INFOSCIENCE TRENDS
Year:2024 | Volume:1 | Issue:1|Papers Count:7

The emergence of large language models has sparked discussion about their role in academic and scholarly publishing and whether their use poses threats to academic integrity. This paper argues for the ethical acceptance and integration of language models into the scholarly publishing process for the purposes of improving writing quality without the need for explicit acknowledgment or additional scrutiny, on the basis that this policy is critical for closing the equity gap for non-native English speakers and researchers from developing countries. Journal-wide policies are proposed that would allow the use of AI for revising writing quality, without singling out specific authors or articles, aligning with existing practices for language editing services, and ensuring confidentiality while promoting fairness and transparency in the publishing process. By removing artificial barriers to participation and acknowledging the diverse linguistic backgrounds of researchers, the scholarly ecosystem can advance toward greater equity and innovation.

This study delves into the transformative potential of genetic algorithms in revolutionizing the selective dissemination of information (SDI) through the optimization of content personalization strategies. The primary objective of this research is to examine the efficacy of genetic algorithms in enhancing SDI systems by adeptly tailoring content selection to align with user preferences. Through a comprehensive exploration of various methodologies and approaches employed in integrating genetic algorithms into SDI systems, this study sheds light on the intricate mechanisms that underpin the optimization of content personalization. The empirical findings underscore the profound impact of genetic algorithms on augmenting the SDI process, showcasing their ability to facilitate personalized content delivery, streamline selection procedures, and dynamically adapt to evolving user preferences. By emphasizing the transformative potential of genetic algorithms, this study not only advances the current knowledge base but also underscores their pivotal role in elevating the performance of SDI systems. Furthermore, this research offers valuable insights into the nuanced design considerations and challenges inherent in deploying genetic algorithms for content personalization within SDI frameworks. The implications of these findings extend beyond academia, providing actionable guidance for researchers and practitioners seeking to develop sophisticated and adaptive SDI systems that effectively cater to individual user needs and preferences. Ultimately, this study paves the way for the development of intelligent information dissemination platforms that prioritize relevance, personalization, and user-centricity.

Mental disorders pose a significant global challenge, particularly accentuated by the escalated prevalence during the COVID-19 pandemic outbreak. In addressing this critical issue, the utilization of web-based telehealth systems emerges as a promising solution to provide essential healthcare services to individuals grappling with mental disorders. In light of this, the primary objective of this study is to share the experience of designing and developing a web-based telehealth system tailored for deployment within a developing country. Using a three-phase model, we conducted interviews with 8 clinical psychologists and 8 general psychologists in the Concept Identification phase, extracting content to alleviate COVID-19-related mental health burdens. In the System Design phase, we collaborated with 24 experts recruited from social media to develop a final prototype for a web-based telehealth system's user interface. The third phase, Pre- and Post-Implementation Assessments, engaged 120 participants. Using the COVID-19 Stress Scale, we measured stress levels before and after participation. Data analysis employed comprehensive statistical tests, calculating Standard Deviation and Mean for an overview, and conducting analytical tests (independent sample t-test, one-way ANOVA, Tukey's post hoc test) for deeper insights. Depression, drug use, stress, anxiety, and sleep problems emerged as shared concerns among all participating psychologists. Data indicated an initial stress score of 128 for participants before engaging with the web-based telehealth. Subsequently, stress scores progressively decreased over quarters: 85 in the first, 44 in the second, and 62 in the third. Significantly, repeated measures analysis of variance highlighted a substantial stress reduction within the COVID-19-affected group. Web-based telehealth presents a solution that overcomes geographical constraints, reduces travel complexities, and eases the financial burden of seeking care. Moreover, it offers increased flexibility in appointment scheduling, enhancing the overall convenience of mental health support. A key advantage of web-based telehealth is its potential to maintain a continuum of care for individuals managing mental health challenges.

The current research aimed to investigate how health professionals evaluate online health information, with a focus on their personality trait of conscientiousness. The study population consisted of a random sample of students and faculty members from two health-related faculties at the Tehran University of Medical Sciences. The research identified several challenges in evaluating health information, including difficulties in determining information quality and the presence of inaccurate information. The participants considered objectivity, currency, and authority as the most important criteria for assessing the quality of health information. Additionally, the study found a significant correlation between conscientiousness and the assessment of credibility. Changing levels of conscientiousness influenced participants' reasons for rejecting or trusting health information. It is worth noting that participants paid less attention to factors such as topic coverage and accuracy, despite their importance. These findings can be used to conduct more comprehensive studies in healthcare and inform policymaking in the context of online health information delivery, including social media platforms. To enhance the understanding of health professionals' evaluation of online health information, future research could consider expanding the sample to include a more diverse range of participants. Additionally, conducting comparative studies on different sources of health information, such as websites and social media platforms, could provide valuable insights for healthcare professionals and policymakers.

The purpose of this paper is to explore the potential impact of quantum computing on academic and public libraries, with a focus on how it can empower these institutions in the information era. This viewpoint article provides an analysis of the advancements in quantum computing and their potential impact on libraries. It examines how quantum computing can enhance various aspects of library operations, including information processing, data storage, search capabilities, data analysis, cybersecurity, and scientific research. The findings reveal that advances in quantum computing have the potential to revolutionize libraries by enabling faster and more efficient information processing, improved data storage and retrieval, enhanced search capabilities, advanced data analysis, strengthened cybersecurity measures, and facilitated scientific research. These advancements can empower libraries to better serve their users in the information era. This paper contributes to the existing literature by highlighting the potential benefits of quantum computing for libraries. It explores the unique opportunities presented by quantum computing technologies and emphasizes their originality and value in transforming libraries into more efficient and effective information hubs in the digital age.

This article presents a thorough examination of eight state-of-the-art AI chatbots that are designed for general use. Through detailed descriptions and empirical research, which include the use of confusion matrices and a pairwise comparison methodology, the study systematically assesses these chatbots based on eight specific criteria. The main objective of this research is to pinpoint the most efficient AI chatbot for general purposes, thereby providing invaluable insights for both users and developers within the AI industry. By utilizing the results of this study, developers can gain access to crucial information and actionable recommendations that help in enhancing the performance of their chatbots. Furthermore, these findings act as a vital resource for budding developers who are venturing into the creation of their own chatbot projects. In a rapidly evolving technological landscape, where AI is increasingly infiltrating various aspects of daily life, such detailed evaluations play a crucial role. They guide both users and developers towards selecting and developing AI chatbots that are not just effective, but also user-friendly and adaptable to the shifting needs and preferences of their user base.

Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering innovative solutions for blood pressure management and control. This article explores the potential impact of AI-enhanced health tools in revolutionizing the approach to managing blood pressure. Various AI models, including machine learning algorithms, deep learning techniques, natural language processing, reinforcement learning, and Bayesian networks, are utilized to analyze data, predict outcomes, and provide personalized recommendations for individuals. These AI models have the capability to extract insights from complex datasets, identify patterns, and tailor interventions based on individual needs and preferences. Despite the promising potential of AI in blood pressure management, several challenges must be addressed. Data quality and privacy concerns, interpretability and transparency of AI algorithms, bias and fairness in decision-making, regulatory and ethical considerations, integration and adoption issues, as well as validation and performance assessments pose significant hurdles in the implementation of AI-enhanced health tools. Overcoming these challenges requires a collaborative effort among healthcare providers, data scientists, ethicists, regulators, and policymakers to ensure the safe, effective, and ethical use of AI in healthcare settings. By addressing these challenges proactively and leveraging the power of AI, healthcare providers can optimize treatment strategies, improve patient outcomes, and empower individuals to take control of their health. The integration of AI in blood pressure management has the potential to transform healthcare delivery, enhance personalized care, and ultimately contribute to better health outcomes for individuals.