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Seasonal Flammability Comparisons Of Native And Exotic Plants In The Post Oak Savannah, Blackland Prairie, And Pineywoods Ecoregions Of Texas, Michael Tiller

Electronic Theses and Dissertations

East Texas’ diverse landscape can present year-round wildfire seasons that can be influenced by seasonal and regional differences in climate and physiography. Greater insight into the fundamental thermal behavior of wildland fuels can aid in fire behavior prediction and development of fire-resistant plant lists. This study focused on estimating seasonal and regional flammability characteristics of five evergreen species: yaupon (Ilex vomitoria), Chinese privet (Ligustrum sinense), greenbrier (Smilax spp.), eastern red cedar (Juniperus virginiana), and escarpment live oak (Quercus fusiformis); and two deciduous species: Chinese tallow (Triadica sebifera) and southern …

Microstructures In A Shear Margin: Jarvis Glacier, Alaska, Christopher Gerbi, Stephanie Mills, Renée Clavette, Seth Campbell, Steven Bernsen, David Clemens-Sewall, Ian Lee, Robert Hawley, Karl Kreutz, Kate Hruby

Dartmouth Scholarship

Microstructures, including crystallographic fabric, within the margin of streaming ice can exert strong control on flow dynamics. To characterize a natural setting, we retrieved three cores, two of which reached bed, from the flank of Jarvis Glacier, eastern Alaska Range, Alaska. The core sites lie ~1 km downstream of the source, with abundant water present in the extracted cores and at the base of the glacier. All cores exhibit dipping layers, a combination of debris bands and bubble-free domains. Grain sizes coarsen on average approaching the lateral margin. Crystallographic orientations are more clustered and with c-axes closer to horizontal nearer …

A Game-Theoretic Analysis Of Childhood Vaccination Behavior: Nash Versus Kant, Philippe De Donder, Humberto Llavador, Stefan Penczynski, John E. Roemer, Roberto Vélez

Cowles Foundation Discussion Papers

Whether or not to vaccinate one’s child is a decision that a parent may approach in several ways. The vaccination game, in which parents must choose whether to vaccinate a child against a disease, is one with positive externalities (herd immunity). In some societies, not vaccinating is an increasingly prevalent behavior, due to deleterious side effects that parents believe may accompany vaccination. The standard game-theoretic approach assumes that parents make decisions according to the Nash behavioral protocol, which is individualistic and non-cooperative. Because of the positive externality that each child’s vaccination generates for others, the Nash equilibrium suffers from a …

An Assessment Of Grass Regeneration Nurseries At The Western Regional Plant Introduction Station, 1994-1997, V. L. Bradley, R. C. Johnson

IGC Proceedings (1977-2023)

The Western Regional Plant Introduction Station (WRPIS), Pullman, WA, USA, maintains over 17,000 accessions of forage and turf grasses that are mostly wind cross-pollinated and highly heterogenic. Regeneration procedures have been refined over the past nine years to include improved isolation distance and increased plant populations for regeneration. The grass regeneration nurseries planted from 1994 through 1997 were evaluated using data recorded in the Germplasm Resources Information Network (GRIN) and it was found that approximately 78% of the regenerations were successful. Reasons for failures were contributed to inadequate plant number, presence of disease, seed shattering, and unsuitable growing environment. Several …

Assessment Of Ensilability Of Six Tropical Grasses Using Three Different Approaches, B. Zierenberg, K. Friedel, A. Glatzle, A. Chudy

IGC Proceedings (1977-2023)

The preparation of well-preserved silages is considered increasingly important in the tropics and subtropics. In these regions silage production played a minor role in the past, as ensilability of tropical grasses is generally considered poor. In this study ensilability of six tropical grasses grown in the Paraguayan Chaco Boreal (Panicum maximum cv. Gatton, Cynodon plectostachyus, Cynodon sp. cv. Tifton 85, Digitaria eriantha var. pentzii, Panicum maximum cv. Tanzania and Digitaria milanjiana) was assessed in three different ways: (1) The chemical determination of the ratio between water soluble carbohydrates (WSC) and buffer capacity (BC), (2) the biological …

Improving The Feeding Value Of Cotton Stalk, Wheat Straw And Rice Straw With Ozonation, Meiji Okamoto, Masaaki Hanada, R. Aoyama, R. Kim, N. Kaiga

IGC Proceedings (1977-2023)

The experiment was conducted to study the effect of ozone treatment on the feed value of cotton stalk, wheat straw and rice straw. These feeds were cut into 2 cm and 4 cm lengths, and rolled before ozonizing. The ozonizing periods were 30 min. 60min and. 120 min. The Acid Detergent Lignin (ADL) concentration of the feeds decreased with ozone treatment. Except for rice straw, the short cutting treatment (2cm) decreased the concentration of ADL and Cellulose of the ozonized cotton stalk and wheat straw. Rolling and ozone treatment were effective in decreasing the ADL concentration of cotton stalk. IVDMD …

Fermentation Quality Of Phasey Bean And Guineagrass Silages, Y. Imura, T. Namihira, Yasuhiro Kawamoto

IGC Proceedings (1977-2023)

Silages were made from guineagrass (Panicum maximum Jacq. var. maximum) and phasey bean (Macroptilium lathyroides (L.) Urb.) at three-growth stages. The silages were investigated in relation fermentation quality. Phasey bean silage showed a better fermentation quality than guineagrass silage. The latic acid to total acid ratio of phasey bean silage was higher than 500g/kg DM, and the volatile basic nitrogen to total nitrogen ratio was lower than 100g/kg. It is concluded that phasey bean is an unique legume suitable for good silage fermentation.

Preserving Innocence: Ending Perceived Adultification And Toxic Masculinity Toward Black Boys, Erik M. Hines, Edward C. Fletcher Jr., Donna Y. Ford, James L. Moore Iii

Journal of Family Strengths

As early as the age of ten, Black boys are viewed as older, guilty until proven innocent by law enforcement (including school resource officers), and encounter a myriad of adverse racialized academic and social experiences (e.g., explicit and implicit biases) (Goff et al., 2014; Noguera, 2008). Dancy (2014) noted how Trayvon Martin, a Black male teen murdered for essentially being seen as threatening and intimidating, was viewed as adult-aged, deviant, troubled, and shiftless. Moreover, Black boys are expected to express minimum “signs” of weakness, vulnerability, and/or sensitivity. The aforementioned social persona may contribute to young Black men and boys not …

Volume 32, Summer 2005 Communication And Theater Association Of Minnesota Journal

Communication and Theater Association of Minnesota Journal

Complete digitized volume (volume 32, Summer 2005) of Communication and Theater Association of Minnesota Journal.

Machine Learning Approach To Stability Analysis Of Semiconductor Memory Element, Ravindra Thanniru, Gautam Kapila, Nibhrat Lohia

SMU Data Science Review

Memory stability analysis traditionally relied heavily on circuit simulation-based approaches that run Monte Carlo (MC) analysis over various manufacturing and use condition parameters. This paper researches application of Machine Learning approaches for memory element failure analysis which could mimic simulation-like accuracy and minimize the need for engineers to rely heavily on simulators for their validations. Both regressor and classifier algorithms are benchmarked for accuracy and recall scores. A high recall score implies fewer escapes of fails to field and is the metric of choice for comparing algorithm. The paper identifies that recall score in excess of 0.97 can be achieved …

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Spotify: You Have A Hit!, Christopher E. Dawson Jr., Steve Mann, Edward Roske, Gauthier Vasseur

SMU Data Science Review

Abstract. Over 87% of the streaming music is owned by four major record labels (Jones, 2018). Yet, the songs owned by those labels account for <1% of the total amount of music created each year. These labels are historically better at identifying talent (though this talent identification is becoming more difficult). Even though Spotify has 36% of the streaming marketing share (T4, 2021), Spotify has not been profitable because of the large licensing costs paid to the large music labels. If Spotify could identify hit songs & artists before the large labels, they would sign those artists and dramatically reduce their licensing costs. Using the Spotify API, this paper will use Spotify data on over 400K songs over the last three years for exploratory data analysis, provide descriptive statistics, perform feature selection, and develop models using LASSO and XGBOOST Classification. The research determined multiple key features and predicted with over 60% accuracy songs which were going to be a hit (defined as >90% popularity).

Reading Level Identification Using Natural Language Processing Techniques, William Arnost, Ellen Lull, Joseph Schueder, Joseph Engler

SMU Data Science Review

This paper investigates using the Bidirectional Encoder Representations from Transformers (BERT) algorithm and lexical-syntactic features to measure readability. Readability is important in many disciplines, for functions such as selecting passages for school children, assessing the complexity of publications, and writing documentation. Text at an appropriate reading level will help make communication clear and effective. Readability is primarily measured using well-established statistical methods. Recent advances in Natural Language Processing (NLP) have had mixed success incorporating higher-level text features in a way that consistently beats established metrics. This paper contributes a readability method using a modern transformer technique and compares the results …

Modeling Electric Energy Generation In Ercot During Extreme Weather Events And The Impact Renewable Energy Has On Grid Reliability, Adam R. Ruthford, Bivin Sadler

SMU Data Science Review

This paper shows the inadequacy of current grid backup supplies, their ongoing threat to grid reliability, and the increased risk of customer blackouts. This paper also examines reliability concerns caused by renewable energy sources under more normal operating conditions. This paper seeks to model the impacts of extreme weather on ERCOTs grid. Unusual weather patterns require that the electrical grid operates in conditions outside normal operating parameters. Electrical system demand can spike well above normal levels. At the same time, weather influences can change the operating envelope of electrical generation equipment. The ERCOT grid has changed from its’ traditional mix …

Predicting Power Using Time Series Analysis Of Power Generation And Consumption In Texas, Joshua Eysenbach, Bodie Franklin, Andrew J. Larsen, Joel Lindsey

SMU Data Science Review

Due to the recent power events in Texas, power forecasting has been brought national attention. Accurate demand forecasting is necessary to be sure that there is adequate power supply to meet consumer's needs. While Texas has a forecasting model created by the Electricity Reliability Council of Texas (ERCOT), constant efforts are required to ensure that the model stays at the state-of-the-art and is producing the most reliable forecasts possible. This research seeks to provide improved short- and medium-term forecasting models, bringing in state-of-the-art deep learning models to compare to ERCOT’s forecasts. A model that is more accurate than ERCOT’s own …

Emotion Integrated Music Recommendation System Using Generative Adversarial Networks, Mrinmoy Bhaumik, Patrica U. Attah, Faizan Javed

SMU Data Science Review

Music can stimulate emotions within us; hence is often called the “language of emotion.” This study explores emotion as an additional feature in generating a playlist with a deep learning model to improve the current music recommendation system. This study will sample emotions from certain subjects for each song in a sample of the data. Since the effect of music on emotion is subjective and is different person to person, this study would need a considerable number of subjects to reduce subjectivity. Due to the limited resources, a portion of the data will be labeled with emotion from subjects and …

Alternative Methods For Deriving Emotion Metrics In The Spotify® Recommendation Algorithm, Ronald M. Sherga Jr., David Wei, Neil Benson, Faizan Javed

SMU Data Science Review

Spotify's® recommendation algorithm tailors music offerings to create a unique listening experience for each user. Though what this recommender does is highly impressive, there is always room for improvement given that these techniques are not fully prescient. This study posits that in addition to creating certain features based on audio analysis, incorporating new features derived from album art color as well as lyrical sentiment analysis may provide additional value to the end user. This team did not find that a significant difference existed between color valence and Spotify® valence; however, all other comparisons resulted in statistically significant difference of means …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Covid-19 - A Graph Network Approach, Nibhrat Lohia, Rajesh Satluri, Suchismita Moharana, Venkat Kasarla

SMU Data Science Review

The effects of COVID-19 and its spreads are attributed to various factors. This study uses CDC open-source data on COVID-19 effected population with features ranging from location to ethnicity, to create a Knowledge Graph to measure the similarity between COVID-19 cases and estimate the risk for people likely affected by COVID-19. This data could be used to find correlations between distinct factors, like ethnicity and pre-existing health conditions, to find the vulnerability of a given COVID-19 patient. Using the Jaccard similarity coefficient, in the knowledge graph, we are able to identify and explore relationships between COVID-19 cases as well as …

Rocket Learn, Daanesh Ibrahim, Jules Stacy, David Stroud, Yusi Zhang

SMU Data Science Review

Abstract. This paper covers the development, testing, and implementation of Reinforcement Learning methods designed to autonomously learn and optimize Rocket League play. This study aims to analyze and benchmark model frameworks commonly used in Reinforcement Learning applications. These models can be applied to tasks ranging in difficulty from simple to superhumanly complex, and this study will begin with and build upon simple models performing simple tasks. It will result in complex models performing difficult tasks. Models will be allowed to train autonomously on the game using mass parallelization to expedite training times with the goal of maximizing reward function scores. …

Real-Time Voice Biometric Speaker Verification, Inderbir Dhillon, Jason Rupp, Aniketh Vankina, Robert Slater

SMU Data Science Review

Abstract. Automated speaker verification has been an area of increased research in the last few years, with a special interest in metric learning approaches that compute distances between speaker voiceprints. In this paper, three metric learning systems are built and compared in a one-shot speaker verification task using contrastive max-margin loss, triplet loss, and quadruplet loss. For all the models, spectrograms are created from speaker audio. Convolutional Neural Network embedding layers are trained to produce compact voiceprints that allow users to be distinguished using distance calculations. Performances of the three models were similar, but the model with the best EER …

Pokégan: P2p (Pet To Pokémon) Stylizer, Michael B. Hedge, Morgan Nelson, Thomas Pengilly, Michael Weatherford

SMU Data Science Review

This paper covers the development, testing, and implementation of an automatic framework for converting common images of pets into a Pokémon cartoon with the style of a Pokémon trading card. The technique will first implement object detection for common animals to facilitate image segmentation and apply the appropriate style transfer model to ensure the most aesthetic stylization. It explores various methods to address artifacts in the results of common neural style transfer techniques using Generative Adversarial Networks (GANs). This research sets up a framework to create an app that converts user-submitted pet pictures to Pokémon styled images using the most …

Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo

SMU Data Science Review

Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …

Urban Traffic Simulation: Network And Demand Representation Impacts On Congestion Metrics, Aaron Faltesek, Balasubramaniam Dakshinamoorthi, Sreeni Prabhala, Akbar Thobani, Anu Kuncheria, Jane Macfarlane

SMU Data Science Review

Traffic simulations are often used by city planners as a basis for predicting the impact of policies, plans, and operations. The complexities underpinning traffic simulations are often not described in detail yet can significantly impact the simulation outcome. Conflating underlying data for simulations is complex and hinders the interest in this type of exploration. This paper aims to elucidate critical features of traffic simulations that drive the generated metrics of the modeled urban environment. Specifically, this paper examines differences in two road graph networks for the metropolitan region of Houston, TX: a reduced network composed of 45,675 road links and …

Intelligent Investment Portfolio Management Using Time-Series Analytics And Deep Reinforcement Learning, Sachin Chavan, Pradeep Kumar, Tom Gianelle

SMU Data Science Review

Abstract. – With globalization, the capital markets have exploded in size and value, making them exceedingly difficult to predict. These days the public has access to real-time data of the market-leading to more participation. As a positive step, this might lead to better wealth distribution in the society, and it also adds to the random nature of the market, making it more unpredictable. The portfolio accounts consisting of stocks and bonds are considered serious investment assets. They can make or break a person’s future. It is also a way of shielding one against market risk or rising inflation. These accounts, …

Identifying Vacant Lots To Reduce Violent Crime In Dallas, Texas, Laura Lazarescou, Andrew Mejia, Tina Pai, Sabrina Purvis, Robert Slater, Owen Wilson-Chavez

SMU Data Science Review

Vacant lots have been associated with community violence for many years. Researchers have confirmed a positive correlation between vacant lots and vacant buildings with increased violence in urban and rural geographies. However, identifying vacant lots has been a challenge, and modeling methods were largely manual and time-intensive. This prevented cities and non-profit organizations from acting on the information since it was expensive and high-risk to develop remediation programs without clearly understanding where or how many vacant lots existed.

The primary objective of this study was to provide a predictive model that accelerates and improves the accuracy of prior land classification …

Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods, Joshua Balson, Matt Chinchilla, Cam Lu, Jeff Washburn, Nibhrat Lohia

SMU Data Science Review

Across the United States, record numbers of wildfires are observed costing billions of dollars in property damage, polluting the environment, and putting lives at risk. The ability of emergency management professionals, city planners, and private entities such as insurance companies to determine if an area is at higher risk of a fire breaking out has never been greater. This paper proposes a novel methodology for identifying and characterizing zones with increased risks of forest fires. Methods involving machine learning techniques use the widely available and recorded data, thus making it possible to implement the tool quickly.

Qualitative Leveraging Natural Language Processing To Establish Judge Incrimination Statistics To Educate Voters In Re-Elections, Aurian Ghaemmaghami, Paul Huggins, Grace Lang, Julia Layne, Robert Slater

SMU Data Science Review

The prevalence of data has given consumers the power to make informed choices based off reviews, ratings, and descriptive statistics. However, when a local judge is coming up for re-election there is not any available data that aids voters in making data-driven decision on their vote. Currently court docket data is stored in text or PDFs with very little uniformity. Scaling the collection of this information could prove to be complicated and tiresome. There is a demand for an automated, intelligent system that can extract and organize useful information from the datasets. This paper covers the process of web scraping …

The Fierce Green Fire: Vol. 12 Issue 9, Wofford College. Department Of Environmental Studies

The Fierce Green Fire

No abstract provided.

The Emission Spectrum Of The Hot Jupiter Wasp-79b From Hst/Wfc3, Trevor O. Foote, Nikole K. Lewis, Brian M. Kilpatrick, Jayesh M. Goyal, Giovanni Bruno, Hannah R. Wakeford, Nina Robbins-Blanch, Tiffany Kataria, Ryan J. Macdonald, Mercedes López-Morales, David K. Sing, Thomas Mikal-Evans, Vincent Bourrier, Gregory Henry, Lars A. Buchhave

Information Systems and Engineering Management Research Publications

Here we present a thermal emission spectrum of WASP-79b, obtained via Hubble Space Telescope Wide Field Camera 3 G141 observations as part of the PanCET program. As we did not observe the ingress or egress of WASP-79b's secondary eclipse, we consider two scenarios: a fixed mid-eclipse time based on the expected occurrence time, and a mid-eclipse time as a free parameter. In both scenarios, we can measure thermal emission from WASP-79b from 1.1 to 1.7 μm at 2.4σ confidence consistent with a 1900 K brightness temperature for the planet. We combine our observations with Spitzer dayside photometry (3.6 and 4.5 …