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Many boundary value problems of mathematical physics are modelled by elliptic differential operators L in a given domain Ω . An effective method for treating such problems is the method of layer potentials, whose essence resides in reducing matters to solving a boundary integral equation. This, in turn, requires inverting a singular integral operator, naturally associated with L and Ω, on appropriate function spaces on ƌΩ. When the operator L is of second order and the domain Ω is Lipschitz (i.e., Ω is locally the upper-graph of a Lipschitz function) the fundamental work of B. Dahlberg, C. Kenig, D. Jerison, E. Fabes, N. Rivière, G. Verchota, R. Brown, and many others, has opened the door for the development of a far-reaching theory in this setting, even though several very difficult questions still remain unanswered. In this dissertation, the goal is to solve a number of open questions regarding spectral properties of singular integral operators associated with second and higher-order elliptic boundary value problems in non-smooth domains. Among other spectral results, we establish symmetry properties of harmonic classical double layer potentials associated with the Laplacian in the class of Lipschitz domains in R2. An array of useful tools and techniques from Harmonic Analysis, Partial Differential Equations play a key role in our approach, and these are discussed as preliminary material in the thesis: --Mellin Transforms and Fourier Analysis; --Calderón-Zygmund Theory in Uniformly Rectifiable Domains; -- Boundary Integral Methods. Chapter four deals with proving invertibility properties of singular integral operators naturally associated with the mixed (Zaremba) problem for the Laplacian and the Lamé system in infinite sectors in two dimensions, when considering their action on the Lebesgue scale of p integrable functions, for 1 < p < ∞. Concretely, we consider the case in which a Dirichlet boundary condition is imposed on one ray of the sector, and a Neumann boundary condition is imposed on the other ray. In this geometric context, using Mellin transform techniques, we identify the set of critical integrability indexes p for which the invertibility of these operators fails. Furthermore, for the case of the Laplacian we establish an explicit characterization of the Lp spectrum of these operators for each p є (1,∞), as well as well-posedness results for the mixed problem. In chapter five, we study spectral properties of layer potentials associated with the biharmonic equation in infinite quadrants in two dimensions. A number of difficulties have to be dealt with, the most significant being the more complex nature of the singular integrals arising in this 4-th order setting (manifesting itself on the Mellin side by integral kernels exhibiting Mellin symbols involving hyper-geometric functions). Finally, chapter six, deals with spectral issues in Lipschitz domains in two dimensions. Here we are able to prove the symmetry of the spectra of the double layer potentials associated with the Laplacian. This is in essence a two-dimensional phenomenon, as known examples show the failure of symmetry in higher dimensions. / Mathematics

Diffuse optical tomography (DOT) is an imaging technique that utilizes near-infrared (NIR) light to probe biological tissue and ultimately recover the optical parameters of the tissue. Broadly, the process for image reconstruction in DOT involves three parts: (1) the detected measurements, (2) the modeling of the medium being imaged, and (3) the algorithm that incorporates (1) and (2) to finally estimate the optical properties of the medium. These processes have long been established in the DOT field but are also known to suffer drawbacks. The measurements themselves tend to be susceptible to experimental noise that could degrade reconstructed image quality. Furthermore, depending on the DOT configuration being utilized, the total number of measurements per capture can get very large and add additional computational burden to the reconstruction algorithms. DOT algorithms are reliant on accurate modeling of the medium, which includes solving a light propagation model and/or generating a so-called sensitivity matrix. This process tends to be complex and computationally intensive and, furthermore, does not take into account real system characteristics and fluctuations. Similarly, the inverse algorithms typically utilized in DOT also often take on a high computational volume and complexity, leading to long reconstruction times, and have limited accuracy depending on the measurements, forward model, and experimental system. The purpose of this dissertation is to address and develop computational methods, especially incorporating deep learning, to improve each of these components. First, I evaluated several time-domain data features involving the Mellin and Laplace transforms to incorporate measurements that were robust to noise and sensitive at depth for reconstruction. Furthermore, I developed a method to find the optimal values to use for different imaging depths and scenarios. Second, I developed a neural network that can directly learn the forward problem and sensitivity matrix for simulated and experimental measurements, which allows the computational forward model to adapt to the system's characteristics. Finally, I employed learning-based approaches based on the previous results to solve the inverse problem to recover the optical parameters in a high-speed manner. Each of these components were validated and tested with numerical simulations, phantom experiments, and a variety of in vivo data. Altogether, the results presented in this dissertation depict how these computational approaches lead to an improvement in DOT reconstruction quality, speed, and versatility. It is the ultimate hope that these methods, algorithms, and frameworks developed as a part of this dissertation can be directly used on future data to further validate the research presented here and to further validate DOT as a valuable imaging tool across many applications.

Yes / This paper demonstrates a non-Fourier prediction methodology of triple-layer human skin tissue for determining skin burn injury with non-ideal properties of tissue, metabolism and blood perfusion. The dual-phase lag (DPL) bioheat model is employed and solved using joint integral transform (JIT) through Laplace and Fourier transforms methods. Parametric studies on the effects of skin tissue properties, initial temperature, blood perfusion rate and heat transfer parameters for the thermal response and exposure time of the layers of the skin tissue are carried out. The study demonstrates that the initial tissue temperature, the thermal conductivity of the epidermis and dermis, relaxation time, thermalisation time and convective heat transfer coefficient are critical parameters to examine skin burn injury threshold. The study also shows that thermal conductivity and the blood perfusion rate exhibits negligible effects on the burn injury threshold. The objective of the present study is to support the accurate quantification and assessment of skin burn injury for reliable experimentation, design and optimisation of thermal therapy delivery.

Yes / A potential flow model of the semi-inverse type is proposed to simulate flow over round crested weirs. This technique involves the construction of only streamlines over the weir instead of constructing the entire flow net. A Serre–Green–Naghdi (SGN) equation is employed to determine the initial free-surface profile, which is solved using a combined finite volume-finite difference scheme. The potential flow equations were numerically solved using a five-point central finite difference scheme. The model was applied to define the pressure and velocity fields in channel controls involving transcritical flow, such as the Gaussian weir, parabolic weir, and semicircular weir. The impact of streamline curvature on pressure and velocity distributions was investigated in the study. The curvature of the streamline strongly influenced the rise and drop of the bed pressures along the test section. A semicircular weir experiment was also conducted to validate the pressure and velocity profiles obtained using the proposed 2-D fluid flow model. The computed pressure and flow profiles from the solution of the potential flow equation agree perfectly with the present experiment and similar experiments available in the literature. In conclusion, the SGN equation provides an excellent initial profile to solve a 2-D ideal fluid flow numerically.

Les modèles à sur-représentation de zéros discrets et continus ont une large gamme d'applications et leurs propriétés sont bien connues. Bien qu'il existe des travaux portant sur les modèles discrets à sous-représentation de zéro et modifiés à zéro, la formulation usuelle des modèles continus à sur-représentation -- un mélange entre une densité continue et une masse de Dirac -- empêche de les généraliser afin de couvrir le cas de la sous-représentation de zéros. Une formulation alternative des modèles continus à sur-représentation de zéros, pouvant aisément être généralisée au cas de la sous-représentation, est présentée ici. L'estimation est d'abord abordée sous le paradigme classique, et plusieurs méthodes d'obtention des estimateurs du maximum de vraisemblance sont proposées. Le problème de l'estimation ponctuelle est également considéré du point de vue bayésien. Des tests d'hypothèses classiques et bayésiens visant à déterminer si des données sont à sur- ou sous-représentation de zéros sont présentées. Les méthodes d'estimation et de tests sont aussi évaluées au moyen d'études de simulation et appliquées à des données de précipitation agrégées. Les diverses méthodes s'accordent sur la sous-représentation de zéros des données, démontrant la pertinence du modèle proposé. Nous considérons ensuite la classification d'échantillons de données à sous-représentation de zéros. De telles données étant fortement non normales, il est possible de croire que les méthodes courantes de détermination du nombre de grappes s'avèrent peu performantes. Nous affirmons que la classification bayésienne, basée sur la distribution marginale des observations, tiendrait compte des particularités du modèle, ce qui se traduirait par une meilleure performance. Plusieurs méthodes de classification sont comparées au moyen d'une étude de simulation, et la méthode proposée est appliquée à des données de précipitation agrégées provenant de 28 stations de mesure en Colombie-Britannique. / Zero-inflated models, both discrete and continuous, have a large variety of applications and fairly well-known properties. Some work has been done on zero-deflated and zero-modified discrete models. The usual formulation of continuous zero-inflated models -- a mixture between a continuous density and a Dirac mass at zero -- precludes their extension to cover the zero-deflated case. We introduce an alternative formulation of zero-inflated continuous models, along with a natural extension to the zero-deflated case. Parameter estimation is first studied within the classical frequentist framework. Several methods for obtaining the maximum likelihood estimators are proposed. The problem of point estimation is considered from a Bayesian point of view. Hypothesis testing, aiming at determining whether data are zero-inflated, zero-deflated or not zero-modified, is also considered under both the classical and Bayesian paradigms. The proposed estimation and testing methods are assessed through simulation studies and applied to aggregated rainfall data. The data is shown to be zero-deflated, demonstrating the relevance of the proposed model. We next consider the clustering of samples of zero-deflated data. Such data present strong non-normality. Therefore, the usual methods for determining the number of clusters are expected to perform poorly. We argue that Bayesian clustering based on the marginal distribution of the observations would take into account the particularities of the model and exhibit better performance. Several clustering methods are compared using a simulation study. The proposed method is applied to aggregated rainfall data sampled from 28 measuring stations in British Columbia.

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