Digital Commons Network^™

Simulation-Based Assessment Of Operational And Retrofit Solutions For Effluent Compliance Of A Capacity-Limited Oxidation Ditch Wwtp, Ahmed Sameh Harash

Theses and Dissertations

Oxidation-ditch wastewater treatment plants (WWTPs) across Egypt's Nile Delta are increasingly carrying hydraulic and organic loads beyond what they were designed for. This work can be considered pioneering as this work compared operational and structural retrofit alternatives for such a plant within a single verified simulation framework. The present study fills that gap by building a digital twin of the Qaha WWTP, a dual-train Carrousel oxidation ditch in Qalyubia Governorate, calibrated to a baseline flow of 7,420 m³/day in the SUMO24 platform (a third-generation, open-source wastewater treatment process simulation software) using the SUMO2c biokinetic model (a simplified variant of Activated …

A Maximum Energy Release Rate Computational Framework For Fatigue Crack Trajectory And Life Prediction Under Mixed-Mode Proportional And Nonproportional Loading, Nahla Helmy Matar

Theses and Dissertations

The problem of mixed-mode fatigue crack growth has been a persistent research challenge. The determination of fatigue crack propagation direction directly impacts the safety and integrity of components and structures, making it crucial in various industrial applications, including welded drilling pipes, aerospace, and automotive engineering. Unlike uniaxial loading, mixed-mode cracks tend to change direction frequently (kinking), and nonproportional loading adds to the complexity of the problem. This research aims to estimate the crack propagation direction and fatigue crack life in linear elastic materials subjected to mixed-mode (I+II) loading conditions under proportional or non-proportional cyclic loading. Numerical software was developed based …

Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu

Journal of Marine Science and Technology–Taiwan

Port and vessel networks increasingly operate on IP/Ethernet backbones with high‑noise, high‑dimensional traffic. We present a lightweight hybrid intrusion‑detection model that couples a variational autoencoder (VAE) with a multilayer perceptron (MLP) and augments training with a boundary‑oriented latent‑space mixup strategy. The VAE models the distribution of normal traffic and identifies anomalies through reconstruction errors. Subsequently, it generates robust latent vectors, enabling the MLP to perform highly accurate supervised classification. On the UNSW‑NB15 dataset, the proposed pipeline attains ≥97% accuracy and an outstanding recall of 99.56% in binary intrusion detection, and visualization of the latent space (PCA) together with reconstruction‑error analyses …

Sar Ship Detection Based On Shallow Feature Guidance, Chenxu Xia, Peng Chen, Ya Zhang, Ying Li

Journal of Marine Science and Technology–Taiwan

Maritime ship detection is of great significance for both military security and civilian applications. Synthetic Aperture Radar (SAR), with its all-weather and all-day imaging capability, plays a vital role in maritime surveillance. Nevertheless, SAR ship targets typically appear small in scale, embedded in complex backgrounds, blurred at boundaries, and easily confused with near-shore features, which pose substantial challenges for accurate detection. To address these issues, we propose a SAR ship detection network that integrates dual enhancements of small-object representation and edge information. The network introduces two key components: the Small Target Refine Pyramid (STRP) to strengthen shallow feature representation for …

Enhancing Shipboard Safety Management Under The Ism Code: An Innovative Risk Assessment Framework With A Stern Tube Case Study, Pi-Yen Lin

Journal of Marine Science and Technology–Taiwan

The shipboard safety management system (SMS) is designed to enhance safe operations, risk management, and emergency response to improve overall ship safety and efficiency. This paper demonstrates the use of an engine room simulator (ERS) for collecting failure modes and applies it to a comprehensive failure analysis of the stern tube lubricating oil system. A new risk closeness coefficient method was developed, integrating expert background knowledge and weighted risk assessments. The analysis, based on multiple expert evaluations, covered five subsystems, eight main components, 23 failure modes, and 112 failure causes. This study presents 26 recommendations for maritime practitioners and onboard …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

Power At Sea Develop Phase, Jake Lauer, Isabella Heinemann, Bailey Gargasz, Zachery Boyer

Mechanical Engineering

This project develops a renewable, wave-powered charging system designed to extend the mission duration of Autonomous Underwater Vehicles by eliminating the need for frequent manual battery replacement. Building upon a prior oscillating water column prototype from the CONCEPT phase, the team redesigned and tested an improved system capable of converting wave-induced air motion into electrical power using a Wells turbine. Key enhancements include doubling the column diameter to increase displaced air volume, integrating a flared inlet and bi-directional nozzle to improve airflow, and selecting corrosion-resistant materials suitable for long-term deployment in marine environments. Multiple prototypes were constructed and evaluated through …

Comparative Performance Of Physiological Vital-Sign Forecasting Under Random And Patient-Wise Splitting Using Deep Learning, Lavanya Vasavi Chittem Reddy

Theses and Dissertations

Physiological vital-sign forecasting estimates future measurements based on recent temporal patterns and can support analysis of continuously recorded monitoring data. This study comparatively evaluated deep feedforward, recurrent, bidirectional recurrent, long short-term memory, and bidirectional long short-term memory architectures for one-step-ahead forecasting of peripheral oxygen saturation, heart rate, and pulse rate. Each model received consecutive observations of peripheral oxygen saturation, heart rate, pulse rate, respiratory rate, and age, while separate single-output models predicted the next value of the selected target.

Random and patient-wise data splitting were compared using identical input definitions, preprocessing procedures, model architectures, and training hyperparameters. The strongest architecture …

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

Developing Imitation Learning Policies By Exploring Diffusion Framework For Quadcopters, Likith Swamireddy

Theses and Dissertations

Imitation learning offers a promising approach in developing near-optimal onboard policies for nonlinear systems, by learning from computationally expensive trajectory planners during offline training. Existing State-of-the-art methods commonly learn deterministic mappings from observations to actions by training fast neural networks to mimic the expert demonstrations, which can limit their ability to capture the action distribution, when the policy is trained on multiple possible expert commands to reach the same target, and this may lead to unsafe scenarios in the obstacle environments. To address these challenges, this study explores Diffusion Models (DMs) as a feedback controller for quadcopters that learns the …

Enhancing Agricultural Sustainability Under Climate Change: A Multi-Scale Framework Integrating Climate Extremes, Resource Efficiency, And Data-Driven Modeling, Shahryar Fazli

Computational and Data Sciences (PhD) Dissertations

Agricultural systems are increasingly challenged by climate variability, where shifting temperature regimes, hydrological variability, and the rising frequency of compound and cascading extremes threaten global food security and resource sustainability. Addressing these challenges requires integrated frameworks that bridge biophysical monitoring, predictive modeling, and adaptive decision-making. This dissertation develops a data-driven, multi-scale framework to quantify and enhance agricultural resilience by integrating remote sensing, climate analytics, and machine learning across the United States, with a focus on California and the Western U.S.

First, hyperspectral and thermal remote sensing data from EMIT and OpenET are integrated to characterize crop nitrogen–water interactions and assess …

Scalable Quality Assessment Of Ground Motion Records Via Interpretable Deep Learning Architectures, Ali Montazeri Namin

All Graduate Theses and Dissertations, Fall 2023 to Present

Earthquake engineers rely on accurate recordings of ground shaking to design safe and resilient buildings. However, sorting through thousands of these recordings to find the reliable ones and throwing out those ruined by sensor errors or background noise is traditionally done by hand. Because modern seismic networks collect massive amounts of earthquake data every day, this manual checking process is much too slow. To fix this, researchers are turning to artificial intelligence to automatically check the quality of these recordings.

While artificial intelligence offers a fast solution, there are limitations. These computer models can become massive and expensive to run, …

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Investigation Of The Production Technology Of Environmentally Safe Urethane Oligomers Based On Local Raw Materials, Bakhtiyor Kudratovich Shaykulov, Dilafruza Ruziboevna Akbarova Gulboeva, Fayzulla Nurmuminovich Nurqulov

Technical science and innovation

This paper investigates the step-by-step poly condensation pathway for synthesizing environmentally friendly, non-isocyanate urethane oligomers derived from local and safe raw materials: urea and ethylene glycol. The influence of thermodynamic parameters on the target product yield was systematically evaluated to determine the optimum technological matrix. Specifically, a 1:2 reactant mass ratio, a stable stationary temperature of 150-155 °C, and a reaction duration of 2 hours under an inert nitrogen atmosphere resulted in a sustainable and reproducible yield of 72%. Thermal degradation and competitive side-product condensations were activated above 160 °C, leading to a sharp drop in overall chemical efficiency. The …

Automatic +1 Diffraction Order Detection In Off-Axis Digital Holographic Interferometry Using An Energy-Based Spectral Model, Nigora Alimdjanovna Akbarova, Dilnoza Ibrokhim Kizi Abdulkhayeva

Technical science and innovation

This paper presents an automatic +1 diffraction order detection algorithm for off-axis digital holographic interferometry (DHI Manual spectral filtering is widely used to find out the desired order of diffraction in the Fourier domain in conventional digital holographic reconstruction. Nevertheless, this approach is very subjective and must be subject to expert guidance, thus, the reconstruction reproducibility and stability can often deteriorate. To address these limitations, this designed scheme constructs a spectral model utilizing energy-maximization as well as adaptive DC suppression and dynamic filter radius estimates. The algorithm itself can automatically observe the distribution of energy in the hologram spectrum and …

Evaluation Of The Metrological Characteristics Of An Ultrasonic Flowmeter For Water Resources, Makhsud Idrisovich Makhmudov, Uktam Farkhodovich Mamirov, Siroj Sobirovich Nurov

Technical science and innovation

This paper presents the results of evaluating the metrological characteristics of a developed ultrasonic flowmeter intended for measuring water flow in open channels. The study analyzes the main sources of measurement errors and classifies them into methodological, instrumental, additional, systematic, and random components. Experimental investigations were carried out to estimate the random and systematic errors under steady-state flow conditions. Using the least-squares method, the systematic error was separated into additive and multiplicative components, providing a basis for calibration and correction of the measurement results. The reliability of the developed measuring instrument was evaluated using the failure rate method based on …

On The Issue Of Developing Diagnostic Systems In Case Of Incompleteness Of Data, Amanulla Azizovich Kadirov, Shohijahon Ulugbek Ugli Akhmetov

Technical science and innovation

This paper addresses the problem of developing diagnostic systems for industrial equipment when initial failure data is incomplete or unavailable. An approach is proposed for generating synthetic data based on a pseudo-failure generator that implements stochastic modeling of degradation processes. A formalized algorithm for generating time-to-failure samples has been developed, including stages of failure physics analysis, statistical model selection, distribution parameterization, and simulation of operating conditions. The Weibull distribution is used as the base model, allowing for the consideration of various stages of the equipment’s life cycle. Additionally, factors that bring the synthetic data closer to real operating conditions are …

Analysis And Optimization Of Intelligent Control Of The Drying Process Under Uncertainty, Azizbek Nodirbekovich Yusupbekov, Marufjon Kobuljonovich Shodiev, Khusniddin Mamarasul Ugli Esonov

Technical science and innovation

This section proposes a multi-criteria Pareto-optimization algorithm, integrated with Herbert Simon's four-stage decision-making scheme (Intelligence–Design–Choice–Implementation) and an ANFIS-based adaptive controller, for simultaneously optimizing the mutually conflicting quality, energy, and time criteria of the wheat-drying process. The algorithm for constructing the set of Pareto-optimal solutions and selecting the compromise solution closest to the ideal point is presented step by step. The implementation of the ANFIS controller in the MATLAB/Simulink environment is comprehensively verified through a simulation block diagram, the Rule Viewer, the Surface Viewer, the training error-function graph, the RMSE convergence graph, and a learning-curve analysis. Training was carried out on …

Mathematical Modelling Of Nitrification And Denitrification Processes Based On Neuro-Fuzzy Bioreactor Models, Mirkhalil Agzamovich Ismailov, Boburbek Zokirjon Ugli Mannobjonov

Technical science and innovation

This paper presents the development and investigation of hybrid neural network and fuzzy models for the mathematical modelling of nitrification and denitrification processes in a biological wastewater treatment bioreactor. A comprehensive approach is proposed, integrating a mechanistic model of the ASM1/ASM2d type with neural networks (LSTM and Gaussian Process Regression), as well as a fuzzy control system based on an extended set of expert rules. A digital twin of the bioreactor was developed to allow for the prediction of the dynamic behavior of key parameters such as NH₄⁺, NO₃⁻, dissolved oxygen, etc. within a prediction range of 1 to 12 …

Developing A Digital Facility Management Framework For Enhancing Operation And Maintenance Of Public Parks In Egypt, Alaa Ezzat, Alia Amer, Yasser M. El Sayed

HBRC Journal

Public parks in Cairo represent important urban assets, however, their operation and maintenance (O&M) face many challenges due to fragmented information, reactive maintenance practices, organizational constraints, and limited digital integration. Digital Facility Management discipline still lacks the tailored framework that adequately addresses the operational needs of public parks in Egypt with all its specific details. This study develops an integrated digital FM framework for improving the efficiency of the management process of public parks. A four-phase methodology was adopted, comprising a literature review, framework development, technical system specification, and expert assessment. The proposed framework integrates BIM, COBie, CMMS, IoT technologies, …

Machine Learning Models For Estimating The Consumed And Remaining Useful Life Of Haul Trucks In An Open-Pit Mine In Peru, Marco Cotrina, Jairo Marquina, Mario Sandoval, Jose Mamani, Johnny Ccatamayo

Journal of Sustainable Mining

The purpose of this study was to develop a machine learning-based model to predict the consumed useful life and estimate the remaining useful life of haul trucks in an open-pit mining operation in Peru. A comparative analysis of multiple machine learning models was conducted, including multiple linear regression (MLR), random forest + PSO, support vector regression (SVR), gradient boosting machine (GBM), decision tree + PSO, and artificial neural networks (ANN-MLP). The models were evaluated using performance metrics such as R², RMSE, and MAE, selecting the optimal model to estimate the remaining useful life based on a theoretical lifespan …

Development Of A Spectral Index And Web Application For Automated Marble Quarry Monitoring: A Sentinel-2 Based Approach For Change Detection And Monitoring, Konstantinos Ntouros, Vasileios Drimzakas - Papadopoulos, Georgios Gkologkinas, Georgios Ntouros, Dimitrios Markou

Journal of Sustainable Mining

This study introduces an automated workflow to monitor marble quarry operations using Sentinel-2 satellite data, providing a cost-effective and efficient tool for regulatory oversight focused on environmental sustainability. At the core of this workflow is the Quarry Change Detection Index (QCDI), a new spectral index specifically developed to leverage the unique spectral characteristics of quarry sites, enhancing the detection of land cover changes associated with quarry expansion. To facilitate practical application, a web-based tool was developed using Google Earth Engine and Streamlit. The user-centric design of this platform features an intuitive interface, allowing users to easily select parameters, visualize data, …

Integrating Gis With Interim Payment Valuation In Road Construction Projects: A Conceptual Framework, Abdulrahman I. Iro, Juma M. Matindana, Julian Ijumulana

Tanzania Journal of Engineering and Technology (TJET)

Abstract

Interim Payment Valuation is a critical process in road construction contract administration, yet conventional valuation practices remain heavily dependent on manual measurements, fragmented documentation, spreadsheets, and professional judgement. These limitations can affect measurement accuracy, transparency, traceability, and the timeliness of payment certification. Although Geographic Information Systems have increasingly been applied to construction planning, quantity measurement, progress monitoring, infrastructure management, and decision support, their integration with contractual and financial processes for interim payment valuation remains insufficiently explored. This study therefore develops a conceptual framework for integrating GIS with Interim Payment Valuation in road construction projects. A PRISMA-guided structured literature review …

Artificial Neural Network-Based Modelling And Prediction Of Key Performance Indicators In Road Construction Projects, Hussein Mativila, John M. Kafuku, Beatus A. T. Kundi

Tanzania Journal of Engineering and Technology (TJET)

Road construction projects in developing countries including Tanzania experience several challenges including cost overruns, schedule delays and poor quality. The causes of these challenges include complex interdependencies project’s uncertainty factors. This makes traditional methods for predicting Key Performance Indicators (KPIs) less effective. This study has developed a robust Artificial Neural Network (ANN) model for accurate modelling and predicting KPIs for road construction projects in Tanzania. The analysis was based on data obtained from 281 projects implemented by TANROADS in 11 regions from 2015 to 2025. Fourteen uncertainty factors were measured on a five-point Likert scale and screened using Principal Component …

The Teaching Stem Center Online: Enhancing Instructional Methods For Underprepared Students' Success, Bassey Akpan, Jan Duncan

The Journal of the Research Association of Minority Professors

This study investigates the experiences and academic outcomes of underprepared and underrepresented undergraduate students enrolled in introductory STEM courses at Texas College, a minority‑serving institution committed to advancing equity in science, technology, engineering, and mathematics education. The research focuses on STEM faculty across Biology, Chemistry, Computer Science, and Mathematics and how the Teaching STEM Center Online (TSC) enhances instructional methods to support this student population.

Using a mixed‑methods design, the study evaluates the implementation and impact of the TSC initiative. This includes faculty development workshops, structured instructional coaching, and the integration of evidence‑based teaching practices. Quantitative data from faculty surveys …

Voltage-Related Power Quality Issues And Impacts On Distribution Networks With Sensitive Loads - A Review, Godwin Elinazi Mnkeni, Jackson Justo, Aviti Thadei Mushi, Bakari M. M. Mwinyiwiwa

Tanzania Journal of Engineering and Technology (TJET)

Voltage disturbances are the most important power quality (PQ) complications that customers and power utilities face in this smart era. The growing adoption of sophisticated electronic equipment and integration of renewable energy sources (RES) into power grids has increased the susceptibility of power distribution networks (PDNs) to voltage sags, swells, interruptions, flicker, and voltage imbalance. These disturbances, mainly caused by upstream faults, switching operations, and RES integration, compromise voltage PQ and system reliability. Consequently, they accelerate equipment degradation, increase electronic waste (e-waste), raise reactive power demand and maintenance costs, increase power losses, and impose substantial economic losses on customers and …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

Size-Tunable Tellurium Quantum Dots By Glancing Angle Deposition, S. M. Sayem, Salim Hussain, Fernando Maia De Oliveira, Ranjitha Kumarapuram Hariharalakshmanan, Gregory Guisbiers, Tansel Karabacak

Faculty Scholarship

Tellurium has a unique helical crystal arrangement and pronounced anisotropy that influence its electronic and optical properties at the nanoscale. This study reports the synthesis of pure tellurium quantum dots (Te QDs) on silicon wafer using glancing angle deposition (GLAD). Quasi-hemispherical dots with lateral sizes ranging from 9 to 28 nm and vertical dimensions of 6 to 10 nm were produced as a function of the deposition duration. A comparison of the dot sizes with the Bohr radii of the charge carriers indicated a regime of strong to intermediate confinement. Structural analyses confirmed the polycrystalline nature and trigonal phase of …

Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry

Engineering and Technology Management Faculty Publications and Presentations

This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …