Verification of design calculations of a wind catcher/tower natural ventilation system with performance testing in real building

A wind catcher/tower natural ventilation system was installed in a seminar room in the building of the School of Construction Management and Engineering, the University of Reading in the UK . Performance was analysed by means of ventilation tracer gas measurements, indoor climate measurements (temperature, humidity, CO2) and occupant surveys. In addition, the potential of simple design tools was evaluated by comparing observed ventilation results with those predicted by an explicit ventilation model and the AIDA implicit ventilation model. To support this analysis, external climate parameters (wind speed and direction, solar radiation, external temperature and humidity) were also monitored. The results showed the chosen ventilation design provided a substantially greater ventilation rate than an equivalent area of openable window. Also air quality parameters stayed within accepted norms while occupants expressed general satisfaction with the system and with comfort conditions. Night cooling was maximised by using the system in combination with openable windows. Comparisons of calculations with ventilation rate measurements showed that while AIDA gave reasonably correlated results with the monitored performance results, the widely used industry explicit model was found to over estimate the monitored ventilation rate.

Quantitative pulsed phase thermography applied to steel plates

Pulsed Phase Thermography (PPT) has been proven effective on depth retrieval of flat-bottomed holes in different materials such as plastics and aluminum. In PPT, amplitude and phase delay signatures are available following data acquisition (carried out in a similar way as in classical Pulsed Thermography), by applying a transformation algorithm such as the Fourier Transform (FT) on thermal profiles. The authors have recently presented an extended review on PPT theory, including a new inversion technique for depth retrieval by correlating the depth with the blind frequency fb (frequency at which a defect produce enough phase contrast to be detected). An automatic defect depth retrieval algorithm had also been proposed, evidencing PPT capabilities as a practical inversion technique. In addition, the use of normalized parameters to account for defect size variation as well as depth retrieval from complex shape composites (GFRP and CFRP) are currently under investigation. In this paper, steel plates containing flat-bottomed holes at different depths (from 1 to 4.5 mm) are tested by quantitative PPT. Least squares regression results show excellent agreement between depth and the inverse square root blind frequency, which can be used for depth inversion. Experimental results on steel plates with simulated corrosion are presented as well. It is worth noting that results are improved by performing PPT on reconstructed (synthetic) rather than on raw thermal data.

Truth in complex adaptive systems models should be based on truth by constructive verification

It is argued that the truth status of emergent properties of complex adaptive systems models should be based on an epistemology of proof by constructive verification and therefore on the ontological axioms of a non-realist logical system such as constructivism or intuitionism. ‘Emergent’ properties of complex adaptive systems (CAS) models create particular epistemological and ontological challenges. These challenges bear directly on current debates in the philosophy of mathematics and in theoretical computer science. CAS research, with its emphasis on computer simulation, is heavily reliant on models which explore the entailments of Formal Axiomatic Systems (FAS). The incompleteness results of Gödel, the incomputability results of Turing, and the Algorithmic Information Theory results of Chaitin, undermine a realist (platonic) truth model of emergent properties. These same findings support the hegemony of epistemology over ontology and point to alternative truth models such as intuitionism, constructivism and quasi-empiricism.

Developing a quantitative approach for determining the in vitro prebiotic potential of dietary oligosaccharides

Prebiotics are nondigestible carbohydrates that beneficially affect the host by selectively stimulating the growth and/or activity of one, or a limited number of, bacteria present in the colon. The selected genera should have the capacity to improve host health (e.g. Bifidobacterium, Lactobacillus). To help identify preferred types, for inclusion into the diet, a quantitative equation [measure of the prebiotic effect (MPE)] is suggested. This will help evaluate, in vitro, the fermentation of dietary carbohydrates and compare their prebiotic effect. Although the approach is not meant to define health values, it is formulated to better inform the choice of prebiotic. It therefore, compares measurements of bacterial changes through the determination of maximum growth rates of predominant groups present in faeces, rate of substrate assimilation and the production of lactic, acetic, propionic and butyric acids. The equation will allow further in vitro comparisons of MPE, leading towards further studies (e.g. in humans) to determine the success of dietary intervention. (C) 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

Alkaline phosphatase activity in pasteurized milk: a quantitative comparison of Fluorophos and colourimetric procedures

Fluorophos and colourimetric procedures for alkaline phosphatase (ALP) testing were compared using milk with raw milk additions, purified bovine ALP additions and heat treatments. Repeatability was between 0.9% and 10.1% for Fluorophos, 3.5% and 46.1% for the Aschaffenburg and Mullen (A&M) procedure and 4.4% and 8.8% for the Scharer rapid test. Linearity (R-2) using raw milk addition was 0.96 between Fluorophos and the Scharer procedure. Between the Fluorophos and the A&M procedures, R-2 values were 0.98, 0.99 and 0.98 for raw milk additions, bovine ALP additions and heat treatments respectively. Fluorophos showed greater sensitivity and was both faster and simpler to perform.

Development of a quantitative tool for the comparison of the prebiotic effect of dietary oligosaccharides

Aims: To develop a quantitative equation [prebiotic index ( PI)] to aid the analysis of prebiotic fermentation of commercially available and novel prebiotic carbohydrates in vitro, using previously published fermentation data. Methods: The PI equation is based on the changes in key bacterial groups during fermentation. The bacterial groups incorporated into this PI equation were bifidobacteria, lactobacilli, clostridia and bacteroides. The changes in these bacterial groups from previous studies were entered into the PI equation in order to determine a quantitative PI score. PI scores were than compared with the qualitative conclusions made in these publications. In general the PI scores agreed with the qualitative conclusions drawn and provided a quantitative measure. Conclusions: The PI allows the magnitude of prebiotic effects to be quantified rather than evaluations being solely qualitative. Significance and Impact of the Study: The PI equation may be of great use in quantifying prebiotic effects in vitro. It is expected that this will facilitate more rational food product development and the development of more potent prebiotics with activity at lower doses.

Quantitative analysis of accuracy of an inertial/acoustic 6DOF tracking system in motion

An increasing number of neuroscience experiments are using virtual reality to provide a more immersive and less artificial experimental environment. This is particularly useful to navigation and three-dimensional scene perception experiments. Such experiments require accurate real-time tracking of the observer's head in order to render the virtual scene. Here, we present data on the accuracy of a commonly used six degrees of freedom tracker (Intersense IS900) when it is moved in ways typical of virtual reality applications. We compared the reported location of the tracker with its location computed by an optical tracking method. When the tracker was stationary, the root mean square error in spatial accuracy was 0.64 mm. However, we found that errors increased over ten-fold (up to 17 mm) when the tracker moved at speeds common in virtual reality applications. We demonstrate that the errors we report here are predominantly due to inaccuracies of the IS900 system rather than the optical tracking against which it was compared. (c) 2006 Elsevier B.V. All rights reserved.

Verification and FPGA circuits of a block-2 fast path-based predictor

This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of size two for a lower hardware solution while maintaining similar input-output characteristic to the algorithm. The blocked solution, here referred to as B2P algorithm, is obtained using graph theory and retiming methods. Verification approaches were exercised to show that prediction performances obtained from the FPP and B2P algorithms differ within one mis-prediction per thousand instructions using a known framework for branch prediction evaluation. For a chosen FPGA device, circuits generated from the B2P algorithm showed average area savings of over 25% against circuits for the FPP algorithm with similar time performances thus making the proposed blocked predictor superior from a practical viewpoint.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.