The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. Big data is a widely-used term without a clear definition. The difference between big data and traditional data can be characterized by four Vs: velocity (speed at which data is generated), volume (amount of data generated), variety (the data can take on different forms), and veracity (the data may be of poor/unknown quality). As many industries begin to recognize the value of big data, organizations try to capture it through means such as: side-channel data in a manufacturing operation, unstructured text-data reported by healthcare personnel, various demographic information of households from census surveys, and the range of communication data that define communities and social networks. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called statistical learning of the data, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies in the process. However, several open challenges still exist in this framework for big data analytics. Recently, data types such as free-text data are also being captured. Although many established processing techniques exist for other data types, free-text data comes from a wide range of individuals and is subject to syntax, grammar, language, and colloquialisms that require substantially different processing approaches. Once the data is processed, open challenges still exist in the statistical learning step of understanding the data. Statistical learning aims to satisfy two objectives, (1) develop a model that highlights general patterns in the data (2) create a signaling mechanism to identify if outliers are present in the data. Statistical modeling is widely utilized as researchers have created a variety of statistical models to explain everyday phenomena such as predicting energy usage behavior, traffic patterns, and stock market behaviors, among others. However, new applications of big data with increasingly varied designs present interesting challenges. Consider the example of free-text analysis posed above. There's a renewed interest in modeling free-text narratives from sources such as online reviews, customer complaints, or patient safety event reports, into intuitive themes or topics. As previously mentioned, documents describing the same phenomena can vary widely in their word usage and structure. Another recent interest area of statistical learning is using the environmental conditions that people live, work, and grow in, to infer their quality of life. It is well established that social factors play a role in overall health outcomes, however, clinical applications of these social determinants of health is a recent and an open problem. These examples are just a few of many examples wherein new applications of big data pose complex challenges requiring thoughtful and inventive approaches to processing, analyzing, and modeling data. Although a large body of research exists in the area of anomaly detection increasingly complicated data sources (such as side-channel related data or network-based data) present equally convoluted challenges. For effective anomaly-detection, analysts define parameters and rules, so that when large collections of raw data are aggregated, pieces of data that do not conform are easily noticed and flagged. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This paper focuses on the healthcare, manufacturing and social-networking industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerably to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process / PHD / The prevalence of big data has rapidly changed the usage and mechanisms of data analytics within organizations. The fields of manufacturing and healthcare are two examples of industries that are currently undergoing significant transformations due to the rise of big data. The addition of large sensory systems is changing how parts are being manufactured and inspected and the prevalence of Health Information Technology (HIT) systems in healthcare systems is also changing the way healthcare services are delivered. These industries are turning to big data analytics in the hopes of acquiring many of the benefits other sectors are experiencing, including reducing cost, improving safety, and boosting productivity. However, there are many challenges that exist along with the framework of big data analytics, from pre-processing raw data, to statistical modeling of the data, and identifying anomalies present in the data or process. This work offers significant contributions in each of the aforementioned areas and includes practical real-world applications. Big data analytics generally follows this framework: first, a digitized process generates a stream of data, this raw data stream is pre-processed to convert the data into a usable format, the pre-processed data is analyzed using statistical tools. In this stage, called ‘statistical learning of the data’, analysts have two main objectives (1) develop a statistical model that captures the behavior of the process from a sample of the data (2) identify anomalies or outliers in the process. In this work, I investigate the different steps of the data analytics framework and propose improvements for each step, paired with practical applications, to demonstrate the efficacy of my methods. This work focuses on the healthcare and manufacturing industries, but the materials are broad enough to have wide applications across data analytics generally. My main contributions can be summarized as follows: • In the big data analytics framework, raw data initially goes through a pre-processing step. Although many pre-processing techniques exist, there are several challenges in pre-processing text data and I develop a pre-processing tool for text data. • In the next step of the data analytics framework, there are challenges in both statistical modeling and anomaly detection o I address the research area of statistical modeling in two ways: - There are open challenges in defining models to characterize text data. I introduce a community extraction model that autonomously aggregates text documents into intuitive communities/groups - In health care, it is well established that social factors play a role in overall health outcomes however developing a statistical model that characterizes these relationships is an open research area. I developed statistical models for generalizing relationships between social determinants of health of a cohort and general medical risk factors o I address the research area of anomaly detection in two ways: - A variety of anomaly detection techniques exist already, however, some of these methods lack a rigorous statistical investigation thereby making them ineffective to a practitioner. I identify critical shortcomings to a proposed network-based anomaly detection technique and introduce methodological improvements - Manufacturing enterprises which are now more connected than ever are vulnerable to anomalies in the form of cyber-physical attacks. I developed a sensor-based side-channel technique for anomaly detection in a manufacturing process.

An increase of 153,375 to 248,418 traumatic brain injuries (TBI) due to incidents in sports and recreation activities has been reported in the past couple of years in the US alone. These are grounds for concern for athletes partaking in sports with a high incidence of TBI’s such as football and soccer. The latter, traditionally not classified as a contact-sport, has attracted research due to participants using their head as an instrument for heading. Voluntary heading, in combination with lenient laws and regulations concerning TBI expose how soccer players are easily at risk of injury. On the other hand football, an aggressive sport by nature, has brought attention to the possible neurocognitive and neurophysiological ramifications of repetitive subconcussive impacts. One of these is in the form of a progressive neurodegenerative pathology known as chronic traumatic encephalopathy (CTE). A priori reasons revealed, led to a need to characterize the most important variables involved in ball-player interactions within soccer simulated gameplay. By understanding these, it would be possible to obtain parameters to design and manufacture new composite-material based protective headgear unlike products that are commercially available nowadays. In addition, development of a testing protocol focused on frequency domain variables - transmissibility and mechanical impedance - would allow to evaluate the performance of football helmets. A focus would be set on low impacts categorized as subconcussive impacts. Incoming velocity and inflation pressure were identified as the most influential variables affecting the peak impact force of a soccer ball. An innovative 6-layer carbon fiber headband, with silicone padding, was manufactured that out-performed existing headgear at attenuating peak linear acceleration. Lastly, quantification of the transmissibility and mechanical impedance indicated poor performance of football helmets below 60 Hz.

Submitted by Pedro Silva Filho (pfsilva@ipen.br) on 2017-11-21T11:56:39Z No. of bitstreams: 0 / Made available in DSpace on 2017-11-21T11:56:39Z (GMT). No. of bitstreams: 0 / O processo de soldagem por fricção e mistura (FSW) tem despertado grande interesse nos últimos anos e tornou-se uma alternativa para unir materiais de baixa soldabilidade, como as ligas de alumínio das séries 2XXX e 7XXX, as quais são empregadas na estrutura das aeronaves, por possuírem elevada relação resistência/peso. O processo FSW, todavia, causa mudanças microestruturais nos materiais soldados, particularmente na zona misturada (ZM) e nas zonas termicamente (ZTA) ou termomecanicamente (ZTMA) afetadas. Estas mudanças geralmente interferem no desempenho frente à corrosão das ligas soldadas. No presente estudo, a resistência à corrosão das ligas de alumínio 2024-T3 e 7475-T761, unidas pelo processo FSW foi investigada em solução 10 mM de NaCl. Ensaios de visualização em gel ágar-ágar e de imersão associados a técnicas microscópicas foram realizados para investigar o efeito do acoplamento galvânico na corrosão das diferentes regiões da junta soldada. Os resultados do ensaio de visualização em gel mostraram que, quando acopladas, a liga 2024 atua como cátodo e a 7475 como ânodo. Os ensaios de imersão revelaram acoplamento galvânico entre as ligas na zona misturada (ZM). A região mais afetada pela corrosão foi a ZTMA da liga 7475, com corrosão intergranular desde as primeiras horas de imersão. A influência do processo de soldagem na resistência à corrosão das duas ligas de alumínio foi investigada por ensaios eletroquímicos. Os ensaios eletroquímicos adotados foram medidas de potencial de circuito aberto (PCA) em função do tempo de exposição ao meio corrosivo, espectroscopia de impedância eletroquímica (EIE) e curvas de polarização potenciodinâmica. Os ensaios de polarização mostraram elevada atividade eletroquímica na zona de mistura indicada pelos altos valores de densidade de corrente em comparação com as demais zonas testadas. Os resultados de EIE globais mostraram que nas primeiras horas de exposição ao eletrólito o processo de corrosão foi predominantemente controlado pela liga 7475; todavia, com o tempo de exposição ao eletrólito, a corrosão passou a ser controlada pela liga 2024. / Dissertação (Mestrado em Tecnologia Nuclear) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP

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La capacité d’une plante à réguler sa croissance racinaire est une composante importante de l’acclimatation aux stress environnementaux. A l’échelle cellulaire, cette régulation est effectuée via le contrôle de la division et de l’élongation des cellules mais les rôles respectifs de chaque processus et leurs interactions sont peu connus. Notamment, l’activité de production de cellules du méristème apical racinaire (RAM) est trop souvent négligée. Dans cette thèse, l’analyse spatiale de la croissance le long de l’apex racinaire et l’analyse temporelle des trajectoires de croissance des cellules ont été couplées pour comprendre les liens existants entre division et élongation cellulaire. Pour cela, j’ai développé un système de phénotypage de la croissance à haute résolution spatio-temporelle qui a été appliqué à l’étude de racines d’un peuplier euraméricain (Populus deltoides × Populus nigra) en réponse à différents stress (stress osmotique, impédance mécanique). Une forte variabilité du taux de croissance racinaire entre individus ainsi que des variations individuelles cycliques de la croissance ont été observées malgré des conditions environnementales contrôlées. L’utilisation de cette variabilité couplée à la quantification de l’activité du RAM a mis en évidence l’importance du taux de production de cellules pour soutenir la croissance racinaire. Ces travaux analysent une nouvelle échelle de variations spatiales et temporelles de la croissance peu prise en compte jusqu’à présent. Hautement applicable à d’autres questions scientifiques, l’analyse du devenir des cellules une fois sortie du RAM est également discutée pour des conditions de croissance non stables / Regulation of root growth is a crucial capacity of plants for acclimatization to environmental stresses. At cell scale, this regulation is controlled through cell division and cell elongation but respective importance of these processes and interactions between them are still poorly known. Notably, the cell production activity of the root apical meristem (RAM) is often excluded. During this thesis, spatial analyses of growth along the root apex were coupled with temporal analyses of cell trajectories in order to decipher the links between cell division and cell elongation. This required the setup of a system for phenotyping root growth at a high spatiotemporal resolution which was applied to study the growth of roots from an euramerican poplar (Populus deltoides × Populus nigra) in response to different environmental stresses (osmotic stress or mechanical impedance). An important variability of root growth rate between individuals as well as individual cyclic variations of growth along time were observed despite tightly controlled environmental conditions. Use of this variability coupled with quantification of the RAM activity led us to a better understanding of the importance of the cell production rate for sustaining root growth. This work analyses a new spatiotemporal scale of growth variability poorly considered. Widely applicable to others scientific questioning, temporal analyses of cell fate once produced in the RAM is also discussed for non-steady growth conditions