Computational Engineering Commons^™

Comprehensive Performance Evaluation Of Devops Infrastructure Under Dynamic Workloads, Abdulrazaq Mamud

College of Graduate Studies: Theses & Dissertations

This research aims to investigate performance optimization and reliability issues related to cloud-based computing environments through an analysis of three key infrastructure components: virtualized CPU resource management, distributed API rate limiting, and web server deployment architectures. This research combines machine learning and system experimentation as a way of exploring the impact of infrastructure-level behaviors on overall system performance and scalability. The first component of the research focuses on analyzing CPU Fragmentation in Virtualized Environments, where unbalanced workload allocation on Virtual CPU Cores causes increased tail latency, resulting in Service Level Agreement violations. Metrics are analyzed using the Random Forest classifier …

Quantifying Hemodynamics In Implantable Cardiac And Renal Support Devices Using Computational Modeling, Alexander D. De Oliveira

Summer Research Program Abstracts

No abstract provided.

Mapping Leo Satellite Internet Performance Using Mobile Starlink Deployment, Annika Govil, Jacob Gray

UROP Posters

High-speed, low-latency internet connectivity on the move is a critical challenge for applications in connected vehicles, disaster response, and remote education. While terrestrial networks such as 4G or 5G are widespread, they lack coverage in remote or rural geographic areas.

Low Earth Orbit (LEO) satellite constellations, such as SpaceX's Starlink, promise global high-bandwidth, low-latency internet. However, their performance is well-documented in stationary scenarios, while data for *mobile* applications is scarce. This project explores the feasibility and real-world performance of LEO satellite internet while in motion.

Current research documents the performance of LEO satellite constellations in stationary settings. However, data concerning …

Towards Computational Methods In Medical Data Analysis: From Speech And Text To Imaging, Kristin Qi

Graduate Doctoral Dissertations

Early detection of cognitive decline and efficient medical image analysis remain critical challenges in healthcare. Traditional clinical assessments are infrequent and resource-intensive, while everyday speech data and unlabeled medical images remain largely unexploited. This dissertation develops computational methods integrating machine learning and artificial intelligence across speech, text, and imaging modalities to address challenges in medical data processing. For cognitive monitoring, this work first introduces methods using voice assistant systems to collect longitudinal speech data in home environments, demonstrating that incorporating historical session patterns significantly enhances detection of mild cognitive impairment. Building on this foundation, a framework combining large language model-driven …

Analysis Of Leakage In Nearshore Pipelines Using Fitness For Service Method To Ensure Mechanical Integrity Based On Api-579 Level 3, Elriandri Elriandri, Dedi Priadi

Journal of Materials Exploration and Findings

The Fitness for Service (FFS) analysis is performed as a quantitative assessment to evaluate the integrity condition of a pipeline. Essentially, FFS assessment helps determine whether equipment components can operate safely despite existing deficiencies. This evaluation is carried out using the Finite Element Method (FEM). In the case of an underwater pipeline that experiences leak due to anchor pull at the flange connection, it undergoes plastic deformation and is lifted approximately 1 meter. As a mitigation step, inspection and repairs have been carried out by the company. Subsequently, modeling is performed to reconstruct the deformation process of the pipeline. Then, …

Assessment Of The Melt Quality Of A 30% Scrap Adc12 Aluminium Alloy Using The Inclusion And Fluidity Measurement Instrument (Ifmi) With Mullite Ceramic Filters, Gusti Ruri Ruri Lestari, Sandya Ananda Riswan, Muhammad Anis, Ahmad Ashari, Paramita Vidya Ayuningtyas, Bambang Suharno Prof, Donanta Dhaneswara

Journal of Materials Exploration and Findings

The increasing demand for sustainable practices in the metal casting industry has driven the use of recycled aluminum alloys such as ADC12. However, the addition of aluminum scrap tends to increase oxide inclusions, which reduce melt fluidity and compromise casting quality. This study utilizes the Inclusion and Fluidity Measurement Instrument (IFMI), equipped with mullite ceramic filters, to assess the melt quality of ADC12 aluminum alloy containing 30% scrap. Fluidity and inclusion characteristics were evaluated at five pouring temperatures (660°C, 680°C, 700°C, 720°C, and 740°C). The results show that fluidity increased with temperature, reaching a peak of 84.6 g·s⁻¹ at 740°C, …

Equipment Criticality Analysis To Determine Asset Integrity Management System Scheme In Supporting Production Optimization Scenarios In The Aeging Field, Donny Andryanto, Donanta Dhaneswara

Journal of Materials Exploration and Findings

This research investigates the production decline and cost increase in the X&Y oil and gas fields from 2022 to 2023. Production fell by 34%, while production costs per barrel rose by 79%. To address these issues, a series of optimization processes are proposed. These aim to restructure and enhance the production facilities to reduce current production costs. The optimizations include reducing pressure at the PPP Platform in 2024. Additional steps include shutting down CPP-ORF 14” pipelines and most processes at CPP 2 Platform by 2027 to convert it into an accommodation platform. Further, the plan involves optimizing the release of …

Morphological Test Of Areca Nut Fiber Ceramic Membrane Using Scanning Electron Microscopy Energy Dispersive X-Ray Mapping Spectroscopy, Amelia Marzain, Siti Umi Kalsum, Marhadi Marhadi, Ahmad Nabil Shahab

Journal of Materials Exploration and Findings

This study investigates the potential of ceramic membranes derived from areca nut fiber as a cost-effective and environmentally sustainable material for the removal of iron (Fe) and manganese (Mn) from groundwater. Two types of membranes were fabricated: one without activation and one chemically activated using 10% sodium hydroxide (NaOH). The morphological and elemental characteristics of both membranes were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) mapping. The concentrations of Fe and Mn before and after treatment were measured using Atomic Absorption Spectroscopy (AAS). The NaOH-activated membrane exhibited a more porous surface structure and higher oxygen content, …

Irregularities In The Dynamics Of Large Networks: Cases On Social Networks And The Internet, Dheeman Saha, Abdullah Mueen, Shuang Luan, Afsah Anwar, Yangsun Hong

Computer Science ETDs

Dynamic networks capture evolving relationships among entities, and irregularities in their behavior often signal significant events. This dissertation investigates such irregularities across social and infrastructural networks. First, we analyze Twitter (prior to its rebranding to X) to characterize influencers, introducing a follower-overlap similarity measure that identifies micro-influencers beyond content-based methods.

We then examine election-related irregularities by comparing suspended and non-suspended accounts during the 2020 U.S. presidential election, revealing behavioral patterns linked to misinformation and coordinated manipulation.

Next, we develop an algorithm to infer post-specific diffusion networks, reconstructing who-saw-from-whom pathways for tweets at scale. This method uncovers structural differences in diffusion …

Prism (Proxy Recognition And Inclusion Scoring Method), Destiny Raburnel, Crystal Tubbs, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

AI-driven automated hiring tools are reshaping how companies find talent, but they often reproduce the hidden biases embedded in their training data. Our project, PRISM (Proxy Recognition and Inclusion Scoring Method), investigates how subtle demographic signals, specifically first names associated with gender and race, influence AI resume screening even when candidates have identical qualifications. We built a controlled dataset of resumes that are identical in every way except for the applicant's first name, with each resume using a racially neutral surname to isolate how first names alone affect scoring. We tested these resumes against job postings in technology, healthcare, and …

A Predictive Model For Multi- Week Respiratory Risk From Red Tide On Florida’S Gulf Coast., Elmer S. Ochaeta

Computer Science and Engineering Faculty Publications

Florida’s Gulf Coast red tide (Karenia brevis) can put toxins into the air, making people cough, irritating the throat, and worsening asthma or other breathing problems especially when winds blow from the ocean toward the beach. Right now, most public updates don’t really help with the question people actually ask when planning a weekend or vacation: “Will going to or close to the beach be risky in the next few weeks?”.

In this project, I build a weekly early warning system that estimates respiratory risk for specific beaches and predicts that risk 2 to 4 weeks ahead. The study covers …

Iso-Detr: A Novel Detection Transformer For Industrial Small Object Detection, Faisal Saeed, Anand Paul

School of Public Health Faculty Publications

Effectively detecting and assessing real-time structural and ecological parameters in contemporary manufacturing environments poses significant challenges, particularly in identifying minute objects within product images. The swift evolution of the industrial sector underscores the necessity for intelligent manufacturing environments to uphold stringent product quality standards. However, accelerating production processes at high speeds heightens the risk of defective product outcomes. This research addresses the challenges inherent in small object detection within industrial contexts, proposing an innovative detection transformer model tailored to modern manufacturing environments. The proposed model integrates a feature-enhanced multi-head self-attention block (FEMSA), merging cross-channel communication network and multiple multi-head self-attention …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

Integrating Machine Learning With An Fps Aim Trainer For Optimal Sensitivity Finding, Sharan Krishna

Computer Science and Software Engineering

First-person shooter (FPS) games often demand high levels of skill in aiming, which leads players to look for external tools to improve their performance. This is where the concepts of aim training and aim trainers come in, becoming an easily accessible outside source for players to strengthen their performance with custom scenarios outside a set game. While many aim trainers exist, they offer limited insight into player performance metrics or adaptability to varying aiming styles. Furthermore, most existing aim trainers lack a standardized way of correlating aim skill with real-world performance or personalized feedback. This aim trainer addresses these limitations …

User Evaluation Of A Virtual Patient For Philippine Medical Education, Ma. Mercedes T. Rodrigo, Samantha Castaneda, James Alvir Maclin V. Alaan, Paolo Santino P. Caoile

Department of Information Systems & Computer Science Faculty Publications

Caladrius is a virtual patient system designed for use in Philippine medical schools. It responds to the need for medical interview training, for greater variety in teaching-learning strategies, and for culturally appropriate technologies. It has two versions: a text-only version whose interface is similar to a chat interface, and an audio version that accepts speech input and responds with speech output. In a prior test of Caladrius, users requested improved audio response time and the inclusion of different patient personalities. A subsequent version of Caladrius was created to comply with these requests. As much of the lag was attributable to …

Thermal Heterogeneity And Lithium Plating During Fast Charging Of Lithium-Ion Battery., Ayodeji Avis Adeniran

Electronic Theses and Dissertations

Presently, widespread adoption of electric vehicles (EVs) is hindered by challenges associated with XFC, including heat generation, lithium plating, and accelerated battery degradation. This dissertation addresses these barriers through two coordinated themes—lithium plating and thermal management—spanning four manuscripts that integrate operando diagnostics, physics-based simulation, and module-level design optimization. The first theme seeks a fundamental investigation into the dynamic interplay between temperature heterogeneity and lithium plating during fast charging, a phenomenon that poses significant limitations on the performance of graphite-based anodes. Firstly, a custom operando cell with a sapphire window was designed to enable synchronized MWIR thermography and optical imaging of …

Analyzing Patterns In Hofstede’S Cultural Dimensions Towards Individual Ai Receptiveness For Affective Experiences, Maggie Yang

Master's Theses

The use of artificial intelligence (AI) has significantly advanced efficient decision-making, with trust in algorithmic decisions shown to vary by cultural upbringing [1]. As AI becomes increasingly embedded in everyday life, it is essential to examine how cultural values affect trust in AI in subjective contexts that extend beyond purely quantitative analysis, like the personal interpretation of art. This work utilizes Hofstede’s cultural dimensions to investigate potential patterns in receptiveness towards AI predictions during art interpretation, providing insight into individual susceptibility to bias in AI-assisted affective analysis.

The study leveraged a cultural dimension survey and a custom Java program connected …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

Comparative Analysis Of Emergent Behaviors Of Three Drone Swarm System Models For Targeting Using Agent-Based Modeling And Simulation, Arsenio T. Gumahad Ii

Engineering Management & Systems Engineering Theses & Dissertations

This dissertation introduces a novel computational simulation framework for evaluating the emergent behaviors of three swarm drone models using Agent-Based Modeling and Simulation (ABMS). The three swarm models are a Leader-Follower swarm model based on Bruckstein's antline theory, a Flocking model based on a simplified Reynolds 'Boids’ model, and a Stigmergic model with pheromone-based coordination. The primary objective of the simulation is to evaluate the performance of these models in delivering a user-defined number of drones of each type to a target area of interest in four separate scenarios, resulting in 50,000 separate simulation trials. Each scenario was structured to …

Simulation Of Stress, Deformation, And Vibration In Unlubricated Ball Bearings At Varying Rotational Speeds, Billy Dwiki, Moh. Hartono

Journal of Mechanical Engineering Science and Technology (JMEST)

Bearings are critical components in rotating machinery, and their performance is highly influenced by operational factors such as rotational speed, load, and lubrication. This study aims to analyze the deformation, stress, and dynamic behavior of SAE 52100 bearings under dry operating conditions at various rotational speeds, as well as to propose mitigation strategies for high-speed industrial applications. A finite element simulation was conducted using ANSYS R19.2 to evaluate the total deformation, equivalent elastic strain, Von Mises stress, and vibrational characteristics of a 6204-type SAE 52100 bearing. The simulations were performed across a speed range of 600 to 2000 RPM under …