Air Force Institute of Technology

Traumatic brain injuries (TBI) frequently occur and can lead to lasting negative cognitive, physical, and mental health outcomes. The biological response to even mild TBIs (mTBI) includes well-characterized inflammatory sequelae that start immediately post-injury, remain for weeks, and can develop into long-term systemic inflammation. Studies have shown that TBI influences multiple physiological systems, including the gastrointestinal tract, through bidirectional communication modulated, in part, by the gut microbiome. Brainstem functioning post-TBI, as measured by acoustic startle sensorimotor processing, might play a role in this feedback loop. The current study investigated pre- to post-TBI (lateral fluid percussion injury model) changes in microbial communities and acoustic startle response in male and female rats. That is, the influence of mTBI on gut microbiome and sensorimotor processing was explored to examine: 1) overall and sex-specific differences in the gut microbiome and taxa in response to mTBI; 2) overall and sex-specific differences in sensorimotor processing following mTBI; and 3) associations between the gut microbiome and sensorimotor processing. Results showed mTBI had a limited effect on microbial diversity overall, and the same was observed in males and females independently. Yet, mTBI was associated with differences in 13 genus-level taxa. Further evaluation highlighted that 11 of the 13 genus-level taxa were sex-specific, with several being known to have short-chain fatty acid-producing capabilities. Alterations in sensorimotor processing were identified following mTBI; however, no sex-specific differences were evident. In addition, no associations were observed between sensorimotor processing and the gut microbiome. This study contributes longitudinal and sex-specific findings to the growing body of research examining the diverse effects of mTBI on the brain and gut microbial communities.

Additive manufacturing offers efficient production of plastic scintillators with nontrivial geometries using vat polymerization, allowing fabrication of geometries which would be difficult or even impossible to produce using conventional subtractive manufacturing. This work presents a novel photocurable scintillator formula that includes coumarin 450 as a tertiary dye to enable high-resolution 3D printing via the manipulation of the 405 nm cure light. Bulk photocured and 3D printed (with and without tertiary dye) samples were compared through observational assessment and spectral response. All samples showed pulse shape discrimination between neutron and gamma events. Inclusion of the tertiary dye has minimal impact on emission spectrum and light output, but significant impact on print resolution as shown by comparison of printed high-complexity geometries and feature resolution test objects. With the use of a cure-limiting dye, unsupported features — such as freestanding pillars — were resolvable down to 0.7 mm. Even finer resolution at or below 0.1 mm was achieved in fully supported, integrated structures printed with off-the-shelf 405 nm desktop 3D printer. Scintillators demonstrated a light output up to 50% of EJ-200 with a PSD figure of merit up to 1.35 at 0.9–1.1 MeVee.

The current state of Quantum computing (QC) is extremely optimistic, and we are at a point where researchers have produced highly sophisticated quantum algorithms to address far reaching problems. However, it is equally apparent that the noisy quantum environment is a larger threat than many may realize. The noisy intermediate scale quantum era can be viewed as an inflection point for the enterprise of QC where decoherence could stagnate progress if left unaddressed. One tactic for handling decoherence is to address the problem from a hardware level by implementing topological materials into the design. In this work, we model several Josephson junctions that are modified by the presence of topological superconducting nanowires in between the host superconductors. Our primary result is a numerical calculation of the energy-phase relationship for topological nanowire junctions which is a key parameter of interest for the dynamics of qubit circuits. In addition to this, we report on the qualitative physical behavior of the bound states as a function of superconducting phase. These results can be used to further develop and inform the construction of more complicated systems, and it is hopeful that these types of designs could manifest as a fault tolerant qubit.

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. To unify this conversation, this article formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This article then presents three artificial intelligence (AI) strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP<inline-formula><tex-math notation="LaTeX">${}^{3}$</tex-math></inline-formula>, an intermediate constraint satisfaction language to be resolved with a general constraint optimization solver. This article solves the other PLP variation instance using a greedy search heuristic. The article finishes by comparing the results of the two different PLP variations.

Proper process parameter calibration is critical to the success of fused deposition modeling (FDM) three‐dimensional (3D) printing, but is time‐consuming and requires expertise. While existing systems for autonomous calibration have demonstrated success in calibrating for a single objective, users may need to balance multiple conflicting objectives. Herein, an easily deployable, camera‐based system for autonomous calibration of FDM printers that optimizes for both part quality and completion time is presented. Autonomous calibration is achieved through a novel, multifaceted computer vision characterization and a multitask learning extension to Bayesian optimization. The system is demonstrated on four popular filament types using two distinct 3D printers. The results show that the system significantly outperforms manufacturer calibration across the machine and material configurations, achieving an average improvement of 32.2% in quality and a 31.2% decrease in completion time with respect to a popular benchmark.

This paper explores the advancement of object detection models within the domain of satellite imagery analysis, focusing on the innovative application of synthetically generated datasets to enhance model performance. Motivated by the inherent challenges of manual dataset annotation, such as errors, limited variability, and geographical biases, this study employs synthetic data generation techniques to create a diverse dataset by overlaying 3D models of 31 different aircraft types onto satellite imagery, creating a dataset of 5000 images containing 27,375 aircraft. This dataset includes a variety of environmental conditions and image transforms aimed at training a more robust and generalizable object detection model. Using a pre-trained model based on the YOLO v8x architecture, an extensive comparison of fine-tuned models trained separately on traditional manually annotated datasets was compared with a fine-tuned model trained on the synthetic dataset. The results show the model trained with synthetic data achieves a performance within 2% of the other models, detecting actual aircraft in diverse circumstances, despite only being trained on synthetic data derived from 3D models. By providing a scalable, efficient, and accurate method for training object detection models, this research demonstrates the possibility of detecting previously unseen objects based only on a 3D model.

The interactions between software and hardware are increasingly important to computer system security. This research collected microprocessor control signal sequences to develop machine learning models that identify software tasks. In contrast with prior work that relies on hardware performance counters to collect data for task identification, this research is based on creating additional digital logic to record sequences of control signals inside a processor’s microarchitecture. The proposed approach considers software task identification in hardware as a general problem, with attacks treated as a subset of software tasks. Three lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during actual (i.e., non-simulated) system operation. Second, experimental design selects hardware and software configurations to train and evaluate machine learning models. The machine learning models significantly outperform a naïve classifier based on Euclidean distances from class means. Various experiment configurations produced a range of balanced accuracy scores. Third, task classification is addressed using decision boundaries defined with thresholds chosen by an optimization strategy to develop non-neural network classifiers. When implemented in hardware, the non-neural network classifiers could require less digital logic to implement compared to neural network models.

Abstract Flight data for deep space satellites launched and operated between 1991 and 2020 is analyzed to generate various reliability metrics. Satellite reliability is first estimated by the Kaplan‐Meier estimator, then parameterized through the Weibull distribution. This general process is applied to a general satellite data set that included all deep space satellites launched between 1991 and 2020, as well as two data subsets. One subset focuses on deployable satellites, while the other introduces a methodology of normalizing satellite lifetimes by satellite design life. Results from the general data set prove deep space satellites suffer from infant mortality while the results from the deployable data subset show deployable deep space satellites are only reliable over short periods of time. Results from the design life normalized data set give promising results, with satellites having a relatively high chance of reaching their design life. Available information regarding specific modes of failure is also leveraged to generate a percent contribution to overall satellite failure for eight distinct failure modes. Satellite failure due to crashing, in‐space propulsion failure, and telemetry system failure are proven to drive both early in life failure and later in life failure, making them the main causes of decreased reliability.

Emerging technologies such as artificial intelligence, machine learning (AI/ML), and autonomy do not align to the traditional Department of Defense acquisition principles and processes for reliability and maintainability (R&M). Because emerging technologies are based in software, it is logical their development should follow an iterative, continuous process like DevSecOps (DSO). To incorporate R&M activities for software-intensive systems, including those that employ AI/ML and autonomy, the Scientific Test and Analysis Techniques Center of Excellence (STAT COE) expanded the DevSecOps Process Assessment Collaboration Tool (DSO-PACT). The DSO-PACT is designed to help program teams define and track lifecycle activities and tasks within a DevSecOps process. This paper outlines a method for integrating R&M into the DSO-PACT, which consisted of identifying R&M activities (overarching processes) and tasks (specific actions) from relevant source documents and aligning them to the appropriate DSO phase. These activities and tasks were organized into a relational database and a dashboard was created to display R&M activities by DSO phase to easily show progress toward each activity as a program evolves and matures. In addition to providing a way to track R&M activities in a continuous process, this tool will help foster horizontal integration because multiple organizations can utilize the same tool across the entire development cycle of emerging technologies.

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 (Laštovička 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).Although extensive research employing satellite data and global models have been conducted on the middle/upper atmosphere and ionosphere in recent years (Froidevaux, et al., 2006;Gettelman et al., 2019;Yu et al., 2019Yu et al., , 2021Yu et al., , 2022;;Cai et al., 2020Cai et al., , 2021Cai et al., , 2022;;Wu et al., 2021;Emmons et al., 2022), it is still a challenge to forecast changes in ionospheric irregularities and dynamic processes in the Earth's upper atmosphere (Tian et al., 2022(Tian et al., , 2023)) with more days' lag to the higher latitudes. It has been proven that the SSW effect can extend beyond the stratosphere and modulates the mesosphere and thermosphere.In summary, the articles in the research topic "Observations and Simulations of Layering Phenomena in the Middle/Upper Atmosphere and Ionosphere" report various prominent layers and their coupling behaviors in the MLT region. This will further advance our knowledge on chemical and dynamics in the upper atmosphere and ionosphere. A future challenge is to predict the changes in Earth's upper atmospheric conditions by comprehending the physical mechanisms governing the coupling between the various layers of the Earth's atmosphere as one whole system. In the face of growing demand for accurate prediction of the upper atmosphere and ionosphere environments, it is crucial to understand both the long-term changes and short-term dynamical coupling processes through trace species in the region. Therefore, we can gain an understanding of how the upper atmospheric layers behave and its fundamental atmospheric interaction processes.