Digital Commons Network^™

Bryson Peter - Part 1, Mark Naison, Steven Payne

Oral Histories

No abstract provided.

Bryson Peter - Part 1 (Addendum), Mark Naison, Steven Payne

Oral Histories

No abstract provided.

Bryson Peter - Part 2, Mark Naison, Steven Payne

Oral Histories

No abstract provided.

Bryson Peter - Part 3, Mark Naison, Steven Payne

Oral Histories

No abstract provided.

Charles Latta, Mark Naison, Steven Payne

Oral Histories

Due to Latta’s initiatives, the BAC has won many awards for community service and social action. He works to make BAC visible to the borough president, congressmen, and councilmen in order to stay involved. In his fifth year as President, BAC hosted the conference for the Northeast Province and the Metro Founders Day, assisting with Atlantic hurricane relief initiatives. Though the COVID-19 pandemic made these initiatives difficult, they still try to persist in their message of outreach that they have held for decades. Latta’s goal is to see the Chapter bring in more people and to develop the ideals and …

Cheryl Simmons Oliver, Mark Naison, Steven Payne

Oral Histories

Simmons-Oliver’s education reflected those same values. Attending Catholic institutions—St. Anthony of Padua School, Cathedral High School, and Marymount Manhattan College—she encountered racial and ethnic diversity that deepened her awareness of inequality and strengthened her commitment to inclusion and justice. Her studies continued beyond college: she earned a Master of Science, a Juris Doctor, and an M.B.A. from various institutions throughout her career, a rare feat for any woman of her generation, and especially for a Black woman in mid-century Americas. For Simmons-Oliver, education was more than achievement; it was a means of self-definition and a way to honor her family’s …

Efficient Routing For Software-Defined Wireless Sensor Networks: A Naïve Bayes Approach, Amine Tcherak, Samia Loucif, Mohamed Ould Khaoua

All Works

Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) applications. Software-Defined Networking (SDN) is an emerging networking paradigm that extends the lifetime of WSNs by transferring the resource-intensive routing task from sensor nodes to a centralized controller. However, many SDN-based routing schemes for WSNs employ inefficient algorithms at the controller. Traditional shortest-path methods often create traffic imbalances across neighboring nodes, while Reinforcement Learning (RL)-based approaches typically generate excessive control traffic. Both issues accelerate energy depletion and reduce network lifetime. Moreover, existing algorithms frequently overlook critical factors, such as buffer occupancy, when selecting relay nodes, which can lead …

Gets: Greenhouse Environment Time Series, Scott Grimshaw, Natalie J. Blades, Grant R. Mcqueen

ScholarsArchive Data

This dataset contains high-frequency environmental measurements from a single greenhouse used to study multivariate statistical process control under strong autocorrelation. The data consist of a continuous Phase 1 monitoring period of approximately four weeks, during which environmental sensors recorded conditions inside the greenhouse once per minute.

The primary variables included in the archived dataset are:

• date – Date-time stamp at one-minute resolution (local greenhouse time).

• co2_ppm – Carbon dioxide concentration in parts per million (ppm).

• humidity_pct – Relative humidity (%).

• soil_temp_F – Soil temperature in degrees Fahrenheit.

Hogfish (Lachnolaimus Maximus) Neurocranium, Nate B. Nutting-Hartman, Naya H. Mondrosch, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

Population Pyramids For 66 Counties And The State Of South Dakota From 1970 To 2020, Weiwei Zhang

Center Demographic Datasets

The product of population pyramids of 1970, 1980, 1990, 2000, 2010, and 2020 for the state of South Dakota and its 66 counties uses U.S. decennial census population counts distributed by the U.S. Census Bureau. Population pyramids are generated using R software, adapting original scripts provided by Nathanael Rosenheim through OPEN Inter-university Consortium for Political and Social Research (ICPSR). Project Citation: Rosenheim, Nathanael. Population Pyramid Data and R Script for the US, States, and Counties 1970 - 2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-01-06. https://doi.org/10.3886/E117081V1

The downloadable zip file contains 67 individual files.
File …

Overflowing: A Visual Memoir, Margaret Gonzalez

Honors Projects

My collection, Overflowing: A Visual Memoir, is a series of mixed-media collages bound into a book, depicting formative experiences that have shaped who I am during my time at Grand Valley State University. They are an autobiographical exploration of self-discovery, expression, gratitude, and turbulence. I fell in love with the art of collage after creating a piece inspired by Hanna Höch for my History of Photography class. Since then, I have continued making collages. I haven’t gotten to take many art classes at Grand Valley, but creating has always been a constant in my life. So, I wanted to culminate …

Is Universal Health Coverage Really Better? Unintended Consequences Of The 2019 Amendment Of The National Health Insurance Act For Humanitarian Sojourners In South Korea, Minji Ju, Minah Kang, Eunice Y. Park

Department of Public Health Scholarship and Creative Works

Background: South Korea achieved universal health coverage (UHC) through the National Health Insurance (NHI). However, humanitarian sojourners under temporary stay permits were initially excluded. Alongside recommendations from the National Human Rights Commission of Korea (NHRCK), the 2019 Amendment of the NHI Act expanded eligibility of the NHI. While this marked significant progress toward greater universality in health care, it also led to unintended consequences for humanitarian sojourners. Methods: This study employed a two-fold approach aligned with the trajectory of the Amendment. First, we conducted semi-structured in-depth interviews to analyze diverse perspectives on the universality of health coverage, the benefits of …

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Polydopamine-Coated Magnetic Nanoparticles Data, William G. Pitt

ScholarsArchive Data

This data archive contains raw data and processed data related to research and development of magnetic nanoparticles that adhere to bacteria. This data is the foundation of data in the MS thesis of Bowen Houser, and publications and presentations related to that research effort. Polydopamine-coated magnetic nanoparticles are found to be very adhesive to gram-positive bacteria, and less adhesive to gram-negative bacteria. These data files contain information for S. aureus, S. epidermidis, S. mutans, E. coli, P. aeruginosa, and N. perflava. Some data include the kinetics of capture.

Data Release Of 5-Fluorouracil From Polylactic Acid Microparticles Containing Magnetic Nanoparticles, William G. Pitt

ScholarsArchive Data

This data archive contains raw data and processed data related to research and development of the release of the chemotherapy drug 5-fluororacil from microparticles of poly(lactic acid) which also contain superparamagnetic magnetite nanoparticles. This data is the foundation of data in the MS thesis of Tyler Green, and publications and presentations related to that research effort.

National-Scale Open Cattle Feedlot Detection Using Deep Learning And High-Resolution Aerial Images: Spatial Distribution And Animal Welfare Analysis, Uilson Ricardo Venancio Aires, Vitor Souza Martins, Dakota Hester, Thainara Lima, Lucas Borges Ferreira

Research Data

Open cattle feedlots are a major form of beef production infrastructure in the United States, characterized by outdoor confinement, high animal densities, and regulated feeding practices. Despite their economic importance, a comprehensive and spatially consistent national database of these facilities remains limited. This dataset provides labeled training data, trained deep learning models, and geospatial detection outputs developed to support automated identification of open cattle feedlots from aerial imagery. The dataset includes a manually curated shapefile of 11,746 open cattle feedlot facilities identified through visual interpretation of high-resolution aerial imagery in highly productive counties of Nebraska, Kansas, and Texas. These labels …

A Molecular Mechanism Of Epithelial To Mesenchymal Transition (Emt): An Evidence Based Lesson Applying Molecular Biology Through The Lens Of Cancer, Sophie Hasson, Melissa Rowland-Goldsmith

Open Educational Resources

This lesson aims to strengthen students’ critical thinking and molecular biology data analysis skills as well as their understanding of concepts related to the hallmark of cancer: tissue invasion and metastasis. Students begin by learning about the invasion of cancer cells, Epithelial-Mesenchymal Transition (EMT), and an important protein involved in EMT: E-cadherin. They then apply molecular biology knowledge to analyze data from multiple papers to infer the relationship between Snail and E-cadherin in different types of cancers. Next, they continue to interpret data from many papers to determine that a repressor transcription factor is present during EMT in cancer cells …

Reinforcement Learning Based Intelligent Optimisation For Bin Packing Problems: A Review, Nadia Dahmani, Amril Nazir, Ikbal Taleb, Syed M.Salman Bukhari

All Works

The convergence of Reinforcement Learning (RL) and Bin Packing Problems (BPP) is a critical field of study that has profound ramifications in logistics, manufacturing, computer, and retail industries. This paper thoroughly examines the progression from simple rule-based tactics to advanced Deep Reinforcement Learning (DRL) techniques in solving BPPs. By conducting a thorough review of 231 papers conducted between 2019 and 2024, we address and provide answers to important research inquiries, such as “To what extent has academic research explored the use of RL for BPP during this time frame?” and “Which specific areas of application and methodologies have been predominantly …

Artificial Intelligence In Waste Management Systems: Applications, Challenges, And Prospects, Imane Belyamani

All Works

Despite global recognition of the climate crisis, greenhouse gas emissions are projected to rise by 8.8 % by 2030, primarily due to inadequate planning, poor implementation, and insufficient financial support. While international initiatives such as the ’Waste to Zero’ coalition launched at the 28th Conference of the Parties to the UNFCCC (COP 28) highlight the urgency of advancing decarbonization and the circularity of waste systems, this review focuses on how artificial intelligence (AI) can accelerate that transformation. It systematically explores the role of AI in advancing waste management practices, with a focus on predictive analytics, route optimization, and machine learning-based …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Macrophyte Community Analysis Of The Tennessee-Tombigbee Waterway, The Pearl River, And The Pascagoula River, Samuel A. Schmid, Nicholas J. Engle-Wrye, Gray Turnage

Research Data

The macrophyte Tennessee-Tombigbee Waterway, Pearl River, and Pascagoula River were surveyed using point surveys. Data collected from these surveys were used to determine differences in species composition among macrophyte communities, how latitude affected species richness, and how the presence/absence of introduced species was affected by native richness. Differences in species composition were measured using NMDS, and the latitude-richness and introduced-native relationships were modeled using linear modeling methods. This record includes the data and code used for these analyses.

Red Snapper (Lutjanus Campechanus) Neurocranium, Gabriella K. Flora, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

Spatial–Temporal Deep Learning For Electric-Vehicle Charging Demand: An Exploratory Study Of Graph Convolutional And Lstm Networks Performance, Maher Alaraj, Carla Martins, Mohammed Radi, Mohamed Darwish, Munir Majdalawieh

All Works

Electric-vehicle (EV) charging is a localized, time-varying load that challenges distribution networks. This study offers practical insights into when spatial graph structure adds value beyond temporal context, utilizing real-world data and a transparent evaluation. We compare Long Short-Term Memory (LSTM) and Graph Convolutional Network (GCN) models for hourly EV-charging energy forecasting, based on 145,778 sessions recorded in Boulder, Colorado (2018–2023). After preprocessing and temporal alignment, temporal covariates (hour, day, month, year) and, when applicable, ZIP-code indicators were engineered. LSTMs were trained with 1 h and 24 h input windows, with or without ZIP features, and evaluated through 5-fold cross-validation. GCNs …

Great Hammerhead (Sphyrna Mokarran) Chondrocranium, Female, Nicholas Burchett, Michelle Bassali, Marissa Rosas, Rileigh Gonzalez, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

Atlantic Goliath Grouper (Epinephelus Itajara) Vertebrae, Nicholas Burchett, David W. Kerstetter, Rileigh Gonzalez, Sierra Rafacz, Ema Nagel

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

The Associations Between Meniscus Tear Location And The Prevalence And Severity Of Cartilage Wear In Different Knee Compartments Using An Arthroscopic Grading System, Tara Korbal, Claudia Leonardi, Amy Bronstone, Vinod Dasa

School of Medicine Faculty Publications

Introduction: Meniscus tears are associated with progressive cartilage degeneration and worsening osteoarthritis. Although several previous studies have explored the relationship between meniscus tears and cartilage damage, few have examined the relationship between tear location and cartilage wear in all knee compartments using an arthroscopic grading system. Objectives: Evaluate the association between meniscus tear location and the prevalence and severity of high-grade cartilage wear in knee compartments. Methods: A total of 210 patients were categorized into groups based on meniscus tear location: medial meniscus tear (MMT, n = 106), lateral meniscus tear (LMT, n = 41), and medial-lateral meniscus tear (MLMT, …

Daniels Distinction Portfolio By Maggie Schoeny, Maggie Schoeny

Finance: Undergraduate Distinction Portfolios

A Daniels Distinction Portfolio of experiential education by Maggie Schoeny

Mahi Mahi (Coryphaena Hippurus) Neurocranium, Ameer Tolani, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

Ladyfish (Elops Saurus) Neurocranium, Kaylee M. Blanchard, David W. Kerstetter

All Scans: Kerstetter Fisheries and Avian Ecology 3D Scan Series

No abstract provided.

Passive Listening: Exploring Interpassivity In Ambient Music, David Chechelashvili, Alan Brown

Staff Scholarship - New Zealand

The landscape of ambient music presents unique challenges for critique and classification due to its wide range of subgenres, functions and goals. Often defined by its atmospheric and immersive qualities, ambient music is traditionally seen as providing a backdrop for relaxation or as a means to block out the harsh reality of the outside world. This dominant theory, while valuable in certain contexts, tends to oversimplify the multifaceted nature of ambient music and ignores the potential for more nuanced listening experiences. The proposed alternative theory of interpassivity challenges existing understandings of ambient music production and consumption modes. Drawing on psychoanalytic …