Matching Items (3)
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- All Subjects: Bees
- All Subjects: data
- Creators: Jones, Donald
- Resource Type: Text
Description
Honey bees (Apis mellifera) are responsible for pollinating nearly 80\% of all pollinated plants, meaning humans depend on honey bees to pollinate many staple crops. The success or failure of a colony is vital to global food production. There are various complex factors that can contribute to a colony's failure, including pesticides. Neonicotoids are a popular pesticide that have been used in recent times. In this study we concern ourselves with pesticides and its impact on honey bee colonies. Previous investigations that we draw significant inspiration from include Khoury et Al's \emph{A Quantitative Model of Honey Bee Colony Population Dynamics}, Henry et Al's \emph{A Common Pesticide Decreases Foraging Success and Survival in Honey Bees}, and Brown's \emph{ Mathematical Models of Honey Bee Populations: Rapid Population Decline}. In this project we extend a mathematical model to investigate the impact of pesticides on a honey bee colony, with birth rates and death rates being dependent on pesticides, and we see how these death rates influence the growth of a colony. Our studies have found an equilibrium point that depends on pesticides. Trace amounts of pesticide are detrimental as they not only affect death rates, but birth rates as well.
ContributorsSalinas, Armando (Author) / Vaz, Paul (Thesis director) / Jones, Donald (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.
ContributorsBrowning, Jacob Christian (Author) / Meuth, Ryan (Thesis director) / Jones, Donald (Committee member) / McCulloch, Robert (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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