Telematics and Informatics Reports最新文献

High-dimensional datasets comprise a few rows but a huge total of features. The increased difficulty data scientists have in efficiently gleaning insights from high-dimensional large data is a direct outcome of this trend. Therefore, a K-Means clustering method (KMC) must be implemented to cleanse this data, with the centroid of KMC being ideally chosen by improved squirrel search algorithm (ISSA). The suggested algorithm features not one but two different types of searches: the leaping search and the progressive search. The linear regression selection strategy is utilised to automatically choose the applicable approach during the evolutionary phase; this increases SSA's stability. Harris Hawks Optimizer (HHO), which replicates the behaviour of a Harris hawk during rabbit predation, is applied to the cleaned data to cluster the massive pattern. However, HHO has problems with low accuracy and early convergence because of its inability to strike a good balance among exploitation. A new variant of HHO, dubbed velocity-guided HHO (VGHHO), including three improvements is proposed to address these drawbacks. By including a velocity operator and an inertia weight into the search equation, we are able to create a unique modified position search equation for use during the exploitation phase. Then, we incorporate a learning mechanism based on refraction and opposition to provide the promising resolutions and aid the swarm in escaping the local optimal solution. After the massive cluster has been built with VGHHO, uproot technology is used to uncover massive patterns. We run the tests on a wide variety of high-dimensional datasets and employ a number of different efficiency metrics. Evidence from these investigations shows that the proposed method produces high-quality mining results.

International policy agendas are increasingly focusing on the 21st century skills needed by future workers in response to Industry 4.0. In this study, we conduct a bibliometric analysis of 2662 articles published by 6579 authors in the last two decades to understand the structure of the scholarly knowledge in this field. We first identify influential articles, documents, journals and trends in this literature. We use co-citation analysis to identify foundational themes in the development of 21st century skills literature, then using bibliometric coupling, we identify communities in the current research front. We then use co-word analysis to identify future directions in the field. Overall, we find that research on 21st century skills has grown exponentially in the past two decades, however, few researchers focus primarily on this topic. The existing research is primarily dominated by psychologists, education researchers and technology researchers. We also find that specific disciplines such as industrial engineering and nursing are prominent contributors in the field, and that critical thinking and computational thinking are key areas of focus.

This study delves into the intricate dynamics of agenda-setting between Twitter and elite news media concerning COVID-19 vaccines. A comprehensive dataset comprising 501,531 US-based, English-language tweets and 7,282 news headlines extracted from The New York Times and The Washington Post was collected from January 1, 2020, to April 30, 2021. To uncover the temporal evolution of content topics, Latent Dirichlet Allocation (LDA) was employed alongside sentiment analysis to gage corresponding valence levels. Granger causality tests were then conducted on the time series of topic sizes and valence scores from tweets and news headlines to explore the intermedia agenda-setting effects. The LDA analysis identified 13 topics, with Twitter discourse predominantly focusing on the top five ranked topics, while news headlines exhibited a more even distribution across all topics. The Granger causality tests revealed tweets-to-news Granger causality for four topics, news-to-tweets Granger causality for four topics, and mutual influence for the remaining five topics. Consequently, the directions of the agenda-setting effects varied depending on the specific discussions' topics. The findings indicated that elite news media wielded greater influence over socially impactful aspects of COVID-19 vaccination, while Twitter exhibited an agenda largely independent of elite news media, centering on highly personal facets of COVID-19 vaccination. Furthermore, the transfer of salience in topics was more pronounced compared to valence.

In the wake of rapid development of livestream shopping and extensive research surrounding it, the fit between elements in livestream shopping has attracted the widespread attention of researchers. Based on the match-up hypothesis and information processing fluency theory, this study explored the impact of various types of fit between elements in livestream shopping on consumers’ purchase intention through processing fluency. The analysis of data sampled in mainland China showed that product–scene fit and anchor–scene fit influence consumers’ perceptual fluency; consumers’ perceptual fluency and pleasure play a mediating role in the effects of the two types of fit on purchase intention. Additionally, consumer–scene fit, consumer–product fit, consumer–anchor fit, and anchor–product fit affect consumers’ conceptual fluency; consumers’ conceptual fluency and trust play a mediating role in the effects of the four types of fit on purchase intention. The main theoretical contribution of this study is to systematically examine the impact of the fit between elements in livestream shopping on consumers’ purchase intention and reveal the influence mechanism from a novel perspective of information processing fluency.

As a general behavior in civil social life, how can personal health information disclosure be affected by social structural factors when using the health application? This study used the Social Capital Theory to examine the personal health information behavior. Nationally representative cross-sectional survey data generated by the China Family Panel Studies(CFPS) in 2020 was utilized(N = 8628). Logistic regression analysis was employed to analyze the data. After controlling for age, gender, and education covariates, social interaction had no significant impact on health APP information disclosure behavior (OR=0.988),there is a significant positive relationship between trust(OR=1.282)/ reciprocity perception(OR=1.134) and health APP information disclosure behavior. More relational social capitals can enhance individual social trust, maintain reciprocity perception, maintain the balance and stability of social exchange relations.This study extends the application of social capital theory in the study of personal health information disclosure, providing theoretical support for the democratization process of social health governance.

The present study explores the domain of Keris classification by employing advanced Convolutional Neural Networks (CNNs) as a potent technique for identifying subtle patterns and cultural characteristics inherent in these renowned Indonesian daggers. The study has presented encouraging findings about the identification of Pamor, Dhapur, and Tangguh categories. However, it is crucial to recognise and confront the inherent constraints associated with this research. The key constraints of the study pertain to the diversity of data, accuracy of labeling, generalizability of the model, and ethical considerations. The acquisition of a comprehensive dataset that effectively encompasses the whole range of Keris patterns offers a significant obstacle. Furthermore, it is crucial to pay careful attention to the accuracy of labeling, since it can be influenced by the subjective character of Keris classification. The important worry lies in guaranteeing the model's capacity to generalise to Keris images that have not been previously encountered, as well as its ability to comprehend and explain its decision-making process. The careful establishment of ethical frameworks is necessary to address ethical problems related to cultural sensitivity and the potential misuse of AI outputs in the realm of cultural heritage. Nevertheless, these constraints offer significant perspectives on potential areas for future investigation and enhancement. Future endeavours may prioritise the augmentation and broadening of the dataset, fostering collaboration with specialists in cultural domains, improving the interpretability of the model, and effectively addressing ethical considerations. The present study not only exhibits potential for expanding artificial intelligence in the domain of cultural preservation, but also contributes to a more profound understanding and recognition of the complex artistry and historical significance encapsulated within the Keris.

Blockchain technology has garnered significant attention in recent years, prompting researchers, entrepreneurs, and businesses to seek viable ways to validate the application of blockchain within their specific use cases. Blockchain decision schemes (BDSs) can assist in this decision-making process, offering a potentially more cost-effective alternative to domain experts. Flow chart blockchain decision schemes (FC-BDSs) constitute 77.5% of all BDSs, and this paper systematically reviews these by standardising and aggregating the most prominent schemes into an open-source package. Central to our approach is the definition of an FC-BDS as a directed acyclic graph (DAG). Upon this mathematical foundation, we engage in a meticulous exploration and analysis of various elements within FC-BDSs. We present an in-depth analysis of the structure of FC-BDSs, exploring features such as vertex count, question categorisation, and outcome distribution. Notably, the majority of FC-BDS questions ask about data and participation (34.1%) above other domains such as security (18.6%) and performance (10.8%). Observations regarding outcomes shows an overall balance in suggesting the usage or avoidance of blockchains; however, there is a discrepancy between the average questions required to reach these outcomes, revealing potential biases within schemes. Further analysis using similarity metrics (based on both structural and semantic features) identifies significant overlaps between FC-BDSs, with some schemes showing over 90% similarity. These observations could be attributed to a more informal publishing routine for FC-BDSs, and help trace the evolution of FC-BDSs over time. The insights drawn from this research provide valuable insights into the broader BDSs landscape, and stand to make significant strides towards the standardisation of FC-BDSs, thereby promoting a more coherent and effective utilisation of these decision-making tools in the realm of blockchain technology application.

This study tested the utility of virtual reality technologies in building public awareness of an environmental issue—the growing pollution of plastic waste in oceans. We conducted an experiment to test whether viewing of VR video would produce two anticipated immersive experiences (e.g., perceived realism and spatial presence) in severely polluted oceans, and to further examine their effects on participants’ pro-environmental attitudes with regards to reducing plastic waste. Results showed that VR viewing led to higher perceived realism and spatial presence in comparisons with 2D video and audio-only conditions; perceived realism contributed significantly to pro-environmental attitude, whereas spatial presence did not. The higher the perceived realism, the stronger the pro-environmental attitude. Implications of the findings for using VR technologies for effective environmental communication are discussed.

The concept of “mobile money” triggers a diverse range of initiatives, involves various actors, and prompts numerous inquiries. This article explores how mobile money serves as an innovative mobile telephony service in Africa. It delves into the actors involved in this emerging mobile money system. The article draws on existing literature on innovation and introduces a methodology to evaluate the innovativeness of mobile money in sub-Saharan African countries. By combining insights from innovation and strategy theories, the scope of analysis spans from 2007, coinciding with the launch of M-Pesa, to 2023, with a specific focus on the West African Economic and Monetary Union (WAEMU) zone. This study asserts that Mobile Money qualifies as an African innovation, despite the apparent dominance of multinationals, particularly telecom operators, who engage in collaborative efforts with banking institutions and employ strategic tactics to secure advantageous positions in this promising new market. Looking ahead, the study sheds light on the potential risks linked to the incorporation of AI (such as recognition and geolocation) in mobile money transactions, along with the associated risk of financial exclusion. Additionally, it prompts further research into the geopolitical aspects surrounding mobile money and the complexities of achieving interoperability.

The Smart Watch has become an important tool for individuals to understand the physical body, construct the technological body and promote the right of self-determination of the body. This study examined the daily self-tracking practices of 25 smartwatch users through participatory observation and semi-structured interviews. On the one hand, the tracking data generated by the smartwatch helps to build the technological body, thus digitizing the body and empowering the individual's body cognition. On the other hand, continuous monitoring results in the body being shaped by technology and data, and people bear the cost of digitization under the power of Technology. This study also noted the care logic of smart watches. When children give their parents smart watches as gifts, a lively and powerful practice of digital care is taking place. The investigation of the self-tracking practice in the use of smart watches is helpful to understand the role of data technology in contemporary life politics from the perspective of human subjectivity.