Indigenous communities around the world are losing their languages at accelerating rates to the effects of the climate crisis and global capitalism. To preserve samples of these languages facing endangerment and extinction, samples of language use (e.g., audio-video recordings, photographs, textual transcriptions, translations, and analyses) are created and stored in language archives: repositories intended to provide long-term preservation of and access to language materials. In recent years, archives of all kinds are considering their origins and audiences. With the emergence of the community paradigm of archiving framework, the roles of archivists, communities, and institutions are under re-examination. Language archives too are reflecting this trend, as it becomes more common for speakers of Indigenous languages (also known as language communities) to document and archive their own languages and histories. As the landscape of language archiving expands, we now see increased emphasis on the re-use of archival material, particularly to support language revitalization—efforts to increase and maintain the use of the language. There are calls for language documentation (and, by extension, language archiving) to prioritize revitalization efforts. This dissertation is a case study of one language archive collection: the Boro Language Resource in the Computational Resource for South Asian Languages (CoRSAL) archive. …

The traditional definition of tourism has been transformed by significant advancements in communication and information technology. The concept of Metaverse, derived from the words "meta" (meaning beyond) and "verse" (meaning universe), has redefined how people experience travel. This innovative concept combines virtual reality, augmented reality, and artificial intelligence to create virtually augmented spaces. However, the tourism industry should clarify and narrow down the definition of Metaverse and its intriguing concept for its successful adoption in the future. Thus, it is crucial to define Metaverse tourism and understand how users will accept it in the near future. This study aims to comprehend the technology behind Metaverse tourism, review current research on the topic, and identify the critical factors related to experiential Metaverse tourism. The paper also examines how computer self-efficacy, novelty seeking, subjective norm, job relevance, perceived usefulness, perceived ease of use, and perceived enjoyment can influence expected user satisfaction and behavioral intention, given the context of situational motivation. The findings have significant implications for theory and management, addressing various questions related to users' perceptions, expectations, design considerations, stakeholder preparations, and performance assessment of metaverse technology in tourism applications.

Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data gathered using geospatial information science (GIS) and remote sensing techniques. Two sets of data were collected: a BBD for a single year of 2020 and a historical BBD covering a five-year period from 2016 to 2020. Three ML algorithms, including random forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were trained using 319,404 and 1,246,261 bridge decks in the BBD and the historical BBD, respectively. Results showed that the use of historical BBD significantly improved the performance of the models compared to BBD. Additionally, random forest and XGBoost, trained using the historical BBD, demonstrated higher overall accuracies and average F1 scores than the ANN model. Specifically, the random forest and XGBoost models achieved overall accuracies of 83.4% and 79.4%, respectively, and average F1 scores of …

Relevance is a core concept in the field of Information Science and a common term in everyday vernacular that generally refers to the usefulness of information. However, relevance has not been sufficiently or consistently defined or explored in the information science literature. Relevance criteria are the factors that information users employ when determining whether information they encounter is relevant. Identifying relevance criteria is a crucial step to understanding relevance. Relevance criteria employed with newer information formats like online video are especially important to study. Online video is now widespread, and people are increasingly likely to rely on video for information. This study identifies relevance criteria employed during relevance assessments of online video through a explanatory sequential mixed-methods study of frequent online video users including students, faculty, librarians, and video professionals. Methods included an online survey and interviews.