Matching Items (4)
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- All Subjects: Bees
- All Subjects: Stochastic modeling
- Creators: Lanchier, Nicolas
Description
Bee communities form the keystone of many ecosystems through their pollination services. They are dynamic and often subject to significant changes due to several different factors such as climate, urban development, and other anthropogenic disturbances. As a result, the world has been experiencing a decline in bee diversity and abundance, which can have detrimental effects in the ecosystems they inhabit. One of the largest factors that impacts bees in today's world is the rapid urbanization of our planet, and it impacts the bee community in mixed ways. Not very much is understood about the bee communities that exist in urban habitats, but as urbanization is inevitably going to continue, knowledge on bee communities will need to strengthen. This study aims to determine the levels of variance in bee communities, considering multiple variables that bee communities can differ in. The following three questions are posed: do bee communities that are spatially separated differ significantly? Do bee communities that are separated by seasons differ significantly? Do bee communities that are separated temporally (by year, interannually) differ significantly? The procedure to conduct this experiment consists of netting and trapping bees at two sites at various times using the same methods. The data is then statistically analyzed for differences in abundance, richness, diversity, and species composition. After performing the various statistical analyses, it has been discovered that bee communities that are spatially separated, seasonally separated, or interannually separated do not differ significantly when it comes to abundance and richness. Spatially separated bee communities and interannually separated bee communities show a moderate level of dissimilarity in their species composition, while seasonally separated bee communities show a greater level of dissimilarity in species composition. Finally, seasonally separated bee communities demonstrate the greatest disparity of bee diversity, while interannually separated bee communities show the least disparity of bee diversity. This study was conducted over the time span of two years, and while the levels of variance of an urban area between these variables were determined, further variance studies of greater length or larger areas should be conducted to increase the currently limited knowledge of bee communities in urban areas. Additional studies on precipitation amounts and their effects on bee communities should be conducted, and studies from other regions should be taken into consideration while attempting to understand what is likely the most environmentally significant group of insects.
ContributorsPhan, James Thien (Author) / Sweat, Ken (Thesis director) / Foltz-Sweat, Jennifer (Committee member) / School of Music (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The decline of honeybee colonies around the world has been linked to the presence of the Varroa destructor, a mite acting as a virus vector for the Acute Bee Paralysis Virus. We developed a model of the infestation of the Apis melliifera honeybee colony by the Acute Bee Paralysis Virus, which is transmitted by the parasitic Varroa destructor. This is a four dimensional system of nonlinear ODE's for healthy and virus infected bees, total number of mites in the colony and number of mites that carry the virus. The Acute Bee Paralysis Virus can be transmitted between infected and uninfected bees, infected mite to adult bee, infected bee to phoretic mite, and reproductive mites to bee brood. This model is studied with analytical techniques deriving the conditions under which the bee colony can fight off an Acute Bee Paralysis Virus epidemic.
ContributorsDavis, Talia Lasandra (Author) / Kang, Yun (Thesis director) / Lanchier, Nicolas (Committee member) / Moore, Marianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
DescriptionUnderstanding the evolution of opinions is a delicate task as the dynamics of how one changes their opinion based on their interactions with others are unclear.
ContributorsWeber, Dylan (Author) / Motsch, Sebastien (Thesis advisor) / Lanchier, Nicolas (Committee member) / Platte, Rodrigo (Committee member) / Armbruster, Dieter (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2021
Description
The main objective of this work is to study novel stochastic modeling applications to cybersecurity aspects across three dimensions: Loss, attack, and detection. First, motivated by recent spatial stochastic models with cyber insurance applications, the first and second moments of the size of a typical cluster of bond percolation on finite graphs are studied. More precisely, having a finite graph where edges are independently open with the same probability $p$ and a vertex $x$ chosen uniformly at random, the goal is to find the first and second moments of the number of vertices in the cluster of open edges containing $x$. Exact expressions for the first and second moments of the size distribution of a bond percolation cluster on essential building blocks of hybrid graphs: the ring, the path, the random star, and regular graphs are derived. Upper bounds for the moments are obtained by using a coupling argument to compare the percolation model with branching processes when the graph is the random rooted tree with a given offspring distribution and a given finite radius. Second, the Petri Net modeling framework for performance analysis is well established; extensions provide enough flexibility to examine the behavior of a permissioned blockchain platform in the context of an ongoing cyberattack via simulation. The relationship between system performance and cyberattack configuration is analyzed. The simulations vary the blockchain's parameters and network structure, revealing the factors that contribute positively or negatively to a Sybil attack through the performance impact of the system. Lastly, the denoising diffusion probabilistic models (DDPM) ability for synthetic tabular data augmentation is studied. DDPMs surpass generative adversarial networks in improving computer vision classification tasks and image generation, for example, stable diffusion. Recent research and open-source implementations point to a strong quality of synthetic tabular data generation for classification and regression tasks. Unfortunately, the present state of literature concerning tabular data augmentation with DDPM for classification is lacking. Further, cyber datasets commonly have highly unbalanced distributions complicating training. Synthetic tabular data augmentation is investigated with cyber datasets and performance of well-known metrics in machine learning classification tasks improve with augmentation and balancing.
ContributorsLa Salle, Axel (Author) / Lanchier, Nicolas (Thesis advisor) / Jevtic, Petar (Thesis advisor) / Motsch, Sebastien (Committee member) / Boscovic, Dragan (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023