The primary objectives of this work are two-fold. First, additive manufacturing (AM) and rapid prototype (RP) testing are evaluated for use in production of a wind tunnel (WT) models. Second, an approach was developed to incorporate stability and control (SandC) WT data into aircraft conceptual design multidisciplinary design optimization (MDO). Both objectives are evaluated in terms of data quality, time, and cost. FDM(TM) and PolyJet AM processes were used for model production at low cost and time. Several models from a representative tailless configuration, ICE 101, were printed and evaluated for strength, cost and time of production. Furthermore, a NACA 0012 model with 20% chord flap was manufactured. Both models were tested in the Virginia Tech (VT) Open-Jet WT for force and moment acquisition. A 1/15th scale ICE 101 model was prepared for manufacturing, but limits of FDM(TM) technology were identified for production. An approach using WT data was adapted from traditional surrogate-based optimization (SBO), which uses computational fluid dynamics (CFD) for data generation. Split-plot experimental designs were developed for analysis of the WT SBO strategy using historical data and for WT testing of the NACA 0012. Limitations of the VT Open-Jet WT resulted in a process that was not fully effective for a MDO environment. However, resolution of ICE 101 AM challenges and higher quality data from a closed-section WT should result in a fully effective approach to incorporate AM and RP testing in an aircraft conceptual design MDO. / Master of Science

Current methods to reduce the native microbiota and potential pathogens on spices include steam treatments and ethylene oxide (EtO) fumigation. The objectives of this research were to identify the effectiveness of a lab-scale steam apparatus and a commercial EtO process on the inactivation of Salmonella enterica or Enterococcus faecium NRRL B-2354 inoculated whole black peppercorns and cumin seeds. Peppercorns and cumin seeds were inoculated with Salmonella or Enterococcus and processed in a lab-scale steam apparatus at 16.9 PSIA and two references temperatures (165°F and 180°F) and in a commercial ethylene oxide fumigation chamber using a standard commercial EtO fumigation process. Cells were enumerated by serial dilution and plating onto TSA with a thin overlay of selective media. Inoculation preparation influenced inactivation of Salmonella on peppercorns with greater reductions reported for TSA-grown cells compared to within a biofilm. To achieve an assured 5-log reduction of TSA-inoculated Salmonella on peppercorns exposure for 125s and 100s at 165°F and 180°F, respectively is required. For cumin seeds temperatures of 165°F for 110s were needed or 65s at 180°F to assure 5 log reduction. EtO fumigation significantly reduced both microorganisms on both spices (p<0.05), however significant variation existed between bags in the same process run. Reductions of Enterococcus were comparable or less than that of Salmonella under the majority of conditions, however a direct linear relationship cannot be used to compare the microbes. This study demonstrates that the effectiveness of Enterococcus faecium NRRL B-2354 as a surrogate for Salmonella can vary between spices and processes. / Master of Science in Life Sciences

Understanding potential cross contamination pathways is essential to reducing the risk of food product contamination. The use of a fluorescing abiotic surrogate (GloGermTM) to visualize the potential spread of bacteria may be beneficial to researchers. To quantify cross contamination during experimental trials in a mock retail deli, a rating method for visualization of fluorescence levels using a trained sensory panel was developed. Panelists feedback led to a pre-defined strategy allowing for characterization of contamination seen in photographs and reduced variability within responses. Following validation, GloGermTM was used to visually represent how bacteria may spread through a deli environment. Six origination sites (slicer blade, meat chub, floor drain, preparation table, employee's glove, employee's hands) were evaluated separately and spread was photographed throughout the mock deli. The trained sensory panel then analyzed the photographs. Five of the six contamination origination sites transferred GloGermTM to surfaces throughout the mock deli. Contamination from the floor drain did not spread to any food contact surfaces. To determine the potential of using a GloGermTM/ bacteria mixture to simultaneously track and sample contamination spread; surfaces were co-inoculated with GloGermTM and bacteria to determine if co-inoculation would affect the recoverability of microorganisms from these surfaces. Three common foodborne bacteria (E. coli O157:H7,Salmonella enterica ser. Enteritidis, Listeria monocytogenes, Listeria innocua) were inoculated on 2 by 2 stainless steel coupons alone and with GloGermTM . There was no significant difference found (p > 0.05) between the recovery of bacteria alone and the mixture for all bacteria. Finally, the use of co-inoculation was further explored by inoculating two contamination origination sites with either bacteria alone (L. monocytogenes and L. innocua) or a GloGermTM/bacteria cocktail. Nine recipient sites were sampled after a series of deli procedures were performed. Generally, no significant differences (p>0.05) were seen between the transfer of bacteria inoculated alone and the transfer of bacteria inoculated with GloGermTM to the selected recipient sites, regardless of contamination source or bacteria. These results suggest there may be potential in using L. innocua in combination with GloGermTM to visually track and sample contamination from a known source throughout a retail deli environment. / Ph. D.

We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method. / Master of Science / Parameter uncertainty quantification seeks to determine both estimates and uncertainty regarding estimates of model parameters. Example of model parameters can include physical properties such as density, growth rates, or even deblurred images. Previous work has shown that replacing data with a surrogate model can provide promising estimates with low uncertainty. We extend the previous methods in the specific field of linear models. Theoretical results are tested on simulated computed tomography problems.

Simulationen elektronischer Strukturen ermöglichen die Bestimmung grundlegender Eigenschaften von Materialien ohne jegliche Experimente. Sie zählen deshalb zu den Standardwerkzeugen, mit denen Fortschritte in materialwissenschaftlichen und chemischen Anwendungen vorangetrieben werden. In den letzten Jahrzehnten hat sich die Dichtefunktionaltheorie (DFT) aufgrund ihrer ausgezeichneten Balance zwischen Genauigkeit und Rechenkosten als die beliebteste Simulationstechnik für elektronische Strukturen etabliert. Jedoch verlangen drängende gesellschaftliche und technologische Herausforderungen nach Lösungen für immer komplexere wissenschaftliche Fragestellungen, sodass selbst die effizientesten DFT-Programme nicht mehr in der Lage sind, Antworten in angemessener Zeit und mit den verfügbaren Rechenressourcen zu liefern. Daher wächst das Interesse an Ansätzen des maschinellen Lernens (ML), die darauf abzielen, Modelle bereitzustellen, die die Vorhersagekraft von DFT-Rechnungen zu vernachlässigbaren Kosten replizieren. In dieser Arbeit wird gezeigt, dass solche ML-DFT Ansätze bisher nicht in der Lage sind, das Vorhersagen der elektronischen Struktur von Materialien auf DFT-Niveau vollständig abzubilden. Davon ausgehend wird in dieser Arbeit ein neuer Ansatz für ML-DFT Modelle vorgestellt. Es wird ein umfassendes Framework für das Training von ML-DFT-Modellen auf Grundlage einer lokalen Darstellung der elektronischen Struktur entwickelt, welcher auch Details wie Strategien zur Datengeneration und Hyperparameteroptimierung beinhaltet. Es werden Ergebnisse vorgestellt, die zeigen, dass mit diesem Framework trainierte Modelle die breite Palette der Vorhersagefähigkeit sowie Genauigkeit von DFT-Simulationen zu drastisch reduzierten Kosten replizieren. Weiterhin wird die allgemeine Nützlichkeit dieses Ansatzes demonstriert, indem Modelle über Längenskalen, Phasengrenzen und Temperaturbereiche hinweg angewendet werden.:List of Tables 10 List of Figures 12 Mathematical notation and abbreviations 14 1 Introduction 19 2 Background 23 2.1 Density Functional Theory 23 2.2 Sampling of Observables 35 2.3 Machine Learning and Neural Networks 37 2.4 Hyperparameter Optimization 46 2.5 Density Functional Theory Machine Learning Models 50 3 Scalable Density Functional Theory Machine Learning Models 59 3.1 General Framework 59 3.2 Descriptors 67 3.3 Data Generation 69 3.4 Verification of accuracy 78 3.5 Determination of Hyperparameters 87 4 Transferability and Scalability of Models 99 4.1 Large Length Scales 100 4.2 Phase Boundaries 108 4.3 Temperature Ranges 117 5 Summary and Outlook 131 Appendices 136 A Computational Details of the Materials Learning Algorithms framework 137 B Data Sets, Models, and Hyperparameter Tuning 145 Bibliography 161 / Electronic structure simulations allow researchers to compute fundamental properties of materials without the need for experimentation. As such, they routinely aid in propelling scientific advancements across materials science and chemical applications. Over the past decades, density functional theory (DFT) has emerged as the most popular technique for electronic structure simulations, due to its excellent balance between accuracy and computational cost. Yet, pressing societal and technological questions demand solutions for problems of ever-increasing complexity. Even the most efficient DFT implementations are no longer capable of providing answers in an adequate amount of time and with available computational resources. Thus, there is a growing interest in machine learning (ML) based approaches within the electronic structure community, aimed at providing models that replicate the predictive power of DFT at negligible cost. Within this work it will be shown that such ML-DFT approaches, up until now, do not succeed in fully encapsulating the level of electronic structure predictions DFT provides. Based on this assessment, a novel approach to ML-DFT models is presented within this thesis. An exhaustive framework for training ML-DFT models based on a local representation of the electronic structure is developed, including minute treatment of technical issues such as data generation techniques and hyperparameter optimization strategies. Models found via this framework recover the wide array of predictive capabilities of DFT simulations at drastically reduced cost, while retaining DFT levels of accuracy. It is further demonstrated how such models can be used across differently sized atomic systems, phase boundaries and temperature ranges, underlining the general usefulness of this approach.:List of Tables 10 List of Figures 12 Mathematical notation and abbreviations 14 1 Introduction 19 2 Background 23 2.1 Density Functional Theory 23 2.2 Sampling of Observables 35 2.3 Machine Learning and Neural Networks 37 2.4 Hyperparameter Optimization 46 2.5 Density Functional Theory Machine Learning Models 50 3 Scalable Density Functional Theory Machine Learning Models 59 3.1 General Framework 59 3.2 Descriptors 67 3.3 Data Generation 69 3.4 Verification of accuracy 78 3.5 Determination of Hyperparameters 87 4 Transferability and Scalability of Models 99 4.1 Large Length Scales 100 4.2 Phase Boundaries 108 4.3 Temperature Ranges 117 5 Summary and Outlook 131 Appendices 136 A Computational Details of the Materials Learning Algorithms framework 137 B Data Sets, Models, and Hyperparameter Tuning 145 Bibliography 161

Yes / Purpose.: To establish whether a clinically exploitable relationship exists between surrogate measures of retinal ganglion cell number and functional sampling density and visual contrast sensitivity in healthy young eyes. Methods.: Psychometric functions for contrast detection were measured at 9° eccentricity in superior and inferior visual field from 20 healthy adults (age 23–43, median 26 years). Functions were compared with corresponding localized regions of retinal nerve fiber layer (RNFL) thickness measured by optical coherence tomography, a surrogate of retinal ganglion cell number, and to grating resolution acuity, a psychophysical surrogate of retinal ganglion cell sampling density. Correlations between psychometric function parameters and retinal ganglion cell surrogates were measured by Spearman's rank correlation. Results.: All measures exhibited a 2- to 4-fold variation in our sample. Despite this, correlations between measures were weak. Correlations between psychometric function parameters (threshold, spread) and RNFL thickness ranged in magnitude from 0.05 to 0.19 (P = 0.43–0.85). Grating resolution was sampling limited for 16 of 20 participants in superior visual field, and for 12 of 20 participants in inferior visual field. Correlations between psychometric function parameters and grating resolution acuities ranged in magnitude from 0.05 to 0.36 (P = 0.12–0.85) when all data were considered, and from 0.06 to 0.36 (P = 0.26–0.87) when only sampling-limited data were considered. Conclusions.: Despite considerable variation in both psychometric functions for contrast detection and surrogate measures of retinal ganglion cell number and sampling density among healthy eyes, relationships between these measures are weak. These relationships are unlikely to be exploitable for improving clinical tests in healthy populations.

Les Algorithmes Évolutionnaires (AEs) ont été très étudiés en raison de leur capacité à résoudre des problèmes d'optimisation complexes en utilisant des opérateurs de variation adaptés à des problèmes spécifiques. Une recherche dirigée par une population de solutions offre une bonne robustesse par rapport à un bruit modéré et la multi-modalité de la fonction optimisée, contrairement à d'autres méthodes d'optimisation classiques telles que les méthodes de quasi-Newton. La principale limitation de AEs, le grand nombre d'évaluations de la fonction objectif,pénalise toutefois l'usage des AEs pour l'optimisation de fonctions chères en temps calcul.La présente thèse se concentre sur un algorithme évolutionnaire, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), connu comme un algorithme puissant pour l'optimisation continue boîte noire. Nous présentons l'état de l'art des algorithmes, dérivés de CMA-ES, pour résoudre les problèmes d'optimisation mono- et multi-objectifs dans le scénario boîte noire.Une première contribution, visant l'optimisation de fonctions coûteuses, concerne l'approximation scalaire de la fonction objectif. Le meta-modèle appris respecte l'ordre des solutions (induit par la valeur de la fonction objectif pour ces solutions); il est ainsi invariant par transformation monotone de la fonction objectif. L'algorithme ainsi défini, saACM-ES, intègre étroitement l'optimisation réalisée par CMA-ES et l'apprentissage statistique de meta-modèles adaptatifs; en particulier les meta-modèles reposent sur la matrice de covariance adaptée par CMA-ES. saACM-ES préserve ainsi les deux propriété clé d'invariance de CMA-ES: invariance i) par rapport aux transformations monotones de la fonction objectif; et ii) par rapport aux transformations orthogonales de l'espace de recherche.L'approche est étendue au cadre de l'optimisation multi-objectifs, en proposant deux types de meta-modèles (scalaires). La première repose sur la caractérisation du front de Pareto courant (utilisant une variante mixte de One Class Support Vector Machone (SVM) pour les points dominés et de Regression SVM pour les points non-dominés). La seconde repose sur l'apprentissage d'ordre des solutions (rang de Pareto) des solutions. Ces deux approches sont intégrées à CMA-ES pour l'optimisation multi-objectif (MO-CMA-ES) et nous discutons quelques aspects de l'exploitation de meta-modèles dans le contexte de l'optimisation multi-objectif.Une seconde contribution concerne la conception d'algorithmes nouveaux pour l'optimi\-sation mono-objectif, multi-objectifs et multi-modale, développés pour comprendre, explorer et élargir les frontières du domaine des algorithmes évolutionnaires et CMA-ES en particulier. Spécifiquement, l'adaptation du système de coordonnées proposée par CMA-ES est coupléeà une méthode adaptative de descente coordonnée par coordonnée. Une stratégie adaptative de redémarrage de CMA-ES est proposée pour l'optimisation multi-modale. Enfin, des stratégies de sélection adaptées aux cas de l'optimisation multi-objectifs et remédiant aux difficultés rencontrées par MO-CMA-ES sont proposées. / Evolutionary Algorithms (EAs) have received a lot of attention regarding their potential to solve complex optimization problems using problem-specific variation operators. A search directed by a population of candidate solutions is quite robust with respect to a moderate noise and multi-modality of the optimized function, in contrast to some classical optimization methods such as quasi-Newton methods. The main limitation of EAs, the large number of function evaluations required, prevents from using EAs on computationally expensive problems, where one evaluation takes much longer than 1 second.The present thesis focuses on an evolutionary algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which has become a standard powerful tool for continuous black-box optimization. We present several state-of-the-art algorithms, derived from CMA-ES, for solving single- and multi-objective black-box optimization problems.First, in order to deal with expensive optimization, we propose to use comparison-based surrogate (approximation) models of the optimized function, which do not exploit function values of candidate solutions, but only their quality-based ranking.The resulting self-adaptive surrogate-assisted CMA-ES represents a tight coupling of statistical machine learning and CMA-ES, where a surrogate model is build, taking advantage of the function topology given by the covariance matrix adapted by CMA-ES. This allows to preserve two key invariance properties of CMA-ES: invariance with respect to i). monotonous transformation of the function, and ii). orthogonal transformation of the search space. For multi-objective optimization we propose two mono-surrogate approaches: i). a mixed variant of One Class Support Vector Machine (SVM) for dominated points and Regression SVM for non-dominated points; ii). Ranking SVM for preference learning of candidate solutions in the multi-objective space. We further integrate these two approaches into multi-objective CMA-ES (MO-CMA-ES) and discuss aspects of surrogate-model exploitation.Second, we introduce and discuss various algorithms, developed to understand, explore and expand frontiers of the Evolutionary Computation domain, and CMA-ES in particular. We introduce linear time Adaptive Coordinate Descent method for non-linear optimization, which inherits a CMA-like procedure of adaptation of an appropriate coordinate system without losing the initial simplicity of Coordinate Descent.For multi-modal optimization we propose to adaptively select the most suitable regime of restarts of CMA-ES and introduce corresponding alternative restart strategies.For multi-objective optimization we analyze case studies, where original parent selection procedures of MO-CMA-ES are inefficient, and introduce reward-based parent selection strategies, focused on a comparative success of generated solutions.

This master thesis deals with the issue of legislation of surrogacy. Development in the area of reproductive medicine as the first "test tube baby" in 1978 and following medical progress has inevitably brought development expansion of this institute. This institute, although that it brings with itself many ethical, social and legal questions, is not legally regulated in the Czech Republic (except one provision, Art. 804 of the Civil Code). Therefore, this master thesis tries to provide a proposal of legislation which might be adopted in the Czech Republic, using primarily analytical and comparative methods. After the surrogacy is explained and forms and models of it are described, the international framework is clarified. Although many states realize actuality of this topic, there has not been any international legislation adopted that would regulate e.g. surrogate tourism, until this day. However, there is a certain guideline, namely the interest of a child, as the European Court of Human Rights judged. State's legislations range on the scale from criminalization (France) to legalization of its commercial form (Ukraine). After evaluation of legislation of some states whose regulations show real functioning of models of surrogacy, it is clear that the United Kingdom of Great Britain and Northern...

In der nichtlinearen Datenreihenanalyse hat sich seit etwa 10 Jahren eine Monte-Carlo-Testmethode etabliert, die Theiler-surrogatmethode, mit Hilfe derer entschieden werden kann, ob eine Datenreihe nichtlinearen Ursprungs sei. Diese Methode wird kritisiert, modifiziert und verallgemeinert. Das, was Theiler untersuchen will braucht andere Surrogatmethoden, die hier konstruiert werden. Und das, was Theiler untersucht braucht gar keine Monte-Carlo-Methoden. Mit Hilfe des in der Arbeit eingeführten Begriffs des Phasensignals werden Testmöglichkeiten dargelegt und Beziehungen zwischen den nichtlinearen Eigenschaften der Zeitreihe und deren Phasenspektrum erforscht. Das Phasensignal wird aus dem Phasenspektrum der Zeitreihe hergeleitet und registriert außerordentliche Geschehnisse im Zeitbereich sowie Phasenkopplungen im Frequenzbereich. <br /> <br /> Die gewonnenen Erkenntnisse werden auf das Problem der Polbewegung angewendet. Die Hypothese einer nichtlinearen Beziehung zwischen der atmosphärischen Erregung und der Polbewegung wird untersucht. Eine nichtlineare Behandlung wird nicht für nötig gehalten. / In the nonlinear data analysis there is a popular Monte Carlo Test method due to Theiler (it was established about 10 years ago), the Theiler surrogate method, which tests whether a time series is of a linear origin. This method is being criticized, modified and generalized in this thesis. What Theiler wants to test, needs other surrogate methods, which are constructed here. And what Theiler really tests, does not need Monte Carlo methods. With the help of the concept of the phase signal, that is introduced here, other test options are possible. The phase signal helps also in investigating the relations between the nonlinear characteristics of the time series and their phase spectrum. The phase signal is derived from the phase spectrum of the time series and registers extraordinary events in the time domain as well as phase couplings in the frequency domain. <br /> <br /> These theoretical approches are applied to the problem of polar motion. The hypothesis of a nonlinear relationship between the atmospheric excitation and the pole movement is examined. A nonlinear treatment is not considered necessary.

This thesis deals with the problem of unwanted children in Czech Republic and looks for answers to solve it. Unwanted children are those children whose parents, for whatever reason, do not want to take care of them. Institutional solution is the solution decided by court. In the text there are presented options of these children and options of their parents. The paper therefore deals with a description of surrogate children care and its forms - surrogate family care and institutional care. Besides it brings also a description of forms system of legal anonymous abandonment of a newborn child. Characterizes children, reasons why parents abandon them and the parents themselves. Mentions causes of placing children into institutions and briefly describes also these institutions, including number of children placed in them. For better orientation it also outlines a gradual historical development of care of unwanted children.