Digital Commons Network^™

Co-Creating An Intervention To Prevent Injuries In Police Force Recruits: A Concept Mapping Study Of Police Force Recruits, Police Force Staff, Health Professionals, And Research Experts, Myles Murphy, Andrea B. Mosler, Garth Allen, Alex Donaldson, Clare L. Ardern, Carolyn A. Emery, Martin Hägglund, Jonathan M. Hodgson, Joanne L. Kemp, Debra Langridge, Tiffany Mcalinden, Thomas Mirco, Sophia Nimphius, Simone Radavelli-Bagatini, Kiera Staley, Vanessa Sutton, Evert Verhagen, Andrea M. Bruder

Research outputs 2022 to 2026

Objective: Police force recruits have a high musculoskeletal injury burden, which results in a substantial economic burden and can lead to attrition. The objective of this study was to identify and prioritise the strategies perceived as important and feasible to reduce the prevalence, incidence, and burden of injury in police force recruits. Design: Mixed-methods concept mapping study. Methods: Forty-eight participants were recruited from four broad groups: police force recruits/officers; police force staff; health professionals; and research experts. Participants brainstormed statements in response to a prompt (“To prevent injury and/or reduce the impact of injury on law enforcement recruit training, I …

Implementation And Effectiveness Of A Food Education Intervention To Promote Plant-Based Foods: A Partially Cluster-Randomized Controlled Trial, Henna Vepsäläinen, Satu Kinnunen, Sari Niinistö, Leena Forma, Suvi T. Itkonen, Tuuli E. Korhonen, Liisa Korkalo, Heli Kuusipalo, Jelena Meinilä, Kaija Nissinen, Susanna Raulio, Ros Sambell, Mari Åkerlund, Suvi M. Virtanen, Maijaliisa Erkkola

Research outputs 2022 to 2026

Background: Legumes can substitute for meat and are thus a particularly important food group for environmental sustainability. As the use of legumes among Finnish children is marginal, there is potential for a substantial increase in their consumption. However, food education delivered through early childhood education and care (ECEC) settings has rarely focused specifically on environmentally sustainable foods, such as legumes. This study describes the implementation and effectiveness of an ECEC-delivered food education intervention within the FoodStep study. Methods: The partially cluster-randomized FoodStep intervention aimed to increase the supply of vegetables, legumes, fruits, berries, and sustainable fish while reducing the provision …

Preventive Behaviors Associated With Fall Incidence Among Older Adults Living In Rural Communities Of Thailand: A Cross-Sectional Study, Rujira Nonsa-Ard, Sirapat Khodseewong, Kasama Wongprachum, Chaiwut Bourneow, Ploypailin Aneknan, Sumattana Glangkarn, Davina Porock

Research outputs 2022 to 2026

Background: The escalating population of older adults poses a significant challenge, particularly in lower-middle-income countries like Thailand. Falls represent a critical public health concern in this demographic. This study aimed to identify risk factors influencing fall occurrence in older adults within a specific rural community context, where the majority of participants remain engaged in farm work.

Methods: A cross-sectional study design was utilized, involving 1,256 participants recruited by the multistage random sampling approach. Data were collected via a questionnaire that included: demographic information, the Thai version of the Barthel Activities of Daily Living (ADL), the Thai Fall Risk Assessment Tool …

A Deep Learning Based Three-Stage Fusion Neural Network With Composite Adaptive Momentum Optimizer For Bangla Cyberbully Identification, Mahin Montasir Afif, Abdullah Al Noman, Kazi Abdullah Jarif, Md Emamul Arefin Islam, Sathya Narayana Sharma K, Mahfujur Rahman, Dipta Gomes, Kazi Tanvir, Md Obaidur Rahaman, Syed Mohammed Shamsul Islam

Research outputs 2022 to 2026

Cyberbullying in Bangla social media is an increasingly critical issue, posing severe risks to mental health and online safety. Despite the growing need for automated detection systems, challenges such as limited linguistic resources and the nuanced characteristics of the Bangla language hinder model performance. In this study, we propose TriB-FNN, a novel and explainable three-stage fusion neural network, designed to effectively capture cyberbully in Bangla social media texts. The model integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Units (GRUs) in a hierarchical pipeline. To optimize training, we employ a Composite Adaptive Momentum (CAM) Optimizer, …

Writing Assessment In Tennessee Community Colleges, Joshua Tucker

Masters Theses and Doctoral Dissertations

This mixed-methods study examined how composition faculty at Tennessee community colleges used margin comments to support student writing, whether those comments reflected principled or novice feedback language, and how instructors understood and described their own commenting practices. Marginal feedback remains the most widely used yet least theoretically examined form of response in first-year writing instruction, and the legislative reforms reshaping Tennessee's community college system, including the corequisite model, TNPromise, and TNReconnect have intensified questions about the pedagogical practices used to support an increasingly diverse student population. Eight full-time English instructors at Tennessee community colleges participated in the study, submitting 771 …

Gesture-Efficient Global Language Input Methods, Daniel Mailman

Masters Theses and Doctoral Dissertations

A central task of computer hardware and software is text entry: sending units of human written language to applications such as word processors and spreadsheets. Input units range from individual lexons (Unicode characters) to lexemes (words and compounds). Standard QWERTY keyboards directly access a limited set of lexons and are used to assemble lexemes one lexon at a time. Non-QWERTY lexons and lexemes often require (e.g.) layout switching, dead-key composition, candidate lists, memorized codes, or relatively sophisticated software input methods (IMEs). This dissertation presents µLex, a framework for gesture-efficient IMEs. µLex IMEs are designed to coexist with application and operating …

Evaluating The Transformation Of Land Cover, Land Surface Temperature, And Urban Heat Island In Sylhet Division Through Geospatial Analysis, Md Fazle Rabbi Joy, Md Abdur Rahim, Md Mahfuzar Rahman, Sumiaya Amin Preota, Mohammed Mahathee Hossain

School of Earth, Environmental, & Marine Sciences Faculty Publications

Rapid and unplanned urbanization has significantly transformed land use/land cover (LULC), contributing to increased land surface temperature (LST) and the emergence of urban heat islands (UHIs), especially in ecologically fragile regions. This study focuses on the Sylhet Division of Bangladesh, assessing LULC changes, LST dynamics, and UHI expansion from 1994 to 2024 using Landsat satellite imagery, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) indices, and field-based LST validation. Unsupervised classification reveals a 191.75% increase in settlement areas and a 25.65% loss in forest cover. Correspondingly, the highest LST zones (> 30 °C) were absent in 1994 but …

The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej

All Works

This study investigates green technology adoption (GTA) among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE), focusing on the influence of corporate sustainability goals (CSG) and sustainability motivation (SM). Utilizing institutional theory, the theory of planned behavior (TPB), and resource-based view (RBV), the research highlights how SMEs integrate environmental, social, governance (ESG) and economic considerations into their CSG to enhance GTA. Addressing a gap in prior research that has largely emphasized external drivers of adoption while underexploring internal organizational mechanisms, the study conceptualizes CSG as strategic intent and models SM as a second-order construct . Based on …

A Journey To The Midwest For Migrant Farmwork: Women's Testimonies Examining Their Lived Experiences Using Autoethnography And Plática Research Methodologies, Yesenia G. Alcalá

Theses & Dissertations

A JOURNEY TO THE MIDWEST FOR MIGRANT FARMWORK: WOMEN’S TESTIMONIES EXAMINING THEIR LIVED EXPERIENCES USING AUTOETHNOGRAPHY AND PLÁTICA

RESEARCH METHODOLOGIES

Yesenia Garza Alcalá, Master of Business Administration

University of the Incarnate Word 2026

The purpose of my research was to share the stories of five women who experienced a yearly migratory routine from Del Rio, Texas, to work in farms and factories in the Midwest, and how that annual journey shaped and educated us. The women are four of my older sisters and me, all of whom traveled and worked in the Princeville, Illinois, migrant camp, where we experienced the …

From Post-Hoc To Pre-Assessment Evaluation: Llms As Collaborative Partners For Simulating Student Performance, Siaw Ling Lo, Ping Fan Ke, Yi Meng Lau

Research Collection School Of Computing and Information Systems

Ensuring the quality of multiple-choice questions (MCQs) remains a complex challenge in higher education, as traditional post-hoc psychometric analyses often identify flaws only after students have been negatively impacted. While Large Language Models (LLMs) offer potential for simulating student performance to validate items before delivery, they are frequently hindered by a “high-accuracy bias” that prevents larger models from realistically mimicking student errors. This paper proposes the Triadic Pedagogical Fit (TPF) framework, which triangulates Model Scale, Task Complexity via Revised Bloom’s Taxonomy, and Instructional Persona to optimize pre-assessment evaluation. Through two studies, we demonstrate that while Mid-scale (12B) models effectively mirror …

A Critical Review Of Pharmaceutical Pollutants In Soil And Air: Ecotoxicological Impacts On Animal, Plant And Microbial Communities - Health Hazards And Waste Management, Md Faisal Amin, Md. Saydur Rahman

School of Integrative Biological & Chemical Sciences Faculty Publications

Pharmaceutical contamination in soil and air has become a critical environmental concern due to its widespread sources, complex behavior, and long-lasting ecological impacts. This review comprehensively explores the presence, fate, and effects of pharmaceutical compounds in soil, highlighting their entry routes, environmental persistence, and biological consequences. Pharmaceuticals infiltrate terrestrial and aquatic environments through various pathways, including agricultural application, pharmaceutical manufacturing waste, sewage sludge, hospital and household discharges, irrigation with contaminated water, and atmospheric deposition. A wide range of drug classes, such as antibiotics, analgesics, non-steroidal anti-inflammatory drugs, antidepressants, anticancer drugs, and hormones, have been identified in both soil and air …

Recent Canadian Immigration Reforms And Their Consequences For Migrant Children And Families’ Health: The “Globalization Of Indifference”?, Rachel Kronick, Marianne Côté-Olijnyk, Efrat Arbel

All Faculty Publications

Despite its reputation as a welcoming nation to refugees, Canada has enforced increasing restrictions on asylum, even in the face of evidence from the United States and around the world of the profound harm such restrictions cause. In this paper, we outline recent Canadian legal and policy reforms and their potential impacts on refugee health equity. Following Farmer's approach of ethnographic case narratives that aim to portray how current policy and legislation are embodied and felt in migrants' lives, we document avoidable harms to migrant children and families. Our data come from qualitative research with inadmissible asylum seekers and narrative …

Correctional Officer Suicide: A Theory-Driven Investigation Of Acquired Capability And Suicide Behavior, Abigail R. Andersen

All-Inclusive List of Electronic Theses and Dissertations

A growing body of research suggests that correctional officers (COs) die by suicide at a significantly higher rate compared to the general population and some other public safety personnel. Despite what is known about this phenomenon, an understanding of the risk factors driving the high rate of suicide among this population remains unclear. Specifically, little attention has been paid to specific risk factors for suicide among this population and there is an overall absence of theory-driven research investigating CO suicide. The present study sought to address the gap in this area of research by investigating potential risk factors for suicide …

Harnessing Digital Twin (Dt) Technology For Food Security And Climate Resilience In Sub-Saharan Africa (Ssa), Henri E.Z. Tonnang, Francis Chianu, Siyabusa Mkuhlani, Francis Muthoni, John Michael Humphries Choptiany, Franck B.N. Tonle, Bonoukpoe M. Sokame, Mercy Lung’Aho

All Peer-Reviewed Publications

Sub-Saharan Africa (SSA) faces chronic food insecurity despite possessing approximately 60% of the world’s uncultivated arable land. Field trials generate evidence but are costly and insufficiently scaled to address accelerating climate and demographic pressures. Digital twin (DT) technology, defined as the continuous, bidirectional virtual replication of physical systems using real-time data, supports monitoring, modelling, and optimisation of agrifood systems. To our knowledge, however, no published synthesis has examined DT agriculture research through the lens of SSA food systems or smallholder farming realities. A PRISMA-compliant systematic review was conducted across bibliographic databases using a pre-defined Boolean search and adapted PICOS eligibility …

Beyond Legacy Indices: A Critical Review Of The Modified Nemerow, Mean Enrichment Quotient, And Aggregated Multivariate Environmental Risk (Amer) Indices For Heavy Metal Pollution In Sediments, Md Shahnul Islam, Anjuman Ara

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Background: Heavy metal pollution in sediments poses persistent ecological risks due to metal toxicity, bioaccumulation, and potential for remobilization. Legacy indices have been widely used for decades but suffer from fundamental flaws, including arbitrary background dependence, lack of grain-size normalization, and poor ecological relevance.

Objective: This review critically evaluates three emerging sediment pollution indices, the Modified Nemerow, Mean Enrichment Quotient (MEQ), and Aggregated Multivariate Environmental Risk (AMER) index, to assess their methodological advances and potential to replace legacy approaches.

Methods: A structured literature search was conducted in Web of Science, Scopus, and Google Scholar (2010–2026). Using an adapted Birch framework, …

Characterization Of Regulation Of Ace2 And Morphogenesis (Ram) Pathway Components In Aspergillus Fumigatus, Devi Naga Jyothi Bale

Theses and Dissertations (ETD)

Aspergillus fumigatus CotA is a conserved protein kinase and predicted orthologue of Cbk1, a terminal kinase of the Regulation of Ace2 and Morphogenesis (RAM) network. In our previous study, disruption of CotA resulted in carbon source dependent invasive growth with the mutant exhibiting preferential growth on glucose but severely impaired growth on alternate carbon sources. This pathway in other fungi has been implicated in the regulation of morphogenesis, stress responses, and virulence, but this study is the first one to characterize it in the pathogenic filamentous fungus.  In yeast, the RAM network includes the CotA ortholog, Cbk1, and associated proteins, …

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

A Data-Driven Koopman Framework For Station-Keeping On Near Rectilinear Halo Orbit, Anusha Sharma Malladi

Theses and Dissertations

Cislunar missions have gained significant attention in recent decades, motivating the need for efficient modeling and reliable control. In this work a Koopman operator based framework is developed for approximating the error dynamics around a reference Near Rectilinear Halo Orbit (NRHO) in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP). A decoder free neural network is used to learn a lifted linear representation of the nonlinear CR3BP dynamics and a residual based approach is used to identify the corresponding control input matrix. The model is then implemented in a receding-horizon target point controller and compared with uncontrolled propagation and a State …

Comparative Performance Of Physiological Vital-Sign Forecasting Under Random And Patient-Wise Splitting Using Deep Learning, Lavanya Vasavi Chittem Reddy

Theses and Dissertations

Physiological vital-sign forecasting estimates future measurements based on recent temporal patterns and can support analysis of continuously recorded monitoring data. This study comparatively evaluated deep feedforward, recurrent, bidirectional recurrent, long short-term memory, and bidirectional long short-term memory architectures for one-step-ahead forecasting of peripheral oxygen saturation, heart rate, and pulse rate. Each model received consecutive observations of peripheral oxygen saturation, heart rate, pulse rate, respiratory rate, and age, while separate single-output models predicted the next value of the selected target.

Random and patient-wise data splitting were compared using identical input definitions, preprocessing procedures, model architectures, and training hyperparameters. The strongest architecture …

Developing Imitation Learning Policies By Exploring Diffusion Framework For Quadcopters, Likith Swamireddy

Theses and Dissertations

Imitation learning offers a promising approach in developing near-optimal onboard policies for nonlinear systems, by learning from computationally expensive trajectory planners during offline training. Existing State-of-the-art methods commonly learn deterministic mappings from observations to actions by training fast neural networks to mimic the expert demonstrations, which can limit their ability to capture the action distribution, when the policy is trained on multiple possible expert commands to reach the same target, and this may lead to unsafe scenarios in the obstacle environments. To address these challenges, this study explores Diffusion Models (DMs) as a feedback controller for quadcopters that learns the …

In-Process Large Language Model Requesting Through Knowledge Graphs For Interfacing With Manufacturing Digital Twins, Reiki Tanaya Karuka, Vinh Nguyen

Michigan Tech Publications

Digital twins (DTs) have potential to improve manufacturing processes by providing in-process monitoring and reducing trial-and-error while improving process prediction and performance. However, identifying anomalies or defects in a DT environment remains a very time intensive process that requires multidisciplinary expertise and significant manual effort in analyzing the system. Large language models (LLMs) offer potential capabilities to interpret system-level information and support user-driven querying, but their effectiveness is limited by hallucinations when reasoning over large and dynamic manufacturing datasets inherent to process monitoring DTs. Knowledge graphs (KGs) have emerged as efficient tools in the industrial domain to efficiently represent data …

A Projector-Rank Partition Theorem For Exact Degrees Of Freedom In Experimental Design, K. G. Nagananda

Mathematics and Statistics Faculty Publications and Presentations

In many experimental designs—split-plots, blocked or nested layouts, fractional factorials, and studies with missing or unequal replication—standard ANOVA procedures no longer tell us exactly how many independent pieces of information each effect truly contributes. We provide a general degrees of freedom (df) partition theorem that resolves this ambiguity. For N observations, we show that the total information in the data (i.e., N −1 df) can be split exactly across experimental effects and randomization strata by projecting the data onto each stratum and counting the df each effect contributes there. This yields integer df—not approximations—for any mix of fixed and random …

Evaluating The Pulse Of Exercise Programs: Developing And Validating The Intervention Usability Scale For Exercise (Iuse) To Enhance Implementation And Adherence, Anne Inger Mørtvedt, Eva Ageberg, Steven Elmer, Kevin Trewartha, Erich Petushek

Michigan Tech Publications

Background: Adherence to exercise interventions is often suboptimal, despite numerous studies documenting barriers and facilitators. Usability may be a critical yet underexplored determinant of adherence. This study aimed to develop and assess the psychometric properties of the Intervention Usability Scale for Exercise (IUSE). Methods: Item generation and content validation involved cognitive interviews and feedback from eight exercise intervention stakeholders and ten target users from the general public. Subsequently, 526 target users from University, Qualtrics and Prolific participant panels assessed exercise programs through an online survey. Dimensionality was assessed using Principal Component Analysis (PCA), Exploratory Factor Analysis (EFA), and bifactor models. …

Examination Of Cleveland State Community College's Employee Professional Development Program: Leveraging Employee Learning And Relationship Building Amidst Significant Institutional Change, Ashley T. Raburn

Masters Theses and Doctoral Dissertations

Within the last decade, Cleveland State Community College has gone through many changes, including transitioning between five different administrations from 2021 to 2025. With this change in administration, there have been many different philosophies of how professional development should be handled at the college. The priority of professional development has shifted between very high and very low, and the ownership of active initiatives was unclear. Recently, college administrators have implemented a program intended to allow PD at the college to have more structure and visibility within the employee community. Literature in both Higher education and other industries discusses the importance …

The Future Of Nature-Based Recreation In Warming Tropical Cities, Perrine Hamel, Emma E. Ramsay, Shawnda A. Morrison, Su Li Heng, Winston T. L. Chow, Beatrice H. Ho, Pearl Min Sze Tan, Moshe Mandelmilch, Lancy Sim, Jason Kai Wei Lee

Research Collection College of Integrative Studies

Nature-based recreation promotes health in tropical cities but is increasingly threatened by rising heat. This review presents recent evidence on the issue of humid heat stress and outdoor recreation in tropical cities and outlines key adaptation strategies – addressing hazard, exposure, and vulnerability – to enable ‘heat-smart’ nature-based activities. Despite existing solutions, critical research gaps remain, especially in integrating social, physiological, and technological insights to better address humid heat stress in tropical urban environments.

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

All Works

This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

All Works

Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

All Works

As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …

Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …