Digital Commons Network^™

Affect Regulation In The Context Of Sexual And Gender Minority Stress: A Scoping Review Protocol, Daphne Y. Liu, Benjamin A. Swerdlow, Shao Yuan Chong, Nadia Kako, Alex Rubin, Nicholas S. Perry

Psychology: Faculty Scholarship

Objective

To provide a broad, comprehensive picture of affect regulation in the context of sexual and gender minority stress, this scoping review aims to identify and synthesize methods, methodologies, and available evidence pertinent to emotion regulation and coping in the context of minority stress among sexual and gender minority (SGM) people.

Introduction

SGM people face disproportionately high rates of mental health problems due to experiences of minority stress and lack of social safety. Theories and growing evidence suggest that affect regulation plays a critical role in SGM people’s well-being in the face of minority stress. Researchers have largely studied emotion …

Statistics Of Glinting Clouds Observed By Dscovr And Geostationary Satellites, Tamás Várnai, Alexander Marshak, Alexander Kostinski

Michigan Tech Publications

This study examines how frequently the specular reflection of sunlight—that is, sun glint—reveals the presence of ice crystals that maintain a steady horizontal orientation. The study analyzes data from the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft and from collocated images taken by geostationary satellites. The analysis of spatio-temporal variations in glint frequency over vegetated land surfaces reveals that (a) year-to-year variations are modest with no clear trends; (b) glints typically occur 7%–8% more frequently than previously estimated; (c) glints are most frequently observed during the May-August period, and over Asia. The results also …

Optimizing Proaftn Classifier With Ant Colony Algorithm: Enhanced Diabetes Detection Benchmarking, Feras Al-Obeidat

All Works

The increasing global prevalence of diabetes highlights the need for accurate diagnostic tools to improve early detection and effective treatment planning. Traditional classification models often struggle to achieve optimal performance due to limitations in parameter tuning and adaptability to complex datasets. To address these limitations, this article introduces PROAnt, an innovative learning approach designed to enhance the robustness and efficiency of the PROAFTN multicriteria classification method. PROAnt leverages the computational power of ant colony optimization (ACO) to dynamically fine-tune and optimize the key parameters, such as intervals and weights, at the core of the PROAFTN classification process. This learning methodology …

A Probabilistic Modeling Analysis Of The Longitudinal Immune Response To Infection And Vaccination Across Demographic Groups And Pulmonary Symptoms, James O'Hanlon, Kaitlyn Sullivan, Lyndsey M. Muehling, Glenda Canderan, Jie Sun, Judith A. Woodfolk, Jeffrey M. Wilson, Rayanne A. Luke

Spora: A Journal of Biomathematics

Antibody and cytokine kinetics describe the dynamic response to immune events such as infection and vaccination. These dynamics are not fully understood, and mathematical characterization may help explain variability across demographic groups and pulmonary symptoms post-acute infection. We fit time-dependent probability models to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data to obtain distributions of longitudinal antibody response and cytokine values. To assess differences between groups, an overlap metric is applied to the modeled response curves. Our antibody models suggest significant differences between male and female populations and demonstrate deficient antibody responses of less-healthy groups such as smokers. Our cytokine …

Optimal Intervention Strategies In Age-Structured Sirvd Model: Epidemiological And Economic Analysis Of Covid-19 In Korea, Joon Chun, Melisa Hendrata

Spora: A Journal of Biomathematics

This study employed an age-structured Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD) model to design optimal COVID‑19 intervention strategies by integrating epidemiological dynamics with economic costs. Using empirical data from South Korea, we estimated transmission parameters and analyzed quarantine-based non-pharmaceutical interventions (NPIs) alongside targeted vaccination scenarios. Our results indicated that a stronger form of mild NPI (upper range of Level I), when combined with targeted vaccination that prioritized seniors, achieved substantial reductions in infection peaks and overall economic burden. These findings underscore the effectiveness of age-specific strategies in epidemic management and offer critical insights for policymakers seeking balanced, resource-efficient approaches.

Inhomogeneous Branching Random Walks: Incorporating Genealogy And Density Effects, Lauren Ajax, Beatrice Durham, Pratima Hebbar, Cade Johnston, Jiayi Zhang

Spora: A Journal of Biomathematics

We introduce a novel framework using inhomogeneous branching random walks (BRWs) to model biological processes, specifically by introducing genealogy-dependence in branching rates and displacement distributions to model bacterial colony growth. Current stochastic models often either assume independent and identical behavior of individual agents or incorporate only spatiotemporal inhomogeneity, ignoring the effect of genealogy-based inhomogeneity on the long-time behavior of these processes. Such asymptotics are of independent mathematical interest and are crucial in understanding the emergence of patterns. We propose several inhomogeneous BRW models in 2D space where displacement distributions and branching rates vary with time, space, and genealogy. A combined …

Infinite Line, Infinite Knowledge: The 'Spera' And Organized Chaos In Lambert's Encyclopedia, The 'Liber Floridus', Ava Romano

Theses and Dissertations

The Liber Floridus is a medieval encyclopedia renowned for its program of circular diagrams, or sperae. Inside this manuscript of 190 chapters, these diagrams frame and embody written knowledge, revealing a connection between encyclopedism and life in the Benedictine monastery by creating a coherent visual organization of chapters.

Flux Crystal Growth Of Potassium Lanthanide Double Vanadates With Lanthanide-Dependent Coordination Number Variance, Hunter B. Tisdale, Hans Conrad Zur Loye

Faculty Publications

A series of potassium rare earth double vanadates is presented. Single crystals of double vanadates with stoichiometry K₃Ln(VO₄)₂ (Ln = La, Pr, Nd, and Sm) and crystallizing in the glaserite structure type were synthesized via a molten alkali chloride/fluoride flux reaction. Different structures were observed depending on the constituent rare earth: K₃La(VO₄)₂ crystallizes in an as-of-yet unreported modification of the typical glaserite structure in the space group P2₁/c, likely due to its larger ionic size that results in an increase in the lanthanide coordination number …

A Visualization-Supported, Hierarchical, Action-Learning Model For Driving Behavior In A V2x Environment, Xuantong Wang, Jing Li, Jecca Bowen

Geography and the Environment: Faculty Scholarship

Understanding human driving decisions is crucial for intelligent transportation research. Most existing studies focus on individual vehicles in limited contexts, which restricts broader applicability of results. Leveraging Vehicle-to-Everything (V2X) infrastructure, this study introduces a machine learning framework to model driving actions and detect outliers across diverse environments. This approach features a semantically enabled clustering method that groups similar driving behaviors based on speed and actions. It also adds a time-series learning model to identify typical driving behaviors across various contexts, thereby enabling detection of abnormal driving actions. A suite of visual tools has been developed to help interpret driving patterns, …

Exploring The Role Of Occupational Therapy In The Delivery Of Trauma-Informed Services To Hospital-Based Clients Ot, Frank Czuba

Journal of Applied Disciplines

Hospitalization can traumatize clients and impact overall health outcomes, leading to increased hospital readmission rates and resulting in a negative impact for both clients and providers. Limited information exists regarding how hospital-based occupational therapists evaluate and address trauma, even though incorporating trauma-informed principles into professional practice can enhance the occupational therapy process. The study explored the comprehension and perceived significance of trauma-informed tenets among occupational therapists delivering services in hospital-based settings. A quantitative research study with an exploratory design was implemented to explore how occupational therapists (OT) understand and utilize trauma-informed practices with their clients in a hospital setting. An …

The Impact Of Natural Cavities On Mining Feasibility In Brazil: Dilemma Between The Sustainability Of Mining Operations And The Environment, Tiago Leite, Felipe Souza, Mariana Silva, Iuri Brandi, Hernani De Lima

Journal of Sustainable Mining

Due to the legislation on natural underground cavities in Brazil, mineral reserves have been blocked, causing a great dilemma between the sustainability of the mineral activity and the preservation of cavities. This study presented an analysis of the evolution of the federal cave legislation, pointing out the main gains and contribution to the theme, in which the importance in the Brazilian scenario of the publication in 2017 of Normative Instruction N° 01, which updated the procedures for the definition of these “other forms of compensation” to the irreversible negative impact on cavities with a high degree of relevance. In this …

A Review Study Of The Relationship Between Coal Fly Ash Characteristics And The Presence Of Rare Earth Metals From Steam Power Plants In Indonesia, Muhammad F. Shafiyurrahman, Shofa Rijalul Haq, Nurkhamim Nurkhamim, Ade Kurniawan, Rofa D Bulopa, Rizki Khoiriah Nasution

Journal of Sustainable Mining

The establishment of a coal mining downstream ecosystem focused on a sustainable and environmentally friendly economy is an important goal for many countries. One strategic step in achieving this goal is the utilization of coal fly ash (CFA) waste as a secondary source of rare earth elements (REE). The use of CFA provides a solution to the environmental and health challenges resulting from mining primary sources. Prior to developing REE extraction technology, CFA characterization must be conducted. Therefore, this study addresses the relationship between CFA characteristics and the presence of REE. Based on the ASTM C618-12 standard, CFA in Indonesia …

Study On Visualization Simulation And System Dynamic Optimization Of The Ore Drawing Process In Mine Chutes, Liang Juan, Zhu Quanjie, Hao Yingnan, Quan Zhongxue, Zhang Zhiwu, Liu Xiaohuic, Ouyang Zhihua

Journal of Sustainable Mining

The ore release process in metal mine chutes is a critical part of the mining operation. Based on the urgent demand for intelligent mining construction, this paper proposes a visualization simulation and dynamic optimization method for the mine chute ore release process using the open-source programs Python and Blender. The main research content includes: designing the simulation method and model for the ore release process, including the main control program, process subprograms, etc.; proposing a visualization simulation method for the mine chute ore release process based on Blender, studying the impact of ore shape and size on ore release efficiency, …

Assessment Of Heavy Metal Contamination In Surface Water Near Tailings Dam, Gera Techane Mengistu, Tesema Kebede, Enatfenta Melaku, Engdasew Temere

Journal of Sustainable Mining

Elevated levels of heavy metals in surface water can lead to the degradation of water quality, especially in mining areas where tailing dams are sources of heavy metal pollution. This study focuses on evaluating the concentrations of heavy metals in surface water near the Legadembi tailings dams. The mean concentrations (in µg/L) of various heavy metals including: arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), molybdenum (Mo), nickel (Ni), lead (Pb), antimony (Sb), selenium (Se), thallium (Tl), uranium (U), vanadium (V), tungsten (W), zinc (Zn), and zirconium (Zr) were determined: 79.7, 0.15, 162.3, 0.66, 18.1, 0.01, …

Toward Sustainable Water Security: A Novel Assessment And Recharge Strategy For North Bekaa Plain, Rida Tadmouri, Amin Shaban, Mohamad Soliman

BAU Journal - Science and Technology

The demand for water in the northern Bekaa Plain is of utmost significance, especially given the substantial portion of land dedicated to irrigated agriculture. Additionally, the region experiences population growth, further exacerbating the water supply challenge. Understanding the groundwater potential zones and identifying appropriate recharge areas are crucial steps towards sustainable water resource management. The study focuses on reaching the best solutions for the study area, by utilizing advanced Geographic Information System (GIS) tools, including overlying slope, rainfall, lithology, attitude, soil, fracture, and stream maps, a Groundwater Potential (GWP) map was generated using ArcGIS. This map serves as a valuable …

Assessment Of Outdoor Thermal Human Comfort Over Houche Al-Oumara, North Lebanon, Ali Adnan Shaar, Ismail Ibrahim Abbas, Mahmoud Ahmed Hewehy, El-Sayed Mohamed Abdel-Hamid Robaa, Mohammed Salah El-Din Mostafa

BAU Journal - Science and Technology

This study examined thermal discomfort in Houche Al-Oumara between 1994 and 2021 using several discomfort indices and analyzing temperature, relative humidity, and rainfall data. The results showed that July and August recorded markedly higher mean temperatures than all other months, with July having the highest temperature overall. In contrast, January was identified as the coldest month. The study found a steady rise in mean air temperature from January to July, followed by a steady decline from July to December. Relative humidity showed the same pattern but in the opposite direction. Fewer than half of the residents experienced thermal discomfort during …

Ai In Society: A Regulatory Framework For Responsible Integration, Ziad Doughan, Sari Itani, Hadi Al Mubasher

BAU Journal - Science and Technology

This review paper studies the influence of Artificial Intelligence (AI) and Machine Learning (ML) on society in various categories in detail. AI and ML have developed rapidly in the past two decades, thus changing our lifestyles. These developments have various positive and negative impacts on society. This paper explores the many societal impacts of AI and ML, in economics, social aspects, ethics, and policy, shedding light on the opportunities and challenges that arise. An interdisciplinary insight is capable of understanding the challenges society faces when it uses AI and ML, locking opportunities that lie ahead and identifying promising paths towards …

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Ai Tips And Traps, Patrick Barry

Books

Based on a series of popular courses and workshops that Professor Patrick Barry has created for students, professionals, and anyone else interested in taking a skills-based approach to artificial intelligence, this book gives you a chance to engage with important AI concepts, experiment with exploratory AI exercises, and then ultimately develop your own customized list of AI traps to try as well as AI traps to avoid.

Agrivoltaics Swot Analysis For Implementation In The United States, Vani Ramesh

UROP Posters

Agrivoltaics is the implementation of solar installations alongside agricultural land in order to improve the efficiency of land usage in both industries. Research in European and Asian countries developing agriculture and solar developments in tandem has indicated that implementation in the United States, including Virginia, is feasible and beneficial. The US has the highest energy use per capita in the world, with the increase of various technologies has resulted in land developments using higher energy levels than previously. While Virginia’s energy demand has increased with the addition of data centers. Increased energy demand and food security requirements have unearthed policy/political …

Deep Learning-Based Co-Current Upward Gas-Liquid Two-Phase Flow Regime Identification In An Annular Conduit, Joshua Robert Macomber

Graduate Theses, Dissertations, and Problem Reports (ETD)

Flow regime identification in co-current upward gas-liquid flow through annular conduits remains a significant challenge in petroleum engineering, with major safety and operational implications. It is also important across industries involving the transport of multiphase fluids. Misidentifying flow regimes can introduce major operational risk, yet regime boundaries in annular gas-liquid flow are often visually complex and context dependent.

The objective of this study was to evaluate the utility of convolutional neural network (CNN) classifiers for flow regime identification. The CNN was trained using annular flow image dataset published by Texas A&M University. The dataset consists of approximately 947 RGB images …

Use Of Generative Ai In Scholarly Research: Challenges And Opportunities, Marco Marabelli, Robert M. Davison, Giovanni Gatti, Ankita Srivastava, Monideepa Tarafdar

Computer Information Systems Faculty Publications

The focus of this panel report is to present evidence of a panel discussion that took place in October 2025 on the use of generative artificial intelligence (GAI) in academic research and publishing. The panel included publishers (Elsevier, Emerald, Gailot Press, MIT Press, Nature/Springer, Wiley) and editors in chief (representing the Information Systems Journal, Journal of the AIS, Journal of Data Science and ICES Journal of Marine Science). Key outcomes of the panel are the following: 1) publishers and editors have some disagreements regarding GAI use; 2) besides disagreements, all recognize that GAI is here to stay, therefore outstanding issues …

Generating Template Banks For Intermediate Duration Gravitational Waves, Michael St. Pierre

Open Access Dissertations

There are a multitude of astrophysical sources which carry the potential to produce perturbations in spacetime, known as gravitational waves, across a wide spectrum of frequencies. Over the brief history of gravitational wave astronomy, short-duration “chirp” signals emitted from black hole mergers, highly energetic “burst” signals emitted from supernovae and Gamma-Ray Bursts (GRBs), and long duration “continuous” signals emitted from galactic neutron stars and the stochastic gravitational wave background have traditionally been at the forefront of search development, leaving a class of intermediate duration  ($10² - 10⁴ seconds) gravitational wave signals poorly suited for detection. The conventional way …

A New Functional Setting For Term Structure Modeling Using The Health-Jarrow-Morton Framework, Michael Pokojovy, Ebenezer Nkum, Thomas M. Fullerton Jr.

Mathematics & Statistics Faculty Publications

The well-known Heath–Jarrow–Morton (HJM) framework provides a universal and efficacious instrument for modeling the stochastic evolution of an entire yield curve by explaining the interest rate dynamics in continuous time under no-arbitrage conditions. Existing implementations involve exponentially weighted function spaces as theoretical settings for the former stochastic evolution. While the choice of weight can have a drastic effect on model calibration and subsequent forecasting, it cannot be estimated from market data and does not allow for any objective interpretation. The proposed approach does not have this shortcoming as it adopts a suitably designed unweighted function space. The HJM equation is …

Aura: An Ai-Powered Multimodal Prototype For Adaptive Apraxia Of Speech Therapy And Communication Support, Omotayo Omoyemi, Rachel K. Johnson

Speech-Language Pathology Faculty Publications

Apraxia of Speech (AOS) is a motor speech disorder that significantly limits communication and requires intensive, long-term therapy. Access to consistent treatment is often constrained by shortages of speech-language pathologists, high costs, and limited opportunities for continuous monitoring outside clinical settings. Recent advances in Artificial Intelligence (AI) provide new opportunities to support scalable and personalized speech therapy.

This paper presents AURA (Adaptive Understanding and Relearning Assistant for Apraxia), a multimodal AI framework designed to support speech therapy, progress monitoring, and communication for individuals with AOS. The system integrates speech analysis, machine learning–based error detection, reinforcement learning for adaptive therapy, and …

Leveraging Machine Learning Techniques To Identify Catastrophic Outliers In Photometric Redshift Estimates, Jason Yoo

Honors Theses

Leveraging the unprecedented data volumes from modern astronomical surveys, this work addresses the critical challenge of identifying catastrophic outliers (COs) in photometric redshift (photo-z) estimates. Reliable redshift estimation is essential for precision cosmology, yet COs (galaxies with severely erroneous photo-z) can introduce significant systematic bias. We develop and evaluate two machine learning approaches for robust CO identification: a multilayer perceptron (MLP) utilizing photo-z point estimates and probability distributions, and a convolutional neural network (CNN) leveraging raw 5-band photometric images. Using datasets emulating LSST conditions, we demonstrate that the MLP model achieves high accuracy and stability across redshift bins, correctly identifying …

Setting The Recycling Narrative: A Thematic Analysis Of Recycling Webpages In The United States, Haley Nolde

Dissertations and Theses

Recycling is widely promoted in the United States as a central strategy for waste diversion and sustainable materials management, yet recycling outcomes remain inconsistent and public understanding is often fragmented. While substantial research examines recycling behavior, infrastructure, material science, and markets, less attention has been paid to how recycling itself is framed and communicated to the public. This thesis addresses that gap by examining narratives produced by recycling thought leader organizations (TLOs) in the United States. Using thematic analysis, this study analyzes publicly available recycling webpages from 99 TLOs, including state government agencies, state-level non-governmental organizations (NGOs), national and regional …

Deep Learning-Based Quantification Of Tall Fescue Abundance In Pastures, Rose E. Binkley, Samuel R. Revolinski, Echo Elizabeth Gotsick, Ray Smith, Zeya Wang, Katsutoshi Mizuta

Plant and Soil Sciences Faculty Publications

Accurate and rapid measurement of tall fescue abundance is essential for efficient forage management and mitigating livestock exposure to potential endophyte-related toxicity. Traditional visual estimation methods are labor-intensive and are subject to change depending on the observer doing the calculations, which limits their scalability and consistency. This study explores the application of deep learning techniques to automate and improve the classification of tall fescue abundance across pasture systems in Kentucky. Ground-truth abundance classes were evaluated using one of the traditional methods with occupancy grid and compared against estimates derived from red-green-blue images. Two convolutional neural network architectures, YouOnlyLookOnce (YOLO) and …

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …