bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spectral Galerkin Approximation and Error Analysis Based on a Mixed Scheme for Fourth‐Order Problems in Complex Regions
Authors: Zheng Jihui , An Jing 
Published: October 2024

ABSTRACTIn this article, an efficient spectral Galerkin method, which is based on a mixed scheme, is proposed and studied for solving fourth‐order problems in complex regions. The fundamental idea behind this approach is to transform the initial problem into an equivalent form in cylindrical coordinates and to reshape the computational domain into a product‐type rectangular one, which facilitates the utilization of spectral methods. However, when considering the equivalent fourth‐order form directly in cylindrical coordinates, it introduces intricate pole conditions and variable coefficients, posing challenges to both theoretical analysis and algorithm implementation. To address this, we employ the orthogonality of Fourier series to further decompose it into a sequence of decoupled two‐dimensional fourth‐order eigenvalue problems. For each such problem, we introduce an auxiliary function to transform it into an equivalent second‐order coupled system. Building on this, we formulate a mixed variational formulation and discrete scheme, and prove the error estimates for eigenvalue and eigenfunction approximations. Furthermore, we extend this algorithm to the two‐dimensional complex domains. Finally, a series of numerical examples are presented, and the numerical results validate the effectiveness of the algorithm and the correctness of the theoretical results.
Boron nitride: The key material in polymer composites for electromobility
Authors: García‐Hernández Zureima , Molina‐Ramírez Oscar , Rivera‐Salinas Jorge E. , Sifuentes‐Nieves Israel , González‐Morones Pablo , Hernández‐Hernández Ernesto 
Published: October 2024

AbstractDespite the continuous development and improvement of many technologies and multifunctional materials for the electric powertrain (ePowertrain) for electric vehicles, there are still technical issues and challenges to address such as thermal management in batteries, electric motors, and power electronic devices, as most of their failures are due to poor thermal management. Consequently, conventional engineering polymer materials already used must be replaced since most of them have low thermal conductivity and are therefore limited in performance for thermal management applications. A key solution is to develop highly thermally conductive polymer composites that combine other features, such as flame‐retardant, electrical insulation, and mechanical and barrier properties, by incorporating fillers into the polymer matrix. This approach has attracted intensive research efforts. In this review, we first examine the key drivers, trends, and solutions of the ePowertrain segment, emphasizing thermal management. Second, special attention is given to the state‐of‐the‐art boron nitride (BN) polymer composites with current or potential applications in the automotive industry, especially, in batteries, electric motors, and power electronics. Third, analysis and prediction of thermal properties of BN polymer composites by finite element simulation are presented. Finally, outlooks for future research in this field are highlighted.Highlights
Thermal management of batteries, electric motors and power electronics, using BN polymer composites, optimizes the functionality of electric vehicles.
Cross‐linked polymers with BNNSs provide resins for high power motors, film capacitors, and Li‐metal battery electrolytes for electric vehicles.
Mathematical modeling and life cycle analysis can predict trends and research gaps in ePowertrain applications.
Artificial Intelligence and Machine Learning Competencies for the Archival Professions
Authors: Arias Hernandez Richard , Fewster Kaila , Penniman Sophie 
Published: October 2024

ABSTRACTThis article presents qualitative research aimed at deriving key competencies for archival students and professionals to leverage Artificial Intelligence (AI) and Machine Learning (ML) to support the ongoing availability and accessibility of trustworthy public records. Methodology and findings are presented. Our primary source of data were interviews conducted between January and July 2023 with 10 archivists, record managers, and digital archives researchers from the UK, Canada, USA, Australia, and New Zealand. All our interviewees had practical experience applying AI and ML to the processing of records in archives or record management offices. Findings from the qualitative data analysis from the interviews are presented and organized in three areas of competencies: knowledge, skills, and attitudes.
From Click to Trip: A Socio‐Technical Perspective on Tourism Live Streaming
Authors: Han Jia Yu , Chua Alton Y.K. , Xu Shao Gui 
Published: October 2024

ABSTRACTTourism live streaming can significantly enhance users' desire to travel. Although previous studies have identified many technical and social enablers influencing users' impulsive travel intention, limited research has examined it from a socio‐technical perspective. This paper aims to develop and validate a theoretical model that explains how visibility, personalization, and metavoicing influence impulsive travel intention through parasocial relationship in tourism live streaming. Empirical results (N = 551) reveal that visibility, personalization, and metavoicing exert both direct positive impacts on impulsive travel intention and indirect effects through the mediation of parasocial relationship. This paper also identifies gender as a moderating role in the proposed model. This paper enriches the literature by not only shedding light on the underlying process through which users develop impulsive travel intention, considering both technical features and social interaction but also stands as one of the earliest to investigate the gender's moderating role in this process.
Participatory Media and Game Preservation: A Taxonomy of YouTube Game‐Related Content
Authors: Newman Michele , Favors Drew , Koughan Lane , Lee Jin Ha 
Published: October 2024

ABSTRACTVideo games are both information objects as well as experiences of play. However, current methods of game preservation often fail to capture the full gaming experience. To address this, one proposed solution involves leveraging player‐generated content as an alternative to emulation. To investigate the viability of this suggestion, we examine the following research questions: (1) What types of gameplay content exist on the platform YouTube? and (2) What are the challenges and opportunities in utilizing this style of community created content for game preservation? We introduce a Taxonomy of YouTube Game‐Related Content to answer these questions and discuss its application, along with the challenges and opportunities that arose during its creation. Our taxonomy and findings suggest that although utilizing this content for game preservation requires additional knowledge of games and their community, as well as consideration of how the platform and creators impact game history, it also presents opportunities to preserve more diverse voices and experiences of play. Furthermore, it suggests YouTube videos offer a potential avenue, not only for preserving the game object, but also for preserving its broader cultural significance and multiple forms of knowledge.
The Geo‐Socio‐Personal Model: Mapping Complex Information Ecosystems for Research‐related Information Behaviors
Authors: Ward Wesley S. , Given Lisa M. 
Published: October 2024

ABSTRACTCreating international collaborative teams is common to address complex research problems in low‐income countries. Teams involve experts from low‐ and high‐income countries who must share information and communicate across interpersonal, organizational, and national contingencies and geographic and temporal borders. Effective information sharing between people and across institutions requires nuanced understandings of various information ecosystems, including the relationships between attitudes, values, and behaviors of individuals and organizations. This paper reports on an evidence‐based model developed from a qualitative study of research teams operating in two countries (Lao People's Democratic Republic and Australia), using constructivist grounded theory methodology. The resulting Geo‐Socio‐Personal (GSP) Model for Complex Information Ecosystems reflects multicultural relationships at four levels: geo‐structural, geo‐social, organizational, and inter−/intra‐personal. The model examines contingencies affecting information behaviors across all four levels, including interpersonal, organizational, and international relations, and the economic, political, and structural levels that posit these relations.
Describing the Ephemeral: Facilitating Rich Search Experiences with Performing Arts Video Collections
Authors: Given Lisa M. , Polkinghorne Sarah , Cattlin Joann , Carlin David 
Published: October 2024

ABSTRACTDigital video collections of dance, theatre, and other performances can provide unprecedented access to these ephemeral art forms. Making these collections searchable requires understanding of users' needs, necessitating collaboration among subject matter, metadata, and information behavior experts. Focusing on the example of Circus Oz, a prominent Australian circus company, this paper describes an exploratory, user‐centered process that draws on qualitative thematic coding techniques to create descriptive metadata schema. This iterative process illustrates the extensive labor involved, and the combination of expertise required, to create schema that extend beyond technical and basic categories to support rich search and discovery of cultural data collections.
Student Perceptions of Generative AI in LIS Coursework
Authors: Kizhakkethil Priya , Perryman Carol 
Published: October 2024

ABSTRACTThe purpose of the study is to inform LIS curriculum development by understanding student perceptions of generative AI tools. Assignments with a generative AI component for two courses (n = 65) were de‐identified and analyzed after the end of the semester using a grounded qualitative approach. Students recognize the need for caution and critical evaluation of generative AI tool output, while mentioning areas of utility in practice, including information retrieval, reference services, teaching, and information access. Responses highlight the importance of information literacy in the use of these tools, and potential implications for practice in information professions. The study contributes to the literature about the need for curriculum development to address this disruptive technology by identifying the professional competencies that are affected.
Investigating Co‐Authorship Networks of Academic and Industry Researchers in Artificial Intelligence
Authors: Liang Lizhen 
Published: October 2024

ABSTRACTResearch teams from the industry, especially big technology companies, have been pushing impactful research work in the field of artificial intelligence (AI), changing the prospects of the field and the careers of many researchers. Research teams from big technology companies usually possess more data, bigger computing infrastructure, and research talent, granting them the advantages in advancing AI research. While most previous work focuses on investigating the advantages the industry has in the field of AI, and how their research publication is different from those published by academic teams, few research has been done to investigate whether working as an industry researcher is beneficial at the individual level. In this work, by analyzing co‐authorship networks of researchers published in AI conferences, we investigate whether working in the industry gives researchers advantages in “intangible” forms, such as social capital, represented by the collaborative relationships they gained or maintained. Our result shows that the many advantages industry researchers possess correlate with the social capital they have, measured by degree centrality, eigenvector centrality, betweenness centrality, and effective size.
Assessing Gender and Racial Bias in Large Language Model‐Powered Virtual Reference
Authors: Liu Jieli , Wang Haining 
Published: October 2024

ABSTRACTTo examine whether integrating large language models (LLMs) into library reference services can provide equitable services to users regardless of gender and race, we simulated interactions using names indicative of gender and race to evaluate biases across three different sizes of the Llama 2 model. Tentative results indicated that gender test accuracy (54.9%) and racial bias test accuracy (28.5%) are approximately at chance level, suggesting LLM‐powered reference services can provide equitable services. However, word frequency analysis showed some slight differences in language use across gender and race groups. Model size analysis showed that biases did not decrease as model size increased. These tentative results highlight a positive outlook on integrating LLMs into reference services, while underscoring the need for cautious AI integration and ongoing bias monitoring.
Divide & Conquer: Critical Informatics Approaches to Disinformation
Authors: May Emma , Paris Britt 
Published: October 2024

ABSTRACTIn this paper, we use a critical informatics approach to investigate institutional disinformation around 2022–23 labor organizing at three higher education institutions: Rutgers University, Temple University, and the University of California. Our contribution to the study of disinformation is the application of critical informatics perspectives that attend to structural power dynamics of disinformation within an institutional context. Understanding the political economic dynamics of disinformation and how these dynamics function can help more solidly contextualize and clarify how and why disinformation exists across different information systems, so that solutions to this social and institutional problem of disinformation can be more appropriately addressed and understood. The study describes disinformation tactics employed by institutional leaders during higher education labor organizing including: non‐performative commitments to “community”, legal threats, misleading victories, and elite capture.
Coconut Libtool: Bridging Textual Analysis Gaps for Non‐Programmers
Authors: Santosa Faizhal Arif , Lamba Manika , George Crissandra , Downie J. Stephen 
Published: October 2024

ABSTRACTIn the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open‐source, web‐based application that utilizes state‐of‐the‐art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people‐first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.
Exploring the Opinion Shifting of Social Media Users Based on Stance Detection
Authors: Sun Ran , An Lu , Li Gang 
Published: October 2024

ABSTRACTFine‐grained mining of stance change among social media users during crises contributes significantly to a comprehensive understanding of the development of public opinion online. This study collected detailed information on 227,281 Twitter users who continuously participated in the discussion about the COVID‐19 vaccine to analyze patterns of opinion shifting. Utilizing the COVID‐Twitter‐BERT pre‐trained model, we detect each user's stance on the COVID‐19 vaccine and construct a time‐series dataset reflecting their stance over time. A prediction model for users' opinion shifting is established based on the LightGBM model, and the SHAP explanation method is used to rank the importance of features to identify key features influencing the opinion shifting prediction. Our research findings indicate that nearly half of the users maintained a consistent stance on vaccines, with a relatively low proportion of shifts between pro‐vaccine and anti‐vaccine stances. The F1 value of the prediction model based on the LightGBM model is 0.92. These findings can assist in monitoring social media user opinions online.
Transitioning to Human‐Centered AI: A Systematic Review of Theories, Scenarios, and Hypotheses in Human‐AI Interactions
Authors: Wang Di , Zheng Kaiyang , Li Chuanni , Guo Jianting 
Published: October 2024

ABSTRACTThis study conducted a systematic review of human‐AI interaction (HAI)over the past decade for the implemented theories and scenarios, and the tested hypotheses to discover the changes in the current transition to human‐centered AI (HCAI). Moving from acceptance theories, Computers are social actors (CASA), anthropomorphism, and the integrative trust model are the most frequent theories. Augmentation scenarios of decision‐making, teamwork, and human‐AI collaborations are common in the latest HAI studies. Users' trust, acceptance, and intention to use an AI system are the main research targets in HAI studies. These trends show a clear transition toward HCAI. This paper also discusses opportunities tied to HAI studies based on the interconnections between the various theories, scenarios, and hypotheses.
Deceiving Dichotomies: On the Interrelation of the Archive and the Cultural Memory Canon
Authors: Youngman Tyler , Modrow Sebastian , Meth Isaac 
Published: October 2024

ABSTRACTThis paper explores the conceptual relationship between archives and canonization by amending Aleida Assmann's cultural memory framework of Canon and Archive to consider ongoing debates in archival theory and practice. The process of canonization concerns the selection, value, and duration of cultural memory. The formation and function of archives are marked by processes of appraisal (record evaluation and selection), acquisition (record transfer), and preservation (record duration). Placing A. Assmann's framework in conversation with the informational frameworks of de Fremery and Buckland, we observe how at any given time (a past/present context), records (documents) are selected (perception) because of their value in signaling memories and community histories (affordances) while serving as a durable medium for restabilizing identities (use) across time (a present/future context).
Will Users Prefer Cultural Heritage Exhibition in Digital Twin? The Mediating Role of Perceived Authenticity under the Moderation of Individual Profiles
Authors: Zhao Liang , Wang Lu , Yu Feng , Zhao Yijun , Wang Yujue 
Published: October 2024

ABSTRACTDigital twin techniques provide unprecedented opportunities in cultural heritage and museum exhibition services. As the techniques intersect with cultural heritage, a compelling question arises: whether users accept the new service with pleasure or show aversion to the technology. In this paper, we compare user attitudes towards digital twin exhibition and traditional physical exhibition through laboratory experiments. Drawing from the Stimulus‐Organism‐Response (SOR) theoretical framework, we incorporate perceived authenticity and individual profiles such as field experience and personality traits to explore the underlying mechanisms and boundary conditions influencing user attitudes towards digital twin exhibitions. It showed that compared with traditional exhibition, users with low level of cultural heritage familiarity, agreeableness trait and openness trait would perceive less authenticity in digital twin exhibition, and in turn report more negative attitudes towards the exhibition. The findings contribute to a better understanding of the fusion of technology and cultural heritage from user's perspective and provide insights into technology‐empowered services of cultural heritage and smart museums.
Immersive Technology in Education
Authors: Bosman Isak de Villiers , Smith Annique Elizabeth , Wong Yan Lau , Ka Kwan Sui Dave , Alemneh Daniel , Chow Anthony 
Published: October 2024

ABSTRACTImmersive technologies like virtual reality (VR) and augmented reality (AR) are transforming higher education by providing engaging, experiential learning opportunities. However, their integration into educational practices presents challenges, including high costs, technological complexity, and the digital divide. This panel explores the application of immersive technologies in higher education, focusing on developing an immersive technology lab designed to support their adoption across the institution. The discussion will cover global perspectives on immersive learning, showcasing varied adoption rates and strategies across different regions. Additionally, the panel will highlight research opportunities in immersive education, emphasizing the need for studies on effective integration, impact on learning outcomes, and strategies to overcome barriers to adoption. The goal is to identify sustainable approaches to expose staff and students to immersive technologies, ensuring equitable opportunities for all stakeholders.
Creative Research Engagement from Design through Application to Dissemination: Maximizing the Research Impact through Creativity
Authors: Budd John M , Senteio Charles , Nesset Valerie , Mansourian Yazdan ,   Members of the ASIS&T Research Engagement Committee
Published: October 2024

ABSTRACTThe value of research is often determined by its impact and ability to catalyze positive change in the life of communities, technological developments, and the advancement of education. However, translating research findings and innovative conceptualizations into practical applications is not always easy for various reasons. As a result, there may be difficulty achieving a buy‐in from community and industry partners who are essential for elevating innovative ideas to the level of working models and tangible outcomes. Similarly, it is not always immediately clear how research findings should be integrated in teaching and learning to improve the relevance of education to community and industry needs. In many situations, creativity and imagination are key to transforming research into impactful applications. Can we reimagine partnerships for the benefit of praxis? Can we design unique methods of research dissemination that will reach and engage a broader community, beyond a specialist circle? Can we present ideas and concepts to the world in a way that excites and motivates people, moving them to action? The presenters will provide examples of work that addresses these questions and will work with the audience to help with guidance in their own work.