Digital Commons Network^™

Large Language Models For Construction Risk Classification: A Comparative Study, Abdolmajid Erfani, Hussein Khanjar

Michigan Tech Publications

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines …

Comprehensive Review Of In Vitro Approaches For Environmental Heavy Metal Exposure, Manas Warke, Madeline English, Camila Padilla, Lexie Gasco, Wendy Leisner, Rupali Datta, Smitha Rao

Michigan Tech Publications

Heavy metals are ubiquitous environmental pollutants, contaminating air, soil, and water via the erosion of natural deposits, as well as originating from anthropogenic sources, such as agriculture, industries, transportation, and landfills. The increasing utilization of heavy metals over the years, combined with the persistent nature of metals in the environment poses a direct threat to human and environment health. Although regulatory limits have been established for toxic metals, assessing the associated health risks using real-life exposure scenarios remains challenging. In this review, we summarize the development and use of in vitro models based two- and three-dimensional cell culture systems, focusing …

Enhancing Network Security: Dynamical Intrusion Detection Systems Leveraging Zero Trust Architecture, Ekramul Haque

Tennessee State University Alumni Theses and Dissertations

This thesis discussed two original methodologies for proposing security frameworks incorporating machine learning (ML) and Zero Trust Architecture (ZTA) principles to manage advanced persistent threats faced by Unmanned Aerial Vehicles (UAVs) and Network intrusion detection systems (IDS). The first methodology examined the use of RF signals and deep learning to identify and classify UAVs. The RF signal characteristics used in the method for detecting UAVs improved the ability to determine RF drone protocols. Although the models led to promising findings, their lack of ability to generalize to new drone types identified the need to improve both the data set and …

Case Article—Optimizing Transportation And Equity In A District Zoning Problem For Orville Public Schools, Kelly M. Sullivan, Ashlea Bennett Milburn

Industrial Engineering Faculty Publications and Presentations

“Optimizing transportation and equity in a district zoning problem for ORville public schools” is a case developed to expose students to an optimization decision problem with ethical considerations incorporated. Specifically, students are asked to solve a school district zoning problem in which all residential addresses in a school district must be assigned to middle schools having capacity limits on the numbers of students that they can accommodate.

Enhancing Non-Player Character Dialogue In Video Gages: An Evaluation Of Large Language Model-Generated Responses, Lam P. Quach

Master's Theses

As video games increasingly emphasize narrative depth and player immersion, the quality of Non-Player Character (NPC) dialogue has become crucial for creating engaging gaming experiences. This thesis investigates the potential of Large Language Models (LLMs) to generate high-quality NPC dialogue by comprehensively evaluating four state-of-the-art models: Gemma 3 27B, Mistral 7B, QWEN 2.5, and LLAMA 3.1. The study employs a mixed-methods approach, combining human evaluation (N=50 participants) with AI-based assessment across five key benchmarks: coherence, personality expression, engagement, style/tone appropriateness, and overall quality. Participants evaluated 32 dialogue samples (8 per model) generated for a fantasy game context featuring two distinct …

The Floodnet Community Engagement Guide, Véronëque Ignace, Sofia Mariyamis, Kendra Krueger, Polly Pierone, Hayley Elszasz, Hannah Eisler Burnett

The Science and Resilience Institute at Jamaica Bay, SRIJB

"At the Intersection of Science, Policy, and Community: The FloodNet NYC Community Engagement Strategy” is a public-facing community engagement guide that documents the strategies, tools, and lessons developed through FloodNet NYC, a cross-sector partnership among researchers at NYU and CUNY and New York City agencies. Designed as a practical resource, this guide shares our approach to community engagement and dissemination so that researchers, practitioners, community organizations, and public agencies can adapt these methods to their own urban climate science projects and other community-centered efforts that address climate challenges.

Grounded in community-based participatory research (CBPR) principles, the guide presents community engagement …

Sainik Vol 1 Issue 1, Sastra Deemed To Be University

SAINIK

No abstract provided.

Hybrid Forecasting Of University Electricity Demand Using Time Series And Deep Learning, Minsoo Baek, Youngguk Seo

Faculty Articles

University buildings are energy-intensive and operate on complex schedules, making electricity demand forecasting particularly challenging. This study develops and evaluates monthly forecasting models for a public university campus in Georgia using six years of data (January 2019–December 2024) that integrate weather variables and academic calendar indicators. Three modeling approaches are compared: Seasonal Autoregressive Integrated Moving Average (SARIMA), SARIMA with exogenous variables (SARIMAX), and a hybrid SARIMAX–Long Short-Term Memory (LSTM) model. Feature selection methods, correlation analysis, Granger causality, Random Forest importance, Recursive Feature Elimination (RFE), and Least Absolute Shrinkage and Selection Operator (LASSO) regression, were applied to optimize input relevance. The …

Single-Molecule Orientation And Localization Microscopy, Sophie Brasselet, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Single-molecule localization microscopy (SMLM) offers enhanced spatial resolution in optical microscopy, providing detailed insights into the spatial organization of proteins in cells at the nanoscale. Over the past decade, SMLM has progressively incorporated the capability to retrieve the orientations of single molecules using their polarized dipolar emission pattern. Here we explore recent advancements in single-molecule orientation and localization microscopy (SMOLM), which yields super-resolved images of molecular three-dimensional (3D) orientations, wobble and 3D positions. This advancement opens possibilities to explore the nanoscale organization and conformation of biological molecules as well as to monitor and design local 3D optical fields in nanophotonics. …

Guiding State-Specific Studies On Highway Disasters: Examining Differences In Postdisaster Funding, Mariam Elazhary, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The highway sector is frequently and severely impacted by natural disasters. To mitigate these effects and reduce the need for large relief budgets or emergency funds, it is crucial to study the resilience of the transportation network. As such, defining resilience in absolute terms is not enough; instead, it is equally important to analyze specific case studies and build a lessons-learned rapport. This is critical because it helps identify unique vulnerabilities and strengths that might be overlooked in broader analyses. However, current research on highway resilience often lacks empirical evidence to support the selection of these unique and significant case …

Proceedings Of The 33rd United States – Japan Bridge Engineering Workshop, David Y. Yang

Civil and Environmental Engineering Faculty Publications and Presentations

This report summarizes the 33rd United States–Japan Bridge Engineering Workshop, an event that convenes bridge engineers from both countries. The workshop serves as a platform to exchange innovative ideas and technologies, as well as to share crucial knowledge and lessons learned, all with the aim of advancing bridge engineering. The 33rd workshop specifically addressed transportation asset management and seismic retrofitting, two subjects of increasing importance to both the United States and Japan. The report details the workshop's discussions on topics relevant to bridge engineers.

Measuring Neck Morphologies Using Upright Magnetic Resonance Imaging, Narayan Yoganandan, Mahmudur Rahman, Ali Warriach, Karthik Banurekha Devaraj, Brian D. Stemper, Aditya Vedantam, Christopher Wolfla, Peter Le

Biomedical Engineering Faculty Research and Publications

Introduction

Because the head supported mass worn by military pilots adds loading to the head-neck complex, morphometrical characteristics of neck muscles are expected to be different from those that do not wear the added mass. To accurately determine segmental neck loads using computational whole-body human or head-neck models, it is important to include accurate morphometrical properties of cervical muscles from the population of interest, in this case military pilots. Routine radiographic imaging (x-rays and computed tomography scans) is less optimal, while magnetic resonance imaging (MRI) is appropriate. Although MRI scanners are generally available in clinical settings, recumbent scanning does not …

Fpga-Based Overlay Accelerators With Massive Parallel Processing Units To Accelerate Deep Neural Networks, Ehsan Kabir

Graduate Theses and Dissertations

Deep neural networks (DNNs) are widely used in applications such as classification, prediction, and regression. Various DNN architectures, such as convolutional neural networks (CNN), multilayer perceptrons (MLP), long short-term memory (LSTM), recurrent neural networks (RNN), and transformers, have become leading machine learning techniques in these applications. They require significant computational resources and have substantial memory demands due to intensive matrix-matrix multiplications and complex data flows. Hence, efficient utilization of on-chip computational and memory resources is essential to maximize parallelism and minimize latency. Designing an optimal tiling scheme that aligns effectively with the architecture is also necessary. Modern FPGAs are equipped …

Validation And Proposed Expansion Of The Data-Driven D Region Model, Kevin M. Watson

Theses and Dissertations

Predicting ionosphere effects on radio wave propagation is critical for communications and over-the-horizon radar systems. The D region is challenging to model for several reasons, one of which is chemical complexity. Ion density changes during sunrise/sunset introduce substantial uncertainty in electron density profiles (EDPs). The Data Driven D Region (D3R) model solves electron densities through an ion chemistry model and assimilates relevant space weather inputs. This study evaluates D3R against the Faraday International Reference Ionosphere (FIRI) and measurements using Very Low Frequency (VLF) propagation paths. Space weather event impacts on D3R EDPs are evaluated during the disturbed period from 8-12 …

Low-Cost Transfers For Cislunar Liaison Navigation, Ka'eo J. Swartzmiller

Theses and Dissertations

CubeSats have an inherent limitation on volume due to their small form factor. Naturally, this extends to the capacity of on-board ΔV, thus limiting the maneuverability of the satellites. This limited maneuverability makes it extremely important to be intentional when planning out mission trajectories. The cislunar region subjects these trajectories to the gravitational forces of both the Earth and the Moon, making it all the more necessary to deliberately plan out maneuvers. The Circular Restricted Three Body Problem (CR3BP) provides a simpler model for estimating the motion of spacecraft within the cislunar region, allowing for the gravitational forces to be …

Implementing Lean Principles To Enhance Warehouse Operations At King Abdulaziz Air Base(Kaab): A Case Study Of Royal Saudi Air Force (Rsaf), Saleh A. Alghamdi

Theses and Dissertations

The Royal Saudi Air Force (RSAF) relies on efficient logistics to sustain readiness. At King Abdulaziz Air Base, warehouse receiving inefficiencies caused delays and waste. This study used Lean principles and a six-month time–motion analysis, with Pareto and Fishbone tools, to identify 55% waste in dead pile and 75% in palletized shipments. Standard times of 5.98 and 6.55 minutes were set. Key recommendations include SOPs, cross-training, forklift certification, layout redesign, and RFID. Lean adoption could save 100+ labor hours and $4,000 annually, improving safety, accuracy, and mission readiness.

A Review Of The United States' Long Term War Support Capabilities In The Indo-Pacific Command Region, Brian J. Mullin Jr.

Theses and Dissertations

This study examines U.S. maritime transportation readiness in the Indo-Pacific, highlighting fleet age, mariner shortages, shipyard decline, and port vulnerabilities. It also considers contested logistics and technological threats. Recommendations include fleet recapitalization, mariner pipeline growth, port diversification, and defensive upgrades. The study concludes that secure sea line assumptions are outdated and calls for greater resilience, with follow-on efficiency analysis proposed for ports and ships.

Irp Commercial Trailer Data Feasibility Study, Brian Howell, Jennifer Walton

Kentucky Transportation Center Research Report

This study evaluates the feasibility for developing and maintaining a comprehensive tractor truck trailer database. The commercial motor vehicle (CMV) community currently lacks a cross-jurisdictional, centralized repository for commercial trailers registered in United States and Canadian jurisdictions. Existing federal databases provide information on interstate commercial motor carriers, vehicles, and drivers but exclude trailers. Similarly, IRP and IFTA do not collect trailer-level data, and trailer data is displayed inconsistently across its customer base. Approximately one fifth of violations in Kentucky during inspections from 2020 to 2022 were attributed to trailers, including worn tires, inadequate brakes or improper registration. The rise of …

Exploring The Adoption Of Construction Technologies In Infrastructure Projects: Bridging Perceptions Of Experts With Real Project Data, Fareed Salih, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Infrastructure projects have progressively adopted construction technologies, yet previous studies have not fully explored technology utilization by comparing the perceptions of experts with actual project data. This research fills this knowledge gap. The authors (1) developed, distributed, and collected a survey completed by 52 construction practitioners to investigate the use of these technologies and their associated benefits; (2) checked the survey reliability and validity using Cronbach's alpha test; (3) investigated the perceptions of utilizing construction technologies across industry sectors; (4) explored the perceptions of benefits across industry sectors; (5) analyzed the impact of construction technologies on project objectives and phases …

Labor Productivity Losses Across Construction Trades: A Machine Learning Approach, Tamima Elbashbishy, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Labor productivity is a major concern in the construction industry. Existing research on construction labor productivity (CLP) within specific trades has produced inconsistent findings due to differences in the factors analyzed. This lack of consistency makes it difficult to identify the most critical drivers of productivity losses across trades. To address this gap, this study adopts a cross-trade analytical approach to systematically identify and evaluate the inefficiencies impacting labor productivity in multiple construction trades. Specifically, the study (1) identified common organizational and project-level inefficiencies that influence labor performance; (2) conducted an expert-based survey to measure the frequency and perceived impact …

Communicating Wastewater-Based Surveillance Data To Drive Action, Kata Farkas, Devrim Kaya, Rasha Maal-Bared, Ahmad I. Al-Mustapha, Sarmila Tandukar, Ishi Keenum, Teemu Gunnar, Aaron Bivins, Matthew J Wade, Kyle Bibby, Tarja M. Pitkänen, Ananda Tiwari

Michigan Tech Publications

As exemplified during the COVID-19 pandemic, wastewater-based surveillance (WBS) can deliver near real-time, population-level pathogen data to guide public health action. Its impact, however, hinges on timely, transparent, and context-specific communication to stakeholders, including health authorities, policymakers, scientists, clinicians, and the public. This review examines current WBS communication practices, identifies persistent challenges, and proposes strategies to enhance relevance. Key challenges include data complexity, lack of standardised communication frameworks, ethical and privacy concerns, and variable stakeholder capabilities. The strategic use of digital platforms, such as dashboards, reports, press releases, and social media, alongside traditional media, can broaden reach and aid interpretation. …

Tissue Engineered Combinatorial Therapeutics For Spinal Cord Injury Repair, Inha Baek

Graduate Theses and Dissertations

Traumatic spinal cord injury (SCI) lead to temporary or permanent sensorimotor deficit due to the complex and multifaceted features of the injury site, making the treatments ineffective. Recent research emphasizes the potential of combinatorial therapeutics approaches combining different therapeutics, such as biomaterials and stem cell transplantation. In this context, nerve composite hydrogels, fabricated from decellularized sciatic nerve (dSN) and spinal cord (dSC) extracellular matrices (ECM), might be promising platforms due to their biocompatibility and ability to mimic native microenvironments. In this study, we developed and characterized nerve mimetic composite hydrogels embedded with human adipose-derived stem cells (hASCs) and investigated their …

Switching Performance Optimization Of Sic Mosfets With Digital Active Gate Drivers, Liyang Du

Graduate Theses and Dissertations

Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have extensive applications in high-power density devices, but they exhibit quick switching transients that are problematic, including electromagnetic interference (EMI), voltage overshoot, and current imbalance between paralleled devices. The purpose of this dissertation is to maximize the switching performance of SiC MOSFETs using digital active gate drivers (AGDs). A three-level gate driving approach is presented to improve current sharing in paralleled SiC MOSFETs through turn-on voltage and gate signal delay adjustment. Closed-loop control with real-time feedback ensures high-reliability operation. For better practicability, a compact AGD structure of a single-path Class-B amplifier-based topology is …

Automation Of Lcmc Logistics Processes: A Delphi Approach, Kline M. Alt

Theses and Dissertations

As the U.S. Air Force confronts growing complexity in system acquisition, the implementation of digital models in system design and logistics process management allows the incorporation of digital tools and the possibility for automation of portions of logistics processes. This thesis investigates where these technologies can be most effectively integrated within the Air Force Life Cycle Management Center logistics enterprise (AFLCMC). Using a three round Delphi study, AFLCMC logistics subject matter expert (SME) opinions were solicited from program-level senior logisticians, program managers to identify high-need areas, key success factors, and potential barriers to adoption. Quantitative consensus from Likert-scale and ordinal …

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Text-Based Propaganda Detection, Vivek Sharma

Dissertations, Theses, and Capstone Projects

In the digital age, the swift consumption of news and the widespread dissemination of propaganda significantly threaten democratic principles and informed decision-making. News organizations, often aligned politically, tailor their content to appeal to specific audiences, thereby perpetuating biases. Despite advancements in detecting propaganda through various computational techniques, including Large Language Models(LLM) and BERT-based approaches, significant challenges persist concerning data availability and the effectiveness of detection tools. This thesis explores the landscape of text-based propaganda and reviews related research in textual analysis. It introduces a novel method for collecting a social-media based dataset, which includes analysis of journalists' tweets to discern …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Multimodal Learning In Real-World Application: Enhancing Feature Representation And Training Strategies, Nana Lin

Graduate Doctoral Dissertations

Multimodal learning has emerged as a critical paradigm for developing intelligent systems that can understand and reason across diverse inputs such as images, text, and audio data. Despite significant advances, effective deployment of multimodal models in practice remains a challenging task. This dissertation explores how multimodal learning can be effectively applied to high-stakes, real-world scenarios, with a focus on enhancing feature representation and training efficiency. Specifically, this research investigates multimodal learning strategies in two key domains: healthcare and surveillance.

In the healthcare domain, we explored the data fusion and alignment approaches for cognitive decline diagnoses. First, we propose the LOVEMA …

The Utilisation Of The Fourth Industrial Revolution (4ir) Technologies In E-Government Service Delivery: A Systematic Literature Review, Arnet Zitha, Noluntu Mpekoa, Sheethal Tom

African Conference on Information Systems and Technology

The integration of Fourth Industrial Revolution (4IR) technologies is transforming e-Government by boosting citizen engagement and enhancing service efficiency. However, gaps still exist in understanding the various applications, impacts, and barriers to adoption. This systematic review synthesises literature from 16 studies published between 2017 and 2025, illustrating how technologies like blockchain, artificial intelligence, big data, Internet of Things, and machine learning are employed and their effects on e-Government service delivery. The review reveals that 4IR technologies continue to play a vital role in e-Government services by addressing security threats, simplifying verification and authentication, building trust, and improving the quality and …