Objectives Lack of familiarity and limited digital literacy frequently limit the ability and willingness of older adults to use digital health tools. This study aimed to investigate the factors influencing the intention to use digital health information and services (DHIAS) and their actual use among indigenous Taiwanese older adults, based on the theory of planned behaviour (TPB) and digital health literacy.Design Cross-sectional quantitative study using a structured questionnaire.Setting Community-based settings at the primary care level; 11 cultural health stations located in Eastern Taiwan.Participants A total of 170 Indigenous elders aged ≥55 years were recruited. Inclusion criteria included: Indigenous descent, aged ≥55 years, cognitively intact and able to communicate in Mandarin. All participants completed the interview-based survey.Primary and secondary outcome measures The primary outcomes were the intention to use DHIAS and its actual use. Predictors included digital health literacy and TPB constructs. All variables were measured using validated or adapted items within the questionnaire.Results The mean score for intention to use DHIAS was 1.50 (SD=0.90) and the mean score for actual behaviour was 1.68 (SD=1.14). Hierarchical regression analyses indicated that subjective norms (β=0.270, p<0.001)—a core construct of TPB—and digital health literacy (β=0.603, p<0.001) significantly predicted intention, accounting for 55.7% of the variance. Intention (β=0.732, p<0.001) and digital health literacy (β=0.146, p<0.025) were significant predictors of actual behaviour, accounting for 72.4% of the variance.Conclusions Digital health literacy and TPB constructs critically influence digital health engagement among older populations. Interventions aimed at improving digital health engagement among older populations should focus on enhancing digital skills and creating socially supportive environments. Future studies should explore culturally tailored strategies to reduce digital disparities in communities with limited healthcare access.
Polymodality of statistical sand grain size distribution is due to the changes in kinematic energy of aquatic environment during the process of sediment deposition in open system-facies. Improving relevance of information about deposition paleoenvironment is of high significance in interpretation of granulometric analysis results. The paper investigates the results of granulometric analysis of sandy-aleuritic deposits confined to different formations in the wells located in the oil fields on the Yamal Peninsula. Based on the kinematic theory of open system transformation, the equation that describes the dependence of grain size on grain kinematic parameters – time period and transport distance – was developed. Therefore, it is possible to calculate these parameters within the studied facies on the basis of available grain sizes and percentage of fraction with diameter range from 0.001 to 1 mm. The aim of this study is to present a new approach to facies identification based on the calculations of kinematic parameters of sand grain flow and fine grains using the equations of open system transformation intensity within the universal kinematic theory. The parameter which was proved the most informative is sediment transport distance during deposition, which is controlled by the size of the settling grains. This parameter is influenced by bed slope angle, grain size, and deposition depth. Comparing the value of this parameter with fraction diameter, it is possible to identify the facies of the studied area.
Phumzile D. Mogoale, Agnieta Pretorius, Refilwe C. Mogase
et al.
Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. Developed countries have largely integrated AI technologies into their learning institutions, while the discipline is in its infancy in developing countries such as South Africa (SA).
Objectives: This study aims to contextualise and recommend the strategy that institutions of higher learning in SA can adopt to integrate AI into their institutions.
Method: A systematic literature review (SLR) method was followed. Publications published between 2018 and 2024 in the Multidisciplinary Digital Publishing Institute (MDPI) and Taylor Francis Online databases using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Following an initial search, 114 documents were retrieved, and, using inclusive criteria, 29 papers were chosen for analysis. The databases were selected because of their unique benefits in terms of accessibility, material breadth, and researcher-specific functions, unlike other sources.
Results: Results show that to integrate AI, the following should be considered: planning, collaborations, training, and ethical standards to guarantee responsible use and productivity. This will enhance teaching and learning, well preparing students for a future whereby AI is widely used in the workplace.
Conclusion: To integrate AI into learning institutions, a tailored approach needs to ensure that the AI technology improves teaching, enhances administrative procedures, and adheres to the institution’s rules and regulations.
Contribution: This article forms a theoretical and methodological contribution to advancing knowledge that may inform policy and practice makers.
Management information systems, Information theory
We investigate information-theoretic limits and design of communication under receiver quantization. Unlike most existing studies, this work is more focused on the impact of resolution reduction from high to low. We consider a standard transceiver architecture, which includes i.i.d. complex Gaussian codebook at the transmitter, and a symmetric quantizer cascaded with a nearest neighbor decoder at the receiver. Employing the generalized mutual information (GMI), an achievable rate under general quantization rules is obtained in an analytical form, which shows that the rate loss due to quantization is $\log\left(1+γ\mathsf{SNR}\right)$, where $γ$ is determined by thresholds and levels of the quantizer. Based on this result, the performance under uniform receiver quantization is analyzed comprehensively. We show that the front-end gain control, which determines the loading factor of quantization, has an increasing impact on performance as the resolution decreases. In particular, we prove that the unique loading factor that minimizes the MSE also maximizes the GMI, and the corresponding irreducible rate loss is given by $\log\left(1+\mathsf {mmse}\cdot\mathsf{SNR}\right)$, where mmse is the minimum MSE normalized by the variance of quantizer input, and is equal to the minimum of $γ$. A geometrical interpretation for the optimal uniform quantization at the receiver is further established. Moreover, by asymptotic analysis, we characterize the impact of biased gain control, showing how small rate losses decay to zero and providing rate approximations under large bias. From asymptotic expressions of the optimal loading factor and mmse, approximations and several per-bit rules for performance are also provided. Finally we discuss more types of receiver quantization and show that the consistency between achievable rate maximization and MSE minimization does not hold in general.
Juanita Zainudin, Nazlena Mohamad Ali, Alan F. Smeaton
et al.
Widely distributed misinformation shared across social media channels is a pressing issue that poses a significant threat to many aspects of society’s well-being. Inaccurate shared information causes confusion, can adversely affect mental health, and can lead to mis-informed decision-making. Therefore, it is important to implement proactive measures to intervene and curb the spread of misinformation where possible. This has prompted scholars to investigate a variety of intervention strategies for misinformation sharing on social media. This study explores the typology of intervention strategies for addressing misinformation sharing on social media, identifying 4 important clusters – cognition-based, automated-based, information-based, and hybrid-based. The literature selection process utilized the PRISMA method to ensure a systematic and comprehensive analysis of relevant literature while maintaining transparency and reproducibility. A total of 139 articles published from 2013–2023 were then analyzed. Meanwhile, bibliometric analyses were conducted using performance analysis and science mapping techniques for the typology development. A comparative analysis of the typology was conducted to reveal patterns and evolution in the field. This provides valuable insights for both theory and practical applications. Overall, the study concludes that scholarly contributions to scientific research and publication help to address research gaps and expand knowledge in this field. Understanding the evolution of intervention strategies for misinformation sharing on social media can support future research that contributes to the development of more effective and sustainable solutions to this persistent problem.
Daniella Bar-Lev, Tuvi Etzion, Eitan Yaakobi
et al.
Enzymatic DNA labeling is a powerful tool with applications in biochemistry, molecular biology, biotechnology, medical science, and genomic research. This paper contributes to the evolving field of DNA-based data storage by presenting a formal framework for modeling DNA labeling in strings, specifically tailored for data storage purposes. Our approach involves a known DNA molecule as a template for labeling, employing patterns induced by a set of designed labels to represent information. One hypothetical implementation can use CRISPR-Cas9 and gRNA reagents for labeling. Various aspects of the general labeling channel, including fixed-length labels, are explored, and upper bounds on the maximal size of the corresponding codes are given. The study includes the development of an efficient encoder-decoder pair that is proven optimal in terms of maximum code size under specific conditions.
Geoffroy Caillat-Grenier, Andrei Romashchenko, Rustam Zyavgarov
We study the connection between mixing properties for bipartite graphs and materialization of the mutual information in one-shot settings. We show that mixing properties of a graph imply impossibility to extract the mutual information shared by the ends of an edge randomly sampled in the graph. We apply these impossibility results to some questions motivated by information-theoretic cryptography. In particular, we show that communication complexity of a secret key agreement in one-shot setting is inherently uneven: for some inputs, almost all communication complexity inevitably falls on only one party.
Research in interdisciplinary fields like biomechanics is published in a variety of journals whose visibility depends on bibliometric indexing that is often driven by citation analysis of bibliometric databases. This study documented variation in publication metrics and research subject categories assigned to 14 biomechanics journals. Authors, citation, and citation rate (CR) were collected for the top 15 cited articles in the journals retrieved from the Google Scholar service. Research subject categories were also extracted for journals from three databases (Dimensions, Journal Citation Reports, and Scopus). Despite the focus on biomechanics for the journals studied, these biomechanics journals have widely varying CR and subject categories assigned to them. There were significant (p=0.001) and meaningful (77-108%) differences in median CR between average, low, and high CR groups of these biomechanics journals. Since CR are primary data used to calculate most journal metrics and there is no one biomechanics subject category, field normalization for journal citation metrics in biomechanics is difficult. Care must be taken to accurately interpret most citation metrics of biomechanics journals as biased proxies of general usage of research, given a specific database, time frame, and area of biomechanics research.
Bibliography. Library science. Information resources
Background: The power disparity between traditional leaders and councillors is a source of concern for the local government’s developmental focus, as the municipality and traditional leaders do not always agree, even though the service delivery is sorely needed by the people they serve. Although many scholars have written about the roles of traditional leaders and municipal councillors, the critical question that remains unanswered is how their relationship works in terms of service delivery.
Aim: This article theoretically investigates the relationship between traditional leaders and municipal councillors and its adverse impacts on rural development.
Setting: South African municipalities.
Methods: The methodology for this article is a literature review guided by a hermeneutic framework. This article adopted a hermeneutic framework to integrate the analysis and interpretation of information collected from the literature.
Results: The power dynamic between traditional leaders and elected councillors has surfaced as a source of concern, as it has the potential to delay and block development.
Conclusion: The article concludes that all local government stakeholders must work to strengthen the relationship between traditional leaders and municipal councillors.
Contribution: This article has the potential to add to theory, policy and practice in terms of strategies to address the relationship between traditional leaders and municipal councillors in local government.
Integrating marketing and distribution businesses is crucial for improving the coordination of equipment and the efficient management of multi-energy systems. New energy sources are continuously being connected to distribution grids; this, however, increases the complexity of the information structure of marketing and distribution businesses. The existing unified data model and the coordinated application of marketing and distribution suffer from various drawbacks. As a solution, this paper presents a data model of “one graph of marketing and distribution” and a framework for graph computing, by analyzing the current trends of business and data in the marketing and distribution fields and using graph data theory. Specifically, this work aims to determine the correlation between distribution transformers and marketing users, which is crucial for elucidating the connection between marketing and distribution. In this manner, a novel identification algorithm is proposed based on the collected data for marketing and distribution. Lastly, a forecasting application is developed based on the proposed algorithm to realize the coordinated prediction and consumption of distributed photovoltaic power generation and distribution loads. Furthermore, an operation and maintenance (O&M) knowledge graph reasoning application is developed to improve the intelligent O&M ability of marketing and distribution equipment.
Energy conservation, Energy industries. Energy policy. Fuel trade
Onur Pusuluk, Mahir H Yeşiller, Gökhan Torun
et al.
The quantum superposition principle has been extensively utilized in the quantum mechanical description of bonding phenomenon. It explains the emergence of delocalized molecular orbitals and provides a recipe for the construction of near-exact electronic wavefunctions. On the other hand, its existence in composite systems may give rise to nonclassical correlations that are regarded as a resource in quantum technologies. Here, we approach the electronic ground states of three prototypical molecules in the light of the framework set by fermionic information theory. By introducing the notion of orbital discord, we additively decompose the pairwise orbital correlations into their classical and quantum parts in the presence of superselection rules. We observe that quantum orbital correlations can be stronger than classical orbital correlations though not often. Moreover, quantum orbital correlations can survive even in the absence of orbital entanglement depending on the symmetries of the constituent orbitals. Finally, we demonstrate that orbital entanglement would be underestimated if the orbital density matrices were treated as qubit states.
Wolfgang G. Stock, Kaja J. Fietkiewicz, Katrin Scheibe
et al.
Participants in real-time online sessions, be it (business) meetings, virtual school lessons, or social live streams, all engage in cyber social interactions. Unlike parasocial interactions, cyber social interactions are characterized by reciprocity and temporal proximity. In contrast to social interactions, they lack spatial proximity and bodily contact. This is a fairly new concept in information science that rose from technological advances and unprecedented circumstances (e.g., the rise of digital economy and knowledge workers being able to work remotely or, more recently, global lockdowns and contact restrictions). As a result, the past ways of working and socializing were transformed by making them, in some cases predominantly, virtual. Regarding the example of social live streaming we exhibit the importance of cyber social interactions for information behavior research. This conceptual article is a plea for information science to engage more in human-human online relations and interactions.
Bibliography. Library science. Information resources
A hallmark of chaotic dynamics is the loss of information with time. Although information loss is often expressed through a connection to Lyapunov exponents -- valid in the limit of high information about the system state -- this picture misses the rich spectrum of information decay across different levels of granularity. Here we show how machine learning presents new opportunities for the study of information loss in chaotic dynamics, with a double pendulum serving as a model system. We use the Information Bottleneck as a training objective for a neural network to extract information from the state of the system that is optimally predictive of the future state after a prescribed time horizon. We then decompose the optimally predictive information by distributing a bottleneck to each state variable, recovering the relative importance of the variables in determining future evolution. The framework we develop is broadly applicable to chaotic systems and pragmatic to apply, leveraging data and machine learning to monitor the limits of predictability and map out the loss of information.
Game dynamics theory, as a field of science, the consistency of theory and experiment is essential. In the past 10 years, important progress has been made in the merging of the theory and experiment in this field, in which dynamics cycle is the presentation. However, the merging works have not got rid of the constraints of Euclidean two-dimensional cycle so far. This paper uses a classic four-strategy game to study the dynamic structure (non-Euclidean superplane cycle). The consistency is in significant between the three ways: (1) the analytical results from evolutionary dynamics equations, (2) agent-based simulation results from learning models and (3) laboratory results from human subjects game experiments. The consistency suggests that, game dynamic structure could be quantitatively predictable, observable and controllable in general.