Digital Commons Network^™

Development Of Ice Releasing Epoxy-Siloxane Marine Coatings, Morgen Hagerott

Coatings and Polymeric Materials

In this study, epoxy-siloxane coatings were prepared and modified using various polysiloxane oil additives with the goal of developing an ice releasing epoxy-siloxane coating. Contact angle, surface energy, and ice adhesion tests were conducted to study the effect each oil additive had on the surface properties of the coatings. Additionally, antifouling and fouling release properties were assessed using two micro-organisms, Cellulophaga lytica and Navicula incerta, and one macro-organism, Amphibalanus Amphitrite (barnacles). The goal of which was to compare the ice and fouling releasing properties of the coatings to see if any correlations could be made between the two. One of …

Sustainable Thermosetting Polymers: Biobased Epoxy Resin Systems And Aqueous Non-Isocyanate Polyurethanes, Raul Setien

Coatings and Polymeric Materials

In recent years, there has been a desire to seek alternatives to petrochemically derived materials. These alternatives can be derived from renewable sources such as vegetable oils. However, methods for achieving sustainability are not limited to only resourcing materials from bio-based sources but can also be achieved by utilizing safer synthesis techniques, reducing cure times, and eliminating volatile organic content in thermoset coating formulations. This dissertation sets out to achieve a goal the sustainability across different fields. Epoxidized sucrose soyate is a versatile bio-based materials that is typically synthesized utilizing hydrogen peroxide and acetic acid. A proposed safer alternative utilizes …

Surface Modifying Amphiphilic Additives For The Improvement Of Fouling-Release Properties Of Siloxane-Polyurethane Coatings, Jackson Benda

Coatings and Polymeric Materials

Since mankind has navigated the world’s oceans, marine biofouling has been a persistent problem with several negative economic and environmental consequences. In modern times, the buildup of biofouling causes a significant reduction in vessel speed, leading to increased power consumption, higher costs of operation, and an increase in greenhouse gas emissions. Two of the most widely used protection strategies to combat this highly complex and dynamic phenomenon include the application of biocide containing anti-fouling (AF), or more environmentally friendly non-toxic fouling-release (FR) coatings. Traditional FR coatings utilize low surface energy components such as polydimethylsiloxane (PDMS), but often suffer mechanical failure …

Modification Of Diglycidyl Ether Of Bisphenol-A Epoxy Primer For Multi-Substrate Application, Gwendorlene Chea

Coatings and Polymeric Materials

Traditional vehicles and aircraft have consisted primarily of steel and aluminum alloys which due to their density, has resulted in various logistic problems including transportation, maneuverability, fuel efficiency. These hindrances have led to a major increase in the incorporation of composites material into this equipment. As the use of these composite increases, multi-substrate coating systems that can provide adequate corrosion protection to metal components, as well as superior adhesion and flexibility to the composites, are needed. The goal of this work is the modification of diglycidyl ether of bisphenol-A epoxy coating systems for improved flexibility and adhesion for the development …

Invited Perspective - Engaging Aspirations To Nurture Communities, Kentaro Toyama

Subsistence Marketplaces

For subsistence communities, the question is whether aspirations can be applied to motivate behavior that is, on the one hand consistent with people’s aspirations, but which might otherwise be difficult to elicit. Could poorer households be encouraged to save, to spend more on their children’s education, or to act against unhealthy social norms? A couple of examples suggest this is not only possible, but highly successful in contexts where other appeals fail.

Creation And Implementation Of The Innovation-Based Learning Framework: A Learning Analytics Approach, Lauren Singelmann

Electrical and Computer Engineering

To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where students learn fundamental engineering concepts and apply them to an innovation project with the goal of producing value outsidethe classroom. The model has been fairly successful, but questions still remain about how to best support students and instructors in open-ended innovation spaces. To answer these questions, learning analytics and educational data mining (LA/EDM) …

The Genomic Alteration Landscape Of Pancreatic Duct Adenocarcinoma, David Adeleke

Genomics and Bioinformatics

By 2030, PDAC is projected to be the second leading cause of cancer-related death in the US. PDAC is a multifactorial disease driven by genomic alterations. Understanding this alteration landscape will both refine the knowledge of disease etiology and enhance disease stratifications, drug design, and targeted treatment. This study aimed to identify novel genetic alterations that are associated with pancreatic cancer biology and prognosis to further refine the genetic focus for therapy development, disease subtyping, and risk assessment in PDAC. To this end, SNV, CNV, and clinical data for PDAC patients were downloaded from the ICGC data portal and analyzed …

Introduction To The Data Mining Techniques In Cybersecurity, Israa Ezzat Salem, Maad Mijwil, Alaa Wagih Abdulqader, Marwa M. Ismaeel, Marwa M. Ismaeel, Anas M. Zein Alaabdin

Mesopotamian Journal of CyberSecurity

As a result of the evolution of the Internet and the massive amount of data that is transmitted every second, as well as the methods for protecting and preserving it and distinguishing those who are authorized to view it, the role of cyber security has evolved to provide the best protection for information over the network. In this paper, the researcher discusses the role of data mining methods in cyber security. Data mining has several uses in security, including national security (for example, surveillance) and cyber security (e.g., virus detection). Attacks against buildings and the destruction of key infrastructure, such …

Efficient Coherent Trust Based Location Aware Clustering For Communication In Mobile Ad Hoc Networks, Ali Alsalamy, Nejood F. Abdulsattar, Dheyaa Abdulameer Mohammed

Mesopotamian Journal of CyberSecurity

Mobile Ad Hoc Networks (MANETs) are the emerging technology for communication and it is used in maximum of the application hence it is noticed by the academic and industrial sectors. Due to the mobile nature of the nodes in MANETs it is complicated to transfer the ecological information. During communication in maximum of the cases link failures and high energy consumption occurred. In order to overcome this issues clustering techniques are introduced in earlier days which is the group of nodes into cluster and each cluster consists of a cluster head (CH) and the other nodes which are present in …

Sql Injection Attack Detection Using Machine Learning Algorithm, Taseer Muhammad, Hamayoon Ghafory

Mesopotamian Journal of CyberSecurity

The structured query language injection attack (SQLIA) is a well-known cyberattack targeting vulnerabilities in web-based applications; it is used to carry out illegal information control language, bypass confirmation measures, and get access to restricted data. There was some consideration given to existing systematic reviews in the literature. Contemporary systematic reviews frequently incorporate both older and more contemporary works in the topic. Therefore, we restricted ourselves to recently published works. For the current study, I used information from 2012 through 2020. Encryption, XML, design coordination, parsing, and machine learning are just some of the methods and systems that can be used …

Using Neural Networks To Model Complex Mathematical Functions, Abdelfatah Kouidere, Mondher Damak

Mesopotamian Journal of Big Data

Accurately modeling highly complex and irregular mathematical functions like fractals, chaos, and turbulence poses longstanding challenges. Traditional physics-based approaches often fail due to analytic intractability and extreme sensitivity. In this work, we pioneer the usage of long short-term memory (LSTM) recurrent neural networks for learning representations of such complex mathematical functions. We train custom-designed deep LSTM architectures on functions including the Lorenz attractor, Mandelbrot set, and Mackey-Glass delay differential equation. The networks achieve excellent quantitative performance across critical evaluation metrics like mean squared error and R-squared. Qualitative visualizations also demonstrate highly precise function replication and generalization. Comparisons to polynomial regression …

Individual Differences And Transfer Of Visual Anticipation In Expert Female Field Hockey Goalkeepers, Khaya Morris-Binelli, Sean Muller, Fleur E.C.A. Van Rens, Allen G. Harbaugh, Simon M. Rosalie

Physiotherapy Papers and Journal Articles

Significance: Visual anticipation is vital for performance in several domains such as driving, military, and high-speed interceptive sports, as it enables performers to handle severe time constraints. There has been little investigation into individual differences of anticipation skill transfer within a domain, which can guide training of anticipation to improve performance.

Purpose: The purpose of this study was to investigate individual differences in the transfer of anticipation within a domain, specifically anticipation of the field hockey penalty corner drag-flick across different opponents.

Methods: Eight female Australian international and national field hockey goalkeepers participated. Goalkeepers completed female and male opponent penalty …

Comparison Of Non-Learned And Learned Molecule Representations For Catalyst Discovery, Qianqian Yao

Computer Science

Catalyst discovery is one very important task in storing renewable energy to address climate change and energy scarcity globally. Catalyst candidates can be represented by molecular descriptors and also can be modeled by graphs. Properties of catalyst candidates can be calculated by Density Functional Theory (DFT). Machine learning algorithms are applied to predict properties of catalyst candidates because DFT is computationally expensive. However, machine learning algorithms cannot operate over some standard molecular formats. Therefore, to represent molecules in the format that is required by machine learning algorithms is the primary task to tackle. Thus, this paper compared non-learned and learned …

Contributing Factors Promoting Success For Females In Computing: A Comparative Study, Bethlehem Gronneberg

Computer Science

Despite the growing global demand for Computer Science (CS) professionals, their high earning potential, and diversified career paths (U.S. BLS 2021, UNESCO 2017), a critical gap exists between enrollment and graduation rates among female students in computing fields across the world (Raigoza 2017, Hailu 2018, UNESCO 2017, Bennedsen and Caspersen 2007). The largest dropout point occurs during the first two years of their CS studies (Giannakos, et al., 2017). The purpose of this parallelly convergent mixed-methods research was to comparatively investigate, describe and analyze factors correlated to the experiences and perceptions of female undergraduates as it relates to their persistence …

Sentiment Analysis Of Covid-19 Vaccination Impact On Twitter Tweets Using Nlp Supervised Learning And Rnn Classification Comparison, Tanvir Ahmed

Computer Science

Twitter provides a platform for exchanging information and opinions on global concerns like the COVID-19 epidemic. During the COVID-19 pandemic, we used a collection of around 16,180 tweets to derive inferences regarding public views toward the vaccine impact once immunizations became widely available to the community. We use natural language processing and sentiment analysis techniques to uncover information regarding the public's perception of the COVID-19 vaccine. Our findings demonstrate that people are more pleased about taking COVID-19 shots than they are about some of the vaccines' side effects. We also look at people's reactions to COVID-19 safety measures after they …

Software Engineering Methodologies In Developing A Railway Condition Monitoring System, Bhavana Bhardwaj

Computer Science

With the continuous growth of rail track geometry irregularities due to aging, environmental factors, and wheel loads, rail track requires frequent maintenance. Railroads often rely on the precise and correct localization and identification of track irregularities that significantly destroy infrastructure and create life-threatening environments. Therefore, monitoring the conditions of the railroad tracks is vitally essential for ensuring safety, reliability, and cost-efficiency of operations. Consequently, agencies inspect all tracks twice a week per federal track safety regulations. However, their existing methods of track inspection are expensive, slow, require track closure, and pose a high risk to workers. The technical constraints of …

Evaluating The Effectiveness Of Music Recommendations Using Cosine Similarities Of Various Combined Feature Sets Consisting Of Metadata And User Generated Tags, Shubhit Kumar

Computer Science

This paper aims to compare and evaluate the methods for recommending songs using metadata, user generated tags, metadata and tags, and metadata and top five tags. Recommendations are calculated using cosine similarities with relevant exceptions (artist filtering, tag filtering, year to decade conversion) investigated.

Tennessee Higher Education Fact Book 2020-2021, Tennessee. Higher Education Commission.

Higher Education Fact Book

No abstract provided.

Articulation And Transfer In Tennessee Higher Education 2021 Report, Tennessee. Higher Education Commission.

Academic Programs and Workforce Reports

No abstract provided.

Academic Supply And Occupational Demand Report 2021, Tennessee. Higher Education Commission.

Academic Programs and Workforce Reports

No abstract provided.

Beginnings, Elizabeth Becker

Dance (MFA) Theses

Researcher Elizabeth Becker uses personal experiences of pregnancy alongside scholarly research on the developmental movement patterns of the human embryo, fetus, and newborn’s first year of life to explore the multiplicity of these movement patterns within and outside the womb. Becker explores the relationship between the fertilization, germinal, embryonic, and fetal stages in relation to a newborn and its mother. These movement patterns within the beginning stages of life are valuable to research because they simulate neurodevelopmental patterns, which help wire the central nervous system in early childhood. These movements also help lay the foundation for sensory-motor development and life-long …

Inverse Boundary Value Problems For Polyharmonic Operators With Non-Smooth Coefficients, Landon Gauthier

Theses and Dissertations--Mathematics

We consider inverse boundary problems for polyharmonic operators and in particular, the problem of recovering the coefficients of terms up to order one. The main interest of our result is that it further relaxes the regularity required to establish uniqueness. The proof relies on an averaging technique introduced by Haberman and Tataru for the study of an inverse boundary value problem for a second order operator.

An Integrated Computational Pipeline To Construct Patient-Specific Cancer Models, Daniel Plaugher

Theses and Dissertations--Mathematics

Precision oncology largely involves tumor genomics to guide therapy protocols. Yet, it is well known that many commonly mutated genes cannot be easily targeted. Even when genes can be targeted, resistance to therapy is quite common. A rising field with promising results is that of mathematical biology, where in silico models are often used for the discovery of general principles and novel hypotheses that can guide the development of new treatments. A major goal is that eventually in silico models will accurately predict clinically relevant endpoints and find optimal control interventions to stop (or reverse) disease progression. Thus, it is …

Cover And Contents

Turkish Journal of Physics

No abstract provided.

On An Econophysics Model, Miron Kaufman

Turkish Journal of Physics

Krugman has proposed a continuous in time and space model for the emergence of polycentric urban areas in the regional space. By using a discrete version of this model on spatial networks, we predicted the distribution of jobs among the different localities inside a couple of economic regions in Ohio and Texas. The time evolution of the distribution of jobs is governed by a market potential. Employment gradually moves towards locations considered relatively attractive if their market potential is above the spatial average. Similarly, jobs move away from locations with below-average market potential. First, I will show that the market …

Dynamic Multicritical Phase Diagrams Of The Mixed Spin (2, 5/2) Blume-Emery-Griffiths Model With Repulsive Biquadratic Coupling, Mustafa Gençaslan, Mustafa Keski̇n

Turkish Journal of Physics

We investigated the dynamic phase transitions (DPTs) in the mixed spin (2, 5/2) Blume-Emery Griffiths model with repulsive biquadratic interaction in the presence of a time-varying magnetic field. We used the path probability method to obtain the set of the dynamic equations. We numerically solved these dynamic equations to characterize the nature of first- and second-order phase transitions and to find the DPT temperatures as well as obtain the phases in the system. We constructed the dynamic phase diagrams (DPDs) in reduced temperature and amplitude of oscillating magnetic field plane. We observed that the DPDs display richer, complex and more …

The Case For Alternate Media Requirement Expressions In Systems Engineering, Jason Kolligs

Dissertations

“Requirements” are often presented as a necessary, if not mandated, component or tool of the systems engineering process. Despite their place in engineering, low quality requirements are the first potential mistakes of an engineering project and their effects are potentially felt downstream in the form of increased costs and schedule overruns. Innovations to improve requirements engineering have historically omitted the requirement media. This research provides the rationale and process heuristics to include alternate media requirement expressions within systems engineering which shifts accountability to higher context requirement expressions that may require less cognitive load while decreasing effort and schedule thereby potentially …

Finding The Right Angle: Lessons From Mathematics For The Legal Writing Classroom, Maria Termini

Faculty Scholarship

No abstract provided.

Whole File Chunk Based Deduplication Using Reinforcement Learning, Xincheng Yuan

Master's Projects

Deduplication is the process of removing replicated data content from storage facilities like online databases, cloud datastore, local file systems, etc., which is commonly performed as part of data preprocessing to eliminate redundant data that requires unnecessary storage spaces and computing power. Deduplication is even more specifically essential for file backup systems since duplicated files will presumably consume more storage space, especially with a short backup period like daily [8]. A common technique in this field involves splitting files into chunks whose hashes can be compared using data structures or techniques like clustering. In this project we explore the possibility …

Darknet Traffic Classification, Nhien Rust-Nguyen

Master's Projects

The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic to block these criminal activities. This research aims to improve darknet traffic detection by assessing Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN) and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN) for classification of network traffic and the underlying application types. We find that our RF model outperforms the state-of-the-art machine learning techniques used by prior work with the CIC-Darknet2020 dataset. To evaluate the robustness of our RF classifier, we degrade …