Digital Commons Network^™

Examining Teaching Competencies And Challenges While Integrating Artificial Intelligence In Higher Education, Xinyue Ren, Min Lun Wu

STEMPS Faculty Publications

The rapid development of artificial intelligence (AI) technologies has demonstrated their affordances and limitations in revolutionizing pedagogical strategies in higher education. Given the lack of guidelines, policies, and resources to assist instructors in efficiently and ethically integrating AI into teaching and learning practices, this systematic review aimed to investigate AI integration competencies and challenges in higher education from the intelligent Technological Pedagogical Content Knowledge (TPACK) perspective. We first applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 23 studies published between 2019 and 2023 that met the inclusion and exclusion criteria. After conducting open coding and …

Designing Tpack-Driven Mobile Learning With Digital Citizenship For Biology Education: Insights From Ngt And Ism Approaches, Cik Jamaliah Abd Manaf, Shakinaz Desa, Muhamad Ikhwan Mat Saad, Helen Crompton

STEMPS Faculty Publications

The rapid digitization of education and the complexity of scientific content demand the integration of digital citizenship in TPACK-based mobile learning for Biology. This study aimed to identify and prioritize relevant digital citizenship practices using a Design and Development Research approach. In Phase One, the Nominal Group Technique with 10 experts evaluated eight proposed practices. Phase Two employed Interpretive Structural Modeling with nine experts to structure hierarchical relationships. Seven practices were retained, with Ethical Use of Digital Resources as the top-level driver. The resulting framework supports ethical and discipline-specific mobile learning models, promoting responsible digital engagement in Biology education.

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. …

Designing Ai-Powered Learning: Adult Learners' Expectations For Curriculum And Human-Ai Interaction, Jinhee Kim, Seongryeong Yu, Rita Detrick, Xi Lin, Na Li

STEMPS Faculty Publications

Despite the potential benefits offered by GenAI technologies to provide innovative solutions to address distinct challenges faced by working adult learners (ALs) in higher education and beyond, there is limited understanding of how best to structure AI-powered learning for this population while ensuring their distinct needs and perspectives are considered. Hence, this study aimed to determine what curriculum and student-AI interaction would be required by situating ALs’ views. Through analyzing 48 e-portfolios and in-depth interviews with 20 ALs from diverse educational and professional backgrounds, the study found that ALs perceived content mastery and developing a lifelong habit of learning as …

Carbon Sequestration Through Conservation Tillage In Sandy Soils Of Arid And Semi-Arid Climates: A Meta-Analysis, Samantha Lynn Colunga, Leila Wahab, Alejandro Fierro-Cabo, Engil Isadora Pujol Pereira

School of Earth, Environmental, & Marine Sciences Faculty Publications

Highlights

• Under CST, SOC increased by 12.74 ± 1.46 % in sandy soils with 5 of the 6 practices contributing to this rise.
• SOC increased up to 15 years post-CST conversion, but more research is needed to assess long-term effects.
• SOC increased by 13% in soils with over 56% sand content under CST.
• SOC increased by 15% in the 0-20 cm depth under CST, with no significant increase at greater depths.
• SOC increased under CST when nitrogen was applied at rates up to 100 kg N ha⁻¹.

Abstract

This meta-analysis assessed soil organic carbon (SOC) percent changes in sandy soils, …

Examining The Presence And Effects Of Coherence And Fragmentation In The Gulf Of Maine Fishery Management Network, Derek A. Katznelson, Antonia Sohns, Dongkyu Kim, Evelyn Roozee, William Donner, Andrew M. Song, Jasper R. De Vries, Owen Temby, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Natural resource management networks cohere due to mutual dependencies and fragment, in part, due to the perceived risks of interaction. However, research on these networks has tended to accept coherence a priori rather than problematizing dependence, and few studies exist on interorganizational risk perception. This article presents the results of a study operationalizing these concepts and measuring the distribution of three types of dependence (capital, legitimacy, and regulatory) and two types of perceived risk (performance and sanction) among nearly fifty stakeholder groups and organizations participating in the management of fisheries in the binational Gulf of Maine. The analysis reveals an …

Nesting Population Trend Of The Leatherback Sea Turtle In Bocas Del Toro Province And Comarca Ngäbe-Buglé, Panama For The Period 2002–2022, Sonia Gutiérrez Parejo, Susan E. Piacenza, Raúl García, Cristina Ordóñez, Roldán A. Valverde

School of Earth, Environmental, & Marine Sciences Faculty Publications

Sea turtle biologists have made sustained efforts to understand the global status of leatherback sea turtle populations. However, despite progress in assessments, demographics, and ecology, key uncertainties persist in tracking leatherback population trends. Trend analyses have historically focused on nesting beaches, with nest counts providing a widely used index for population abundance. Here, we analysed 20 years of annual nest abundance at four main nesting beaches (Soropta, Bluff, Playa Larga and Chiriquí) in Bocas del Toro province and the Comarca Ngäbe-Buglé, Panama, which constitute the largest nesting leatherback sea turtle population in Central America. We conducted daily nest counts during …

Isolation And Molecular Identification Of Pathogens Causing Sea Turtle Egg Fusariosis In Key Nesting Beaches In Costa Rica, Keilor E. Cordero-Umaña, Ruth Hernando-Martínez, María Martínez-Ríos, Jaime Restrepo, Roldán A. Valverde, Laura Martín-Torrijos, Pilar Santidrián Tomillo, Javier Diéguez-Uribeondo

School of Earth, Environmental, & Marine Sciences Faculty Publications

The global rise of fungal pathogens presents an emerging threat to biodiversity, with significant risks to species such as endangered sea turtles. The fungal disease known as sea turtle egg fusariosis (STEF) is associated with high embryo mortality rates and represents a substantial conservation challenge. This disease is caused by two fungal species, namely Fusarium falciforme (Ff) and Fusarium keratoplasticum (Fk), and their identification is essential for guiding future efforts to address potential fungal infections, particularly on important nesting beaches such as those in Costa Rica. In this study, we conducted fungal isolations from sea turtle eggshells …

Forecasting Covid-19 Cases, Hospital Admissions, And Deaths Based On Wastewater Sars-Cov-2 Surveillance Using Gaussian Copula Time Series Marginal Regression Model, Hueiwang Anna Jeng, Norou Diawara, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Modeling efforts are needed to predict trends in COVID-19 cases and related health outcomes, aiding in the development of management strategies and adaptation measures. This study was conducted to assess whether the SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalizations, and deaths using copula-based time series modeling. SARS-CoV-2 RNA load in wastewater in Chesapeake, VA, was measured using the RT-qPCR method. A Gaussian copula time series (CTS) marginal regression model, incorporating an autoregressive moving average model and Gaussian copula function, was used as a forecasting model. Wastewater SARS-CoV-2 viral loads were correlated with …

Machine Learning Models For Pancreatic Cancer Survival Prediction: A Multi-Model Analysis Across Stages And Treatments Using The Surveillance, Epidemiology, And End Results (Seer) Database, Aditya Chakraborty, Mohan D. Pant

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pancreatic cancer is among the most lethal malignancies, with poor prognosis and limited survival despite treatment advances. Accurate survival modeling is critical for prognostication and clinical decision-making. This study had three primary aims: (1) to determine the best-fitting survival distribution among patients diagnosed and deceased from pancreatic cancer across stages and treatment types; (2) to construct and compare predictive risk classification models; and (3) to evaluate survival probabilities using parametric, semi-parametric, non-parametric, machine learning, and deep learning methods for Stage IV patients receiving both chemotherapy and radiation. Methods: Using data from the SEER database, parametric models (Generalized Extreme Value, …

Mapping Engineering Ethics Education, R Tormey, T Børsen, Shannon Chance, T T Lennerfors, Diana Adela Martin, G Bombaerts

Research Outputs: 2025-Present

Writing a handbook implies describing the fundamental information needed by those teaching or researching in a field. This is a challenging task when the field is still maturing. This handbook grew from the idea that we could ‘collaboratively write’ engineering ethics education. In this introduction, we define the field in three dimensions: engineering, ethics, and education. The latter dimension is divided into three parts: the subjects that shape the teaching of engineering ethics education, the understanding of learners and learning that informs the pedagogical choices made, and the fusion of these two in the pedagogical methods used in teaching engineering …

“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 …

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. …

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 …

Icrop+: An Edge-Boosted Crop Disease Detection System Via Tinyml And Lora Communication, Xu Tao, Jackson Butcher, Simone Silvestri, Sajal K. Das

Computer Science Faculty Research & Creative Works

Crop disease detection is essential for controlling dis-ease spread and minimizing agricultural losses. In this demo, we present an implementation of iCrop+, an end-to-end autonomous crop disease detection system that integrates on-device AI, low-power long-range communication (LoRa), and server-based deep learning to create a hybrid architecture suitable for real-world deployment. The prototype efficiently balances local processing and remote inference through category-based optimization, adaptive classification, and intelligent data transmission, ensuring that only the most informative segments are transmitted to the server. Built on low-cost devices such as Raspberry Pi, LoRa transceiver modules, and a laptop, the demo showcases its potential for …