Potassium catalysis in the pyrolysis behaviour of short rotation willow coppice

Short rotation willow coppice (SRC) and a synthetic biomass, a mixture of the basic biomass components (cellulose, hemicellulose and lignin), have been investigated for the influence of potassium on their pyrolysis behaviours. The willow sample was pre-treated to remove salts and metals by hydrochloric acid, and this demineralised sample was impregnated with potassium. The same type of pre-treatment was applied to components of the synthetic biomass. Characterisation was performed using thermogravimetric analysis with measurement of products by means of Fourier transform infrared spectroscopy (TGA-FTIR) and pyrolysis-gas chromatography-mass spectrometry (PY-GC-MS). A comparison of product distributions and kinetics are reported. While the general features of decomposition of SRC are described well by an additive behaviour of the individual components, there are some differences in the magnitude of the influence of potassium, and on the products produced. For both SRC and the synthetic biomass, TGA traces indicate catalytic promotion of both of the two-stages of biomass decomposition, and potassium can lower the average apparent first-order activation energy for pyrolysis by up to 50 kJ/mol. For both SRC and synthetic biomass the yields and distribution of pyrolysis products have been influenced by the presence of the catalyst. Potassium catalysed pyrolysis increases the char yields markedly and this is more pronounced for synthetic biomass than SRC. Gas evolution profiles during pyrolysis show the same general features for both SRC and synthetic biomass. Relative methane yields increase during the char formation stage of pyrolysis of the potassium doped samples. The evolution profiles of acetic acid and formaldehyde change, and these products are seen in lower relative amounts for both the demineralised samples. A greater variation in pyrolysis products is observed from the treated SRC samples compared to the different synthetic biomass samples. Furthermore, substituted phenols from lignin pyrolysis are more dominant in the pyrolysis profiles of the synthetic biomass than of the SRC, implying that the extracted lignins used in the synthetic biomass yield a greater fraction of monomeric type species than the lignocellulosic cell wall material of SRC. For both types of samples, PY-GS-MS analyses show that potassium has a significant influence on cellulose decomposition markers, not just on the formation of levoglucosan, but also other species from the non-catalysed mechanism, such as 3,4-dihydroxy-3-cyclobutene-1,2-dione. © 2007 Elsevier Ltd. All rights reserved.

Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Practical, Computation Efficient High-Order Neural Network for Rotation and Shift Invariant Pattern Recognition

In this paper, a modification for the high-order neural network (HONN) is presented. Third order networks are considered for achieving translation, rotation and scale invariant pattern recognition. They require however much storage and computation power for the task. The proposed modified HONN takes into account a priori knowledge of the binary patterns that have to be learned, achieving significant gain in computation time and memory requirements. This modification enables the efficient computation of HONNs for image fields of greater that 100 × 100 pixels without any loss of pattern information.

Tunable multiwavelength SOA fiber laser with ultra-narrow wavelength spacing based on nonlinear polarization rotation

A tunable multiwavelength fiber laser with ultra-narrow wavelength spacing and large wavelength number using a semiconductor optical amplifier (SOA) has been demonstrated. Intensity-dependent transmission induced by nonlinear polarization rotation in the SOA accounts for stable multiwavelength operation with wavelength spacing less than the homogenous broadening linewidth of the SOA. Stable multiwavelength lasing with wavelength spacing as small as 0.08 nm and wavelength number up to 126 is achieved at room temperature. Moreover, wavelength tuning of 20.2 nm is implemented via polarization tuning.

Three key regimes of single pulse generation per round trip of all-normal-dispersion fiber lasers mode-locked with nonlinear polarization rotation

We show experimentally and numerically new transient lasing regime between stable single-pulse generation and noise-like generation. We characterize qualitatively all three regimes of single pulse generation per round-trip of all-normal-dispersion fiber lasers mode-locked due to effect of nonlinear polarization evolution. We study spectral and temporal features of pulses produced in all three regimes as well as compressibility of such pulses. Simple criteria are proposed to identify lasing regime in experiment. © 2012 Optical Society of America.

Evaluation of the ECOSSE model for simulating soil organic carbon under Miscanthus and short rotation coppice-willow crops in Britain

Acknowledgements This work contributes to the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI). We acknowledge the E-OBS data set from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).

Moving to Capture Children's Attention : Developing a Methodology for Measuring Visuomotor Attention

Funding: Authors LH and MMW are part of the Healthy Children, Healthy Families Theme of the National Institute of Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) Yorkshire and Humber (www.clahrc-yh.nihr.ac.uk/). Please note, the views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR and the Department of Health. At different points in time this programme of research has been supported by a Medical Research Council (MRC; www.mrc.ac.uk) scholarship, an MRC Centenary Early Career Award and a grant from The Waterloo Foundation (TWF reference: 1285/1986; www.waterloofoundation.org.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Courbe de rotation de la galaxie à partir de la photométrie d'amas situés à la périphérie du disque

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Courbe de rotation de la galaxie à partir de la photométrie d'amas situés à la périphérie du disque

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

3D considerations for motion perception and visuomotor transformations

Moving through a stable, three-dimensional world is a hallmark of our motor and perceptual experience. This stability is constantly being challenged by movements of the eyes and head, inducing retinal blur and retino-spatial misalignments for which the brain must compensate. To do so, the brain must account for eye and head kinematics to transform two-dimensional retinal input into the reference frame necessary for movement or perception. The four studies in this thesis used both computational and psychophysical approaches to investigate several aspects of this reference frame transformation. In the first study, we examined the neural mechanism underlying the visuomotor transformation for smooth pursuit using a feedforward neural network model. After training, the model performed the general, three-dimensional transformation using gain modulation. This gave mechanistic significance to gain modulation observed in cortical pursuit areas while also providing several testable hypotheses for future electrophysiological work. In the second study, we asked how anticipatory pursuit, which is driven by memorized signals, accounts for eye and head geometry using a novel head-roll updating paradigm. We showed that the velocity memory driving anticipatory smooth pursuit relies on retinal signals, but is updated for the current head orientation. In the third study, we asked how forcing retinal motion to undergo a reference frame transformation influences perceptual decision making. We found that simply rolling one's head impairs perceptual decision making in a way captured by stochastic reference frame transformations. In the final study, we asked how torsional shifts of the retinal projection occurring with almost every eye movement influence orientation perception across saccades. We found a pre-saccadic, predictive remapping consistent with maintaining a purely retinal (but spatially inaccurate) orientation perception throughout the movement. Together these studies suggest that, despite their spatial inaccuracy, retinal signals play a surprisingly large role in our seamless visual experience. This work therefore represents a significant advance in our understanding of how the brain performs one of its most fundamental functions.

Siblings’ Sex is linked to Mental Rotation Performance in Males but not Females

Research has consistently found sex differences in mental rotation. Twin research has suggested that females with male co-twins perform better than females with female co-twins on mental rotation. Because twins share both pre-natal and post-natal environments, it is not possible to test whether this advantage is due to in-uterine transmission of testosterone from males to females or due to socialisation processes. The present study explored whether the advantage of females with brothers can be observed in non-twin siblings. Participants (N = 1799) were assessed on mental rotation. The observed group differences were overall small: males performed significantly better than females; females with sisters performed similarly to females with brothers; importantly, males with brothers performed significantly better than both female groups. The results suggest that sex differences in mental rotation are driven by the group of males with brothers.

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.