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This thesis deals with the Problem of Distinction, i.e. what explains the distinction of two substances, especially when they are qualitatively indiscernible? It argues that the best solution to this problem is an ontology of haecceities, properties like "being identical with X" which is unique for X and responsible for its individuation. This is achieved in two steps. In the first half of the thesis (Chapters 2 to 5), the nature of the Problem of Distinction is clarified and Principles of Evaluations of the solution to the Problem of Distinction are set out. Then based on these Principles, the thesis argues against the main extant non-haecceity solutions to the Problem of Distinction including the Spacetime Points Solution, Bare Particularism, Trope Theory, and the Universal Bundle Theory. In the second half of the thesis (Chapters 6 to 9), it develops a novel ontology of haecceities. A Haecceity Mereology with three rules is proposed. According to this ontology, the reality is constituted by two fundamental kinds of properties, universals and haecceities. An individual substance is a special mereological sum of a haecceity and its correspondent universals. Hence, there are two distinct substances because of the distinction of two haecceities. Besides solving the Problem of Distinction, the ontology of haecceities also explains many other things such as the unity of substances. Further, we argue that, although our knowledge of substances is epistemically fallible, the relationship between a haecceity and its correspondent universals is metaphysically necessary.

This study mainly investigated how the mechanical behaviour of alumina trihydrate (ATH) filled Poly (methyl methacrylate) (PMMA) composites is affected by changes in certain parameters such as the volume fraction of and the size of the ATH particles. Although numerous studies have been conducted in this area, the effect of the microstructure is very difficult to explain because of the many factors which are involved and affect the mechanical behaviour of particle-filled composites. In this study, a systematic study, where volume fraction and particle diameter are varied, is presented. Modelling technique, both micromechanical analytical and finite element models, are used to achieve understanding of the experimentally observed mechanical behaviour of the composites. Five groups of composites were tested: Composite A: 15 μm, 34.7 vol. %, B: 8 μm, 39.4 vol. %, C: 15 μm, 39.4 vol. %, D: 25 μm, 39.4 vol. % and E: 15 μm, 44.4 vol. %. Flexural tests, uniaxial tensile tests, single edge notch bending (SENB) tests were performed at different temperatures. The elastic modulus of the composites increased as the filler content increased, whilst the change in particle diameter hardly affected the elastic modulus. Due to the competition between the reinforcing effect of the rigid particles and the weakening effect of the particle agglomeration on the composites, the increase in filler content showed little effect on the strength of the composites. On the other hand, as the particle diameter increased, the strength of the composite decreased. The fracture toughness, G_IC, of the composites is negatively correlated with the filler content, whilst increased with the increasing particle diameter. The FE simulation has great advantages comparing to micromechanical analytical models. For predicting the elastic modulus of the particle filled composites, the FE simulation can make as good prediction as the Lielens model, the prediction of which agreed well with the experimental data. The FE simulation can also predict the strength of particle-filled composites if an appropriate crack initiation stress of the matrix is used. The stress field obtained using the FE simulation led to a better understanding of how the microstructures of the composites affect their mechanical behaviour. In addition, the application of the SEM image-converted RVEs, i.e. real microstructure approach, takes into consideration the effect of particle agglomeration, which is another big improvement comparing to micromechanical analytical models.

Modelling surface runoff can be beneficial to operations within many fields, such as agriculture planning, flood and drought risk assessment, and water resource management. In this study, we built a data-driven model that can reproduce monthly surface runoff at a 4-km grid network covering 13 watersheds in the Chesapeake Bay area. We used a random forest algorithm to build the model, where monthly precipitation, temperature, land cover, and topographic data were used as predictors, and monthly surface runoff generated by the SWAT hydrological model was used as the response. A sub-model was developed for each of 12 monthly surface runoff estimates, independent of one another. Accuracy statistics and variable importance measures from the random forest algorithm reveal that precipitation was the most important variable to the model, but including climatological data from multiple months as predictors significantly improves the model performance. Using 3-month climatological, land cover, and DEM derivatives from 40% of the 4-km grids as the training dataset, our model successfully predicted surface runoff for the remaining 60% of the grids (mean R2 (RMSE) for the 12 monthly models is 0.83 (6.60 mm)). The lowest R2 was associated with the model for August, when the surface runoff values are least in a year. In all studied watersheds, the highest predictive errors were found within the watershed with greatest topographic complexity, for which the model tended to underestimate surface runoff. For the other 12 watersheds studied, the data-driven model produced smaller and more spatially consistent predictive errors.

Since the discovery of pathogenic mitochondrial DNA (mtDNA) mutations in the 1980's, these mutations have been shown to be involved in a number of human diseases and have caught increasing attention. The emergence of next generation sequencing technology has allowed us to do more comprehensive studies of these mutations in the mitochondrial genome, especially for low frequency heteroplasmic mutations. In my research projects, I combined experimental and computational approaches to investigate mtDNA heteroplasmies in different medical disciplines. I first conducted a literature review to summarize recent findings on the implications of mtDNA mutations in human diseases, with a focus on complex diseases such as cancer, neurodegenerative diseases and aging. My first project was to investigate mtDNA heteroplasmy and copy number variations in a general healthy population. This population-based study indicated that both mtDNA quality and quantity decreased with age. I further found that mtDNA copy number was associated with serum bicarbonate level and white blood cell counts, while aggregate heteroplasmy load was associated with blood apolipoprotein B level. These results suggested that maintaining optimal mtDNA copy number and preventing the expansion of heteroplasmy could promote healthy aging. In my second project, I first identified heteroplasmies in 466 pairs of DNA and RNA sequencing data. I verified that most of the heteroplasmies were transcribed to RNA regardless of their pathogenic potentials. I then experimentally tested whether the heteroplasmy frequencies could change over time. My test showed that a heteroplasmy with ~50% frequency could decrease to ~1% in only 28 days. Moreover, these observed heteroplasmy dynamics could significantly affect certain gene expression levels. My third project focused on mtDNA fragments circulating in blood stream in a cell-free format. I developed an experiment protocol tailored for sequencing short DNA fragments from plasma samples. After analyzing the sequencing data, I found that the fragment length of mtDNA in plasma is much shorter than that of nuclear DNA. We also demonstrated that mtDNA heteroplasmy was detectable in the plasma sample, suggesting its potential to serve as a biomarker in different clinical applications.

Micro sphere resonators that hold optical whisper gallery modes (WGMs) provide an innovative option for implementing micro optical resonators. The unique properties of the micro sphere resonator make it capable to introduce resonance for various optical phenomenon, like stimulated Raman scattering (SRS), in micron scale. This thesis illustrates the characteristics of micro sphere resonators and demonstrates the resonance-enhanced SRS in micro sphere resonators with reduce threshold power. Both theoretical and experimental results are presented. Coupling model for WGM is derived in transfer matrix method. Simulation analyses for mode pattern of WGMs are solved based on mathematic model and finite element method (FEM). Fabrication of devices like silica binocular tapered fiber couplers and silica micro spheres is developed. The measurement setup is established, based on which characteristics of silica micro sphere resonators are measured. The result is compared to theoretical analysis to obtain the Q-factor of the resonator. The correspondence of the Q-factor on the coupling condition and the degradation of the Q factor over time are further discussed. SRS in silica micro sphere resonators is demonstrated with a reduced threshold power as low as 1 mW, which is 2 to 3 orders lower than that in fiber-based devices. Fabrication and measurement on chalcogenide glass micro sphere resonators are presented, according to which the threshold for SRS is also calculated. Further work is needed to improve the Q-factor of the chalcogenide glass micro sphere resonator for demonstrating the resonance-enhanced SRS in this material.