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Surveying subterranean biodiversity of the Tongass National Forest using traditional survey approaches and environmental DNA (eDNA) metabarcoding
Get PDFThe Tongass National Forest (TNF) is a large temperate rainforest in southeastern Alaska, much of which is underlain by karst. Timber harvest, among other threats, can have detrimental effects on karst habitats; however, the subterranean fauna of this region is understudied and lacks a baseline for monitoring. I spent three field seasons conducting biological assessments of caves in the TNF using two approaches: traditional specimen-based sampling and eDNA metabarcoding. To improve the reference sequence database, I generated 213 novel sequences from invertebrate specimens collected from 35 caves. I also collected 125 water samples from 39 sites for environmental DNA (eDNA) metabarcoding analysis, targeting two mitochondrial genes, cytochrome oxidase subunit 1 (COI) and 16S ribosomal RNA. Over 400 unique taxa were identified, including several new records and species of conservation concern. Distance-based linear models of community composition revealed differences in the taxa recorded by our two approaches. This study provided the first comprehensive biological assessments of subterranean fauna of the TNF and demonstrated the utility of complementing environmental DNA and traditional survey approaches
Neoplatonism and Nicene Christianity in the fourth and Fifth Centuries CE: Power, the Dispersal of Ideas, and the Lives of the Clergy
Get PDFThe Justifications of Anti-Vaccination Beliefs
Get PDFhttps://louis.uah.edu/research-horizons/1377/thumbnail.jp
Geometric Patterns Describing Cahracters of Lie Algebras
Get PDFhttps://louis.uah.edu/research-horizons/1386/thumbnail.jp
Observational analysis of small-scale structures across the Earth’s bow shock
Get PDFThis work identifies and characterizes magnetic structures in the solar wind and magnetosheath across the Earth’s bow shock. I investigate the differences between the properties of small-scale flux rope (SFR) structures immediately upstream and downstream of the bow shock by employing two data analysis methods: one based on wavelet transforms and the other based on the Grad-Shafranov (GS) detection and reconstruction techniques. 676 hours in the solar wind, and 1051 hours in the magnetosheath, of in situ magnetic field and plasma data from the Magnetospheric Multiscale (MMS) and Time History of Events and Macroscale Interactions during Substorms (THEMIS) missions were used to identify these coherent structures. I investigate the difference between the properties of the magnetic structures in different near-Earth regions. The magnetic structures with varying degrees of Alfvénicity are characterized in a systematic manner as they move across boundaries in near-Earth space. I identified thousands of SFR event intervals in each region, and established an inventory of events with high magnetic helicity. I report the parameters associated with the SFRs such as scale size, duration, and magnetic helicity density, and a direct comparison of the statistical properties of the structures from these two regions is performed. The GS-based method is extended to identify structures with significant remaining plasma flow aligned with the local magnetic field, and yielded a unique set of additional parameters that allowed us to evaluate the distributions of the Walén test slope, magnetic flux, and the orientation of the z-axes of the structures. In general, it is found that the distributions of various parameters follow power laws. The SFR structures seem to be compressed in the magnetosheath, as compared with their counterparts in the solar wind. A significant rotation in the z-axis defining the orientation of the structures is also seen across the bow shock. The implications for the elongation of the SFRs in the magnetosheath along one spatial dimension are also discussed
Utilization of artificial intelligence to predict surface energy budget
No full textEstimating heat and moisture exchange between the land and atmosphere has several important practical applications, including water resource management, air pollution forecasting, and atmospheric propagation modeling. Turnkey systems for measuring the surface energy budget typically cost between 50,000. This study explores the use of artificial intelligence (AI) to predict sensible heat flux (SHF) and latent heat flux (LHF) using inexpensive surface meteorology data and downwelling solar radiation measurements as inputs. Observations from Amer- iFlux sites were used to train several AI models—Convolutional Neural Networks (CNNs), One Dimensional Transformers (1D Transformers), Artificial Neural Net- works (ANNs), and Long Short-Term Memory (LSTM) networks. Among these, the 1D Transformer model demonstrated the best performance, with an average root mean squared error (RMSE) of 46 W/m² and a correlation coefficient of 0.88 for SHF, and an RMSE of 48 W/m² with a correlation of 0.85 for LHF, establishing the feasibility of using AI to predict components of the surface energy budget using meteorological data and downwelling solar radiation as predictors
Expression, purification, and characterization of the angiopoietin-like 4 protein N-terminus
No full textThis research focused on developing protocols to express and purify the N-terminus of angiopoietin-like protein 4 (nANGPTL4), and identify its structural characteristics to understand its possible contribution to cancer metastasis. Bacteria expression was optimized by adding magnesium, calcium, and glycerol to the initial Luria-Bertani (LB) broth incubation, then transferring to Terrific Broth (TB) for induction. Purification was enhanced through overnight nickel resin suspension, extended sonification, and buffer adjustments. Protein recovery was further improved by transferring size exclusion columns to G50 superfine resin. Protein purity was analyzed using SDS-PAGE and native gel electrophoresis, while protein content was determined through UV-Vis spectroscopy. Circular dichroism and 1D 1H NMR data suggested the presence of alpha helices in nANGPTL4. The research provides a foundation for future studies, with ongoing optimization of protein crystal screens for X-ray diffraction or microED analysis. These findings contribute to understanding nANGPTL4’s potential role in tumor progression and metastasis inhibition
Safe reinforcement learning for trajectory tracking of mobile robots with minimal intermittent observations
No full textAutonomous wheeled mobile robots (WMRs) are widely used for safe operation in safety-critical systems, such as robotic visual inspection of confined spaces in energy infrastructure, warehouse automation, delivery robots, and autonomous vehicles. The operating environments for these safety-critical systems are often uncertain. Therefore, in such environments, it is essential for WMRs to reliably follow predetermined paths while effectively maintaining lane position and avoiding collisions. However, frequent observations required for control execution to account for environmental uncertainty result in increased sensing, computation, and energy costs. This necessity drives the research for safe and resource-aware trajectory-tracking control methods. Although several state-of-the-art trajectory tracking control schemes exist, these methods do not simultaneously address the challenges of safety and optimality under intermittently available sensing and computation. This research addresses the problem of optimal, safe trajectory tracking control for WMRs by developing a safe reinforcement learning (RL)-based trajectory tracking control framework integrated with event-based sensing and computation. The first part of the research reviews the state-of-the-art approaches for safe, resource-aware, and optimal control frameworks for WMRs in uncertain environments. It primarily focuses on 1) traditional control approaches for WMRs, 2) defining the rationale for selecting the control barrier function (CBF) as the safety certificate in the trajectory tracking control algorithm, and 3) the event-triggered control (ETC) that can reduce sensing and computation costs. In the second step, a near-optimal event-based sampling and optimal tracking control scheme under input constraints for WMRs is developed by extending an existing event-based RL-based control. The optimal trajectory tracking controller employs event-based adaptive dynamic programming and reinforcement learning to approximate the near-optimal controller under limited sensing and computation under input constraint. Numerical simulation results indicate a 61.2\% reduction in computation and sensing. In the third and final step, the event-based optimal trajectory tracking control is extended to incorporate safety by reformulating the cost function using CBF. The effectiveness of the proposed safe, event-triggered reinforcement learning-based control algorithm is validated through MATLAB-based numerical simulations in a lane-keeping scenario with safety constraints
Design, development, and analysis of a pulsed power system using a thyristor to fire a coaxial plasma gun
No full textA first of a kind fiber-optically controlled and thyristor switched coaxial plasma gun pulsed power system has been designed, built, and fired at up to 2 kV and 6.4 kA, with a 6.65 MW momentary peak power output from the capacitor. Total system firing jitter from initial digital signal to 90% peak current rise has been measured at 78.15 ns, representing 0.18% of the total pulse time of 44.10 µs. This includes both thyristor turn-on and coaxial plasma gun breakdown. Standard deviation of peak current over repeated firings has been measured to be 21.09 A (0.33%). The system has experienced 100+ firings at 2 kV without a single misfire. A voltage profiling analysis has been performed where voltage differentials across system components, including the thyristor, were collected temporally for the entire pulse. High speed and long-exposure photography was used to verify coaxial plasma gun operation and mass-injection/gas-puff system timing. This study sets the groundwork for plasma-load pulsed power systems using thyristors, as well as informing expanded voltage and current capability, enabling fine timing control and multisystem synchronization, allowing greater repeatability and reliability, and informing multithyristor array systems
Maude Valérie White During the British Musical Renaissance
No full textDespite the societal and professional constraints placed on female composers during the British Musical Renaissance, Maude Valérie White navigated these challenges by adapting her compositions to commercial expectations while maintaining artistic integrity.https://louis.uah.edu/honors-399/1022/thumbnail.jp