Digital Commons Network^™

Conversion Relationships And Three-Dimensional Storage And Utilization Modes Of Four Water Resources In An Open-Pit Mining Area, Chen Tianci, Xu Zhimin, Sun Yajun, Fang Jie, Wang Qiangmin, Xiong Xiaofeng, Zhu Yuhao, Lu Zihan, Wan Fei

Coal Geology & Exploration

Background Open-pit coal mines in China are primarily distributed in arid and semi-arid regions such as Xinjiang and Inner Mongolia. However, the contradiction between coal mining and groundwater resource conservation is increasingly prominent in these regions. Specifically, a substantial amount of mine water inflow produced during coal mining tends to lead to the further loss of groundwater resources within the influence range of a mining area. Concurrently, the failure of efficient mine water storage intensifies the regional water shortage.Methods This study investigated a typical open-pit coal mine in eastern Inner Mongolia. Using methods such as field survey and sampling, …

Multi-Scale Color Correction And Contrast Enhancement Via Leaf In Wind Optimization For Improved Weld Defect Detection In Non- Destructive Testing, Senthil Anand N Mr

Theses and Dissertations

Image enhancement is an essential process in numerous fields, including industrial inspection, medical imaging, remote sensing, and photography, as it improves image quality for accurate analysis and interpretation. Among the advanced image enhancement techniques, Focused Super Resolution (FSR) with Self-Attention Single Candidate Optimizer-based Generative Adversarial Networks (GANs) is specifically designed for weld defect detection, while Advanced Image Enhancement through Multi-scale Color Correction and Contrast Stretching using Leaf in Wind Optimization focuses on enhancing the overall visual quality of images. Although both approaches aim to improve image quality, they differ significantly in their objectives and application areas. The FSR method concentrates …

Efficient Small Tool Detection In Construction Via Lightweight Deep Neural Networks, Maryam Soleymani

LSU Master's Theses

Construction sites are dynamic and inherently hazardous environments, where small hand tools—although essential—pose serious safety risks due to their frequent use, portability, and tendency to be misplaced or dropped. This study introduces a novel and lightweight deep learning-based architecture, Lightweight Small Tool Detection (LSTD), specifically designed for fast detection of small tools in unstructured and challenging construction environments. Recognizing that small object detection remains a persistent limitation in existing computer vision models, particularly under poor lighting or cluttered backgrounds, LSTD integrates advanced modules for enhanced feature extraction, fusion, and classification. It achieves notable improvements in accuracy, recall, and computational efficiency …

Cost-Effective Automated Uhi Mapping With Ai: A Case Study Of A Scalable Framework For Climate Equity In San José, California, Martin Alvarez Lopez

Master's Theses

Urban areas experience the Urban Heat Island (UHI) effect, with higher temperatures than rural areas, disproportionately impacting low-income communities. Mapping UHIs is a process that usually requires significant amount of human resources, and is not scalable. The lack of accurate and detailed UHI maps makes it difficult for decision makers to design effective mitigation strategies. In this work we introduce a cost-effective, scalable, and universally applicable UHI mapping framework that leverages open-source data and AI-driven feature extraction from remote sensing imagery. Using various causative factors such as city characteristics, anthropogenic heat, city canyons, and meteorological variables, we create UHI maps …

Sirilla: Predicting Traffic Flow Via Stacked Decentralized Federated Learning, Andrew Selvia

Master's Theses

Billions of people today rely on traffic predictions to optimize their travels. Digital mapping services deliver accurate predictions by learning from vast troves of historical data. Impressive as these systems are, their assumptions do not always apply. They depend on an endless flow of sensitive user data to a central authority, a stable Internet connection, and trustworthiness on both sides of the traditional client-server model. This thesis explores a novel architecture which bucks those assumptions. In the proposed model, traffic data remains on edge devices which individually train models via federated learning. Beyond the obvious privacy benefits, this architecture enables …

Uptake, Distribution, And Activity Of Pluronic F68 Adjuvant In Wheat And Its Endophytic Bacillus Isolate, Anthony Cartwright, Mohammad Zargaran, Anagha Wankhade, Astrid Jacobson, Joan E. Mclean, Anne J. Anderson, David W. Britt

Biological Engineering Student Research

Surfactants are widely utilized in agriculture as emulsifying, dispersing, anti-foaming, and wetting agents. In these adjuvant roles, the inherent biological activity of the surfactant is secondary to the active ingredients. Here, the hydrophilic non-ionic surface-active tri-block copolymer Pluronic^® F68 is investigated for direct biological activity in wheat. F68 binds to and inserts into lipid membranes, which may benefit crops under abiotic stress. F68’s interactions with Triticum aestivum (var Juniper) seedlings and a seed-borne Bacillus spp. endophyte are presented. At concentrations below 10 g/L, F68-primed wheat seeds exhibited unchanged emergence. Root-applied fluorescein-F68 (fF68) was internalized in root epidermal cells and …

Investigating The Impact Of Agent Openness On Planning In Multi-Agent Systems, Bala Subramanyam Duggirala

School of Computing: Dissertations, Theses, and Student Research

Multi-agent systems (MAS) possess significant potential for modeling real-world scenarios requiring coordinated actions (like wildfire fighting or ridesharing) among autonomous entities or agents (e.g., wildfire fighting agents) in complex, dynamic environments. Effective decision-theoretic planning (where each agent must carefully consider both the immediate and the future situations or states, and coordinate with the other agents (neighbors) to evaluate what needs to be done at present) within MAS, especially multiagent planning, where the planning agent directly models its neighbors in order to estimate their optimal actions, is critical, yet challenged by factors like partial observability, openness, and diverse agent types with …

Sbv_Dps: A Stacking-Bagging-Voting Nested Ensemble Based Diabetes Prediction System Using K-Fold Cross Validation, Sourabh Shastri, Sachin Kumar, Paramjit Kour, Vibhakar Mansotra

Mansoura Engineering Journal

With the advancement of machine learning techniques, the introduction of the most accurate model has become a necessity. In real-world scenarios, every model has some constraints and assimilates errors, so their performance is not always highly efficient; this sparked the development of ensemble learning. The ensemble approach aims to consolidate the strengths of existing approaches and minimize their weaknesses or decision-making risks. The proposed diabetes prediction system encases a resampling filter, applied to balance the dataset and model builder method, i.e., without the SBV ensemble and with the SBV ensemble method. The model is initially built without using the SBV …

Cloud-Assisted Multi-Channel Data Broadcasting, Debabrata Maji

Master’s Dissertations

The convergence of Internet of Things (IoT) and cloud computing has transformed technology, impacting commerce, industrial production, data management, etc. Multi- Channel Broadcast Encryption (MCBE), first introduced by Phan et al. (ASIACCS 2013), is a cryptographic encryption primitive used for both IoT and Cloud that permits a sender to e!ciently and securely encrypt several messages for di”erent groups of receivers. After thoroughly exploring the existing literature, we observe that none achieves the robust provable security within the standard model. This paper addresses this gap, aiming to achieve adaptive INDistinguishable under full-IDentity Chosen-Ciphertext Attack (IND-ID-CCA) security by constructing an e!cient identity-based …

Influence Of Zinc Oxide Nanoparticles On The Efficiency Of Oxytetracycline Removal From Wastewater Using Continuous Catalytic Ozonation, Sarmad Al-Anssari, Hassanain A. Hassan, Maha K. Mohsin, Ahmed A. Mohammed

Research outputs 2022 to 2026

Antibiotics must be fully eliminated before they are released into the environment. In most cases, conventional wastewater treatment systems are not built to handle polar microcontaminants such as antibiotics. Oxytetracycline (OTC) is one of these antibiotics, an environmental hazard contaminant in aqueous solutions. Therefore, an advanced treatment method is needed for wastewater contaminated with antibiotics. In this study, we employed zinc oxide nanoparticles (ZnO), and the catalytic ozonation procedure was employed to increase the ozonation efficiency. A continuous experiment was carried out to compare the effectiveness of catalytic and single ozonation in degrading OTC in a continuous reactor. The flow …

Humanization Strategy Of The Public Urban Space In Cairo, Rania Badawy Shokry

Mansoura Engineering Journal

An accurate scientific definition of the term “humanization of cities” has not yet crystallized, which refers the term to the word from which it is derived. She is "human". Obviously, anything contrary to it is inhuman; starting from wild nature or a life entirely dependent on vehicles. If a person cannot dispense with vehicles when carrying out his daily activities, then here we know that the standard of humanization in a place is low. However, if the planning of a city or neighborhood takes into account the needs of the population and the human standard, then the city may reach …

Turning Data Into Guardrails-Decoding Financial Vulnerability Through Behavioural Signs, Debanwita Hajra

Master’s Dissertations

This report presents the work I did during my internship at Hongkong and Shanghai Banking Corporation (HSBC), Kolkata. As a financial institution, the strength of the bank is fundamentally rooted in the behavior and reliability of its customers. Understanding this behavior is not only desirable; it is essential for the security, risk mitigation and future strategic planning of the bank. To do this, banks must invest in a thorough analysis of the financial behavior of their customers to detect early signs of risk and act accordingly. I worked in the Finance Support Team within the Data and Analytics division, where …

Performance Analysis Of University Wifi Using 802.11e Information Elements, Douglas Christopher Hales

Theses and Dissertations

Wireless networks, including IEEE 802.11 (WiFi), continue to become more important for many uses, including university classrooms. Factors that impact the performance of these networks have changed greatly over time, with increased scale at which they are used, and growing dependence of latency sensitive applications. In order to better understand the performance of current WiFi networks, a methodology was created to capture and analyze beacon frames with optional 802.11e information elements (IE), using Quality of Service enhanced Basic Service Set or QBSS load (channel utilization) and station count. This methodology is able to collect channel use more frequently than many …

Incorporating Industry-Standard Technical Writing Into A Materials Testing Laboratory, Caleb Levi Head

Theses and Dissertations

The fluid analysis and materials testing laboratories are crucial for engineering students to learn experimental procedures and data interpretation. Engineers spend a significant portion of their workday on report writing, yet many feel their undergraduate education did not adequately prepare them for this task. At the University of Arkansas at Little Rock, students were given an industry-influenced lab report template to expand their knowledge of the necessary information required in technical writing, thereby teaching and improving their technical writing skills actively. These reports were evaluated qualitatively and quantitatively against previous submissions over two semesters in the mechanical engineering fluids and …

A Comprehensive Study Of Transportation Projects Impact In Congested Cities Through Travel Demand Modeling And Decision-Making Tools: A Case Study Of Zagazig City, Egypt, Amr M. Sakr, Metwally Gouda Mohamed Altaher, Mahmoud El-Saied Ali Solyman, Mohamed Ibrahim El-Sharkawi Attia

Mansoura Engineering Journal

Transportation projects are major resource-intensive projects that largely influence the transportation system’s resilience, human health, and quality of life. This study aims to assess and prioritize four transportation projects in Zagazig City, Egypt, in an attempt to build a more sustainable transport system. The travel demand model is used to determine various traffic characteristics and environmental impacts, and decision-making tools are used to evaluate the proposed projects. Benefit-cost analysis (BCA) is employed to investigate the economic feasibility of the proposed projects, and two hybrid multiple-criteria decision-making (MCDM) methods are used to prioritize the proposed projects. Through the work conducted in …

Algorithmic Optimization Of Filter Bank: Harnessing Rapid Convergence And Its Applications, Keerthana B Ms

Theses and Dissertations

A novel optimization framework is proposed for the design of both uniform and non-uniform filter banks, aimed at improving the accuracy and computational efficiency of biomedical signal classification tasks, with a particular emphasis on the detection of sleep disorders and Alzheimer’s disease.The proposed algorithm is grounded in multirate signal processing theory and aims to achieve Near Perfect Reconstruction (NPR) with minimal computational overhead. The process begins with the optimization of a uniform cosine-modulated filter bank (CMFB), achieved through iterative frequency-domain analysis and parameter tuning. This is followed by the derivation of a non-uniform filter bank via selective merging of bandpass …

Algorithmic Optimization Of Filter Bank: Harnessing Rapid Convergence And Its Applications, Keerthana B

Theses and Dissertations

A novel optimization framework is proposed for the design of both uniform and non-uniform filter banks, aimed at improving the accuracy and computational efficiency of biomedical signal classification tasks, with a particular emphasis on the detection of sleep disorders and Alzheimer’s disease. The proposed algorithm is grounded in multirate signal processing theory and aims to achieve Near Perfect Reconstruction (NPR) with minimal computational overhead. The process begins with the optimization of a uniform cosine-modulated filter bank (CMFB), achieved through iterative frequency-domain analysis and parameter tuning.

This is followed by the derivation of a non-uniform filter bank via selective merging of …

Hourly Sulfur Dioxide Observations Over North America: First Retrieval Results From Tempo, Can Li, Nickolay A. Krotkov, Joanna Joiner, Simon Carn, Vitali Fioletov, Chris Mclinden, Debora Griffin, Xiong Liu, Heesung Chong

Michigan Tech Publications

We present the first sulfur dioxide (SO2) retrievals from Tropospheric Emissions: Monitoring of Pollution (TEMPO), the first geostationary atmospheric composition sensor to cover North America, along with some potential applications of TEMPO SO2 data. We show that high resolution (∼10 km²) TEMPO measurements can be used to produce good quality SO2 retrievals with relatively small noise and biases. We demonstrate that hourly TEMPO data are useful for monitoring volcanic hazards, by providing frequent updates on the plume location and additional information on the plume height or winds. With the large number of measurements from TEMPO, it is also …

Rethinking Green Building Development Through Policy And Operational Metrics: Insights From Baton Rouge And Beyond, Oluwafemi Awolesi

LSU Doctoral Dissertations

This dissertation explores the challenges and opportunities associated with the development, adoption, and performance of green buildings in regions with relatively low certification uptake, drawing insights from Baton Rouge and its parish system as a case study. It addresses three primary research questions: (1) How can the adoption rate of green buildings be enhanced? (2) How do certified buildings perform compared to their non-certified counterparts? (3) What operational metrics can be applied to better evaluate building performance in the future?

Using a mixed-methods approach, the study examines stakeholder perceptions in East Baton Rouge, Louisiana, investigates energy performance, indoor environmental quality, …

Integrated Evaluation Of Airtightness, Acoustics, Thermal Performance, And Life-Cycle Carbon Emissions In Code-Compliant Wood-Framed And Thin-Shell Concrete Dome Residential Structures, Eduardo Ibanez Vasquez

Theses and Dissertations

The residential construction industry faces increasing demands for energy-efficient and sustainable housing solutions. In the U.S., residential buildings significantly contribute to energy consumption and greenhouse gas (GHG) emissions, highlighting the need for enhanced building envelope performance. This study compares four residential structures: a traditional code-compliant wood-framed structure built in 1970, a traditional code-compliant wood-framed structure built in 2016, a modular code-compliant wood framed structure built in 2024, and a thin-shell concrete dome built in 2022. The analysis focuses on airtightness, acoustic insulation, passive thermal performance, and life-cycle carbon emissions, including both embodied (cradle-to-gate) and operational energy use. Field-based methods included …

Collaborative Class Zine: Weekly Reflection Assignment With Powerpoint Template, Brett Whysel

Open Educational Resources

Abstract

This collaborative class zine assignment uses a shared PowerPoint format to deepen student learning while building community. Students contribute visual reflections (graphics, diagrams, quotes, or doodles) weekly or periodically throughout the semester. Faculty provide reflection prompts about key takeaways and unclear concepts (sample prompts included). The resource includes both sample LMS instructions and a ready-to-use PowerPoint zine template that works across all disciplines. By combining metacognitive reflection with creative expression, students consolidate learning by finding new connections and applications. The collaborative format creates community connections and joy while students learn from peers' diverse perspectives.

A Longitudinal Analysis Of Morphological Shape Variation Of Spleen In Patients With Fontan Surgery, Varatharajan Nainamalai, Håvard Bjørke Jenssen, Mostafa Rezaeitaleshmahalleh, Djinaud Prophete, Jordan Gosnell, Sarah Khan, Marcus Haw, Jingfeng Jiang, Joseph Vettukattil

Michigan Tech Publications

Background: Splenic size serves as a surrogate biomarker for predicting portal vein hyper-tension and liver abnormalities in subjects with Fontan Associated Liver Disease (FALD). We analyze the long-term shape variation of the spleen in FALD subjects using morphological shape features of radiomic features. Methods: We used 154 (84 from computed tomography and 70 from magnetic resonance) image volumes obtained from 36 individuals who underwent stage 3 Fontan procedure and 145 computed tomography images from controls to assess splenomegaly. To understand the splenomegaly, thirteen shape features of the spleen over three 10-year intervals, and variations between controls and FALD subjects were …

Fundamental Exploration Of Soot Formation And Morphology From A Molecular Modeling Perspective, Khaled Mosharraf Mukut

Dissertations (1934 -)

Soot formation remains one of the least understood phenomena in combustion science, posing significant challenges due to its complex physicochemical nature and considerable environmental and health impacts. This dissertation presents a comprehensive molecular-level investigation into the fundamental mechanisms governing soot inception, particle growth, and morphological evolution through state-of-the-art reactive molecular dynamics (RMD) simulations of acetylene pyrolysis. A novel computational analysis utility, Molecular Arrangement and Fringe Identification and Analysis from Molecular Dynamics (MAFIA-MD), was developed to accurately characterize soot particle formation, providing detailed insights into chemical composition, internal structure, and surface characteristics. Critical physicochemical markers defining the boundary between gas-phase species …

The Effect Of Transformer Based Pre-Trained Language Models On Biomedical Relation Extractions, Eman Saad, Sherif Kishk, Amr Ali-Eldin, Ahmed I. Saleh

Mansoura Engineering Journal

Biomedical relation extraction represents a critical advancement in healthcare research. As the volume of biomedical publications continues to grow exponentially, efficient extraction of entity relationships has become essential for accelerating knowledge discovery, drug development, and precision medicine initiatives. The field has evolved from focusing on simple binary relations, such as Protein-Protein Interactions (PPI), which are fundamental to understanding cellular processes and therapeutic development, to addressing more complex multi-class classification challenges like Drug-Drug Interactions (DDI) and Chemical-Protein Interactions (CPI).Pre-trained language models have become the cornerstone of modern approaches of extracting biomedical relations. Models like BioBERT, PubMedBERT, and SciBERT, along with general-purpose …

Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie

Iraqi Journal for Computer Science and Mathematics

COVID-19, caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization (WHO) and rapidly spread worldwide from late 2019. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic tool, its sensitivity ranges from only 60% to 70%, leading to false negatives. Computed Tomography (CT) imaging has emerged as a valuable alternative for accurate diagnosis; however, the quality of CT images is often degraded by motion-induced blur and additive noise, particularly in children, individuals with mental health conditions, or those with phobias of CT scans. This study aims to enhance COVID-19 CT image quality …

Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar

Iraqi Journal for Computer Science and Mathematics

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

The Future Of Intelligent Industrial Systems: Plc, Node-Red, And Iot/Iiot, Firas Ahmed Hussein, Mohammad Tariq Yaseen, Mohammed Obaid Mustafa

AUIQ Technical Engineering Science

Integrating Programmable Logic Controllers (PLCs) with Node-RED and IoT/IIoT has emerged as a transformative technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and smart manufacturing and …

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.