Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Gordon Bradley, John Hiles, Arnold Buss. Includes bibliographical references (p. 107-108). Also available online.

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For decades our military has been designed and funded as a conventionally superior force on the battlefield employing the most devastating and advanced weapon systems the world has ever seen (World, War I, II, Operation Desert Storm, and Phase I of Operation Iraqi Freedom). However, in low intensity conflicts (LICs) or irregular warfare (IW) campaigns, U.S. forces are faced with an irregular enemy, one that does not choose to fight our forces directly but rather through unconventional or indirect methods. For over 60 years, the Department of Defense has had an appalling record of protecting its air bases and personnel while deployed around the world in support of low intensity conflicts. However, the way the military defends and protects these air bases still revolves around a Cold War threat, a conventional threat. The strategy of global power projection and forward presence are the cornerstone to U.S. defense. To enhance combat capabilities in the Air Force and to defeat irregular warfare (IW) forces in any environment, the answer lies within the concept of Integrated Swarming Operations (ISO); the complete integration of a highly trained security force, skilled in the employment of successful counterinsurgency tactics, techniques, and procedures (TTPs), with today's most sophisticated Command, Control, Communications, Computer and Intelligence, Surveillance, and Reconnaissance (C4ISR) platforms into a battlefield swarm. In doing so, ISO allows security forces to achieve their three critical air base defense Mission Essential Tasks (METs) of 1) tactical ISR, 2) intercepting the threat, and 3) application of force as well as the Air Force's Integrated Base Defense (IBD) Objectives of "See First, Understand First, and Act First." / US Air Force (USAF) author.

For insectivorous bats living in temperate areas such as Nova Scotia, underground sites are a critical resource for over-wintering, as well as for swarming in the late summer and early fall, which is when mating occurs. The objectives of this study were to 1) identify additional abandoned mines and caves in Nova Scotia that are important swarming sites, and 2) quantitatively characterize factors which best differentiate between caves and mines that are used for swarming/hibernating, and those that are not. Acoustic and/or trapping surveys of 17 abandoned mines and nine caves in Nova Scotia were conducted in 2010. Five site characteristics were analysed to explain differences between used and unused sites. Surveys indicated that twelve of the 26 sites are used by bats during the swarming period. Results of a logistic regression analysis of nine a priori selected models indicated that chamber length was the best predictor of swarming.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2005. / Thesis Advisor(s): Phillip E. Pace Includes bibliographical references (p. 109). Also available online.

Chronic, low-grade inflammation is an underlying condition across a globally increasing number of debilitating diseases. These diseases include obesity, atherosclerosis, and diabetes and their resultant low-grade inflammation can be effectivity modeled with low dose stimulants such as lipopolysaccharide (LPS). While the innate immunity plays a significant role in fighting infectious disease, an initial exposure to low dose LPS hinders secondary infection clearance and pre-disposes murine models for fatal sepsis. Neutrophils are the most prevalent circulating innate immune cell and their homotypic aggregation, or swarming, is a key mechanism in clearing pathogens greater than 20 μm in size. We hypothesize that neutrophil swarming ability is altered when in a low dose LPS primed state; potentially leading to an overall altered innate immune response in the face of infection. However, an in vitro model does not currently exist to reliably quantify and compare neutrophil swarms across treatment groups. Here we propose a novel model utilizing fungal zymosan coated beads as a uniform target to which neutrophils may swarm. / Master of Science / White blood cells are critical for our body’s ability to fight off infection. The pathogens that cause infections come in many forms including fungus, viruses, and bacteria. However, in many debilitating inflammatory diseases such as heart disease and obesity, chronic inflammation prevents one’s white blood cells from being able to properly fight off infection. In order to study white blood cell function without the variability that is analogous to living pathogens, we propose a model system that simulates an artificial pathogen target where both the target and the surrounding environment can be precisely controlled. This system can then be used to study white blood cell function, specifically how it may be impacted under inflammatory conditions.

Unmanned Aerial Vehicles (UAVs) are becoming vital warfare platforms because they significantly reduce the risk of human life while accomplishing important missions. A UAV can be used for example, as stand-in sensor for the detection of mobile, low-probability-of-intercept battlefield surveillance and fire control emitters. With many UAVs acting together as a swarm, the location and frequency characteristics of each emitter can be accurately determined to continuously provide complete battlefield awareness. The swarm should be able to act autonomously while searching for targets and relaying the information to all swarm members. In this thesis, two methods of autonomous control of a UAV swarm were investigated. The first method investigated was the Particle Swarm Optimization (PSO) algorithm. This technique uses a non-linear approach to minimize the error between the location of each particle and the target by accelerating particles through the search space until the target is found. When applied to a swarm of UAVs, the PSO algorithm did not produce the desired performance results. The second method used a linear algorithm to determine the correct heading and maneuver the swarm toward the target at a constant velocity. This thesis shows that the second approach is more practical to a UAV swarm. New results are shown to demonstrate the application of the algorithm to the swarm movement.

abstract: I investigate two models interacting agent systems: the first is motivated by the flocking and swarming behaviors in biological systems, while the second models opinion formation in social networks. In each setting, I define natural notions of convergence (to a ``flock" and to a ``consensus'', respectively), and study the convergence properties of each in the limit as $t \rightarrow \infty$. Specifically, I provide sufficient conditions for the convergence of both of the models, and conduct numerical experiments to study the resulting solutions. / Dissertation/Thesis / Masters Thesis Mathematics 2018

We explore the behavior of an integrodifferential equation used to model one-dimensional biological swarms. In this model, we assume the motion of the swarm is determined by pairwise interactions, which in a continuous setting corresponds to a convolution of the swarm density with a pairwise interaction kernel. For a large class of interaction kernels, we derive conditions that lead to solutions which spread, blow up, or reach a steady state. For a smaller class of interaction kernels, we are able to make more quantitative predictions. In the spreading case, we predict the approximate shape and scaling of a similarity profile, as well as the approximate behavior at the endpoints of the swarm (via solutions to a traveling wave problem). In the blow up case, we derive an upper bound for the time to blow up. In the steady state case, we use previous results to predict the equilibrium swarm density. We support our predictions with numerical simulations. We also consider an extension of the original model which incorporates external forces. By analyzing and simulating particular cases, we determine that the addition of an external force can qualitatively change the behavior of the system.

In this work, I seek to model swarms of fruit flies, drosophila melanogaster, whose flights are characterized by straight flight segments interrupted by rapid turns called saccades. These flights are reminiscent of Levy-distributed random walks which are known to lead to efficient search behavior. I build two types of model for swarms of foraging fruit flies, whose behavior depends on swarm density and chemoattractant concentration, using rules inspired by experimentally observed flight patterns. First I will present a Lagrangian model where the path of each individual fly is tracked. I will also consider an Eulerian model where the fruit fly density evolves as a function of time and position in space. I will discuss the advantages and disadvantages of the two models and the relationship between them.