Physics-Informed Machine Learning to Predict Extreme Weather Events

Extreme weather events refer to unexpected, severe, or unseasonal weather events, which are dynamically related to specific large-scale atmospheric patterns. These extreme weather events have a significant impact on human society and also natural ecosystems. For example, natural disasters due to extreme weather events caused more than $90 billion global direct losses in 2015. These extreme weather events are challenging to predict due to the chaotic nature of the atmosphere and are highly correlated with the occurrence of atmospheric blocking. A key aspect for preparedness and response to extreme climate events is accurate medium-range forecasting of atmospheric blocking events. Unlike the conventional approach based on numerical weather forecasting, we propose a new machine learning approach to make binary classification predictions based on recurring patterns from multi-dimensional data of time-evolving atmospheric flow patterns. This approach enables us to focus on the intrinsic connection between extreme weather events and the surrounding large-scale atmospheric patterns. We build an empirical model using Convolutional Neural Networks to classify the 2D atmospheric flow patterns images to predict whether that would cause an extreme weather event or not. We retrieve the spatio-temporal data from the dataset by converting them into coarse-graining images and categorizing and labeling them to predict extreme weather events. These categorized images are then fed to the Neural Network to give us the final prediction. We use a CNN with 4 Convolutional layers, which provides the best accuracy compared to when we have more or fewer layers

Quasi-experimental analysis of new mining developments as a driver of deforestation in Zambia

Mining is a vital part of the global, and many national, economies. Mining also has the potential to drive extensive land cover change, including deforestation, with impacts reaching far from the mine itself. Understanding the amount of deforestation associated with mining is important for conservationists, governments, mining companies, and consumers, yet accurate quantification is rare. We applied statistical matching, a quasi-experimental methodology, along with Bayesian hierarchical generalized linear models to assess the impact on deforestation of new mining developments in Zambia from 2000 to present. Zambia is a globally significant producer of minerals and mining contributes ~ 10% of its gross domestic product and ~ 77% of its exports. Despite extensive deforestation in mining impacted land, we found no evidence that any of the 22 mines we analysed increased deforestation compared with matched control sites. The extent forest lost was therefore no different than would likely have happened without the mines being present due to other drivers of deforestation in Zambia. This suggests previous assessments based on correlative methodologies may overestimate the deforestation impact of mining. However, mining can have a range of impacts on society, biodiversity, and the local environment that are not captured by our analysis

The Story of Burley Tobacco Farming in Bethel, Watauga County, North Carolina: Cultural Meanings and Economic Impacts

During the twentieth century, tobacco farming characterized the culture and economy of many southern Appalachian mountain communities, including Bethel, Watauga County, North Carolina. Since 2004, following the end of the federal tobacco program, tobacco farming in the mountains has all but ended. In 2011, only three farmers raised tobacco in Bethel, the last tobacco farming community in the county. At one time, hundreds of farmers grew tobacco every year in Watauga County. What was once an important crop and way of life in the mountains is now gone. Although tobacco farming often provided partial portions of incomes in the mountains, tobacco farming, as part of diversified farm operations, was critical to the maintenance and sustainability of agrarian cultures and economies. Now, without tobacco farming, agrarian communities in the mountains face a tenuous future. This thesis examines the culture and economy of tobacco farming in Bethel, Watauga County, North Carolina from its origins, in the 1930s, to today

An Autonomous Discord Bot to Improve Online Course Experience and Engagement: Lessons Learned Amid the COVID-19 Pandemic

The COVID-19 pandemic pushed many educational institutions to adopt online learning models for most or all of their courses. As a result, the effectiveness of remote learning is more important now than ever before. In this paper, we report on work that was conducted in the Spring of 2021 at Utah Valley University. We explored the use of Discord as a delivery mechanism for online course content during the 2020-2021 school year. We also developed a Discord bot to autonomously track attendance. Based on our experience to date, the Discord bot appears to enhance remote learning. We describe the design, implementation, and deployment of our bot. We also discuss what worked well, as well as areas for improvement. In future semesters we plan to collect data by which we may begin to answer fundamental questions about the impact of such bots on remote learning

Conserved noncoding sequences highlight shared components of regulatory networks in dicotyledonous plants

Conserved noncoding sequences (CNSs) in DNA are reliable pointers to regulatory elements controlling gene expression. Using a comparative genomics approach with four dicotyledonous plant species (Arabidopsis thaliana, papaya [Carica papaya], poplar [Populus trichocarpa], and grape [Vitis vinifera]), we detected hundreds of CNSs upstream of Arabidopsis genes. Distinct positioning, length, and enrichment for transcription factor binding sites suggest these CNSs play a functional role in transcriptional regulation. The enrichment of transcription factors within the set of genes associated with CNS is consistent with the hypothesis that together they form part of a conserved transcriptional network whose function is to regulate other transcription factors and control development. We identified a set of promoters where regulatory mechanisms are likely to be shared between the model organism Arabidopsis and other dicots, providing areas of focus for further research