Engineering Commons^™

Toward Secure And Practical Machine Learning-Based Access Control: A Framework With Real-World Constraints And Adversarial Analysis, Olusesi Balogun, Mohammad Ghasemigol, Zhipeng Cai, Daniel Takabi

School of Cybersecurity Faculty Publications

Attribute-Based Access Control (ABAC) frameworks coordinate access requests based on subject, object, and environment attributes, as well as policy rules, and are widely used in corporate security systems. Recently, machine learning has been applied to ABAC to address policy-generation imbalances, misassigned privileges, and attribute leakages. However, existing MLBAC techniques do not consider the structural constraints and attribute interdependencies present in traditional ABAC systems. Moreover, these frameworks have not been extensively evaluated under black-box attack scenarios. To address these gaps, we propose extensions to MLBAC that integrate structural constraints, attribute dynamism, and attribute weighting into the MLBAC objective function. Additionally, we …

A Hybrid Cnn-Lstm Surrogate Model For Hyper-Resolution Spatiotemporal Flood Forecasting In Norfolk, Virginia, Yidi Wang, Jonathan L. Goodall, Chetan Kumar, Diana Mcspadden, Sergio A. Barbosa, Binata Roy, Ali Shahabi, Navid Tahvildari

Data Science Faculty Publications

Study region

Norfolk, Virginia, United States

Study focus

Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features.

New hydrologic insights for …

Stress-Strain Behavior Of Saturated Clays From Vane Shear Test Results: A Numerical Approach, Gustavo Rodrigues De Avila Oliveira

Master’s Theses

The vane shear test (VST) is widely used to estimate the undrained shear strength of soft cohesive soils; however, it does not directly provide constitutive information such as the stress–strain response. Smoothed Particle Hydrodynamics (SPH), a mesh-free numerical method, offers an attractive alternative for modeling the large deformations involved in the VST and extracting additional information from the test.

This study develops and calibrates a two-dimensional SPH model to simulate the field vane shear test and investigate the stress–strain behavior of soft soils. The VST was implemented within an SPH framework, while field and laboratory data were used to define …

The Development Of A Convenient And Consistent Methodology For Flight Proficiency To Certify Multi-Rotor Uas Pilots For State Departments Of Transportation, Colin H. Dees, Joseph M. Burgett

The Professional Constructor

To fly an unmanned aircraft system (UAS), commonly referred to as a “drone,” the Federal Aviation Administration (FAA) requires pilots to pass a knowledge test. There is no requirement at the state or federal level for drone operators to demonstrate the ability to operate a UAS. The National Institute of Science and Technology (NIST) has created an exam for public and private entities to assess basic UAS flight proficiency. It is the only nationally recognized flight proficiency protocol. NIST does not provide a scoring recommendation and leaves it to the user to determine the minimum criteria to pass. There is …

Feasibility Of Upcycling Spent Lithium-Ion Battery To Carbon Dioxide Capture Adsorbent, Chimezie Frank Onwudinjo

Master’s Theses

This study investigates the feasibility of repurposing spent lithium-ion battery (SLIB) to lithium orthosilicate (Li₄SiO₄), a high temperature carbon dioxide sorbent. Two synthesis pathways including conventional acid-leaching method (Scenario 1) and a pyrolysis-based route (Scenario 2) were explored. Additionally, techno-economic analysis (TEA) and lifecycle assessment (LCA) of the two processes were also performed. Different analytical characterization techniques were performed to understand the material properties of Li₄SiO₄ including surface area, crystallinity, morphology and thermal stability. CO₂ capture performance of the synthesized Li₄SiO₄ was tested in a thermogravimetric analyzer (TGA) using …

Control System Emendation And Ai-Driven Optimization For Enhancing A Smart Residential Microgrid, Anthony Nyoyoko

Master’s Theses

This thesis began with a simple idea: to restore the control system of a smart residential microgrid to respond intelligently to electricity prices while remaining safe, reliable, and practical on low-cost hardware. At the start, the goal was to design an economically aware microgrid that could look at electricity prices from the PJM market and make better operational decisions than traditional rule-based control. The motivation was straightforward. As residential renewable energy adoption increases, microgrids are expected to do more than just supply power. They are expected to respond to price volatility, integrate renewable generation, and operate reliably using embedded controllers …

Discovering Naturally Occurring Antifreeze Peptides From Microbiome By Integrating Protein Language Models And Molecular Dynamics Simulation, Ibrahim A. Imam, Trevor Morey, Yuexu Jiang, Duolin Wang, Dong Xu, Qing Shao

Chemical and Materials Engineering Faculty Publications

Antifreeze peptides inhibit ice crystal growth and recrystallization, and are promising components of cryoprotective formulations for cell, tissue, and food preservation, as well as anti-icing surface coatings. However, the discovery of new antifreeze peptides has been hindered by their sequence diversity and the limited scalability of experimental screening. In this study, we identify novel antifreeze peptide candidates from a microbiome-derived sequence library using ensemble machine learning and molecular dynamics (MD) simulations. We developed an ensemble classifier composed of 10 adapter-tuned protein-language models and a random forest meta-learner. After training on a curated dataset of 73 766 sequences, we applied this …

Effect Of Protective Mutation On Structure And Dynamics Of Apoe: A Molecular Dynamics Simulation Study, Newton A. Ihoeghian, Usman L. Abass, Ibrahim A. Imam, Qing Shao

Chemical and Materials Engineering Faculty Publications

Apolipoprotein E (APOE) plays a significant role in determining the risk of Alzheimer’s disease (AD). Three mutations—APOE3–R136S, APOE3–V236E, and APOE4–R251G—have been reported to reduce the risk of AD. Unveiling the molecular mechanism behind this reduction could lay a foundation for developing therapeutics for AD. To shed light on this subject, we investigate the mutation-induced variation in structural and dynamic properties of APOE3–R136S, APOE3–V236E, and APOE4–R251G in explicit solvent using molecular dynamics simulations. The APOE2, APOE3, and APOE4 were used as the reference. The analysis unveiled that the three protective mutations may exert protection through different mechanisms. The R215G mutation makes …

Tiny Plastic, Big Trouble: How Polystyrene Nanoparticles Impact Dna-Damage Repair Deficient Cervical Cancer Cells, Jordan D. Berezowitz, Mira C. Fish, Lauren E. Mehanna, Breanna Knicely, Claire E. Rowlands, Eva M. Goellner, Brittany E. Givens

Chemical and Materials Engineering Faculty Publications

Microplastics are becoming increasingly abundant waste products; therefore, the risk of human exposure is also increasing. The cytotoxic consequences of microplastic exposure, particularly in cancer, have yet to be explored. We obtained commercially available polystyrene nanoparticles of uniform size (86.61 ± 6.41 nm) and confirmed the chemical composition and shape using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), respectively. We evaluated colloidal stability over a range of concentrations from 1–1000 µg mL⁻¹ using hydrodynamic diameter and zeta potential, determining that higher concentrations exhibit greater colloidal stability compared to lower concentrations. Specifically, the zeta potential increased from …

The Role Of Laminin Isoforms In Glioblastoma Migration, Aseel Ahmed, Faezeh Ghobadi, Hanna Devillier, Qi Cai

Distinguished Undergraduate Researcher Program

Glioblastoma (GBM) remains the most prevalent and lethal invasive brain tumor, denoted by a median survival of 12-15 months despite standard procedures such as safe surgical resection, radiotherapy, and chemotherapy. This limited effectiveness largely arises from the infiltrative nature of GBM cells, which interact with the brain’s extracellular matrix (ECM) by migrating along blood vessels and axonal pathways. The ECM contains the interstitial matrix (IM) and the
basement membrane (BM) made up of proteins and glycans which interact with the malignant cells. While research has mainly been focused on understanding how GBM cells interact with IM components, our knowledge about …

The Healing Power Of Nanoparticles: Bioengineering Gelatin Films With Lignin-Graft-Plga Nanoparticles For Enhanced Tissue Repair, Marie Howe, Cristina Sabliov, Thanida Chuacharoen, Willyam Nikiema

Distinguished Undergraduate Researcher Program

Fish gelatin, a biocompatible material, can be methacrylated to enable its photo-crosslinking and formation of hydrogels for tissue-repair applications (1). This study investigated the incorporation of lignin-grafted PLGA nanoparticles (LNPs) into 3D-printed fGelMA hydrogels as controlled drug delivery systems. Lignin has UV absorbing properties, which could potentially interfere with photocrosslinking of fGelMA (2). These LNPs possess hydrophobic PLGA core and hydrophilic lignin forming the shell providing ample opportunities for delivery of drugs of different chemistries (3). Fluorescent LNPs (FLNPs) were engineered by covalently bonding a fluorophore to the shell prior to nanoparticle synthesis to allow for fluorophore tracking. The understanding …

Innovative Smart Sensing System For Accurate Monitoring Of Uhpc Setting Time, Ted Atera, Khalilullah Taj, Masoud Pasbani, Yen-Fang Su

Distinguished Undergraduate Researcher Program

Ultra-High-Performance Concrete (UHPC) is an advanced concrete material known for its strength and durability. Its superior mechanical properties make it an ideal material for demanding structural applications such as bridges. Accurate setting time measurements are crucial in helping to optimise the performance of UHPC. All conventional methods and standards for measuring the setting time of concrete rely on using expensive specialised equipment, such as the Vicat apparatus, which assesses penetration depth or resistance. However, these methods may not be well-suited for UHPC due to its rapid surface drying while the interior remains fresh. This prevents free needle penetration and leads …

Novel Methods For Environmental Fluoride Measurement, Cable Warren

Chemistry & Biochemistry Dissertations

Fluoride analysis has been an important focus of analytical analysis for many years and will continue to be so going forward. Today, fluorinated compounds, specifically per/polyfluoroalkyl substances (PFAS), better known as “forever chemicals” have captured the moment and are the subject of vast research and regulation. Under this context, I have researched new methods of fluoride separation and concentration in complex media as well as a novel method of PFAS destruction and total organic fluorine analysis for screening of PFAS in aqueous samples. Through research and work on micro-scale detection methods, an understanding of the state of the art and …

Labor Savings From Mechanical Rice Transplanting In Bangladesh, India, Nepal, And The Philippines: A Systematic Review And Meta-Analysis, Rodrigo S. David

ASEAN Journal on Science and Technology for Development

Rice production in South and Southeast Asia faces serious challenges, including acute labor shortages and rising wages, which threaten regional food security. Mechanical rice transplanting offers a promising solution; however, evidence on labor savings, especially region-specific data, remains inconsistent. This systematic review and meta-analysis aimed to quantify labor savings from mechanical versus manual transplanting across Bangladesh, India, Nepal, and the Philippines.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across four major scientific databases (2000–2026) using Boolean search terms for mechanical transplanting and labor outcomes. Of 284 initial records, 52 met eligibility …

Climate And Seasonal Influences On Indo-Pacific Sea Level: Insights From 30 Years Of Satellite And Tide Gauge Data, Karlina Triana

ASEAN Journal on Science and Technology for Development

We analyze Indo-Pacific sea-level variability using monthly satellite altimetry (1993–2025) validated against records from 16 tide-gauge stations. Cross-comparisons show strong agreement, especially in the western Pacific, confirming the reliability of altimetry for regional assessments. Seasonal SLA variability is largest in the Bay of Bengal and South China Sea and reflects monsoonal forcing, whereas interannual fluctuations in the eastern Indian and western Pacific oceans are dominated by ENSO and modulated by PDO. Harmonic decomposition isolates annual and semi-annual cycles, and an EOF/PCA framework identifies the leading modes: EOF1 (30.8%) captures basin-scale interannual variability and EOF2 (20.9%) reflects the seasonal cycle. Spectral …

Metagenomic Polymorphic Toxin Effector And Immunity Profiling Predicts Microbiome Development And Disease-Related Dysbiosis, Hunter W. Schroer, Francesco Beghini, Juan Antonio Raygoza Garay, Nicholas A. Christakis, Dustin E. Bosch

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease …

Development Of Feature Tokenizer Deep Learning Model For Fault Diagnosis In Marine Propulsion System, Pratik Anand Deshpande, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the …

Development Of An Active Heave Compensation System On A Cost Effective Microcontroller For A Fiber Optic Tether Management System, Frances Truong

Open Access Master's Theses

Fiber optic tether systems are used for real-time data transmission to remotely operated vehicles (ROV) due to their ease of deployment and light weight, but are prone to damage from tension loads. An active heave compensation system applied to a fiber optic tether system can address these challenges by actively managing the tether payout to reduce peak loads. Tether management systems with active heave compensation have been in use for larger systems such as work class vehicles and offshore oil rigs, but few are developed for small maritime systems. This work focuses on the development and incorporation of an active …

Gpu-Accelerated Biased Random-Key Genetic Algorithms: Framework Optimization And Llm-Driven Configuration, Fnu Harishjitu Saseendran

Open Access Master's Theses

This thesis presents two complementary contributions to the field of GPU-accelerated evolutionary metaheuristics for combinatorial optimization, organized in manuscript format.

The first manuscript, “BrkgaCuda 3.0: A Redesigned Multi-GPU Framework for Biased Random-Key Genetic Algorithms,” presents a ground-up architectural redesign of BrkgaCuda 2.0 that enables a true multi-GPU island model for Biased Random-Key Genetic Algorithms (BRKGA). The BRKGA island model evolves multiple semi-independent populations that periodically exchange elite solutions, a structure that maps naturally to multi-GPU parallelism; however, BrkgaCuda 2.0 is confined to a single GPU. BrkgaCuda 3.0 introduces an IslandManager that distributes populations across any number of GPUs, with multiple …

Design Of A Communication And Control System For A Mesopelagic Vertical Profiling Robot, Julian Blanco

Open Access Master's Theses

The VAMPIRE (Visual and Acoustic Mesopelagic Profiler for Interdisciplinary Research) is a new platform capable of collecting simultaneous environmental, acoustic, and visual data in the Mesopelagic ocean. This work describes the communication and control systems of the vehicle, and presents preliminary data from initial deployments in the open ocean. A system architecture was developed to allow high level control of the vehicle with several layers of redundancy and automatic fail over systems allowing for safe operation.  A one dimensional motion model is developed and a system identification is performed to better understand the vehicle's vertical maneuverability. This model is used …

Dynamics Of Single-Walled Carbon Nanotubes In Structured Complex Fluids, Sepehr Yari

Open Access Master's Theses

Cancer survival depends strongly on the stage at which the disease is detected, motivating the development of biosensors capable of identifying early changes in cellular state. Single-walled carbon nanotubes (SWCNTs) are promising intracellular biosensors because their intrinsic near-infrared fluorescence, photostability, and tunable surface chemistry and lenght enable prolonged measurements in living cells. Disease such as cancer can progress and alter intracellular physical properties, suggesting that SWCNT motion may provide a physical readout of changes in cellular state. Interpreting this motion is challenging, however, because the cytoplasm is structurally heterogeneous and contains both passive fluctuations and active transport processes. As a …

Using Network Models To Understand Biological Signaling Architecture, Russ White, Emily Brown Reeves, Gerald L. Fudge

Faculty Publications

Engineers have developed abstract network models to better understand the recurring problems faced by communication systems. This paper argues that these models can be generalized to describe biological communications systems given that they share many requirements with human-designed systems, including functional requirements and physical constraints. Leveraging collaboration, biologists and engineers can work together to use well-understood communication systems, designed to carry data across a computer network, as a model for analyzing less well-understood biological communication systems in order to make predictions and uncover previously unknown functionalities. To illustrate this approach, we apply the Recursive Internet Network Architecture model (RINA) to …

Performance Of Shcc Brick Infill And Nsm Reinforcement Systems For Strengthening Rc Deep Beams With Large Openings, Ahmed Badr El-Din, Mohamed Ghalla, Galal Elsamak, Ayman El-Zohairy

Faculty Publications

The presence of large web openings in reinforced concrete (RC) deep beams often disrupts the natural load-transfer mechanism, leading to severe reductions in shear strength and premature failure. This study introduces a novel hybrid strengthening system incorporating strain-hardening cementitious composite (SHCC) bricks, stainless-steel and galvanized-steel sheets, and near-surface-mounted (NSM) steel reinforcements to restore and enhance the shear performance of RC deep beams with large openings. A comprehensive experimental program was conducted on sixteen beams under concentrated loading to evaluate the influence of different infill and strengthening configurations on load capacity, stiffness, ductility, and energy absorption. The results revealed that the …

Long Short-Term Memory (Lstm) -Based Neural Network Model For Optimizing Composite Manufacturing Process Using Autoclave, Sourav Bolar, Steven Corns, Nayan Pundhir, Kumbla Chandrashekhara

Engineering Management and Systems Engineering Faculty Research & Creative Works

Producing high-quality fiber-reinforced composites requires precise temperature control during autoclave curing, as even small variations can lead to defects that compromise strength and reliability. At the same time, manufacturers aim to reduce energy use and shorten curing cycles without sacrificing material performance. To address these challenges, this study develops a data-driven Long Short-Term Memory (LSTM) neural network model capable of forecasting temperature evolution inside the autoclave throughout the curing cycle. The model is trained on time-series temperature data collected from multiple sensing locations, enabling it to learn the spatial and temporal trends that govern heat flow during curing. Data augmentation …

Work-In-Progress: Evaluating Feasibility Of Band Matrix Solvers For Scaling Up Extreme Learning Machine Method, Anton Akusok, Kaj Mikael Björk, Amaury Lendasse, Leonardo Espinosa Leal

Engineering Management and Systems Engineering Faculty Research & Creative Works

This work presents the results of the potential of band linear system solvers for improving the scalability of the Extreme Learning Machine (ELM) method at large model sizes. The model is tested on the standard MNIST dataset with a range of solvers provided by the SciPy Python library. The results are analyzed taking into consideration the overall performance and the performance impact of band solvers across different matrix bandwidths, as well as the performance versus runtime analysis. The findings show potential in applying the proposed method to very large ELM models with narrow band matrices.

Sme Ai Outreach In Finland—A Case Study, Kaj Mikael Björk, Anton Akusok, Amaury Lendasse, Leonardo Espinosa-Leal

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper presents a project (work in progress) where entrepreneurship and higher education in AI (from Master level to postdoc level) are integrated in order to produce a dual effect; helping SMEs to gain insight in how AI can aid in the corporate environment and to expose AI researchers to the real-life situations in the company world. If successful, the companies are made ready for the AI revolution and the researchers more equipped for corporate settings. The project is ongoing, so this paper addresses a work-in-progress project. The paper reflects on the project as well on some aspects that need …

Design Of Novel Gating Systems For Steel Castings, K. Balasubramanian, Laura Bartlett, M. Xu

Materials Science and Engineering Faculty Research & Creative Works

Gating systems play an important role in determining the quality and mechanical properties of castings. To understand the efficiency of gating systems, four systems, namely pressurized system, non-pressurized system, naturally pressurized system with a side riser and a naturally pressurized system with a top riser, were studied. The naturally pressurized systems were provided with overflows which collected the incoming metal swirl. Parameters like velocity of metal flow, air entrapment, microporosity and Niyama criterion were considered, and a design was developed with a common pouring basin. 8630 alloy was poured into two molds using a teapot ladle. The inclusion analysis revealed …

Microstructure And Properties Of Oxide Dispersion-Strengthened Alloys, Ertugrul Demir, Seung Min Ha, Anish Ranjan, Xingshuo Zhang, Aaron Penders, Mukesh Bachhav, Xiaochun Li, Lin Shao, Alexander Demblon, Haiming Wen, Enrique Lavernia

Materials Science and Engineering Faculty Research & Creative Works

Oxide dispersion-strengthened (ODS) alloys are a critical class of structural materials for extreme environments, owing to their unique combination of high-temperature strength, thermal stability, and radiation tolerance, enabled by a very high density of nanoscale oxide dispersoids. These features make ODS alloys attractive for advanced nuclear systems, aerospace applications, and other harsh-service conditions where conventional alloys rapidly degrade. Despite decades of development, key challenges remain in understanding how nanoscale oxides interact with matrix microstructures, alloy chemistry, and irradiation-induced defects to control macroscopic performance. This review provides a focused, mechanism-based synthesis of the microstructural features that govern the properties of ODS …

Catalytic Hydropyrolysis Of Beetle Killed Trees For The Production Of Transportation Biofuels, Oluwanisola Makinde, Fernando L.P. Resende, Michael Asama, Demian F. Gomez

Jasper Department of Chemical Engineering Faculty Publications and Presentations

We conducted catalytic hydropyrolysis of beetle-killed trees: Pine, Ash tree, and Redbay in a micro-pyrolyzer (Py/GC–MS) and investigated the performance of heterogeneous catalysts like HZSM-5, NiMo-HZSM- 5, and NiRe-HZSM- 5. We also investigated the effects of temperature, catalyst-to- biomass ratio, and hydrogen pressure on product yield. Our findings reveal that aromatic yields increase with temperature and catalyst-to- biomass ratio but decline at higher hydrogen pressures; additionally, higher catalyst acidity enhances both total hydrocarbon production and selectivity toward C7–C8 aromatics, with each feedstock exhibiting distinct optimal conditions. The type of metal doped on the HZSM-5 zeolite plays an important role in …

Elevated Temperature Flexure Behavior Of Continuous Carbon Fiber Reinforced Zrb2–Zrsi2 Ultrahigh Temperature Ceramic Matrix Composites, Jacob Stacy, Aaron Ginsparg, Jason Lonergan, Jeremy Watts, Gregory Hilmas

Materials Science and Engineering Faculty Research & Creative Works

Ultrahigh temperature ceramic matrix composites (UHTCMCs) were fabricated from unidirectional prepreg tapes consisting of a matrix of ZrB₂ with 5, 10, and 15 vol.% ZrSi₂ additions and continuous polyacrylonitrile carbon fibers and were densified at 1600°C in a hot press. The relative matrix densities ranged from 88% to 93% with interlayer spacings of ∼72 µm and fiber volume fractions between 30% and 36%. Phenolic resin additions were utilized to react with ZrSi₂ acting as a transient sintering aid to form ZrC and SiC phases. Elastic moduli of the UHTCMCs decreased with increasing temperature during 4-pt flexure testing. …