Forum and Ethical Culture Club Records

The Forum, originally established as the Ethical Culture Club in Brookings, South Dakota, in 1906, began as a liberal theological alternative to church attendance and later became a platform for open discussions on topics such as child labor laws, public libraries, and peace advocacy. Renamed The Forum in 1910, the group attracted college-affiliated members and professionals, fostering intellectual engagement and community action. This collection includes the club’s undated constitution, correspondence, programs, program notes, a 1923 historical account, records of Ethical Culture Club meetings, and minutes from Forum meetings, offering insight into the organization’s discussions and impact

Session 2 : \u3cem\u3eCreating a User-friendly Shiny App for Reproducible Two-Sample Mendelian Randomization Studies\u3c/em\u3e

We present a Shiny app that supports and facilitates two-sample Mendelian randomization studies with genome-wide association study (GWAS) summary statistics. The proliferation of GWAS and the sharing of their marginal SNP association statistics have enabled researchers to address causal inference questions between two complex traits. Two-sample Mendelian randomization posits a causal relationship between a putative exposure and a putative outcome. Our Shiny app will enable researchers to input GWAS summary statistics for the putative outcome and putative exposure. The app supports diverse sensitivity analyses to assess the assumptions that underlie Mendelian randomization. To ensure computational reproducibility, the user can download a Rmarkdown file with all analysis code from our app. We also briefly discuss anticipated issues with app deployment

Generative AI for Synthetic Data Creation: Building Mastery-Focused Educational Datasets

Synthetic data is artificially generated data that mimics the statistical properties of real world data without exposing sensitive information. It is used in analysis, research, and deployments. Educational technology (EdTech) is an area where synthetic data can solve the problems of data scarcity, privacy concerns, regulatory compliance, bias reduction, data quality, data integrity, and cost efficiency. Our research aims to generate synthetic educational dataset by leveraging generative AI techniques such as Autoencoder, variational autoencoder and Copula-GAN. Our experimental results shows the significant progress in generating educational dataset and represents the data distribution of synthetic and real data

Bayesian Machine Learning Approach for Corn Yield Prediction Using Satellite Imagery and Topographic Data

In an era of climate change and growing global food demand, accurate crop yield prediction is pivotal for leveraging advanced technologies to enhance crop management and sustainability. This study compares the prediction performance of several Bayesian Machine Learning method using high-resolution PlanetScope imagery and topographic data. In specific, the Bayesian Linear Regression, Bayesian Random Forest, Bayesian Splines, Bayesian Additive Regression Trees, and Bayesian Neural Network were developed to incorporate uncertainty quantification and achieve enhanced predictive accuracy. Our finding shows that the Bayesian Random Forest outperform the other model in term of crop yield prediction

2025 SDSU Data Science Symposium Schedule

2025 SDSU Data Science Symposiu

Session 7 : \u3cem\u3eUsing Machine Learning to Predict Attrition in a Federal Nutrition Education Program\u3c/em\u3e

Attrition poses a significant challenge to the effectiveness of federal nutrition education programs, hindering their ability to achieve widespread impact. This study employs machine learning techniques to develop a predictive model for identifying participants at high risk of dropping out of the Expanded Food and Nutrition Education Program (EFNEP). Analysis is conducted using standardized EFNEP program data (pre-program 24-hour dietary recalls, food and physical activity questionnaires, and demographic information) on over 1.25 million adult participants from 2013 to 2022. Three machine learning algorithms (logistic regression, XGBoost, and random forest) were evaluated, with the XGBoost model achieving the highest predictive accuracy. Key predictors of attrition included Cooperative Extension region, funding tier, land-grant university type (1860 vs. 1890), enrollment year, household income, age, race, residence, number of children, number of foods consumed in 24-hour dietary recall, and physical activity level. These findings provide valuable insight to EFNEP administrators, enabling them to proactively identify at-risk program participants and implement targeted interventions to improve retention and program impact

Young: Gertrude Stickney Young Papers

The Gertrude Stickney Young Papers document her significant academic, historical, and civic contributions to South Dakota. A long-time faculty member at South Dakota State University and author of historical works, Young played a pivotal role in shaping the university’s intellectual environment and preserving the state’s history. Key works in the collection include South Dakota: An Appreciation (1944) and Dakota Again (1950), which reflect her dedication to local history. Additionally, her other writings, such as Dr. Frederick A. Stafford in Dakota, 1884-1922 and William Joshua Cleveland, 1845-1910, highlight her broad historical interests. The collection also contains clippings, correspondence, and manuscripts, including personal items and works like Glimpses of South Dakota State College (1957). Other materials document her involvement in local organizations and her collaborations with figures like Ada Caldwell. This collection is a valuable resource for understanding the cultural, educational, and civic history of South Dakota, as well as Young\u27s lasting influence on the state\u27s historical scholarship

Stewart, Beryl Papers

Rhea Beryl DeHaven Stewart was a teacher and a farm homemaker. She served on the South Dakota Board of Education from 1947 to 1959 and was a contributor of poetry and prose to many publications. This is a collection of manuscripts and published works of Beryl Stewart as well as material she collected and scrapbooks she created throughout her career

Estimation of Parameters of the Truncated Normal Distribution with Unknown Bounds

The expectation-maximization (EM) algorithm is a commonly used iterative algorithm for providing parameter estimates of distributions for truncated samples when the truncation points or number of missing observations are known. There is also literature for estimating the unknown bounds of truncated distributions. However, there are no works that accommodate both parameter and bound estimation. In this work, we propose a methodology and an iterative algorithm known as an expectation-solution (ES) algorithm to estimate the location, scale, and truncation parameters of the truncated normal distribution. A preliminary simulation study illustrates the utility of this methodology

Active Bio-nanocomposites from Litchi Seed Starch, Tamarind Kernel Xyloglucan, and Lignin Nanoparticles to Improve the Shelf-life of Banana (\u3cem\u3eMusa acuminata\u3c/em\u3e)

Valorization of agricultural byproducts to biodegradable packaging films aids in reducing plastic dependency and addressing plastic perils. Herein, starch (LSS) from litchi seeds and xyloglucan (XG) from tamarind kernels were recovered, and composite films were developed. The XG addition strengthened the weak polymer networks of LSS and improved rheological, molecular, morphological, mechanical, and water vapor barrier properties. The incorporation of lignin nanoparticles (LNPs) into the LSS-XG network further increased the tensile strength (14.83 MPa), elastic modulus (0.41 GPa), and reduced surface wettability (80.07°), and water vapor permeability (5.63 ± 0.38 × 10−7 g m−1s−1Pa−1). The phenolic hydroxyls of LNPs imparted strong UV-shielding and free radical scavenging abilities to films. These attributes aided in preserving the quality of coated banana fruits with minimal weight loss and color change. Overall, this research highlights the potential transformation of underutilized abundant byproducts into sustainable active bio-nanocomposites for food packaging and shelf-life extension of fruits