Dissertations / Theses on the topic 'Deep peat'

The study seeks to develop a field water management system for agriculture in peat soils in Malaysia, with an overall approach of integrating the engineering and agronomic aspects associated with crop production in deep peat areas. This includes the determination of soil physical parameters essential for field drainage design. The main experiments were carried out on a 10.9 hectare plot of land, initially drained 15 years earlier. The results were compared with data collected from a newly opened area and an area drained 40 years earlier.

Organic soils are formed as deceased plant and animal wildlife is deposited and decomposed in wet environs. These soils have loose structures, low undrained strengths, and high natural water contents, and require improvement before they can be used as foundation materials. Previous researchers have found that the deep mixing method effectively improves organic soils. This study presents a quantitative and reliable method for predicting the strength of one organic soil treated with deep mixing. For this thesis, organic soils were manufactured from commercially available components. Soil-binder mixture specimens with different values of organic matter content, OM, binder content, water-to-binder ratio, and curing time were tested for unconfined compressive strength (UCS). Least-squares regression was used to fit a predictive equation, modified from the findings of previous researchers, to this data. The equation estimates the UCS of a deep-mixed organic soil specimen using its total water-to-binder ratio and mixture dry unit weight. Soil OM is incorporated into the equation as a threshold binder content, aT, required to improve a soil with a given OM; the aT term is used to calculate an effective total water-to-binder ratio. This thesis reached several important conclusions. The modified equation was successfully fitted to the data, meaning that the UCS of some organic soil-binder mixtures may be predicted in the same manner as that of inorganic soil-binder mixtures. The fitting coefficients from the predictive equations indicated that for the soils and binder tested, specimens of organic soil-binder mixtures have a greater relative gain of UCS immediately after mixing compared to specimens of inorganic soil-binder mixtures. However, the inorganic mixtures generally have a greater relative gain of UCS during the curing period. The influence of curing temperature was found to be similar for organic and inorganic mixtures. For the organic soils and binder tested in this research, aT may be expressed as a linear or power function of OM. For both functions, the value of aT was negligible at values of OM below 45%, which reflects the chemistry of the organic matter in the peat moss. For projects involving deep mixing of organic soils, the predictive equation will be used most effectively by fitting it to the results of bench-scale testing and then checking it against the results of field-scale testing.
Master of Science
Organic soils are formed continuously as matter from deceased organisms – mainly plants – is deposited in wet environs and decomposes. Organic soils are most commonly found in swamps, marshes, and coastal areas. These soils make poor foundation materials due to their low strengths. Deep mixing, or soil mixing, involves introducing a binder like Portland cement or lime into soil and blending the soil and binder together to form columns or blocks. Upon mixing, cementitious reactions occur, and the soil-binder mixture gains strength as it cures. Deep mixing may be performed using either a dry binder, known as dry mixing, or a binder-water slurry, referred to as wet mixing. Deep mixing may be used to treat either inorganic or organic soils to depths of 30 meters or greater. Contractor experience has shown that deep mixing is one of the most effective methods of improving the strength of organic soils. Lab-scale studies (by previous researchers) of wet mixing of inorganic soils have found that the strength of soil-binder mixtures can be expressed as a function of mixture curing time and curing temperature, as well as the quantity of binder used, or binder factor, and the consistency of the binder slurry. No corresponding expression has been generated for wet mixing of organic soils, although many studies on the subject have been performed by previous researchers. The goal of this research was to generate such an expression for one organic soil. The soil used was made of sphagnum peat moss, an organic material commonly found in nature, and an inorganic clay used by previous researchers in studies of deep mixing in inorganic soils. The binder used in this research was a Portland cement. For this research, 43 unique soil-binder mixtures were manufactured. Each mixture involved a unique combination of soil organic matter content, binder factor, and binder slurry consistency. After a soil-binder mixture was made, it was divided, placed into cylindrical molds, and allowed to cure. The temperature of the curing environment of the mixture was monitored. Mixture compressive strength was assessed after 7, 14, and 28 days of curing using two cylindrically molded specimens of the mixture. Data on mixture strength was then evaluated to assess whether it could be expressed as a function of the variables tested. iv This research determined that the strength of at least some organic soils improved with wet mixing can be expressed as a function of soil organic matter content, binder factor, binder slurry consistency, and mixture curing time and curing temperature. The function will likely prove useful to deep mixing contractors, who routinely perform lab-scale deep mixing trials on samples of the soils to be improved in the field. Assuming wet mixing is used, the results of the trials are used to select values of binder factor and binder slurry consistency for the project. The function generated from this research will allow deep mixing contractors to select these values more reliably during the lab-scale phase of their work.

The Goukou Wetland is a 700 ha unchannelled valley bottom wetland near the town of Riversdale in the Western Cape of South Africa. The wetland is approximately 16 km long and between 200 and 800 m wide, with peat deposits up to 8 m deep that get progressively shallower downstream. The Goukou Wetland is one of the last remaining intact peatlands of significant size in the Western Cape. However, there is increasing human pressure on these peat wetlands, where the dominant plant is palmiet (Prionium serratum), which is endemic to the Western and Eastern Cape Provinces of South Africa. Palmiet is viewed as a problem plant by farmers as it is believed to block waterways and promote inundation of arable land and infrastructure. Many landowners therefore actively remove palmiet from peatlands, threatening the integrity of these wetlands. Although the hydrogeomorphic origin of large, non-peat floodplain and valley bottom wetlands has been investigated in South Africa, unchannelled valley-bottom wetlands with deep peat accumulations are rare features and have not been well studied. The hydrogeomorphic factors leading to peat accumulation have been documented elsewhere in Southern Africa, where aggradation due to sedimentation along trunk streams may block a tributary stream, elevating the local base level of the tributary, creating the accommodation space for organic sedimentation. Alternatively, sedimentation along a trunk stream at the toe of a tributary stream may similarly block a trunk stream, promoting organic sedimentation along the trunk stream upstream of the tributary. This pattern of peat accumulation is associated with declining peat thickness upstream of the blocked valley. In the case of the Goukou Wetland, however, peat depth and organic content was found to increase consistently upstream from the toe to the head of the wetland. The Goukou Wetland was graded along its length, with gradient increasing consistently upstream in response to longitudinal variation in discharge. There was no clear relationship between peat formation and tributary streams blocking the wetland. Instead, the distribution of peat and the extent of the wetland appeared to be controlled by the plant palmiet, whose clonal nature and robust root, rhizome and stem system allowed it to grow from channel banks and islands into fast-flowing river channels, slowing river flows and ultimately blocking the channel. The promotion of diffuse flows within the dense, monospecific stands of palmiet creates conditions conducive to water retention and peat accumulation. By growing across the full width of the valley floor, the plant is able to constrict the stream, trapping sediment and slowing flows such that the fluvial environment is changed from a fast flowing stream to one with slow, diffuse flow. These processes appear to lead to the formation of organic sediment, accumulating to form a deep peat basin. The sustained input of water from the folded and fractured quartzite lithologies of the Cape Supergroup that make up the Langeberg Mountains, which provide the bulk of the water supply to the wetland, is also important in promoting permanent flooding in the wetland. A feature that characterized the wetland was the fact that bedrock across the valley beneath the peat deposits exhibited a remarkably uniform elevation. This suggests that over long periods of time (tens to hundreds of thousands of years), bedrock has been laterally planed across the valley floor. It is proposed that valley widening associated with lateral planning of Uitenhage Formation rocks has taken place during periods of episodic very high flows. During these episodes, erosion cuts into the peat wetland and valley sides, cutting to bedrock and planing the valley floor to a uniform elevation for a given distance from the head of the wetland. Periods of episodic degradation are followed by periods of renewed peat accumulation associated with palmiet establishment, such that the wetland valley is shaped by repeated cycles of cutting and filling. Palmiet can be considered an “ecosystem engineer” that is integral to the formation of these deep peat basins. Removal of palmiet from these systems is likely to have negative consequences for the wetland and its functions in that water storage will be reduced, erosion will increase dramatically, and the water-purification function of the wetlands will be lost. Management of these wetlands, which are close to the geomorphic threshold slopes for their size, is therefore essential if they are to be preserved for the benefit of human well-being.

There has in recent years been interdisciplinary research on utilizing machine learning for detecting and classifying neurodegenerative disorders with the sole goal of outperforming state-of-the-art models in terms of metrics such as accuracy, specificity, and sensitivity. Specifically, these studies have been conducted using existing networks on ”novel” methods of pre-processing data or by developing new convolutional neural networks. As of now, no work has looked into how different normalization techniques affect a deep or shallow convolutional neural network in terms of numerical stability, its performance, explainability, and interpretability. This work delves into what normalization technique is most suitable for deep and shallow convolutional neural networks. Two baselines were created, one shallow and one deep, and applied eight different normalization techniques to these model architectures. Conclusions were drawn based on our analysis of numerical stability, performance (metrics), and methods of Explainable Artificial Intelligence. Our findings indicate that normalization techniques affect models differently regarding the mentioned aspects of our analysis, especially numerical stability and explainability. Moreover, we show that there should indeed be a preference to select one method over the other in future studies of this interdisciplinary field.

Populations that utilize deep water running (DWR) are described in Chapter I. A review of the literature concerning maximal and submaximal responses during DWR, shallow water running and swimming is presented in Chapter II. The protocols to elicit maximal responses during DWR and treadmill running (TMR), subject characteristics, and statistical methods employed are described in Chapter III. The results, presented in Chapter IV, indicate that the DWR protocol is a reliable test for eliciting peak oxygen consumption and heart rate. Furthermore, the metabolic responses during DWR are lower than TMR. Chapter V discusses factors which might limit maximal responses during DWR. Chapter VI contains suggestions for further research. Raw data are presented in Appendix A.

This thesis investigates the demographics of paleo-populations of white-tailed deer (Odocoileus virginianus) as a means of testing the hypothesis that this species was domesticated or managed as a vital cultural and economic resource by the ancient Maya in Mesoamerica. To do so it employs a set of standardized bone measurements derived from a modern population and compares them with 1100 deer bone samples recovered by archaeologists from Maya sites dating from 450 B.C. to the late 16th century. These measurements were also applied to modern white-tailed deer specimens representing a discrete population from south eastern Florida of know age, and sex, for use as a baseline. The recorded measurements were used for side by side comparisons and to generate log ratios testing population stature and sexual dimorphism represented in the archaeological materials. Changes in deer stature and mortality profile over time are examined and tested against standard methods for the detection of herd management strategies, that may potentially reveal deer domestication or resource management. Pathologies common to white-tailed deer are identified and their potential for assessing the ontological age of mature deer is investigated. The results show variations in white-tailed deer stature over time and space, suggesting dynamic alterations in prey selection that may be reflective of changes in Maya social complexity.

La Tomographie par Émission de Positons (TEP) est une méthode d'imagerie médicale nucléaire permettant de mesurer l'activité métabolique d'un organe par la dégradation d'un radio-traceur injecté. Cette méthode d'imagerie peut être utilisée pour l'observation de l'activité métabolique cérébrale à l'aide d'un radio-traceur adéquat, tel que le 18F-Fluorodésoxyglucose. Dans le cadre d'une étude clinique, des patients cérébro-lésés ayant des troubles de la conscience ont eu une chirurgie d'implantation d'électrodes de Stimulation Cérébrale Profonde (SCP). Afin d'effectuer un suivi des patients avant et après la procédure de SCP, et parce qu'elle est compatible avec la présence d'électrode, l'imagerie TEP est utilisée. Nous nous posons la question suivante, comment caractériser les variations entre deux imageries TEP afin de mesurer précisément l'éffet d'un traitement ? Par construction les valeurs obtenues en imagerie TEP dépendent de nombreux facteurs. Si le poids du patient ainsi que la quantité injectée de radio-traceur marqué sont classiquement normalisés en utilisant la méthode des 'Standard Uptake Value' (SUV), la glycémie, entre autre ne l'est pas. Pour cette raison, calculer les variations d'activités entre deux imageries TEP est un problème délicat. Nous proposons une fonction pour calculer les cartes de variation métabolique de deux acquisitions TEP basée sur une approche voxel du ratio des imageries TEP. Nous l'appliquons à l'étude des patients stimulés (SCP) avec troubles de la conscience. Plus spéciffiquement, nous nous intéressons à la comparaison des imageries TEP intra-patient (avant versus après SCP), mais aussi à la comparaison interpatient (patient versus référence). Dans le processus de création des cartes intra-patient, les imageries TEP sont recalées rigidement avec une acquisition pondérée T1 d'Imagerie par Résonance Magnétique (IRM) structurelle. Du fait de déformations majeures liées aux lésions cérébrales, un masque cérébral précis est créé manuellement par un expert clinique. Dans le processus de création des cartes inter-patient, les imageries TEP des patients sont recalées de manière élastique à une imagerie de référence, un atlas (groupe témoin), que nous construisons. Dans ce cas, un masque semi-automatique de l'intérieur de la boîte crânienne est réalisé. Les résultats peuvent être affinés par l'application supplémentaire d'un masque manuel déformé. Un des points clefs de la méthode est de calculer une normalisation spécifique à chaque imagerie, les rendant comparables, afin de calculer une caractérisation quantitative des variations métaboliques cérébrales. Les cartes de variation métabolique cérébrale obtenues sont ensuite comparées aux évaluations et effets cliniques observés afin de juger de leur pertinence
Positron Emission Tomography is a nuclear medicine imaging method, allowing measure of an organe metabolic activity through degradation of an injected radio-tracer. This methode can be used, with the appropriate radio-tracer, such as 18F-Fluorodeoxyglucose, for observation of cerebral metabolic activity. Through a clinical study, brain damaged patients with counciousness disorders had an implantation surgery of Deep Brain Stimulation (DBS) electrodes. To be able to do the follow up of the patient before and after the DBS procedure, and because it's compatible with electrodes, PET imaging is used. We ask ourself the following question, how to characterize variations between two PET images, to precisely mesure the impact of a treatment ? By construction, PET imaging obtained values depend of numerous factors. If patient weight and injected radio-tracer are classicaly normalized, using the `Standard Uptake Value' (SUV) method, glycemia for exemple is not. For this reason, compute activity variations between two PET images is a delicate problem. We propose a specific function to allow computation of metabolic variation maps for two PET acquisitions, based on a voxel approach of the PET imaging ratio. We apply it to the study of stimulated patients (DBS) with counciousness disorders. More specifically, we are interested in intra-patient PET imaging comparison (before versus after DBS), but also in inter-patient comparison (patient versus reference). During the intra-patient maps creation process, PET patient images are rigidly registered with a T1 weighted structural Magnetic Resonance Imaging (MRI) acquisition. Due to major deformation caused by cerebral injuries, a precise brain mask is created by a clinical expert. During the inter-patient maps creation process, PET patient imaging are non-rigidly registered to a reference imaging, an Atlas we build. In this case, a semi automatic mask of the inside skull is computed. Results can be further improved by the supplementary application of a deformed manual mask. One of the method key elements, is to estimate a specific normalization for each imaging, making them comparable, in order to calculate quantitative charaterisation of cerebral metabolic variations. Cerebral metabolic variation maps obtained are then compared to observed clinical assesments and effects to judge their relevance

The Deer Horn property is located 150 km south of Smithers in west-central British Columbia and covers 51 km². The deposit is an intrusion-related polymetallic system enriched in Au-Ag-Te-W-Cu with lesser amounts of Bi-Pb-Zn-Mo; the Au and Ag are hosted in telluride minerals. The quartz-sulfide vein system containing the main zones of Au-Ag-Te mineralization and sericite alteration is found in the hanging wall of a local, spatially related thrust fault. The age of the sericite alteration is 56 ± 2 Ma. Biotite K-Ar ages of 57–48 Ma for the nearby Nanika granodiorite intrusive suite indicates that it is likely genetically responsible for the Au-Ag-Te mineralizing event. The telluride minerals are 0.1–525 μm and commonly form whole euhedral to subhedral grains or composite grains of Ag-, Bi-, Pb-, and Au-rich telluride minerals (e.g., hessite, tellurobismuthite, volynskite, altaite, and petzite). Panchromatic cathodoluminescence imaging revealed four generations of quartz. Locally, oscillatory zoning observed in quartz II suggests the participation of hydrothermal fluids. Fine-grained veinlets of quartz III and IV intersect quartz I and II, which is evidence of at least two shearing events; veinlets of calcite intersect all generations of quartz. Three types of fluid inclusions were observed: (1) aqueous liquid and vapour inclusions (L-V); (2) aqueous carbonic inclusions (L-L-V); and (3) carbonic inclusions (V-rich). Fluid inclusions that are thought to be primary or pseudosecondary and related to the telluride mineralization were tested with microthermometry. Homogenization temperatures are 130.0–240.5 °C for L-V inclusions and 268.0–336.4 °C for L-L-V inclusions. Four of eight aqueous carbonic inclusions had solid CO2 melting temperatures from –56.8 to –62.1 °C, indicating the presence of 0.5–13.2% dissolved methane in these inclusions. Sulfur isotope analysis of ³⁴S/³²S using 20 samples of pyrite was conducted. δ³⁴S readings are close to 0 (from –1.6 to 1.6 per mil) and confirm that the sulfur is very likely magmatic/igneous in origin.
Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
Graduate

Gliomas are the most common primary brain tumours and are associated with poor prognosis. Among them, diffuse gliomas – which include their most aggressive form glioblastoma (GBM) – are known to be highly infiltrative. The diagnosis and follow-up of gliomas rely on positron emission tomography (PET) and magnetic resonance imaging (MRI). However, these imaging techniques do not currently allow to assess the whole extent of such infiltrative tumours nor to anticipate their preferred invasion patterns, leading to sub-optimal treatment planning. Mathematical tumour growth modelling has been proposed to address this problem. Reaction-diffusion tumour growth models, which are probably the most commonly used for diffuse gliomas growth modelling, propose to capture the proliferation and migration of glioma cells by means of a partial differential equation. Although the potential of such models has been shown in many works for patient follow-up and therapy planning, only few limited clinical applications have seemed to emerge from these works. This thesis aims at revisiting reaction-diffusion tumour growth models using state-of-the-art medical imaging and data processing technologies, with the objective of integrating multi-parametric PET/MRI data to further personalise the model. Brain tissue segmentation on MR images is first addressed with the aim of defining a patient-specific domain to solve the model. A previously proposed method to derive a tumour cell diffusion tensor from the water diffusion tensor assessed by diffusion-tensor imaging (DTI) is then implemented to guide the anisotropic migration of tumour cells along white matter tracts. The use of dynamic [S-methyl-11C]methionine ([11C]MET) PET is also investigated to derive patient-specific proliferation potential maps for the model. These investigations lead to the development of a microscopic compartmental model for amino acid PET tracer transport in gliomas. Based on the compartmental model results, a novel methodology is proposed to extract parametric maps from dynamic [11C]MET PET data using principal component analysis (PCA). The problem of estimating the initial conditions of the model from MR images is then addressed by means of a translational MRI/histology study in a case of non-operated GBM. Numerical solving strategies based on the widely used finite difference and finite element methods are finally implemented and compared. All these developments are embedded within a common framework allowing to study glioma growth in silico and providing a solid basis for further research in this field. However, commonly accepted hypothesis relating the outlines of abnormalities visible on MRI to tumour cell density iso-contours have been invalidated by the translational study carried out, leaving opened the questions of the initialisation and the validation of the model. Furthermore, the analysis of the temporal evolution of real multi-treated glioma patients demonstrates the limitations of the formulated model. These latter statements highlight current obstacles to the clinical application of reaction-diffusion tumour growth models and pave the way to further improvements.
Les gliomes sont les tumeurs cérébrales primitives les plus communes et sont associés à un mauvais pronostic. Parmi ces derniers, les gliomes diffus – qui incluent la forme la plus agressive, le glioblastome (GBM) – sont connus pour être hautement infiltrants. Le diagnostic et le suivi des gliomes s'appuient sur la tomographie par émission de positons (TEP) ainsi que l'imagerie par résonance magnétique (IRM). Cependant, ces techniques d'imagerie ne permettent actuellement pas d'évaluer l'étendue totale de tumeurs aussi infiltrantes ni d'anticiper leurs schémas d'invasion préférentiels, conduisant à une planification sous-optimale du traitement. La modélisation mathématique de la croissance tumorale a été proposée pour répondre à ce problème. Les modèles de croissance tumorale de type réaction-diffusion, qui sont probablement les plus communément utilisés pour la modélisation de la croissance des gliomes diffus, proposent de capturer la prolifération et la migration des cellules tumorales au moyen d'une équation aux dérivées partielles. Bien que le potentiel de tels modèles ait été démontré dans de nombreux travaux pour le suivi des patients et la planification de thérapies, seules quelques applications cliniques restreintes semblent avoir émergé de ces derniers. Ce travail de thèse a pour but de revisiter les modèles de croissance tumorale de type réaction-diffusion en utilisant des technologies de pointe en imagerie médicale et traitement de données, avec pour objectif d'y intégrer des données TEP/IRM multi-paramétriques pour personnaliser davantage le modèle. Le problème de la segmentation des tissus cérébraux dans les images IRM est d'abord adressé, avec pour but de définir un domaine propre au patient pour la résolution du modèle. Une méthode proposée précédemment permettant de dériver un tenseur de diffusion tumoral à partir du tenseur de diffusion de l'eau évalué par imagerie DTI a ensuite été implémentée afin de guider la migration anisotrope des cellules tumorales le long des fibres de matière blanche. L'utilisation de l'imagerie TEP dynamique à la [S-méthyl-11C]méthionine ([11C]MET) est également investiguée pour la génération de cartes de potentiel prolifératif propre au patient afin de nourrir le modèle. Ces investigations ont mené au développement d'un modèle compartimental pour le transport des traceurs TEP dérivés des acides aminés dans les gliomes. Sur base des résultats du modèle compartimental, une nouvelle méthodologie est proposée utilisant l'analyse en composantes principales pour extraire des cartes paramétriques à partir de données TEP dynamiques à la [11C]MET. Le problème de l'estimation des conditions initiales du modèle à partir d'images IRM est ensuite adressé par le biais d'une étude translationelle combinant IRM et histologie menée sur un cas de GBM non-opéré. Différentes stratégies de résolution numérique basées sur les méthodes des différences et éléments finis sont finalement implémentées et comparées. Tous ces développements sont embarqués dans un framework commun permettant d'étudier in silico la croissance des gliomes et fournissant une base solide pour de futures recherches dans le domaine. Cependant, certaines hypothèses communément admises reliant les délimitations des anormalités visibles en IRM à des iso-contours de densité de cellules tumorales ont été invalidée par l'étude translationelle menée, laissant ouverte les questions de l'initialisation et de la validation du modèle. Par ailleurs, l'analyse de l'évolution temporelle de cas réels de gliomes multi-traités démontre les limitations du modèle. Ces dernières affirmations mettent en évidence les obstacles actuels à l'application clinique de tels modèles et ouvrent la voie à de nouvelles possibilités d'amélioration.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

Le cancer du sein métastasé nécessite un suivi régulier. Au cours du traitement, des images de TEP- scan sont régulièrement acquises puis interprétées selon des recommandations telles que PERCIST pour décider d’un éventuel ajustement thérapeutique. Cependant, PERCIST se concentre seulement sur la lésion présentant l’activité tumorale la plus élevée. L’objectif de cette thèse est de développer des outils permettant de prendre en compte toutes les zones actives à l’aide du TEP-scan, afin de suivre au mieux l’évolution du cancer du sein. Notre première contribution est une méthode pour la segmentation automatique d’organes actifs (cerveau, vessie). Notre deuxième contribution formule la segmentation de lésions sur les images de suivi comme un problème de recalage d’images. Pour résoudre le recalage longitudinal d’images TEP corps entier, nous avons développé une nouvelle méthode nommée MIRRBA (Medical Image Registration Regularized By Architecture), qui combine les avantages des méthodes conventionnelles et de celles utilisant l’apprentissage profond. Nous avons validé trois approches (conventionnelle, apprentissage profond et MIRRBA) sur une base de données privées d’images TEP longitudinales obtenues dans le contexte de l’étude EPICURE. Finalement, notre troisième contribution est l’évaluation de biomarqueurs extraits des segmentations de lésions obtenues grâce au recalage. Nous proposons donc un nouvel outil automatisé pour améliorer suivi du cancer du sein métastasé
Metastatic breast cancer requires constant monitoring. During follow-up care, PET images are regularly acquired and interpreted according to specific guidelines, such as PERCIST, to decide whether or not the treatment should be adapted. However, PERCIST focuses only on one lesion representing tumor burden. The objective of this PhD thesis is to assist physicians monitormetastatic breast cancer patients with longitudinal PET images and improve tumor evaluation by providing them tools to consider all regions showing a high uptake. Our first contribution is a method for the automatic segmentation of active organs (brain, bladder, etc). Our second contribution formulates the segmentation of lesions in the followup examination as an image registration problem.The longitudinal full-body PET image registration problem is addressed, in this thesis, with our novel method called MIRRBA (Medical Image Registration Regularized By Architecture), which combines the strengths of both conventional and DL-based approaches within a Deep Image Prior (DIP) setup. We validated the three types of approaches (conventional, DL and MIRRBA) on a private longitudinalPET dataset obtained in the context of the EPICURE project. Finally, the third contribution is the evaluation of the biomarkers extracted from lesion segmentations obtained from the lesion registration step. We propose a new tool for the monitoring of metastatic breast cancer

Cette thèse porte sur l’application de la technique de corrélation de bruit sismologique ambiant pour l'imagerie et le suivi des réservoirs géothermiques de Rittershoffen (ECOGI) et de Soultz-sous-Forêts (GEIE-EMC). La forte variabilité spatio-temporelle des sources du bruit de fond sismologique dans la gamme de période 0.2-7s limite la reconstruction des fonctions de Green. Cela induit des erreurs dans la construction des modèles de vitesse. Deux approches sont proposées pour s’affranchir des effets de la non-uniformité spatiale du bruit. Par ailleurs, la variabilité temporelle des sources de bruit est un facteur limitant pour le suivi du réservoir. On estime que les perturbations de vitesse doivent être de l’ordre de 0.1% à 1% pour pouvoir être détectées par les réseaux disponibles. Ce seuil n’a pas été franchi lors de la construction du site Rittershoffen mais une modification probable des propriétés diffractantes du milieu a été observée à la suite d’une stimulation
This work focuses on the application of the ambient seismic noise correlation technique for the imaging and monitoring of deep geothermal reservoirs near Rittershoffen (ECOGI) and Soultz-sous-Forêts (GEIE-EMC). The strong spatial and temporal variability of the noise sources in the period range 0.2-7s limits the reconstruction of the Green’s functions. This results in significant errors in the velocity models. Two approaches are proposed to overcome the spatial non-uniformity of the noise and to improve the quality of the velocity models. Besides that, the temporal variability of the noise sources is a limiting factor for monitoring purposes. We estimate that the speed variations should be larger than 0.1% to 1% to be detected by the available networks. This threshold was not reached at Rittershoffen during the drillings or the stimulations. However, a probable change of the diffracting properties of the medium was observed following a hydraulic stimulation

La sclérose en plaques (SEP) est la maladie neurologique évolutive la plus courante chez les jeunes adultes dans le monde et représente donc un problème de santé publique majeur avec environ 90 000 patients en France et plus de 500 000 personnes atteintes de SEP en Europe. Afin d'optimiser les traitements, il est essentiel de pouvoir mesurer et suivre les altérations cérébrales chez les patients atteints de SEP. En fait, la SEP est une maladie aux multiples facettes qui implique différents types d'altérations, telles que les dommages et la réparation de la myéline. Selon cette observation, la neuroimagerie multimodale est nécessaire pour caractériser pleinement la maladie. L'imagerie par résonance magnétique (IRM) est devenue un biomarqueur d'imagerie fondamental pour la sclérose en plaques en raison de sa haute sensibilité à révéler des anomalies tissulaires macroscopiques chez les patients atteints de SEP. L'IRM conventionnelle fournit un moyen direct de détecter les lésions de SEP et leurs changements, et joue un rôle dominant dans les critères diagnostiques de la SEP. De plus, l'imagerie par tomographie par émission de positons (TEP), une autre modalité d'imagerie, peut fournir des informations fonctionnelles et détecter les changements tissulaires cibles au niveau cellulaire et moléculaire en utilisant divers radiotraceurs. Par exemple, en utilisant le radiotraceur [11C]PIB, la TEP permet une mesure pathologique directe de l'altération de la myéline. Cependant, en milieu clinique, toutes les modalités ne sont pas disponibles pour diverses raisons. Dans cette thèse, nous nous concentrons donc sur l'apprentissage et la prédiction des altérations cérébrales dérivées des modalités manquantes dans la SEP à partir de données de neuroimagerie multimodale
Multiple Sclerosis (MS) is the most common progressive neurological disease of young adults worldwide and thus represents a major public health issue with about 90,000 patients in France and more than 500,000 people affected with MS in Europe. In order to optimize treatments, it is essential to be able to measure and track brain alterations in MS patients. In fact, MS is a multi-faceted disease which involves different types of alterations, such as myelin damage and repair. Under this observation, multimodal neuroimaging are needed to fully characterize the disease. Magnetic resonance imaging (MRI) has emerged as a fundamental imaging biomarker for multiple sclerosis because of its high sensitivity to reveal macroscopic tissue abnormalities in patients with MS. Conventional MR scanning provides a direct way to detect MS lesions and their changes, and plays a dominant role in the diagnostic criteria of MS. Moreover, positron emission tomography (PET) imaging, an alternative imaging modality, can provide functional information and detect target tissue changes at the cellular and molecular level by using various radiotracers. For example, by using the radiotracer [11C]PIB, PET allows a direct pathological measure of myelin alteration. However, in clinical settings, not all the modalities are available because of various reasons. In this thesis, we therefore focus on learning and predicting missing-modality-derived brain alterations in MS from multimodal neuroimaging data

碩士
國立中央大學
化學工程與材料工程研究所
96
The deep dyeing effect of the disperse dye on polyester microfiber in the presence of cationic auxiliaries synthesized by (3-acrylamidopropyl)trimethyl ammonium chloride (APTAC) and acrylonitrile (AN) as monomers and ammonium persulfate (APS) as the initiator has been developed. The different parameters, dyeing temperature, auxiliary components, auxiliary content, dyeing holding time, dyeing temperature rate and auxiliary’s molecular weight, were discussed. Colour shade depth ( K/S ), wash fastness test and SEM photographs of dyed microfiber were measured. The result showed that the K/S of the dyed microfiber was increased and maintain good wash fastness, with adding cationic auxiliaries (especially with low molecular weight) in the dye bath or decreasing dyeing temperature. The highest value of K/S, 550 (increase 25%), of dyed microfiber was obtained by adding auxiliary.

abstract: Alzheimer’s Disease (AD), a neurodegenerative disease is a progressive disease that affects the brain gradually with time and worsens. Reliable and early diagnosis of AD and its prodromal stages (i.e. Mild Cognitive Impairment(MCI)) is essential. Fluorodeoxyglucose (FDG) positron emission tomography (PET) measures the decline in the regional cerebral metabolic rate for glucose, offering a reliable metabolic biomarker even on presymptomatic AD patients. PET scans provide functional information that is unique and unavailable using other types of imaging. The computational efficacy of FDG-PET data alone, for the classification of various Alzheimer’s Diagnostic categories (AD, MCI (LMCI, EMCI), Control) has not been studied. This serves as motivation to correctly classify the various diagnostic categories using FDG-PET data. Deep learning has recently been applied to the analysis of structural and functional brain imaging data. This thesis is an introduction to a deep learning based classification technique using neural networks with dimensionality reduction techniques to classify the different stages of AD based on FDG-PET image analysis. This thesis develops a classification method to investigate the performance of FDG-PET as an effective biomarker for Alzheimer's clinical group classification. This involves dimensionality reduction using Probabilistic Principal Component Analysis on max-pooled data and mean-pooled data, followed by a Multilayer Feed Forward Neural Network which performs binary classification. Max pooled features result into better classification performance compared to results on mean pooled features. Additionally, experiments are done to investigate if the addition of important demographic features such as Functional Activities Questionnaire(FAQ), gene information helps improve performance. Classification results indicate that our designed classifiers achieve competitive results, and better with the additional of demographic features.
Dissertation/Thesis
Masters Thesis Computer Science 2017

Continued technology scaling has introduced many new challenges in VLSI design. Instantaneous switching of the gates yields high current flow through them that causes large voltage drop at the supply lines. Such high instantaneous currents and voltage drop cause reliability and performance degradation. Reliability is an issue as high magnitude of current can cause electromigration, whereas, voltage drop can slow down the circuit performance. Therefore, designing power supply lines emphasizes the need of computing maximum current through them. However, the development of digital integrated circuits in short design cycle requires accurate and fast timing and power simulation. Unfortunately, simulators that employ device modeling methods, such as HSPICE are prohibitively slow for large designs. Therefore, methods which can produce good maximum current estimates in short times are critical. In this work a compact model has been developed for maximum current estimation that speeds up the computation by orders of magnitude over the commercial tools.

碩士
國立中興大學
運動與健康管理研究所
100
Nowadays, the population of pet owners is increasing. Through pet companionship, the owners can receive relaxation and pleasure of mind. On the other side, the benefits of the companionship will increase physical activities and cultivate habit of taking exercise. 　　The purpose of this study was to investigate the relationship between attachment of pet owners and attitudes of principles for deep knowledge. The two questionnaires were used as the research tool, including Lexington Attachment to Pet Scale (LAPS) (Johnson, Garrity, & Stallones, 1992) and Nine Principles for Deep Knowledge Scale (PDKS). PDKS had content validity and convergent validity. In this study, the content validity was measured by back translation method and Cronbach’s value (α =.921) was measured by internal consistency, and indicated that LAPS was a useful tool. There were 209 pet owners (Male=80, Female=129) who had taken pets to Veterinary Medical Teaching Hospital of National Chung Hsing University participated in this study. The statistical methods of descriptive statistics, Pearson’s Correlation ,t-test, one-way ANOVA, Simple Linear Regression and Logistic Regression of software SPSS 19.0 were used to exam the relationships between attachment to pet owners’ attitude toward the principles for deep knowledge in their daily lives. The result of this study showed that the degree of attachment to pets had a positive correlation with attitude of deep knowledge. Additionally, there were statistically significant differences between the gender and the two variables of attachment to pets and attitude of deep knowledge. Therefore, the owners（N=148,70.8%） used to carry their pets to exercise demonstrated higher levels in the two variables of attachment to pets and attitude toward the principles for deep knowledge. The finding of this study proved the concept that pet companionship can increase both of the mental and interpersonal supports. Hence, to be aware of the degree of attachment to pets will predict the attitude toward the principles for deep knowledge and to improve owners’ daily lives and habits.

Decision of problems of influencing of interconnection effects of peak and latitudinal impulsive modulation in the process of deep print for making of document in composition of CALS-technologies on quality of images needs explanation of many theoretical aspects. Interconnection of effects of amplitude and latitudinal impulsive modulation in the process of deep print for making of documentation in composition of CALS-technologies discovers itself as characteristic damages on the different areas of reproduction.