One of the state-of-the-art highlights of deep learning in the past ten years is the introduction of generative adversarial networks (GANs), which had achieved great success in their ability to generate images comparable to real photos with minimum human intervention. These networks can generalise to a multitude of desired outputs, especially in image-to-image problems and image syntheses. This thesis proposes a computer graphics pipeline for 3D rendering by utilising generative adversarial networks (GANs). This thesis is motivated by regression models and convolutional neural networks (ConvNets) such as U-Net architectures, which can be directed to generate realistic global illumination effects, by using a semi-supervised GANs model (Pix2pix) that is comprised of PatchGAN and conditional GAN which is then accompanied by a U-Net structure. Pix2pix had been chosen for this thesis for its ability for training as well as the quality of the output images. It is also different from other forms of GANs by utilising colour labels, which enables further control and consistency of the geometries that comprises the output image. The series of experiments were carried out with laboratory created image sets, to pursue the possibility of which deep learning and generative adversarial networks can lend a hand to enhance the pipeline and speed up the 3D rendering process. First, ConvNet is applied in combination with Support Vector Machine (SVM) in order to pair 3D objects with their corresponding shadows, which can be applied in Augmenter Reality (AR) scenarios. Second, a GANs approach is presented to generate shadows for non-shadowed 3D models, which can also be beneficial in AR scenarios. Third, the possibility of generating high quality renders of image sequences from low polygon density 3D models using GANs. Finally, the possibility to enhance visual coherence of the output image sequences of GAN by utilising multi-colour labels. The results of the adopted GANs model were able to generate realistic outputs comparable to the lab generated 3D rendered ground-truth and control group output images with plausible scores on PSNR and SSIM similarity index metrices.

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[pt] Na exploração de campos offshore, é frequente a necessidade transportar óleo e gás por dutos de produção ou de transferência que percorrem grandes distâncias no leito submarino. Em relação aos gasodutos, podem ocorrer a formação de condensado ao longo desses, o que afeta drasticamente a capacidade de entrega e a modalidade operacional, sendo a remoção do condensado realizada por pigs. Além disso, devido a existência de condições extremas como baixas temperaturas e altas pressões, pode ocorrer a formação de plug de hidrato nos gasodutos. Nesse caso, são previstas operações especiais para a remoção do plug de hidrato, onde, durante o procedimento, pode ser gerado condensado. Dessa forma, é importante que essas operações sejam simuladas durante a fase de projeto e de operação para avaliar a efetividade dos procedimentos e os valores das variáveis de processo de forma a garantir a segurança operacional do sistema. O objetivo deste estudo é investigar numericamente o processo de deslocamento de pigs e de plugs de hidrato ao longo de tubulações, na presença de condensado. A formação de condensado é obtida através de um modelo de equilíbrio de fases, que utiliza a equação de Peng-Robinson para o cálculo das propriedades termodinâmicas para ambas as fases. O escoamento bifásico é modelado como unidimensional. A solução das equações de conservação de massa, quantidade de movimento linear e energia, acopladas ao balanço de forças para prever o deslocamento do pig/plug são resolvidas utilizando o método numérico de diferenças finitas. A modelagem do escoamento e do modelo termodinâmico, englobando o equilíbrio de fases e as propriedades termodinâmicas, foram validadas com soluções analíticas e dados da literatura. Estudos de casos de deslocamento de pig e de plug de hidrato na presença de condensado foram realizados e os resultados obtidos foram bastante satisfatórios, permitindo concluir que os modelos desenvolvidos podem ser utilizados para a análise e previsão das operações de passagem de pig e de remoção de plugs de hidrato na presença de condensado. / [en] In offshore oilfield exploration, gas is often transported through long-distance transfer pipelines or production pipelines on the seabed. Along the pipeline, condensate may be formed, which dramatically affect the delivery capacity and the operational mode, requiring condensate removal employing pigs. Further, due to the presence of extreme conditions encountered in these pipelines, such as low temperatures and high pressures, hydrate plug can also be formed inside the gas pipelines. In this case, special procedures are foreseen to remove the hydrate plug, during which condensate may be generated. Thus, it is essential that these operations should be simulated throughout the design and operation stages to evaluate the efficacy of these procedures and the process variable values in order to guarantee the system s operational safety. The purpose of this study is to investigate numerically the process of displacement of pigs and hydrate plugs along pipelines, in the presence of condensate. The condensate formation is obtained through a phase behavior model (FLASH), which employs the Peng-Robinson equation to calculate the thermodynamic properties for both phases. Two-phase flow is modeled as one-dimensional. The conservation equations of mass, linear momentum and energy, coupled with the force balance to predict the displacement of the pig/plug are solved, using the numerical method of finite differences. The flow and thermodynamic models were validated with analytical solutions and literature data. The validation of the thermodynamic model included the phase equilibrium and thermodynamic properties. Case studies of displacement of pig and hydrate plug in the presence of condensate were performed and the results obtained were very satisfactory, allowing to conclude that the developed models can be used for the analysis and prediction of the pigging operations and removal of hydrate plugs in the presence of condensate.

Yes / Research with prisoners’ families is limited in the context of learning difficulties/disabilities (LD) and autism spectrum. Life-story interviews with mothers reveal an extended period of emotional and practical care labour, as the continuous engagement with their son’s education and experiences of physical and emotional abuse are explored. Prior to their son’s incarceration, mothers spoke of stigma and barriers to support throughout their childrearing, as well as limited or absent preventative/positive care practices. Subsequently prisons and locked wards seem to feature as a progression. Mothers have experienced abuse; physical and/or emotional, as well as lives that convey accounts of failure. Not their failure, but that of the systems. A care ethics model of disability assists an analysis of the narratives where care-less spaces are identified. Interrelated experiences merging emotional responses to extended mothering, the external forces of disabilism and destructive systems, lead to proposing a rehumanising of care practices within for example, education and the criminal justice system. / The Leverhume Trust (RF-2016-613\8)

<p dir="ltr">The success of machine learning techniques in widespread applications has taught us that with respect to accuracy, the more data, the better the model. However, for fairness, data quality is perhaps more important than quantity. Existing studies have considered the impact of data preprocessing on the accuracy of ML model tasks. However, the impact of preprocessing on the fairness of the downstream model has neither been studied nor well understood. Throughout this thesis, we conduct a systematic study of how data quality issues and data preprocessing steps impact model fairness. Our study evaluates several preprocessing techniques for several machine learning models trained over datasets with different characteristics and evaluated using several fairness metrics. It examines different data preparation techniques, such as changing categories into numbers, filling in missing information, and smoothing out unusual data points. The study measures fairness using standards that check if the model treats all groups equally, predicts outcomes fairly, and gives similar chances to everyone. By testing these methods on various types of data, the thesis identifies which combinations of techniques can make the models both accurate and fair.The empirical analysis demonstrated that preprocessing steps like one-hot encoding, imputation of missing values, and outlier treatment significantly influence fairness metrics. Specifically, models preprocessed with median imputation and robust scaling exhibited the most balanced performance across fairness and accuracy metrics, suggesting a potential best practice guideline for equitable ML model preparation. Thus, this work sheds light on the importance of data preparation in ML and emphasizes the need for careful handling of data to support fair and ethical use of ML in society.</p>

A Pipeline Analytical Program and Dredging Knowledge{Base Expert{System (DKBES) determines a pipeline dredge's production and resulting cost and schedule. Pipeline dredge engineering presents a complex and dynamic process necessary to maintain navigable waterways. Dredge engineers use pipeline engineering and slurry transport principles to determine the production rate of a pipeline dredge system. Engineers then use cost engineering factors to determine the expense of the dredge project. Previous work in engineering incorporated an object{oriented expert{system to determine cost and scheduling of mid{rise building construction where data objects represent the fundamental elements of the construction process within the program execution. A previously developed dredge cost estimating spreadsheet program which uses hydraulic engineering and slurry transport principles determines the performance metrics of a dredge pump and pipeline system. This study focuses on combining hydraulic analysis with the functionality of an expert{system to determine the performance metrics of a dredge pump and pipeline system and its resulting schedule. Field data from the U.S. Army Corps of Engineers pipeline dredge, Goetz, and several contract daily dredge reports show how accurately the DKBES can predict pipeline dredge production. Real{time dredge instrumentation data from the Goetz compares the accuracy of the Pipeline Analytical Program to actual dredge operation. Comparison of the Pipeline Analytical Program to pipeline daily dredge reports shows how accurately the Pipeline Analytical Program can predict a dredge project's schedule over several months. Both of these comparisons determine the accuracy and validity of the Pipeline Analytical Program and DKBES as they calculate the performance metrics of the pipeline dredge project. The results of the study determined that the Pipeline Analytical Program compared closely to the Goetz eld data where only pump and pipeline hydraulics a ected the dredge production. Results from the dredge projects determined the Pipeline Analytical Program underestimated actual long{term dredge production. Study results identi ed key similarities and di erences between the DKBES and spreadsheet program in terms of cost and scheduling. The study then draws conclusions based on these ndings and o ers recommendations for further use.