A study was conducted in 2004 and 2005 to determine the effect of liquid hog manure fertilization on greenhouse gas emissions from the surface of a grassland pasture in south-eastern Manitoba. The objectives of this research were to determine the effects of manure application, itstiming and soil moisture on greenhouse gas emissions from pasture soil, cattle dung and urine patches. Nitrous oxide (N2O), methane (CH4) and carbon dioxide (CO2) emissions were determined from grassland soil surface, and from cattle dung and artificial urine patches. Liquid hog manure treatments were no manure (Control); 153 kg ha-1 of available-nitrogen (N) (two year average) in spring (Spring); and 149 kg ha-1 as half-rate applications in fall and spring (Split). Four field experiments were conducted on grassland plots. The static-vented chamber technique was used to estimate gas emission rates. Two of the experiments focused on the effects of manure application timing and soil moisture on greenhouse gas emissions from the grassland soil surface. The other two experiments focused on the effects of manure application and soil moisture on greenhouse gas emissions from cattle dung and artificial urine patches. Fresh cattle dung was collected from steers grazing adjacent pastures receiving the same three manure treatments. Artificial cattle urine treatments were generated by converting blood urea concentrations of the steers into urine-N concentrations. Manure application increased (P≤0.01) cumulative N2O emissions from the grassland soil surface with Control, Split and Spring treatments averaging 7, 43 and 120 mg N2O-N m-2, respectively. Of the two manure treatments, the Spring treatment emitted higher (P≤0.10) N2O emissions than the Split treatment. Soil moisture was a major factor influencing the quantity and type of greenhouse gas emissions, with saturated areas emitting CH4 during warm periods, whereas drier areas emitted N2O. Nitrous oxide emissions from these dry areas were higher in manure-treated plots. Spring application increased root density by 45% in the top 5 cm of soil compared to the Control. An increase in soil organic carbon with root density may offset any increase in greenhouse gas emissions caused by manure treatment. Cattle dung from Split and Spring treatments had higher cumulative N2O emissions (30 and 82 mg N2O-N m-2, respectively) compared to dung from Control pastures (6 mg N2O-N m-2) over two study years. Dung from the Spring treatment emitted more N2O (P≤0.01) than the other two treatments. All cattle dung patches emitted CH4 after deposition though unaffected by manure treatment. Artificial urine having highest N concentration had greater (P≤0.05) cumulative N2O emissions (690 mg N2O-N m-2) than urine with the lowest N concentration (170 mg N2O-N m-2). Drier soil locations emitted more N2O from cattle dung and artificial urine patches than wetter areas. This study demonstrated that Split application of liquid hog manure to grassland emitted less N2O than a complete application in spring. Moisture greatly affected the location of N2O and CH4 emissions. Drier areas emitted more N2O than wetter ones. Particularly, the findings indicate a need to assess grassland on periodically saturated soils as sources rather than sinks for CH4. Application of manure increased greenhouse gas emissions from cattle dung and urine patches with urine potentially having the greatest impact because of their higher emissions of N2O. An increase in root growth seems to offset greenhouse gas emissions from manure application.

This thesis examines how the non-Newtonian characteristics of liquid hog manure affect the flow conditions within a steady-flow anaerobic digester. There are three main parts to this thesis. In the first part of this thesis, the physical properties of liquid hog manure and their variation with temperature and solids concentration are experimentally determined. Naturally¬¬-settled manure sampled from an outdoor storage lagoon is studied, and density, viscosity, and particle size distribution are measured. Hog manure with total solids concentrations of less than 3.6% exhibits Newtonian behaviour; manure between 3.6% and 6.5% total solids is pseudoplastic, and fits the power law; manure with more than 6.5% total solids exhibits non-Newtonian and time-dependent characteristics. The second part of this thesis investigates the flow of Newtonian and non-Newtonian fluids—represented by tap water and xanthan gum solution, respectively—within four lab-scale reactor geometries, using residence time distribution (RTD) experiments. The effect of reactor geometry, flow rate, and fluid viscosity are evaluated. In the third part of this thesis, flow conditions within lab-scale and pilot-scale anaerobic digester reactors are simulated using three-dimensional modeling techniques. The RTDs of lab-scale reactors as predicted by the 3D numerical models compare well to the experimental results. The 3D models are also validated using data from particle image velocimetry (PIV) experiments. Finally, the viscous properties of liquid hog manure at 3% and 8% total solids are incorporated into the models, and the results are evaluated.

Classical Swine Fever in the Lao Peoples’ Democratic Republic: Virological, Epidemiological and Clinical studies. Classical swine fever (CSF) is a highly contagious virus infection of swine caused by classical swine fever virus (CSFV). The CSF virus is a member of the genus Pestivirus of the family Flaviviridae. Classical swine fever is believed to be endemic in Lao Peoples’ Democratic Republic (Lao PDR). Infectious diseases, including CSF, are a major constraint to pig production in developing countries such as Lao PDR. The aim of this thesis was to investigate aspects and present data regarding the nature of CSF pertinent to Lao PDR. An introduction to Lao PDR, local pig production and a review of pertinent CSF literature is presented in Chapter 1. Low levels of veterinary infrastructure have exacerbated infectious disease problems in developing countries. Chapter 2 of this thesis described the construction and refurbishment of a project laboratory in Lao PDR for the diagnosis of viral diseases, in particular CSF virus Furthermore, a diagnostic specimen submission system was adapted to the local domestic and economic conditions. Poor diagnostic facilities and lack of disease reporting systems in Lao PDR have allowed diseases to spread largely unchecked due to low levels of recognition. The process of development and assessment of appropriate diagnostic assays to the local conditions is presented and discussed in Chapter 3. ELISA and RT-PCR technologies for CSF virus detection in clinical specimens were implemented. Variations to RT-PCR methodologies were also investigated to determine the most suitable technique for the local situation. Results indicated that the RT-PCR methodology was more sensitive than ELISA for the detection of CSF virus in fresh clinical specimens. Notably, the situation was reversed when decomposed samples were tested. Methodologies for the preservation and detection of CSF virus in samples subjected to local tropical condition were also investigated. The proprietary reagent RNAlater ™ was found to be suitable for the preservation of CSF virus RNA under local conditions. Methodologies for CSF virus serology using the ELISA technique are also described. The majority of the pigs in Lao PDR are raised within village small-holder systems, with indigenous breeds being the most popular. Very little is known about the response of indigenous breed pigs to CSF virus infection. Chapter 4 described the pathogenicity of a Lao strain of CSF virus (Lao/Kham225) in both indigenous (Moo Laat) and imported pig breeds (Large white/Landrace cross-breed). Statistically significant (p = 0.05) differences in the breed-related susceptibility to CSF infection were demonstrated in clinical and haematological responses, and post-mortem pathology. The results demonstrated the course of CSF infection in the Large white/Landrace cross-breed was generally more acute than in the indigenous breed. Investigations into the epidemiology of CSF in Lao PDR are presented in Chapter 5. The distribution of CSF outbreaks during the period of mid-1997 to the end of 1999 was investigated and crude incidence results were calculated. Serological surveillance to determine the serological prevalence of CSF virus antibodies in selected regions of Lao PDR was performed during 1997 to 1999. Structured serological surveillance was performed in Vientiane Municipality, Bhorikhamxai, Khammouane and Savannakhet provinces during the survey period. Passive serological surveillance using samples from eight abattoirs in Lao PDR was also performed. Statistically significant (p = 0.05) intra- and inter-provincial differences were noted in a number of the surveys. The success of CSF vaccination via post-vaccination serology was also assessed. The results of the investigations determined that vaccination to prevent CSF infection was insufficient and post-vaccination responses were variable. Phylogenetic and phylogeographic studies to determine the genetic characteristics of Lao PDR and other regional CSF virus isolates are presented in Chapter 6. The 5’-non-coding region and the E2 gene of CSF viruses were investigated to determine genetic relationships between Lao PDR and regional isolates. Genetic typing of all field virus isolates using phylogenetic analysis techniques indicated that all viruses belonged to genogroup 2. Phylogeographic analysis of field viruses revealed a delineation of sub-genogroup allocation on a geographic basis. Members of the sub-genogroup 2.1 originated in Northern and Central regions of Lao PDR. Conversely, members of the sub-genogroup 2.2 originated in Southern and Central regions of Lao PDR. All Vietnamese viruses examined belonged to sub-genogroup 2.2. Phylogenetic analysis indicated that the Vietnamese viruses were largely distinct from Lao and Thai CSF viruses. With the exception of one virus isolate, all Thai viruses also belonged to sub-genogroup 2.2. With the exception of one Vietnamese vaccine virus, all vaccines examined belonged to genogroup 1. A general discussion of the results presented in all chapters, as well as implications for future research into this field, are presented in Chapter 7.

Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-09T14:45:09Z No. of bitstreams: 2 dissertacao Andre Costa.pdf: 10722387 bytes, checksum: bff242b1a21e34e27f228538f8f5d6b1 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-09T14:45:10Z (GMT). No. of bitstreams: 2 dissertacao Andre Costa.pdf: 10722387 bytes, checksum: bff242b1a21e34e27f228538f8f5d6b1 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-11-25 / Capes / Um detector de pedestres básico geralmente possui dois componentes principais: um que seleciona regiões da imagem que possivelmente contêm um pedestre (gerador de candidatos) e outro que classifica estas regiões em grupos de pedestres e não-pedestres (classificador). Estes classificadores normalmente baseiam-se em extratores de características, que são transformações que alteram a intensidade ou cor original dos pixels de uma imagem em uma nova representação, para ressaltar algum tipo de conhecimento sobre o conteúdo da imagem. Quando o ambiente é não-controlado, fatores externos podem influenciar negativamente no desempenho do classificador. Baixa resolução, ruído, desfoque e oclusão são alguns efeitos que podem ser gerados por estes fatores, degradando a qualidade das imagens obtidas e, consequentemente, das características extraídas. Esta dissertação propõe-se a avaliar como extratores de características comportam-se nesse tipo de ambiente. Estes cinco tipos de degradação foram simulados nas bases de imagem usadas nos experimentos: INRIA Person e Caltech Pedestrian. Como estamos interessados apenas na etapa de classificação, as imagens foram transformadas em janelas de tamanho fixo na etapa de pré-processamento. Os experimentos usam uma combinação de extratores de características (HOG, LBP, CSS, LGIP e LTP) e modelos de aprendizagem (AdaBoost e SVM linear) para formar classificadores. Os classificadores foram treinados com as imagens intactas e testados com imagens em diversos níveis de degradação. O HOG (42%) e LTP (54%) foram superiores aos demais em aproximadamente metade dos pontos de teste na INRIA Person e Caltech Pedestrian, respectivamente. Foi confirmada a queda de desempenho do LBP quando exposto a ruído, mostrando que o LGIP e o LTP amenizam isso. Também observou-se que o CSS é robusto a ruído, mas gera características fracas no geral. Por fim, notou-se que classificadores que combinam mais de um extrator de características foram superiores aos individuais em boa parte dos pontos de teste. Combinando-se todos os extratores, tem-se um classificador superior em 95,8% das situações ao criado somente com o melhor extrator no geral (HOG, na base da INRIA, e LTP, na base da Caltech).

Dynamic development of nuclear magnetic resonance spectroscopy (NMR) allowed fast acquisition of experimental data which determine structure and dynamics of macromolecules. Nevertheless, due to lack of appropriate computational methods, NMR spectra are still analyzed manually by researchers what takes weeks or years depending on protein complexity. Therefore automation of this process is extremely desired and can significantly reduce time of protein structure solving. In presented work, a new approach to automated three-dimensional protein NMR spectra analysis is presented. It is based on Histogram of Oriented Gradients and Bayesian Network which have not been ever applied in that context in the history of research in the area. Proposed method was evaluated using benchmark data which was established by manual labeling of 99 spectroscopic images taken from 6 different NMR experiments. Afterwards subsequent validation was made using spectra of upstream of N-ras protein. With the use of proposed method, a three-dimensional structure of mentioned protein was calculated. Comparison with reference structure from protein databank reveals no significant differences what has proven that proposed method can be used in practice in NMR laboratories.

Object recognition is a much studied computer vision problem, where the task is to find a given object in an image. This Master Thesis aims at doing a MATLAB implementation of an object recognition algorithm that finds three kinds of objects in images: electrical outlets, light switches and wall mounted air-conditioning controls. Visually, these three objects are quite similar and the aim is to be able to locate these objects in an image, as well as being able to distinguish them from one another. The object recognition was accomplished using Histogram of Oriented Gradients (HOG). During the training phase, the program was trained with images of the objects to be located, as well as reference images which did not contain the objects. A Support Vector Machine (SVM) was used in the classification phase. The performance was measured for two different setups, one where the training data consisted of photos and one where the training data consisted of digitally generated images created using a 3D modeling software, in addition to the photos. The results show that using digitally generated images as training images didn’t improve the accuracy in this case. The reason for this is probably that there is too little intraclass variability in the gradients in digitally generated images, they’re too synthetic in a sense, which makes them poor at reflecting reality for this specific approach. The result might have been different if a higher number of digitally generated images had been used.

In the last decades there has been such a fundamental development in the technologies including technologies focusing on face detection and identification supported by computer vision. Algorithm optimization has reached the point, when face detection is possible on mobile devices. At the outset, this work analy-ses common used algorithms for face detection and identification, for instance Haar features, LBP, EigenFaces and FisherFaces. Moreover, this work focuses on more up-to-date approaches of this topic, such as convolutional neural networks, or FaceNet from Google. The goal of this work is a design and its subsequent im-plementation of an automated, monitoring system designated for a lab, which is based on aforementioned algorithms. Within the design of the monitoring system, algorithms are compared with each other and their success rate and possible ap-plication in the final solution is evaluated.

Porous ceramics and porous ceramic composites are emerging functional materials that have found numerous industrial applications, especially in energy conversion processes. They are characterized by random microstructure and high porosity. Examples are ceramic candle filters used in coal-fired power plants, gas-fired infrared burners, anode and cathode materials of solid oxide fuel cells, etc. In this research, both experimental and theoretical work have been conducted to characterize and to model the mechanical behavior and durability of this novel class of functional material. Extensive experiments were performed on a hot gas candle filter material provided by the McDermott Technologies Inc (MTI). Models at micro-/meso-/macro- geometric scales were established to model the porous ceramic material and fiber reinforced porous ceramic material. The effective mechanical properties are of great technical interest in many applications. Based on the average field formalism, a computational micromechanics approach was developed to estimate the effective elastic properties of a highly porous material with random microstructure. A meso-level analytical model based on the energy principles was developed to estimate the global elastic properties of the MTI filament-wound ceramic composite tube. To deal with complex geometry, a finite element scheme was developed for porous material with strong fiber reinforcements. Some of the model-predicted elastic properties were compared with experimental values. The long-term performance of ceramic composite hot gas candle filter materials was discussed. Built upon the stress analysis models, a coupled damage mechanics and finite element approach was presented to assess the durability and to predict the service life of the porous ceramic composite candle filter material. / Ph. D.

The aim of this thesis is to provide methods for 2D segmentation and 2D/3D tracking, that are both fast and robust to imperfect image information, as caused for example by occlusions, motion blur and cluttered background. We do this by combining high level shape information with simultaneous segmentation and tracking. We base our work on the assumption that the space of possible 2D object shapes can be either generated by projecting down known rigid 3D shapes or learned from 2D shape examples. We minimise the discrimination between statistical foreground and background appearance models with respect to the parameters governing the shape generative process (the 6 degree-of-freedom 3D pose of the 3D shape or the parameters of the learned space). The foreground region is delineated by the zero level set of a signed distance function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities. We obtain the differentials of this energy with respect to the parameters governing shape and conduct searches for the correct shape using standard non-linear minimisation techniques. This methodology first leads to a novel rigid 3D object tracker. For a known 3D shape, our optimisation here aims to find the 3D pose that leads to the 2D projection that best segments a given image. We extend our approach to track multiple objects from multiple views and propose novel enhancements at the pixel level based on temporal consistency. Finally, owing to the per pixel nature of much of the algorithm, we support our theoretical approach with a real-time GPU based implementation. We next use our rigid 3D tracker in two applications: (i) a driver assistance system, where the tracker is augmented with 2D traffic sign detections, which, unlike previous work, allows for the relevance of the traffic signs to the driver to be gauged and (ii) a robust, real time 3D hand tracker that uses data from an off-the-shelf accelerometer and articulated pose classification results from a multiclass SVM classifier. Finally, we explore deformable 2D/3D object tracking. Unlike previous works, we use a non-linear and probabilistic dimensionality reduction, called Gaussian Process Latent Variable Models, to learn spaces of shape. Segmentation becomes a minimisation of an image-driven energy function in the learned space. We can represent both 2D and 3D shapes which we compress with Fourier-based transforms, to keep inference tractable. We extend this method by learning joint shape-parameter spaces, which, novel to the literature, enable simultaneous segmentation and generic parameter recovery. These can describe anything from 3D articulated pose to eye gaze. We also propose two novel extensions to standard GP-LVM: a method to explore the multimodality in the joint space efficiently, by learning a mapping from the latent space to a space that encodes the similarity between shapes and a method for obtaining faster convergence and greater accuracy by use of a hierarchy of latent embeddings.

Root-lesion nematodes, Pratylenchus spp., are serious pathogens of potato plants worldwide. Several management practices can control Pratylenchus spp.; however, they all have shown some limitations. Therefore, environmentally-safe, low-cost, and effective control strategies are needed as possible alternative to currently used strategies. This thesis was designed to assess if liquid hog manure (LHM) holds such potential. The objectives of this thesis were to determine: (i) the prevalence and identity of species of Pratylenchus spp. in Manitoba potato fields, (ii) if short-chain volatile fatty acids (VFA) in LHM are the constituents responsible for the manure’s toxicity to Pratylenchus spp. using solution exposure experiments (iii) the effectiveness of LHM in killing Pratylenchus spp. in soil, and (iv) the impact of LHM on nematode communities. Pratylenchus spp. were detected in 39% of 31 potato fields surveyed in Manitoba with population densities ranging, for positive fields, from 45 to 631 nematodes kg-1 fresh soil. Morphometrics of female nematodes and molecular diagnosis (using species-specific PCR primers) showed that the species of Pratylenchus present in the potato fields to be P. neglectus. Potato, cv. Russet Burbank, showed to be a poor host to two populations of Pratylenchus spp. from Manitoba potato fields. Accordingly, P. neglectus does not seem to be a limitation to potato production in Manitoba; thus, P. penetrans, the most widely spread and damaging species to potato was used in the successive studies of assessing the use of LHM to control Pratylenchus spp. in potato fields. VFA (acetic, propionic, n-butyric, isobutyric, n-valeric, isovaleric, and n-caproic acids) accounted for the majority of the lethal effect of LHM to P. penetrans under acidic conditions. VFA in LHM killed Pratylenchus spp. in soil and acidification seemed to enhance its ability when VFA concentration in the manure is low. LHM did not act as a soil fumigant eliminating soil trophic interactions but increased bottom-up food web interactions. VFA in LHM persisted in the soil for four days with biological degradation being their mode of loss. In conclusion, LHM is potentially an effective and low-cost strategy to control Pratylenchus spp. and its efficacy can be improved by acidification. / October 2009