The vocal folds are an essential component of human speech production and communication. Advancements in voice research allow for improved voice disorder treatments. Since in vivo analysis of vocal fold function is limited, models have been developed to simulate vocal fold motion. In this research, synthetic and computational vocal fold models were used to investigate various aspects of vocal fold vibratory characteristics. A series of tests were performed to quantify the effect of varying material and geometric parameters on the models' flow-induced responses. First, the influence of asymmetric vocal fold stiffness on voice production was evaluated using life-sized, self-oscillating vocal fold models with idealized vocal fold geometry. Asymmetry significantly influenced glottal jet flow, glottal area, and vibration frequency. Second, flow-induced responses of simplified and MRI-based synthetic models were compared. The MRI-based models showed remarkable improvements, including less vertical motion, alternating convergent-divergent glottal profile patterns, and mucosal wave-like movement. Third, a simplified model was parametrically investigated via computational modeling techniques to determine which geometric features influenced model motion. This parametric study led to identification and ranking of key geometric parameters based on their effects on various measures of vocal fold motion (e.g., mucosal wavelike movement). Incorporation of the results of these studies into the definition of future models could lead to models with more life-like motion.

The voice is the primary mode of communication for humans. Because the voice is so important, voice disorders tend to severely diminish quality of life. A better understanding of the physics of voice production can help to improve treatment of voice disorders. For this thesis research a self-oscillating synthetic vocal fold model was developed, compared with previous synthetic vocal fold models, and used to explore the physical effects of augmentation injections on vibration dynamics. The research was conducted in two stages. First, four vocal fold models were evaluated by quantifying onset pressure, frequency, maximum glottal gap, flow rate, and medial surface motion. The newly developed model, differentiated from the other models by the inclusion of more layers, adjusted geometry, and an extremely soft superficial lamina propria layer, was included in this study. One of the models, created using MRI-derived geometry, had the most defined mucosal wave. The newly-developed model had the lowest onset pressure, flow rate, and smallest maximum glottal width, and the model motion compared very well with published excised human larynx data. Second, the new model was altered to simulate bowing by decreasing the volume of the body layer relative to that of a normal, unbowed model. Two models with varying degrees of bowing were created and tested while paired with normal models. Pre- and post-injection data (onset pressure, vibration frequency, glottal flow rate, open quotient, and high-speed image sequences) were recorded and compared. General pre- to post-injection trends included decreased onset pressure, glottal flow rate, and open quotient, and increased vibration frequency. Additionally, there was a decrease in mucosal wave velocity and an increase in phase angle. The thesis results are anticipated to aid in better understanding the physical effects of augmentation injections, with the ultimate goal of obtaining more consistent surgical outcomes, and also to contribute to the advancement of voice research through the development of the new synthetic model.

Synthetic, self-oscillating vocal fold (VF) models are physical models whose life-like vibration is induced and perpetuated by fluid flow. Self-oscillating VF models, which are often fabricated life-size from soft silicone elastomers, are used to study various aspects of voice biomechanics. Despite their many advantages, the development and use of self-oscillating VF models is limited by the casting process used to fabricate the models. Consequently, this thesis focuses on the development of 3D printing processes for fabricating silicone VF models. A literature review is first presented which describes three types of material extrusion 3D printing processes for silicone elastomers, namely direct ink writing (DIW), embedded 3D printing, and removable-embedded 3D printing. The review describes each process and provides recent examples from literature that show how each has been implemented to create silicone prints. An embedded 3D printing process is presented wherein a set of multi-layer VF models are fabricated by extruding silicone ink within a VF-shaped reservoir filled with a curable silicone support matrix. The printed models successfully vibrated during testing, but lacked several desirable characteristics which were present in equivalent cast models. The advantages and disadvantages of using this fabrication process are explored. A removable-embedded 3D printing process is presented wherein shapes were fabricated by extruding silicone ink within a locally-curable support matrix then curing the silicone ink and proximate matrix. The printing process was used to fabricate several geometries from a variety of silicone inks. Tensile test results show that printed models exhibit relatively high failure strains and a nearly isotropic elastic modulus in directions perpendicular and parallel to the printed layers. A set of single-material VF models were printed and subjected to vibration testing. The printed models exhibited favorable vibration characteristics, suggesting the continued use of this printing process for VF model fabrication. A micro-slicing process is presented which is capable of creating gcode for 3D printing multiple materials in discrete and mixed ratios by utilizing a previously-sliced single-material shape and a material definition. An important advantage of micro-slicing is its ability to create gcode with a mixed-material gradient. Initial test results and observations are included. This micro-slicing process could be used in material extrusion 3D printing

Data splitting is commonly used in machine learning to split data into a train, test, or validation set. This approach allows us to find the model hyper-parameter and also estimate the generalization performance. In this research, we conducted a comparative analysis of different data partitioning algorithms on both real and simulated data. Our main objective was to address the question of how the choice of data splitting algorithm can improve the estimation of the generalization performance. Data splitting algorithms used in this study were variants of k-fold, Kennard-Stone, SPXY ( sample set partitioning based on joint x-y distance), and random sampling algorithm. Each algorithm divided the data into two subset, training/validation. The training set was used to fit the model and validation for the evaluation. We then analyzed the different data splitting algorithms based on the generalization performances estimated from the validation and the external test set. From the result, we noted that the important determinant for a good generalization is the size of the dataset. For all the data sample methods applied on small data set, the gap between the performance estimated on the validation and test set was significant. However, we noted that the gap reduced when there was more data in training or validation. Too many or few data in the training set can also lead to bad model performance. So it is importance to have a reasonable balance between the training/validation set sizes. In our study, KS and SPXY was the splitting algorithm with poor model performance estimation. Indeed these methods select the most representative samples to train the model, and poor representative samples are left for model performance estimation. / Datapartitionering används vanligtvis i maskininlärning för att dela data i en tränings, test eller valideringsuppsättning. Detta tillvägagångssätt gör det möjligt för oss att hitta hyperparametrar för modellen och även uppskatta generaliseringsprestanda. I denna forskning genomförde vi en jämförande analys av olika datapartitionsalgoritmer på både verkliga och simulerade data. Vårt huvudmål var att undersöka frågan om hur valet avdatapartitioneringsalgoritm kan förbättra uppskattningen av generaliseringsprestanda. Datapartitioneringsalgoritmer som användes i denna studie var varianter av k-faldig korsvalidering, Kennard-Stone (KS), SPXY (partitionering baserat på gemensamt x-y-avstånd) och bootstrap-algoritm. Varje algoritm användes för att dela upp data i två olika datamängder: tränings- och valideringsdata. Vi analyserade sedan de olika datapartitioneringsalgoritmerna baserat på generaliseringsprestanda uppskattade från valideringen och den externa testuppsättningen. Från resultatet noterade vi att det avgörande för en bra generalisering är storleken på data. För alla datapartitioneringsalgoritmer som använts på små datamängder var klyftan mellan prestanda uppskattad på valideringen och testuppsättningen betydande. Vi noterade emellertid att gapet minskade när det fanns mer data för träning eller validering. För mycket eller för litet data i träningsuppsättningen kan också leda till dålig prestanda. Detta belyser vikten av att ha en korrekt balans mellan storlekarna på tränings- och valideringsmängderna. I vår studie var KS och SPXY de algoritmer med sämst prestanda. Dessa metoder väljer de mest representativa instanserna för att träna modellen, och icke-representativa instanser lämnas för uppskattning av modellprestanda.

The Late Cretaceous Trail Member of the Ericson Sandstone represents a regionally extensive fluvial system that transported sediments from the Sevier fold and thrust belt and Uinta Mountain uplift to the Western Interior Seaway. The Trail Member is a petroleum reservoir target that has unpredictable production rates due to the unknown behavior and connectivity of channel sandstones. The abundant outcrop, wellbore, and core data available allows for a comprehensive analysis of how the fluvial architecture, connectivity, and reservoir quality change along 65 km of depositional dip. Observations made at Flaming Gorge and Clay Basin (most landward field locations) suggest a highly mobile fluvial system that was influenced by both autogenic channel clustering and allogenic forcing. Evidence is seen for movement along the Sevier fold and thrust belt and early Laramide uplift of the Uinta Mountains. Specifically, three zones identify temporal tectonic changes throughout deposition of the Trail Member. The Upper and Lower Trail zones represent times of low accommodation as the fluvial system must avulse and move laterally to find available space. The Middle Trail zone represents a higher accommodation setting with internal autogenic channel clustering. This shows that on a finer timescale, autogenic processes control sediment distribution, while on a longer timescale, external drivers, specifically tectonics, control the distribution of sediment in the Trail fluvial system. Significant changes were observed within the Trail Member towards the basin. At Northern Colorado, lenticular, fluvial-dominated sands are still common, preserved organic and woody material, mud cracks, and increased bioturbation are observed that are not present elsewhere. The sandstone channels are slightly wider, have more common occurrences of low flow-regime sedimentary structures such as ripples and mud cracks, and appear to be more individually isolated with thin fine-grained material surrounding the channels. On a larger scale, photogrammetric analysis shows a rapid lateral change (0.3 km) from a sand-rich, channel-dominated expression to a mud-rich, channel-poor character. These observations suggest a lower energy fluvial system focused within a possible incised valley showing that the fluvial system is being influenced primarily by eustatic forces, rather than tectonics. Subsurface data from twelve wells located north of the Northern Colorado locality show a rapid (15 km) increase in thickness (97 m to 182 m) and decrease in net-to-gross (89.3% to 65.3%). Early subsidence of the Washakie sub-basin just east of the wells could account for the rapid increase in accommodation. Another possible explanation for the rapid thickness increase to the northeast could be the presence of an incised valley. These possibilities show the complexity of the environment within which the Trail Member fluvial system deposited sediments.

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Within the area of context-sensitive code completion there is a need for accurate predictive models in order to provide useful code completion predictions. The traditional method for optimizing the performance of code completion systems is to empirically evaluate the effect of each system parameter individually and fine-tune the parameters. This thesis presents a genetic algorithm that can optimize the system parameters with a degree-of-freedom equal to the number of parameters to optimize. The study evaluates the effect of the optimized parameters on the prediction quality of the studied code completion system. Previous evaluation of the reference code completion system is also extended to include model size and inference speed. The results of the study shows that the genetic algorithm is able to improve the prediction quality of the studied code completion system. Compared with the reference system, the enhanced system is able to recognize 1 in 10 additional previously unseen code patterns. This increase in prediction quality does not significantly impact the system performance, as the inference speed remains less than 1 ms for both systems. / Inom området kontextkänslig kodkomplettering finns det ett behov av precisa förutsägande modeller för att kunna föreslå användbara kodkompletteringar. Den traditionella metoden för att optimera prestanda hos kodkompletteringssystem är att empiriskt utvärdera effekten av varje systemparameter individuellt och finjustera parametrarna. Det här arbetet presenterar en genetisk algoritm som kan optimera systemparametrarna med en frihetsgrad som är lika stor som antalet parametrar att optimera. Studien utvärderar effekten av de optimerade parametrarna på det studerade kodkompletteringssystemets pre- diktiva kvalitet. Tidigare utvärdering av referenssystemet utökades genom att även inkludera modellstorlek och slutledningstid. Resultaten av studien visar att den genetiska algoritmen kan förbättra den prediktiva kvali- teten för det studerade kodkompletteringssystemet. Jämfört med referenssystemet så lyckas det förbättrade systemet korrekt känna igen 1 av 10 ytterligare kodmönster som tidigare varit osedda. Förbättringen av prediktiv kvalietet har inte en signifikant inverkan på systemet, då slutledningstiden förblir mindre än 1 ms för båda systemen.

The isothermal crystallization and melting temperatures of poly(ε-caprolactone) were correlated using fast differential scanning calorimetry. The melting kinetics was found to be independent of isothermal crystallization temperature and time. The conventional Hoffman-Weeks method could not be used to determine the equilibrium melting temperature because the observed melting temperatures were greater than the crystallization temperatures by a constant, so the Gibbs-Thomson method was used instead, yielding an equilibrium melting temperature of 103.4 ± 2.3°C. A modification was proposed to the non-linear Hoffman-Weeks equation that included a non-linear undercooling dependence for the kinetic fold surface free energy upon crystallization and permitted accurate modeling of the observed melting behavior. The isothermal crystallization rates of four narrow molecular weight poly(ethylene oxide) fractions were characterized using fast differential scanning calorimetry for crystallization temperatures spanning 100°C range with the lower limit approaching the glass transition. A transition from homogeneous to heterogeneous primary nucleation was observed at −5°C. The kinetic analysis suggested that the crystal growth geometry depends strongly on temperature, where rod-like structures begin to appear near the glass transition temperature, highly branched solid sheaves grow throughout the homogeneous primary nucleation temperature range, and spherulites grow in the heterogenous primary nucleation range. Poly(δ-valerolactone) was synthesized using microwave-assisted techniques. Narrow molecular weight fractions were obtained using successive precipitation fractionation. Preliminary isothermal crystallization studies suggest that conventional thermal analysis methods are not adequate to measure the melting temperatures accurately due to reorganization during heating. / Doctor of Philosophy / Plastics may be classified into two general categories: those which form ordered domains upon solidification, i.e. undergo crystallization, and those which remain disordered upon solidification, i.e. form glasses. This work is focused on studying the crystallization and melting processes in two linear polymers, poly(ε-caprolactone) and poly(ethylene oxide), using new experimental technology. In the case of poly(ε-caprolactone), the experimental data could not be rationalized by existing theories, and we have proposed modifications to these theories that explained the results. In the case of poly(ethylene oxide), the application of new experimental technology resulted in previously unreported data that indicated novel behavior at very low crystallization temperatures. In the last portion of this work, poly(δ-valerolactone) was made using a novel approach. Conventional experimental approaches to measuring the crystallization and melting behavior were shown to be inadequate.

Cette thèse s'inscrit dans le cadre de l'estimation d'une densité, considéré du point de vue non-paramétrique et non-asymptotique. Elle traite du problème de la sélection d'une méthode d'estimation à noyau. Celui-ci est une généralisation, entre autre, du problème de la sélection de modèle et de la sélection d'une fenêtre. Nous étudions des procédures classiques, par pénalisation et par rééchantillonnage (en particulier la validation croisée V-fold), qui évaluent la qualité d'une méthode en estimant son risque. Nous proposons, grâce à des inégalités de concentration, une méthode pour calibrer la pénalité de façon optimale pour sélectionner un estimateur linéaire et prouvons des inégalités d'oracle et des propriétés d'adaptation pour ces procédures. De plus, une nouvelle procédure rééchantillonnée, reposant sur la comparaison entre estimateurs par des tests robustes, est proposée comme alternative aux procédures basées sur le principe d'estimation sans biais du risque. Un second objectif est la comparaison de toutes ces procédures du point de vue théorique et l'analyse du rôle du paramètre V pour les pénalités V-fold. Nous validons les résultats théoriques par des études de simulations. / This thesis takes place in the density estimation setting from a nonparametric and nonasymptotic point of view. It concerns the statistical algorithm selection problem which generalizes, among others, the problem of model and bandwidth selection. We study classical procedures, such as penalization or resampling procedures (in particular V-fold cross-validation), which evaluate an algorithm by estimating its risk. We provide, thanks to concentration inequalities, an optimal penalty for selecting a linear estimator and we prove oracle inequalities and adaptative properties for resampling procedures. Moreover, new resampling procedure, based on estimator comparison by the mean of robust tests, is introduced as an alternative to procedures relying on the unbiased risk estimation principle. A second goal of this work is to compare these procedures from a theoretical point of view and to understand the role of V for V-fold penalization. We validate these theoretical results on empirical studies.

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