One of the main drivers influencing consumers in the purchasing of red meat is the level of visible fat, and this is particularly important in lamb, with lamb often being perceived as fatty. Consumer-driven preference for leaner meat, coupled with the meat processing industries preference for a reduction in carcass fat, increasing lean meat yield and reducing waste, have led to continued selection for lean growth and reduced fatness in several meat producing species The perception of lamb being fatty could be directly targeted in isolation by reducing overall fat levels, however there are related effects on meat (eating) quality, and the combined improvement and consistency of meat (eating) quality and the reduction of overall fatness is more complicated. It is apparent that fat content plays a significant role in meat (eating) quality. Generally four major fat depots are recognised in animal carcasses, these are: subcutaneous (under the skin); internal organ associated; intermuscular (between muscles and surrounding muscle groups); and intramuscular (marbling, between muscle fibres), the latter generally regarded as having the greatest association with meat (eating) quality. X-ray computed tomography (CT) can measure fat, muscle and bone in vivo in sheep and CT predictions of carcass composition have been used in commercial UK sheep breeding programmes over the last two decades. Together with ultrasound measures of fat and muscle depth in the loin region, CT measured carcass fat and muscle weights have contributed much to the success of breeding for leaner carcasses and increased lean meat yield. Recently it has also been considered that x-ray computed tomography provides the means to simultaneously estimate IMF and carcass fat in vivo. Thus the aim of this project is to investigate the use of two and three-dimensional x-ray computed tomography techniques in the estimation of meat (eating) quality traits in sheep, and to further investigate the genetic basis of these traits and the possibility of their inclusion into current breeding programmes. The primary approach was the use of two-dimensional x-ray computed tomography, determining the most accurate combination of variables to predict IMF and mechanical shear force in the loin. The prediction of mechanical shear force was poor with accuracies ranging from Adj R2 0.03 – 0.14, however the prediction of IMF in the loin was more promising. CT predicted carcass fat weight accounted for a moderate amount of variation in IMF (R2 =0.51). These accuracies were significantly improved upon by including other information from the CT scans (i.e. fat and muscle densities, Adj R2 >0.65). Average muscle density in a single or multiple scans accounted for a moderate amount of the variation in IMF (Adj R2 = 0.51-0.60), and again accuracies R2 >0.65 were achieved, independent of CT-measured fat areas or predicted fat weights. Similar results were achieved with the use of three-dimensional CT scanning techniques (Adj R2 0.51 – 0.71), however, there was a dramatically increased requirement for image analysis when compared to two-dimensional techniques, and the increase in accuracy was not significant. This suggests that the current method of two-dimensional image capture is sufficient in the estimation of IMF in vivo in sheep. The prediction equations developed as part of this work were applied across divergent breed types (Texel, Scottish Blackface and Texel cross mule), to investigate the transferability of the prediction equations directly across to other breeds of sheep. As part of this study, the IMF levels across the breed types and sexes were also compared and found that IMF was significantly affected by breed type (P<0.001) with Scottish Blackface lambs having higher levels of IMF when compared to Texel cross mule lambs, and the lowest levels of IMF were in the purebred Texel lambs at the same liveweight or similar levels of carcass fatness. Sex also had a significant effect on IMF across breeds (P<0.001) with females having higher levels of IMF at similar levels of both carcass fat and liveweight, and within breed, females had significantly higher levels of IMF in both the purebred Texel and Scottish Blackface lambs, when compared at similar levels of carcass fat and liveweight (P<0.05). Using the models previously developed in purebred Texel to predict IMF in the Scottish Blackface and Texel cross mule, accuracies were found to be R2 = 0.57 – 0.64 and R2 = 0.37 – 0.38 respectively. Providing evidence that the equations are transferable across to some breeds more successfully than others, however, given that there is currently no method of accurately estimating IMF in vivo, accuracies across to both breeds are acceptable. The genetic parameter estimation was unsuccessful using the same research-derived dataset as previously employed in the study. However the ambition was always to investigate the genetic relationships between traits in a large industry dataset, exploiting the wealth of commercial CT information available. These investigations were considerably more successful, and among the first to present genetic parameters of novel CT-derived IMF estimates. The results found moderate heritability estimates of h2 0.31 and 0.36 for the final selected prediction equations, with clear indications that one model not including CT predicted carcass fat or any other fat measures, was more independent of these measures and the two separate prediction methods were highly genetically correlated with each other (rg = 0.89). The results from this study show that not only is it possible to accurately estimate IMF levels in the loin of Texel sheep using CT scanning, but that, until breed specific predictions are developed, the methods developed in this study are transferable across some breed types. The results also show that CT predicted IMF is heritable, independent of overall fatness and has the potential to be included in current breeding programmes. These findings can now be used to develop breeding programmes which enable breeders to make the best use of CT scanning technology to improve carcass composition while maintaining or possibly improving aspects of meat (eating) quality.

No description available.

In X-ray computed tomography, motion of the object (breathing, for example) while X-ray projections are acquired for tomographic reconstruction leads to mo- tion artifacts in the reconstructed image. Object motion (such as that of breathing lungs) during acquisition of a computed tomography scan causes artifacts in the reconstructed image due to the reason that the source and detectors require a finite amount of time to rotate around the object while acquiring measurements even as the object is changing with time. With traditional reconstruction algorithms, the object is assumed to be stationary while data is acquired. However, in the case of dynamic tomography, the projection data that is acquired is not consistent, as it is data measured from an object that is deformed at each view angle of measurement. In this work, we propose a method for estimation of general (non-rigid) small motion for dynamic tomography from motion-corrupted projection data. For a static object, the Helgason-Ludwig consistency conditions impose some structure on the moments of the projections. However in the case of dynamic object (result- ing in motion-corrupted projections) this is violated. In the proposed method, we estimate motion parameters of the general motion model from the moments of the dynamic projections. The dynamic object can be modeled as f (g(x, t)) where g is a time-dependent warping function. The non-linear problem of solving a system involving composition of functions is dealt with in the Fourier transform space where it simplifies into a problem involving multiplicatively separable functions. The system is then linearized to solve for object motion. We assume a general basis function in our model. For numerical simulations, we use polynomial and B-spline basis functions as special cases of the basis functions. Simulation is performed by applying known deformations to the Shepp-Logan phantom, to a head slice of the Visible Human phantom and a thorax slice of the Zubal phantom. Simulations are performed for projections generated by parallel- beam and fan-beam geometry. Simulation for fan-beam geometry are performed by rebinning the motion corrupted fan beam projections to parallel beam projections, followed by the proposed motion estimation method. Simulation for the Visible Human phantom and the thorax slice of the Zubal phantom are performed for fan- beam geometry. Poisson noise is also added to the generated dynamic projections before motion estimation is performed. To solve the ill-posed problem of motion estimation by the proposed method, we use a Tikhonov type regularization that involves minimizing an objective function that is the sum of a data discrepancy term, a term that penalizes temporal variation of motion, and another term to penalize large magnitudes of motion. Using the estimated motion, the original image has been reconstructed from the motion corrupted projection data, with the knowledge of the underlying motion which is estimated by the proposed algorithm, by an algebraic technique similar to the dynamic SART algorithm from the literature. Here, a SART-type coefficient matrix is computed using ray tracing with rays whose paths are warped according to the estimated motion. The dynamic image at t = 0 is then reconstructed with using the computed dynamic SART matrix.

Orientador: Alexandre Campane Vidal / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Geociências / Made available in DSpace on 2018-08-24T11:53:39Z (GMT). No. of bitstreams: 1 Belila_AlineMariaPocas_M.pdf: 4962104 bytes, checksum: f61b885a8c2ed37b65751400d6efc90b (MD5) Previous issue date: 2014 / Resumo: A caracterização de reservatórios carbonáticos é um trabalho complexo devido a alta heterogeneidade dessas rochas. Com as recentes descobertas dos reservatórios barremianos/aptianos pertencentes a fase rifte da Bacia de Santos, observa-se a importância de novos estudos que auxiliem a interpretação e caracterização destes reservatórios. Com este propósito, foram estudadas sucessões carbonáticas aflorantes da Formação Morro do Chaves, Bacia de Sergipe-Alagoas, análogas aos reservatórios da Bacia de Santos. A caracterização destas rochas foi realizada através do estudo de afloramentos para avaliar aspectos de escala mais ampla e descrição petrográfica para identificação de características em micro-escala, visando a identificação das potenciais fácies reservatório com base nos dados petrofísicos. O espaço poroso foi caracterizado utilizando imagens de tomografia computadorizada de raios-x, determinando a heterogeneidade, anisotropia e volume elementar representativo das amostras. Para a utilização dos dados de tomografia, é proposto um novo método de segmentação de imagens tridimensionais baseado na rede neural de Self-Organizing Maps. A partir dos resultados, foram determinados o valor da porosidade absoluta e conectividade do espaço poroso, validando o método como uma ferramenta consistente para a análise quantitativa e qualitativa do espaço poroso em rochas heterogêneas / Abstract: The carbonate reservoir characterization is a complex activity due to the high heterogeneity of these rocks. The recent Barremian/Aptian reservoirs discoveries belonging to the rift phase of the Santos Basin, shows the importance of further studies to assist the reservoirs interpretation and characterization. For this purpose, outcropping carbonate successions of Morro do Chaves Formation in Sergipe-Alagoas Basin were studied, analogous to Santos Basin reservoirs. The rock characterization was developed through the study of outcrops to evaluate aspects of larger scale and petrographic description to assess micro-scale characteristics in order to identify of potential reservoir facies based on petrophysical data. The pore space was characterized using x-ray computed tomography images, determining the heterogeneity, anisotropy and representative elementary volume of the samples. For the tomography data, a new method is proposed for three-dimensional image segmentation based on neural network Self-Organizing Maps. From the results, we determined the absolute value of porosity and pore space connectivity, validating the method as a consistent tool for quantitative and qualitative analysis of the pore space in heterogeneous rocks / Mestrado / Geologia e Recursos Naturais / Mestra em Geociências

This study is carried out to develop the workflow from image acquisition to numerical simulation for asphalt concrete (AC) microstructure. High resolution computed tomography (CT) images are acquired and the image quality is improved using digital image processing (DIP). Non-uniform illumination which results in inaccurate phase segmentation is corrected by applying an illumination profile to correct the background and flat-fields in the image. Distance map based watershed segmentation is used to accurately segment the phases and separate the aggregates. Quantitative analysis of the microstructure is used to determine the phase volumetric relationship and aggregates characteristics. The results of the phase reconstruction and internal structure quantification using this procedure shows a very high level of reliability. Numerical simulations are carried out in Two dimensions (2D) and Three dimensions (3D) on the processed AC microstructure. Finite element analysis (FEM) is used to capture the strength and deformation mechanisms of the AC microstructure. The micromechanical behaviour of the AC is investigated when it is considered as a continuum and when considered as a multi-phase model. The results show that the size and arrangement of aggregates determines the stress distribution pattern in the mix.

The behavior of the asphalt mixtures under large deformations, for example an unconfined compaction is of high practical importance. Quantitative measurement of the spatial distribution of internal structure of asphalt mixtures is crucial to study deformation behavior of asphalt mixtures. Deformation of granular material under an unconfined compaction is investigated in this study, as a groundwork for further research on deformation behavior of asphalt mixtures. Two sets of 3D images of specimens are obtained using X-Ray computed tomography (CT) under an unconfined compaction. Digital image analysis procedure is developed to segment different phases for characterizing spatial distribution of internal structure. Comparative volumetric relationship before and after compaction showed that air void distribution is not changed heavily due to absence of interlocking. Initial and final spatial positions of individual granules are investigated to trace their movement under compaction. It is shown that X-Ray CT could be a useful tool to characterize internal structure of asphalt mixtures and its evolution during deformation.

Additive manufacturing (AM) allows building complex shapes with high accuracy. The X-ray Computed Tomography (XCT) is one of the promising non-destructive evaluation techniques for the evaluation of subsurface defects in an additively manufactured component. Automatic defect detection and segmentation methods can assist part inspection for quality control. However, automatic detection and segmentation of defects in XCT data of AM possess challenges due to contrast, size, and appearance of defects. In this research different deep learning techniques have been applied on publicly available XCT image datasets of additively manufactured cobalt chrome samples produced by the National Institute of Standards and Technology (NIST). To assist the data labeling image processing techniques were applied which are median filtering, auto local thresholding using Bernsen’s algorithm, and contour detection. A convolutional neural network (CNN) based state-of-art object algorithm YOLOv5 was applied for defect detection. Defect segmentation in XCT slices was successfully achieved applying U-Net, a CNN-based network originally developed for biomedical image segmentation. Three different variants of YOLOv5 which are YOLOv5s, YOLOv5m, and YOLOV5l were implemented in this study. YOLOv5s achieved defect detection mean average precision (mAP) of 88.45 % at an intersection over union (IoU) threshold of 0.5. And mAP of 57.78% at IoU threshold 0.5 to 0.95 using YOLOv5M was achieved. Additionally, defect detection recall of 87.65% was achieved using YOLOv5s, whereas a precision of 71.61 % was found using YOLOv5l. YOLOv5 and U-Net show promising results for defect detection and segmentation respectively. Thus, it is found that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第20445号 / 農博第2230号 / 新制||農||1050(附属図書館) / 学位論文||H29||N5066(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 吉村 剛, 教授 藤井 義久, 教授 松浦 健二 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21140号 / 農博第2266号 / 新制||農||1057(附属図書館) / 学位論文||H30||N5114(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 藤井 義久, 教授 吉村 剛, 教授 松浦 健二 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

<p>Through thousands of years of evolution, natural biological systems have optimized their structures to thrive in diverse ecological conditions. Extracting and leveraging the inherent design principles of these biological systems can provide inspiration for the development of advanced lightweight structural materials. To effectively facilitate this transition, it is crucial to understand the specific mechanisms by which the microstructure of biological materials influences their mechanical properties. This dissertation focuses on understanding microstructure-property correlations in three biological systems: Venus flower basket, Cholla cactus, and Organ pipe coral.</p> <p>The Venus flower basket exhibits a cylindrical cage-like structure made from a complex network of silica fibers which exhibit a core-shell like layered architectures. A novel multimodal approach involving nanoindentation, ex situ and in situ fiber testing, and post-failure fractography was utilized to precisely understand the impact of the layered structure on the tensile and fracture behavior of fibers. The observation of fibers in real-time revealed, for the first time, that the initiation of failure occurs at the fiber's surface and progressively advances towards the core, traversing multiple layers. The concentric layers encompassing the central core act sacrificially, employing various toughening mechanisms to protect the core. Furthermore, nanoindentation experiments performed in situ in water shed light on the significance of the layered fiber structure in a marine environment. Another interesting system is the Cholla cactus. In arid environments, Cholla cactus produces porous wood with a mesh-like structure. To comprehensively understand the structure, properties, and designs of Cholla cactus wood, various techniques such as x-ray tomography, scanning electron microscopy, nanoindentation, and finite element simulations were employed. The structure and function of different wood components was investigated from both biological and mechanical behavior perspectives. The impact of the unique structure of wood components on the design of engineering materials is discussed. Finally, the dissertation focuses on the Organ pipe coral, which exhibits a hierarchical structure comprising vertical tubes and horizontal platforms at the macrostructure level. At the microstructure level, cells are formed through a unique arrangement of micrometer-sized plates made of calcium carbonate. Nanoindentation was used to assess the impact of this hierarchical structure on micromechanical properties. The results unveiled distinct toughening mechanisms operating at different length scales within the coral.</p> <p>17</p> <p>By gaining a precise understanding of the correlations between microstructure and properties in various biological materials, this research provides valuable insights for the design of advanced architected structural materials. The unique interplay between microstructure, function, and properties is discussed.</p>