Digital Commons Network^™

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 …

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 …

Temporal Evolution Of Workforce Fatality-Linked Factors In The Transportation Construction Sector, Mariam Elazhary, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Despite advancements in safety measures, fatalities in the transportation sector remain disproportionately higher than in other industries. Transportation sector fatalities arise from a combination of demographic, environmental, and occupational factors that amplify the overall risk. While strides have been made in understanding the factors contributing to fatalities, there remains a critical gap in research focused on their temporal evolution. The goal of this study is to investigate the temporal evolution of fatality-linked association factors in the transportation construction sector. The methodology encompassed three stages. Stage 1 was preprocessing the data: the US Census of Fatal Occupational Injuries, covering 3,792 fatality …

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 …

A Comprehensive Review On The Recovery Of Lithium From Lithium-Ion Batteries And Spodumene, Asad Ali, Sadia Afrin, Abdul Hannan Asif, Yasir Arafat, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

The growing demand for Lithium-Ion batteries (LIBs) for use within varied electronics and electric vehicles (EVs) has raised concerns about the sustainability of lithium extraction from natural deposits. This study provides a comprehensive comparison of lithium recovery through mining of spodumene deposits, and the recovery of lithium from used batteries via recycling, in terms of technological, economic and environmental impacts. Lithium recovered through mining and refining operations prompt significant land disruption and soil contamination and possess large ecological, water and carbon footprints. Contrastingly, lithium recovered through battery recycling undergoes successive heat and chemical treatments, leading to waste minimisation and a …

Acoustic Emission Sensing For Assessing Compressive Strain In Impacted Thermoplastic Composites, M. M. Shahzamanian, Li Ai, Sydney Houck, Md Mushfiqur Rahman Fahim, Sourav Banerjee, Paul Ziehl

Civil Engineering Faculty Publications

Composite materials used in aircraft during in-flight service are vulnerable to impacts, which can lead to undetected damage and deterioration. Impact incidents can cause considerable structural harm that might not be immediately apparent, resulting in a decline in the mechanical properties of material and overall performance. There are currently limited advanced models capable of assessing the state of compressive strain (or stress) in a compression after impact (CAI) test based on acoustic emission (AE) data. Developing such a model may enable real-time monitoring and evaluation during in-flight service, providing users with alerts when composite material failure is approaching. This paper …

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.

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 …

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. …

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 …

Toward A Generalizable Perceptual Hashing Framework For Image Manipulation Detection, Priyanka Samanta

Dissertations, Theses, and Capstone Projects

This thesis contributes to research in adversarial image manipulation detection. The primary motivation is the increasing need to verify digital images, especially for legal evidence, journalistic proof, or social media content—where manipulated or fabricated images can mislead, defame, or distort reality. A key application and contribution of this work is the development of eWitness, a blockchain application that generates and registers image provenance at capture time to enable independent verification of authenticity. The secret sauce behind the system is SmartHash, a novel and efficient perceptual hashing algorithm designed for real-world deployment in systems like eWitness. Unlike existing algorithms, SmartHash targets …

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 …

Low-Cost Cutaneous Protoporphyrin Ix (Ppix) Detection (Cpd) Device For Follow-Up Monitoring Of Patients After Photodynamic Therapy, Md Asaduzzaman Rasel

Graduate Masters Theses

Background: Photodynamic Therapy (PDT) utilizes specific wavelengths of light to activate photosensitizing chemical compounds, known as photosensitizers, which induce the generation of cytotoxic reactive oxygen species (ROS) for the targeted destruction of cancer cells. Among various photosensitizers for PDT, Protoporphyrin IX (PpIX) is widely employed in oncology and dermatology due to its natural in situ generation via the metabolic conversion of 5- aminolevulinic acid (ALA), a non-phototoxic prodrug. Systemic administration of ALA after 3-6 hr drug delay leads to peak PpIX accumulation in tissues, facilitating therapeutic and diagnostic applications. However, PpIX can persist in the skin for 24–48 hours post-treatment, …

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 …

Human-Machine Communication: Complete Volume. Volume 10

Human-Machine Communication

This is the complete volume of HMC Volume 10.

Modeling, Control, And Learning For Whole-Body Manipulation With Large-Scale Soft Robots, Curtis C. Johnson

Theses and Dissertations

This dissertation addresses the challenge of enabling soft robots to perform contact-rich manipulation tasks by overcoming limitations in hardware, modeling, control, and learning. First, we present hardware advancements with PneuDrive, a pneumatic control system for actuating soft robots. We also introduce Baloo, a hybrid soft-rigid robotic torso which achieves a single-arm payload capacity of 19.3 kg and robust whole-body grasping capabilities. We also develop a full-body tactile sensing system enabling piezoresistive sensor arrays to cover large areas (1 square meter) with over 8000 distributed sensing points. Second, we focus on developing tractable dynamic models based on the Recursive Newton-Euler algorithm …

Design And Optimization Of Anode Catalysts For Direct Ethanol Fuel Cells: Advances And Challenges In C-C Bond Activation And Selective Modulation Of The C1 Pathway, Kai-Chi Qin, Meng-Tian Huo, Yu Liang, Si-Yuan Zhu, Zi-Hao Xing, Jin-Fa Chang

Journal of Electrochemistry

Direct ethanol fuel cells (DEFCs) are a promising alternative to conventional energy sources, offering high energy density, environmental sustainability, and operational safety. Compared to methanol fuel cells, DEFCs exhibit lower toxicity and a more mature preparation process. Unlike hydrogen fuel cells, DEFCs provide superior storage and transport feasibility, as well as cost-effectiveness, significantly enhancing their commercial viability. However, the stable C–C bond in ethanol creates a high activation energy barrier, often resulting in incomplete electrooxidation. Current commercial platinum (Pt)- and palladium (Pd)-based catalysts demonstrate low C–C bond cleavage efficiency (

Hydrosocial Ocean Commons: Offshore Wind Energy In Oregon, Bryce Ashlyn Sprauer

Dissertations and Theses

The ocean has been culturally and legally conceptualized as a commons, a space of subsistence inherited by all. Enclosure or privatization creates overlapping jurisdictions of ocean space. The ocean is also the world's most significant carbon capture, absorbing billions of tons of anthropogenic carbon dioxide since the Industrial Revolution. Now, the ocean offers an additional opportunity to decarbonize energy production and reduce greenhouse gas emissions through 'new frontiers' of offshore wind technology; however, this potential is entangled within neoliberalism. Offshore wind is entering a fragmented legal landscape where the social-legal process of ocean jurisdiction remains contested. As the "new frontier" …

Upcycling Waste To Wealth: Cuo-Sio₂/Reduced Graphene Nanocomposite From Pomegranate Peels For One-Pot Low-Temperature Conversion Of Waste Oils Into Valuable Fatty Acid Monomers, Attia Attia, Shahenda Mahran Dr., Maria Centeno Prof., Basudeb Saha

Petroleum Engineering and Gas

and hence reduce the price of the fatty acid monomers. This study reports for the first time a novel, environmentally benign, highly active copper oxide-silica oxide/reduced graphene oxide (CuO-SiO2/RGO), heterogeneous nano-catalyst derived from waste pomegranate peels, for the one-pot, low-temperature synthesis of fatty acid monomers from high-acid-value waste vegetable oil (WVO). The synthesised nano-catalyst was extensively characterised using XRD, FT-IR, TEM, SEM, EDX and TGA-DTA. Further, it was utilised to synthesise fatty acidrich oleic phenoxypropyl acrylate (OPA) monomer from high acid value WVO via a single-step reaction.

Investigation Of Co-Current And Counter-Current Multiphase Flow In Upstream Drilling And Production Applications, Hazem Fleifel

LSU Doctoral Dissertations

In the domain of upstream Petroleum Engineering, the dynamics of co-current and counter-current multiphase flows present huge challenges, significantly impacting the efficiency of associated processes. This study investigates two specific applications concerning these flow behaviors, involving both laboratory experiments and transient computer simulations. This work investigates two distinct well control and production enhancement processes, involving multiphase flow dynamics: the bullheading kill procedure and liquid-assisted gas lift (LAGL).

The first topic (Topic 1) explores the bullheading process, a type of well control methods, by challenging the traditional understanding that gas removal efficiency increases monotonically with injection pump flow rate. Through 1D …

3d Advanced Geological Prediction Based On Reflected Seismic Waves And Its Application Effects, Cai Sheng

Coal Geology & Exploration

Objective For the advance geological prediction of tunnels based on reflected seismic waves, the traditional 2D observation method tends to cause spatial positioning deviations of anomalies. To overcome this limitation, this study developed a 3D seismic wave-based advance geological prediction method according to the actual geometries of tunnels. Methods Using the technical path consisting of theoretical analysis, numerical simulation, and engineering verification, this study solved key technical challenges including 3D wavefield reconstruction, spatial modeling, and migration imaging in the 3D detection system. Based on the principle of geometric seismology, this study demonstrated the enhancement effects of 3D observation on the …

Digital Rock Modeling And Elastic Simulations Of Fractured-Vuggy Carbonate Reservoirs, Zhu Wei, Zhao Luanxiao, Wang Yirong

Coal Geology & Exploration

Objective The Tarim Basin exhibits extensively distributed deep fractured-vuggy carbonate reservoirs. Therefore, determining the seismic and petrophysical characteristics of carbonate reservoirs with a fracture and vug system will provide significant guidance for the exploration, discovery, drilling, and exploitation of favorable reservoirs in the basin. Variations in the filling state of dissolution fractures and vugs will lead to significantly different elastic and attenuation characteristics of the reservoirs. Methods Based on geological insights into the geometric structural and filling characteristics of the dissolution fracture-vug system in the outcrop sections of carbonate strata in the Tarim Basin, this study proposed a digital rock …

Advances In Research On Geological Evaluation Of Compressed Air Energy Storage In Underground Gas Storage Facilities, Jiang Wen, Huang Leqing, Zhang Songhang, Ma Huiying

Coal Geology & Exploration

Background Against the backdrop of achieving peak carbon dioxide emissions and carbon neutrality globally, renewable energy has developed rapidly and is gradually changing the world's energy mix. However, the intermittent and fluctuating nature of photovoltaic and wind power energy is prone to cause grid frequency fluctuations and reduced power supply reliability. Compressed air energy storage (CAES) technology has emerged as the key to the stable operation of the green power grid thanks to its large capacity and low cost.Methods Based on a systematic review of domestic and international literature in recent years, this study organizes the current theoretical and …

Pseudo-2d Mt Inversion Technique For Small-Frame Tem And Its Application In Advance Geological Prediction, Han Ziqiang, Wanma Longzhi, Li Xiangfeng, Yang Yongqiang, Zhu Xiaoming, Luo Chengxian, Tang Li

Coal Geology & Exploration

Objective To address the bottlenecks of low computational efficiency and impracticality of multi-dimensional inversion in the advance geological prediction based on the transient electromagnetic method (TEM), this study developed a pseudo-two-dimensional (2D) magnetotelluric (MT) inversion technique for small-frame TEM based on time-frequency transformation. Methods First, this study corroborated the significant similarity between the late-time apparent resistivity derived using small-frame TEM and the Cagniard apparent resistivity in the MT method under the parameters commonly used in advance geological prediction. Then, the optimal time-frequency transformation coefficient (C_TF) was introduced to achieve a rapid conversion from the late-time apparent resistivity …

Using Statistical Clustering Of Trajectory Data To Support Analysis Of Subject Movement In A Virtual Environment, Martin Galicia Avila, Douglas Timmer, Alley C. Butler

Manufacturing & Industrial Engineering Faculty Publications

Gracia de Luna conducted experiments with an HMD virtual environment in which human subjects were presented with surprise distractions. His collected data for head, dominant hand, and non-dominant hand included 6 DOF human subject trajectories. This paper examines this data from 57 human subject responses to those surprise virtual environment distractions using statistical trajectory clustering algorithms. The data is organized and processed with a Dynamic Time Warping (DTW) algorithm and then analyzed using the Density Based Spatial Clustering (DBSCAN) algorithm. The K-means method was used to determine the appropriate number of clusters. Chi Squared goodness of fit was used to …

Learning Neural Point Processes For Long Event Sequences, Zhuoqun Li

LSU Doctoral Dissertations

This research presents a comprehensive series of studies aimed at advancing learning neural point processes for long event sequences, with applications spanning disaster resilience, crime forecasting, and healthcare. Structured around three interconnected studies, this work addresses core challenges in temporal point process (TPP) modeling, efficient handling of long event sequences, and improving accuracy over extended forecasting horizons by reinforcement learning.

The first study proposes the Sparse Transformer Hawkes Process (STHP) to model long asynchronous event sequences. Traditional neural network-based TPPs struggle with long event sequences due to computational inefficiencies. To address this, the STHP model combines two components: a temporal …

Exploring Arabic Large Language Models: A Comprehensive Review, Lamar Aljahdali, Joud Kaki

Effat Undergraduate Research Journal

This paper presents a comprehensive review of Arabic large language models (LLMs), exploring their capabilities, limitations, and potential impact on the Arabic NLP landscape. We analyze the performance of prominent LLMs, including JAIS, AraBERT, and BLOOM, highlighting their strengths and weaknesses on various NLP tasks. The review delves into critical challenges faced by Arabic LLMs, such as domain adaptation, cross-lingual capabilities, and ethical considerations. Additionally, the paper emphasizes the importance of responsible development and deployment practices for LLMs, ensuring fairness, transparency, and cultural sensitivity.

Advancing Rfid Systems: From Head Orientation To Robotic Localization Using Passive Tags, Guilherme Ricardo Mendes Da Silva Barreto De Figueiredo

Doctoral Dissertations

No abstract provided.

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Contributing Factors To Low Airspeed On Climb-Out As A Precursor To Loss Of Control In Flight, Owen D. Poborsky

Journal of Aviation Technology and Engineering

The nature of aviation is not without inherent risk. Conducting training flight operations increases these inherent risks. Flight data monitoring and voluntary safety action programs seek to reduce risk, as seen in their success at commercial airlines. These successes are slowly permeating into general aviation through programs such as the National General Aviation Flight Information Database (NGAFID). The NGAFID is a program funded by the Federal Aviation Administration that allows general aviation operators to have the ability to collect and share de-identified flight data on an aggregate level to identify trends and increase safety margins during flight operations. This study …