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Effects of a Modified Heat Treatment on the Quasi-static and Dynamic Behavior of Additively Manufactured Lattice Structures
No full textThe flexibility of additive manufacturing techniques that produce parts from powders layer-by-layer directly from a digital model enabled the fabrication of complex lightweight lattice structures with precisely engineered mechanical properties. Herein, an investigation of the quasi-static and dynamic behavior of additively manufactured (AM) triply periodic minimal surface (TPMS) lattice structures before and after a novel post-process heat treatment step is conducted. The specimens were fabricated out of Inconel 718, a nickel–chromium-based superalloy, using a selective laser melting technique with three different topologies, namely, gyroid, primitive, and I-WP. The quasi-static tests were conducted at a strain rate of 0.002 s-1 and dynamic experiments were conducted using a split Hopkinson pressure bar at three different strain rates, 600 s-1, 800 s-1, and 1000 s-1. It was shown that while the strain rate does not significantly affect the mechanical responses of the lattice structures, the heat treatment step dramatically changes their behavior. Results demonstrated that after the heat treatment, the yield strength of the I-WP specimens increased by 65.2% under a quasi-static load. Also, flow stress after yielding in the dynamic tests was shown to increase around 9.6% for I-WP specimens and up to 12.8% for gyroid specimens. The specific energy absorption values were 10.5, 19.1, and 10.7 for I-WP, gyroid, and primitive, respectively, before the heat treatment, and changed to 19.6, 19.8, and 15.4 after the heat treatment. The results confirm that by precisely designing the architecture of a lattice structure and implementing a modified heat treatment process, it is possible to optimize the weight, strength, and energy absorption capability of this type of metamaterial
Diagonals of self-adjoint operators II: non-compact operators
No full textGiven a self-adjoint operator T on a separable infinite-dimensional Hilbert space we study the problem of characterizing the set D(T) of all possible diagonals of T. For operators T with at least two points in their essential spectrum σess(T), we give a complete characterization of D(T) for the class of self-adjoint operators sharing the same spectral measure as T with a possible exception of multiplicities of eigenvalues at the extreme points of σess(T). We also give a more precise description of D(T) for a fixed self-adjoint operator T, albeit modulo the kernel problem for special classes of operators. These classes consist of operators T for which an extreme point of the essential spectrum σess(T) is also an extreme point of the spectrum σ(T). Our results generalize a characterization of diagonals of orthogonal projections by Kadison, Blaschke-type results of Müller and Tomilov, and Loreaux and Weiss, and a characterization of diagonals of operators with finite spectrum by the authors
Seasonal Variability and Predictability of Monsoon Precipitation in Southern Africa
Get PDFRainfed agriculture is the mainstay of economies across Southern Africa (SA), where most precipitation is received during the austral summer monsoon. This study aims to further our understanding of monsoon precipitation predictability over SA. We use three natural climate forcings, El Niño–Southern Oscillation, Indian Ocean Dipole (IOD), and the Indian Ocean Precipitation Dipole (IOPD)—the dominant precipitation variability mode—to construct an empirical model that exhibits significant skill over SA during monsoon in explaining precipitation variability and in forecasting it with a five-month lead. While most explained precipitation variance (50%–75%) comes from contemporaneous IOD and IOPD, preconditioning all three forcings is key in predicting monsoon precipitation with a zero to five-month lead. Seasonal forecasting systems accurately represent the interplay of the three forcings but show varying skills in representing their teleconnection over SA. This makes them less effective at predicting monsoon precipitation than the empirical model
Association of Blast Exposure in Military Breaching with Intestinal Permeability Blood Biomarkers Associated with Leaky Gut
Get PDFInjuries and subclinical effects from exposure to blasts are of significant concern in military operational settings, including tactical training, and are associated with self-reported concussion-like symptomology and physiological changes such as increased intestinal permeability (IP), which was investigated in this study. Time-series gene expression and IP biomarker data were generated from “breachers” exposed to controlled, low-level explosive blast during training. Samples from 30 male participants at pre-, post-, and follow-up blast exposure the next day were assayed via RNA-seq and ELISA. A battery of symptom data was also collected at each of these time points that acutely showed elevated symptom reporting related to headache, concentration, dizziness, and taking longer to think, dissipating ~16 h following blast exposure. Evidence for bacterial translocation into circulation following blast exposure was detected by significant stepwise increase in microbial diversity (measured via alpha-diversity p = 0.049). Alterations in levels of IP protein biomarkers (i.e., Zonulin, LBP, Claudin-3, I-FABP) assessed in a subset of these participants (n = 23) further evidenced blast exposure associates with IP. The observed symptom profile was consistent with mild traumatic brain injury and was further associated with changes in bacterial translocation and intestinal permeability, suggesting that IP may be linked to a decrease in cognitive functioning. These preliminary findings show for the first time within real-world military operational settings that exposures to blast can contribute to IP
Study Protocol: Identifying Transcriptional Regulatory Alterations of Chronic Effects of Blast and Disturbed Sleep in United States Veterans
Get PDFInjury related to blast exposure dramatically rose during post-911 era military conflicts in Iraq and Afghanistan. Mild traumatic brain injury (mTBI) is among the most common injuries following blast, an exposure that may not result in a definitive physiologic marker (e.g., loss of consciousness). Recent research suggests that exposure to low level blasts and, more specifically repetitive blast exposure (RBE), which may be subconcussive in nature, may also impact long term physiologic and psychological outcomes, though findings have been mixed. For military personnel, blast-related injuries often occur in chaotic settings (e.g., combat), which create challenges in the immediate assessment of related-injuries, as well as acute and post-acute sequelae. As such, alternate means of identifying blast-related injuries are needed. Results from previous work suggest that epigenetic markers, such as DNA methylation, may provide a potential stable biomarker of cumulative blast exposure that can persist over time. However, more research regarding blast exposure and associations with short- and long-term sequelae is needed. Here we present the protocol for an observational study that will be completed in two phases: Phase 1 will address blast exposure among Active Duty Personnel and Phase 2 will focus on long term sequelae and biological signatures among Veterans who served in the recent conflicts and were exposed to repeated blast events as part of their military occupation. Phase 2 will be the focus of this paper. We hypothesize that Veterans will exhibit similar differentially methylated regions (DMRs) associated with changes in sleep and other psychological and physical metrics, as observed with Active Duty Personnel. Additional analyses will be conducted to compare DMRs between Phase 1 and 2 cohorts, as well as self-reported psychological and physical symptoms. This comparison between Service Members and Veterans will allow for exploration regarding the natural history of blast exposure in a quasi-longitudinal manner. Findings from this study are expected to provide additional evidence for repetitive blast-related physiologic changes associated with long-term neurobehavioral symptoms. It is expected that findings will provide foundational data for the development of effective interventions following RBE that could lead to improved long-term physical and psychological health
The Impact of Data Preparation and Model Complexity on the Natural Language Classification of Chinese News Headlines
Get PDFGiven the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was studied. The simplest model (Naïve Bayes) achieved 85.1% accuracy on a holdout dataset, while the most complex model (Neural Network using BERT) demonstrated 89.3% accuracy. The most useful data preparation steps were identified, and another goal examined the underlying complexity and computational costs of automating the categorization process. It was discovered the BERT model required 170x more time to train, was slower to predict by a factor of 18,600, and required 27x more disk space to save, indicating it may be the best choice for low-volume applications when the highest accuracy is needed. However, for larger-scale operations where a slight performance degradation is tolerated, the Naïve Bayes algorithm could be the best choice. Nearly one in four records in the Toutiao dataset are duplicates, and this is the first published analysis with duplicates removed
Optimal Trajectory Solutions for Unmanned Pursuer/Evader Offensive Counterair Including Engagement Zone
Get PDFThis work considers a two-actor scenario of a faster, unmanned pursuer with an engagement zone (EZ) and a non-maneuvering mobile evader. The pursuer aims to capture an evader with a circular EZ. The EZ is dynamic and shifts as a function of the evader’s velocity vector. As the evader’s strategy changes, the EZ relative to the pursuer adjusts based on relative heading and speed of the two agents. Using nonlinear optimal control techniques, the optimal trajectory and minimum time to engage the evader are determined for various pursuer initial headings and positions through MATLAB simulation. The results build a control strategy given the scenario, with analytic solution validation. Results lay a foundation to model pursuer-evader capture scenarios with a dynamic EZ, leading to general guidelines for real-time control strategies
A Nonlinear Least Squarees Approach to Orbit Determination in Near-Earth Orbits
Get PDFThis work explores relevant scenarios to implement a Frequency on Arrival method using nonlinear least squares to determine a transmitting satellite’s orbit based only on measurements of the received radio frequency. Multiple relevant collection geometries, such as a target in prograde low earth orbit, in retrograde low earth orbit, in sun synchronous orbit, in near-perigee highly elliptical orbit, in near-apogee highly elliptical orbit, and in geosynchronous orbit, are simulated to collect theoretical received frequency data. Once data collection has been simulated, the theoretical apparent frequencies recorded are used in a nonlinear least squares method with differential correction to find a precise orbit determination to determine the feasibility of using this frequency-based orbit determination method in any practical scenarios. The results of this work show that with an accurate enough initial guess, a single receiver is sufficient for orbit determination. It is apparent that an improvement to the performance of the orbit determination method is noticeable when two or more receivers are used. Additional findings suggest that the geometry of the collection platform diversity does not have as large of an effect on a rapidly moving target as it does on slower moving targets out in geosynchronous orbit or near the apogee of a highly elliptical orbit
Scene Decomposed Blind Deconvolution and Neural Network based Multi-Frame Image Restoration Techniques for Astronomical Imagery
Get PDFGround based astronomical imaging is an important method in gaining situational awareness of orbiting and far off objects in space. This method of imaging is accessible to everyone that can look up into the sky, but the accessibility to digital telescope systems allows for more exciting methods of extracting information. A use case for these telescopes is finding nearby objects to larger brighter known objects. The number satellites in low-earth orbit and geosynchronous earth orbit is becoming more congested as these orbits increase in population. Tens of thousands of satellites and debris now exist in this orbit, with the number expected to grow. This drives a need to know the proximity of potential unknown objects to known assets. Using larger telescopes increases the number of photons a camera can collect, giving us the ability to peer further into space or conversely find fainter objects nearby. This paper focuses on the algorithmic methods of image restoration and detection of objects within ground based imagery, seeking to quantify statistical and neural network methods that perform these tasks
An Analysis of Electrical Energy Resilience Technologies as Applied to Air Force Operations
Get PDFAn analysis of 46 Resilient Energy Devices and Technology Concepts was conducted to determine their suitability for use in supporting Air Force Operations both at home station and abroad. The research consisted of two endeavors: an extensive literature review and a rank-ordering matrix. The dual nature of the efforts was designed to maximize usability and understanding for the End User, who may not be familiar with some principles of energy technologies, resilience, or design. The results showed the superiority of novel Solid (Metal) Fuels and Lead-Acid Batteries for Energy Storage and Thermoelectric Generators, Solar Photovoltaic Panels, Geothermal Extraction, Diesel Generators, and Solar Thermal Generation (in Building Integrated/Added Systems) for Energy Generation. Additionally, deep analysis of the principles behind resilience and failures of electrical energy in a military context led to strongly recommending installing these devices or connecting already-installed devices into Microgrids or alternatively using them in a Combined or Hybrid System configuration to maximize operational redundancy and decentralization of resources. Finally, the outcomes led to the conclusion that Compressed Natural Gas, Hydrogen, and Uranium for Storage and both Microreactors and Conventional Nuclear Reactors for Generation are generally not recommended for use at most installations, with their disbenefits outweighing their benefits