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.
Open Access Policy
Virginia Tech's open access policy enables researchers to deposit the accepted version of scholarly articles with no embargo.
Theses and Dissertations
Virginia Tech was first in the world to require ETDs in 1997, and continues to add scans of older theses and dissertations.
Open Textbooks
More than 40 freely available and openly licensed textbooks are among our most downloaded items.
Communities in VTechWorks
Select a community to browse its collections.
Recent Submissions
An Integrative Review of Curricular Integration as a Curriculum Development Strategy in Health Professions Education
Ryan, Shane Michael (Virginia Tech, 2025-03-14)
This integrative literature review examines the concept of curricular integration as a curriculum development strategy in health professions education. The review synthesizes existing research on the definition, theoretical foundations, implementation, and efficacy of integrated curricula, which seek to connect diverse disciplines and provide a more holistic, student-centered approach to learning. Key elements of successful curricular integration include interdisciplinary faculty collaboration and the contextualization of knowledge within real-world settings. Theoretically, curricular integration supports deeper learning, enhances clinical reasoning, and improves knowledge transfer, preparing students for complex health science professions. However, challenges related to the variability in defining and evaluating curricular integration are identified, and the need for standardized metrics and more robust longitudinal studies is emphasized. The review concludes that while curricular integration shows significant promise in improving health professions education, further research is needed to refine implementation strategies, evaluate its long-term impact, and ensure its alignment with evolving expectations of professional practice.
An Economic Approach to Optimize Capital Allocation
Rohan Malhotra (Rohan Malhotra, 2025-02-15)
A central problem that investors face is how to manage risk on transactions with positive expectation of profit while maximizing the rate of growth of capital. One way to define the problem is given a transaction with a positive expectation of profit, how much risk should the investor take on the transaction to maximize long term growth of its capital base by maximizing risk adjusted return while minimizing ruin. In this paper we explore the use of Kelly Criterion, which is to maximize the expected value of the logarithm of Insurers capital (“maximize expected logarithmic utility”) to find solutions to the problem. The criterion is known to economists and financial theorists by names such as the “geometric mean maximizing portfolio strategy”, maximizing logarithmic utility, the growth-optimal strategy, including the capital growth criterion. We prove these concepts using a hypothetical investor with a fixed probability of total loss. This approach can be generally utilized in allocating capital for investments.
Keywords: Kelly criterion, Betting, Long run investing, Portfolio allocation, Logarithmic utility, Capital growth
Learning Optimal Solutions via an LSTM-Optimization Framework
Yilmaz, Dogacan; Büyüktahtakın, İ. Esra (Springer, 2023-06-06)
In this study, we present a deep learning-optimization framework to tackle dynamic mixed-integer programs. Specifically, we develop a bidirectional Long Short Term Memory (LSTM) framework that can process information forward and backward in time to learn optimal solutions to sequential decision-making problems. We demonstrate our approach in predicting the optimal decisions for the single-item capacitated lot-sizing problem (CLSP), where a binary variable denotes whether to produce in a period or not. Due to the dynamic nature of the problem, the CLSP can be treated as a sequence labeling task where a recurrent neural network can capture the problem's temporal dynamics. Computational results show that our LSTM-Optimization (LSTM-Opt) framework significantly reduces the solution time of benchmark CLSP problems without much loss in feasibility and optimality. For example, the predictions at the 85% level reduce the CPLEX solution time by a factor of 9 on average for over 240,000 test instances with an optimality gap of less than 0.05% and 0.4% infeasibility in the test set. Also, models trained using shorter planning horizons can successfully predict the optimal solution of the instances with longer planning horizons. For the hardest data set, the LSTM predictions at the 25% level reduce the solution time of 70 CPU hours to less than 2 CPU minutes with an optimality gap of 0.8% and without any infeasibility. The LSTM-Opt framework outperforms classical ML algorithms, such as the logistic regression and random forest, in terms of the solution quality, and exact approaches, such as the (`, S) and dynamic programming-based inequalities, with respect to the solution time improvement. Our machine learning approach could be beneficial in tackling sequential decision-making problems similar to CLSP, which need to be solved repetitively, frequently, and in a fast manner.
Battery asset management with cycle life prognosis
Liu, Xinyang; Zheng, Zhuoyuan; Büyüktahtakın, İ. Esra; Zhou, Zhi; Wang, Pingfeng (Elsevier, 2021-12)
Battery Asset Management problem determines the minimum cost replacement schedules for each individual asset in a group of battery assets that operate in parallel. Battery cycle life varies under different operating conditions including temperature, depth of discharge, charge rate, etc., and a battery deteriorates due to usage, which cannot be handled by current asset management models. This paper presents a new battery asset management methodology where battery cycle life prognosis is integrated with parallel asset management to reduce lifecycle cost of the Battery Energy Storage Systems (BESS). For the battery failure time prognosis, a nonlinear physics-based battery capacity fade model is developed and incorporated in parallel asset management model to update battery capacity over time. Experiment results have shown that the developed battery asset management methodology can be conveniently used to facilitate BESS asset management decision making thereby decreasing asset lifecycle costs.
Optimizing surveillance and management of emerald ash borer in urban environments
Bushaj, Sabah; Büyüktahtakın, İ. Esra; Yemshanov, Denys; Haight, Robert G. (Wiley, 2020-05-21)
Emerald ash borer (EAB), a wood-boring insect native to Asia, was discovered near Detroit in 2002 and has spread and killed millions of ash trees throughout the eastern United States and Canada. EAB causes severe damage in urban areas where it kills high-value ash trees that shade streets, homes, and parks and costs homeowners and local governments millions of dollars for treatment, removal, and replacement of infested trees. We present a multistage, stochastic, mixed-integer programming model to help decision-makers maximize the public benefits of preserving healthy ash trees in an urban environment. The model allocates resources to surveillance of the ash population and subsequent treatment and removal of infested trees over time. We explore the multistage dynamics of an EAB outbreak with a dispersal mechanism and apply the optimization model to explore surveillance, treatment, and removal options to manage an EAB outbreak in Winnipeg, a city of Manitoba, Canada. Recommendation to Resource Managers Our approach demonstrates that timely detection and early response are critical factors for maximizing the number of healthy trees in urban areas affected by the pest outbreak. Treatment of the infested trees is most effective when done at the earliest stage of infestation. Treating asymptomatic trees at the earliest stages of infestation provides higher net benefits than tree removal or no-treatment options. Our analysis suggests the use of branch sampling as a more accurate method than the use of sticky traps to detect the infested asymptomatic trees, which enables treating and removing more infested trees at the early stages of infestation. Our results also emphasize the importance of allocating a sufficient budget for tree removal to manage emerald ash borer infestations in urban environments. Tree removal becomes a less useful option in small-budget solutions where the optimal policy is to spend most of the budget on treatments.