impossible. To address the related uncertainties, we derive a stochastic fluvial process model on the basis of the convective Exner equation that uses the statistics (mean and variance) of river velocity as input parameters. These statistics allow for quantifying the uncertainty in riverbed topography, river discharge and position of the river channel. In order to couple the velocity statistics and the fluvial process model, the perturbation method is employed with a non-stationary spectral approach to develop the Exner equation as two separate equations: the first one is the mean equation, which yields the mean sediment thickness, and the second one is the perturbation equation, which yields the variance of sediment thickness. The resulting solutions offer an effective tool to characterize alluvial aquifers resulting from fluvial processes, which allows incorporating the stochasticity of the paleoflow velocity.

Moving on (part 1): The physics of power and curriculum history

This is the first of two papers that map (dis)continuities in notions of power from Aristotle to Newton to Foucault. They trace the ways in which bio-physical conceptions of power became paraphrased in social science and deployed in educational discourse on the child and curriculum from post-Newtonian times to the present. The analyses suggest that, amid ruptures in the definition, role, location and meaning given 'power' historically in various 'physical' and 'social' cosmologies, the naming of 'power' has been dependent on 'physics', on the theorization of motion across 'Western' sciences. This first paper examines some (dis)continuities in regard to histories of motion and power from Aristotelian 'natural science' to Newtonian mechanics.

In Perpetual Motion: Theories of Power, Educational History, and the Child

"In Perpetual Motion is an "historical choreography" of power, pedagogy, and the child from the 1600s to the early 1900s. It breaks new ground by historicizing the analytics of power and motion that have interpenetrated renditions of the young. Through a detailed examination of the works of John Locke, Jean-Jacques Rousseau, Johann Herbart, and G. Stanley Hall, this book maps the discursive shifts through which the child was given a unique nature, inscribed in relation to reason, imbued with an effectible interiority, and subjected to theories of power and motion. The book illustrates how developmentalist visions took hold in U.S. public school debates. It documents how particular theories of power became submerged and taken for granted as essences inside the human subject. In Perpetual Motion studiously challenges views of power as in or of the gaze, tracing how different analytics of power have been used to theorize what gazing could notice."--BOOK JACKET.

A closed-form optimal tuning of mass dampers for one degree-of-freedom systems under rotating unbalance forcing

This paper is focused on the study of a vibrating system forced by a rotating unbalance and coupled to a tuned mass damper (TMD). The analysis of the dynamic response of the entire system is used to define the parameters of such device in order to achieve optimal damping properties. The inertial forcing due to the rotating unbalance depends quadratically on the forcing frequency and it leads to optimal tuning parameters that differ from classical values obtained for pure harmonic forcing. Analytical results demonstrate that frequency and damping ratios, as a function of the mass parameter, should be higher than classical optimal parameters. The analytical study is carried out for the undamped primary system, and numerically investigated for the damped primary system. We show that, for practical applications, proper TMD tuning allows to achieve a reduction in the steady-state response of about 20% with respect to the response achieved with a classically tuned damper. Copyright © 2015 by ASME.

Molecular modeling of Bt Cry1Ac (DI–DII)–ASAL (Allium sativum lectin)–fusion protein and its interaction with aminopeptidase N (APN) receptor of Manduca sexta

Genetic engineering of Bacillus thuringiensis (Bt) Cry proteins has resulted in the synthesis of various novel toxin proteins with enhanced insecticidal activity and specificity towards different insect pests. In this study, a fusion protein consisting of the DI–DII domains of Cry1Ac and garlic lectin (ASAL) has been designed in silico by replacing the DIII domain of Cry1Ac with ASAL. The binding interface between the DI–DII domains of Cry1Ac and lectin has been identified using protein–protein docking studies. Free energy of binding calculations and interaction profiles between the Cry1Ac and lectin domains confirmed the stability of fusion protein. A total of 18 hydrogen bonds was observed in the DI–DII–lectin fusion protein compared to 11 hydrogen bonds in the Cry1Ac (DI–DII–DIII) protein. Molecular mechanics/Poisson–Boltzmann (generalized-Born) surface area [MM/PB (GB) SA] methods were used for predicting free energy of interactions of the fusion proteins. Protein–protein docking studies based on the number of hydrogen bonds, hydrophobic interactions, aromatic–aromatic, aromatic–sulphur, cation–pi interactions and binding energy of Cry1Ac/fusion proteins with the aminopeptidase N (APN) of Manduca sexta rationalised the higher binding affinity of the fusion protein with the APN receptor compared to that of the Cry1Ac–APN complex, as predicted by ZDOCK, Rosetta and ClusPro analysis. The molecular binding interface between the fusion protein and the APN receptor is well packed, analogously to that of the Cry1Ac–APN complex. These findings offer scope for the design and development of customized fusion molecules for improved pest management in crop plants.

Geometry-free stochastic analysis of BDS triple frequency signals

The paper presents a geometry-free approach to assess the variation of covariance matrices of undifferenced triple frequency GNSS measurements and its impact on positioning solutions. Four independent geometryfree/ ionosphere-free (GFIF) models formed from original triple-frequency code and phase signals allow for effective computation of variance-covariance matrices using real data. Variance Component Estimation (VCE) algorithms are implemented to obtain the covariance matrices for three pseudorange and three carrier-phase signals epoch-by-epoch. Covariance results from the triple frequency Beidou System (BDS) and GPS data sets demonstrate that the estimated standard deviation varies in consistence with the amplitude of actual GFIF error time series. The single point positioning (SPP) results from BDS ionosphere-free measurements at four MGEX stations demonstrate an improvement of up to about 50% in Up direction relative to the results based on a mean square statistics. Additionally, a more extensive SPP analysis at 95 global MGEX stations based on GPS ionosphere-free measurements shows an average improvement of about 10% relative to the traditional results. This finding provides a preliminary confirmation that adequate consideration of the variation of covariance leads to the improvement of GNSS state solutions.