yes / This work outlines a novel approach for determining thermal contact resistance (TCR) in micromoulding. The proposed technique aims to produce TCR predictions with known confidence values and combines experimental evidence (temperature fields and contact angle measurements) with various mathematical modelling procedures (parametric representation of surfaces, finite element analysis and stochastic processes). Here, emphasis is made on the mathematical aspects of the project. In particular, we focus on the description of the parametric surface representation technique based on the use of partial differential equations, known as the PDE method, which will be responsible for characterizing and compressing micro features in either moulds or surface tools. / EPSRC

In chronic lymphocytic leukemia (CLL), the presence of multiple subsets expressing ‘stereotyped’ B-cell receptors (BCRs) has implicated antigen(s) in leukemogenesis. These stereotyped subsets display similar immunoglobulin (IG) gene usage, almost identical complementarity determining region 3’s and may share clinical features. For instance, subsets #1 (IGHV1/5/7/IGKV1-39) and #2 (IGHV3-21/IGLV3-21) have inferior outcome compared to non-subset patients, whereas subset #4 (IGHV4-34/IGKV2-30) display a favourable prognosis. The aim of this thesis was to investigate genomic aberrations, gene expression patterns and methylation profiles in stereotyped subsets and compare epigenetic profiles in CLL and mantle cell lymphoma (MCL). In paper I, we investigated genomic aberrations in subsets #2, #4 and #16 and in non-stereotyped samples (n=101) using high-density 250K SNP arrays. Subset #2 and non-subset #2 IGHV3-21 cases displayed a higher frequency of aberrations than subset #4 cases. The high incidence of del(11q) in both subset #2/non-subset #2 may reflect the adverse survival reported for IGHV3-21 patients. In contrast, the lower frequency of genetic events and lack of poor-prognostic aberrations in subset #4 may partially explain their indolent disease. In paper II, we analysed the global RNA expression in subset #4, #16 and non-subset IGHV4-34 CLL patients (n=25). Subsets #4 and 16 showed distinct gene expression profiles, where genes involved in cell regulatory pathways were significantly lower expressed in subset #4, in line with their low-proliferative disease. In paper III, a genome-wide methylation array was applied to investigate methylation profiles in subsets #1, #2 and #4 (n=39). We identified differential methylation patterns for all subsets and found affected genes to be involved in e.g. apoptosis and therapy resistance. When performing functional annotation, a clear enrichment of genes involved in adaptive immunity was observed. These genes were preferentially methylated in subset #1 when compared to either subset #2 or #4, possibly due to different antigen responses. In paper IV, the genome-wide methylation profiles for 30 CLL and 20 MCL patients were investigated. Distinct methylation profiles were observed, where MCL displayed a more homogeneous profile. Homeobox transcription factor genes showed a higher degree of methylation in MCL, while apoptosis-related genes and proliferation-associated genes were methylated in CLL. In summary, this thesis demonstrates that stereotyped CLL subsets display differences in gene expression profiles, genetic aberrations and methylation patterns, underscoring the functional relevance of subgrouping according to BCR stereotypy. The distinct methylation profiles of CLL and MCL suggests that different epigenetic mechanisms are involved in the pathogenesis of these B-cell malignancies.

Additive Manufacturing (AM) is on the verge of causing a downfall to conventional manufacturing with its huge potential in part manufacture. With an increase in demand for customized product, on-demand production and sustainable manufacturing, AM is gaining a great deal of attention from different industries in recent years. AM is redefining product design by revolutionizing how products are made. AM is extensively utilized in automotive, aerospace, medical and dental applications for its ability to produce intricate and lightweight structures. Despite their popularity, AM has not fully replaced traditional methods with one of the many reasons being inferior surface quality. Surface texture plays a crucial role in the functionality of a component and can cause serious problems to the manufactured parts if left untreated. Therefore, it is necessary to fully understand the surface behavior concerning the factors affecting it to establish control over the surface quality. The challenge with AM is that it generates surfaces that are different compared to conventional manufacturing techniques and varies with respect to different materials, geometries and process parameters. Therefore, AM surfaces often require novel characterization approaches to fully explain the manufacturing process. Most of the previously published work has been broadly based on two-dimensional parametric measurements. Some researchers have already addressed the AM surfaces with areal surface texture parameters but mostly used average parameters for characterization which is still distant from a full surface and functional interpretation. There has been a continual effort in improving the characterization of AM surfaces using different methods and one such effort is presented in this thesis. The primary focus of this thesis is to get a better understanding of AM surfaces to facilitate process control and optimization. For this purpose, the surface texture of Fused Deposition Modeling (FDM) and Laser-based Powder Bed Fusion of Metals (PBF-LB/M) have been characterized using various tools such as Power Spectral Density (PSD), Scale-sensitive fractal analysis based on area-scale relations, feature-based characterization and quantitative characterization by both profile and areal surface texture parameters. A methodology was developed using a Linear multiple regression and a combination of the above-mentioned characterization techniques to identify the most significant parameters for discriminating different surfaces and also to understand the manufacturing process. The results suggest that the developed approaches can be used as a guideline for AM users who are looking to optimize the process for gaining better surface quality and component functionality, as it works effectively in finding the significant parameters representing the unique signatures of the manufacturing process. Future work involves improving the accuracy of the results by implementing improved statistical models and testing other characterization methods to enhance the quality and function of the parts produced by the AM process.