The not-so-extreme white dwarf of the CV GD 552

GD 552 is a cataclysmic binary which was previously believed to be composed of an M-star and a white dwarf, the latter having an extreme mass of 1.4 solar masses. In a recent paper we showed that this is not compatible with new observational evidence and presented an alternative model in which the white dwarf has a typical mass and the companion is a brown dwarf, making the system a likely member of the elusive group of CVs which have already evolved through minimum orbital period. Here we present additional spectroscopical evidence supporting this conclusion by means of skew mapping.

Context-Aware Body Area Networks (CABAN) for interactive smart environments:Interference characterization

Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains or even to enter new and unknown environments. This network mobility is unlike node mobility in that sensed changes in inter-network interference level may be used to identify opportunities for intelligent inter-networking, for example, by merging or splitting from other networks, thus providing an extra degree of freedom. This paper introduces the concept of context-aware bodynets for interactive environments using inter-network interference sensing. New ideas are explored at both the physical and link layers with an investigation based on a 'smart' office environment. A series of carefully controlled measurements of the mesh interconnectivity both within and between an ambulatory body area network and a stationary desk-based network were performed using 2.45 GHz nodes. Received signal strength and carrier to interference ratio time series for selected node to node links are presented. The results provide an insight into the potential interference between the mobile and static networks and highlight the possibility for automatic identification of network merging and splitting opportunities. © 2010 ACM.

TEXTILE TECHNOLOGIES IN CONCRETE ENVIRONMENTS."

Girli Concrete is a cross disciplinary funded research project based in the University of Ulster involving a textile designer/ researcher, an architect/ academic and a concrete manufacturing firm.
Girli Concrete brings together concrete and textile technologies, testing ideas of
concrete as textile and textile as structure. It challenges the perception of textiles as only the ‘dressing’ to structure and instead integrates textile technologies into the products of building products. Girli Concrete uses ‘low tech’ methods of wet and dry concrete casting in combination with ‘high tech’ textile methods using laser cutting, etching, flocking and digital printing. Whilst we have been inspired by recent print and imprint techniques in architectural cladding, Girli Concrete is generated within the depth of the concrete’s cement paste “skin”, bringing the trades and crafts of both industries together with innovative results.
Architecture and Textiles have an odd, somewhat unresolved relationship. Confined to a subservient role in architecture, textiles exist chiefly within the categories of soft furnishings and interior design. Girli Concrete aims to mainstream tactility in the production of built environment products, raising the human and environmental interface to the same specification level as the technical. This paper will chart:
The background and wider theoretical concerns to the project.
The development of Girli Concrete, highlighting the areas where craft becomes
art and art becomes science in the combination of textile and concrete
technologies.
The challenges of identifying funding to support such combination technologies,
working methods and philosophies.
The challenges of generating and sustaining practice within an academic
research environment
The outcomes to date

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Adaptive Bacteria Colony Picking in Unstructured Environments Using Intensity Histogram and Unascertained LS-SVM Classifier

Features analysis is an important task which can significantly affect the performance of automatic bacteria colony picking. Unstructured environments also affect the automatic colony screening. This paper presents a novel approach for adaptive colony segmentation in unstructured environments by treating the detected peaks of intensity histograms as a morphological feature of images. In order to avoid disturbing peaks, an entropy based mean shift filter is introduced to smooth images as a preprocessing step. The relevance and importance of these features can be determined in an improved support vector machine classifier using unascertained least square estimation. Experimental results show that the proposed unascertained least square support vector machine (ULSSVM) has better recognition accuracy than the other state-of-the-art techniques, and its training process takes less time than most of the traditional approaches presented in this paper.

A decrease in bulk water and mannitol and accumulation of trehalose and trehalose-based oligosaccharides define a two-stage maturation process towards extreme stress resistance in ascospores of Neosartorya fischeri (Aspergillus fischeri)

Difference equation approach to two-thermocouple sensor characterization in constant velocity flow environments

Thermocouples are one of the most popular devices for temperature measurement due to their robustness, ease of manufacture and installation, and low cost. However, when used in certain harsh environments, for example, in combustion systems and engine exhausts, large wire diameters are required, and consequently the measurement bandwidth is reduced. This article discusses a software compensation technique to address the loss of high frequency fluctuations based on measurements from two thermocouples. In particular, a difference equation (DE) approach is proposed and compared with existing methods both in simulation and on experimental test rig data with constant flow velocity. It is found that the DE algorithm, combined with the use of generalized total least squares for parameter identification, provides better performance in terms of time constant estimation without any a priori assumption on the time constant ratios of the thermocouples.

The performance of concrete exposed to marine environments: predictive modelling and use of laboratory/on site test methods

This paper reports an approach by which laboratory based testing and numerical modelling can be combined to predict the long term performance of a range of concretes exposed to marine environments. Firstly, a critical review of the test methods for assessing the chloride penetration resistance of concrete is given. The repeatability of the different test results is also included. In addition to the test methods, a numerical simulation model is used to explore the test data further to obtain long-term chloride ingress trends. The combined use of testing and modelling is validated with the help of long-term chloride ingress data from a North Sea exposure site. In summary, the paper outlines a methodology for determining the long term performance of concrete in marine environments.

Microbiology of sugar-rich environments: Diversity, ecology and system constraints

Microbial habitats that contain an excess of carbohydrate in the form of sugar are widespread in the microbial biosphere. Depending on the type of sugar, prevailing water activity and other substances present, sugar-rich environments can be highly dynamic or relatively stable, osmotically stressful, and/or destabilizing for macromolecular systems, and can thereby strongly impact the microbial ecology. Here, we review the microbiology of different high-sugar habitats, including their microbial diversity and physicochemical parameters, which act to impact microbial community assembly and constrain the ecosystem. Saturated sugar beet juice and floral nectar are used as case studies to explore the differences between the microbial ecologies of low and higher water-activity habitats respectively. Nectar is a paradigm of an open, dynamic and biodiverse habitat populated by many microbial taxa, often yeasts and bacteria such as, amongst many others, Metschnikowia spp. and Acinetobacter spp., respectively. By contrast, thick juice is a relatively stable, species-poor habitat and is typically dominated by a single, xerotolerant bacterium (Tetragenococcus halophilus). A number of high-sugar habitats contain chaotropic solutes (e.g. ethyl acetate, phenols, ethanol, fructose and glycerol) and hydrophobic stressors (e.g. ethyl octanoate, hexane, octanol and isoamyl acetate), all of which can induce chaotropicity-mediated stresses that inhibit or prevent multiplication of microbes. Additionally, temperature, pH, nutrition, microbial dispersion and habitat history can determine or constrain the microbiology of high-sugar milieux. Findings are discussed in relation to a number of unanswered scientific questions.

Connectivity and Physical Activity: Using Footpath Networks to Measure the Walkability of Built Environments

There is now a strong body of research that suggests that the form of the built environment can influence levels of physical activity, leading to an increasing interest in incorporating health objectives into spatial planning and regeneration policies and projects. There have been a number of strands to this research, one of which has sought to develop “objective” measurements of the built environment using Geographic Information Science (GIS) involving measures of connectivity and proximity to compare the relative “walkability” of different neighbourhoods. The development of the “walkability index” (e.g. Leslie et al 2007, Frank et al 2010) has become a popular indicator of spatial distribution of those features of the built environment that are considered to have the greatest positive influence on levels of physical activity. The success of this measure is built on its ability to succinctly capture built environment correlates of physical activity using routinely available spatial data, which includes using road centre lines as a basis of a proxy for connectivity.

This paper discusses two key aspects of the walkability index. First, it follows the suggestion of Chin et al (2008) that the use of a footpath network (where available), rather than road centre lines, may be far more effective in evaluating walkability. This may be particularly important for assessing changes in walkability arising from pedestrian-focused infrastructure projects, such as greenways. Second, the paper explores the implication of this for how connectivity can be measured. The paper takes six different measures of connectivity and first analyses the relationships between them and then tests their correlation with actual levels of physical activity of local residents in Belfast, Northern Ireland. The analysis finds that the best measurements appear to be intersection density and metric reach and uses this finding to discuss the implications of this for developing tools that may better support decision-making in spatial planning.