Advanced data analysis for traction force microscopy and data-driven discovery of physical equations

The plummeting cost of collecting and storing data and the increasingly available computational power in the last decade have led to the emergence of new data analysis approaches in various scientific fields. Frequently, the new statistical methodology is employed for analyzing data involving incomplete or unknown information. In this thesis, new statistical approaches are developed for improving the accuracy of traction force microscopy (TFM) and data-driven discovery of physical equations. TFM is a versatile method for the reconstruction of a spatial image of the traction forces exerted by cells on elastic gel substrates. The traction force field is calculated from a linear mechanical model connecting the measured substrate displacements with the sought-for cell-generated stresses in real or Fourier space, which is an inverse and ill-posed problem. This inverse problem is commonly solved making use of regularization methods. Here, we systematically test the performance of new regularization methods and Bayesian inference for quantifying the parameter uncertainty in TFM. We compare two classical schemes, L1- and L2-regularization with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. We find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. We further combine the Bayesian L2 regularization with the computational speed of Fast Fourier Transform algorithms to develop a fully automated method for noise reduction and robust, standardized traction-force reconstruction that we call Bayesian Fourier transform traction cytometry (BFTTC). This method is made freely available as a software package with graphical user-interface for intuitive usage. Using synthetic data and experimental data, we show that these Bayesian methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Next, we employ our methodology developed for the solution of inverse problems for automated, data-driven discovery of ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic differential equations (SDEs). To find the equations governing a measured time-dependent process, we construct dictionaries of non-linear candidate equations. These candidate equations are evaluated using the measured data. With this approach, one can construct a likelihood function for the candidate equations. Optimization yields a linear, inverse problem which is to be solved under a sparsity constraint. We combine Bayesian compressive sensing using Laplace priors with automated thresholding to develop a new approach, namely automatic threshold sparse Bayesian learning (ATSBL). ATSBL is a robust method to identify ODEs, PDEs, and SDEs involving Gaussian noise, which is also referred to as type I noise. We extensively test the method with synthetic datasets describing physical processes. For SDEs, we combine data-driven inference using ATSBL with a novel entropy-based heuristic for discarding data points with high uncertainty. Finally, we develop an automatic iterative sampling optimization technique akin to Umbrella sampling. Therewith, we demonstrate that data-driven inference of SDEs can be substantially improved through feedback during the inference process if the stochastic process under investigation can be manipulated either experimentally or in simulations

Mesh-Free and Finite Element-Based Methods for Structural Mechanics Applications

The problem of solving complex engineering problems has always been a major topic in all industrial fields, such as aerospace, civil and mechanical engineering. The use of numerical methods has increased exponentially in the last few years, due to modern computers in the field of structural mechanics. Moreover, a wide range of numerical methods have been presented in the literature for solving such problems. Structural mechanics problems are dealt with using partial differential systems of equations that might be solved by following the two main classes of methods: Domain-decomposition methods or the so-called finite element methods and mesh-free methods where no decomposition is carried out. Both methodologies discretize a partial differential system into a set of algebraic equations that can be easily solved by computer implementation. The aim of the present Special Issue is to present a collection of recent works on these themes and a comparison of the novel advancements of both worlds in structural mechanics applications

Vibration

Physiological and biomechanical responses of humans to vibrations during manned space flight and threshold data on tolerances to various vibrational modes and condition

NASA Thesaurus. Volume 2: Access vocabulary

The NASA Thesaurus -- Volume 2, Access Vocabulary -- contains an alphabetical listing of all Thesaurus terms (postable and nonpostable) and permutations of all multiword and pseudo-multiword terms. Also included are Other Words (non-Thesaurus terms) consisting of abbreviations, chemical symbols, etc. The permutations and Other Words provide 'access' to the appropriate postable entries in the Thesaurus

The Open Ocean Hydrofoil Ship: Will it Come of Age?

Problems encountered during development of the hydrofoil ship have restricted exploitation of its unique characteristics in an open ocean environment. An examination of pertinent physical and technical constraints is undertaken to assess their impact upon the future. The focus of this examination is centered upon the evolution of the fully submerged foil type ship during the past decade with emphasis upon the commitment directed toward its development by the United States Government. The developmental process induced by this commitment is found to be impeded by physical and technical factors, but organizational and traditional constraints are also instrumental in retarding progress. Recent successes indicate a reversal of past trends and suggest that the ocean going hydrofoil ship may soon add another dimension to surface water-borne transportation

Advanced constellation and demapper schemes for next generation digital terrestrial television broadcasting systems

206 p.Esta tesis presenta un nuevo tipo de constelaciones llamadas no uniformes. Estos esquemas presentan una eficacia de hasta 1,8 dB superior a las utilizadas en los últimos sistemas de comunicaciones de televisión digital terrestre y son extrapolables a cualquier otro sistema de comunicaciones (satélite, móvil, cable¿). Además, este trabajo contribuye al diseño de constelaciones con una nueva metodología que reduce el tiempo de optimización de días/horas (metodologías actuales) a horas/minutos con la misma eficiencia. Todas las constelaciones diseñadas se testean bajo una plataforma creada en esta tesis que simula el estándar de radiodifusión terrestre más avanzado hasta la fecha (ATSC 3.0) bajo condiciones reales de funcionamiento.Por otro lado, para disminuir la latencia de decodificación de estas constelaciones esta tesis propone dos técnicas de detección/demapeo. Una es para constelaciones no uniformes de dos dimensiones la cual disminuye hasta en un 99,7% la complejidad del demapeo sin empeorar el funcionamiento del sistema. La segunda técnica de detección se centra en las constelaciones no uniformes de una dimensión y presenta hasta un 87,5% de reducción de la complejidad del receptor sin pérdidas en el rendimiento.Por último, este trabajo expone un completo estado del arte sobre tipos de constelaciones, modelos de sistema, y diseño/demapeo de constelaciones. Este estudio es el primero realizado en este campo

Reception performance studies for the evaluation and improvement of the new generation terrestrial television systems

270 p.La industria de la TV ha experimentado grandes cambios en las últimas décadas. Las expectativas cada vez mayores de los espectadores y la reducción del espectro disponible para los servicios de TV han provocado la necesidad de sistemas más robustos de Televisión Digital Terrestre (TDT).El primer intento de cumplir estos requisitos es el estándar europeo DVB-T2 (2009). La publicación de un nuevo estándar significa el inicio de un proceso de evaluación del rendimiento del mismo mediante, por ejemplo, estudios de cobertura u obtención de valores de umbral de relación señal / ruido (SNR). Al inicio de esta tesis, este proceso estaba casi terminado para recepción fija y móvil. Sin embargo, la recepción en interiores no se había estudiado en detalle. Por esta razón, esta tesis completa la evaluación de DVB-T2 en interiores y define una nueva metodología de evaluación optimizada para este escenario.A pesar de que DVB-T2 emplea tecnologías muy avanzadas, el sistema se definió hace casi diez años y desde entonces han aparecido nuevas técnicas avanzadas, como por ejemplo nuevos códigos de corrección de errores o la nueva técnica de multiplexación por división en capas (LDM). Estas nuevas técnicas tampoco han sido evaluadas en entornos de interior, por lo que esta tesis incluye el análisis de las mismas evaluando su idoneidad para mejorar el rendimiento de DVB-T2. Además, se ha comprobado que los algoritmos tradicionales de los receptores TDT no están optimizados para los nuevos escenarios en los que se consideran las señales multicapa y recepción móvil. Por esta razón, se han propuesto nuevos algoritmos para mejorar la recepción en este tipo de situaciones.El último intento de hacer frente a los altos requisitos actuales de TDT es el estándar americano ATSC 3.0 (2016). Al igual que con DVB-T2, se necesita proceso completo de evaluación del sistema. Por ello, en esta tesis se han realizado simulaciones y pruebas de laboratorio para completar el estudio de rendimiento de ATSC 3.0 en diferentes escenarios

Leveraging elasticity theory to calculate cell forces: From analytical insights to machine learning

Living cells possess capabilities to detect and respond to mechanical features of their surroundings. In traction force microscopy, the traction of cells on an elastic substrate is made visible by observing substrate deformation as measured by the movement of embedded marker beads. Describing the substrates by means of elasticity theory, we can calculate the adhesive forces, improving our understanding of cellular function and behavior. In this dissertation, I combine analytical solutions with numerical methods and machine learning techniques to improve traction prediction in a range of experimental applications. I describe how to include the normal traction component in regularization-based Fourier approaches, which I apply to experimental data. I compare the dominant strategies for traction reconstruction, the direct method and inverse, regularization-based approaches and find, that the latter are more precise while the former is more stress resilient to noise. I find that a point-force based reconstruction can be used to study the force balance evolution in response to microneedle pulling showing a transition from a dipolar into a monopolar force arrangement. Finally, I show how a conditional invertible neural network not only reconstructs adhesive areas more localized, but also reveals spatial correlations and variations in reliability of traction reconstructions

Acronym dictionary

This reference was originally compiled as a tool for abstracters who need to know the expansion of acronyms they may encounter in the texts they are analyzing. It is a general rule of abstracting at the NASA Center For Aerospace Information (CASI) that acronyms are expanded in the abstract to enhance both information content and searchability. Over the last 22 years, abstracters at CASI have recorded acronyms and their expansions as they were encountered in documents. This is therefore an ad-hoc reference, rather than a systematic collection of all acronyms related to aerospace science and technology