Mitre Corporation

About: Mitre Corporation is a company organization based out in Bedford, Massachusetts, United States. It is known for research contribution in the topics: Air traffic control & National Airspace System. The organization has 4884 authors who have published 6053 publications receiving 124808 citations. The organization is also known as: Mitre & MITRE.

Advanced distributed simulation through the Aggregate Level Simulation Protocol

Abstract: The venerable problem-solving technique of simulation finds itself in the midst of a revolution. Where once it was regarded as a "technique of last resort" for systems analysis, today simulation is widely applied to support myriad purposes, including: training, interaction, visualization, hardware testing and decision support in real-time. Advanced distributed simulation (ADS) is the US Department of Defense (DoD) nomenclature used to describe the cooperative utilization of physically distributed simulations toward a common objective. The Aggregate Level Simulation Protocol (ALSP), under the auspices of ADS, provides a mechanism for the integration of existing simulation models to support training via theater-level simulation exercises. Consisting of a collection of infrastructure software and protocols for both inter-model communication through a common interface and time advance using a conservative Chandy-Misra based algorithm, the ALSP has supported an evolving "confederation of models " since 1992. A review of the history and design of ALSP is presented and serves to outline directions for future investigation.

Reduced Incidence of Long-COVID Symptoms Related to Administration of COVID-19 Vaccines Both Before COVID-19 Diagnosis and Up to 12 Weeks After

Abstract: Both clinical trials and studies leveraging real-world data have repeatedly confirmed the three COVID-19 vaccines authorized for use by the Food and Drug Administration are safe and effective at preventing infection, hospitalization, and death due to COVID-19 and a recent observational study of self-reported symptoms provides support that vaccination may also reduce the probability of developing long-COVID. As part of a federated research study with the COVID-19 Patient Recovery Alliance, Arcadia.io performed a retrospective analysis of the medical history of 240,648 COVID-19-infected persons to identity factors influencing the development and progression of long-COVID. This analysis revealed that patients who received at least one dose of any of the three COVID vaccines prior to their diagnosis with COVID-19 were 7-10 times less likely to report two or more long-COVID symptoms compared to unvaccinated patients. Furthermore, unvaccinated patients who received their first COVID-19 vaccination within four weeks of SARS-CoV-2 infection were 4-6 times less likely to report multiple long-COVID symptoms, and those who received their first dose 4-8 weeks after diagnosis were 3 times less likely to report multiple long-COVID symptoms compared to those who remained unvaccinated. This relationship supports the hypothesis that COVID-19 vaccination is protective against long-COVID and that effect persists even if vaccination occurs up to 12 weeks after COVID-19 diagnosis. A critical objective of this study was hypothesis generation, and the authors intend to perform further studies to substantiate the findings and encourage other researchers to as well.

Big data, big data quality problem

Abstract: A USAF sponsored MITRE research team undertook four separate, domain-specific case studies about Big Data applications. Those case studies were initial investigations into the question of whether or not data quality issues encountered in Big Data collections are substantially different in cause, manifestation, or detection than those data quality issues encountered in more traditionally sized data collections. The study addresses several factors affecting Big Data Quality at multiple levels, including collection, processing, and storage. Though not unexpected, the key findings of this study reinforce that the primary factors affecting Big Data reside in the limitations and complexities involved with handling Big Data while maintaining its integrity. These concerns are of a higher magnitude than the provenance of the data, the processing, and the tools used to prepare, manipulate, and store the data. Data quality is extremely important for all data analytics problems. From the study's findings, the "truth about Big Data" is there are no fundamentally new DQ issues in Big Data analytics projects. Some DQ issues exhibit return-s-to-scale effects, and become more or less pronounced in Big Data analytics, though. Big Data Quality varies from one type of Big Data to another and from one Big Data technology to another.

Fabrication of n(+)/p InP solar cells on silicon substrates

Abstract: InP films were grown by MOCVD on Si GaAs substrates (as well as on InP substrates, included as controls), and were used to fabricate solar cells, using the Spitzer et al. (1987) technique. Contact to the substrate was made with Al-Ti-Pd-Ag to the Si wafers and with Au-Zn alloy to the GaAs wafers, while contract to the front was made with Cr-Au-Ag. Air mass zero efficiencies were found to be 7.1 percent for Si-substrate cells and 9.4 percent for GaAs-substrate cells.

A new bound on the local density of sphere packings

Abstract: It is shown that a packing of unit spheres in three-dimensional Euclidean space can have density at most 0.773055..., and that a Voronoi polyhedron defined by such a packing must have volume at least 5.41848... These bounds are superior to the best bounds previously published [5] (0.77836 and 5.382, respectively), but are inferior to the tight bounds of 0.7404... and 5.550... claimed by Hsiang [2]. Our bounds are proved by cutting a Voronoi polyhedron into cones, one for each of its faces. A lower bound is established on the volume of each cone as a function of its solid angle. Convexity arguments then show that the sum of all the cone volume bounds is minimized when there are 13 faces each of solid angle 4?/13.