Bentzenmckenzie0517

Detection and separation of gas-phase volatile organic compounds (VOCs) is of great importance for many applications including air quality monitoring, toxic gas detection and medical diagnostics. A lack of small and low-cost detectors limits the potential applications of VOC gas sensors, especially in the areas of consumer products and the 'Internet of Things'. Most of the commercially available low-cost technologies are either only capable of measuring a single VOC type, or only provide a total VOC concentration, without the ability to provide information on the nature or type of the VOC. We present a new approach for improving the selectivity of VOC detection, based on temporally resolved thermal desorption of VOCs from a nanoporous material, which can be combined with any existing VOC detector. This work uses a nanoporous silica material that adsorbs VOC molecules, which are then thermally desorbed onto a broadband VOC detector. Different VOCs are desorbed at different temperatures depending on their boiling point and affinity to the porous surface. The nanoporous silica is inert; VOC adsorption is proportional to the concentration of VOC in the environment, and is fully reversible. An example of a detection system using a commercial total VOC photoionization detector and a nanoporous silica preconcentrator is demonstrated here for six different VOCs, and shows potential for discrimination between the VOCs.Accurate measurements of 235U enrichment within metallic nuclear fuels are essential for understanding material performance in a neutron irradiation environment, and the origin of secondary phases (e.g. uranium carbides). In this work, we analyse 235U enrichment in matrix and carbide phases in low enriched uranium alloyed with 10 wt% Mo via two chemical imaging modalities-nanoscale secondary ion mass spectrometry (NanoSIMS) and atom probe tomography (APT). Results from NanoSIMS and APT are compared to understand accuracy and utility of both approaches across length scales. NanoSIMS and APT provide consistent results, with no statistically significant difference between nominal enrichment (19.95 ± 0.14 at% 235U) and that measured for metal matrix and carbide inclusions.Wayfinding is a major challenge for visually impaired travelers, who generally lack access to visual cues such as landmarks and informational signs that many travelers rely on for navigation. Indoor wayfinding is particularly challenging since the most commonly used source of location information for wayfinding, GPS, is inaccurate indoors. We describe a computer vision approach to indoor localization that runs as a real-time app on a conventional smartphone, which is intended to support a full-featured wayfinding app in the future that will include turn-by-turn directions. Our approach combines computer vision, existing informational signs such as Exit signs, inertial sensors and a 2D map to estimate and track the user's location in the environment. An important feature of our approach is that it requires no new physical infrastructure. While our approach requires the user to either hold the smartphone or wear it (e.g., on a lanyard) with the camera facing forward while walking, it has the advantage of not forcing the user to aim the camera towards specific signs, which would be challenging for people with low or no vision. We demonstrate the feasibility of our approach with five blind travelers navigating an indoor space, with localization accuracy of roughly 1 meter once the localization algorithm has converged.Functional connectivity between brain regions is often estimated by correlating brain activity measured by resting-state fMRI in those regions. The impact of factors (e.g, disorder or substance use) are then modeled by their effects on these correlation matrices in individuals. A crucial step in better understanding their effects on brain function could lie in estimating connectomes, which encode the correlation matrices across subjects. Connectomes are mostly estimated by creating a single average for a specific cohort, which works well for binary factors (such as sex) but is unsuited for continuous ones, such as alcohol consumption. Alternative approaches based on regression methods usually model each pair of regions separately, which generally produces incoherent connectomes as correlations across multiple regions contradict each other. In this work, we address these issues by introducing a deep learning model that predicts connectomes based on factor values. The predictions are defined on a simplex spanned across correlation matrices, whose convex combination guarantees that the deep learning model generates well-formed connectomes. We present an efficient method for creating these simplexes and improve the accuracy of the entire analysis by defining loss functions based on robust norms. find more We show that our deep learning approach is able to produce accurate models on challenging synthetic data. Furthermore, we apply the approach to the resting-state fMRI scans of 281 subjects to study the effect of sex, alcohol, and HIV on brain function.In MRI practice, it is inevitable to appropriately balance between image resolution, signal-to-noise ratio (SNR), and scan time. It has been shown that super-resolution reconstruction (SRR) is effective to achieve such a balance, and has obtained better results than direct high-resolution (HR) acquisition, for certain contrasts and sequences. The focus of this work was on constructing images with spatial resolution higher than can be practically obtained by direct Fourier encoding. A novel learning approach was developed, which was able to provide an estimate of the spatial gradient prior from the low-resolution (LR) inputs for the HR reconstruction. By incorporating the anisotropic acquisition schemes, the learning model was trained over the LR images themselves only. The learned gradients were integrated as prior knowledge into a gradient-guided SRR model. A closed-form solution to the SRR model was developed to obtain the HR reconstruction. Our approach was assessed on the simulated data as well as the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 15 subjects. The experimental results demonstrated that our approach led to superior SRR over state-of-the-art methods, and obtained better images at lower or the same cost in scan time than direct HR acquisition.