ItemPredicting the Spread of Asian Giant Hornets in North America and Identifying Positive Sightings using a Convolutional Neural Network(2021-02) Crooks, Sabrina; Davidson, Shirley; Norton, Madeline; Cline, KellySoil temperatures, traveling distances, clutch sizes, and mortality rates among other factors must be analyzed to determine the potential spread of Asian Giant Hornets in North America. Our goals were: to create models to calculate this population growth and spread, to create a convolutional neural network which could correctly distinguish between positive and negative reported sightings, and to format a website which would allow for the spread to be tracked by both the Washington State Department of Agriculture and concerned citizens. We created three models to represent the population of Asian Giant Hornets. Model one was the simplest and did not include any limitations to population growth. Model two brings into account the mortality rate of queens that freeze in winter soil. Model three expands upon the second model by adding in the carrying capacity of a region based on the minimum necessary distance between two hives of Asian Giant Hornets. To best handle the influx of misidentified hornet images, we created a model using a convolutional neural network (CNN) to classify the images. Using the image data provided, the CNN was trained using a large amount of the data to identify what was and what was not an Asian Giant Hornet. This CNN was able to identify a Asian Giant Hornet with 90% accuracy. The convoluted neural network and the population predictive models were combined into a website. This website will allow pictures of reported sightings to be uploaded. Once uploaded, the trained CNN will analyze the photo and determine its verification ID, positive or negative. ItemA Winning Design for Soccer Teams: Three-Node Networks and Increased Pass Frequency(2020-02) Crooks, Sabrina; Davidson, Shirley; Norton, Madeline; Cline, KellyWe found qualitative indicators of team success for the Huskies soccer team. Success was measured in terms of the match outcome: win, loss, or tie. These indicators of success included the total number of passes per game, the type and frequency of node networks used, and the length of passes. These passes in a typical winning game included three different types of passing networks, two-node, three-node, and four-node, where the number of nodes indicates the players involved within the network. We found that while all three of these network types contributed to winning, the best indicator of success was an increase in the frequency of the three-node network utilization. We also found that shorter and simpler passes were more indicative of the team’s success in the match. We created five quantitative models that focused mainly on whether it was a home or away game, the starting formation, and the starting player line-up. We determined the top three players in all three positions, defense, mid-field, and offense, based upon the frequency in which they received a passed ball throughout the season. We hypothesized that other indicators of the team’s success would include which individuals started each game.