Digital Commons Network^™

Impact Of Ionizable Lipid Variation On The Immunogenicity Of Lipid Nanoparticles, Chloe M. Krueger, Abbey L. Stokes, Shilpi Agrawl, Christopher E. Nelson

Annual Student Research Poster Session

Lipid nanoparticles (LNPs) are a leading nonviral delivery system for nucleic acid therapeutics due to their scalability and efficiency. However, certain formulations may trigger undesired immune responses. This study aimed to assess the inflammatory potential of LNPs formulated with different ionizable lipids using a murine macrophage reporter cell line (RAW-IRCs). RAW-IRCs express GFP upon successful mRNA delivery and mRFP1 upon activation of inflammatory pathways. A library of LNPs was synthesized via vortex mixing and characterized for hydrodynamic diameter, polydispersity index, and encapsulation efficiency. Treated RAW-IRCs were analyzed by flow cytometry to evaluate GFP and mRFP1 expression. Our results highlight seven …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

The Effect Of Fascicular Elastin On The Mechanical And Functional Properties Of Healthy, Damaged, And Healing Tendon, Shawn Pavey

McKelvey School of Engineering Graduate Student Theses & Dissertations

Mechanical properties of tendon are highly influenced by structural protein composition and microscopic sub-structures. Within the largest subunit of tendon, the fascicle, the role of the elastin protein remains understudied despite impressive extensibility and fatigue resistance of its resulting elastic fibers. While previous work catalogued contributions of fascicular elastin across tendon type and species, the anticipated effects of elastin in fatigue and healing have not yet been explored. While previous knockout mouse models showed that disruption of elastic fibers led to altered mechanical properties (e.g., increased linear modulus), these models depended on heterozygous elastin deficiency or indirect knockout of proteins …

Approximating The Maximum Weighted Independent Set Problem Empirically, Sue Sue

College of Engineering Summer Undergraduate Research Program

In this project we investigate approximation algorithms for the maximum weighted independent set (MWIS) problem in random graphs that approximate real-world graphs, such as social networks. The problem involves finding a large collection of nodes in a network (vertices in a graph) such that no two nodes are directly connected to one another. Applications of the MWIS problem are broad and include clique-finding algorithms as well as the use of large independent sets in distributed algorithms. We build on prior work by the mentor with a SURP 2024 and senior project, in which preliminary findings suggested that a standard greedy …

Motor Subsystem For A Tensegrity-Based Robotic Exoskeleton, Presley Sacavitch, Israel Villegas

College of Engineering Summer Undergraduate Research Program

Tensegrity structures are composed of stiff rods and elastic cables suspended in a flexible tension network. In particular, the biotensegrity model proposes that all biological systems exhibit tensegrity-like characteristics across multiple scales, ranging from the cellular level to the musculoskeletal system of tendons, ligaments, and fascia, to the human body as a whole. Compared to the traditional biomechanical models used in exoskeleton design, it can be a more accurate representation of how motion emerges from natural forms, but further work is needed to fully understand the heterarchical nature of human anatomy. This project will focus on developing a powered electrical …

Validation And Trend Analysis Of Satellite-Derived Surface Water Temperature Observations Over Adirondack Lakes, Marzi Azarderakhsh, Carolien Mossel, Abdou Rachid Bah, Aisha Malik, Fahmeda Khanom, Jonathan Borrelli, Pete Mcintyre, Hamidreza Norouzi, Kevin Rose

Publications and Research

This study aims to validate and evaluate satellite remote sensing observations from the Landsat series over 135 lakes in the Adirondack State Park, located in upstate New York, and to examine their surface temperature trends over the past 40 years. It utilizes data from the Moderate Resolution Imaging Spectroradiometer (MODIS), along with Landsat 5 and 7. Park-scale results were derived by extracting MODIS surface temperatures within the park boundary, while lake-scale results were estimated using Landsat 5 (1984-2012) and Landsat 7 (1999-2023) observations. In addition, field observations were utilized to perform a comprehensive validation and evaluation of satellite-based surface temperature …

2025 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2025.

Ai-Driven Autonomous Manufacturing: A Novel Taxonomy, Vision-Guided Planning, And Diversity-Aware Active Learning Frameworks, Ibrahim Yousif

Theses and Dissertations

Manufacturers face two opposing challenges: the escalating demand for customized products and the pressure to reduce lead times. Current manufacturing equipment, although reliable, operates at the limits of its technology and lacks adaptability to dynamic environments. This trade-off has been described as the optimal degree of automation, a threshold beyond which further automation incurs more cost than benefit. Smart manufacturing has introduced adaptive, data-driven systems, but many deployments still lack the contextual adaptability and autonomous decision-making required to handle unplanned disruptions. This underscores the need for intelligent, autonomous systems capable of real-time adaptation without costly reconfiguration, minimizing human intervention during …

Multiscale Geometric Analysis In Endovascular Therapies: Ex Vivo, In Silico Approaches, Dima Hussein Ali Bani Hani

Theses and Dissertations

In this work, we address a persistent global challenge of vascular disease, specifically peripheral artery disease (PAD), and arteriovenous fistula (AVF) complications in patients with end-stage kidney disease (ESKD). The important role of vascular geometry in disease intervention plans and the development of endovascular therapeutic strategies (e.g., Drug-coated balloon (DCBs)) is critical in addressing these challenges.

This study evaluates the feasibility of co-delivery of paclitaxel (PTX) and valsartan (VAL) using urea-based coatings in DCB, analyzes the coating morphology and microstructural changes to improve therapeutic results. A computational finite element model was developed to complement experimental work, simulate tissue-coating interactions, compute …

Characterization Of The Fatigue Threshold Behavior Of Uhmwpe, Bethany B. Smith, Anurag Roy, Robert O. Ritchie, Lisa A. Pruitt

Faculty Journal Articles

Ultra-high-molecular-weight-polyethylene (UHMWPE) has been the material of choice for bearings in total joint replacements (TJRs) for decades as a result of its excellent wear resistance, chemical inertness, energetic toughness, low friction, and biocompatibility. Utilization of this polymer in orthopedic devices requires oxidation, wear, and fatigue resistance. Balancing these important properties by tailoring processing techniques and modulating microstructural features has been an ongoing endeavor in the field. Research into the clinical applications of UHMWPE has primarily focused on the challenges of wear and oxidation while studies into the realm of fatigue have been more limited. Literature gaps exist in fully understanding …

Gut Microbiome Dynamics In Heart Failure And The Therapeutic Potential Of Nmeg-Cgrp, Kamryn Michael Gleason

Theses and Dissertations

Heart failure (HF) is increasingly recognized as a multisystem disease often linked to gut dysbiosis; however, its specific effects on gut microbial composition remain poorly understood. This study examined long-term changes in the gut microbiome in a murine HF model induced by transverse aortic constriction (TAC) and evaluated the effects of NMEG-CGRP. TAC mimics pressure overload-induced cardiac dysfunction, replicating key features of HF. CGRP, a neuropeptide with vasodilatory and cardioprotective effects, shows potential as a therapy for HF but is limited by rapid degradation. A stabilized analog, NMEG-CGRP, was used to assess its impact on cardiac and gastrointestinal health. Mice …

Performance Limiting Factors For Silicon Anodes In Lithium-Ion Batteries, Najmaddin Bashirzada

Theses and Dissertations

The rising global demand for high-performance and sustainable energy storage solutions has placed lithium-ion batteries (LIBs) at the forefront of technological progress. However, the energy density limits of traditional graphite anodes require the investigation of alternative materials. Silicon (Si), with its high theoretical capacity, is a promising anode material. Still, its practical use faces major challenges such as volumetric expansion, unstable solid electrolyte interphase (SEI) formation, and low conductivity. This thesis examines the electrochemical behavior and limitations of Si-based electrodes using coin cells and ex-situ three-electrode cells. A comparison of electrochemical testing methods – i.e., Galvanostatic Intermittent Titration Technique (GITT) …

Multi-Layer Decision Making For Long-Term Autonomous Mission Based On Dual Process Theory, Shruti Jadhav

Theses and Dissertations

Unmanned aerial vehicles (UAVs) are increasingly used in precision agriculture, where extended autonomous operation is required for monitoring, intervention, and field management. However, achieving long-term autonomy remains challenging due to battery constraints, environmental uncertainty, and the need to balance exploration with event-driven tasks. To address these challenges, a multi-layer decision-making framework inspired by Dual Process Theory (DPT) is developed. The framework combines reactive return-tobase strategies, exploratory navigation, and directional bias from prior missions, with a conflict-monitoring mechanism that adapts system behavior based on real-time conditions. The approach is implemented in a simulated agricultural grid environment, demonstrating improved adaptability and coverage …

Additively Manufactured Bioinspired Microstructures For Active Surface Modification, Zefu Ren

Doctoral Dissertations and Master's Theses

Advancements in additive manufacturing have facilitated the development of bioinspired microstructures, which hold promise for applications, such as liquid transport, self-cleaning, and anti-icing. However, the controllability of these microstructures remains an area requiring further exploration. This research explores the design, fabrication, and active control of 3D-printed bioinspired microstructures for dynamic wettability modulation. First, the anisotropic scales of butterfly wings were replicated through optimized two-photon polymerization printing strategies, achieving directional droplet motion controlled by structural geometry and arrangement. The reversed wetting trend compared with natural wings revealed key insights into the structure–performance relationship. Next, microstructures were integrated with dielectric elastomer actuators …

Resilience Engineering Via Bifurcation And Ecological Network Analysis: Demonstrated In An Electric Power Case Study, Rogelio Gracia Otalvaro

Doctoral Dissertations and Master's Theses

Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is …

Soft Sensing Of Biological Oxygen Demand In Industrial Wastewater Using Machine Learning Models, Muhammad Hassnain, Sarada M.W. Lee, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Traditional methods for determining biological oxygen demand (BOD) from industrial water resource recovery facilities (WRRFs) are time-consuming and often impractical for real-time process control. This study explores the application of machine learning (ML) and artificial intelligence (AI) models for the prediction of final effluent BOD (F-BOD) based on physicochemical and operational parameters by leveraging nineteen years of historical laboratory and instrumentation data from the WRRF of an essential oil manufacturing plant. The predictions from these models are then used to simulate the process dynamics, assessing the optimal operational boundary conditions for all input parameters at which the target (F-BOD) falls …

Biomass Materials And Their Application In 4d Printing, Zhongda Yang, Jian Li, Yanling Guo, Yangwei Wang, Wen Zhao, Wei Zhao, Yanju Liu, Laichang Zhang

Research outputs 2022 to 2026

Four-dimensional (4D) printing technology is a revolutionary development that produces structures that can adapt in response to external stimuli. However, the responsiveness and printability of smart materials with shape memory properties, which are necessary for 4D printing, remain limited. Biomass materials derived from nature have offered an effective solution due to their various excellent and unique properties. Biomass materials have been abundant in resources and low in carbon content, contributing to the then-current global green energy-saving goals, including carbon peaking and carbon neutrality. This review focused on different sources of biomass materials used in 4D printing, including plant-based, animal-based, and …

Design Of A Robust Adaptive Cascade Fractional-Order Proportional–Integral–Derivative Controller Enhanced By Reinforcement Learning Algorithm For Speed Regulation Of Brushless Dc Motor In Electric Vehicles, Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better …

Economic Viability And Environmental Sustainability: A Cost-Benefit Analysis Of Green Technologies In Mineral Extraction, Tshinkobo Bukasa Orphea, Agyingi Babaca Agyingib, Xiangrui Meng

Journal of Sustainable Mining

This research offers a novel approach to comparing green technologies’ economic profitability and environmental sustainability of their mineral extraction based upon econometric and life cycle assessment methodologies. Quantitative results show attractive results with an NPV of $2,014,001 and an IRR of 17%. In the third year, the project nets at $300,000, or 63%, at a 7% discount rate. However, soil protection remains challenging, but pollution coefficients are improved, as evidenced by environmental impact assessments (EIAs). The findings in the study further underscore how regulatory frameworks and market drivers dictate the use of green technology. The economic, environmental and regulatory costs …

Reengineering Resilience: Bio-Resilience Bonds For Financing Microbial Infrastructure And Climate Equity, Reece Buckley

COP30

This policy proposal introduces Bio-Resilience Bonds (BRBs), a performance-based financial instrument designed to monetise microbial ecosystem services as measurable climate infrastructure. Microbial ecosystems are crucial for climate resilience, yet they are often overlooked in mainstream adaptation f inance frameworks. Their ability to regulate carbon and nitrogen cycles, reduce methane emissions and enhance soil and water stability (Delgado-Baquerizo et al., 2016) makes them essential assets for climate mitigation and adaptation. With global adaptation needs exceeding £2.7 trillion (UNEP, 2024), this oversight indicates a systemic failure to recognise biology as a form of infrastructure. BRBs transform microbial outputs into localised key performance …

Stabilized Weak-Gradient Discontinuous Finite Elements With Optimal Error Estimates For Second-Order Elliptic Pdes, Aymen Laadhari

Mathematical Modelling and Numerical Simulation with Applications

This work introduces an accurate finite element approach employing a new stabilized discrete weak gradient, designed for second-order elliptic problems on arbitrary conforming meshes. We formulate the approach within a discontinuous Galerkin framework and derive a consistent and coercive bilinear form. Appropriate error analysis on a model problem confirms optimal convergence. Building on the core analysis, we extend the method to more challenging settings, including time-dependent heterogeneous scenarios and a biophysically realistic optimal-control model of photobleaching in the budding yeast cell. We further illustrate the versatility of the weak-gradient construction by applying it to an unsteady level-set equation relevant to …

Application Of Dematel Based On Bipolar Neutrosophic Sets For Sustainable Agriculture Practices, Lazim Abdullah, Nor Liyana Amalini Binti Mohd Kamal

Neutrosophic Systems with Applications

The development of natural capital is a fundamental objective within sustainable agricultural systems, where the optimization of both crop and livestock production is vital to addressing global food demands. Despite this imperative, major agricultural sectors such as paddy and rubber production, often fall short of satisfying consumption needs. This study aims to identify and prioritize the most influential criteria for sustainable agriculture using the Bipolar Neutrosophic Set-based Decision-Making Trial and Evaluation Laboratory (BNS-DEMATEL) method. Expert evaluations were elicited from five agricultural specialists using linguistic assessments to analyze the performance and interdependencies among sustainability criteria. Computational analyses were conducted using MATLAB …

Drawing On Uncertainty Methodologies Of Neutrosophic Hypersoft Sets In Cognitive Computing-Driven Healthcare Systems, Mona Mohamed, Nurhan Alaa

Neutrosophic Systems with Applications

A new paradigm called cognitive computing simulates human reasoning and decision-making through integrating advanced techniques such as artificial intelligence (AI) and natural language processing (NLP). Cognitive computing systems, in contrast to traditional systems, can handle both structured and unstructured data, adjust to new information, and offer context-sensitive insights. This study examines how cognitive computing improves decision-making, personalization, and human-machine collaboration in various fields. Cognitive computing in the healthcare sector processes clinical notes, imaging data, and electronic health records to help physicians with diagnosis, treatment planning, and patient engagement. This study examines key applications, including their role in diagnostic support, where …

Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …

Multi Features Data Clustering Using Novel Statistical Method With Application To Color Images, Husham Y. A. Alameen, Ali Karah Bash

Iraqi Journal for Computer Science and Mathematics

The efficiency and performance of the color image clustering algorithms are determined by various factors, including accuracy, data size, speed, and reliability (the absence of randomness in the results). Some applications, like microscopes analyzing images of biological objects or telescopes observing planetary motion prioritize accuracy over execution time. In contrast, surveillance cameras and moving object tracking prioritize speed and reliability over accuracy. This study introduces a novel algorithm that balances these four factors by clustering data with multiple features linked through specific relationships. The proposed algorithm has been practically applied to RGB color images. Traditional clustering methods, such as K-means, …

09.29.2025 Ored Connect, Liz Williamson

ORED Newsletter

• Contest for Biosafety and Biosecurity Month
• ORED Website Walk Through training
• MIG Applications
• Field Fest

Plasmonic Nanoparticle Integration On Fiber Optic: Role Of Organic Linker And Functionalization Length In Refractive Index Sensing, Shela Ilmiyah, Mohamad Syahadi, Muflikhah Muflikhah, Yuwana Pradana, Zainul Arifin Iman Supardi, Iwan Darmadi, Lia Aprilia

Makara Journal of Science

Nanoplasmonic fiber optic sensors leverage the optical fiber’s inherent compactness and surface sensitivity via evanescent-field interactions with the localized surface plasmon resonance of the metallic nanoparticles. One of the popular approaches is self-assembly owing to its relatively low cost. Despite its low cost, the self-assembly technique has low reproducibility and exhibits low sensitivity because of low nanoplasmonic coverage on the fiber optic surface. In this study, several fabrication techniques were explored to assess the refractive index sensitivity by comparing three common organosilanes, i.e., (3-Aminopropyl)triethoxysilane (APTES), (3-Aminopropyl)trimethoxysilane, and (3-Mercaptopropyl)trimethoxysilane. Comparative analyses assessed the anchoring efficiency of the three silanes, sensor reproducibility, …

Warpage-Resistant, Under-Extrusion-Free, High-Surface-Quality Additive Manufacturing Process For Polyethylene-Based Composite Radiation Shielding Material, Duo Xu, Volodymyr Korolovych, You Lyu, Jacqueline Aslarus, Domingo R. Flores-Hernandez, Simo Pajovic, William T. Heller, Lembit Sihver, Svetlana V. Boriskina

Informatics and Engineering Systems Faculty Publications

Polyethylene (PE) is one of the best shielding materials for primary space radiation due to its high hydrogen content. For effective secondary neutron shielding, boron-rich fillers are incorporated to enhance performance. The semicrystalline nature and high thermal expansion coefficient of PE impede its adoption for in situ additive manufacture in space via the fused deposition modeling (FDM) 3D printing. We developed an optimized PE blend to mitigate the effects of under-extrusion and warpage. Guided by studies on extrusion and warpage, we developed an optimal set of printing parameters for the proposed PE blend. The optimum PE blend─both in its pure …

A Novel Scaffold-Based Model For Stage Ii Pressure Injury Simulation: Protocol Development And Future Directions In Tissue Engineering, Skyler B. Wrubleski

Undergraduate Research and Scholarship Symposium

This study developed a 3D tissue-engineered scaffold to mimic human sacral skin for modeling pressure injuries. The scaffold consisted of a composite hydrogel of sodium alginate, gelatin, and tannic acid, crosslinked with calcium chloride. Swelling tests showed moderate hydration capacity, while wound mimic formation using a custom 3D-printed apparatus produced consistent morphology and dimensions comparable to human dermal tissue. The scaffold maintained structural integrity under prolonged compressive forces, demonstrating viability for pressure injury studies. Future work will incorporate a bilayer model with keratinocytes and fibroblasts and introduce elastin to enhance elasticity and mechanical properties. This approach aims to improve in …

Antibacterial Activity Of Zno/Geopolymer Composite Granules And Their Potential Application In Water Treatment, Ziyan Tirta Maulitia, Putri Nur Angelina, Pipit Erlita Sari, Sri Sugiarti, Irma Isnafia Arief, Zaenal Abidin

Makara Journal of Science

Water pollution has become a major global concern owing to its complexity and widespread impact. Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) are the most common bacterial contaminants in water sources, which pose significant threats to public health. To mitigate water contamination by these pathogenic microorganisms, developing and implementing effective water treatment technologies are essential. A promising approach involves using antibacterial water filtration systems. Herein, zinc oxide (ZnO) nanoparticles were synthesized via hydrothermal and precipitation methods as antibacterial agents for water treatment. These preparation methods required considerably low synthesis time. A capping agent was …