Digital Commons Network^™

The Application Of Statistical Modeling To Identify Genetic Associations With Mild Traumatic Brain Injury Outcomes, Caroline Schott

Masters Theses

"Traumatic brain injury (TBI) is a growing health concern, with millions of TBI diagnoses in the United States each year. The vast majority of TBI diagnoses are mild traumatic brain injuries (mTBI), which can be challenging to manage due to variation in symptoms and outcomes. Most individuals with mTBI successfully recover quickly, but a small subset has a delayed recovery. Although the factors that contribute to this variation in recovery are not clearly understood, it is possible that genetic differences may play a role. Very few studies have investigated the association between single nucleotide polymorphisms (SNPs) with mTBI outcomes and …

Sentiment Strength And Topic Recognition In Sentiment Analysis, Esi A.R. Adeborna

Masters Theses

"Current sentiment analysis methods focus on determining the sentiment polarities (negative, neutral or positive) in users’ sentiments. However, in order to correctly classify users’ sentiments into their right polarities, the strengths of these sentiments must be considered. In addition to classifying users’ sentiments into their correct polarities, it is important to determine the sources and topics under which users’ sentiments fall. Sentiment strength helps as to understand the levels of customer satisfaction toward products and services. Sentiment topics on the other hand, helps to determine the specific product/service areas associated with user sentiments. This paper proposes two sentiment analysis approaches. …

A Hybrid Framework For Critical Infrastructures Interdependency Modeling, Simulation, And Analysis, David Corder Hinton

Masters Theses

"Flow system models, also known as flow network models, encompass vastly complex, ever-expanding problem sets which comprise the foundation for maintenance, operation, and improvement of critical infrastructures around the world. The stable operation of these vast critical infrastructures is fundamental to the continued advancement of modern society. These infrastructures are tightly interdependent and vulnerable to interruption by both natural circumstance and malicious targeting. This necessitates representation of such critical infrastructures and their multi-domain interdependencies in defense focused constructive and virtual simulation environments as a matter of national interest and security. By breadth exploration of the problem space, this work body …

Flight Software Development For A University-Class Microsatellite Mission, Yumeka Nagano

Masters Theses

"The purpose of this research is to present the software development process used by the Command and Data Handling (C&DH) subsystem as a part of a microsatellite mission underway at the Missouri University of Science and Technology. The mission's objective is to evaluate the performance of an innovative cold gas propulsion system for use in formation flying applications and the incorporation of a stereoscopic imaging sensor used to measure the satellite pair's relative position and velocity vectors in real time. C&DH uses a Raspberry Pi 3 Model B as the flight computer running a Linux environment, and the flight code …

Tropospheric Correction For Insar Using Machine Learning, Ngo Hi Kenny Yue

Masters Theses

"Interferometric Synthetic Aperture Radar (InSAR) is a popular technique for studying Earth's surface deformation caused by phenomena like earthquakes and subsidence. However, its accuracy is limited by tropospheric delays caused by water vapor in the atmosphere. This limitation can be overcome by using methods that correct for tropospheric noise, such as statistical, empirical, and predictive approaches. This study explores the potential of using machine learning algorithms to predict the zenith total delay caused by tropospheric effects in InSAR measurements. The study employs two different machine learning algorithms, random forest and neural networks, to learn the relationship between numerical weather prediction …

Computer Vision In Adverse Conditions: Small Objects, Low-Resoltuion Images, And Edge Deployment, Raja Sunkara

Masters Theses

"Computer vision based on deep learning is an essential field that plays a significant role in object detection, image classification, semantic segmentation, instance segmentation, and other applications. However, these models face significant challenges in adverse conditions, such as small objects, low-resolution images, and edge deployment. These challenges limit the accuracy and efficiency of computer vision algorithms, making it difficult to obtain reliable results.

The primary objective of this thesis is to assess the performance of deep learning- based computer vision models in challenging conditions and provide viable solutions to overcome the obstacles. The study will specifically address three key challenges, …

Meta-Analysis Of Mesenchymal Stem Cell Gene Expression Data From Obese And Non-Obese Patients, Dakota William Shields

Masters Theses

"The prevalence of gene expression microarray datasets in public repositories gives opportunity to analyze biologically interesting datasets without running the laboratory aspect in house. Such experimentation is expensive in terms of finances, time, and expertise, which often results in low numbers of replicates. Meta-analysis techniques attempt to overcome issues due to few biological or technical replicates by combining separate experiments together to increase statistical power. Proper statistical considerations help to offset issues like simultaneous testing of thousands of genes, unintended hybridization, and other noises.

Microarrays contain light intensities from tens of thousands of hybridized probes giving a measure of gene …

Investigation Of Defect Production And Displacement Energies In Wurtzite Aluminum Nitride, Sean Anderson

Masters Theses

"Aluminum Nitride is an active element of sensors that monitor the performance and well-being of the nuclear reactors due to its piezoelectric properties. Yet, the variations of its properties under irradiation are largely unexplored. We report the results of the molecular dynamics simulations of the structural changes in AlN under irradiation via the knock-on atom technique. By creating and evolving the irradiation cascades due to energetic particle interaction with the atom of the crystalline lattice we determine the rate of the defect production as a function of the deposited energy. Further, we determine a displacement energy, a key characteristic that …

Nanobubble-Induced Aggregation Of Ultrafine Particles: A Molecular Dynamics Study, Eric Bird, Zhi Liang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Nanobubble-induced aggregation (NBIA) of fine and ultrafine particles in liquid is a promising method for enhancing floatation rates in mineral processing, cleaning contaminants from water, and reviving marine ecosystems. Although the current experimental techniques can measure the nanobubble capillary force between two surfaces with controlled approach speed, they are not capable of imaging NBIA dynamics of fine/ultrafine particles by real-time observation with nanoscale spatial resolution. In this work, we use molecular dynamics (MD) simulations to study dynamics of NBIA of Ag particles in a Lennard-Jones fluid system. The molecular-level modeling allows us to study microscopic details of NBIA dynamics that …

Application Of A Variable Path Length Repetitive Process Control For Direct Energy Deposition Of Thin-Walled Structures, Elias B. Snider, Douglas A. Bristow

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Direct Energy Deposition (DED) Additive Manufacturing is Well Suited to Fabricating Large Thin-Walled Metal Structures Such as Rocket Nozzles but Suffers from Layer-To-Layer Defect Propagation. Propagating Defects May Exhibit as Slumping or a Ripple in Bead Geometry. Recent Works Have Used Repetitive Process Control (RPC) Methods for Additive Manufacturing to Stabilize the Layer-Wise Defect Propagation, But These Methods Require Repetition of the Same Path. However, Typical Thin-Wall DED Applications, Sometimes Referred to as Vase Structures, Have Changing Paths with Each Layer Such as Expanding or Contracting Diameters and Changing Profiles. This Paper Presents an Extension to Optimal RPC that Uses …

Final-Stage Densification Kinetics Of Direct Current–Sintered Zrb2, Austin D. Stanfield, Steven M. Smith, Suzana Filipović, Nina Obradović, Vladimir Buljak, Gregory E. Hilmas, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

Final-stage sintering was analyzed for nominally phase pure zirconium diboride synthesized by borothermal reduction of high-purity ZrO₂. Analysis was conducted on ZrB₂ ceramics with relative densities greater than 90% using the Nabarro–Herring stress–directed vacancy diffusion model. Temperatures of 1900°C or above and an applied uniaxial pressure of 50 MPa were required to fully densify ZrB₂ ceramics by direct current sintering. Ram travel data were collected and used to determine the relative density of the specimens during sintering. Specimens sintered between 1900 and 2100°C achieved relative densities greater than 97%, whereas specimens sintered below 1900°C failed to …

The Effect Of Crystal Anisotropy On Fracture Toughness And Strength Of Zrb2 Microcantilevers, Tamás Csanádi, Ahmad Azizpour, Marek Vojtko, William Fahrenholtz

Materials Science and Engineering Faculty Research & Creative Works

The influence of crystal anisotropy on the micromechanical properties of ceramic grains plays an important role in the design of the macromechanical performance of bulk polycrystalline samples. To this end, the effect of crystal orientation on fracture toughness and strength was investigated by microcantilever bending experiments combined with finite element method (FEM) simulations in grains of a polycrystalline ZrB2 sample. The sample was prepared by hot pressing and the crystal orientations were determined by electron backscatter diffraction after careful surface preparation. The bending tests were carried out on notched and unnotched microcantilevers cut from specific grains along the prismatic (⊥ …

Real-Time Air Gap And Thickness Measurement Of Continuous Caster Mold Flux By Extrinsic Fabry-Perot Interferometer, Abhishek Prakash Hungund, Hanok Tekle, Bohong Zhang, Ronald J. O'Malley, Jeffrey D. Smith, Rex E. Gerald, Jie Huang

Materials Science and Engineering Faculty Research & Creative Works

Mold Flux plays a critical role in continuous casting of steel. Along with many other functions, the mold flux in the gap between the solidifying steel shell and the mold serves as a medium for controlling heat transfer and as a barrier to prevent shell sticking to the mold. This manuscript introduces a novel method of monitoring the structural features of a mold flux film in real-time in a simulated mold gap. A 3-part stainless-steel mold was designed with a 2 mm, 4 mm and, 6 mm step profile to contain mold flux films of varying thickness. An Extrinsic Fabry-Perot …

System Level Pdn Impedance Optimization Utilizing The Zeros Of The Decoupling Capacitors, Yifan Ding, Shuang Liang, Francesco De Paulis, Matteo Cocchini, Samuel Connor, Matthew Doyle, Albert E. Ruehli, Chulsoon Hwang, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

System-Level Power Distribution Network (PDN) Impedance Optimization Utilizing the Zeros of the Decoupling Capacitors (Decaps) is Discussed in This Paper. an Example of a Practical PDN Application is Proposed to Validate the Poles and Zeros Algorithm (P&Z) Presented. the System-Level PDN is with the Printed Circuit Board (PCB), Package (PKG), and Chip, as Well as the Low-Frequency Decaps on the PCB and the On-PKG Decoupling Capacitors. the PDN Optimization Results Are Compared with Those from the Genetic Algorithm (GA) to Show the Reasonableness and Validity of the P&Z Algorithm.

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …

Multi-Layered Energy Management Framework For Extreme Fast Charging Stations Considering Demand Charges, Battery Degradation, And Forecast Uncertainties, Waqas Ur Rehman, Jonathan W. Kimball, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

To achieve a cost-effective and expeditious charging experience for extreme fast charging station (XFCS) owners and electric vehicle (EV) users, the optimal operation of XFCS is crucial. It is however challenging to simultaneously manage the profit from energy arbitrage, the cost of demand charges, and the degradation of a battery energy storage system (BESS) under uncertainties. This paper, therefore, proposes a multi-layered multi-time scale energy flow management framework for an XFCS by considering long- and short-term forecast uncertainties, monthly demand charges reduction, and BESS life degradation. In the proposed approach, an upper scheduling layer (USL) ensures the overall operation economy …

Assessment Of The Dimensionless Groups-Based Scale-Up Of Gas–Solid Fluidized Beds, Faraj M. Zaid, Haider Al-Rubaye, Thaar M. Aljuwaya, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

The most common scale-up approach for gas–solids fluidized beds is based on matching the governing dimensionless parameters. in the literature, this approach has been validated only by means of measuring global parameters between different sizes of fluidized beds. However, such global measurements are not sufficient to depict all the interplaying hydrodynamic phenomena and hence verify the scale-up relationships. Therefore, to assess this approach, an advanced gas–solids optical probe and pressure transducer measurement techniques have been applied to quantify local hydrodynamic parameters in two different sized fluidized beds. Four different sets of experimental conditions were designed and conducted to examine the …

Blockchain-Enabled Authenticated Key Agreement Scheme For Mobile Vehicles-Assisted Precision Agricultural Iot Networks, Anusha Vangala, Ashok Kumar Das, Ankush Mitra, Sajal K. Das, Youngho Park

Computer Science Faculty Research & Creative Works

Precision Farming Has a Positive Potential in the Agricultural Industry Regarding Water Conservation, Increased Productivity, Better Development of Rural Areas, and Increased Income. Blockchain Technology is a Better Alternative for Storing and Sharing Farm Data as It is Reliable, Transparent, Immutable, and Decentralized. Remote Monitoring of an Agricultural Field Requires Security Systems to Ensure that Any Sensitive Information is Exchanged Only among Authenticated Entities in the Network. to This End, We Design an Efficient Blockchain-Enabled Authenticated Key Agreement Scheme for Mobile Vehicles-Assisted Precision Agricultural Internet of Things (IoT) Networks Called AgroMobiBlock. the Limited Existing Work on Authentication in Agricultural Networks …

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

Lasa: Location-Aware Scheduling Algorithm In Industrial Iot Networks With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The Synchronized Single-hop Multiple Gateway (SHMG) is a framework recently proposed to support mobility into 6TiSCH, the standard network architecture defined for Industrial Internet of Things (IIoT) deployments. SHMG supports industrial applications with stringent requirements by adopting the Shared-Downstream Dedicated-Upstream (SD-DU) scheduling policy, which allocates to Mobile Nodes (MNs) a set of dedicated transmission opportunities for uplink data. Such allocation is performed on all the Border Routers (BRs) of the network without considering the location of MNs. Transmission opportunities are reserved also in BRs far from the current location of the MN, resulting in a waste of resources that limits …

Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under …

One-Shot Federated Learning For Leo Constellations That Reduces Convergence Time From Days To 90 Minutes, Mohamed Elmahallawy, Tie (Tony) Tie Luo

Computer Science Faculty Research & Creative Works

A Low Earth orbit (LEO) satellite constellation consists of a large number of small satellites traveling in space with high mobility and collecting vast amounts of mobility data such as cloud movement for weather forecast, large herds of animals migrating across geo-regions, spreading of forest fires, and aircraft tracking. Machine learning can be utilized to analyze these mobility data to address global challenges, and Federated Learning (FL) is a promising approach because it eliminates the need for transmitting raw data and hence is both bandwidth and privacy friendly. However, FL requires many communication rounds between clients (satellites) and the parameter …

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

Key Factors Affecting Labor Productivity In Offsite Construction Projects, Rayan H. Assaad, Islam H. El-Adaway, Makarand Hastak, Kim Lascola Needy

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Offsite construction has been shown to possess many advantages and benefits in dealing with the construction industry's challenges, which include poor labor productivity. Nevertheless, all previous productivity-related research studies have focused mainly on factors impacting labor productivity in traditional stick-built construction methods. This paper filled this knowledge gap by following a multistep interrelated research methodology. First, a research team of 19 construction professionals and academics developed and distributed an industry survey to (1) quantify the likelihood of occurrence and relative impact of risk factors that adversely impact labor productivity in offsite construction projects; (2) identify and prioritize key risk factors …

Extending The Nno Ballistic Limit Equation To Foam-Filled Dual-Wall Systems, William P. Schonberg

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

A key component in the quantitative assessment of the risk posed to spacecraft by the micrometeoroid and orbital debris (MMOD) environment is frequently referred to as a ballistic limit equation (BLE). A frequently used BLE for dual-wall configurations (which are commonly used on spacecraft to protect them against the MMOD environment) is the New Non-Optimum, or "NNO", BLE. In design applications where a BLE is needed for a new structural system that has not yet been tested, but resembles to a fair degree a dual-wall system, it is common practice to equivalence the materials, thicknesses, etc., of the new system …

Epitaxial Electrodeposition Of Ordered Inorganic Materials, Jay A. Switzer, Avishek Banik

Chemistry Faculty Research & Creative Works

Conspectus The quality of technological materials generally improves as the crystallographic order is increased. This is particularly true in semiconductor materials, as evidenced by the huge impact that bulk single crystals of silicon have had on electronics. Another approach to producing highly ordered materials is the epitaxial growth of crystals on a single-crystal surface that determines their orientation. Epitaxy can be used to produce films and nanostructures of materials with a level of perfection that approaches that of single crystals. It may be used to produce materials that cannot be grown as large single crystals due to either economic or …

Erratum: New Estimates Of The Magnitude Of The Sea-Level Jump During The 8.2 Ka Event, (Geology (2022), 50(1), (86–90), (10.1130/G49296.1)), Jonathan Obrist-Farner, Mark Brenner, Jeffery R. Stone, Marta Wojewódka-Przybył, Thorsten Bauersachs, Andreas Eckert, Marek Locmelis, Jason H. Curtis, Susan R.H. Zimmerman, Alex Correa-Metrio, Lorenz Schwark, Edward Duarte, Antje Schwalb, Etienne Niewerth, Paula Gabriela Echeverría-Galindo, Liseth Pérez

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

No abstract provided.

Efficient And Accurate Coded Target Decoding For 3d Reconstruction Of Soil Specimens In Triaxial Test, Xiaolong Xia, Xiong Zhang

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Coded Targets Have Been Widely Used for Solving the Corresponding Problem in Photogrammetry for High-Accuracy Three-Dimensional Measurements. Accurate and Efficient Recognition and Identification of Coded Targets Are of Great Importance in Coded Target-Based Photogrammetry. in This Paper, an Efficient and Accurate Method for Coded Target Decoding Was Developed. in This Method, Blob Analysis Was Performed to Recognize the Coded Targets. Then, Image Processing, the RANSAC Algorithm, and the Interpolation Technique Were Applied Respectively to Decode the Coded Targets, Identify Falsely Identified Coded Targets, and Recover Missing Coded Targets. Interpolation Was Also Performed on the Membrane, Which Can Significantly Increase the …

Assessment Of The Dimensionless Groups-Based Scale-Up Of Gas–Solid Fluidized Beds, Faraj M. Zaid, Haider Al-Rubaye, Thaar M. Aljuwaya, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

The most common scale-up approach for gas–solids fluidized beds is based on matching the governing dimensionless parameters. In the literature, this approach has been validated only by means of measuring global parameters between different sizes of fluidized beds. However, such global measurements are not sufficient to depict all the interplaying hydrodynamic phenomena and hence verify the scale-up relationships. Therefore, to assess this approach, an advanced gas–solids optical probe and pressure transducer measurement techniques have been applied to quantify local hydrodynamic parameters in two different sized fluidized beds. Four different sets of experimental conditions were designed and conducted to examine the …