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
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Recent Submissions
Foreign expansion strategy and performance
Mas-Ruiz, Francisco J.; Nicolau, Juan Luis; Ruiz-Moreno, Felipe (Emerald, 2002-08-01)
The aim of this study is to examine the determining factors of a firm's performance, as a direct consequence of its diversification strategy in its expansion into foreign markets, considering certain factors like the market, the product and the company itself. As a novelty, the methodology employed uses the event-study to estimate the excess of returns generated by its shares on the Stock Market, based on a sample, of 35 expansion announcements into external markets corresponding to 11 diversifying companies. A regression analysis is also carried out to examine the impact of these factors, market, product and company, on the excesses in returns observed. The empirical application, carried-out in Spain, has allowed us to detect that, on average, the impact of the news about a company's expansion on the returns on its shares is positive; its determining factors being the speciality of the product offered and the level of development in the target country.
LLMs for Semantic Web Query
Chen, Yinlin (2023-11-09)
The emergence of Large Language Models like GPT-4 offers unprecedented capabilities in understanding human intent and generating text. This tutorial explores the intersection of LLMs and semantic web applications, focusing on how these models can automatically generate queries that adhere to metadata standards. Participants will engage in hands-on exercises that demonstrate the integration of LLMs into a sample semantic web application. This session will offer conceptual understanding and practical skills for metadata practitioners, developers, and researchers. The aim is to enable attendees to leverage the capabilities of LLMs in enhancing semantic web applications.
Target audience: Metadata practitioners, developers, researchers, and those interested in Large Language Models
Expected learning outcomes:
Understand LLMs and their capabilities.
Gain hands-on experience and learn to generate metadata-compliant queries using LLMs.
Discuss potential applications and limitations of LLMs in the semantic web.
Tutorial style: Presentation, demonstration, hands-on practice, discussion and Q&A
Prior knowledge required:
Basic familiarity with semantic web technologies, such as RDF or SPARQL
Some basic Python programming skills
Participants are recommended to have:
A dual-monitor setup or two computers to more easily follow along with hands-on exercises while also watching the presentation
Streamlining DBpedia Queries with Natural Language Using Large Language Models
Chen, Yinlin (2023-07-10)
The capability to query knowledge bases like DBpedia using natural language is an emerging approach in the semantic web and linked data. This presentation highlights the use of GPT, a large language model (LLM), to examine its potential for interpreting natural language queries and retrieving information from linked data repositories.
Think of the convenience of querying DBpedia with questions such as "Where was Albert Einstein born?" or "Who won the Nobel Prize in Literature?". To retrieve such information today, one must understand and write SPARQL queries. LLMs, like GPT-4, have the potential to translate these natural language queries into SPARQL, thereby making DBpedia more accessible to those without technical expertise in SPARQL.
This approach improves the search experience and paves the way for more intuitive interaction with linked data. While there are challenges to this approach, including ensuring the accuracy of generated SPARQL queries and handling ambiguous natural language inputs, the integration of GPT-4 with DBpedia opens up a new avenue in information retrieval. This presentation will explore this promising approach, demonstrating its potential to modify our interaction with linked data and influence its practical use in the future.
Computational Analysis of a Mutated μ-opioid Receptor Bound to a Morphinan Antagonist
Marques, Luiza Dias; Luthango, Philade; Wright, Bailee; Gregory, Logan (2023-07-21)
Opium is a depressant drug, derived from the opium poppy (Papaver somniferum L.), and holds one of the earliest records of medicinal plant use. Deriva ves of opium, codeine and morphine, are extensively used in therapeu c pain relief treatments.The mu (μ) opioid receptor is produced from the OPRM1 gene and acts as the primary receptor for most opioid drugs, which are highly addic ve. Narcan is used to counter the effects of an opium overdose; if Narcan binds to the receptor more effec vely, its inhibitory effect will increase and it can bind more favorably than opium or opium deriva ves in the event of an overdose. Researchers know that differences in the receptors' structure and func on influence how the body responds to opioids. In this study, we mutated the Tryptophan-318 residue into an Arginine residue in order to observe whether it would result in a more ideal binding arrangement of narcan. We performed molecular docking in order to obtain the RMSD and affinity values for each predicted posi on of narcan with and without the inves gated muta on. On average, the Y318R μ-opioid receptor produced poses with higher binding affinity than the original protein. The op mum affinity value for the Y318R protein was also lower than that of the original protein. This led to the conclusion that muta ng Tryptophan-318 into Arginine enhances the binding of the morphinian antagonist to the μ-opioid receptor, making it more effec ve at countering the effects of an opium overdose. This research encourages further experimenta on regarding the muta on of addi onal residues and in turn the process of drug discovery can be improved.
Huperzine A with Idebenone: Using Molecular Docking to analyze a potential Combination Therapy for Alzheimer’s Disease by Inhibition of the Acetylcholinesterase Enzyme
Saleem Hashmi, Faeeza; Ramos, Laura; Bolorchuluun, Khishigbuyan; Hughes, Charli; Khojamuratova, Aynahan (2023-07-21)
Those suffering from Alzheimer’s disease (AD) have low concentrations of Acetylcholine (ACh), a neurotransmitter involved in learning, memory, and muscle contraction. Acetylcholinesterase (AChE), a cholinergic type enzyme, plays a crucial role in concluding neurotransmission and degrading ACh. The accumulation of the AChE protein in AD patients results in decreased levels of ACh, and contributes to the buildup of amyloid-beta (Aβ) plaques that are crucial in the onset of AD. This study aims to examine how Idebenone could work as a potential inhibitor in addition to Huperzine A for AChE inhibition as a possible combination therapy for Alzheimer's Disease. Structure files of the inhibitors and AChE (PDB ID: 1VOT) were manipulated using PyMOL, GNINA, and Google Colab for molecular visualization and docking to find bond energies and possible ligand-protein interactions. The results confirmed that Idebenone has a significant binding affinity, though relatively lower than Huperzine A and in a different location, to be a useful component in this combination therapy due to its variety of interactions within AChE’s binding cavity including hydrogen bonds, hydrophobic and aromatic interactions. Huperzine A, with its stronger binding affinity, also exhibited positive interactions with amino acids surrounding the binding cavity of AChE, including hydrophobic and aromatic interactions. Most notably, the placement of each of these molecules did not significantly impact one another. Therefore, we can conclude that when used in conjunction, the two inhibitors target distinct locations in the wide binding pocket of AChE, and complement one another in the areas each can not cover alone. Their neuroprotective qualities also aid in the treatment of the disease. Future research could explore longer inhibitors to optimize binding within AChE's binding cavity and test the efficacy of this combination therapy on other neurodegenerative diseases involving cholinergic dysfunction.