Browsing by Department "Basic & Applied Sciences"
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    A Comparative Study on Two Strategies for Distributed Classification
    (Middle Tennessee State University, 2018-05-30) Xu, Honglan ; Wu, Qiang ; Hong, Don ; Liu, Yeqian ; Green, Lisa ; Basic & Applied Sciences
    Distributed learning is an effective tool to process big data. An easy and effective distributed learning approach is the divide and conquer method. It first partitions the whole data set into multiple subsets. A base learning algorithm is then applied to each subset. Finally the results from these subsets are coupled together. In the classification setting, many classification algorithms can be used in the second stage. Typical ones include the logistic regression and support vector machines. For the third stage, both voting and averaging can be used as the coupling strategies. In this thesis, empirical studies are done to thoroughly compare the effectiveness of these two coupling strategies. Averaging is found to be more effective in most scenarios.
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    A Duality between hypergraphs and cone lattices
    (Middle Tennessee State University, 2018-03-22) French, Zack ; Hart, James ; Sarkar, Medha ; Ye, Dong ; Basic & Applied Sciences
    In this paper, we introduce and characterize the class of lattices that arise as the
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    A SCIENCE FACULTY MOTIVATION ANALYSIS TO ADOPT EVIDENCE-BASED TEACHING
    (Middle Tennessee State University, 2019) Carroll, Penny ; Gardner, Grant ; Brinthaupt, Tom ; Grinath, Anna ; Bleiler-Baxter, Sarah ; Seipelt-Thiemann, Rebecca ; Basic & Applied Sciences
    The most common teaching practice in America’s universities is lecturing and this study investigated faculty reasons for continuing this practice in their particular departmental contexts. This study examined data from faculty surveys, interviews, classroom observations and departmental artifacts using the expectancy-value theory as an analytical framework for understanding faculty motivational components to adopt evidence-based teaching. Some key expectancies and values identified in this study included class size, the need for credible evidence, department communication, department teaching expectations, and achieving tenure. This work also updated previous EVT models by identifying new expectancies and values to consider when working at a local level with faculty. This study proposes a new professional development model for future use when working with faculty on the research-to-practice gap issues.
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    Adopting Reform-based Pedagogy in Post-secondary Microbiology Education
    (Middle Tennessee State University, 2016-11-11) Bonner, Jeffery Wayne ; Barlow, Angela ; Gardner, Grant ; Howard, Robert ; Rowell, Ginger ; Goodin, Terry ; Basic & Applied Sciences
    Current emphasis on improving student learning and retention in post-secondary science education can potentially motivate veteran faculty to reconsider what is often a traditional, instructor-centered instructional model. Alternative models that foster a student-centered classroom environment are more aligned with research on how students learn. These models often incorporate active-learning opportunities that engage students in ways that passively taking notes in an instructor-centered classroom cannot. Although evidence is mounting that active-learning is an effective strategy for improving student learning and attitude, university professors, without formal pedagogical knowledge and training, can face uncertainty about where to start and how to implement these strategies.
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    An Examination of Resources that Impact the Learning Experience of Underprepared Community College Students in a Redesigned Co-Requisite Statistics Course
    (Middle Tennessee State University, 2017-11-12) Smith, Derek ; Chappell, Michaele ; Butler, Kyle ; Lischka, Alyson ; Martin, Mary ; Oslund, Eric ; Basic & Applied Sciences
    Students entering post-secondary institutions underprepared for their college-level mathematics requirements are often required to enroll in developmental courses. These classes typically do not count towards graduation requirements and result in added time and money for a student’s program of study. Furthermore, the literature has found that students just below the threshold of college-ready classification have experienced negative effects related to persistence, which may be explained by the frustration of the additional course work and the stigma some individuals experience when labeled a remedial student. Various reform efforts have been introduced to restructure the curricula and instructional methods to reduce the amount of time needed for underprepared students to satisfy their educational requirements.
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    An Examination of the Statistical Problem-Solving Process as a Potential Means for Developing an Understanding of Argumentation
    (Middle Tennessee State University, 2017-03-30) Baum, Brittany ; Barlow, Angela ; Chappell, Michaele ; Rowell, Ginger ; Strayer, Jeremy ; Basic & Applied Sciences
    As part of the recent history of the mathematics curriculum, reasoning and argument have been emphasized throughout mathematics curriculum standards. Specifically, as part of the Common Core State Standards for Mathematics, the Standards for Mathematical Practice were presented, which included the expectation that students develop arguments and carefully consider others’ arguments. Due to its emphasis on reasoning and argument, argumentation is one possible way students can meet the expectations of these standards. However, when used in mathematics, argumentation is commonly limited to proofs. Therefore, the use of argumentation in mathematics in ways apart from proofs is needed. Through an examination of students in a college-level introductory statistics classroom, this study examined how engaging in the statistical problem-solving process served as an avenue for developing students’ understanding of argumentation.
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    Analysis of MTSU Student Retention Data
    (Middle Tennessee State University, 2016-03-23) Baghernejad, Danielle Marcella ; Wu, Qiang ; Calahan, Rebecca ; Green, Lisa ; Li, Cen ; Basic & Applied Sciences
    Student retention is a challenging task in higher education, since in general more students remaining in the university means better academic programs and higher revenue. Thus, improving retention rates can not only help current students achieve academic success, but help future students as well. The objective of this thesis is to employ data mining and predictive tools on student data to predict student retention among the freshman students. In particular, we aim to identify freshman students who are more likely to drop out so that preemptive actions can be taken by the university. Through data analysis, relevant variables are identified to incorporate into models for prediction. Missing values are taken into consideration, and missing value imputation methods are explored.
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    Analysis of Thermal Motion Effects on the Electron Density via Computational Simulations
    (Middle Tennessee State University, 2014-10-24) Michael, John Robert ; Koritsanszky, Tibor ; Volkov, Anatoliy ; Kong, Jing ; Khaliq, Abdul ; Melnikov, Yuri ; Basic & Applied Sciences
    The Electron Density (ED) of a molecular structure can only be observed for large ensembles of molecules packed tightly in crystal structures in the solid state. Even then it cannot truly be observed, instead experimental measurements are taken via X-Ray Diffraction (XRD) and the resulting data is fitted to a theoretical ED model describing the probability of finding an electron inside an infinitesimal volume element.
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    Arginyl aminopeptidase-like 1 (RNPEPL1): from hypothetical reading frame toward functional characterization
    (Middle Tennessee State University, 2014-07-17) Maynard, Karen Beasley ; Seipelt - Thiemann, Rebecca ; Bailey, Frank ; Cahoon, A ; Kline, Paul ; Robertson, J ; Basic & Applied Sciences
    Arginyl aminopeptidase-like 1 (RNPEPL1) was first reported as a hypothetical reading frame during various genomic studies to complete the human genome. This study aimed to move the gene from hypothetical status to verified, as well as move toward establishing a biological role or function in inflammation based on gene family similarity.
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    BIFURCATION ANALYSIS IN APOPTOSIS (RECEPTOR CLUSTERING)
    (Middle Tennessee State University, 2018) Spears, Genesis Amelia ; Basic & Applied Sciences
    Apoptosis is a designed cell death mechanism involved in biological processes. Apoptosis can either be activated by extrinsic pathway or by the intrinsic pathway. A major part of the external apoptosis pathway is the death receptor Fas which, on binding to its associated ligand FasL, they eventually form the death-inducing signaling complex. FasL promotes clustering for open Fas and activates open stable Fas, forming locally stable signaling platforms through neighborhood-induced receptor interactions. The model exhibits a bifurcation called hysteresis, providing an upstream mechanism for bistability and robustness to decide if the cell lives or dies. At low receptor concentrations, the bistability depends on three states of FasL. The irreversible bistability, representing a committed cell death decision, emerges at high receptor concentrations. Furthermore, the model suggests a mechanism by which cells may function as bistable life/death switches which are independent of their downstream dynamic components. This will be illustrated by simulations.
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    Bioinformatics Tools and Applications for Rainbow Trout
    (Middle Tennessee State University, 2017-04-03) Al-Tobasei, Rafet ; Salem, Mohamed ; Carroll, Hyrum ; Li, Cen ; Seipelt -Thiemann, Rebecca ; Phillips, Joshua ; Basic & Applied Sciences
    Rainbow trout is one of the widely used aquaculture species for food worldwide. Due to its commercial importance, various genomic resources are available for the trout including a draft reference genome, microRNA repertoire, quantitative trait loci and single nucleotide polymorphisms (SNPs) associated with different production traits. However, many of these genomic resources still need improvement in terms of quality and quantity. The only available genome draft is not completely annotated, and lacks non-coding RNA and some protein coding genes. Similarly, majority of the previous work aimed at identification of trait-associated genetic markers were not robust due to limitation of genomic resources that were previously available.
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    Collective Behavior Modeling Through Velocity Alignment
    (Middle Tennessee State University, 2019) Zanussi, Jacy Thor ; Sinkala, Zachariah ; Ding, Wandi ; Khaliq, Abdul ; Basic & Applied Sciences
    We construct a model for collective behavior phenomena by undermining the assumption that the rate of change of position equals velocity in the particle Cucker-Smale model for flocking. Conditions for collective behavior are proven and three continuous models for segregation are presented with simulations for two of them. Future avenues of research and a variety of applications such as social science, engineering, and business trends are discussed in the conclusion.
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    COMPUTATIONAL IMPROVEMENTS FOR STOCHASTIC SIMULATION WITH MULTILEVEL MONTE CARLO
    (Middle Tennessee State University, 2016-06-09) Colgin, Zane ; Khaliq, Abdul ; Sinkala, Zachariah ; Melnikov, Yuri ; Robertson, William ; Basic & Applied Sciences
    In this work we implement various techniques to improve the multilevel Monte Carlo (MLMC) method as it is applied to a variety of stochastic models. In each case we were able to show a quantifiable computational benefit.
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    COMPUTATIONAL MODELING OF BLOCH SURFACE WAVES IN ONE-DIMENSIONAL PERIODIC AND APERIODIC MULTILAYER STRUCTURES
    (Middle Tennessee State University, 2017-03-27) Koju, Vijay ; Robertson, William ; Khaliq, Abdul ; Bedekar, Vishwas ; Baba, Justin ; Basic & Applied Sciences
    Photonic crystals and their use in exciting Bloch surface waves have received immense attention over the past few decades. This interest is mainly due to their applications in bio-sensing, wave-guiding, and other optical phenomena such as surface field enhanced Raman spectroscopy. Improvement in numerical modeling techniques, state of the art computing resources, and advances in fabrication techniques have also assisted in growing interest in this field. The ability to model photonic crystals computationally has benefited both the theoretical as well as experimental communities. It helps the theoretical physicists in solving complex problems which cannot be solved analytically and helps to acquire useful insights that cannot be obtained otherwise. Experimentalists, on the other hand, can test different variants of their devices by changing device parameters to optimize performance before fabrication. In this dissertation, we develop two commonly used numerical techniques, namely transfer matrix method, and rigorous coupled wave analysis, in C++ and MATLAB, and use two additional software packages, one open-source and another commercial, to model one-dimensional photonic crystals. Different variants of one-dimensional multilayered structures such as perfectly periodic dielectric multilayers, quasicrystals, aperiodic multilayer are modeled, along with one-dimensional photonic crystals with gratings on the top layer.
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    Data Mining and Machine Learning Algorithms for Workers' Compensation Early Severity Prediction
    (Middle Tennessee State University, 2016-06-21) Mathews, David ; Hong, Don ; Wu, Qiang ; Green, Lisa ; Hart, James ; Basic & Applied Sciences
    Although the number of workers' compensation claims have been declining over the last two decades, average cost per claim has been steadily increasing. Identifying factors that contribute to severe claims and effectively managing those claims early in the claim life-cycle could reduce costs for employers and insurers. This research project utilizes machine learning algorithms to predict a binary severity outcome variable. A text mining algorithm, Correlated Topics Model, was used to convert textual description fields to topics. Support Vector Machines and Regularized Logistic Regression were implemented for severity classification and variable selection, respectively. Due to the asymmetric severity outcomes in the training data, a balancing method for matching the volume of severe/non-severe claims was employed. Optimal model parameters for both algorithms were selected based on a profitability metric and 10-fold cross-validation. Discussion of data processing techniques and mathematical exposition of machine learning algorithms are provided. Open source statistical programming software, R, was utilized in this project.
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    Decomposition of Cubic Graphs on the Torus and Klein Bottle
    (Middle Tennessee State University, 2015-10-30) Bachstein, Anna Caroline ; Ye, Dong ; Zha, Xiaoya ; Stephens, David ; Basic & Applied Sciences
    It was conjectured by Hoffman-Ostenhof that the edge set of every cubic graph can be decomposed into a spanning tree, a matching, and a family of cycles. This conjecture was verified for many graphs such as the Peterson graph, prisms over cycles, and Hamiltonian graphs. Later the conjecture was also verified for 3-connected cubic graphs on the plane and protective plane by Kenta Ozeki and Dong Ye. In this paper we will verify the conjecture for 3-connected cubic graph on the torus and Klein bottle.
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    DEVELOPING A PERSONALIZED ARTICLE RETRIEVAL SYSTEM FOR PUBMED
    (Middle Tennessee State University, 2016-06-21) Pitigala, Sachintha Prasad ; Li, Cen ; Seo, Suk ; Wallin, John ; Wu, Qiang ; Basic & Applied Sciences
    PubMed keyword based search often results in many citations not directly relevant to the
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    Developing Meaning for Algebraic Procedures: An Exploration of the Connections Undergraduate Students Make Between Algebraic Rational Expressions and Basic Number Properties
    (Middle Tennessee State University, 2013-07-16) Yantz, Jennifer Lynne ; Barlow, Angela ; Holmes Rowell, Ginger ; Stephens, Chris ; Strayer, Jeremy ; Vanosdall, Rick ; Basic & Applied Sciences
    The attainment and retention of later algebra skills in high school has been identified as a factor significantly impacting the postsecondary success of students majoring in STEM fields. Researchers maintain that learners develop meaning for algebraic procedures by forming connections to the basic number system properties. The present study investigated the connections participants formed between algebraic procedures and basic number properties in the context of rational expressions.
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    Distributed Computing Approaches to Pathfinding Problems
    (Middle Tennessee State University, 2016-06-19) Myers, Robert Vital ; Phillips, Joshua ; Gu, Yi ; Barbosa, Salvador ; Basic & Applied Sciences
    The problem of determining the existence of a path between vertices in problem domains with large graphs is outpacing the increases in commonly available processor speeds. This presents a growing need for pathfinding algorithms which can capitalize on parallel approaches. These approaches are often based on parallelizing the search on a single machine. However, some problems may be so large that it becomes appropriate to use distributed computing. This research explores the Distributed Fringe Search algorithm as a more conducive approach for pathfinding problems over multiple distributed machines. The work presented here is novel in its extension of DFS by developing the Distributed Computing Fringe Search. Additionally, this research proposes the Hash Distributed Fringe Search that utilizes space abstraction techniques for work distribution and a more uniform memory requirement. Finally, results are presented to show the impact of the approaches in large searches; these results inform suggestions for future work.
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    ECOLOGICAL LITERACY, URBAN GREEN SPACE, AND MOBILE TECHNOLOGY: EXPLORING THE IMPACTS OF AN ARBORETUM CURRICULUM DESIGNED FOR UNDERGRADUATE BIOLOGY COURSES
    (Middle Tennessee State University, 2017-11-09) Phoebus, Patrick Eugene ; Rutledge, Michael ; Sadler, Kim ; Barlow, Angela ; Kim, Jwa ; Walck, Jeffrey ; Basic & Applied Sciences
    Increasing individual ecological literacy levels may help citizens make informed choices about the environmental challenges facing society. The purpose of this study was to explore the impacts of an arboretum curriculum incorporating mobile technology and an urban greenspace on the ecological knowledge, environmental attitudes and beliefs, and environmental behaviors of undergraduate biology students and pre-service K-8 teachers during a summer course.