Convergence Of Technologies Research Articles

Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption

The Metaverse has emerged as a convergence of technologies and conceptions with the potential to reshape how we interact, participate, learn, and entertain ourselves in the digital age. It has gained substantial attention recently due to its immersive, interconnected, and virtual environment for meeting evolving customer and market demands. As technology advances, firms implementing the Metaverse may gain a competitive edge and create new revenue streams. However, the service sectors still face challenges in adopting and successfully implementing Metaverse. Therefore, we attempted to establish a theoretical foundation to understand the challenges of Metaverse adoption in the service sector and to develop an integrated framework to analyse the barriers using the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL) techniques. To achieve this, we first identify barriers from extensive literature and categorise them into six broad categories based on experts’ opinions. Then, we ranked each category and the barriers constituting them based on their criticality through FAHP. The Security and Privacy category has the highest weight among the six categories of barriers, followed by Governance and Standardisation, Infrastructure, User Behaviour and psychology, Feasibility, and Organisational culture and Stakeholder commitment. Further, we developed the causal-effect relationships among the six categories and analysed those categories through the degree of prominence and relationships. We found that Security and Privacy is one of the most influencing barriers. Then, we analysed our proposed decision-making model under different scenarios through sensitivity analysis. This study offers valuable insights into the relationships among the barriers and how they can impact progress towards achieving specific goals or objectives.

Watching Women: Surveillance and Spectatorship in Early Science Fiction Television

Abstract This article analyzes episodes of early science fiction anthology series to examine how fears of television's ability to turn its gaze on the home circulated around women, both as objects of surveillance and as television spectators, in the 1950s and 1960s. Media historians have called attention to the importance of women in defining the role of new technologies and have detailed the gendered popular constructions of early television. This article stems from such work, addressing the time when audiences and producers alike were grappling with the imagined potential of television's visuality and liveness to impose visibility on its viewers. Early episodes of Tales of Tomorrow, The Twilight Zone, and The Outer Limits expose anxieties about the power of television as a surveillance mechanism to enact control over audiences. Formally and narratively, these episodes complicate the division between seeing and being seen, particularly for women viewers, while enacting strategies to contain and discipline women. They mobilize women's navigation of visual subjectivity and objectification only to eschew their ability to look and reify their status as objects to be looked at. By taking a closer look at early television surveillance narratives, this article demonstrates how women's television spectatorship has always entailed a complex negotiation of imposed looking relations that paradoxically attempt to deny the female gaze even as they depend on it. Understanding this historical context helps illuminate the gendered power dynamics of spectatorship and visibility in our present media landscape, in which the convergence of TV and surveillance technologies realizes the imagined fears of the mid-twentieth century.

Enhancing Data Preservation and Security in Industrial Control Systems through Integrated IOTA Implementation

Within the domain of industrial control systems, safeguarding data integrity stands as a pivotal endeavor, especially in light of the burgeoning menace posed by malicious tampering and potential data loss. Traditional data storage paradigms, tethered to physical hard disks, are fraught with inherent susceptibilities, underscoring the pressing need for the deployment of resilient preservation frameworks. This study delves into the transformative potential offered by distributed ledger technology (DLT), with a specific focus on IOTA, within the expansive landscape of the Internet of Things (IoT). Through a meticulous examination of the intricacies inherent to data transmission protocols, we present a novel paradigm aimed at fortifying data security. Our approach advocates for the strategic placement of IOTA nodes on lower-level devices, thereby streamlining the transmission pathway and curtailing vulnerabilities. This concerted effort ensures the seamless preservation of data confidentiality and integrity from inception to storage, bolstering trust in the convergence of IoT and DLT technologies. By embracing proactive measures, organizations can navigate the labyrinthine terrain of data management, effectively mitigate risks, and cultivate an environment conducive to innovation and progress.

Wireless Charging of Electric Vehicle on Highways in Running Mode by Solar Energy

Abstract: The adoption of electric vehicles (EVs) is on the rise globally, driven by the need for sustainable transportation solutions to mitigate climate change and reduce dependence on fossil fuels. However, the infrastructure for charging EVs remains a significant challenge, hindering widespread adoption. In response, innovative approaches such as wireless charging technology, coupled with solar energy, have emerged to address these challenges. This paper presents an overview of wireless charging systems for electric vehicles, integrated with solar energy, aiming to provide a sustainable and convenient solution for EV owners. The integration of wireless charging and solar energy offers numerous benefits, including convenience, efficiency, and environmental sustainability. By eliminating the need for physical connections and harnessing the power of the sun, electric vehicles can be charged seamlessly and sustainably, reducing reliance on the grid and lowering carbon emissions. This paper discusses the principles behind wireless charging technology, the design considerations for integrating solar energy, and the potential impact on the future of sustainable transportation. Additionally, it addresses the challenges and opportunities associated with this innovative approach, highlighting the need for further research and development to realize its full potential. Overall, the convergence of wireless charging technology with solar energy represents a promising solution to the challenges facing the widespread adoption of electric vehicles, contributing to a cleaner and greener transportation ecosystem.

Design and Optimization of English-Speaking Teaching Model Using Virtual Reality Technology

The advent of virtual reality (VR) technology has ushered in a new era in language education, offering innovative avenues for immersive and interactive learning experiences. This paper introduces a groundbreaking English speaking teaching model designed to harness the power of VR technology. Rooted in the imperative for authentic language practice, our model transports learners into virtual environments meticulously crafted to replicate real-life scenarios. Here, learners engage in conversational interactions, from everyday dialogues to professional exchanges, facilitated by interactive conversational agents. Complementing this immersive experience are adaptive learning algorithms that tailor exercises to individual proficiency levels, alongside multi-modal feedback mechanisms providing comprehensive guidance on pronunciation, intonation, and language usage. Through iterative refinement and a rigorous design methodology, our model seeks to enhance learner engagement, motivation, and proficiency across diverse skill levels.Preliminary pilot studies underscore the model's promise, with participants reporting heightened satisfaction, motivation, and perceived progress in English speaking skills. As we tread further into this innovative domain, future research avenues beckon, including investigations into long-term effectiveness, scalability, and integration into formal education systems. The convergence of VR technology and language education represents a paradigm shift, poised to redefine the dynamics of language acquisition and proficiency in an increasingly interconnected world.

Robust Segmentation of COVID-19 Chest X-Ray Images: Analysis of Variant k-Means Based Clustering Algorithms

Computer aided diagnosis (CADx) become one the most famous method in diagnostic medical field due to the high reliability and efficiency. Recently, the coronavirus disease (COVID-19) has become severe global pandemic. Particularly, the Chest X-ray (CXR) imaging has become an essentiality in COVID-19 detection. As a result, the convergence of CADx technology with Chest X-ray analysis has achieved great efficiency in COVID-19 diagnosis. Therefore, the research value of CADx in COVID-19 diagnosis is exceptionally high. This study aims to evaluate different k-means based clustering algorithms and identifying the one with the highest overall accuracy. First of all, 150 COVID-19 CXR open-source images are acquired from Kaggle and Github. All the images will be unified into a same image size with 1000*1000 pixels and quality during the image pre-processing. Next, the resized images are enhanced by the Modified Global Contrast Stretching (MGCS) enhancement method to increase the quality of images. Then, the traditional k-means, k-medians, k-medoids and fast k-means clustering methods have been implemented in the image segmentation. At the same time, five different numbers <2, 4, 6, 8, 10> of clusters also tested out in this study. Lastly, all the segmented is proceeded to the segmentation performance based on sensitivity, specificity, accuracy, precision, recall and F-score. The result proves that the k-medoids clustering algorithm with 2 clusters archived the best overall segmentation performance as it obtained the highest sensitivity, accuracy, recall and F-score with 66.14%, 87.98%, 0.6614 and 0.7327.

Smart Sensors and Smart Data for Precision Agriculture: A Review.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

Ethical Dimensions of ICT Integration in Higher Education: A Comprehensive Review

In contemporary higher education, the integration of Information and Communication Technology (ICT) has become indispensable, revolutionizing teaching and learning methodologies. However, alongside the benefits, ICT integration brings forth a myriad of ethical considerations that must be addressed to foster a responsible digital culture within educational institutions. This review article delves into the multifaceted realm of ethics surrounding ICT usage in higher education, synthesizing existing literature to provide insights into the ethical challenges faced by educators, administrators, and policymakers. The review explores diverse topics such as digital equity, privacy concerns, academic integrity, and responsible technology use. Moreover, it analyzes the ethical dilemmas arising from the convergence of technology, pedagogy, and morality, offering a sophisticated examination of how educational stakeholders navigate these challenges. Emphasizing the necessity of ethical awareness and proactive actions, the study underscores the importance of fostering a culture of responsible digital citizenship within the educational ecosystem. Ultimately, the paper advocates for intensified efforts to address ethical concerns in ICT integration, ensuring that the transformative potential of technology in education is harnessed while upholding the values and integrity of learning in the digital age. Keywords: ICT integration, Higher education, Ethical issues, Teaching and learning, Digital citizenship

Uncovering the impact of technology convergence on innovation quality: From the perspective of ego‐network dynamics pathway

AbstractThis paper examines the mediating effect of R&D alliance network dynamics on the relationship between technology convergence and firm innovation quality, it further investigates the moderating effect of degree assortativity in the inventor network. An empirical analysis is conducted based on a panel negative binomial regression model with data of Chinese high‐tech firms listed on the Shanghai and Shenzhen Stock from 2000 to 2018. The results show that novel technology convergence not only contributes to innovation quality directly but also indirectly through ego‐network expansion. The relationship between reinforced technology convergence and innovation quality is inverted U‐shaped, partially mediated by ego‐network stability. In addition, degree assortativity suppresses the effects of novel technology convergence on ego‐network expansion and innovation quality and steepens the inverted U‐shaped relationships of reinforced technology convergence with ego‐network stability and innovation quality. This paper contributes to the relevant literature by revealing the links between technology convergence, ego‐network dynamics, and innovation quality, and emphasizing the importance of inventor collaboration ties formation mechanism in knowledge searching and convergent innovation. Furthermore, the conclusion provides managerial suggestions for high‐tech firms to coordinate internal and external resources to improve technical impacts.

Changes in concentration performance and alternating attention after short-term virtual reality training in E-athletes: a pilot study

In the dynamic landscape of e-sports, where intense competitive gaming demands high cognitive abilities, concentration performance and alternating attention play a pivotal role. E-sports encompass diverse genres, each requiring specific cognitive functions. Maintaining unwavering focus is crucial, as split-second decisions can determine victory. The study explores the potential of Virtual Reality (VR) training to enhance concentration performance and alternating attention, shedding light on the importance and possibilities of optimizing cognitive abilities for e-athletes. VR training emerges as a promising intervention, offering immersive environments for cognitive exercises. The study investigates the impact of VR training on concentration performance and alternating attention in amateur e-athletes, utilizing standardized tests. A randomized controlled trial with 66 participants reveals significant improvements in the VR training group, highlighting the adaptability and plasticity of cognitive processes. The findings suggest that VR training can enhance concentration abilities, providing valuable insights for e-sports and potentially extending to other fields requiring sustained attention and rapid task-switching. The study underscores the convergence of cognitive psychology, neuroscience, and VR technology, paving the way for innovative training methodologies and advancements in e-sports performance.

Leadership and the dynamics of projects: Ray Levitt's insights on project leadership

Projects operate in dynamic settings, with changing technologies, and they are themselves evolving forms of organizing. This paper contains insights from Ray Levitt, an operating partner at Blackhorn Ventures and an Emeritus Professor at Stanford, where he led the Global Project Center. It is an extracted and edited version of conversation on a panel discussion at the inaugural Project Leadership in a Changing World Symposium in 2021. The main parts unpack how leaders of complex projects, such as those found in civil engineering construction, face challenges in innovating, particularly in incorporating new technologies that are being developed by start-ups; and how the nature of leadership changes across different project stages and relates to notions of agile leadership. The conversation with Ray Levitt provides contributions to the debate on project leadership in a changing world, on 1) the convergence of technologies affecting project leadership; 2) the importance of success criteria; 3) the changing nature of project leadership through the lifecycle; 4) agile leadership, and 5) the political skills and sensitivities to get project started. These insights can be built on by used by project scholars interested in project leadership in a changing world. In the final section we discuss how they might provoke new research on the leadership and digital innovation in an age of AI and on temporal understandings of project leadership and its discontents in a changing world.

Returning to and continuing the traditional Chinese family system via WeChat: Digitally mediated families in liquid China

This article rethinks digitally mediated family, contending that WeChat facilitates the integration of digitally mediated Chinese family units and family practices into an entire system within traditional patterns. WeChat can serve as the hub for this kind of family mediation, since it is the dominant mega-platform in the smartphone-based Chinese polymedia environment, possessing superior technological convergence and sociocultural affordances. Based on qualitative data from 48 Chinese internal migrants, this article characterizes the entire system of the traditional Chinese family mediated by WeChat. First, the static Chinese family units converged on WeChat inherit spatial and hierarchical structures from the traditional models. Second, individuals’ WeChat usage for dynamic family practices is guided by Chinese family ethics, particularly those related to intergenerational and gendered ethics. Lastly, intimacy practices within Chinese families are digitally mediated to maintain ethically guided features in the Chinese style. This article emphasizes the role of polymedia-mediated ambient co-presence in activating implicit Chinese-style intimacy practices in the digital family spheres. Overall, when Chinese people log in to WeChat, they experience a return to traditional Chinese families, playing roles that contribute to the continuity of the entire traditional Chinese family system. This article suggests that the digitally mediated traditional family system ensures family stability in a highly modernized society where population mobility has become normalized.

On the need for legal regulation of the use of NBICS convergent technologies in criminal proceedings

On the need for legal regulation of the use of NBICS convergent technologies in criminal proceedings

간호정보활용능력, 근거기반실무역량 및 문제해결능력 향상을 위한 다학제간 팀티칭교육 효과

Objectives This study was conducted to confirm the nursing information literacy, evidence-based practice competency, and problem-solving ability of nursing students before and after education through ‘Nursing Information and Digital Convergence’ multidisciplinary team teaching education.
 Methods This study conducted a convenience sample of second-year nursing students at a university, and the data collection period was from March 17 to May 30, 2023. The collected data was used in R 4.2.2 version and analyzed using descriptive statistics and paired t-test according to the characteristics of the variables. The relationship between variables was analyzed using Pearson's Correlation and the sobel test after path analysis to determine the mediating effect of evidence-based practical competency in the influence on nursing information literacy and problem-solving ability in the pre and post test. For the sem function and sobel test of the lavaan package, the sobel function of the multilevel package was used.
 Results First, nursing information literacy, evidence-based practical competency, and problem-solving ability all showed significant increases before and after multidisciplinary team teaching. Second, the positive correlation between nursing information literacy and evidence-based practical competency, nursing information literacy and problem-solving ability, and evidence-based practical competency and problem-solving ability before and after multidisciplinary team teaching increased significantly. Third, the impact of information literacy on problem-solving ability was B=.431, which gave a significant positive influence at the p<.001 level, and the Sobel test results showed that there was a partial mediation effect at the p<.001 level.
 Conclusions Through this study, we were able to identify the nursing information literacy, evidence-based practical competency, and problem-solving ability of nursing students before and after teaching through the ‘Nursing Information and Digital Convergence’ multidisciplinary team teaching education. It was found that educational design for multidisciplinary team teaching education through convergence with various majors was necessary when administering nursing information-related courses.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

Tokenomics in the Metaverse: understanding the lead–lag effect among emerging crypto tokens

The convergence of blockchain and immersive technologies has resulted in the popularity of Metaverse platforms and their cryptocurrencies, known as Metaverse tokens. There has been little research into tokenomics in these emerging tokens. Building upon the information dissemination theory, this research examines the role of trading volume in the returns of these tokens. An empirical study was conducted using the trading volumes and returns of 197 Metaverse tokens over 12 months to derive the latent grouping structure with spectral clustering and to determine the relationships between daily returns of different token clusters through augmented vector autoregression. The results show that trading volume is a strong predictor of lead–lag patterns, which supports the speed of adjustment hypothesis. This is the first large-scale study that documented the lead–lag effect among Metaverse tokens. Unlike previous studies that focus on market capitalization, our findings suggest that trade volume contains vital information concerning cross-correlation patterns.

“Stay tuned!"

“Stay tuned!"

Synergy in Action: Integrating Environmental Monitoring, Energy Efficiency, and IoT for Safer Shared Buildings

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, the integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces is relevant. The outcomes of the study reveal the delicate nature of all the involved components, which need to be carefully developed in an integrated manner.

REVIEWING THE ROLE OF AI IN DRONE TECHNOLOGY AND APPLICATIONS

This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors.
 Keywords: Role, AI, Drone, Applications, Technology.

Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability.