Digital Commons Network^™

Enhancing Urban Refueling Efficiency: Strategies For Sustainable Mobility And Economic Impact, Navid Hashemi Taba, Iftekhar Ahmad, Ahdieh Sadat Khatavakhotan

Research outputs 2022 to 2026

In response to increasing concerns about operational inefficiencies and environmental impacts at petrol stations, this operational research investigates strategies to enhance refueling efficiency across 103 stations in Perth. A pilot study employing observational methods and time-motion analysis established ideal benchmarks for core refueling activities. Empirical analysis of driver behavior during high-demand periods—often driven by fuel price discounts—and regular off-peak hours revealed significant performance gaps, highlighting substantial opportunities for efficiency improvements. Eliminating non-essential tasks could yield time savings of up to 67% during peak periods and 45% during off-peak periods. Economic analysis demonstrated mutual benefits when refueling processes align with these …

Green-Synthesised Nh₂-Mil-101(Cr) Mof–Reinforced Starch Bioplastic Films For Sustainable Food Packaging, Shima Jafarzadeh, Yasaman Esmaeili, Moon Paul, Seyedeh Zahra Haeri, Colin J. Barrow, Mina Dokouhaki, Masoumeh Zargar, Minoo Naebe

Research outputs 2022 to 2026

This study introduces a green, aqueous synthesis approach for NH₂-MIL-101(Cr), avoiding toxic organic solvents while preserving its high crystallinity, porosity (>1000 m2/g), and amino functionality. To develop sustainable packaging materials the synthesized MOF was incorporated into sago starch-based bioplastic films across control 0%, 1%, 3%, and 5% treatments. Nanoparticle characterizations via SEM, TEM, FTIR, XRD, and nitrogen adsorption-desorption isotherms confirmed a porous, crystalline structure (100–300 nm) with a surface area exceeding 1000 m2/g. Film characterizations using SEM, FTIR, color analysis, tensile testing, TGA, and UV-Vis spectroscopy revealed that the 3% treatment offered the best balance, with tensile strength increasing …

Multiple-Nozzle Sprays: Comparison To Single-Nozzle Sprays And Role Of Directional Conditional Filtering, Farzad Hossain, Matthew J. Cleary, Yasir M. Al-Abdeli, Mehdi Khiadani

Research outputs 2022 to 2026

Spray-based droplet evaporation systems used in desalination and cooling applications depend on both nozzle arrangement and the interpretation of measured droplet behaviour. This study examines (i) how multi-nozzle configurations differ from single-nozzle characteristics and (ii) how ensemble-averaged measurements change when droplets are separated according to their motion direction (upward or downward). Atmospheric water sprays generated by full-cone spray nozzles were investigated using Particle Image Velocimetry (PIV) for droplet axial velocity measurements and Shadowgraph imaging to determine droplet size and count. Directional filtering was performed using Large Eddy Simulation (LES), validated against experiments. Single- and dual-nozzle configurations were tested with horizontal …

A Novel Multi−Agent Deep Reinforcement Learning Framework For Fast Frequency Response In Inverter−Based Hybrid Power Plants, Muhammad Ikram, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

The increasing penetration of inverter−based resources (IBR) in power grids necessitates novel control techniques to deliver advanced ancillary services such as fast frequency response (FFR), crucial for ensuring power system stability. Existing schemes−droop control, virtual synchronous generator (VSG), and hybrid approaches−require full observability, and centralized control, which are unsuitable for utility−scale hybrid power plant (HPP) with a distributed grid−forming inverter (GFMI). In addition, it lacks adaptive control capabilities in response to real−time disturbances according to the IEEE 2800−2022 standard. To address these challenges, this paper proposes a design of a novel multi−agent deep reinforcement learning (MADRL) framework for utility−scale IBR−dominated …

Evaluation Of The Effect Of Vibration On Signal Reflection In Coaxial Cable Connectors For Vibration Sensing In Aircraft Structures And Systems, Saidanvar Esanjonovich Valiev, Anthony C. Okafor, Jeremiah J. Rittenhouse, Daniel S. Stutts, Jie Huang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper investigates the effects of vibration on signal reflection (S11) in aerospace data transmission line (ADTL) and commercial data transmission line (DTL) connectors for their alternative use as vibration sensors. The impact of vibration on the S11 signal was investigated on five ADTL and four DTL connectors at six vibration frequencies (20 Hz, 40 Hz, 80 Hz, 160 Hz, 320 Hz, and 640 Hz) and four vibration accelerations (0.5G, 1 G, 2 G, and 4G). The experiment was conducted using a split-plot design with a cable type assigned as the main-plot factor, with vibration frequency and acceleration as subplot …

Multiplexed Fabry-Pérot High-Temperature Sensing Based On Dispersive Microwave-Photonic Frequency-Time Domain Analysis, Ruimin Jie, Chen Zhu, Bohong Zhang, Koustav Dey, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We propose and experimentally demonstrate a multiplexed high-temperature Fabry-Pérot (FP) fiber sensing system interrogated by dispersive microwave-photonic frequency-time domain analysis (DM-FTDA). In the proposed architecture, incoherent broadband probing light is modulated by radio-frequency (RF) signals and then reflected by a parallel network of hollow-core photonic crystal fiber FP (HCPCF-FP) sensors. A chirped fiber Bragg grating provides strong dispersion to map the composite FP spectral response into a well-defined microwave transfer function. Unlike conventional optical Fourier-domain multiplexing that requires deliberate cavity-length allocation, the proposed approach achieves multiplexing via delay-dominated discrimination. Distinct delay fibers are assigned to each sensor branch, and an …

Creating A Framework For Quantifying Wildland Urban Interface (Wui) Fire Vulnerability Factors, Ryan L. Delalio

Construction Management

Wildfires have been the focus of geospatial research for decades. Climate change and increased suburban development in California are leading to an increase of Wildland Urban Interface (WUI). These zones are at higher wildfire risk, and a metric is needed to determine the most vulnerable communities to help them assess their personal wildfire risk. By analyzing current WUI wildfire vulnerability literature, the main contributing factors behind the impact of wildfires in California emerge. These factors are then paired with datasets that contain geospatial information about the factors to help us find the locations of highest vulnerability. This paper serves as …

International Construction In El Salvador, Daniel Roberts

Construction Management

My senior project focused on participating in international construction operations in El Salvador through hands-on involvement in the construction of a steel warehouse facility and a small residential home. The project emphasized structural concrete placement, field coordination, construction sequencing, and adaptation to regional construction practices. Work included assisting with concrete foundations, slab preparation, reinforcing steel placement, masonry work, and general site coordination activities. The experience provided practical exposure to labor productivity, material availability, scheduling challenges, and construction methods commonly used in developing regions. The project also highlighted the importance of communication, problem-solving, and flexibility when working in an international construction …

A Near-Field Communication (Nfc) Multi-Sensor Node With Optimized Read Range And Adaptive Power Management For Remote Monitoring, Rishin Patra, Hilary Scott Nkimbeng Cho, Jin W. Choi

Michigan Tech Publications

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned …

S-[2-(Hydroxyamino)-2-Oxoethyl]-O-Octyl-Dithiocarbonate Ester (Ehaox): Performance And Adsorption Mechanism, Yue Zhou, Jiaxiang Li, Hongli Fan, Zhilong Li, Yuesheng Gao, Tian Lin, Haiyan Tang, Tingting Wang, Wei Sun

Michigan Tech Publications

This study explores the interfacial interactions between a novel collector, S-[2-(hydroxyamino)-2-oxoethyl]-O-octyl-dithiocarbonate ester (EHAOX), and bastnaesite mineral surfaces during selective flotation. Compared to conventional octylhydroxamic acid (OHA), EHAOX demonstrated superior recovery, achieving an over 40% absolute increase in recovery compared to octylhydroxamic acid (OHA) at the same collector concentration (1 × 10⁻⁵ mol/L) and pH 8.0, and selectivity for bastnaesite, attributed to its optimized hydrophobicity (supported by CLogP calculations). Mechanistic studies via zeta potential, UV/vis spectroscopy, and FTIR revealed the formation of a complex composite via chemisorption between EHAOX and Ce³⁺ sites on the bastnaesite surface, with coordination occurring …

Automated Optimization Of Membrane Bioreactor Performance Using Machine Learning, Janessa Cayabyab, Isabella Lopez, Oluwatomisin Opaleye, Jessica Stone

Bioengineering Senior Theses

Membrane bioreactors (MBRs) are increasingly employed in biomanufacturing due to their ability to produce high-quality effluent and support complex biological processes. However, membrane fouling remains a critical operational challenge, driving increased energy consumption, frequent cleaning cycles, elevated transmembrane pressure (TMP), and accelerated membrane degradation. Conventional operator-driven cleaning strategies are reactive by nature and insufficient for managing the dynamic, nonlinear fouling behavior characteristic of full-scale MBR systems.

This study presents a data-driven machine learning pipeline for predicting TMP and fouling behavior in an industry-scale MBR system, with the goal of optimizing cleaning schedules and extending membrane lifespan. A Random Forest regression …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Adaptive Multimodal Smart Home Control On A Raspberry Pi 5 Using Hand Gestures, Voice Cues, And User Feedback, Vaibhav Bora

Theses

A real time multimodal smart home control system deployed on a Raspberry Pi 5 is presented. The system combines hand gestures, short voice cues, and proximity aware interaction to execute household commands such as light brightness control, fan speed adjustment, and stop or kill switch actions. Lightweight gesture and keyword spotting voice classifiers were trained offline and exported to TensorFlow Lite for efficient on device inference. For more natural spoken phrases, the system additionally integrates a locally deployed pretrained offline ASR component rather than a speech recognizer trained from scratch. Using a USB camera and microphone, the system operates fully …

Robustness Of Ai-Driven Histopathology Under Real-World Adversarial Examples, Ruchik N. Yajnik

Theses

This robustness of histopathology classification models under adversarial and real-world perturbations resembling clinical artifacts is being investigated.

Using whole-slide images from the CAMELYON17 cohort, four representative architectures—ResNet-18, ResNet-50, HIPT-2MLP, and ViT-B/16 —are benchmarked across controlled pixel-level distortions and artifact-like transformations. Adversarial methods include iterative Fast Gradient Sign, Projected Gradient Descent, Salt-and-Pepper noise, and the Adversarial Watermark—Stain Shift (AWSS). Three defense strategies—Randomized Smoothing, Adversarial Training, and an Artifact Detector—are evaluated for their ability to preserve diagnostic accuracy and model reliability. Structured perturbations consistently degrade performance, with transformer-based models showing the greatest sensitivity. The benchmark developed here offers a reproducible framework for …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Vergence Task-Based Neural Pathways With Binocularly Normal Vision And Comorbid Persistent Post-Concussive Symptoms -Convergence Insufficiency, Ayushi Sangoi

Dissertations

Binocular dysfunctions are more prevalent in the persistent post-concussive symptoms (PPCS) population than in the general population. The most prevalent binocular disorder is convergence insufficiency (CI), affecting 3-17% of the general population and up to 10 times as many people with PPCS. CI makes it difficult to fuse or maintain fusion on targets at near, and its symptoms include double or blurry vision and headaches when performing close-range tasks such as reading, which can exacerbate PPCS symptoms. Given controversy over the subjectivity and effectiveness of diagnostic tools and symptom surveys for both PPCS and CI, understanding why CI has high …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Significant Crash Characteristics Associated With E-Scooter And E-Bike Crashes, Aimee Jefferson

Dissertations

Micromobility devices—namely e-scooters and e-bikes—have rapidly gained popularity in the United Sates, rising from 35 million annual shared rides in 2017 to over 133 million in 2023 (NACTO 2024). But also increasing is the number of injuries associated with these devices; however, most research emphasizes injury and demographic patterns rather than crash characteristics that would inform prevention strategies. Most existing crash research also relies on small sample sizes, lacks nuance distinguishing between involved parties (motorists, pedestrians, single device), and fails to distinguish between bicycle and micromobility crash patterns despite micromobility devices often being instructed to use conventional bicycle facilities. These …

Glass Transition Temperature Of Plga Nanoparticles And The Application In Drug Delivery, Guangliang Liu

Dissertations

The glass transition temperature (T_g) of poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles plays a crucial role in governing molecular mobility, diffusion, and consequently, drug release kinetics. However, the interaction among residual surfactant, drug effect, nanoscale confinement, and release medium on T_g remains insufficiently characterized. This study aims to bridge this gap by correlating the thermal behavior of PLGA nanoparticles with their drug release behavior under physiologically relevant conditions.

In the present study, PLGA nanoparticles were synthesized using both nano-emulsion and surfactant-free nano-precipitation approaches. The influence of residual surfactants - poly(vinyl alcohol) (PVA) and didodecyldimethylammonium bromide (DMAB) - was systematically …

Membrane-Engineered Nanotherapeutic Platforms For Drug Delivery: Hollow Fiber Membrane Synthesis Of Lipid Nanoparticles, Biomimetic Nanocarriers, And Nanobubbles, Zhixiang Liu

Dissertations

Lipid-based nanocarriers have emerged as a cornerstone technology for RNA therapeutics, enabling effective intracellular delivery for applications ranging from vaccination to gene regulation. However, current manufacturing approaches, particularly microfluidic-based platforms, face inherent limitations in scalability, throughput, and structural tunability due to their reliance on confined channel geometries and restricted mixing architectures. Addressing these challenges requires fundamentally new strategies that decouple nanoparticle formation from traditional microscale flow constraints while maintaining precise control over physicochemical properties.

This dissertation presents a comprehensive framework for the design, engineering, and application of advanced lipid-based nanocarriers, centered on a hollow fiber membrane (HFM)—assisted nanopore-mediated assembly platform. …

Transcranial Ac Modulation Of Cerebellar Nuclear Activity In Awake Animals, Nuran Kavakli

Dissertations

Entrainment of cerebellar nuclear (CN) cells via cerebellar transcranial alternating current stimulation (ctACS) has been reported in animals under ketamine/xylazine anesthesia. Our main objective was to demonstrate modulation of CN activity in unanesthetized, freely moving animals using ctACS. Multi-channel carbon-fiber electrodes were implanted into the interpositus nucleus for recording multi-unit (MU) activity, and thin-film electrodes were implanted subcutaneously over the posterior cerebellum for stimulation. A frequency-domain-based metric was developed to quantify modulation from MU signals. The results demonstrated modulation in a wide range of frequencies (4 Hz-300 Hz) as in anesthetized animals. In contrast, the amplitude of the peak in …

Omama-Db: The Oregon-Massachusetts Mammography Database, Avanih Kanamarlapudi

Graduate Masters Theses

Public datasets for training AI models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of 2D mammograms and 3D tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images us ing a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoen coder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations …

Towards Interpretable Transfer Learning With Limited Data, Youxiang Zhu

Graduate Doctoral Dissertations

Modern foundation models are typically trained with large-scale data to ensure good performance. However, certain tasks cannot scale with large amounts of data due to cost and practical constraints, resulting in limited performance. To address this, in this dissertation, I introduce model-task alignment, a general methodology for making a foundation model work well with tasks with limited data. The methodology comprises two parts: aligning downstream tasks with foundation models and aligning foundation models with downstream tasks. To study and validate this methodology, I first focus on speech-based dementia detection, a representative task with limited data, and then extend to general …

Ai-Augmented Financial Advisors: Comparing Ai And Human Analyst Investment Recommendations In Agreement, Performance, And Firm Size Effects, Crystal Chen

Honors College Theses

This study examines the level of agreement and performance between artificial intelligence (AI) generated investment recommendations and human analyst recommendations across U.S. publicly traded firms. Using a sample of twelve companies categorized by firm size (large, mid, and small), the study collects buy, hold, or sell recommendations from generative AI systems and human analysts. Agreement between AI-to-AI and AI-to-human recommendations is measured using Cohen’s Kappa agreement. Portfolio performance is evaluated by constructing equal-weighted portfolios for each recommendation source and size category. Risk-adjusted returns are measured using the Sharpe ratio over 1-, 2-, and 3-month periods. Furthermore, the study tests whether …

Electrospun Nanofiber Scaffolds For In Vitro 3d Tissue Engineering, Victoria E. Santillan, Samerender Nagam Hanumantharao, Stephanie Bule, Ronish M. Shrestha, Carter Rodzik, Alan Mendoza Estrada, Stephen L. Farias, Marina Tanasova, Smitha Rao

Michigan Tech Publications

Tissue engineering is widely used in research for investigating cellular proliferation, behavior, and responses to various stimuli. However, the predictive value of preclinical studies using cell culture plates is limited by the inability to recapitulate the complexity of the physiological microenvironment. Synthetic three-dimensional (3D) scaffolds can be engineered to mimic the complex morphology of the extracellular matrix of native tissues and can serve as physiologically relevant platforms for preclinical studies. In this study, 3D electrospun scaffolds were characterized to aid in breast cancer research. Unlike previous studies that focused primarily on scaffold fabrication or cell viability, this work systematically evaluates …

Drilling Fluid Enhancement Via Addition Of Sustainably Synthesized Nanoparticles, Hasan A. Abbood, Sarmad Al-Anssari

Research outputs 2022 to 2026

The addition of nanoparticles to the drilling fluids for geological projects is a promising application for enhancing the stability, rheological properties, and overall performance of the mud. This study aims to thoroughly analyze and compare the effects of silica and alumina nanoparticles on the properties and effectiveness of polymer drilling mud. Silica and alpha-alumina nanoparticles were synthesized using the sol-gel process and characterized through several techniques, including X-ray diffraction, Fourier-transform infrared spectroscopy, field-emission scanning electron microscopy, and atomic force microscopy. The effects of silica and alumina nanoparticles on various properties of drilling mud were then measured using a permeable plugging …

Software Engineering: Ai-Enhanced Full Stack Development, Huixin Wu, Haiying Xiao

Open Educational Resources

This course provides instruction in full‑stack web application development using modern JavaScript technologies. Students design, implement, and deploy web‑based solutions while applying AI‑assisted development tools in an ethical and effective manner. The curriculum emphasizes problem‑solving, system design, teamwork, and industry‑standard best practices, with dedicated focus on cloud‑native development—including containerization, distributed services, and modern deployment pipelines—as well as API‑first architecture, enabling students to build scalable, maintainable, and interoperable applications aligned with current industry expectations.