Engineering Commons^™

Low Dissolved Oxygen Operation Enhances Biological Phosphorus Removal In Wastewater Treatment, Haley Morgan

Civil & Environmental Engineering Theses & Dissertations

Biological phosphorus (bio-P) removal occurs when bacteria are exposed to anaerobic/aerobic conditions that stimulate polyphosphate-accumulating organisms (PAOs) to uptake phosphorus. In most treatment plants, dissolved oxygen (DO) concentrations in the aeration tanks are commonly at or above 2.0 mg O₂/L, which is often referred to as the most optimal aeration condition for bio-P. If bio-P can happen efficiently at lower DO (< 0.3 mg O₂/L), then the energy demand of aeration can be decreased appreciably. This research was conducted in a biological nutrient removal (BNR) pilot plant to assess the performance of bio-P at progressively lower DO operation. This …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Design And Fabrication Of Compact Mimo Array Antenna With Tapered Feed Line For 5g Applications, F. Taher, Mohamed Fathy Abo Sree, Hesham A. Mohamed, Hussein Hamed Ghouz, Sarah Yehia Abdel Fatah

All Works

This paper presents the design, fabrication, measurement, and evaluation of two multiple input multiple output antenna elements (MIMOs) optimized for performance in the 5G frequency band. The MIMO antennas are integrated onto Roger/Druoid 5880 substrates with a dielectric constant of 2.2, housed within a compact volume measuring 20×24.14×0.79mm3. Initially, a thorough parametric study is conducted using the CST Electromagnetic (EM) Simulator to analyze and optimize the antenna's performance according to 5G application requirements. Subsequently, in the antenna design phase, calculations are made for the envelope correlation coefficient (ECC > 0.01dB) and diversity gain (DG > 9.9dB), yielding favorable outcomes. Fabrication and measurement …

High Gain Defected Slots 3d Antenna Structure For Millimetre Applications, Arkan Mousa Majeed, Fatma Taher, Taha A. Elwi, Zaid A. Abdul Hassain, Sherif K. El-Diasty, Mohamed Fathy Abo Sree, Sara Yehia Abdel Fatah, Umi Aisah Asli

All Works

The antenna is structured in three dimensions, employing a conductive cylindrical cone as its base. This cone configuration is achieved through the etching of an elliptical slot array onto the antenna. To enhance its performance, a conductive circular reflector is situated beneath the cone, thereby augmenting its gain. The antenna demonstrates operational bandwidth across various frequencies: Ultra-Wideband (UWB) operates at approximately 5 GHz, extending to about 15 GHz; Wideband (WB) is cantered at roughly 20 GHz, while narrowband operates at approximately 27 GHz. Within the frequency range of interest, the antenna's gain varies between 3dBi and 15dBi. Geometric specifications of …

Accelerating High-Throughput Phonon Calculations Via Machine Learning Universal Potentials, Huiju Lee, Vinay I. Hegde, Chris Wolverton, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

Phonons play a critical role in determining various material properties, but conventional methods for phonon calculations are computationally intensive, limiting their broad applicability. In this study, we present an approach to accelerate high-throughput harmonic phonon calculations using machine learning universal potentials (MLIPs) combined with an efficient training dataset generation strategy. Instead of computing phonon properties from a large number of supercells with small atomic displacements of a single atom, our approach uses a smaller subset of supercell structures where all atoms are randomly displaced by 0.01 to 0.05 UŮ, significantly reducing computational costs. We train a state-of-the-art MLIP based on …

Design And Characterization Of Radiation-Hardened Gaas Hbt-Based Optical Transceivers, Collin Burt

Electrical and Computer Engineering ETDs

Optical transceivers are desirable for their galvanic isolation and natural immunity to electromagnetic interference compared with direct electrical connections. This is true in normal operating environments as well as radiation environments. This work realizes and characterizes GaAs HBT-based optical transceivers across temperature and a range of radiation environments including high dose rate, total ionizing dose, neutron, and heavy ion. The transceiver designs are discussed and specific environmental design considerations are highlighted. Final results for each environment are provided and compared with existing transceivers.

Understanding The Breadth And Impact Of The Ias [Presidents Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Heat-Pipe-Based Thermal Management System Design For A 250 Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Machine Learning For Reactor Power Monitoring With Limited Labeled Data, C. L. Stewart, B. L. Goldblum, R. G. Abbott, L. Appleby, Brett J. Borghetti, V. Hollingshead, J. H. Whetzel

Faculty Publications

Real-time reactor power monitoring is critical for a variety of nuclear applications, spanning safety, security, operations, and maintenance. While machine learning methods have shown promise in monitoring reactor power levels, there is limited research on their efficacy in label-starved environments. The goal of this work is to assess the feasibility of classifying nuclear reactor power level using multisource data in scenarios with limited labels. Data were collected using low-resolution multisensors at four nuclear reactor facilities: two large research reactors and two TRIGA reactors. Within each pair, one reactor dataset served as the source and the other as the target in …

Intersectional Predictors Of Early Mathematics Identity Among Underrepresented Engineering-Interested Students, Douglas D. Havard, Adriana Quirós-Arauz

Education Faculty Articles and Research

This study examines the intersectional factors influencing early mathematics identity development among underrepresented secondary students (grades 9-10) with aspirations in engineering. Mathematics identity is a well-established predictor of long-term persistence in engineering, making its early formation critical to understanding student retention in the engineering pipeline. Grounded in Bronfenbrenner's bioecological framework, this study situates learning within nested layers of influence. Using data form the nationally representative High School Longitudinal Study of 2009 (HSLS:09), which includes over 23,000 9th-graders, a hierarchical multiple regression analysis was conducted. The analysis examined intersections of race and gender identity across 16 variables spanning individual, micro-, meso-, …

Ultrasonic Spray Coating Of Carbon Fibers For Composite Cathodes In Structural Batteries, Thomas Burns, Liliana Delatte, Gabriela Roman-Martinez, Kyra Glassey, Paul Ziehl, Monirosadat Sadati, Ralph E. White, Paul T. Coman

Faculty Publications

Structural batteries, also known as “massless batteries”, integrate energy storage directly into load-bearing materials, offering a transformative alternative to traditional Li-ion batteries. Unlike conventional systems that serve only as energy storage devices, structural batteries replace passive structural components, reducing overall weight while providing mechanical reinforcement. However, achieving uniform and efficient coatings of active materials on carbon fibers remains a major challenge, limiting their scalability and electrochemical performance. This study investigates ultrasonic spray coating as a precise and scalable technique for fabricating composite cathodes in structural batteries. Using a computer-controlled ultrasonic nozzle, this method ensures uniform deposition with minimal material waste …

Performance Comparison Of Flat Sheet And Hollow Fibre Air Gap Membrane Distillation: A Mathematical And Simulation Modelling Approach, Hamed Kariman, Hussein A. Mohammed, Masoumeh Zargar, Mehdi Khiadani

Research outputs 2022 to 2026

Membrane Distillation (MD) systems could address current challenges related to global water demand. Among MD systems, Air Gap Membrane Distillation (AGMD) systems play crucial roles in addressing this challenge. AGMD systems can be categorized into two main structural groups: flat sheet and hollow fibre forms. It is important to study, analyse, model and compare these two systems under the same operational conditions. In this study, a detailed mathematical model of flat sheet and hollow fibre AGMD modules under the same conditions (i.e., membrane type, membrane area, porosity, and pore radius of membrane) has been introduced. The simulation of system performance …

From A Learning To A Smart Nation: The Rise Of The Digitalization Megatrend And Singapore's Development, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose: The purpose of this article is to discuss the “learning nation” concept and examine the characteristics and implications of using the “learning” premodifier in this nation-building program. Design/methodology/approach: This article reviews how the “learning” aspect is inter-related to a series of national information and communication technology masterplans and includes a comparative analysis of the related premodifier “smart” as Singapore sets forth its ambition to become a “smart nation” as part of the digitalization megatrend. A print media indicator and Google Trends form part of the methodology to ascertain the rise of digital technology over a certain period. The former …

Dual Operation Aggregation Graph Neural Networks For Solving Flexible Job-Shop Scheduling Problem With Reinforcement Learning, Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang

Research Collection School Of Computing and Information Systems

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods …

Learning-Guided Bi-Objective Evolutionary Optimization For Green Municipal Waste Collection Vehicle Routing, Shubing Liao, Yixin Xu, Yunyun Niu, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a …

Llm-Enhanced Multiple Instance Learning For Joint Rumor And Stance Detection With Social Context Information, Ruichao Yang, Jing Ma, Wei Gao, Hongzhan Lin

Research Collection School Of Computing and Information Systems

The proliferation of misinformation, such as rumors on social media, has drawn significant attention, prompting various expressions of stance among users. Although rumor detection and stance detection are distinct tasks, they can complement each other. Rumors can be identified by cross-referencing stances in related posts, and stances are influenced by the nature of the rumor. However, existing stance detection methods often require post-level stance annotations, which are costly to obtain. We propose a novel LLM-enhanced Multiple Instance Learning (MIL) approach to jointly predict post stance and claim class labels, supervised solely by claim labels, using an undirected microblog propagation model. …

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

Endothelial Dysfunction Promotes Age-Related Reorganization Of Collagen Fibers And Alters Aortic Biomechanics In Mice, Liya Du, Jeffrey Rodgers, Nazli Gharraee, Olivia Gary, Tarek Shazly, John F. Eberth, Susan M. Lessner

Faculty Publications

Endothelial dysfunction, defined as a reduction in the bioavailability of nitric oxide (NO), is a risk factor for the occurrence and progression of various vascular diseases. This study investigates the effect of endothelial dysfunction on age-related changes in aortic extracellular matrix (ECM) microstructure and the relationship between microstructural adaptation and the mechanical response. Here, we used groups of NOS3 knockout (KO), NOS3 heterozygotes (Het), and wild-type (WT) B6 mice (controls) to study changes in hemodynamic parameters, collagen fiber organization, and both active and passive aortic mechanics using biaxial pressure myography over a time course from 1.5 to 12 mo. Our …

Shear-Thinning Hydrogel For Delayed Delivery Of A Small Molecule Metalloproteinase Inhibitor Attenuates Myocardial Infarction Remodeling, Joshua E. Mealy, William M. Torres, Lisa A. Freeburg, Shayne C. Barlow, Alison A. Whalen, Chima V. Maduka, Tarek Shazly, Jason A. Burdick, Francis G. Spinale

Faculty Publications

No abstract provided.

Justification Of Permissible Discharges Of Water Users Into Water Bodies, Mariia S. Stroganova, Ivan V. Antonov, Andrey V. Epifanov

CHEMISTRY AND CHEMICAL ENGINEERING

The purpose of this work is developing an algorithm for ecological and technological rationing of the permissible load on water bodies using geoinformation systems. The algorithm takes into account the self-purification of aquatic ecosystems using a three-component model. Initially, the article provides an overview of methods for determining the permissible discharge masses of pollutants for developing new approaches to rationing. The algorithm of ecological and technological rationing of the load on water bodies and geoinformation systems for its implementation is proposed. The main models used to assess the ecological state of an aquatic ecosystem are considered. A three-component model of …

Separation Of Fatty Acids From Horse Fat And Their Analysis, Sherzod Y. Rakhmanov, Madina O. Khamidovа, Dilshod P. Rakhimov, Rashidah Sokur, Nuriddin T. Kenjaev

CHEMISTRY AND CHEMICAL ENGINEERING

A distinctive feature of horse fat from other animal fats is the presence of amino acids and keratin in its composition, which are not found in other oils, including polyunsaturated fatty acids, proteins, microelements, vitamins A, E, PP, B. The aim of this work is to separate fatty acids from horse fat and analyze them. The process of separating fatty acids included the stages of saponification with sodium hydroxide, decomposition of the resulting soaps in the presence of concentrated sulfuric acid. The study of the composition of the separated fatty acids by the chromatograph mass spectrometric method of analysis established …

Synthesis, Crystal Structure And Some Physicochemical Properties Of Cerium Complexes Containing Beta-Diketonate Ligands, Feruza F. Fayzullayeva, Salmon M. Mukhammadiev, Gulbeka Yu. Mamatova, Shahlo Sh. Daminova

CHEMISTRY AND CHEMICAL ENGINEERING

The objective of this study is to synthesize and characterize novel Ce(III) coordination compounds utilizing the β-diketonate ligand (1,3-diphenylpropane-1,3-dione). The structures of the resulting complexes were determined through single crystal X-ray diffraction (XRD). In these complexes, the oxygen atoms of the ligand form bidentate bonds with the metal ion. The geometry surrounding the cerium ion is described as distorted square antiprismatic, influenced by the flexibility and steric hindrance of the dibenzoylmethane (dbm) ligand. Additional characterization of the newly synthesized complex was performed using IR spectroscopy, thermal analysis, and scanning electron microscopy (SEM).

Synthesis Of Acetylene Diols In The Catalytic System Of Cu(Otf)2/Thf/Tbabr, Abduvakhаb Ikramov, Lochinbek Q. Ablakulov, Odiljon E. Ziyadullayev, Sardor M. Rakhmatov, Mirjalol E. Ziyadullayev

CHEMISTRY AND CHEMICAL ENGINEERING

The aim of the work was the synthesis of acetylenediols in the Cu(OTf)₂/THF/TBABr catalytic system. The synthesis of acetylenic diols based on the alkynylation process of aliphatic and heterocyclic ketones containing two carbonyl groups in their molecules in the presence of alkyne hydrocarbons was studied for the first time using the Cu(OTf)₂/THF/TBABr catalytic system. The effects of temperature, reaction time, nature of solvent and catalyst, and molar amount of starting materials on the synthesis of acetylenic alcohols in the selected system were studied, and based on the results obtained, alternative conditions for the process were found.

Synthesis And Study Of New Complex Compounds Of Copper(Ii) And (5-Methyl-1,3,4-Thiadiazole-2-Ylsulfanil)-Acetic Acid, Ekaterina V. Kinshakova, Aziz K. Atashov, Batirbay S. Torambetov, Shakhnoza A. Kadirova

CHEMISTRY AND CHEMICAL ENGINEERING

The purpose of this study is to synthesize new copper (II) coordination compounds with a heterocyclic polydentate ligand (5-methyl-1,3,4-thiadiazole-2-ylsulfanyl) -acetic acid and to study their structure. The structure of the complexes was established by the method of X-ray diffraction analysis (XRD) of single crystals, it was shown that the complexes have a polymer structure and a coordination polyhedron of the square pyramid type, however, one of them is mono-nuclear composition (C₁₀H₁₄CuN₄O₆S₄*2H₂O)_n, and the second is bi-nuclear composition of cluster type (C₂₀H₂₀Cu_{2 …}

Synthesis Of Acetylene-Based Hetarene Derivatives In The Presence Of Catalysts, Abdulla T. Umrzokov, Abduvaxаb Ikramov, Khusnitdin M. Vapoev, Yusuf K. Zhumanov, Sardor M. Kodirov

CHEMISTRY AND CHEMICAL ENGINEERING

The aim of the study is to synthesize hetaren derivatives of acetylene amination reactions using local natural raw materials as catalyst carriers. Various grades of Oltintag and Angren kaolins, various grades of Navbakhor bentonite, as well as the opaque rock of the Karmaninsky deposit were used as catalyst carriers. Thermal, acidic and hydrothermal treatment was carried out on the appropriate sorbents, with the production of catalysts of various modifications from sorbents and active components. The processes of catalytic condensation of acetylene with ammonia were studied using modified catalysts. The effect of the reaction temperature, the active components of the catalyst, …

Bismuth Cuprates Produced By Solar Energy Showing Anomalous Resistance, Dilbara Dj. Gulamova, Sirojiddin Z. Mirzaev, Sirojiddin Kh. Bobokulov, Timur I. Gulamov

CHEMISTRY AND CHEMICAL ENGINEERING

The aim of the study is to obtain bismuth superconducting cuprates with higher superconducting properties. The advantages of superconducting materials in power engineering, disadvantages of synthesis based on solid-phase and melt principles, and the prospect of using nontrivial methods to obtain high critical parameters are presented. The advantages of synthesis under the influence of concentrated solar energy are shown. In order to obtain a superconductor with high critical properties, a massive, long-term stable bismuth cuprate was synthesized using solar technology. It pushes out an external magnetic field and has a nanostructured layered microstructure with caverns containing submicron-sized particles. The phase …

Predicting Battery Efficiency: A Theoretical Approach, Haiti Schafers

SACAD: Scholarly Activities

The Kansas ElectroRally Races continuously demand faster electric vehicles (EV). When designing a vehicle one of the most important components is the battery. This study was designed to find the following; What is the optimal throttle percentage to run a 36V 20Ah (768Wh) LiFePO4 Rechargeable Battery Pack[1].

The optimal throttle percentage was found with methods detailed in a different section. Based on the performance of the older cars this study choose to focus on 50% and 70%, while obtaining other relevant data to further that inference.

Leveraging Blockchain And Machine Learning To Promote Child Labor-Free Sustainable Development, Ahmad Musamih, Abduraouf Hassan, Khaled Salah, Raja Jayaraman, Mohammed Omar, Ibrar Yaqoob

Industrial Engineering: Faculty & Staff Publications

Child labor has been on the rise in recent years, which necessitates improved identification and reporting mechanisms. Current labor management systems, which are often manual or article-based, lack traceability, audit, privacy, security, and trust features. This leads to challenges in detecting and reporting violations, particularly in large or remote areas. The persistence of this issue undermines the achievement of Sustainable Development Goals (SDGs) and highlights the important role of Corporate Social Responsibility (CSR) in addressing this challenge. Our article proposes a solution combining machine learning and blockchain to automate child labor detection and ensure traceable, auditable, private, and secure reporting. …

Estimating Snow Coverage Percentage On Solar Panels Using Drone Imagery And Machine Learning For Enhanced Energy Efficiency, Ashraf Saleem, Ali Awad, Amna Mazen, Zoe Mazurkiewicz, Ana Dyreson

Michigan Tech Publications

Snow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a …

03.31.2025 Ored Connect, Liz Williamson

ORED Newsletter

Indirect Cost Rate Agreement

NIH Transition to Common Forms

NIH Implementation of New Initiatives and Policies