All Faculty Deposits

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https://hdl.handle.net/10919/70873

The "All Faculty Deposits" collection contains works deposited by faculty and appointed delegates from the Elements (EFARs) system. For help with Elements, see Frequently Asked Questions on the Provost's website. In general, items can only be deposited if the item is a scholarly article that is covered by Virginia Tech's open access policy, or the item is openly licensed or in the public domain, or the item is permitted to be posted online under the journal/publisher policy, or the depositor owns the copyright. See Right to Deposit on the VTechWorks Help page. If you have questions email us at vtechworks@vt.edu.

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Now showing 1 - 20 of 7563

Building the Next Generation of Transformational Leaders: Identifying Perceptions and Existing Opportunities
Oyedare, Israel; Kaufman, Eric K. (American Association for Agricultural Education, 2026-05-20)
As the world faces dynamic challenges affecting societies and organizations, there has been an increasing call for the development of transformational leadership among youth. This focus is critical for preparing for the future and for inspiring hope, trust, and cooperation within any given system. Unfortunately, despite the popularity of the transformational leadership domain, the emphasis has largely been on adult leaders. Thus, this study explores the perspectives of 4-H Extension agents about transformational leadership among youth and existing opportunities available for youth to build their transformational leadership skills. The insights are gleaned from interviews with 15 Extension agents working with 4-H—the largest youth development organization in the United States. The findings are relevant to multiple stakeholders, including 4-H Extension services, for-profit and nonprofit organizations, and academic institutions.
Ebola Control Needs Integrated Epidemics–Logistics Optimization
Büyüktahtakın, İ. Esra (2026-05-22)
The rapidly expanding Bundibugyo Ebola outbreak in the Democratic Republic of the Congo and Uganda underscores a persistent implementation gap: risk signals do not automatically become timely, feasible action. This Comment argues that integrated epidemics–logistics optimization can help decision makers allocate treatment, isolation, diagnostics, transport, and reserve capacity under uncertainty, scarce resources, and socio-political constraints. The analysis highlights four operational priorities: early targeted deployment, dynamic reallocation, equity-aware response, and protected surge capacity.
Leadership From The Land with Dr. Eric Kaufman & Brian Zimmerman [Podcast Episode]
Allen, Scott J.; Kaufman, Eric K.; Zimmerman, Brian (Practical Wisdom for Leaders with Scott J. Allen, Ph.D., 2026-05-13)
In this episode of Practical Wisdom for Leaders, host Scott J. Allen welcomes Dr. Eric Kaufman of Virginia Tech and Cleveland Metroparks CEO Brian Zimmerman for a conversation exploring the deep roots of leadership development in agricultural education and community life. Drawing from their experiences in FFA, 4-H, land-grant universities, and public service, Kaufman and Zimmerman reflect on how leadership is cultivated through practice, mentorship, responsibility, and collaboration rather than positional authority alone. Together, they examine the historical influence of agricultural leadership programs on modern leadership education, the importance of experiential learning, and the role of community networks in solving complex societal challenges. The discussion highlights themes of empowerment, collective leadership, lifelong learning, and the enduring value of developing leaders who can work across differences to move communities forward.
Developing Youth's Capacity to Lead Through Complexities: Exploring 4-H Extension Agents’ Perception on Barriers and Opportunities
Oyedare, Israel; Kaufman, Eric K. (Wiley, 2026-05-15)
As the world faces dynamic challenges affecting societies and organizations, there has been an increasing call for the development of transformational leadership among youth. This focus is critical for preparing for the future and for inspiring hope, trust, and cooperation. Unfortunately, despite the popularity of the transformational leadership domain, the emphasis has largely been on adult leaders. Thus, this study explores 4-H Extension agents’ perspectives about barriers to and support areas for fostering transformational leadership development (TLD) among youth. Interviews revealed primary barriers and strategies that influence success with youth TLD, acknowledging the adults can both inhibit and facilitate the process. The findings have important implications for multiple stakeholders, including 4-H Extension services, community organizations, and academic institutions.
Strengthening S-STEM Pathways: Lessons Learned from Working Engineering Students
Johnson, Taylor; Richardson, Amy Jo; Rodriguez, Sarah L.; Lee, Walter C. (2026-05-22)
The theory of privacy interests: A Heideggerian Onto-epistemological perspective toward privacy actions
Brown, Nicholas James; Mindel, Vitali; Lowry, Paul Benjamin; Nottingham, Quinton (2026)
Prevailing information privacy theories have advanced understanding of how users evaluate privacy risks. Yet, they leave important aspects of user behavior insufficiently explained, particularly why some consumers proactively protect their personal information, whereas others remain passive despite expressing similar privacy concerns. Much of this research relies on privacy concerns as the primary indicator of privacy attitudes. Privacy concerns are crucial, but they largely capture users’ risk-focused evaluations of data collection, use, and disclosure. They therefore explain how users react to perceived privacy threats better than how users develop sustained engagement in protecting their privacy. We propose and test the Theory of Privacy Interests to explain this consumer-side engagement. By privacy interests, we mean users’ experiential engagement with protecting their personal information—the extent to which privacy protection is meaningful to them, they feel competent to pursue it, and they believe their actions can influence privacy outcomes. This construct refers to users’ privacy-protective interests, not to the economic or strategic interests of firms, platforms, or other producers that supply privacy-adjacent digital environments. Drawing on the Heideggerian onto-epistemological framework, we conceptualize privacy interests as experience-based, skillful engagement with privacy protection that develops through users’ repeated interactions with digital environments, privacy risks, and privacy-protective practices. We empirically examine privacy interests and privacy concerns as distinct but complementary constructs. Across three scale-development data collections and four survey studies with 3,922 participants, we find that privacy interests are more effective in explaining proactive privacy actions. In contrast, privacy concerns are more effective in explaining reactive privacy actions. This research operationalizes the Heideggerian onto-epistemological framework to shift attention from users’ risk evaluation to users’ sustained engagement in actualizing privacy protection. In doing so, our research offers a complementary explanation for variation in consumer privacy behavior and advances research on privacy attitudes, privacy actions, and the privacy paradox.
Teaching Followership as a Collaborative Approach to Leadership with Eric Kaufman [Podcast episode]
Oyedare, Israel; Kaufman, Eric K. (The Future of Leadership Podcast with Israel Oyedare, 2026-03-21)
In this episode, I’m joined by Eric Kaufman, a professor of Leadership Studies at Virginia Tech, for an engaging conversation on developing followership programs in higher education. We also explore key topics such as followership discourse and research, followership education, and the cataloguing of followership materials—a Virginia Tech grant-funded project where he served as Co-Principal Investigator.
Understanding the Influence of Data Breaches on Patients’ Willingness to Share Protected Health Information: A Mixed Methods Study of a Construals Privacy Calculus Perspective
Singh, Tripti; Di Gangi, Paul M.; Johnston, Allen C.; Bott, Gregory J.; Lowry, Paul Benjamin (2026-01)
Patients are increasingly faced with balancing the efficacy of their care against the privacy of their protected health information (PHI). Research has established that healthcare data breaches erode patients’ willingness to share new health information, yet withholding PHI results in poorer medical outcomes. This study integrates construal level theory and privacy calculus to introduce a construals privacy calculus perspective of PHI sharing post-data breach. This perspective argues that contextual differences in breach characteristics affect patients’ perceived PHI risks and disclosure decisions. Results of a mixed-methods research design indicate that psychologically distant breaches are significantly less disruptive to disclosure intentions than psychologically closer data breaches. Perceived PHI sensitivity also serves as a model moderator based on qualitative interview data and a meta-inference post hoc analysis of our quantitative study. Overall, this study contributes to understanding PHI disclosure amidst increasing incidents of healthcare data breaches, where PHI disclosures and healthcare privacy are context- and situation-specific phenomena.
An adaptive K-means and reinforcement learning (RL) algorithm to effective vaccine distribution
Cibaku, Elson; Büyüktahtakın, İ. Esra (Elsevier, 2026-01)
We present a new adaptive reinforcement learning (RL) approach, integrated with a K-means clustering algorithm and guided by simulated annealing, to address the capacitated vehicle routing for vaccine distribution (CVRVD) problem. This integrated method provides an efficient and scalable solution for optimizing vaccine distribution logistics. By incorporating cost factors related to travel distance, inventory levels, and penalty terms – while adhering to delivery time windows – our approach improves both operational efficiency and vaccine allocation effectiveness. Experimental results demonstrate that our K-means supported RL algorithm significantly outperforms traditional solvers in tackling this NP-hard problem, particularly in large-scale scenarios. Specifically, our approach can efficiently solve CVRVD instances with up to 1,000 facilities—scenarios that are computationally intractable for exact methods. We demonstrate the effectiveness of the adaptive K-means supported RL algorithm using data from New Jersey, USA, where facility-level vaccination data were available through the state's Immunization Information System. Beyond vaccine distribution, our method has broad applicability in logistics and transportation, enabling more efficient and cost-effective allocation of critical resources such as vaccines and medical supplies.
Assessing the impact of DMOs' photo curation practices on Instagram user engagement
Han, Yutong; Zach, Florian J.; Xiang, Zheng (Elsevier, 2026-10-01)
Content curation is a crucial practice for destination marketing organizations (DMOs). DMOs leverage user-generated content to shape tourists' perceptions and influence user engagement. This includes curating user-generated photos on Instagram. Drawing on signaling theory, this study investigates the content curation practices for user-generated photos employed by DMOs across the 50 U.S. states and Washington, D.C. Employing deep learning and econometric analysis on their Instagram accounts from January 2013 to December 2024, we find a curvilinear relationship between curation intensity and user engagement. Moreover, content alignment between DMO-generated and user-generated photos positively influences user engagement and moderates the relationship between curation intensity and user engagement. Hence, while prior research supports the value of content curation, optimal engagement requires hitting a sweet spot for intensity while ensuring high alignment. Practically, the findings provide guidance for DMOs to craft social media practices, emphasizing the integration of user-generated content into digital marketing campaigns.
Discovering heuristics with Large Language Models (LLMs) for mixed-integer programs: Single-machine scheduling
Cetinkaya, Ibrahim Oguz; Büyüktahtakın, İ. Esra; Shojaee, Parshin; Reddy, Chandan K. (Pergamon-Elsevier, 2026-02)
Our study contributes to the scheduling and combinatorial optimization literature with new heuristics discovered by leveraging the power of Large Language Models (LLMs). We focus on the single-machine total tardiness (SMTT) problem, which aims to minimize total tardiness by sequencing n jobs on a single processor without preemption, given processing times and due dates. We develop and benchmark two novel LLM-discovered heuristics, the EDD Challenger (EDDC) and MDD Challenger (MDDC), inspired by the well-known Earliest Due Date (EDD) and Modified Due Date (MDD) rules. In contrast to prior studies that employed simpler rule-based heuristics, we evaluate our LLM-discovered algorithms using rigorous criteria, including optimality gaps and solution time derived from a mixed-integer programming (MIP) formulation of SMTT. We compare their performance against state-of-the-art heuristics and exact methods across various job sizes (20, 100, 200, and 500 jobs). For instances with more than 100 jobs, exact methods such as MIP and dynamic programming become computationally intractable. Up to 500 jobs, EDDC improves upon the classic EDD rule and another widely used algorithm in the literature. MDDC consistently outperforms traditional heuristics and remains competitive with exact approaches, particularly on larger and more complex instances. This study shows that human-LLM collaboration can produce scalable, high-performing heuristics for NP-hard constrained combinatorial optimization, even under limited resources when effectively configured.
A Roadmap to Holographic Focused Ultrasound Approaches to Generate Thermal Patterns
Cengiz, Ceren; Eger, Zekeriya Ender; Acar, Pinar; Legon, Wynn; Shahab, Shima (Wiley, 2026-04)
In therapeutic focused ultrasound (FUS), such as thermal ablation and hyperthermia, effective acousto-thermal manipulation requires precise targeting of complex geometries, sound wave propagation through irregular structures and selective focusing at specific depths. Acoustic holographic lenses (AHLs) provide a distinctive capability to shape acoustic fields into precise, complex and multifocal FUS-thermal patterns. Acknowledging the underexplored potential of AHLs in shaping ultrasound-induced heating patterns, this study introduces a roadmap for acousto-thermal modeling in the design of AHLs. Three primary modeling approaches are studied and contrasted using four distinct shape groups for the imposed target field. They include pressure-based (BSC-TR and ITER-TR), temperature-based (IHTO-TR), and machine learning (ML)-based (GaN and Feat-GAN) methods. Novel metrics including image quality, thermal efficiency, control, and computational time are introduced, providing each method’s strengths and weaknesses. The importance of evaluating target pattern complexity, thermal and pressure requirements, and computational resources is highlighted for selecting the appropriate methods. For lightly heterogeneous media and targets with lower pattern complexity, BSC-TR combined with error diffusion algorithms provides an effective solution. As pattern complexity increases, ITERTR becomes more suitable, enabling optimization through iterative forward and backward propagations controlled by different error metrics. IHTO-TR is recommended for highly heterogeneous media, particularly in applications requiring thermal control and precise heat deposition. GaN is ideal for rapid solutions that account for acousto-thermal effects, especially when model parameters and boundary conditions remain constant. In contrast, Feat-GaN is effective for moderately complex shape groups and applications where model parameters must be adjusted.
Fighting fibrin with fibrin: Vancomycin delivery into coagulase-mediated Staphylococcus aureus biofilms via fibrin-based nanoparticle binding
Scull, Grant; Aligwekwe, Adrian; Rey, Ysabel; Koch, Drew; Nellenbach, Kimberly; Sheridan, Ana; Pandit, Sanika; Sollinger, Jennifer; Pierce, Joshua G.; Flick, Matthew J.; Gilbertie, Jessica; Schnabel, Lauren; Brown, Ashley C. (Wiley, 2024-12-01)
Staphylococcus aureus skin and soft tissue infection is a common ailment placing a large burden upon global healthcare infrastructure. These bacteria are growing increasingly recalcitrant to frontline antimicrobial therapeutics like vancomycin due to the prevalence of variant populations such as methicillin-resistant and vancomycin-resistant strains, and there is currently a dearth of novel antibiotics in production. Additionally, S. aureus has the capacity to hijack the host clotting machinery to generate fibrin-based biofilms that confer protection from host antimicrobial mechanisms and antibiotic-based therapies, enabling immune system evasion and significantly reducing antimicrobial efficacy. Emphasis is being placed on improving the effectiveness of therapeutics that are already commercially available through various means. Fibrin-based nanoparticles (FBNs) were developed and found to interact with S. aureus through the clumping factor A (ClfA) fibrinogen receptor and directly integrate into the biofilm matrix. FBNs loaded with antimicrobials such as vancomycin enabled a targeted and sustained release of antibiotic that increased drug contact time and reduced the therapeutic dose required for eradicating the bacteria, both in vitro and in vivo. Collectively, these findings suggest that FBN-antibiotic delivery may be a novel and potent therapeutic tool for the treatment of S. aureus biofilm infections.
Exploring stress and coping skills of medical students: a repeated cross-sectional cohort study
Musick, David W.; Criss, Tracey W.; Rudd, Mariah J.; Mutcheson, R. Brock; Harrington, Daniel P.; Knight, Aubrey L. (2026-02-19)
Objectives: To examine stressors and coping skills as re-flected in the student population at a southeastern United States medical school, including identifying key stressors over time and coping mechanisms used. Methods: Repeated cross-sectional cohort, mixed-methods study conducted between 2016 and 2022 at a four-year med-ical school program. Participants were students from seven classes, with two classes providing data during each of their four years of medical school. A census sampling approach was used, with survey data collected annually from each class across four years. Two surveys were used: the Perceived Stress Scale (PSS) and a modified Coping Orientation to Problems Experienced (COPE) Inventory. Open-text ques-tions captured qualitative responses. Statistical analysis in-cluded Welch’s t-tests, Pearson correlations, and Cronbach’s alpha reliability testing. Qualitative data were examined through inductive thematic analysis. Results: Students reported moderate levels of perceived stress across all four years with fluctuations identified by year of study. There were no statistically significant differences in perceived stress based on student gender; however, qualita-tive findings identified gender differences related to coping strategies. Thematic analysis of qualitative data revealed three recurring categories of stressors: academic workload, residency application and match pressures, and personal life challenges. Stressors shifted from academic in the pre-clini-cal years to career concerns during the clinical years. Conclusions: This study highlights the presence of stress throughout medical school and underscores the importance of adaptive coping strategies and the need for phase-specific interventions to support student well-being. Future research should evaluate the effectiveness of interventions in reducing stress across training stages.
Testing and Microcracking Assessment of Cement-Treated Full-Depth Reclamation
Tong, Bilin; Amarh, Eugene; Diefenderfer, Brian K.; Brand, Alexander S.; Hasibul Hasan, Rahat; Katicha, Samer; Benavides Ruiz, Carolina; Flintsch, Gerardo W. (SAGE Publications, 2026-04-21)
Full-depth reclamation (FDR) has gained increasing recognition as an efficient and cost-effective pavement rehabilitation method by recycling up to 100% of existing materials on-site, with Portland cement-stabilized FDR (FDR–PC) providing enhanced structural integrity. A comprehensive understanding of the mechanical properties of FDR–PC is essential to optimize its design and improve implementation efficiency. This study investigated the mechanical characteristics of FDR–PC, the interrelationships among various tests, and assessed the use of microcracking on constructed accelerated pavement test sections. Tests conducted included compressive strength (CS), flexural strength, elastic modulus, and shrinkage behavior in the laboratory, and deflection testing using a falling weight deflectometer. Four constructed FDR–PC pavement sections, including 3.25% and 5.5% cement content (by weight), both with and without induced microcracking, were built to study the shrinkage concerns observed during practice associated with FDR–PC. In addition, the influence of the microcracking technique was evaluated. The findings include: (1) a correlation factor of 1.46 to account for specimen-size effects in FDR–PC CS testing; (2) a slower loading rate than that specified in ASTM C469 may be more appropriate for characterizing FDR–PC; (3) the American Concrete Institute-based modulus predictions tend to overestimate FDR–PC stiffness; (4) the American Association of State Highway and Transportation Officials model provided the most accurate 7-day modulus of rupture (MoR) estimates; (5) length change test results were influenced by density and cement content; (6) strong correlations were observed among all evaluated mechanical properties; and (7) microcracked mixtures gained stiffness over time, with greater initial reduction in the lower-stiffness FDR–PC and significant stiffness recovery in the high-stiffness FDR–PC.
Historical availability of arable land affects contemporaneous female labor and health outcomes
Jha, Chandan Kumar; Sarangi, Sudipta (PLOS, 2025-08-04)
We contribute to the understanding of mechanisms underlying deep-rooted gender norms by exploring the link between the historical availability of arable land and contemporary gender outcomes. We argue that an abundance of arable land in historical times, i.e., pre-industrial period, required more workers in the fields resulting in norms where women worked and contributed from outside the home as well. Consequently, these societies emphasized women’s health due to its positive effect on their productivity in the fields. Moreover, this economic contribution provided women greater bargaining power in the allocation of intrahousehold resources. The historical availability of arable land for a nation is measured as the weighted mean of the shares of its constituent ethnic groups’ ancestral lands suited to cereal agriculture. Consistent with these arguments, we show that countries with more ancestral arable land have higher female labor force participation rates and better health outcomes, measured by maternal mortality ratio and female-male life expectancy gap. We then illustrate the ‘persistence of norm’ mechanism, by showing that ancestral arable land measured at the district level is positively associated with individual-level attitudes regarding women’s participation in the labor market.
A Comparative Analysis of Transfected and Integrated Auxin Reporter Systems Reveals Sensitivity Advantages in Protoplast Transient Expression Assays
Taylor, Joseph S.; Villaseñor, Eric A.; Rashkovsky, James; Simson, Jaime; Wright, R. Clay; Bargmann, Bastiaan O. R. (2025-02-28)
Reporter-gene activation studies using transient transformation of protoplasts are a powerful tool for the investigation of transcriptional regulation in plants. Here, we perform a comparative analysis of reporter-gene activation sensitivity using an integrated versus a co-transfected reporter-gene construct in Arabidopsis seedling mesophyll protoplasts. The DR5 synthetic auxin-responsive promoter was used to assay the response to auxin treatment and over-expression of activator Auxin Response Factors. We show that sensitivity, as measured by the fold-change in fluorescent-protein reporter-gene expression, is significantly increased by using a co-transfected reporter-gene construct.
An Interpretable Ensemble Heuristic for Principal-Agent Games with Machine Learning
Baswapuram, Avinashh Kumar; Chen, Chen; Cai, Wenbo; Büyüktahtakın, İ. Esra (2026-04)
This paper addresses the challenge of enhancing public policy decision-making by efficiently solving principal-agent models (PAMs) for public-private partnerships, a critical yet computationally demanding problem. We develop a fast, interpretable, and generalizable approach to support policy decisions under these settings. We propose an interpretable ensemble heuristic (EH) that integrates Machine Learning (ML), Operations Research (OR), and Game Theory. First, we reformulate a PAM as a mixed-integer program to improve efficiency. Next, we solve thousands of PAM instances under varying config- urations to generate training data for ensemble tree-based ML models that identify key solution patterns. These patterns form a hierarchical heuristic that provides feasible and interpretable solutions. We demonstrate the EH’s efficacy in managing the Emerald Ash Borer (EAB) infestation, an urgent public-policy threat to U.S. ash trees. Empirical results show that the EH produces high-quality solutions with 1-2% optimality gaps while significantly reducing computational time compared to exact optimization. Furthermore, the heuristic explains predictions using an average of 4.5 of 9 input features, enhancing transparency. Our findings demonstrate that the EH promotes rapid, informed, and accountable policy decisions by balancing interpretability with computational efficiency. Practically, it supports real-time simulations for stakeholders without specialized ML or OR expertise. Methodologically, it demonstrates a robust integration of optimization and machine learning to solve complex policy models. Beyond the EAB application, this approach provides a scalable framework for real-time decision support where transparency and justification are paramount.
Strengthening S-STEM Pathways: Lessons Learned From Cross-Sector Partnerships
Moyer, Stephen; Richardson, Amy Jo; Newcomer, James (2026-04-22)
Synthesizing data products, mathematical models, and observational measurements for lake temperature forecasting
Holthuijzen, Maike; Gramacy, Robert; Carey, Cayelan; Higdon, Dave; Thomas, R. Quinn (2025-06-01)
We present a novel forecasting framework for lake water temperature, which is crucial for managing lake ecosystems and drinking water resources. The General Lake Model (GLM) has been previously used for this purpose, but, similar to many process-based simulation models, it: requires a large number of inputs, many of which are stochastic; presents challenges for uncertainty quantification (UQ); and can exhibit model bias. To address these issues, we propose a Gaussian process (GP) surrogate-based forecasting approach that efficiently handles large, high-dimensional data and accounts for input-dependent variability and systematic GLM bias. We validate the proposed approach and compare it with other forecasting methods, including a climatological model and raw GLM simulations. Our results demonstrate that our bias-corrected GP surrogate (GPBC) can outperform competing approaches in terms of forecast accuracy and UQ up to two weeks into the future.

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