Information Science Research Articles

A Comprehensive Survey on the Role of Law in Different Applications in Computer Science

One significant tool for addressing the concerns associated with the use of one of the applications in computer science is the law. Numerous laws and rules governing the use of computer science are designed to safeguard users, customers, and society at large. Law uses norms created by governmental and social institutions to control behavior. Programming is used to explore digital information in computer science. Computers got more potent and smaller as they evolved through generations, utilizing various technologies such as integrated circuits. Cyber law regulates online behavior and deals with matters pertaining to intellectual property, privacy, domain names, and other legal concerns. A few of the many concerns that the law must address in order to mitigate the electronic crimes are protecting privacy and security, upholding justice, limiting civil and criminal liability, ensuring safety, and controlling the application of AI in the workplace. Guidelines that people and organizations have to follow when utilizing computer science applications, making sure that these requirements are met in full. Additionally, as companies must commit to recording AI usage procedures and making clear the use of data and algorithms, the regulation fosters accountability and openness. By doing this, the possibility of prejudice and mistakes when utilizing AI is decreased. This survey provides a self-contained introduction to cybercrimes and types of cybercrimes. We also present ways to combat cybercrime and limit its spread.

The Effects of a Science Vocabulary Intervention on Children’s Oral Language Development

This study investigated the effects of a shared reading and guided play vocabulary intervention on the science vocabulary acquisition of children who were at risk for oral language difficulties. Fifty-five children from five preschool programs participated in a 15-day science vocabulary intervention. Children randomly assigned to the treatment group (n = 29) received explicit science vocabulary instruction through shared readings of informational science books and follow-up scientific investigations. Children in the business-as-usual group (n = 26) received typical instruction from their classroom teacher. Vocabulary knowledge was measured at pre- and posttest using two standardized vocabulary assessments and one researcher-developed science vocabulary test. Results of the researcher-developed test showed that the treatment group significantly outperformed the BAU group on explicitly taught science vocabulary words (p < 0.001). No significant differences were found between the two groups on measures of general vocabulary knowledge. Limitations and practical implications of this study are discussed.

General Definitions of Information, Intelligence, and Consciousness from the Perspective of Generalized Natural Computing

Atoms themselves have no thoughts and cannot be thinking. Why does the human body, which is composed of atoms, have consciousness? The widely used concepts of information and intelligence in today’s science, which are related to this, do not yet have appropriate general definitions. Answering these interesting questions is a crucial issue for technological development in the historical context of human society entering the era of intelligence. The key lies in how to fully utilize the existing fundamental theories subtly related to information science. Here we attempt to give the definition of general information and general intelligence from the perspective of generalized natural computing, based on the least action principle, Hamilton-Jacobi equation, dynamic programming, reinforcement learning, and point out the relationship between the two. The least action principle for describing conservative systems can be seen as an intelligent manifestation of natural matter, and its equivalent form, the Hamilton-Jacobi equation, can be extended to describe quantum phenomena and is a special case of continuous dynamic programming equations. Dynamic programming is an efficient optimization method under deterministic models, while reinforcement learning, as a manifestation of biological intelligence, is its model-free version. The statement that reinforcement learning is the most promising machine learning method has a profound physical foundation. General information is defined as the degree to which a certain environmental element determines the behavior of the subject. General intelligence is defined as the automatic optimization ability of the action or value function of a system with a certain degree of conservatism. Intelligence is a basic property of material systems, rather than an emergent property that only complex systems possess. Consciousness is an advanced intelligent phenomenon, a reconstruction of quasi conservative systems based on complex systems.

Preface: 3rd International Conference on Modern Medicine Technology and Clinical Science (MMTCS 2024)

We are glad to introduce you that the 2024 3rd International Conference on Modern Medicine Technology and Clinical Science (MMTCS 2024) was successfully held in Tokyo, Japan from August 17-18, 2024. This conference brought together innovative academics and industrial experts in the fields of modern medicine technology, pharmaceutical engineering, and clinical science and application to a common forum. The primary goal of the conference is to promote research and developmental activities, and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and Q &amp; A discussion. All the papers have been processed to meet the requirements of international publication standard. We selected a bunch of high-quality papers and compiled them into proceedings after rigorously reviewing. We would like to express our sincere gratitude to the distinguished keynote speakers, as well as all the participants. We also want to thank the publisher for publishing the proceedings. May the readers could enjoy and gain some valuable knowledge from the MMTCS 2024 proceedings. We are expecting more and more experts and scholars from all over the world to join this international event next year. The Scientific Committee of MMTCS 2024 Tokyo, Japan

Enhanced ovarian cancer survival prediction using temporal analysis and graph neural networks

Ovarian cancer is a formidable health challenge that demands accurate and timely survival predictions to guide clinical interventions. Existing methods, while commendable, suffer from limitations in harnessing the temporal evolution of patient data and capturing intricate interdependencies among different data elements. In this paper, we present a novel methodology which combines Temporal Analysis and Graph Neural Networks (GNNs) to significantly enhance ovarian cancer survival rate predictions. The shortcomings of current processes originate from their disability to correctly seize the complex interactions amongst diverse scientific information units in addition to the dynamic modifications that arise in a affected person`s nation over time. By combining temporal information evaluation and GNNs, our cautioned approach overcomes those drawbacks and, whilst as compared to preceding methods, yields a noteworthy 8.3% benefit in precision, 4.9% more accuracy, 5.5% more advantageous recall, and a considerable 2.9% reduction in prediction latency. Our method’s Temporal Analysis factor uses longitudinal affected person information to perceive good-sized styles and tendencies that offer precious insights into the direction of ovarian cancer. Through the combination of GNNs, we offer a robust framework able to shoot complicated interactions among exclusive capabilities of scientific data, permitting the version to realize diffused dependencies that would affect survival results. Our paintings have tremendous implications for scientific practice. Prompt and correct estimation of the survival price of ovarian most cancers allows scientific experts to customize remedy regimens, manipulate assets efficiently, and provide individualized care to patients. Additionally, the interpretability of our version`s predictions promotes a collaborative method for affected person care via way of means of strengthening agreement among scientific employees and the AI-driven selection help system. The proposed approach not only outperforms existing methods but also has the possible to develop ovarian cancer treatment by providing clinicians through a reliable tool for informed decision-making. Through a fusion of Temporal Analysis and Graph Neural Networks, we conduit the gap among data-driven insights and clinical practice, proposing a capable opportunity for refining patient outcomes in ovarian cancer management operations.

Cognition, horizon and practice: formation of the nonsynchronism in theoretical research of information science

Purpose The nonsynchronism in theoretical research of information science (IS) is an interesting phenomenon that has not been sufficiently examined. This study is to reveal the mechanism of its formation from a microscopic perspective.Design/methodology/approach A qualitative research approach was adopted and 22 professors from different IS fields were interviewed. The data were analyzed with a six-step thematic analysis method.Findings The nonsynchronism in IS theoretical research is reflected in three dimensions. Theory cognition and theory practice shape each other under the constraint of theory horizon. Researchers with similar theory cognition and practice form a theoretical generation. The inter-generational gap may exist among IS researchers in different research areas, at different ages, or located in different countries or regions.Research limitations/implications This study reveals the formation mechanism of the nonsynchronism in IS theoretical research from a microscopic perspective of individual researcher’s theory using practice and offers new insights into the dynamics of the structural evolution of IS theoretical knowledge.Practical implicationsThe results of this study can help to promote internal or external communication in IS and enhance theoretical education for doctoral students.Originality/valueThis study identifies and theoretically defines a few core themes, theory horizon, theory cognition, theory practice and theoretical generation, and reveals their relationships. On this basis, a conceptual model of nonsynchronism in theoretical research is developed.

Evaluating AI Excellence: A Comparative Analysis of Generative Models in Library and Information Science

ABSTRACT This study compares the performance of GPT-3.5, GPT-4, Bard, and Gemini in answering Library and Information Science (LIS) questions. Sixteen questions were used for assessment, with two independent examiners scoring the initial and two successive responses from each AI system. Statistical analyses, including one-way Analysis of Variance (ANOVA), independent sample t-test, and one-sample t-test, were employed to identify performance differences. The results revealed consistency in responses generated across iterations for all AI systems. Significant differences were observed among the AI models, with Bard consistently underperforming compared to GPT-3.5, GPT-4, and Gemini. The study uncovered variability in the examiners’ scoring and emphasized the need for multiple evaluators in AI assessment.

How to make sense of generative AI as a science communication researcher? A conceptual framework in the context of critical engagement with scientific information

A guiding theory for a continuous and cohesive discussion regarding generative artificial intelligence (GenAI) in science communication is still unavailable. Here, we propose a framework for characterizing, evaluating, and comparing AI-based information technologies in the context of critical engagement with scientific information in online environments. Hierarchically constructed, the framework observes technological properties, user experience, content presentation, and the context in which the technology is being used. Understandable and applicable for non-experts in AI systems, the framework affords a holistic yet practical assessment of various AI-based information technologies, providing both a reflection aid and a conceptual baseline for scholarly references.

Mathematical Information Retrieval: A Review

Mathematical formulas are commonly used to demonstrate theories and basic fundamentals in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning research in the STEM domain results in the mass production of scientific documents that contain both textual and mathematical terms. In scientific information, the definition of mathematical formulas is expressed through context and symbolic structure that adheres to strong domain-specific notions. Whereas the retrieval of textual information is well-researched, and numerous text-based search engines are present. However, textual information retrieval systems are inadequate for searching scientific information containing mathematical formulas, including simple symbols to complicated mathematical structures. The retrieval of mathematical information is infancy, and it requires the inclusion of new technologies and tools to promote the retrieval of scientific information and the management of digital libraries. This paper provides a comprehensive study of mathematical information retrieval, highlights their challenges and future opportunities.

AI Tools in LIS Research: Navigating Opportunities and Challenges for Scholarly Advancement

ABSTRACT This study examines the integration of AI tools in Library and Information Science (LIS) research to understand their implications on academic practices. Using a careful survey-based methodology, the study explores the advantages of AI tools, related challenges, and potential behavioural impacts, mainly focusing on whether they unintentionally promote satisfaction among researchers. Findings reveal an ideal position for AI tool utilisation, highlighting varied degrees of usage and perceived benefits like increased efficiency and expanded research scope. However, challenges encompass ethical concerns, integration complexities, and diverging opinions regarding the tools’ potential impact on researcher diligence. This study contributes significantly by addressing the less-explored behavioural implications of AI tool integration in research, offering refined insights into their multifaceted impact on academic practices. It underscores the need for a balanced and informed approach to AI integration, considering their transformative potential and ethical considerations in scholarly pursuits.

On-Surface Atomic Scale Qubit Platform.

Recent advances in scanning probe microscopy combined with electron spin resonance have revealed that localized electron spins on or near surfaces can be utilized as building blocks for the bottom-up assembly of functional quantum-coherent nanostructures. In this perspective, we review the recent advances, lay out advantages of this platform and outline the challenges that lie ahead on the way to the application of on-surface atomic spins to quantum information science and quantum computing.

Mapping the Mpox discourse: A network and sentiment analysis

Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.

Problem-based learning with library e-resources to enhance student information literacy self-efficacy

PurposeThis study investigates the effectiveness of problem-based learning (PBL) in information literacy curricula using library e-resources to enhance information literacy self-efficacy (ILSE) of students in two universities in Indonesia, in Java Island (University A) and Sumatra Island (University B).Design/methodology/approachA comparison of the effectiveness of the approach in the two universities forms the focus of the study, which has adopted a single group quasi-experimental design which was conducted in one online teaching-learning session. The authors compared the pre-test and post-test scores of 65 library and information science (LIS) students from both universities.FindingsThe results show that the treatment enhanced ILSE effectively. After the treatment, University A students performed better than those at University B.Research limitations/implicationsThe factors which affected the success of PBL using library e-resources have not been identified in this study, a task for a future qualitative research study.Practical implicationsThis study will both inspire the use of library e-resources in learning activities and promote ILSE.Originality/valueIntegrating PBL with library e-resources provides opportunity to identify the advantages of library e-resources in supporting student ILSE, resulting a better learning achievement.

Promoting Language and Scientific Literacy Through Children's Literature: A Systematic Literature Review

ABSTRACT Children's literature is widely recognized for its potential to enhance student development. Despite existing research and publications on the topic, a thorough study on integrating children's literature into language and scientific literacy has not been widely conducted. This systematic literature review aimed to comprehensively explore current literature on innovative strategies and the effects of integrating children's literature to improve language and scientific literacy. Utilizing the Systematic Literature Review (SLR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methods, this review analyzed 54 studies from the Scopus database spanning 2010 to 2024. The Publish or Perish and VOSviewer applications supported the identification, screening, eligibility, and inclusion stages. Scopus-indexed articles totaling 275 were initially identified and filtered into 54 relevant pieces. The findings highlight that integrating children's literature into science education effectively bridges language and scientific literacy, enhancing cognitive development and comprehension. The potential of this interdisciplinary teaching model suggests practical applications for educators seeking to enrich their instructional strategies by employing diverse literary forms such as informational texts, biographies, comics, dramas, and science fiction. Ultimately, this review emphasizes children’s literature's significant role in advancing language and scientific literacy in educational contexts. Keywords: children’s literature, language literacy, scientific literacy

A review of the literature on spirituality and religion in information research – 1990 to 2022

PurposeMore than eight in ten people worldwide identify with a religious group. In addition, people often engage with spiritual and religious content despite having no formal beliefs or affiliations. Spirituality remains a prominent feature of Western and Westernised information-based societies and cultures; however, people’s everyday interactions with spiritual and religious information have received disproportionate attention in information and library science research. Accordingly, this paper aims to understand how scholars have explored religion and spirituality in information research and identify current and emerging trends in the literature.Design/methodology/approachThis paper analyses 115 peer-reviewed articles, 44 book chapters, 24 theses and 17 unrefereed papers published between 1990 and 2022 to present a narrative review of how scholars have explored spirituality and religion in information research. The reviewed literature is first organised into spirituality-related and religion-related articles and thereafter analysed in Internet studies, information behaviour studies and galleries, libraries, archives and museums-related research groups.FindingsOur analysis indicates scholars in Internet studies have researched both established and alternative religious interactions, and emerging research agendas seek to explore intersections between traditional religious authority and modern Internet-facilitated engagements. Information behaviour scholars have examined interactions in Christianity and Islam, focused primarily on Western contexts and conventional interactions, with emerging research aiming to explore diverse contextual and methodological combinations. Finally, GLAM researchers have investigated the practicality, suitability, and appropriateness of spirituality and religion-related service provisions; however, a clear research agenda is currently lacking in spirituality and religion information research more broadly.Originality/valueThis paper is the first review of the spirituality and religion-related information research spanning Internet studies, information behaviour studies and galleries, libraries, archives and museums research domains.

AI anxiety and fear: A look at perspectives of information science students and professionals towards artificial intelligence

The rapid integration of artificial intelligence (AI) within society and the emergence of the fourth industrial revolution (4IR), has ignited a spectrum of emotions in society, ranging from enthusiasm to anxiety. This study investigates the depths of AI anxiety and fear among a population of information science students and professionals. Utilising a survey of over 200 current students and professionals, this study explores the connections between age, gender identity, ethnicity, geographic location, educational attainment and residence, and the levels of anxiety and fear associated with AI and the 4IR. The findings reveal nuanced relationships, with age, ethnicity, academic achievement and regional context serving as critical differentiators in 4IR and AI anxiety within this population. Students and professionals alike may benefit from seeking further education about this emerging technology.

Student attitudes toward quantum information science and technology in a high school outreach program

[This paper is part of the Focused Collection in Investigating and Improving Quantum Education through Research.] With the current growth in quantum information science and technology (QIST), there is an increasing need to prepare precollege students for postsecondary QIST study and careers. This mixed methods, explanatory sequential research focused on students’ affective outcomes from a one-week, 25-h summer program for U.S. high school students in grades 10–12. The workshop structure was based upon psychosocial theories of self-determination and planned behavior, where QIST aspirations may be facilitated and viewed as achievable choices if students acquire disciplinary knowledge, self-efficacy, normative expectancy of their capacity in the field, and awareness of vocational roles. The program featured lectures, demonstrations, and hands-on experiences in classical and quantum physics and quantum computing. Students’ attitudes toward QIST (N=77)—including self-efficacy, self-concept, relevance, career aspirations, and perceptions of quantitative fluency—showed improvement with a medium effect size, even though treatment students entered the program with more positive QIST attitudes when compared with a control group of high school physics students (N=65). Postprogram interviews with n=12 participants identified several explanatory themes: (i) Students tended to comprehend classical and quantum topics taught through multiple representations, regardless of whether they had taken physics previously; (ii) students experienced some challenges with mathematics and science concepts that support quantum understanding, yet they revealed a willingness to learn new concepts outside of their comfort zone; (iii) students expressed motivation for pursuing science, technology, engineering, and mathematics and/or quantum-related careers in the future, as well as increased QIST self-concept, largely through understanding the relevance of QIST in solving technological problems; and (iv) students reported increased self-efficacy in understanding QIST topics and performing related tasks. This informal summer program showed promise in promoting positive student attitudes toward QIST, a critical emerging field in advancing technological solutions for global challenges. Published by the American Physical Society 2024

Preface: 3rd International Conference on Architecture, Geotechnical Engineering and Construction Technology (AGECT 2024)

The 2024 3rd International Conference on Architecture, Geotechnical Engineering and Construction Technology (AGECT 2024) was held in Barcelona, Spain from August 10-11, 2024. The conference brings together innovative scholars and industry experts in the fields of architecture design, urban planning, building materials, structural engineering, civil engineering, geotechnical engineering, and construction technology into a common forum. The main objective of AGECT 2024 was to promote research and development activities. Another goal is to promote the exchange of scientific information between researchers, engineers, PHDs, students, and practitioners working in the conference and workshop. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&amp;A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 8-15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-40 minutes to hold their speeches. We would like to acknowledge all of those who supported AGECT 2024. The help and contribution of each individual and institution was instrumental in the success of the conference. In particular, we would like to thank the organizing committee for its valuable inputs in shaping the conference program and reviewing the submitted papers. We are sure that the proceedings will serve as an important research source of references and knowledge, which will lead to not only scientific and engineering findings but also new products and technologies. With best regards, Conference Organizing Chair Barcelona, Spain

Self representation based methods for tensor completion problem

Tensor, the higher-order data array, naturally arises in many fields, such as information sciences, seismic data reconstruction, physics, video inpainting and so on. In this paper, we intend to provide a new model to recover a tensor, based on self-representation, for the all-mode unfoldings of the desired tensor, regardless of the tensor rank. The suggested idea generalizes self-representation to tensor and recovers an incomplete tensor by reconstructing one fiber by others in such a way that they all belong to the same subspace. We design least-square and low-rank self-representation algorithms based on the Linearized Alternating Direction Method utilizing this concept. We show that the proposed algorithms converge to the rank-minimization of the incomplete tensor.

Demystifying multilingual LIS journals in Scopus: Languages, coverage, metrics, and implications

This study aimed to investigate the languages, countries, geographic distribution, and commonly used journal metrics of multilingual Library and Information Science (LIS) journals indexed in Scopus. The Scimago Journal Rank (SJR) portal was used as a proxy for Scopus-indexed LIS journals. Through examination of the homepages of 249 LIS journals listed in SJR, 42 multilingual LIS journals were identified. For this study, multilingual journals were defined as those publishing in a combination of English and other languages. The findings revealed that although English-only journals have historically dominated the field, a significant number of multilingual journals have been indexed in Scopus over the past two decades. The 42 identified multilingual journals span 25 countries and encompass 18 languages, reflecting a diverse linguistic landscape in LIS scholarship. Notably, 67% are Open Access (OA) and affiliated with major publishers, including university presses. Spain and Brazil emerge as key players, accounting for 27% of all identified multilingual journals, highlighting the importance of their regional languages in LIS scholarship. However, the study also uncovered disparities in journal metrics and rankings, with multilingual journals consistently scoring lower than their English-only counterparts. Despite efforts to include regional and local journals in major databases, these results underscore the persistent challenges multilingual publications face in achieving and maintaining Scopus indexing.