Adaptive trajectory planning for flight management systems

Current Flight Management Systems (FMS) can autonomously fly an aircraft from takeoff through landing but may not provide robust operation to anomalous events. We present an adaptive trajectory planner capable of dynamically adjusting its world model and re-computing feasible flight trajectories in response to changes in aircraft performance characteristics. To demonstrate our approach, we consider the class of situations in which an emergency landing at a nearby airport is desired (or required) for safety considerations. Our system incorporates a constraint-based search engine to select and prioritize emergency landing sites, then it synthesizes a waypoint-based trajectory to the best airport based on post-anomaly flight dynamics. We present an engine failure/fuel starvation case study and illustrate the utility of our approach during a simulated thrusting power failure for a B-747 over the Bay Area

Development and Application of Computational Biology tools for Biomedicine

Biomolecular simulation can be considered as a virtual microscope for molecular biology, allowing to gain insights into the sub-cellular mechanisms of biological relevance at spatial and temporal scales that are difficult to observe experimentally. It provides a powerful tool to link the laws of physics with the complex behavior of biological systems. Dramatic recent advancements in achievable simulation speed and the underlying physical models will increasingly lead to molecular views of large systems. These improvements may largely affect biological sciences. In this thesis, I have applied computational molecular biology approaches to different biological systems using state of the art structural bioinformatics and computational biophysics tools (Chapter 3). My principal focus was on the computational design of molecular imprinted polymers (MIPs), which have recently attracted significant attention as cost effective substitutes for natural antibodies and receptors in chromatography, sensors and assays. I have used molecular modelling in the optimization of polymer compositions to make high affinity synthetic receptors based on Molecular Imprinting. In particular, I developed a new free of charge protocol that can be performed within just few hours that outputs a list of candidate monomers which are capable of strong binding interactions with the template. Furthermore, I have produced a new computational method for the calculation of the ideal monomer: template stoichiometric ratio to be used in the lab for the MIPs synthesis. These protocols have been implemented as a webserver that is available at http://mirate.di.univr.it/. In parallel, I have also investigated the modelling of much more complex MIPs systems by the introduction of some factors e.g. solvent and cross-linker molecules that are also essential in the polymerisation process. A novel algorithm, which mimics a radical polymerization mechanism, has been written for application in the rational design of MIPs (Chapter 4). Moreover, I have been involved in the field of computational molecular biomedicine. Indeed, in Chapters 5 and 6 I describe the work done in collaboration with two labs at the Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona. In Chapter 5, starting from unpublished experimental data I have computationally characterized the interaction of ACOT8 with HIV-1 Nef accessory protein. I have performed a detailed structural and functional characterization of these two proteins in order to infer any possible functional details about their interactions. The bioinformatics predictions were then confirmed by wet-lab experiments. I have also carried out a detailed structural and functional characterization of two pathogenic mutations of AGT-Mi (Chapter 6). In particular, I have used classical molecular dynamics (MD) simulations to study the possible interference with the dimerization process of AGT-Mi exerted by I244T-Mi and F152I-Mi mutants. Those variants are associated with Primary Hyperoxaluria type 1 disease. In Chapter 7, I present the coarse-grained MD simulations of Membrane/Human ileal bile-acid-binding protein Interactions. This study was carried out in collaboration with the NMR group at the University of Verona and it is a part of an extensive research aimed at better understanding of the main biomolecular interactions in crowded cellular environments. MD simulations results were in agreement with experimental findings

Uses and applications of artificial intelligence in manufacturing

The purpose of the THESIS is to provide engineers and personnels with a overview of the concepts that underline Artificial Intelligence and Expert Systems. Artificial Intelligence is concerned with the developments of theories and techniques required to provide a computational engine with the abilities to perceive, think and act, in an intelligent manner in a complex environment. Expert system is branch of Artificial Intelligence where the methods of reasoning emulate those of human experts. Artificial Intelligence derives it\u27s power from its ability to represent complex forms of knowledge, some of it common sense, heuristic and symbolic, and the ability to apply the knowledge in searching for solutions. The Thesis will review : The components of an intelligent system, The basics of knowledge representation, Search based problem solving methods, Expert system technologies, Uses and applications of AI in various manufacturing areas like Design, Process Planning, Production Management, Energy Management, Quality Assurance, Manufacturing Simulation, Robotics, Machine Vision etc. Prime objectives of the Thesis are to understand the basic concepts underlying Artificial Intelligence and be able to identify where the technology may be applied in the field of Manufacturing Engineering

Reproducibility and physiological factors pertinent to the study of the acute effects of exercise on traditional and alternative measures of vascular and autonomic function in young and older adults

Measuring age-related endothelial dysfunction may provide a prognostic marker of cardiovascular diseases beyond traditional cardiovascular risk factors. Flow-mediated slowing (FMS) may address flow-mediated dilation (FMD) major caveats including larger measurement and biological variability, still, applanation tonometry FMS reproducibility is unknown. The acute model permits investigating the mechanisms underlying aerobic exercise anti-atherogenic and sympatholytic effects which preserve neurovascular homeostasis through aging. Thus, this dissertation aimed to investigate the reproducibility and physiological factors relevant to the study of exercise acute effects on traditional and alternative measures of vascular and autonomic function in young and older adults. Reproducibility assessments of applanation tonometry FMS and FMD were conducted on twenty-four males (aged 23-75 years) healthy and active male adults. Participants also performed walking or running randomized acute bouts of high-intensity interval training (HIIT), moderate-intensity continuous training, or a non-exercise condition. FMS was not a reproducible method with poorer reproducibility (CV: 141%) than FMD (CV: 23%). We found no age-associated response patterns on FMD, and heart-rate variability indexes to exercise in active young and older adults. FMD remained unchanged following exercise, whilst only HIIT reduced cardiovagal modulation, likely representing the initial trigger for vagal adaptations, returning to baseline 60-min into recovery.A disfunção endotelial inerente ao envelhecimento pode ser preditiva de doenças cardiovasculares independentemente dos factores de risco tradicionais, assim a sua avaliação é crucial. A desaceleração fluxo-mediada (DFM) pretende colmatar as lacunas da vasodilatação fluxo-mediada (VFM): a elevada variabilidade biológica e de medição. Contudo, a reprodutibilidade da DFM medida por tonometria de aplanação é desconhecida. O modelo agudo possibilita investigar os mecanismos subjacentes aos efeitos ateroscleróticos e simpatolíticos do exercício aeróbio preservando a homeostasia neurovascular durante o envelhecimento. O objetivo desta dissertação consistiu em examinar a reprodutibilidade e os factores relevantes para o estudo dos efeitos agudos do exercício em medidas tradicionais e alternativas de função endotelial e autonómica em homens jovens e idosos. A reprodutibilidade da DFM e da VFM foi avaliada em 24 homens (23-75 anos), saudáveis e fisicamente ativos. Adicionalmente, duas sessões de treino aeróbio (contínuo vs intervalado) e uma de controlo foram ainda realizadas aleatoriamente. A DFM apresentou uma reprodutibilidade inferior (CV: 141%) à da VDM (CV: 23%). As respostas ao exercício da VFM e da variabilidade da frequência cardíaca não diferiram entre jovens e idosos. A VFM permaneceu inalterada no pós-exercício, já a modulação cardiovagal diminui apenas no pós-treino intervalado de alta intensidade retornando a níveis basais após 60-min de recuperação

Advancing efficiency and robustness of neural networks for imaging

Enabling machines to see and analyze the world is a longstanding research objective. Advances in computer vision have the potential of influencing many aspects of our lives as they can enable machines to tackle a variety of tasks. Great progress in computer vision has been made, catalyzed by recent progress in machine learning and especially the breakthroughs achieved by deep artificial neural networks. Goal of this work is to alleviate limitations of deep neural networks that hinder their large-scale adoption for real-world applications. To this end, it investigates methodologies for constructing and training deep neural networks with low computational requirements. Moreover, it explores strategies for achieving robust performance on unseen data. Of particular interest is the application of segmenting volumetric medical scans because of the technical challenges it imposes, as well as its clinical importance. The developed methodologies are generic and of relevance to a broader computer vision and machine learning audience. More specifically, this work introduces an efficient 3D convolutional neural network architecture, which achieves high performance for segmentation of volumetric medical images, an application previously hindered by high computational requirements of 3D networks. It then investigates sensitivity of network performance on hyper-parameter configuration, which we interpret as overfitting the model configuration to the data available during development. It is shown that ensembling a set of models with diverse configurations mitigates this and improves generalization. The thesis then explores how to utilize unlabelled data for learning representations that generalize better. It investigates domain adaptation and introduces an architecture for adversarial networks tailored for adaptation of segmentation networks. Finally, a novel semi-supervised learning method is proposed that introduces a graph in the latent space of a neural network to capture relations between labelled and unlabelled samples. It then regularizes the embedding to form a compact cluster per class, which improves generalization.Open Acces