Suchergebnisse

"Civil Effects Test Group"--Cover. ; "Project 36.4"--Cover. ; "WT-1485 ; AEC Category: Health and Safety ; Military Category: 28"--Cover. ; "Issuance Date: June 11, 1962"--Cover. ; "March 1961." ; At head of title: Report to the Test Director. ; Mode of access: Internet. ; This bibliographic record is available under the Creative Commons CC0 "No Rights Reserved" license. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.

World War II (WW II) was the largest and deadliest conflict in history, and it involved virtually every part of the world between 1939 and 1945. During WWII, air power was implemented to achieve strategic objectives for the first time in history, and strategic bombing became a fundamental part of military combat. The U.S. Department of Defense defines strategic bombing as a military strategy used with the goal of "destroying enemy military and infrastructure targets and lowering their morale." The Allied forces relied on strategic bombing extensively during the conflict, attempting to break the morale of the enemy population by ceaselessly bombing cities, lines of communication, and factories throughout Italy, Germany, Japan, and their territories. This project aims to analyze and present some of the main characteristics of the Allied aerial military operations carried out during WWII throughout the world. This visualization will help historians, strategists, or simple WWII enthusiasts have a more intuitive view of the strategies and tactics behind aerial warfare in WWII. The Allied countries included in this analysis are the ones from the following countries: United States, United Kingdom, New Zealand, and Australia. The data used for this project come from the Theatre History of Operations Reports, or THOR, an historical data collection project developed and researched by U.S. Air Force Lt. Col. Jenns Robertson. The database was created in 2006 and was made available to the public upon approval of the U.S. Department of Defense in 2012. The dataset was downloaded from the data sciene online community data.world in June 2020 and was cleaned and processed using Python and Excel. All the visualizations were made using Tableau Desktop. I have uploaded some screenshots of my charts that outline some of the findings of this project:

Air Mobility Command (AMC) is combatting manning mismatches with antiquated processes and equipment in Aerial Port operations, and has chosen to address these issues through an initiative called "Aerial Port of the Future." This initiative is designed to understand challenges in Aerial Ports and implement technological tools, where appropriate, to aid AMC Airmen in their duties of transporting goods and personnel through military channels. Oftentimes, personnel are forced to utilize technology that may not meet their needs, and in turn may resist implementation of the imposed technology. Therefore, first, this research identifies potential technologies designed to improve Aerial Port efficiency and effectiveness. Next, through a survey of Aerial Port technicians, this study evaluates the attitudes and perceptions of personnel toward implementation and use of the selected technologies. Overall, the study provides AMC the necessary insight to increase the chances of successful implementation of "Port of the Future" technology.

Military Operations Research, V. 13, No. 1, pp 23-33. ; We construct an optimization model that assists commanders, operators and planners to effectively deploy and employ unmanned aerial vehicles (UAVs) in special operations missions. Specifically, we consider situations where targets (e.g., insurgents) enter a region of interest and a small special operations team is assigned to search and detect these targets. The special operations team is equipped with short-range surveillance UAVs. We combine intelligence regarding the targets with availability and capability of UAVs in an integer linear programming model. The goal is to detect the largest number of targets with the given resources. The model prescribes optimal deployment locations for the ground units and optimal time-phased search areas for the UAVs. The model has been implemented successfully in four field experiments. Preliminary empirical evidence indicates that the model provides 50% increase in detection opportunities compared with a plan manually generated by experienced commanders.