Embora a ablação a laser venha sendo bastante utilizada em materiais em geral, pouco é entendido sobre o comportamento deste processo perto de uma interface separando dois materiais distintos. Neste contexto, o principal objetivo deste trabalho foi realizar um estudo macroscópico e microscópico dos processos que envolvem a ablação a laser em regime de femtossegundos em materiais homogêneos e estruturados. No caso de materiais estruturados, o estudo focou-se em uma situação de interface, na qual ocorrem mudanças nas propriedades de ablação. Baseado nos resultados, nós pretendemos obter subsídios científicos para entender as aplicações da ablação em regime de pulsos ultracurtos para estruturas estratificadas, tais como de dentes, ossos, interface resina-dente, dente-metal, e outras. Diferentes técnicas experimentais foram idealizadas para determinar a progressão da ablação dentro do material e obter dados extraídos da superfície. Utilizando luz espalhada de uma fonte externa, o processo de ablação foi temporalmente monitorado, permitindo determinar a velocidade de ablação em materiais transparentes, assim como perfis típicos de ablação nestes materiais. Em um segundo experimento, nosso estudo permitiu quantificar a variação da geometria de ablação perto de uma interface separando dois materiais distintos. Nossos dados foram suficientes para prever a ocorrência de uma descontinuidade no perfil da ablação entre dois meios: resina A e resina B, mostrando uma repentina descontinuidade do diâmetro da cavidade ablacionada. Adicionalmente, foi realizada uma análise dos aspectos morfológicos de diferentes tecidos biológicos irradiados e nosso estudo mostrou a eficiência da ablação utilizando laser de femtossegundos no processamento de tecidos duros e a possibilidade de utilizar esses sistemas sem causar danos térmicos e mecânicos nos tecidos remanescentes. Finalmente nós aplicamos a microperfuração a laser para produzir micro-poros na superfície de tecidos biológicos (fígados), melhorando a penetração do medicamento ALA e a aumentando a profundidade de tratamento. / Although laser ablation has been long used in general materials, little is known regarding the behavior of theses process near an interface separating two distinct materials. In this context, the main aim of this work was to perform a microscopic and macroscopic study of the processes that include femtosecond laser ablation in homogeneous and/or structured materials. In the case of structured materials, the study focused on an interface situation, in which sudden changes occurred in the properties. Based on the results, we aimed to obtain scientific subsidies to understand the application of ultrashort pulses to stratified structures, such as teeth, bones, resin-teeth or metal-teeth interface, and others. Distinct experimental techniques were used to determinate the ablation progression into the materials and to obtain data extracted from their surface. By using the scattered light from an external source, the ablation process was monitored temporally, allowing to determine the velocity of ablation in transparent materials, besides determining the typical profiles of ablated cavities in these materials. In a second experiment, our study allowed quantifying the overall variation in the ablation geometry that takes place on the interface of two different materials. Our data were sufficient to predict the occurrence of a discontinuity in the ablation profile on the interface between two media: resin A and resin B, showing a sudden discontinuity of the ablated cavity diameter. In addition, an analysis of the morphological aspects of different biological tissues irradiated by femtosecond laser pulses was performed and a comparative study showed the ablation efficiency of the femtosecond lasers in hard tissues processing and the possibility of using these systems with no thermal and mechanic damage. Finally, we applied a laser micromachining producing micro-pores on the tissue surface, improving the ALA penetration and increasing the treatment depth.

Non-invasively reosolving spatial distribution of tissue metabolites serves as a diagnostic tool to in-vivo metabolism thus making magnetic resonance spectroscopic imaging (MRSI) a very useful application. The tissue concentrations of various metabolites reveal disease state and pseudo-progression of tumors. Also, bio-chemical changes manifest much earlier than structural changes that are achieved using standard magnetic resonance imaging(MRI). However, MRSI has not achieved its potential due to several technical challenges that are specic to it. Several technical advances in the eld of MRI does not translate to MRSI. The specic limitations which make MRSI challenging include long scan times, poor spatial resolution, extremely low signal to noise ratio (SNR). In the last few decades, research in MRSI has focused on advanced data acquisition and reconstruction methods, however they cannot achieve high resolution and feasible scan time. Moreover there are several artifacts that lead to increase of spatial resolution not to mention starved SNR. Existing methods cannot deal with these limitations which considerably impacts applications of MRSI. This thesis work we revisit these problems and introduce data acquisition and reconstruction techniques to address several such challenges. In the first part of the thesis we introduce a variable density spiral acquisition technique which achieves high SNR corresponding to metabolites of interest while reducing truncation artifacts. Along with that we develop a novel compartmentalized reconstruction framework to recover high resolution data from lipid unsuppressed data. Avoiding lipid suppression not only reduces scan time and reliability but also improves SNR which is otherwise reduced even further with existing lipid suppression methods. The proposed algorithm exploits the idea that the lipid and metabolite compartment reside in low-dimensional subspace and we use orthogonality priors to reduce overlap of subspaces. We also look at spectral artifacts like Nyquist ghosting which is a common problem with spectral interleaving. Especially in echo-planar spectroscopic imaging (EPSI), one of the most popular MRSI techniques, maintaining a spatial and spectral resolution requires interleaving. Due to scanner inconsistencies spurious peaks arise which makes quantication inecient. In this thesis a novel structural low-rank prior is used to reduce and denoise spectra and achieve high resolution ESPI data. Finally we look at accelerating multi-dimensional spectroscopic problems. Resolving spectra in two dimensions can help study overlapping spectra and achieve more insight. However with an increased dimension the scan time increases. We developed an algorithm for accelerating this method by recovering data from undersampled measurements. We demonstrate the performance in two applications, 2D infra red spectroscopy and 2D MR spectroscopy . The aim of the thesis is to solve these challenges in MRSI from a signal processing perspective and be able to achieve higher resolution data in practical scan time to ultimately help MRSI reach its potential.

Recovering a linear combination of exponential signals characterized by parameters is highly significant in many MR imaging applications such as parameter mapping and spectroscopy. The parameters carry useful clinical information and can act as biomarkers for various cardiovascular and neurological disorders. However, their accurate estimation requires a large number of high spatial resolution images, resulting in long scan time. One of the ways to reduce scan time is by acquiring undersampled measurements. The recovery of images is usually posed as an optimization problem, which is regularized by functions enforcing sparsity, smoothness or low rank structure. Recently structured matrix priors have gained prominence in many MRI applications because of their superior performance over the aforementioned conventional priors. However, none of them are designed to exploit the smooth exponential structure of the 3D dataset. In this thesis, we exploit the exponential structure of the signal at every pixel location and the spatial smoothness of the parameters to derive a 3D annihilation relation in the Fourier domain. This relation translates into a product of a Hankel/Toeplitz structured matrix, formed from the k-t samples, and a vector of filter coefficients. We show that this matrix has a low rank structure, which is exploited to recover the images from undersampled measurements. We demonstrate the proposed method on the problem of MR parameter mapping. We compare the algorithm with the state-of-the-art methods and observe that the proposed reconstructions and parameter maps have fewer artifacts and errors. We extend the structured low rank framework to correct field inhomogeneity artifacts in MR images. We introduce novel approaches for field map compensation for data acquired using Cartesian and non-Cartesian trajectories. We adopt the time segmentation approach and reformulate the artifact correction problem into a recovery of time series of images from undersampled measurements. Upon recovery, the first image of the series will correspond to the distortion-free image. With the above re-formulation, we can assume that the signal at every pixel follows an exponential signal characterized by field map and the damping constant R2*. We exploit the smooth exponential structure of the 3D dataset to derive a low rank structured matrix prior, similar to the parameter mapping case. We demonstrate the algorithm on spherical MR phantom and human data and show that the artifacts are greatly reduced compared to the uncorrected images. Finally, we develop a structured matrix recovery framework to accelerate cardiac breath-held MRI. We model the cardiac image data as a 3D piecewise constant function. We assume that the zeros of a 3D trigonometric polynomial coincides with the edges of the image data, resulting in a Fourier domain annihilation relation. This relation can be compactly expressed in terms of a structured low rank matrix. We exploit this low rank property to recover the cardiac images from undersampled measurements. We demonstrate the superiority of the proposed technique over conventional sparsity and smoothness based methods. Though the model assumed here is not exponential, yet the proposed algorithm is closely related to that developed for parameter mapping. The direct implementation of the algorithms has a high memory demand and computational complexity due to the formation and storage of a large multi-fold Toeplitz matrix. Till date, the practical utility of such algorithms on high dimensional datasets has been limited due to the aforementioned reasons. We address these issues by introducing novel Fourier domain approximations which result in a fast and memory efficient algorithm for the above-mentioned applications. Such approximations allow us to work with large datasets efficiently and eliminate the need to store the Toeplitz matrix. We note that the algorithm developed for exponential recovery is general enough to be applied to other applications beyond MRI.

This thesis encompasses a body of experimental work on the use of structured light in quantum cryptographic protocols. In particular, we investigate the ability to perform quantum key distribution through various quantum channels (fibre, free-space, underwater) in laboratory and realistic conditions. We first demonstrate that a special type of optical fibre (vortex fibre) capable of coherently transmitting vector vortex modes is a viable quantum channel. Next, we describe the first demonstration of high-dimensional quantum cryptography using structured photons in an urban setting. In particular, the prevalence of atmospheric turbulence can introduce many errors to a transmitted key; however, we are still able to transmit more information per carrier using a 4-dimensional scheme in comparison to a 2-dimensional one. Lastly, we investigate the possibility of performing secure quantum communication with twisted photons in an uncontrolled underwater channel. We find that though it is possible for low-dimensional schemes, high-dimensional schemes suffer from underwater turbulence without the use of corrective wavefront techniques.

Traumatic injuries lead to articular cartilage lesion formation and result in the development of osteoarthritis. Recent research suggests that the early stage of mechanical injuries involve cell death (apoptosis and necrosis) and inflammation. In this thesis, we focus on building mathematical models to investigate the biological mechanism involving chondrocyte death and inflammatory responses in the process of cartilage degeneration. Chapter 1 describes the structure of articular cartilage, the process of carti- lage degeneration, and reviews of existing mathematical models. Chapter 2 presents a delay-diffusion-reaction model of cartilage lesion formation under cyclic loading. Computational methods were used to simulate the impact of varying loading stresses and erythropoietin levels. The model is parameterized with experimental results, and is therefore clinically relevant. Due to numerical limitations using delay differential equations, a new model is presented using tools for population dynamics. Chapter 3 presents an age and space-structured model of articular cartilage lesion formation un- der a single blunt impact. Age structure is introduced to represent the time delay in cytokine synthesis and cell transition. Numerical simulations produce similar tempo- ral and spatial patterns to our experimental data. In chapter 4, we extend our model under the cyclic loading setting. Chapter 5 builds a spatio-temporal model adapted from the former models, and investigates the distribution of model parameters using experimental data and statistical methods. Chapter 6 concludes.

Alzheimer's Disease and Related Disorders (ADRD) are a major public health problems. Major sources of care provision are family members in the community and these ADRD caregivers encounter a variety of stressor. Currently there continues to be a need to develop and test Internet based interventions designed to reduce stress for caregivers for persons with ADRD. The web-based Structured Written Emotional Expressions (SWEE) was developed to manage ADRD caregivers stress related to caregiving experiences through writing about their thoughts and feelings. However, differences between provided services by researchers (the web-based SWEE) and the desired services of ADRD caregivers could be a barrier to ADRD caregivers' acceptance and use of the web-based SWEE. The purpose of this study was to assess the acceptability of implementing a web-based nursing intervention for ADRD caregivers and to describe participants' experiences in using the website to understand ADRD caregivers' website usage. An experimental design was used to determine whether the web-based SWEE helped to manage ADRD caregivers' stress through writing interventions. In addition, the UTAUT model was employed for a theoretical framework to explain and predict the web-based SWEE usage behavior by ADRD caregivers. The Finding Meaning Through Caregiving Scale (FMTCS) was used to evaluate finding-meaning related to caregiving experiences as a mediator between performance expectancy and behavioral intention to use in the UTAUT model. Furthermore, the web-based research methods were assessed throughout the web-based SWEE implementing process. Both web-based and paper-based methods were used for recruiting potential participants. Most people who contacted the researcher were recruited by the web-based method. Structural Equation Modeling (SEM) was used for test ADRD caregivers' acceptability of the web-based SWEE and direct content analysis was used for describing participants' experiences in using the web-based SWEE. Fifty people completed the study out of the 90 people who enrolled. Of these 50 participants, 31 completed the study as intended and on schedule. The research showed a good model fit with a Chi-square value (df=43) of 57.191 (p>0.05). The findings showed that performance expectancy had a significant effect on participants' behavioral intention to use (β=0.620, p<0.01) and that effort expectancy also affected the behavioral intention to use the web-based SWEE (β=0.293, p<0.01). Performance expectancy showed stronger effects than effort expectancy. This model explained 52% of variance in behavioral intention to use. However, the effects of facilitating conditions on actual usage and effects of behavioral intention to use on actual usage were not supported by this research. The finding-meaning measure did not show a significant mediating effect on the relationship between performance expectancy and behavioral intention to use. Findings suggested that recruitment methods which use the Internet were an effective way to find potential study participants. Regardless of the topic, the writing intervention helped ADRD caregivers to express stress related to caregiving experiences. In addition, the perceived usefulness of this nursing intervention (performance expectancy) and the perceived ease of use (effort expectancy) were two important constructs which predicted and explained the acceptance of the web-based SWEE by ADRD caregivers. Finally, even though the UATAUT model was only partially supported by a good model fit, this study's findings showed the potential of the UTAUT model for providing health consumer information systems in nursing.

Spatial augmented reality is a particular kind of augmented reality technique that uses projector to blend the real objects with virtual contents. Coincidentally, as a means of 3D shape measurement, structured light illumination makes use of projector as part of its system as well. It uses the projector to generate important clues to establish the correspondence between the 2D image coordinate system and the 3D world coordinate system. So it is appealing to build a system that can carry out the functionalities of both spatial augmented reality and structured light illumination. In this dissertation, we present all the hardware platforms we developed and their related applications in spatial augmented reality and structured light illumination. Firstly, it is a dual-projector structured light 3D scanning system that has two synchronized projectors operate simultaneously, consequently it outperforms the traditional structured light 3D scanning system which only include one projector in terms of the quality of 3D reconstructions. Secondly, we introduce a modified dual-projector structured light 3D scanning system aiming at detecting and solving the multi-path interference. Thirdly, we propose an augmented reality face paint system which detects human face in a scene and paints the face with any favorite colors by projection. Additionally, the system incorporates a second camera to realize the 3D space position tracking by exploiting the principle of structured light illumination. At last, a structured light 3D scanning system with its own built-in machine vision camera is presented as the future work. So far the standalone camera has been completed from the a bare CMOS sensor. With this customized camera, we can achieve high dynamic range imaging and better synchronization between the camera and projector. But the full-blown system that includes HDMI transmitter, structured light pattern generator and synchronization logic has yet to be done due to the lack of a well designed high speed PCB.

<div> <div> <div> <p>Researchers studying persuasion have mostly focused on modeling arguments to understand how people’s beliefs can change. However, in order to convince an audience the speakers usually adapt their speech. This can be seen often in political campaigns when ideas are phrased - framed - in different ways according to the geo-graphical region the candidate is in. This practice suggests that, in order to change people’s beliefs, it is important to take into account their previous perspectives and topics of interest. </p><p><br></p> <p>In this work we propose ChangeMyStance, a novel task to predict if a user would change their mind after being exposed to opposing views on a particular subject. This setting takes into account users’ beliefs before a debate, thus modeling their preconceived notions about the topic. Moreover, we explore a new approach to solve the problem, where the task is decomposed into ”simpler” problems. Breaking the main objective into several tasks allows to build expert modules that combined produce better results. This strategy significantly outperforms a BERT end-to-end model over the same inputs. </p> </div> </div> </div>

In child welfare agencies, the Structured Decision-Making (SDM) Safety and Risk Assessment tools are utilized to support social workers in assessing families and make informed decisions while investigating child abuse and neglect. In the past, numerous studies have evaluated the strengths and weaknesses the SDM safety and risk assessment tools; however, studies have neglected social workers’ perspectives on using the tools during their investigations. Using a quantitative approach, this study examines social workers’ perceptions on the usefulness of the SDM safety and risk assessment tools during their investigations. IS social work participants from one California county agency completed an online questionnaire using Qualtrics software. The data was analyzed using statistical tests including frequencies, independent samples t-tests, and ANOVA. In order to compare groups of participants’ perceptions of the SDM safety and risk tool, we created a composite score to reflect participants’ overall perceptions of the tool. The results revealed no statistically significant differences in social workers’ perceptions about the tool based on participants’ work experiences (more or less than 5-years’ experience) or by participants’ job titles (social worker III, IV, and V). Because these findings cannot be generalized to social workers in other counties, future research should survey social workers from various counties in California to obtain more generalizable results.

Nursing faculty are responsible for graduating competent students and being competent themselves; however, the required competencies of clinical nursing faculty who instruct students in the clinical area are unidentified. The practice problem addressed in this project was the lack of a structured, organized process for identifying initial and ongoing competencies of a clinical nursing facility. The purpose of this project was to explore nursing faculty clinical competency and provide a multimethod, multispecialty approach for implementing clinical nursing faculty competency. The target population was clinical nursing faculty (n= 30) in an academic setting. This project explored the impact of a multimethod, multispecialty approach for assessment and evaluation of clinical nursing faculty competency. The project was guided by Benner's theory of novice to expert; Roger's theory of diffusion and innovation; and the plan, do, check, act model. The study analyzed the data obtained from clinical nursing faculty demographics, and competency validation of 3 clinical and 3 academic, remediation, and retesting outcomes. Descriptive statistics and t test were utilized in analyzing the data. The project findings revealed the clinical nursing faculty members are 100% clinically competent and 68.7% academically competent in the areas evaluated. The project findings have implications for social change through role modeling of leadership skills by clinical nursing faculty and improving student clinical instruction by cultivating competent clinical nursing faculty.