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Training Schedule for the 56th Maintenance Group
Get PDFThe 56th Equipment Maintenance Squadron (56 EMS) provides equipment maintenance and back shop maintenance for the F-35 Joint Strike Fighter. The squadron executes thousands of sorties and flight hours annually. This operations tempo requires maintenance to prevent equipment failures, minimization of aircraft downtime, insurance of safety and compliance, and training of maintenance personnel. The squadron incorporates periodic training sessions to train maintenance personnel skills needed by airmen. This research investigates the optimization of these training sessions employing mixed integer programming (MIP). A multi-objective MIP model is developed to address the complex needs of various training activities, such as: training regiments, maintenance activities, assets, and constraints. We explore how the current training schedules and methodology support the squadron operations. The paper demonstrates the MIP model for a problem size of 148 Airmen. The results of the model generated from the solver indicate a theoretical increase in the effectiveness of training sessions, leading to enhanced maintenance readiness. A Pareto front of solutions is generated to support decision makers perform tradespace analysis. These solutions represent feasible alternatives that offer optimal trade-offs between the two objectives: maximal training of workers and maximal completion of tasks
Improving synthesis and testing conditions for bio-inspired carbon nanotube artificial hair sensors: Design, construction, reproduction, and calibration
No full textTechniques For Addressing Extreme Class Imbalance for Artificial Neural Networks Training
Get PDFThis research examined the class imbalance problem while training convolutional neural networks (CNN) by applying different techniques to combat this common issue. This research used a modified CIFAR-10 dataset along with a curated aerial image dataset. Methods covered included undersampling, oversampling, synthetic minority oversampling technique, Edited Nearest Neighbors and combinations of the aforementioned methods. This research found that undersampling methods tended to outperform oversampling methods. While undersampling methods showed a decrease in overall accuracy, the increase in minority class prediction performance was promising enough to warrant further investigation
Factors Affecting the Retention of Active Duty Airmen
Get PDFThis study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons identified
Human Capital Impacts in Military Acquisition
Get PDFThe Department of Defense has historically struggled to control both cost and schedule growth within acquisitions programs. Many studies have investigated these issues, but very few have explored the impact of human capital in improving performance outcomes. This study provides a description of current acquisition team manning and performs contingency table analysis to evaluate the impact of personnel, base, and ACAT on cost and schedule performance. The results of the study suggest that personnel has little to no impact on performance metrics of any kind, indicating that teams are allocated effectively. The study also suggests that ACAT 3 programs are less likely to have performance issues of any kind, while base has no impact on performance. While the study is limited by a small sample size; it is an important first extensive look at the issue, especially as it relates to schedule
Association of Homelessness and Diet on the Gut Microbiome: A United States-Veteran Microbiome Project (US-VMP) Study
No full textMilitary 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
Simulation Analysis of Applicant Scheduling and Processing Alternatives at a Military Entrance Processing Station
No full textEligibility for enlistment into the US military is assessed by the United States Military Entrance Processing Command (USMEPCOM), an independent agency that reports to the Office of the Secretary of Defense (OSD) and not to any specific branch of military service. This research develops a discrete-event simulation for applicant processing operations at a Military Entrance Processing Station (MEPS) to investigate the viability of potential alternatives to the current applicant arrival and processing operation. Currently, all applicants arrive to the MEPS at the beginning of the processing day in a single batch. This research models and compares two alternatives with the status quo: split-shift processing, by which applicant arrivals occur in two batches: one at 06:00 and one at 11:00 and appointment-based processing, by which applicants may arrive during one of three, four, six, or eight appointment windows. Express-lane processing is also explored, in which applicants are allowed to bypass select processing stations. Experimental results indicate that split-shift processing is not viable under the current processing model due to an unacceptable decrease in applicant throughput. Results from appointment-based scenarios are mixed, with the critical factors being the time between appointment batches and their associated arrival times
Relative Vectoring using Dual Object Detection for Autonomous Aerial Refueling
Get PDFOnce realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process of capturing an image, finding 2D points in the image, matching those points to 3D object features, and analytically solving for the object pose. However, we extend this pipeline by simultaneously performing these operations across two objects instead of one using machine learning and add a fifth stage that transforms the two pose estimates into a relative vector. Furthermore, we propose a novel supervised learning method using bounding box corrections such that our trained artificial neural networks can accurately predict 2D image points corresponding to known 3D object points. Simulation results show that this method is reliable, accurate (within 3 cm at contact), and fast (45.5 fps)
Quantifying Variation across 16S rRNA Gene Sequencing Runs in Human Microbiome Studies
Get PDFRecent microbiome research has incorporated a higher number of samples through more participants in a study, longitudinal studies, and metanalysis between studies. Physical limitations in a sequencing machine can result in samples spread across sequencing runs. Here we present the results of sequencing nearly 1000 16S rRNA gene sequences in fecal (stabilized and swab) and oral (swab) samples from multiple human microbiome studies and positive controls that were conducted with identical standard operating procedures. Sequencing was performed in the same center across 18 different runs. The simplified mock community showed limitations in accuracy, while precision (e.g., technical variation) was robust for the mock community and actual human positive control samples. Technical variation was the lowest for stabilized fecal samples, followed by fecal swab samples, and then oral swab samples. The order of technical variation stability was inverse of DNA concentrations (e.g., highest in stabilized fecal samples), highlighting the importance of DNA concentration in reproducibility and urging caution when analyzing low biomass samples. Coefficients of variation at the genus level also followed the same trend for lower variation with higher DNA concentrations. Technical variation across both sample types and the two human sampling locations was significantly less than the observed biological variation. Overall, this research providing comparisons between technical and biological variation, highlights the importance of using positive controls, and provides semi-quantified data to better understand variation introduced by sequencing runs
A Unified Digital Twin Approach Incorporating Virtual, Physical, and Prescriptive Analytical Components to Support Adaptive Real-Time Decision-Making
No full textBased on an overview of the historical and rapidly expanding literature on digital twins, this paper identifies fundamental capabilities that outline a general and adaptable twin that supports system development, real-time interactions, prescribing courses of action, and actualizing them. We relate these capabilities to business analytics concepts and decision-making processes geared toward rapid adaptation to changing situations. This leads to a general digital twin architecture supporting a system throughout its lifecycle implemented with components including Internet of Things (IoT) devices, a virtual reality environment, network communications, and an analytic simulation. The success of this architecture revolves around an authoritative data source, the Highly Integrated Virtual Environment (HIVE). We demonstrate the architecture and capabilities through a transporter system example. This demonstration highlights important timing and synchronization questions critical to fulfilling the twin’s fundamental role of reacting to evolving real-world conditions. It identifies the importance of lags in decisions, relates it to prescriptive response time and the rate of evolution of the underlying system, and quantifies this impact with new metrics of effectiveness lag and relevancy decay