Extraction of fibre network architecture by X-ray tomography and prediction of elastic properties using an affine analytical model

This paper proposes a method for extracting reliable architectural characteristics from complex porous structures using micro-computed tomography (μCT) images. The work focuses on a highly porous material composed of a network of fibres bonded together. The segmentation process, allowing separation of the fibres from the remainder of the image, is the most critical step in constructing an accurate representation of the network architecture. Segmentation methods, based on local and global thresholding, were investigated and evaluated by a quantitative comparison of the architectural parameters they yielded, such as the fibre orientation and segment length (sections between joints) distributions and the number of inter-fibre crossings. To improve segmentation accuracy, a deconvolution algorithm was proposed to restore the original images. The efficacy of the proposed method was verified by comparing μCT network architectural characteristics with those obtained using high resolution CT scans (nanoCT). The results indicate that this approach resolves the architecture of these complex networks and produces results approaching the quality of nanoCT scans. The extracted architectural parameters were used in conjunction with an affine analytical model to predict the axial and transverse stiffnesses of the fibre network. Transverse stiffness predictions were compared with experimentally measured values obtained by vibration testing. © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Applying quality control in building energy modelling: Comparative simulation of a high performance building

Quality control is considered from the simulator's perspective through comparative simulation of an ultra energy-efficient building with EE4-DOE2.1E and EnergyPlus. The University of Calgary's Leadership in Energy and Environmental Design Platinum Child Development Centre, with a 66% certified energy cost reduction rating, was the case study building. A Natural Resources Canada incentive program required use of EE4 interface with DOE2.1E simulation engine for energy modelling. As DOE2.1E lacks specific features to simulate advanced systems such as radiant cooling in the CDC, an EnergyPlus model was developed to further evaluate these features. The EE4-DOE2.1E model was used for quality control during development of the base EnergyPlus model and simulation results were compared. Advanced energy systems then added to the EnergyPlus model generated small difference in estimated total annual energy use. The comparative simulation process helped identify the main input errors in the draft EnergyPlus model. The comparative use of less complex simulation programs is recommended for quality control when producing more complex models. © 2009 International Building Performance Simulation Association (IBPSA).

Kinetic control of catalytic CVD for high-quality graphene at low temperatures.

Low-temperature (∼600 °C), scalable chemical vapor deposition of high-quality, uniform monolayer graphene is demonstrated with a mapped Raman 2D/G ratio of >3.2, D/G ratio ≤0.08, and carrier mobilities of ≥3000 cm(2) V(-1) s(-1) on SiO(2) support. A kinetic growth model for graphene CVD based on flux balances is established, which is well supported by a systematic study of Ni-based polycrystalline catalysts. A finite carbon solubility of the catalyst is thereby a key advantage, as it allows the catalyst bulk to act as a mediating carbon sink while optimized graphene growth occurs by only locally saturating the catalyst surface with carbon. This also enables a route to the controlled formation of Bernal stacked bi- and few-layered graphene. The model is relevant to all catalyst materials and can readily serve as a general process rationale for optimized graphene CVD.

Model development & application for inland navigational canals

Several agencies in the United Kingdom have interest in the water quality of old navigational canals that have fallen into disuse after the decline of commercial canal transportation. The interested agencies desired a model to predict the water quantity and quality of inland navigational canals in order to evaluate management options to address the issues in the natural streams to which they discharge. Inland navigational canals have unique drivers of their hydrology and water quality compared to either natural streams, irrigation canals, or larger navigational canals connected to seas or oceans. Water in an inland canal is typically sourced from a reservoir and artificially pumped to a summit reach; its movement downhill is controlled by the activity of boats and overflow weirs. Stagnant impoundments between locks, which might normally be expected to result in a decrease in the concentration of sediment-associated pollutants, actually have surprisingly high levels of sediment due to boat traffic. Algal growth in the stagnant reach can be high. This paper describes a canal model developed to simulate hydrology and water quality in inland navigational canals. This model was successfully applied to the Kennet and Avon Canal to predict hydrology, sediment generation and transport, and algal growth and transport. The model is responsive to external influences such as sunlight, temperature, nutrient concentrations, boat traffic, and runoff from the contributing catchment area.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.

Quality practices and problems in free software projects

Free software and open source projects are often perceived to be of high quality. It has been suggested that the high level of quality found in some free software projects is related to the open development model which promotes peer review. While the quality of some free software projects is comparable to, if not better than, that of closed source software, not all free software projects are successful and of high quality. Even mature and successful projects face quality problems; some of these are related to the unique characteristics of free software and open source as a distributed development model led primarily by volunteers. In exploratory interviews performed with free software and open source developers, several common quality practices as well as actual quality problems have been identified. The results of these interviews are presented in this paper in order to take stock of the current status of quality in free software projects and to act as a starting point for the implementation of quality process improvement strategies.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

A systematic approach for improving the quality of official statistics: Case of Korean information and telecommunication industry

With the rapid growth of information and communication technology (ICT) in Korea, there was a need to improve the quality of official ICT statistics. In order to do this, various factors had to be considered, such as the quality of surveying, processing, and output as well as the reputation of the statistical agency. We used PLS estimation to determine how these factors might influence customer satisfaction. Furthermore, through a comparison of associated satisfaction indices, we provided feedback to the responsible statistics agency. It appears that our model can be used as a tool for improving the quality of official ICT statistics. © 2008 Elsevier B.V. All rights reserved.

A global model for effective use and evaluation of e-learning in health.

Healthcare systems worldwide face a wide range of challenges, including demographic change, rising drug and medical technology costs, and persistent and widening health inequalities both within and between countries. Simultaneously, issues such as professional silos, static medical curricula, and perceptions of "information overload" have made it difficult for medical training and continued professional development (CPD) to adapt to the changing needs of healthcare professionals in increasingly patient-centered, collaborative, and/or remote delivery contexts. In response to these challenges, increasing numbers of medical education and CPD programs have adopted e-learning approaches, which have been shown to provide flexible, low-cost, user-centered, and easily updated learning. The effectiveness of e-learning varies from context to context, however, and has also been shown to make considerable demands on users' motivation and "digital literacy" and on providing institutions. Consequently, there is a need to evaluate the effectiveness of e-learning in healthcare as part of ongoing quality improvement efforts. This article outlines the key issues for developing successful models for analyzing e-health learning.

Air quality and climate impacts of alternative bus technologies in Greater London.

The environmental impact of diesel-fueled buses can potentially be reduced by the adoption of alternative propulsion technologies such as lean-burn compressed natural gas (LB-CNG) or hybrid electric buses (HEB), and emissions control strategies such as a continuously regenerating trap (CRT), exhaust gas recirculation (EGR), or selective catalytic reduction with trap (SCRT). This study assessed the environmental costs and benefits of these bus technologies in Greater London relative to the existing fleet and characterized emissions changes due to alternative technologies. We found a >30% increase in CO2 equivalent (CO2e) emissions for CNG buses, a <5% change for exhaust treatment scenarios, and a 13% (90% confidence interval 3.8-20.9%) reduction for HEB relative to baseline CO2e emissions. A multiscale regional chemistry-transport model quantified the impact of alternative bus technologies on air quality, which was then related to premature mortality risk. We found the largest decrease in population exposure (about 83%) to particulate matter (PM2.5) occurred with LB-CNG buses. Monetized environmental and investment costs relative to the baseline gave estimated net present cost of LB-CNG or HEB conversion to be $187 million ($73 million to $301 million) or $36 million ($-25 million to $102 million), respectively, while EGR or SCRT estimated net present costs were $19 million ($7 million to $32 million) or $15 million ($8 million to $23 million), respectively.