Swimming performance strongly influences fitness in aquatic organisms and is closely tied to external body morphology. Although this connection has been closely examined at the individual and species level, few studies have focused on this relationship as it pertains to functional group assemblages. Using functional groups based on similarities in habitat use and morphology, I tested the hypothesis that swimming performance can be reliably predicted by functional group composition. I measured swimming performance as burst speed using a simulated predator attack and as prolonged speed using a step-endurance test in a laboratory flume. I measured morphology using geometric morphometric techniques. A difference in swimming behavior in four of the seven species was observed in the step-endurance test. Benthic species exhibited bracing behavior as an alternative to body-caudal fin (BCF) propulsion in the prolonged speed trials. Swimming performance exhibited a weak relationship with functional groups based on habitat or morphology. Rather a species-based model was the best predictor of swimming performance. Although species exhibited variation in swimming performance, body size was the strongest predictor of absolute swimming performance across all models. Relative swimming performance (measured in body lengths/sec) was negatively related to body size. The results of this study suggest that functional groups are not always reliable predictors of performance and they necessitate empirical testing to validate their effectiveness. This study also provides critical swimming performance data for previously unstudied Great Basin fishes which could be valuable for predicting fish passage through culverts, weirs and fish ladders.

Access to education is a central part of federal higher education policy, and federal grant and loan programs are in place to make college degrees more attainable for students. However, there is still controversy about whether there are unintended consequences of implementing and maintaining these programs, and whether they are effectively achieving the goal of increased accessibility. In order to answer questions about whether three specific types of federal aid cause higher tuition rates and whether these programs increase graduation rates, four ordinary least squares regression models were estimated. They include changes in both in-state and out-of-state tuition sticker prices, graduation rates, as well as changes in three types of federal aid, and other variables indicative of the value of a degree for four-year public universities in Arizona, California, Georgia, and Florida for years 2001-2011. The regressions indicate a positive effect of Pell Grants on in-state and out-of-state tuition and fees, a positive effect of disbursed subsidized federal loans on the change in number of degrees awarded, and a positive effect of Pell Grants on graduation rates.

In this thesis, we develop some inferential results for one-shot device testing data analysis. These extend and generalize existing methods in the literature. First, a competing-risk model is introduced for one-shot testing data under accelerated life-tests. One-shot devices are products which will be destroyed immediately after use. Therefore, we can observe only a binary status as data, success or failure, of such products instead of its lifetime. Many one-shot devices contain multiple components and failure of any one of them will lead to the failure of the device. Failed devices are inspected to identify the specific cause of failure. Since the exact lifetime is not observed, EM algorithm becomes a natural tool to obtain the maximum likelihood estimates of the model parameters. Here, we develop the EM algorithm for competing exponential and Weibull cases. Second, a semi-parametric approach is developed for simple one-shot device testing data. Semi-parametric estimation is a model that consists of parametric and non-parametric components. For this purpose, we only assume the hazards at different stress levels are proportional to each other, but no distributional assumption is made on the lifetimes. This provides a greater flexibility in model fitting and enables us to examine the relationship between the reliability of devices and the stress factors. Third, Bayesian inference is developed for one-shot device testing data under exponential distribution and Weibull distribution with non-constant shape parameters for competing risks. Bayesian framework provides statistical inference from another perspective. It assumes the model parameters to be random and then improves the inference by incorporating expert's experience as prior information. This method is shown to be very useful if we have limited failure observation wherein the maximum likelihood estimator may not exist. The thesis proceeds as follows. In Chapter 2, we assume the one-shot devices to have two components with lifetimes having exponential distributions with multiple stress factors. We then develop an EM algorithm for developing likelihood inference for the model parameters as well as some useful reliability characteristics. In Chapter 3, we generalize to the situation when lifetimes follow a Weibull distribution with non-constant shape parameters. In Chapter 4, we propose a semi-parametric model for simple one-shot device test data based on proportional hazards model and develop associated inferential results. In Chapter 5, we consider the competing risk model with exponential lifetimes and develop inference by adopting the Bayesian approach. In Chapter 6, we generalize these results on Bayesian inference to the situation when the lifetimes have a Weibull distribution. Finally, we provide some concluding remarks and indicate some future research directions in Chapter 7. / Thesis / Doctor of Philosophy (PhD)

Accurately modeling aerodynamic forces and moments are crucial for understanding thebehavior of an aircraft when performing various maneuvers at different flight conditions.However, this task is challenging due to complex nonlinear dependencies on manydifferent parameters. Currently, Computational Fluid Dynamics (CFD), wind tunnel,and flight tests are the most common methods used to gather information about thecoefficients, which are both costly and time–consuming. Consequently, great efforts aremade to find alternative methods such as machine learning. This thesis focus on finding machine learning models that can model the static and thedynamic aerodynamics coefficients for lift, drag, and pitching moment. Seven machinelearning models for static estimation were trained on data from CFD simulations.The main focus was on dynamic aerodynamics since these are more difficult toestimate. Here two machine learning models were implemented, Long Short–TermMemory (LSTM) and Gaussian Process Regression (GPR), as well as the ordinaryleast squares. These models were trained on data generated from simulated flighttrajectories of longitudinal movements. The results of the study showed that it was possible to model the static coefficients withlimited data and still get high accuracy. There was no machine learning model thatperformed best for all three coefficients or with respect to the size of the training data.The Support vector regression was the best for the drag coefficients, while there wasno clear best model for the lift and moment. For the dynamic coefficients, the ordinaryleast squares performed better than expected and even better than LSTM and GPR forsome flight trajectories. The Gaussian process regression produced better results whenestimating a known trajectory, while the LSTM was better when predicting values ofa flight trajectory not used to train the models. / Att noggrant modellera aerodynamiska krafter och moment är avgörande för att förståett flygplans beteende när man utför olika manövrar vid olika flygförhållanden. Dennauppgift är dock utmanande på grund av ett komplext olinjärt beroende av många olikaparametrar. I nuläget är beräkningsströmningsdynamik (CFD), vindtunneltestningoch flygtestning de vanligaste metoderna för att kunna modellera de aerodynamiskakoefficienterna, men de är både kostsamma och tidskrävande. Följaktligen görs storaansträngningar för att hitta alternativa metoder, till exempel maskininlärning. Detta examensarbete fokuserar på att hitta maskininlärningmodeller som kanmodellera de statiska och de dynamiska aerodynamiska koefficienterna för lyftkraft,luftmotstånd och stigningsmoment. Sju olika maskininlärningsmodeller för destatiska koefficienterna tränades på data från CFD–simuleringar. Huvudfokus lågpå den dynamiska koefficienterna, eftersom dessa är svårare att modellera. Härimplementerades två maskininlärningsmodeller, Long Short–Term Memory (LSTM)och Gaussian Process Regression (GPR), samt minstakvadratmetoden. Dessa modellertränades på data skapad från flygbanesimuleringar av longitudinella rörelser. Resultaten av studien visade att det är möjligt att modellera de statiskakoefficienterna med begränsad data och ändå få en hög noggrannhet. Ingen avde testade maskininslärningsmodelerna var tydligt bäst för alla koefficienterna ellermed hänsyn till mängden träningsdata. Support vector regression var bäst förluftmotstånds koefficienterna, men vilken modell som var bäst för lyftkraften ochstigningsmomentet var inte lika tydligt. För de dynamiska koefficienterna presterademinstakvadratmetoden bättre än förväntat och för vissa signaler även bättre än LSTMoch GPR. GPR gav bättre resultat när man uppskattade koefficienterna för enflygbanan man tränat modellen på, medan LSTM var bättre på att förutspå värdenaför en flybana man inte hade tränat modellen på.

No description available.

Die generalisierte Quantilregression, einschließlich der Sonderfälle bedingter Quantile und Expektile, ist insbesondere dann eine nützliche Alternative zum bedingten Mittel bei der Charakterisierung einer bedingten Wahrscheinlichkeitsverteilung, wenn das Hauptinteresse in den Tails der Verteilung liegt. Wir bezeichnen mit v_n(x) den Kerndichteschätzer der Expektilkurve und zeigen die stark gleichmßige Konsistenzrate von v-n(x) unter allgemeinen Bedingungen. Unter Zuhilfenahme von Extremwerttheorie und starken Approximationen der empirischen Prozesse betrachten wir die asymptotischen maximalen Abweichungen sup06x61 |v_n(x) − v(x)|. Nach Vorbild der asymptotischen Theorie konstruieren wir simultane Konfidenzb änder um die geschätzte Expektilfunktion. Wir entwickeln einen funktionalen Datenanalyseansatz um eine Familie von generalisierten Quantilregressionen gemeinsam zu schätzen. Dabei gehen wir in unserem Ansatz davon aus, dass die generalisierten Quantile einige gemeinsame Merkmale teilen, welche durch eine geringe Anzahl von Hauptkomponenten zusammengefasst werden können. Die Hauptkomponenten sind als Splinefunktionen modelliert und werden durch Minimierung eines penalisierten asymmetrischen Verlustmaßes gesch¨atzt. Zur Berechnung wird ein iterativ gewichteter Kleinste-Quadrate-Algorithmus entwickelt. Während die separate Schätzung von individuell generalisierten Quantilregressionen normalerweise unter großer Variablit¨at durch fehlende Daten leidet, verbessert unser Ansatz der gemeinsamen Schätzung die Effizienz signifikant. Dies haben wir in einer Simulationsstudie demonstriert. Unsere vorgeschlagene Methode haben wir auf einen Datensatz von 150 Wetterstationen in China angewendet, um die generalisierten Quantilkurven der Volatilität der Temperatur von diesen Stationen zu erhalten / Generalized quantile regressions, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We denote $v_n(x)$ as the kernel smoothing estimator of the expectile curves. We prove the strong uniform consistency rate of $v_{n}(x)$ under general conditions. Moreover, using strong approximations of the empirical process and extreme value theory, we consider the asymptotic maximal deviation $\sup_{ 0 \leqslant x \leqslant 1 }|v_n(x)-v(x)|$. According to the asymptotic theory, we construct simultaneous confidence bands around the estimated expectile function. We develop a functional data analysis approach to jointly estimate a family of generalized quantile regressions. Our approach assumes that the generalized quantiles share some common features that can be summarized by a small number of principal components functions. The principal components are modeled as spline functions and are estimated by minimizing a penalized asymmetric loss measure. An iteratively reweighted least squares algorithm is developed for computation. While separate estimation of individual generalized quantile regressions usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 150 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations

This study adds to the existing research concerning ecological relationships between suicide rates, social integration, and urbanicity in the U.S. Age-sex-race adjusted five-year averaged suicide rates for 1993-1997 and various measures of urbanicity are used. Some proposed relationships held true, while others indicate that social integration and urbanicity are so intertwined in their effects on suicide that no clear, unidirectional pattern emerges. The religious affiliation measure captured unique variations in the role religion plays in this relationship; depending on how urbanicity was measured. Findings suggest closer attention needs to be paid to how both urbanicity and religious affiliation are measured. Overall, vast regional variation exists in suicide rates and the role of urbanization can be misunderstood if not properly specified.

<p>Real-time hybrid simulation (RTHS) has demonstrated certain advantages over conventional large-scale testing. In an RTHS, the system that is under study is partitioned into a numerical and a physical substructure, where the numerical part is comprised of those elements that are easier to model mathematically, while the physical part consists of those that present a complex behavior difficult to capture in a numerical model. The most complex part of this study is the isolation system, a technology used to protect structures against earthquakes by modifying how they respond to ground motions. Unbonded Fiber Reinforced Elastomeric Isolators (UFREIs) are devices that can accomplish this task and have gained attention in recent years because of their modest but valuable features that make them suitable for implementation in low-rise buildings and in developing countries because of their low cost. Our end goal for this work is to enable the testing of scaled versions of these elastomeric isolators to understand their behavior under shear tests and realistic loading. </p> <p>A testing instrument was designed and constructed to apply a uniaxial compressive force up to 22kN and a shear force of 8kN simultaneously to the specimens. A testing program was conducted where four primary sources of signal distortion were identified as caused by the servo-hydraulic system. From these results, a mechanics-based model was developed to understand better the dynamics that the sliding table can introduce to the measured signals accounting for inertial and dissipative forces. Two Bouc-Wen models were implemented to simulate the behavior of the UFREIs. The first only accounts for the hysteretic behavior of the isolator, and the second accounts for the additional nonlinearities found in the isolator’s behavior. These models were assembled in a virtual RTHS which is available to users interested in learning the applications of RTHS of a base-isolated structure with a nonlinear component.</p> <p>An RTHS experiment was conducted in the IISL where the control system comprised a delay compensator and a proportional-integral controller, which exhibited a good tracking performance with minimal delay and low RMSE. However, it can increase the distortion of the oil-column resonance in the measured signals. The simulation captures the behavior of the isolated structure for small displacements. However, it underestimates the displacement of the full-scale specimen for large displacements. The RTHS showed a better approximation of the displacement of the full-scale structure than the theoretical behavior approximated by the Bouc-Wen models.</p>

No description available.

No description available.