Digital Commons Network^™

A Case Study Using The Transparency Framework And Artificial Intelligence To Promote Effective Writing And Student Success In A Writing-Intensive Course, Elizabeth A. Brown, Maria Kronenburg, Ashlee Steeley, Diana Tagbor

Health Behavior, Policy & Management Faculty Publications

Program evaluation data suggest that undergraduate students struggle with writing in a clear and concise manner and appropriately citing. Faculty implemented the plan-do study-act cycle to pilot the Transparency in Learning and Teaching (TILT) project framework and to explore the use of artificial intelligence (AI) and discuss approaches to using AI, along with the TILT framework, in a writing-intensive course to identify the pros and cons of using ChatGPT in an online classroom. The TILT framework reinforces adult learning by helping students clearly understand the assignment's purpose and establish a clear relationship between assignment and students' professional lives. Faculty encouraged …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

“I Can Do This”: Resilience Of Women Students In Engineering And Technology Courses In Portugal, Bill Williams, Shannon Chance

Research Outputs: 2025-Present

To understand the factors that influenced female students to choose their degree programs and complete them successfully, we gathered data from 11 women undergraduate students in Chemical Engineering, Biotechnology, and Construction Management programs at a Portuguese polytechnic. All participants completed their courses within the two years following the interviews. The two authors analyzed the data gathered via interviews with individual students using thematic analysis and present four generated themes along with their implications.The findings suggest that further research is warranted on the role of short-cycle programs within the Portuguese polytechnic sector in providing routes to help young women overcome obstacles …

Rock Hill Economic Development Records - Accession 1771, Rock Hill Economic Development, Rock Hill History

Manuscript Collection

The Rock Hill Economic Development Records document planning, redevelopment, and economic growth initiatives in Rock Hill, South Carolina, from the late eighteenth century through the early twenty-first century, with the bulk of the materials dating from the mid-twentieth century through the early 2000s. The collection contains photographs, city and regional planning studies, architectural and engineering plans, maps, land plats, newspaper clippings, brochures, and organizational records that illustrate evolving approaches to urban development, historic preservation, transportation, and downtown revitalization.

Central to the collection are comprehensive planning documents produced or utilized by the City of Rock Hill and the Rock Hill Economic …

Engaging Student Nurses In Lifestyle Management: The Role Of Technology-Based Interventions And Mobile Health Applications, Daniela Austin

School of Psychology Theses

Despite their knowledge of health risks and their future role as health promoters, pre-registered nursing students frequently struggle with unhealthy behaviours, including high rates of overweight and obesity. This thesis investigated these challenges in a student nurse population in the South West of England, exploring lifestyle patterns, perceived barriers and motivators, and the potential of mobile health (mHealth) interventions to support behaviour change.Using a multi-method approach across four studies, the research examined students’ current health behaviours, attitudes towards digital tools, and the feasibility and acceptability of theory-driven interventions. The first study, a cross-sectional survey, assessed diet, physical activity, and attitudes …

What Helps And Hinders Staff Supporting People With Intellectual Disabilities Regarding Sex And Relationships, Laurence Constable, Deanna Gallichan

School of Psychology Theses

Background Studies have suggested that staff experience multiple obstacles to providing effective support around relationships and sexuality. The literature has suggested several potential solutions yet it is not clear what impedes the implementation of these. Understanding these barriers could improve care. Methods Two focus groups were carried out with a total of 13 participants recruited from the Southwest of England. Participants were recruited from client-facing roles across care providers, education provision, and social care provision. The focus groups were analysed using reflexive thematic analysis. Findings Four themes were drawn from the data. These were ‘Fear hinders care’, ‘Care is led …

Ethnographically Informed Research Exploring The Impact Of Embedding Trauma-Informed Principles In A Befriending Programme For Refugees., Zahraa Abdulreda

School of Psychology Theses

Refugees often experience trauma from war, displacement, and post-migration stressors, leading to collective and transgenerational trauma. This study explored the impact of embedding trauma-informed principles (TIP) within a refugee befriending programme, where refugees were paired with local volunteers. Ethnographic Action informed research was used through completing interviews, observations, and a focus group. Participants included five befrienders and seven refugees, alongside observational data. Thematic and framework analyses identified five key themes: The Relationship, Safety for Befrienders, Inclusivity, Supporting Befriendees with Integration, and Challenges of Befriending. TIP enhanced understanding of how community-based, trauma-informed interventions can support refugee recovery and resilience. The study …

Laugh, I Nearly Cried: A Qualitative, Reflexive Thematic Analysis Of The Impact Of Humour In Psychotherapy From Client And Therapist Perspectives, Elina Broholm

School of Psychology Theses

Humour in psychotherapy is commonly occurring, yet seldom discussed as a resource in the training and education of therapists, nor considered from the perspective of clients in therapy. This qualitative study explored how clients and therapists perceive the impact of humour in psychotherapy on the therapeutic alliance, experience, and process. 12 clients and eight therapists contributed their perspectives on humour’s impact on the therapeutic experience, considering both its helpful and unhelpful contributions. Not all humour is equal, and special consideration was given to how different humour styles may impact psychotherapy. Reflexive thematic analysis from 20 semi-structured interviews suggested that humour …

Exploring The Caring Experiences Of Parent-Carers Of People With Learning Disabilities Using Reflexive Thematic Analysis, Lauren Moore

School of Psychology Theses

BackgroundPrevious research has suggested that providing care for a relative significantly impacts emotional distress. This research took an exploratory approach to understanding the caring experiences specifically of parents of people with learning disabilities (PWLD).MethodTen parents of people with learning disabilities took part in semi-structured, online interviews about their experiences of caring. Interview data was analysed using Reflexive Thematic Analysis.FindingsAnalysis generated five main themes which were interpreted to summarise the caring experience of these parents: Lifechanging, Emotional, Responsibility, Abandoned, Future. There were many experiences which were shared by participants, though various factors can impact these realities.ConclusionThis research concludes that parenting a …

Clbbm And Clplt2 Function Redundantly During Both Male And Female Gametophytes Development In Watermelon, Qiyan Liu, Dongfen Han, Jinfan Chen, Jie Wang, Denghu Cheng, Xiner Chen, Jiao Jiang, Shujjuan Tian, Jiafa Wang, Man Liu, Haizheng Xiong, Li Yuan

Horticulture Faculty Publications and Presentations

As a prerequisite for sexual reproduction, gametophyte development is an interesting process involving cell proliferation, differentiation and specialization. Gametogenesis has been extensively explored in model plants, but the regulatory mechanism of gametophyte development largely remains unknown in Cucurbitaceae species. In present study, we have shown that watermelon ClBBM and ClPLT2, two AP2/ERF transcription factors, participated in both male and female gametophyte development. Clbbm and Clplt2 single mutants resembled wild-type phenotypes in both vegetative and reproductive development. But Clbbm/Clplt2 double mutant showed partial pollen abortion and bore less seeds comparing to WT. Our results indicated that the abnormal pollen grains were …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Using Preformed Particle Gels To Control Transport In Geothermal Reservoirs: Mathematical Modeling, Philip Winterfeld, Baojun Bai, Yu Shu Wu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

We are developing swellable Preformed Particle Gels (PPG), which can control preferential fluid and heat flow through fracture networks to increase the performance of EGS reservoirs. Part of this development is a mathematical model and numerical simulator to simulate PPG treatments by considering coupled thermal-hydraulic-mechanical effects, and gel swelling kinetics and plugging efficiency, from which an optimized gel treatment design and operation can be achieved. The starting point for our mathematical model is the TOUGH2-CSM formulation and code. The TOUGH2-CSM fluid and heat flow formulation is based on the TOUGH2 one for multiphase, multicomponent, and multi-porosity systems, with the latter …

Applications Of Uav In Landslide Research: A Review, Boneng Chen, Jeremy Maurer, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Uncrewed aerial vehicles (UAVs), commonly known as drones, have gained significant popularity in landslide research due to their operational flexibility, near real-time planning capabilities, cost-effectiveness, high-resolution data outputs, and enhanced safety. This review aims to provide an analysis of UAV-based landslide investigation methodologies and their applications. It begins with an examination of UAV platforms and UAV-equipped sensors employed in landslide research, evaluating their advantages and limitations to guide appropriate platform and sensor selection. The review then explores UAV applications across three primary landslide investigation domains: landslide mapping, landslide monitoring, and landslide hazard assessment, highlighting some representative achievements in each domain. …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework For Enhanced Nuclear Reactor Power Prediction, James Daniell, Kazuma Kobayashi, Ayodeji Alajo, Syed Bahauddin Alam

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can be computationally intensive and may not fully capture the complexities of real-world reactor behavior. This paper introduces a novel hybrid digital twin-focused multi-stage deep learning framework that addresses these limitations, offering a faster and more robust solution for predicting the final steady-state power of reactor transients. By leveraging a combination of feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state …

Aqueous Carbonation Of Steel Slags: A Comparative Study On Mechanisms, Nannan Zhang, Gao Deng, Wenyu Liao, Hongyan Ma, Chuanlin Hu

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study investigated the aqueous carbonation mechanisms of three typical steel slags: ladle metallurgy furnace (LMF) slag containing high Al content, electric arc furnace (EAF) slag featuring high Si content and relatively low Al content, and ladle-arc fusion (LAF) slag with medium-Al content. It was found that the carbonation kinetics of the three slags were similar and followed the surface coverage model within the first 6 h of carbonation. Initially, the carbonation process was primarily governed by the reaction product precipitation. After 3 h of carbonation, the process was dominated by mineral dissolution, controlled by the uncovered reactive sites. The …

Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …

V2vdiscs: Vehicle To Vehicle Distributed Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. …

J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …

Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall …

Yolo-Based Miner Detection Using Thermal Images In Underground Mines, Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

Computer Science Faculty Research & Creative Works

Well-designed and effective in-mine robots can expedite miner self-rescue during emergencies and reduce fatalities. These in-mine robots for miner self-rescue can carry out diverse tasks such as scouting (including object detection and autonomous navigation), and payload delivery. However, robots that can effectively detect humans in a dark underground mine do not yet exist. This paper investigates challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time, in low-light conditions. The research team collected 500 thermal images in the Missouri University of Science & Technology Experimental Mine with the help of …

Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

Computer Science Faculty Research & Creative Works

In the original published article, Figure 3 appears with the Fig. 1 caption, Figure 1 appears with the Fig. 2 caption, and Figure 2 appears with the Fig. 3 caption. The article has been updated to correct this error.

Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, And Challenges, Anurag Satpathy, Manmath Narayan Sahoo, Chittaranjan Swain, Paolo Bellavista, Mohsen Guizani, Khan Muhammad, Sambit Bakshi

Computer Science Faculty Research & Creative Works

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In …

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

Message From The Phd Dissertation Showcase Chairs, Sanjay Kumar Madria, Anita Graser

Computer Science Faculty Research & Creative Works

No abstract provided.

V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us …

Securing Federated Learning From Distributed Backdoor Attacks Via Maximal Clique And Dynamic Reputation System, Priyesh Ranjan, Ashish Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a distributed learning paradigm that leverages the computational strength of local devices to collaboratively train a model. The clients train the local model on their respective devices and submit the weight updates to the server for aggregation. This paradigm allows the clients to experience diverse data without sharing their local data with other participants or the server. However, FL is susceptible to backdoor attackers that deliberately train the model on altered data, essentially trying to get favor on a specific subtask separated from the main task. In this work, we focus on powerful backdoor attackers who …

Iterative Recommendations Based On Monte Carlo Sampling And Trust Estimation In Multi-Stage Vehicular Traffic Routing Games, Doris E.M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das

Computer Science Faculty Research & Creative Works

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the …

Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint …

Citrus: Cost And Ischemia Time Reduction Using Urban Air Mobility Solutions For Organ Transport, Debjyoti Sengupta, Anurag Satpathy, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Urban Air Mobility (UAM) involves the use of both piloted and autonomous aerial vehicles, ranging from small unmanned aerial vehicles (UAVs), such as drones, to larger passenger-carrying personal air vehicles (PAVs). This ground-breaking approach holds the potential to transform healthcare logistics by facilitating the fast and efficient transportation of organs between hospitals, addressing critical mobility challenges in healthcare delivery. However, scheduling organ transport is fraught with challenges, including (1) the limited availability of UAM vehicles at specific hospital branches, (2) the critical Cold Ischemia Time (CIT) for various organs, and (3) the high flying costs associated with moving organs from …