Montaigne, a Study

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Ferromagnetic bubble clusters in Y0.67_{0.67}Ca0.33_{0.33}MnO3_3 thin films

We studied the ferromagnetic topology in a Y$_{0.67}$Ca$_{0.33}$MnO$_3$ thin film with a combination of magnetic force microscopy and magnetization measurements. Our results show that the spin-glass like behavior, reported previously for this system, could be attributed to frustrated interfaces of the ferromagnetic clusters embedded in a non-ferromagnetic matrix. We found temperature dependent changes of the magnetic topology at low temperatures, which suggests a non-static Mn$^{3+}$/Mn$^{4+}$ ratio

Machine learning for the harsh environment: applications in sea ice classification and satellite magnetic fault recovery

This thesis presents the development of two machine learning navigation modules for harsh environment applications. The first application investigates semantic segmentation using neural networks for sea ice detection and classification in polar oceans. Two popular generic architectures, SegNet and PSPNet101 are used to segment images. Transfer learning is performed using two custom datasets, one with four classes: ice, ocean, vessel, and sky, i.e., sea ice detection dataset, and the second with eight classes: ocean, vessel, sky, lens artifacts, first-year ice, new ice, grey ice, and multiyear ice, i.e., sea ice classification dataset. The Nathaniel B. Palmer imagery, which captured 2-month footage of the icebreaker completing an Antarctic expedition was used in the creation of both datasets. A subset of the dataset was labeled to generate a 240-image training set for sea ice detection achieving an accuracy of 98% classification for the 26-image test set. The sea ice classification dataset consists of 1,090 labeled images achieving accuracies of 98.3% or greater for all ice types for the 104-image test set. The second application investigates a new attitude error parameterization and a machine learning regression model for small satellite attitude fault recovery systems experiencing magnetometer bias faults. A simulation environment is developed to mimic an orbit of the international space station, and simulates both the magnetometer and the fine sun sensor on-board a small satellite. A right quaternion error parameterization is presented to ensure consistent error bound growth during the eclipse period of orbits where only a subset of sensor data is available. Using the improved error bounds a fault detection method using Mahalanobis distance is implemented to flag any faults in the system. After the fault detection, the fault recovery uses a regression sliding window optimizer to determine the unknown magnetometer bias that the sensor encounters. The proposed method demonstrates improved root mean squared error and error bound consistency achievable using the right error formulation for magnetic bias fault detection and recovery applications of small satellites

Murine Gut Microbial Communities Influenced by Physical Activity and Diet But Not Gender

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Exercise Attenuates Weight Gain and Modulates Satiety Hormones in Female Mice

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Gut Microbiota Contribute to Exercise Capacity and Metabolic Profile in a Wildtype and Longevity Model Mouse

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The Acute Effect of Exposure to Barefoot Running on VO2 Peak, Fatigue, and Time to Exhaustion in Recreational Runners

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An investigation of using various diesel-type fuels in homogeneous charge compression ignition engines and their effects on operational and controlling issues

Homogeneous charge compression ignition (HCCI) engines appear to be a future alternative to diesel and spark-ignited engines. The HCCI engine has the potential to deliver high efficiency and very low NOx and particulate matter emissions. There are, however, problems with the control of ignition and heat release range over the entire load and speed range which limits the practical application of this technology. The aim of this paper is to analyse the use of different types of diesel fuels in an HCCI engine and hence to find the most suitable with respect to operational and control issues. The single-zone combustion model with convective heat transfer loss is used to simulate the HCCI engine environment. n-Heptane, dimethyl ether and bio-diesel (methyl butanoate and methyl formate) fuels are investigated. Methyl butanoate and methyl formate represent surrogates of heavy and light bio-diesel fuel respectively. The effects of different engine parameters such as equivalence ratio and engine speed on the ignition timing are investigated. The use of internal exhaust gas recirculation is investigated as a potential strategy for controlling the ignition timing. The results indicate that the use of bio-diesel fuels will result in lower sensitivity of ignition timing to changes in operational parameters and in a better control of the ignition process when compared with the use of n-heptane and dimethyl ether

Antibiotics Reduce While Forced-Exercise Increases Inflammation in the Small Intestine

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