Ultrasonic synthesis of silica-alumina nanomaterials with controlled mesopore distribution without using surfactants

A novel sol-gel process has been developed for the synthesis of amorphous silica-aluminas with controlled mesopore distribution without the use of organic templating agents, e.g., surfactant molecules. Ultrasonic treatment during the synthesis enables production of precursor sols with narrow particle size distribution. Atomic force microscopy analysis shows that these sol particles are spherical in shape with a narrow size distribution (i.e., 13-25 nm) and their aggregation during the gelation creates clusters containing similar sized interparticle mesopores. A nitrogen physiadsorption study indicates that the mesoporous materials containing different Si/Al ratios prepared by the new synthesis method has a large specific surface area (i.e., 587-692 m(2)/g) and similar pore sizes of 2-11 nm. Solid-state Al-27 magic angle spinning (MAS) NMR shows that most of the aluminum is located in the tetrahedral position. A transmission electron microscopy (TEM) image shows that the mesoporous silica-alumina consists of 12-25 nm spheres. Additionally, high-resolution TEM and electron diffraction indicate that some nanoparticles are characteristic of a crystal, although X-ray diffraction and Si-29 MAS NMR analysis show an amorphous material.

Template Matching: Matched Spatial Filters and Beyond

Template matching by means of cross-correlation is common practice in pattern recognition. However, its sensitivity to deformations of the pattern and the broad and unsharp peaks it produces are significant drawbacks. This paper reviews some results on how these shortcomings can be removed. Several techniques (Matched Spatial Filters, Synthetic Discriminant Functions, Principal Components Projections and Reconstruction Residuals) are reviewed and compared on a common task: locating eyes in a database of faces. New variants are also proposed and compared: least squares Discriminant Functions and the combined use of projections on eigenfunctions and the corresponding reconstruction residuals. Finally, approximation networks are introduced in an attempt to improve filter design by the introduction of nonlinearity.

Direct Estimation of Motion and Extended Scene Structure from a Moving Stereo Rig

We describe a new method for motion estimation and 3D reconstruction from stereo image sequences obtained by a stereo rig moving through a rigid world. We show that given two stereo pairs one can compute the motion of the stereo rig directly from the image derivatives (spatial and temporal). Correspondences are not required. One can then use the images from both pairs combined to compute a dense depth map. The motion estimates between stereo pairs enable us to combine depth maps from all the pairs in the sequence to form an extended scene reconstruction and we show results from a real image sequence. The motion computation is a linear least squares computation using all the pixels in the image. Areas with little or no contrast are implicitly weighted less so one does not have to explicitly apply a confidence measure.

An Analog VLSI Chip for Estimating the Focus of Expansion

For applications involving the control of moving vehicles, the recovery of relative motion between a camera and its environment is of high utility. This thesis describes the design and testing of a real-time analog VLSI chip which estimates the focus of expansion (FOE) from measured time-varying images. Our approach assumes a camera moving through a fixed world with translational velocity; the FOE is the projection of the translation vector onto the image plane. This location is the point towards which the camera is moving, and other points appear to be expanding outward from. By way of the camera imaging parameters, the location of the FOE gives the direction of 3-D translation. The algorithm we use for estimating the FOE minimizes the sum of squares of the differences at every pixel between the observed time variation of brightness and the predicted variation given the assumed position of the FOE. This minimization is not straightforward, because the relationship between the brightness derivatives depends on the unknown distance to the surface being imaged. However, image points where brightness is instantaneously constant play a critical role. Ideally, the FOE would be at the intersection of the tangents to the iso-brightness contours at these "stationary" points. In practice, brightness derivatives are hard to estimate accurately given that the image is quite noisy. Reliable results can nevertheless be obtained if the image contains many stationary points and the point is found that minimizes the sum of squares of the perpendicular distances from the tangents at the stationary points. The FOE chip calculates the gradient of this least-squares minimization sum, and the estimation is performed by closing a feedback loop around it. The chip has been implemented using an embedded CCD imager for image acquisition and a row-parallel processing scheme. A 64 x 64 version was fabricated in a 2um CCD/ BiCMOS process through MOSIS with a design goal of 200 mW of on-chip power, a top frame rate of 1000 frames/second, and a basic accuracy of 5%. A complete experimental system which estimates the FOE in real time using real motion and image scenes is demonstrated.

An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network

P-glycoprotein (P-gp), an ATP-binding cassette (ABC) transporter, functions as a biological barrier by extruding cytotoxic agents out of cells, resulting in an obstacle in chemotherapeutic treatment of cancer. In order to aid in the development of potential P-gp inhibitors, we constructed a quantitative structure-activity relationship (QSAR) model of flavonoids as P-gp inhibitors based on Bayesian-regularized neural network (BRNN). A dataset of 57 flavonoids collected from a literature binding to the C-terminal nucleotide-binding domain of mouse P-gp was compiled. The predictive ability of the model was assessed using a test set that was independent of the training set, which showed a standard error of prediction of 0.146 +/- 0.006 (data scaled from 0 to 1). Meanwhile, two other mathematical tools, back-propagation neural network (BPNN) and partial least squares (PLS) were also attempted to build QSAR models. The BRNN provided slightly better results for the test set compared to BPNN, but the difference was not significant according to F-statistic at p = 0.05. The PLS failed to build a reliable model in the present study. Our study indicates that the BRNN-based in silico model has good potential in facilitating the prediction of P-gp flavonoid inhibitors and might be applied in further drug design.

Thermodynamic properties of ethanol and gasoline blended fuel

The heat capacities (C-p) of five types of gasohol (50 wt % ethanol and 50 wt % unleaded gasoline 93(#) (E50), 60 wt % ethanol and 40 wt % unleaded gasoline 93(#) (E60), 70 wt % ethanol and 30 wt % unleaded gasoline 93(#) (E70), 80 wt % ethanol and 20 wt % unleaded gasoline 93(#) (E80), and 90 wt % ethanol and 10 wt % unleaded gasoline 93(#) (E90), where the "93" denotes the octane number) were measured by adiabatic calorimetry in the temperature range of 78-320 K. A glass transition was observed at 95.61, 96.14, 96.56, 96.84, and 97.08 K for samples from the E50, E60, E70, E80, and E90 systems, respectively. A liquid-solid phase transition and a solid-liquid phase transition were observed in the respective temperature ranges of 118-153 and 155-163 K for E50, 117-150 and 151-164 K for E60, 115-154 and 154-166 K for E70, 113-152 and 152-167 K for E80, and 112-151 and 1581-167 K for E90. The polynomial equations of Cp and the excess heat capacities (C-p(E)), with respect to the thermodynamic temperature, were established through least-squares fitting. Based on the thermodynamic relationship and the equations obtained, the thermodynamic functions and the excess thermodynamic functions of the five gasohol samples were derived.

Adaptive denoising in spectral analysis by genetic programming

Rowland, J.J. and Taylor, J. (2002). Adaptive denoising in spectral analysis by genetic programming. Proc. IEEE Congress on Evolutionary Computation (part of WCCI), May 2002. pp 133-138. ISBN 0-7803-7281-6

Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning

Ellis, D. I., Broadhurst, D., Kell, D. B., Rowland, J. J., Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat by Fourier Transform Infrared Spectroscopy and machine learning. ? Applied and Environmental Microbiology, 68, (6), 2822-2828 Sponsorship: BBSRC

Automatic 3d free form shape matching using the graduated assignment algorithm.

Liu, Yonghuai. Automatic 3d free form shape matching using the graduated assignment algorithm. Pattern Recognition, vol. 38, no. 10, pp. 1615-1631, 2005.

Prediction of Physical Properties of Yeast Cell Suspensions using Dielectric Spectroscopy

D.J. Currie, M.H. Lee and R.W. Todd, 'Prediction of Physical Properties of Yeast Cell Suspensions using Dielectric Spectroscopy', Conference on Electrical Insulation and Dielectric Phenomena, (CEIDP 2006), Annual Report, pp 672 ? 675, October 15th -18th 2006, Kansas City, Missouri, USA. Organised by IEEE Dielectrics and Electrical Insulation Society.

Truth Recovery and Justice after Conflict: Managing Violent Pasts

Breen Smyth, M. (2007). Truth Recovery and Justice after Conflict: Managing Violent Pasts. Abingdon: Routledge. RAE2008

Na-23, Si-29, and C-13 MAS NMR investigation of glass-forming reactions between Na2CO3 and SiO2

Winter, Rudolf; Jones, A.R.; Greaves, G.N.; Smith, I.H., (2005) 'Na-23, Si-29, and C-13 MAS NMR investigation of glass-forming reactions between Na2CO3 and SiO2', Journal of Physical Chemistry B 109(49) pp.23154-23161 RAE2008

Estimation of long-run parameters in unbalanced cointegration

This paper analyses the asymptotic properties of nonlinear least squares estimators of the long run parameters in a bivariate unbalanced cointegration framework. Unbalanced cointegration refers to the situation where the integration orders of the observables are different, but their corresponding balanced versions (with equal integration orders after filtering) are cointegrated in the usual sense. Within this setting, the long run linkage between the observables is driven by both the cointegrating parameter and the difference between the integration orders of the observables, which we consider to be unknown. Our results reveal three noticeable features. First, superconsistent (faster than √ n-consistent) estimators of the difference between memory parameters are achievable. Next, the joint limiting distribution of the estimators of both parameters is singular, and, finally, a modified version of the ‘‘Type II’’ fractional Brownian motion arises in the limiting theory. A Monte Carlo experiment and the discussion of an economic example are included.

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Wydział Historyczny

Modelo de Aceitação da Tecnologia adaptado às compras online

Dissertação apresentada à Universidade Fernando Pessoa como parte dos requisitos de obtenção do grau de Mestre em Ciências da Comunicação, ramo de Marketing e Publicidade