Digital Commons Network^™

Human Rights In Technology -- A Need For A New Norm, Els De Busser

Case Western Reserve Journal of International Law

The field of cyber security has relied on norms quite heavily to govern the behavior of states and non-state actors in cyberspace. However, existing norms do not offer guidance on integrating attention to human rights into the design and development of digital consumer products. This Paper introduces a way to foresee the human rights impact of new technology combined with a form of governance that regulates problems we do not know exist yet. (Abstract from author.)

Exploring Topological Defects In Janus Bilayers Of Cr(I,Cl)3 And Cr(I,Br)3, Taksh Patel, Suyash Rijal, Charles Paillard, Changsong Xu, L. Bellaiche

2025 Research Poster Competition

Chromium halides such as CrI3 have attracted interest recently. They possess the remarkable property of sustaining long-range magnetic order down to the thickness of only a few layers [1, 2]. However, CrI3 lacks Dzyaloshinskyii– Moriya interaction (DMI) due to inversion center present between the adjacently bonded Chromium atoms.

The inversion center can be removed through the fabrication of Janus Monolayers by adding another trihalide along with Iodine to synthesize the material, that is to grow Janus monolayers Cr(I, X)₃ where X is another halide such as Cl or Br [3].

Previous work has predicted non-trivial spin textures such as …

Bioinspired Gradient Porous Hip Implant Design To Prevent Stress Shielding Using Stress Mapping And Bone Remodeling Theory, Babak Ziaie, Xavier Velay, Waqas Saleem

Research Outputs: 2025-Present

Total Hip Arthroplasty (THA) is a widely performed surgical procedure, with increasing frequency worldwide. Despite its high success rate, many patients require revision surgeries due to complications such as aseptic loosening, primarily resulting from stress shielding, cortical hypertrophy, and micromotion. These issues often stem from a mechanical mismatch between the bone and the solid dense implant, particularly a mismatch in stiffness. A potential solution lies in the use of porous or lattice structures in implant design, which offer lower stiffness and improved biocompatibility by facilitating bone ingrowth, cell seeding, and vascularization. Among these designs, Triply Periodic Minimal Surfaces (TPMS), especially …

Diagnosing Statistical Education Needs Of Health Science Learners, Amy S. Nowacki, Ann M. Brearley, Robert A. Oster, Emily Slade, Katrina L. Devick, Matthew J. Hayat, Sally W. Thurston

Biostatistics Faculty Publications

Many types of health science learners, including clinical and translational scientists, students, researchers, and clinicians, seek to increase their knowledge of biostatistics. These learners are heterogeneous in their field, career stage and career focus. Based on the collective experience of an expert panel with over 115 years teaching statistics to health science learners, we propose a framework for considering the needs of health science learners motivated by their career goals. This framework defines four types of health science learners seeking statistical training: (a) consumers, (b) “milestone makers”, (c) biomedical researchers with statistical support, and (d) biomedical researchers without statistical support. …

Using Analysis Of Teaching And Assessment Practices To Revise Implementation Of A Force Unit In An 8th Grade Science Course, Caleb Christine

Graduate Research Papers

One thing I have noticed from my fellow teachers is a desire to improve how we teach and to engage our students in active learning. As a new teacher I have that same desire. Accomplishing my goal of being an effective teacher while also improving how the material is presented requires an approach that includes evaluating not only how the students are performing, but also how they are responding to my instruction.

I used self reflection journals to keep track of how I was presenting material in my classroom. I also collected information from completion of ungraded assignments and investigations. …

The Impacts Of A Problem-Based Learning Unit On Computational Thinking Skills In A 7th Grade Lego Robotics Classroom, Madison Beeler

Graduate Research Papers

The primary focus of this study was to determine what impact problem-based learning (PBL) has on developing computational thinking skills, self-efficacy, and earth science understanding in a middle school robotics classroom. Twenty-eight students took part in a 36-day instructional unit based on the 5E Learning Cycle. Students explored Martian geology, analyzing rock data collected by the Perseverance rover mission to make connections between landforms, integrating Earth science with computational thinking tasks. Pre and post-test data assessed growth of computational thinking and self-efficacy. Statistical analysis showed significant growth in student self-efficacy, while gains in computational thinking were not statistically significant. Students …

Citizen-Centric Building Energy Retrofits: Understanding And Supporting Citizens As Occupants, Participants, Decision-Makers, And Learners In Home Energy Retrofits, Nikhil Singh Yaduvanshi

Civil Engineering Dissertations - Archive

Residential building retrofits remain significantly under-adopted despite substantial technological advances, economic incentives, and policy efforts, creating a critical gap between technical potential and actual implementation in achieving climate goals. Assessment of citizen involvement in building retrofit processes plays a critical role in formulating effective home retrofit program designs to ensure maximum adoption rates and avoid energy waste. Although, existing retrofit programs and decision-making models recognize the importance of citizen involvement, the extent of citizen capabilities and the effects of citizen-centered approaches on retrofit adoption and building performance has not been comprehensively examined. The adoption and success of retrofit programs depends …

Assessment Of The Feasibility Of Utilizing Plastic Waste In Infrastructure To Promote Circular Economy In Bangladesh, Md Lutfor Rahman

Civil Engineering Dissertations - Archive

This study presents a comprehensive assessment of municipal solid waste (MSW) and plastic waste characterization across Bangladesh, with a focus on circular economy opportunities and the long-term economic viability of plastic road. Using on-site waste characterization data for 13 major cities from four geographic zones of Bangladesh —Northwest, Northeast, Southwest, and Southeast—the research identifies food waste as the dominant component of the national waste stream (67.3%), followed by plastic (12.4%), textiles (7.8%), and paper (4.7%). Among plastics, low-density polyethylene (LDPE) overwhelmingly dominates (89.7%), highlighting the continued prevalence of single-use flexible plastics.

A life cycle cost analysis (LCCA) was conducted comparing …

Implementation And Performance Evaluation Of Plastic Road Mix Design In Parking Lot, Ishraq Faruk

Civil Engineering Dissertations - Archive

The uncontrolled proliferation of plastic waste has emerged as a global environmental crisis, with high-density polyethylene (HDPE) constituting a substantial fraction of the waste stream due to its extensive use in packaging and its resistance to degradation. In parallel, asphalt pavements particularly in urban infrastructure such as parking lots experience accelerated deterioration from raveling, rutting, and cracking as a result of thermal cycling, repetitive loading, and inadequate drainage. These converging challenges present an opportunity for innovative, circular-economy-based sustainable pavement technologies that simultaneously improve surface durability and mitigate plastic waste accumulation.

This research investigates the full-scale implementation and monitoring of HDPE …

Transforming Lives For Eternity: 2024-2025 Year End Summary, Southern Adventist University

Strategic Plans

Southern Adventist University's Strategic Plan Year End Summary for the year 2024-2025.

Deep Learning For Irish Garden Bird Identification: Exploring The Role Of Cnn-Lstm In Video-Based Recognition, Antonina Dolynenko

ICT

Bird populations are widely used as indicators of ecosystem health, but traditional monitoring based on manual observation is labour-intensive and difficult to scale. Recent advances in deep learning and low-cost edge hardware offer new opportunities for automated, real-time bird identification in gardens and other local habitats. This thesis investigates whether video-based deep learning models can reliably classify common Irish garden birds from short motion-triggered clips and how temporal modelling compares to image-based models.

A primary dataset of 20-second clips was collected in a private garden in Ireland using a Raspberry Pi with a high-resolution camera and a YOLO-based trigger to …

Premier League Results Predictions, Laura Consuegra

ICT

This project applies machine learning techniques to predict outcomes in the English Premier League, one of the most prestigious and widely followed football competitions worldwide. By analysing historical and real-time match data, including performance metrics such as goals scored, shots on target, and cards received, predictive models are developed to forecast match results with higher accuracy. The study evaluates the effectiveness of these models and explores the influence of key statistical features on team performance. The findings aim to provide strategic insights for fans, bookmakers, and coaching staff, supporting performance evaluation, tactical decision-making, and a deeper understanding of the factors …

Clustering And Predictive Modelling Of Cryptocurrencies: An Empirical Study Of Lead–Lag Dynamics And Forecasting Performance., Viktor Varga

ICT

Cryptocurrency markets pose analytical challenges due to their volatility, non-stationarity, and complex interdependencies. Traditional econometric models have struggled with these features, while machine learning models often face overfitting and limited interpretability. This thesis has developed an integrated framework combining clustering, lead–lag detection, and predictive modelling to examine whether structural and temporal dependencies in cryptocurrency markets can improve forecasting performance.

Hourly OHLCV data for a selection of liquid cryptocurrencies from the Binance API over a thirteen-month period has been analysed. Dynamic Time Warping (DTW) has been used to identify asset clusters, while cross-correlation, Granger causality, and Vector Autoregression (VAR) have been …

Understanding Model Behaviour And Interpretability In Time Series Forecasting: A Deep Dive Into Lstm And Gru With Xai Techniques., Federico Ariton

ICT

Time-series forecasting is widely used in data analytics, yet the interpretability of deep learning models remains a key challenge. This study compares Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models using a dual interpretability framework that combines attention mechanisms and SHAP analysis. Two feature sets were evaluated across assets with different volatility regimes: Solana and Shiba Inu (high volatility), Bitcoin (moderate volatility), and Apple (low volatility). Results show that autoregressive features achieved the lowest forecasting errors, while volatility- and momentum-based indicators provided stronger interpretability. GRU performed best in moderate-volatility conditions, whereas LSTM demonstrated more consistent performance across varying …

Traditional Vs Deep Learning Approaches For Efficient Electricity Consumption Prediction., Lucas Sant’Ana

ICT

Accurate forecasting of electricity demand is critical for reliable energy planning, resource allocation, and policy design. Traditional statistical models, such as ARIMA, SARIMA, and ARIMAX, have been widely applied but remain constrained by linear assumptions, limited temporal memory, and inflexibility in handling multiple exogenous drivers. In this study, we systematically compare these approaches with multivariate Long Short-Term Memory (LSTM) networks, which are capable of capturing nonlinear dependencies, long-term temporal dynamics, and multivariate interactions. Historical electricity consumption data were combined with weather variables, including temperature, wind speed, and rainfall, and pre-processed through cleaning, scaling, and temporal alignment. Statistical baselines and deep …

Credit Card Fraud Detection., Sonia Ndonga

ICT

This capstone project applies machine learning to detect credit card fraud, addressing a critical financial threat to banks and payment providers. Using an anonymised dataset of 284,807 transactions, which is highly imbalanced with only 0.172% fraudulent cases, three models—Logistic Regression, Random Forest, and Gradient Boosting—were developed and evaluated. The pipeline incorporates data preprocessing, feature engineering, hyperparameter tuning, cross-validation, and interpretability analysis using SHAP values, SHAPASH, and permutation importance. Random Forest achieved the highest performance with an ROC AUC of 0.97 and Average Precision of 0.66. The study also considers fairness, threshold optimisation, and practical deployment strategies, providing a robust automated …

Enhancing Fake News And Rumor Detection Using Metadata, Context, And User Interaction With A Hybrid Gcn-Transformer., Lucas Romulo Zuin Gigli

ICT

The increasing influence of social media has significantly impacted how news spreads among users, making the detection of misinformation, including fake news and rumours, a critical task. Previous research has explored content-based and metadata-driven approaches, while recent advancements have leveraged Graph Neural Networks (GNNs) such as GCNs, GATs, and SAGE to analyse user interactions and propagation patterns. This thesis investigates the effectiveness of a Hybrid Graph-Transformer Convolutional Neural Network (GCN-Transformer) for fake news detection, combining graph-based learning with Transformer attention mechanisms. Through in-depth data analysis and advanced detection techniques, this model aims to enhance predictive performance and mitigate the spread …

Analysis Of Customer Churn In The Banking Sector And Its Mitigating Factors., Fiachra O Callaghan

ICT

This project investigates customer churn within the banking sector, analysing a dataset of 10,000 customers to identify patterns and potential drivers of attrition. By leveraging data analytics and visualization techniques, we aim to understand the demographic and behavioural factors influencing customer decisions to leave for competing banks. The study applies the CRISP-DM framework to structure the analysis, enabling iterative refinement of objectives and hypotheses as insights emerge. Findings indicate that certain customer segments, such as those aged 40–60, are more prone to churn, highlighting opportunities for targeted retention strategies, including fee reductions and tailored loyalty programmes. By integrating academic research …

Credit Card Default Prediction Using Machine Learning., Yassine Zohair

ICT

This project investigates the use of machine learning to predict credit card payment defaults, aiming to help banks and credit card companies mitigate financial losses. Using historical customer data, including demographics, income, education, and previous payment behaviour, three machine learning algorithms were implemented to forecast the likelihood of default. Techniques such as cross-validation, hyperparameter tuning, and SHAPASH were applied to improve model performance and interpretability. Accurate prediction of potential defaulters enables financial institutions to take proactive measures, such as adjusting credit limits or providing targeted financial guidance, thereby enhancing risk management and customer retention.

Leaf Classification Using Convolutional Neural Networks And Vision Transformers, Louis Wilkie

ICT

This study investigates deep learning techniques for plant image classification using four publicly available datasets: Folio, Flavia, Swedish, and Sugarcane. Three modelling approaches—Convolutional Neural Networks (CNN), Vision Transformers (ViT), and hybrid CNN-ViT ensembles—are evaluated. Models leverage PyTorch implementations and pretrained weights from the Hugging Face hub, with hyperparameter tuning applied to optimize accuracy. Transfer learning with pretrained ViT models achieved the highest performance, attaining 100% accuracy on Folio, Flavia, and Swedish datasets, and 94.07% on Sugarcane. CNN and CNN-ViT models showed comparable results, but pretrained CNN-ViTs were more resource-efficient. The findings highlight the importance of evaluating multiple pretrained architectures to …

Enhancing Uk Electricity Price Forecasting Using Deep Learning., Stephen Cooke

ICT

Accurate short-term electricity price forecasting (EPF) is crucial for efficient operation of the UK’s multi-layered power market, impacting generators, traders, the ESO, and policymakers. Prices are highly volatile and non-linear due to renewables, demand fluctuations, and market coupling across Day-Ahead, Intraday, and Balancing Mechanism venues. Traditional statistical models often fail under such dynamics, while machine learning and deep learning approaches—particularly LSTM, GRU, and hybrid architectures—effectively capture temporal dependencies and exogenous drivers. Empirical evidence shows that these models outperform classical baselines, enabling more accurate scheduling, risk management, and financial savings.

Evaluating The Effectiveness Of Variational Mode Decomposition(Vmd)-Enhanced Hybrid Models For Forecasting Volatile Price And Indicator Time Series., Danny Allen

ICT

Forecasting volatile time series, such as commodity prices and market volatility indices, is critical for financial and operational decision-making. This study evaluates the impact of Variational Mode Decomposition (VMD) on hybrid forecasting models combining ARIMA, GARCH, and LSTM. Using gold, platinum, and VIX datasets, models were assessed via MAE, RMSE, and computational time. Results show that VMD improves ARIMA forecasts, and achieves optimal performance when combined with ARIMA-LSTM using performance weighting reducing MAE by 0.62-3.81 and reducing RMSE by 1.48-6.20. Conversely, VMD integration with ARIMA-GARCH and LSTM decreased accuracy. While VMD enhances predictive performance, it increases computational time substantially, increasing …

Exploring The Determinants Of Life Expectancy At Birth: Predicting And Forecasting Global Health Trends Using Statistical, Machine Learning, And Deep Learning Models., Emma Rath

ICT

Accurate life expectancy forecasting is essential for health policy planning, yet research comparing statistical, machine learning, and deep learning approaches under real-world constraints remains limited. This study evaluates ARIMA/ARIMAX, tree-based, and neural network models using Irish and global datasets, considering small samples, missing data, and COVID-19 shocks. ARIMAX with lagged socioeconomic variables outperformed LSTM and other ML/DL methods. Income-based stratification improved predictive accuracy and interpretability, with SHAP analysis highlighting GDP per capita for developed countries and school enrolment and trade indicators for developing contexts. Results provide practical guidance for policymakers and establish limits for model complexity under constrained health data.

Improving The Completeness Of Food Composition Databases Using Predictive Analysis., Carla Arenhart

ICT

This study investigates the use of machine learning regression models to impute missing micronutrient values in Food Composition Databases (FCDBs), focusing on the FAO/INFOODS dataset. A cascading prediction methodology leverages nutrient interdependencies to systematically estimate missing values. Four models—Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Deep Neural Networks (DNN)—were evaluated using MAE, MSE, RMSE, and R². RF and GBM achieved the highest predictive accuracy for protein, phosphorus, calcium, and magnesium, demonstrating that ML-based predictive analytics can provide a more reliable alternative to traditional imputation methods. These findings support improved dietary assessments, nutritional research, and data-driven …

Test Stand Development: An Analysis Of Excavation Forces Within A Simulated Lunar Environment, Faith D. Gaertner

Dissertations, Master's Theses and Master's Reports

Space exploration has rapidly developed in recent years as scientists look to the lunar surface for water ice in permanently shadowed regions. The excavation and decomposition of water outside of Earth’s atmosphere could provide in-situ resources for propellant manufacturing and sustaining life. However, without informed design parameters for lunar exploration and infrastructure development, time will be wasted on systems unfit for the harsh environment. Testing facilities that simulate the environmental conditions are required to better understand the forces these complex systems may face while extraterrestrial.

The force test stand is a unique mechanism that collects tensile and compressive force data …

Near-Infrared Hemicyanine And Bodipy Sensors For Monitoring Mitochondrial Viscosity In Live Cancer Cells, Drosophila Melanogaster Larvae, And Adpkd Kidney Tissue, Joseph Peters

Dissertations, Master's Theses and Master's Reports

Two near-infrared fluorescent sensors, sensor A and sensor B, were synthesized and characterized. Sensor A was a hemicyanine derived structure, and sensor B was a BODIPY derived sensor. The sensors were tested to measure their ability to monitor viscosity, and sensor A was used to monitor viscosity in live cells. Both sensors were found to be effective in monitoring the viscosity level through optical measurements with glycerol. Sensor A was found to be successful in monitoring mitochondrial viscosity in HeLa MBA-MD-453 and MDA-MB-231 cell lines. It was also successful in monitoring viscosity in Drosophila melanogaster larvae as well as …

Life Cycle Assessment Of Renewable Hydrocarbon Fuels Produced By The New Cool Gas To Liquid (Cool Gtl) Process, Elochukwu Okoye

Dissertations, Master's Theses and Master's Reports

The Cool Gas-to-Liquid (Cool GTL) process converts biogas and captured CO₂ into renewable hydrocarbon fuels, contributing to GHG emissions reduction in transportation. This report explains a Life Cycle Assessment (LCA) study that was completed to evaluate the environmental impact of different feedstocks, hydrogen sources, and electricity inputs. The analysis considers biogas from food waste, manure, and landfill sources, as well as biogenic, fossil, and direct air capture (DAC) CO₂ feedstocks. Results show that biogas pathways provide the greatest GHG reductions, with the best case (food and manure biogas + solar hydrogen + solar electricity) achieving a net-negative GWP …

Life Cycle Assessment And Life Cycle Cost Assessment Of A Floating Treatment Wetland In A Wastewater Lagoon Located In Copper Harbor, Michigan, Erin Browne

Dissertations, Master's Theses and Master's Reports

A full-scale floating treatment wetland was implemented in a wastewater treatment lagoon in Copper Harbor, Michigan. Copper Harbor is a small, rural community located in a cold climate. Many studies have focused on the performance of floating treatment wetlands in mesocosm studies in warmer climates, but there is not a lot of research conducted on the feasibility and performance of full-scale floating treatment wetlands in wastewater lagoons, specifically in cold climates. The 2024 treatment season was the first season with the floating treatment wetland implemented in the wastewater lagoon. A preliminary assessment of the cost and environmental impacts of a …

Process To Design And Analyze Dynamic Environment Test Fixtures (Pdadye), Cora J. Taylor

Dissertations, Master's Theses and Master's Reports

Vibration qualification testing is an important requirement in many industries. In-lab testing is intended to evaluate a component’s life expectancy by exposing it to a dynamic environment similar to what it may experience in its field condition. To successfully perform these tests, it is imperative that the field boundary conditions (BCs) of the component of interest are replicated as closely as possible in the lab. One approach to achieving accurate dynamic environment testing is to design a fixture that sufficiently represents the impedance of the next-structure (field structure). The process to design and analyze dynamic environment test fixtures (PDADyE) is …

Evaluating Challenges And Solutions For Buckthorn Classification In Shadowed Environments, Moses Tangwam

Dissertations, Master's Theses and Master's Reports

Invasive species, such as buckthorns, pose significant ecological threats by displacing native vegetation and reducing biodiversity. This study examines the impact of shadows on the classification accuracy of buckthorns using drone-based multispectral imagery collected in a forested area near Michigan Technological University. Shadows impacted approximately 70% of the imagery, notably distorting reflectance in key spectral bands such as near-infrared (NIR) and red edge. The study evaluated vegetation indices like NDVI and the performance of machine learning models, specifically the Random Forest classifier, under these shadowed conditions. Conventional shadow correction techniques, including histogram normalization, Shadow Index (SI), and Inverse Distance Weighting …