VTechWorks

VTechWorks provides global access to Virginia Tech scholarship, including journal articles, books, theses, dissertations, conference papers, slide presentations, technical reports, working papers, administrative documents, videos, images, and more by faculty, students, and staff. Faculty can deposit items to VTechWorks from Elements, including journal articles covered by the University open access policy. Email vtechworks@vt.edu for help.

Recent Submissions

Computational Modeling of Carbon Nanotube-Reinforced Polymer Composites using Machine Learning and Peridynamics

Shah, Kavan Nailesh (Virginia Tech, 2026-09-25)

In this work, data-driven structure-property relations for Carbon Nanotube (CNT)-polymer composites are established using Machine Learning. The stochastic microstructure of computationally generated CNT-polymer composite statistical volume elements (SVEs) is quantified using two-point correlation functions. The two-point correlation functions are represented in a lower-dimensional subspace via Principal Component Analysis (PCA) to make the correlation functions more tractable for use in Machine Learning algorithms for property prediction. Linear regression and Artificial Neural Network (ANN) models are used to establish structure-property linkages that predict the effective stiffness, electrical conductivity, and piezoresistivity coefficients from the principal scores of the correlation functions. It is found that the linear regression and ANN models predict the effective stiffness accurately, but fail to predict the electrical and electro-mechanical properties with sufficient accuracy. It is inferred that the lower-dimensional principal space representation of the two-point correlation functions captures the CNT volume fraction and CNT orientation distribution, both of which control the effective stiffness of the SVEs. However, this representation fails to adequately capture important features which control the electrical conductivity and piezoresistivity of the SVEs, which include the formation of conductive paths between CNTs and the sensitivity of these paths to perturbations in the positions of CNTs due to applied strain. To address these limitation, the SVEs are statistically characterized using two-point cluster functions and two-point blocking functions that capture information about the connectedness of CNTs. In addition, the auto-correlations, two-point cluster functions, and two-point blocking functions of the binarized conductivity contours associated with the SVEs are constructed to capture the conductivity enhancement of the polymer due to the presence of CNTs. Each of these two-point functions is represented in a lower-dimensional space using Principal Component Analysis (PCA). Structure-property linkages are established using Artificial Neural Networks (ANNs) that predict the conductivity and piezoresistivity of the SVEs using these PC scores as input. It is found that the auto-correlations of the binarized conductivity contours constitute the most predictive feature set for the conductivity and piezoresistivity. Furthermore, the two-point cluster and blocking functions fail to reveal additional predictive information about the properties over their auto-correlation counterparts. Finally, the established reduced-order models are used to assign microstructure-informed material properties to the CNT-reinforced polymer binder a multiscale electro-mechanical simulation of a polymer-bonded energetic (PBE). A non-ordinary state-based peridynamic (NOSB-PD) solver is developed to simulate the response of the PBE. A novel bond-based interface is incorporated into the NOSB-PD framework to model the interactions between the different constituent phases. Furthermore, a kinetic damping relaxation (KDR)-based quasi-static solver is incorporated into NOSB-PD to find configurations corresponding to static equilibrium under applied loads. The quasi-static electro-mechanical response of the PBE is then simulated using the developed PD solver.

Bridging the Language Divide: How Local Governments Implement Housing-Related Language Access for Asian Immigrant Communities with Limited English Proficiency in Metropolitan Washington

Sayed, Shaheera (Virginia Tech, 2026-09-25)

For the roughly 25 million U.S. residents with limited English proficiency (LEP), navigating housing systems poses uniquely complex challenges: applications, lease agreements, recertifications, and grievance procedures are conducted overwhelmingly in English, and the consequences of misunderstanding a document or missing a deadline can mean the loss of shelter itself. Despite decades of federal mandates requiring meaningful language access, implementation remains uneven and underfunded, and the capacity that has been built is built for Spanish, leaving linguistically diverse communities, particularly Asian immigrant populations, without equivalent provision. This dissertation asks how housing related agencies in the Washington, D.C. metropolitan region construct, implement, and sustain language access for LEP communities, and where in that process Asian LEP communities can be rendered institutionally invisible. The analysis is organized around Institutional Linguistic Capital (ILC), a framework developed in this dissertation that synthesizes Bourdieu's (1991) linguistic capital, street-level bureaucracy theory (Lipsky, 2010), and administrative burden scholarship (Herd and Moynihan, 2018). ILC treats languages as resources that institutions value, accumulate, ration, and convert, holding capacity in three forms: operational, institutional, and embodied. Paper 1 develops the framework and names its default failure trajectory the cascade of invisibility; each empirical paper then examines one form of capacity across the full sequence. Paper 2 develops AI-augmented policy auditing, applying large language model pipelines with critical discourse analysis and human validation to 905 unique documents from three jurisdictions, and finds that inequity lies not in frequency of mention, since languages appear roughly in proportion to LEP population share, but in conversion: Spanish is named alongside staff, departments, and dedicated lines, while other languages receive recognition that commits little to no material capacity. Paper 3 draws on 25 interviews with 29 participants across six jurisdictions to identify administrative home, funded mandates, and point-of-service verification as the structural features producing unequal access, and shows rationing to be self-reproducing, since provision shapes the demand data that later justify provision. Paper 4 examines frontline practice under federal retrenchment, introducing protective discretion and embodied institutional linguistic capital to explain how workers convert personal linguistic and cultural resources into public capacity, and at what cost. Read together, the papers reveal a cascade of invisibility operating through distinct mechanisms at each level, a persistent compliance-access gap, and a sustainability paradox in which the features that make language access work are the most vulnerable to disruption. The dissertation contributes a transferable theoretical framework, an auditing methodology that agencies can apply to their own documents, and documentation of Asian LEP marginalization at a moment when federal rollbacks have shifted responsibility for multilingual inclusion to local jurisdictions.

The Impact of Teacher Grading Practices and Student Performance in Virginia's Standards of Learning (SOL) Assessments

Basile, Victoria (Virginia Tech, 2026-09-24)

This dissertation examined the relationship of grading practices on student learning outcomes, specifically on student performance on Virginia Standards of Learning (SOL) assessments. The purpose of this study was to compare Standards-Based Grading (SBG) and Traditional Grading to identify (a) the extent to which course grades correlate with SOL outcomes in each grading system, and (b) the association between students' course grades and the number of SOL test attempts required to earn verified credit. Using a quantitative and correlational design, data were collected from multiple high schools in two participating Northern Virginia school districts and analyzed using descriptive statistics, independent-samples t-tests, Spearman's rank-order correlations, chi-square analyses, and crosstab analyses. Results indicated that higher course grades were associated with higher SOL scores in both grading systems, with a larger observed correlation in the SBG group than in the Traditional Grading group. Students in the SBG group also had slightly higher mean SOL scores, although the practical difference between grading systems was negligible. Higher course grades were associated with fewer SOL assessment attempts in both grading systems. The findings suggest that course grades are associated with SOL performance and assessment attempts and provide evidence for educational leaders evaluating grading practices and their alignment with measures of student proficiency. The findings may also inform discussions among leaders within the Virginia Department of Education regarding grading practices, verified credit, and diploma policies intended to promote fairness, rigor, and accurate measurement of student learning.

Inverse Screening of Metal-Organic Frameworks for Low-to-Mid-Pressure Carbon Dioxide Adsorption using Geometric, AP-RDF, and RAC Descriptors

Rengasamy, Padmapriya (Virginia Tech, 2026-09-24)

Metal-Organic Frameworks (MOFs) are promising materials for carbon dioxide (CO₂) capture because of their high porosity, tunable chemistry, and structural diversity. However, the enormous design space of MOFs makes exhaustive experimental and computational screening prohibitively expensive. Consequently, there is a growing need for computational approaches that can rapidly identify promising materials while reducing the cost of materials discovery. This thesis investigates the use of geometric, revised autocorrelation (RAC), and atomic property-weighted radial distribution function (AP-RDF) descriptors for predicting low-to-mid-pressure CO₂ adsorption using machine learning (ML) models. The predictive capabilities of different descriptor combinations are systematically evaluated to establish an accurate surrogate modeling framework. The best-performing model, selected based on predictive accuracy, is subsequently used for interpretability analyses to quantify the relative contribution of different descriptor groups to model predictions and to examine how descriptor importance varies across pressure conditions. These findings facilitate a deeper understanding of the factors influencing CO₂ adsorption predictions. The best-performing XGBoost model using the combined geometric, RAC, and AP-RDF descriptor set achieved R² values of 0.739 and 0.904 at 0.015 and 0.15 bar, respectively. Descriptor ablation and SHAP analyses showed that geometric descriptors provided the strongest overall predictive contribution, while AP-RDF and RAC descriptors supplied complementary information; surrogate-guided screening further demonstrated that high-performing MOFs could be efficiently recovered from a large candidate pool using limited training data. Building upon these insights, surrogate-guided inverse screening is employed to efficiently recover top-performing MOFs from a large candidate pool of 208,849 MOFs using limited training data. The descriptor-level insights obtained from the interpretability analyses are further evaluated through chemistry-aware matched-analog screening, which tests whether AP-RDF and RAC information can help identify higher working capacity candidates among MOFs with similar pore geometry compared with geometry-only selection. Across 234 matched neighborhoods, chemistry-aware guided selection achieved a median working capacity gain of 5.85 wt%, compared with 1.20 wt% for geometry-only selection and 0.15 wt% for the repeated-random benchmark. Predicted and actual guided gains showed strong agreement (Spearman correlation = 0.90), while the grouped chemistry-aware contribution was also strongly associated with actual guided gain (Spearman correlation = 0.89). These results demonstrate that the developed surrogate framework can support both large-scale inverse screening of MOF databases and the selection of higher working capacity candidates among geometrically similar MOFs, while providing descriptor-level information that helps interpret the model predictions and prioritize promising candidates for subsequent simulation or experimental evaluation.

Fundamentals of Business (4e) test bank

Slagel, Koehler (2026-09-24)

This test bank aligns with the open textbook Fundamentals of Business, fourth edition and includes 995 peer-reviewed questions, of which 696 are multiple choice, 167 are fill-in-the-blank, and 132 are essay-style. Applied and conceptual questions are available for each chapter and cover topics from the book. Note: The previously published (2019) test bank aligns with the second edition of Fundamentals of Business. While the updated test bank has substantive overlap, it contains additional topics and organization out of scope for the previous texts. The test bank is available to any instructor who has adopted Fundamentals of Business, fourth edition in their course. Available test bank materials are provided in ONE downloadable zip folder and are organized by format, including: Blackboard/Moodle, Canvas, D2L Brightspace, txt, and XLS. INSTRUCTIONS FOR TEST BANK ACCESS
Requested files will be released via email only after steps 1–3 have been completed and reviewed. STEP 1. Complete the User Verification Process Form. Once submitted, you will receive an email confirming your request. STEP 2. Find the "Test bank.zip" file on the left column of this page. Click on it, add a note, and press submit to request access. STEP 3. Email a copy of your course syllabus to openeducation@vt.edu. Please note: - Access approvals are processed only during regular business hours. Please allow at least seven full business days for processing. - By completing the steps above, you agree to the Terms of Use (outlined below) and confirm intent to adopt Fundamentals of Business, fourth edition for your course. Formats
Please note that we are unable to provide additional file types, support for converting, uploading, or assistance with reformatting files. Please contact your local learning management system (LMS) manager for additional support. Terms of use
Test bank questions and answers are the copyrighted property of their creators. Verified users are responsible for secure handling of the test bank. While every effort is given to ensure security of test bank questions or answers and release only to verified users, no warranties are given regarding the security of the test bank. Virginia Tech and the test bank authors can authorize and require, but cannot control what you or any other verified user does. - Verified users may reproduce, modify, and redistribute test bank questions and answers in the context of educational exams, quizzes, and assessments only. - Verified users must take reasonable measures to prevent students from duplicating and redistributing questions and answers; - Verified users must not share test bank questions and answers beyond the institution with which the verified user reported an affiliation; give exams back to students to keep or otherwise distribute; release the test bank or answers to students as a self study-tool; publicly post any portions of the test bank; allow students to retain permanent or long-term access to exam materials; use the test bank or portions there of for revenue-generating activity. Distribution
The information in the test bank is of a proprietary nature, produced by or for faculty of public institutions of higher education as a result of collaborative study, research, and peer review. Because it is intended to be used in student assessment the information has not been publicly released or published. If you become aware of public distribution of the test bank or portions thereof shared outside of a secure electronic environment, assessment context, or other security breach please inform us immediately at: openeducation@vt.edu. Liability
The test bank and test bank items are provided "as is." Users of this resource assume all risks and further agree to hold Virginia Tech, the Commonwealth of Virginia and their employees and agents, and project contributors harmless from any and all actions related to use of this program. View errata | Report an error AI statement
This resource was partially developed using generative artificial intelligence. ChatGPT generated a significant amount of questions based off of the textbook. The author reviewed the output and edited the questions further. The test bank was then reviewed by students and peers, and went through professional copy editing. Acknowledgments
The project was made possible in part by the Open Education Initiative of the University Libraries at Virginia Tech and by funding from a VIVA Open Grant from VIVA, Virginia’s Academic Library Consortium, a program of the State Council of Higher Education. The VIVA Open Grant Program aims to improve academic success for Virginia students by eliminating the costs of textbooks and other course materials and providing day one access to materials while also empowering faculty to create resources tailored to individual course needs and pedagogical goals. Contributors
Faculty and staff contributors: Ron Poff provided project oversight. Anita Walz provided project oversight, planning, drafted terms of use, and constructed parts of the test bank. Koehler Slagel reviewed existing questions and developed additional questions. Kindred Grey coordinated peer review and professional copyediting, and constructed parts of the test bank. Student reviewers: Kunj Patel and Ashley Zou Peer reviewers: Rachel Hawkins, Business Professor, Tacoma Community College, Tacoma, WA.

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