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    Structure from Motion with Planar Homography Estimation: A Real-time Low-bandwidth, High-resolution Variant for Aerial Reconnaissance

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    We propose a new algorithm variant for Structure from Motion (SfM) to enable real-time image processing of scenes imaged by aerial drones. Our new SfM variant runs in real-time at 4 Hz equating to an 80× computation time speed-up compared to traditional SfM and is capable of a 90% size reduction of original video imagery, with an added benefit of presenting the original two-dimensional (2D) video data as a three-dimensional (3D) virtual model. This opens many potential applications for a real-time image processing that could make autonomous vision–based navigation possible by completely replacing the need for a traditional live video feed. The 3D reconstruction that is generated comes with the added benefit of being able to generate a spatially accurate representation of a live environment that is precise enough to generate global positioning system (GPS) coordinates from any given point on an imaged structure, even in a GPS-denied environment

    Natural Language Processing Analysis of Online Reviews for Small Business: Extracting Insight from Small Corpora

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    Receiving and acting on customer input is essential to sustaining and growing any service organization, particularly a small family business whose livelihood depends on strong relationships with its customers. The competitive advantage offered by advanced analytical approaches for supporting decisions is not trivial, and enterprises across virtually all domains of society are investing heavily in this emerging discipline. Natural Language Processing (NLP) is a subset of computer science that employs computational approaches to analyze human language; it is effective at extracting insight from text data but frequently requires large corpora to train its models, in the scale of thousands or millions of documents. This restricts its accessibility to those large enterprises with the capability to capture, store, manage, and analyze such corpora. This research explores a pilot study that applies NLP approaches, specifically topic modeling and large language models (LLM), to assist a small, family-owned business in assessing its strengths and weaknesses based on customer reviews. The relevant corpora of online Facebook, Google Reviews, TripAdvisor, and Yelp reviews is far smaller than ideal, numbering only in the hundreds. Results demonstrate that coherent and actionable insights from big-data approaches are obtainable and that small organizations are not automatically excluded from the benefits of these advanced analytical approaches, with complementary employment of both topic modeling and LLM presenting the greatest potential for similarly-positioned organizations to exploit

    Optofluidic Passive Parity-time-symmetric Systems

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    This research introduces a novel methodology of harnessing liquids to facilitate the realization of parity-time (PT)-symmetric optical waveguides on highly integrated microscale platforms. Additionally, we propose a realistic and detailed fabrication process flow, demonstrating the practical feasibility of fabricating our optofluidic system, thereby bridging the gap between theoretical design and actual implementation. Extensive research has been conducted over the past two decades on PT-symmetric systems across various fields, given their potential to foster a new generation of compact, power-efficient sensors and signal processors with enhanced performance. Passive PT-symmetry in optics can be achieved by evanescently coupling two optical waveguides and incorporating an optically lossy material into one of the waveguides. The essential coupling distance between two optical waveguides in air is usually less than 500 nm for near-infrared wavelengths and under 100 nm for ultraviolet wavelengths. This necessitates the construction of the coupling region via expensive and time-consuming electron beam lithography, posing a significant manufacturing challenge for the mass production of PT-symmetric optical systems. We propose a solution to this fabrication challenge by introducing liquids capable of dynamic flow between optical waveguides. This technique allows the attainment of evanescent wave coupling with coupling gap dimensions compatible with standard photolithography processes. Consequently, this paves the way for the cost-effective, rapid and large-scale production of PT-symmetric optofluidic systems, applicable across a wide range of fields

    Editorial: Observations and Simulations of Layering Phenomena in the Middle/upper Atmosphere and Ionosphere

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    The middle/upper atmosphere and ionosphere are the transition between neutral and ionized components of the Earth’s atmosphere, including stratosphere, mesosphere, thermosphere, ionospheric E region and ionospheric F region (Laơtovička et al., 2006; Xu, et al., 2007; Smith, 2012). The atmospheric thermal structure and composition are significantly affected by dynamical processes through coupling. The layering phenomena such as mesospheric metal layers, sporadic E layers, and noctilucent clouds are important tracers to study mechanisms of the vertical coupling from the lower to the upper atmosphere (Dou et al., 2010; Plane, 2012; Xue et al., 2013)

    Association of Homelessness and Diet on the Gut Microbiome: A United States-Veteran Microbiome Project (US-VMP) Study

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    Military veterans account for 8% of homeless individuals living in the United States. To highlight associations between history of homelessness and the gut microbiome, we compared the gut microbiome of veterans who reported having a previous experience of homelessness to those from individuals who reported never having experienced a period of homelessness. Moreover, we examined the impact of the cumulative exposure of prior and current homelessness to understand possible associations between these experiences and the gut microbiome. Microbiome samples underwent genomic sequencing and were analyzed based on alpha diversity, beta diversity, and taxonomic differences. Additionally, demographic information, dietary data, and mental health history were collected. A lifetime history of homelessness was found to be associated with alcohol use disorder, substance use disorder, and healthy eating index compared to those without such a history. In terms of differences in gut microbiota, beta diversity was significantly different between veterans who had experienced homelessness and veterans who had never been homeless (P = 0.047, weighted UniFrac), while alpha diversity was similar. The microbial community differences were, in part, driven by a lower relative abundance of Akkermansia in veterans who had experienced homelessness (mean; range [in percentages], 1.07; 0-33.9) compared to veterans who had never been homeless (2.02; 0-36.8) (P = 0.014, ancom-bc2). Additional research is required to facilitate understanding regarding the complex associations between homelessness, the gut microbiome, and mental and physical health conditions, with a focus on increasing understanding regarding the longitudinal impact of housing instability throughout the lifespan.IMPORTANCEAlthough there are known stressors related to homelessness as well as chronic health conditions experienced by those without stable housing, there has been limited work evaluating the associations between microbial community composition and homelessness. We analyzed, for the first time, bacterial gut microbiome associations among those with experiences of homelessness on alpha diversity, beta diversity, and taxonomic differences. Additionally, we characterized the influences of diet, demographic characteristics, military service history, and mental health conditions on the microbiome of veterans with and without any lifetime history of homelessness. Future longitudinal research to evaluate the complex relationships between homelessness, the gut microbiome, and mental health outcomes is recommended. Ultimately, differences in the gut microbiome of individuals experiencing and not experiencing homelessness could assist in identification of treatment targets to improve health outcomes

    Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning

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    Emulator embedded neural networks, which are a type of physics informed neural network, leverage multi-fidelity data sources for efficient design exploration of aerospace engineering systems. Multiple realizations of the neural network models are trained with different random initializations. The ensemble of model realizations is used to assess epistemic modeling uncertainty caused due to lack of training samples. This uncertainty estimation is crucial information for successful goal-oriented adaptive learning in an aerospace system design exploration. However, the costs of training the ensemble models often become prohibitive and pose a computational challenge, especially when the models are not trained in parallel during adaptive learning. In this work, a new type of emulator embedded neural network is presented using the rapid neural network paradigm. Unlike the conventional neural network training that optimizes the weights and biases of all the network layers by using gradient-based backpropagation, rapid neural network training adjusts only the last layer connection weights by applying a linear regression technique. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy. The proposed method is demonstrated on multiple analytical examples, as well as an aerospace flight parameter study of a generic hypersonic vehicle

    A Comparison of Quaternion Neural Network Backpropagation Algorithms

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    This research paper focuses on quaternion neural networks (QNNs) - a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. Previous studies have shown that QNNs outperform real-valued neural networks in basic tasks and have potential in high-dimensional problem spaces. However, research on QNNs has been fragmented, with contributions from different mathematical and engineering domains leading to unintentional overlap in QNN literature. This work aims to unify existing research by evaluating four distinct QNN backpropagation algorithms, including the novel GHR-calculus backpropagation algorithm, and providing concise, scalable implementations of each algorithm using a modern compiled programming language. Additionally, the authors apply a robust Design of Experiments (DoE) methodology to compare the accuracy and runtime of each algorithm. The experiments demonstrate that the Clifford Multilayer Perceptron (CMLP) learning algorithm results in statistically significant improvements in network test set accuracy while maintaining comparable runtime performance to the other three algorithms in four distinct regression tasks. By unifying existing research and comparing different QNN training algorithms, this work develops a state-of-the-art baseline and provides important insights into the potential of QNNs for solving high-dimensional problems

    Shock-wave Tolerant Phase Reconstruction Algorithm for Shack–Hartmann Wavefront Sensor Data

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    We develop a phase reconstruction algorithm for the Shack–Hartmann wavefront sensor (SHWFS) that is tolerant to phase discontinuities, such as the ones imposed by shock waves. In practice, this algorithm identifies SHWFS locations where the resultant tilt information is affected by the shock and improves the tilt information in these locations using the local SHWFS observation-plane irradiance patterns. The algorithm was shown to work well over the range of conditions tested with both simulated and experimental data. In turn, the reconstruction algorithm will enable robust wavefront sensing in transonic, supersonic, and hypersonic environments

    Western Florida Panhandle Electric Transmission Grid Substations, Lines, and Towers \u3cb\u3e[Data set]\u3c/b\u3e

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    The upload consists of 5 different datasets pertaining to the electric transmission grid in the nine counties of the western Florida Panhandle. The area largely coincides with the former operation area of the Gulf Power Company (GPCO) but is not limited to this utility. The five datasets describe the substations, lines, and transmission towers of the grid. The data were created and validated through a variety of datasets from the utility, national data, and state-level information. In total, 195 substations, 1800 miles of transmission lines, and over 18,000 transmission towers were cataloged and described spatially in the data. The spatial files are uploaded as feature classes within a ArcGIS geodatabase. Three metadata files are provided, one each for substations, transmission lines, and transmission towers, which describe the process and sources for creating each data as well as a detailed list of all fields in the files


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