Convergence Of Technologies Research Articles

Visualisation and prediction of Covid-19 data using random forest regression

The outbreak of COVID-19 has spread among several parts of the world. The data pool increases tremendously, which needs excellent attention by researchers of various domains to analyze and determine the measures to handle it. Hence, researchers worldwide are looking into Artificial Intelligence (AI) to resolve the challenges due to this COVID-19. It could be stated that AI can examine huge data mounds so that several new findings can be determined. AI could be deployed in various fields, such as the pharmaceutical industry, the analysis and development of vaccines and antibodies, and drug designing. Due to the impressive progress that AI has made in the latest few years, still, it proves to be the essential quality of the technology and evidence of humans' creativity towards the contribution of developing tools and products which could be statistically and computationally complex. It is observed that AI technology aids in tracing the outburst, patient diagnosis and fastening the procedure of finding a treatment. This work provides an overview of COVID-19, along with the convergence of technologies that could be applied in various sections to handle this pandemic. An extensive exploration of multiple techniques and models has been implemented for the prediction of COVID-19 has been done. Additionally, numerous models have been deployed for predictions of covid states. It has been inferred that XGBoost showed considerable progress in the prophecy.

Digital humanities

Digital humanities is a result of the convergence of digital technology with social sciences and humanities. It is one of the key research areas around the world by the library and information science profession, and in cognate areas such as art and archeology. Focus groups, digital humanities centres are created for intensive researches which are located mainly in libraries as their core programmes. The library techniques and methods such as knowledge organisation, knowledge management, search and retrieval design, education, and emerging areas such as metadata, semantic mapping, ontology, thesaurus construction, digital curation, discovery services, find application in digital humanities. A new area of study, historical informatics complements the digital humanities. India has vast potential application of digital humanities with its diverse culture, customs and languages and so on. India is reckoned as one of the advanced countries in the IT sector, hence recognising the socio-cultural legacy and diverse historical background, it is viewed that digital humanities has many opportunities and applications for LIS professionals in India to contribute substantially. This paper brings together the concepts, tools, and applications of digital humanities and proposes a course of study in LIS curriculum.

Digital Double Diamond (DiDD) Model Analysis of National Competitiveness Under Digital Transformation: An Application to Healthcare and Pharmaceutical Industries in South Korea and Switzerland

The healthcare and pharmaceutical industries are rapidly changing under the digital transformation environment in the 2020s. Investments in this field are given priority, both at the business level and also at the national level, to enhance national competitiveness. However, a very limited number of studies examine the impact of digital transformation on the broader scope of the industry and national competitiveness. Furthermore, the existing studies and competitiveness indices focus rather on general country-level innovation indicators. To mend this gap and to provide a more specific framework to evaluate digital transformation in the healthcare and pharmaceutical industries, this study proposes a new model called the Digital Double Diamond (DiDD), which is an extension of Porter’s Diamond model for national competitiveness. The DiDD model consists of three factors: endogenous factor, centric factor, and exogenous factor containing nine variables and 36 sub-variables that capture the impact of digital transformation in healthcare and pharmaceutical industries to assess national competitiveness. The new DiDD model emphasizes the importance of having a balance between the traditional national competitiveness and digital convergence elements in the healthcare and pharmaceutical industries. As an illustration of the new DiDD model, we compare South Korea and Switzerland’s digital transformation competitiveness in the healthcare and pharmaceutical industries.

The changing ethnic news media landscapes: implications of technological convergence in multicultural Pakistan and Russia

ABSTRACT Media representation of ethnic minorities and communities is important in multicultural societies that reflect ethnic cultural diversity. Therefore, the role of ethnic news media becomes crucial to give voices to marginalized communities, especially in the digital age when their cultural practices, issues and concerns can reach faster beyond the national territories. With technological advancements, ethnic news media practices are also transforming. Ethnic journalists, like their colleagues from mainstream news media, now use integrated digital tools to produce and disseminate news. However, quite less is known about how technology-driven changes are reshaping the ethnic news media landscape, and if such developments have any implications to strengthen the communicative power and representation of ethnic communities in culturally pluralistic societies. This study hence reflects upon the cases of Pakistan and Russia. Informed by the framework of technological convergence and cultural discourse studies (CDS), this study investigates the key implications of technological convergence in Pakistan’s and Russia’s ethnic news media. Moreover, this study primarily uses the qualitative method of in-depth interviews (face-to-face and online) and offers an inductive thematic analysis of the qualitative data.

Grindr Tourism Among Tourists, Locals, and Immigrants: Dating App Impacts for Social Relations, Gay Tourism, and Digital Convergence

Dating app tourism is a phenomenon that intersects both digital mobile media applications and tourism networks. As such, it represents a new but little-examined frontier in the study of sexuality, technology, and identity formation. Using a qualitative sociological approach, this exploratory study identifies Grindr tourism practices and their consequences for gay tourism, tourist-local relations, and digitally mediated social life. With Tel Aviv as the research site, Grindr tourism practices were analyzed using 19 in-depth interviews and six audio diaries. The concept of embedded learning is used to understand the communication that results from contemporary mobile media-integrated tourism. Grindr tourism practices are viewed as mutually beneficial by tourists, locals, and immigrants. Findings indicate that Grindr tourism contributes to tourists’ embedded learning about travel destinations, immigrants’ acclimation and friendship networks, and locals’ self-ascribed cosmopolitan, multicultural identities. The research also uncovered wider issues affecting sexuality, communication, migration movements, ethnicity, and the economic bodies that support large-scale tourism. Specifically, Grindr is used as a tool to arrange independent, mobile, non-institutionalized travel that serves as an alternative to LGBT + tourism industry institutions. Not only is Grindr tourism indicative of shifting travel practices, but it also reflects dating apps’ overarching tendency toward the convergence of multiple social functions into one platform.

Text mining method to identify artificial intelligence technologies for the semiconductor industry in Korea

Semiconductors are among the most important core technologies contributing to the Fourth Industrial Revolution. The United States, Taiwan, and China have been investing heavily in semiconductor research and development. To achieve international competitiveness in the semiconductor industry, Korea needs to establish a research and development (R&D) roadmap for small- and medium-sized enterprises (SMEs). Our study identified trends in the semiconductor industry by analyzing the characteristics of core technologies based on patents that disclose technologies instead of holding exclusive ownership. Specifically, we analyzed registered patents concerned with artificial intelligence and machine learning pertaining to the semiconductor industry, which are attracting considerable attention. Using the Korea Intellectual Property Rights Information Service database, we identified 3569 patent specifications related to AI technology and the semiconductor industry. The text mining and network analysis results indicated that the application of deep neural networks is the most important and affects various aspects of R&D. Particularly, AI technology is actively studied for monitoring manufacturing and etch processes. Additionally, technology convergence among virtual reality, visualization, smart factories, and etching technology was identified. The analysis results identify promising technologies related to semiconductors and provide insights that would enable SMEs in the Korean semiconductor industry to establish a technology roadmap.

A Study on the Development of Interactive Technology for the Convenience of the Visually Impaired

In this paper, as human-computer interface (HCI) technology advances, opportunities are analyzed to provide a better service environment for the visually impaired. The system and user interaction for the visually impaired will become possible through the convergence of technologies that include location-based services, wireless body sensor networks, and smart devices, which can have direct and positive impacts. The system connects to the user's mobility assistance device through a smartphone app and helps the user walk to identify and guide through obstacles. This interactive technology is expected to provide a convenient environment for visually impaired users.

Internet of things application in automated resource management systems

The article summarizes scientific knowledge about the state and potential of the development of the Internet of Things (IoT) industry in Ukraine and analyzes already implemented initiatives to promote its spread in the areas of health care and urban management. The study aims to systematize and analyze the available scientific information about the opportunities and potential revealed by integrating the ERP system and the Internet of Things as a convergent management technology. Based on the research results, a model of combining Internet of Things technologies and resource management of companies in ERP systems is presented; the advantages provided by the integration of IoT and ERP systems are systematized; the problems associated with determining the economic efficiency of the integration of IoT and ERP systems are summarized based on the analysis of applied cases in the areas of health care and urban management. The research used methods of systematization, meta-analysis, and synthesis of research from the fields of information technologies, economics, management, and law. In addition, a case analysis was used to understand the practical aspects of the implementation of convergent technologies based on the combination of management and information technologies. Attention is focused on the fact that future directions of research are related to the expansion of technical capabilities for processing large volumes of IoT data from connected devices and applications to create integration between devices and control systems at enterprises; development of networks and protocols to eliminate performance gaps, provide valuable information on improving customer interaction, speed up customer problemsolving processes, and expand the capabilities of the IoT as a service management technology. Most cloud-based ERP applications with IoT elements have specific requirements for functionality and quality of service, including communication, computing, and data storage. Meeting these requirements can be challenging, especially regarding network performance, resulting in bottlenecks in real-time application performance.

Intelligent cryptocurrency trading system using integrated AdaBoost-LSTM with market turbulence knowledge

The Bitcoin market is firmly positioned as a global asset market. However, due to its extremely high volatility and the lack of a custom Bitcoin trading system, investors find it difficult to establish an effective investment strategy in this market. In this study, we build an intelligent Bitcoin trading system to maximize profitability by predicting the market. We propose a fusion approach combining technology with economic knowledge to achieve accurate predictions. Firstly, we provide an ensemble prediction framework that integrates the AdaBoost algorithm with the LSTM deep learning model as a form of technological convergence. This dramatically improves the predictive performance due to the high representational capacity of LSTM and the overfitting minimization capability of AdaBoost. Secondly, to account for the repeated structural volatility in the market, we combine the econometrics Markov regime-switching model with the AdaBoost-LSTM model to predict the period of market turbulence caused by structural breaks in the regime. Our prediction model suggests that market turbulence lasting longer than 21 days could be a potentially high-risk investment in the future. Finally, we have developed a Bitcoin-customized trading algorithm that maximizes rewards by predicting upward movements in prices and warning of high-risk investments during a long-lasting turbulent regime. In trading tests, our system yielded a cumulative return of 2.4 times and 4.0 times on 1-day and 5-day forecasts, respectively, compared to other representative trading strategies such as the stochastic oscillator.

A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

A flexible OT testbed for evaluating on-device implementations of IEC-61850 GOOSE

The growing convergence of Information Technology and Operational Technology has enhanced communication and visibility across power grids. This, coupled with the growing use of Distributed Energy Resources in power grids, has enhanced the grid capabilities while also creating a larger attack surface for malicious actors. A common protocol vulnerable to these attacks is the IEC-61850 GOOSE protocol due to its low-latency requirements, multicast packet delivery method, and lack of encryption. In this paper, we evaluate the security implications of different hardware implementations of this protocol by contrasting device response and recovery of two commercial off-the-shelf Intelligent Electronic Devices from separate manufacturers. The cyberattacks utilized in this paper are research-established GOOSE attacks with results measured in device latency and GOOSE endpoint response success.

Technological synergies as antecedents of sustainable development: deep-tech versus shallow-tech perspective

ABSTRACT Prior research suggests that synergies between deep and shallow technologies could create unprecedented capacity to achieve sustainable development. However, there is little understanding if these synergies are already achieved and how different technologies work together on a country level to enable its sustainable development. We respond to this question starting from the contingency theory and adopting a configurational perspective to inductively explore the interaction effects of two key deep technologies (artificial intelligence and Internet of things) and two key shallow technologies (broadband and cloud computing) on a country’s sustainability performance. We employ the fuzzy-set Qualitative Comparative Analysis (fsQCA) on a data set of 79 countries, allowing us to embrace the synergies brought by technological convergences. Our results determine how deep and shallow technologies complement and substitute each other and thereby create multiple technological pathways towards high and low sustainable development at the national level. In this way, this study offers significant implications for research, policy, and practice concerned with the issues lying at the intersection of technology and sustainability.

An Analysis of Critical Cybersecurity Controls for Industrial Control Systems

Industrial Control Systems (ICS) comprise software, hardware, network systems, and people that manage and operate industrial processes. Supervisory Control and Data Acquisition Systems (SCADA) and Distributed Control Systems (DCS) are two of the most prevalent ICS. An ICS facilitates the effective and efficient management and operation of industrial sectors, including critical infrastructure sectors like utilities, manufacturing, and water treatment facilities. An ICS collects and integrates data from various field controllers deployed in industrial contexts, enabling operators to make data-driven decisions in managing industrial operations. Historically, ICS were isolated from the internet, functioning as part of air-gapped networks. However, the efficiency improvements brought about by the emergence of Information Technology necessitated a shift towards a more connected industrial environment. The convergence of Information and Operational Technology (IT/OT) has made ICS vulnerable to cyberattacks. Due to the crucial nature of the infrastructure that ICS manage, cyberattacks against ICS may cause critical infrastructure sectors to experience downtime. This may have a crippling impact on a country's well-being and essential economic activities. Given the proliferation of cyber warfare, cyberattacks against ICS are increasingly significant at present, as was the case during the 2015 attack on Ukraine's power infrastructure, which was successful in causing a blackout that affected over 200 000 persons. The threat actors used malicious software known as "BlackEnergy3", which was created to interfere with the regular operation of the ICS in charge of controlling electrical substations. This was the first known instance of malicious software causing blackouts. In response to increasing cyberattacks against ICS, the SANS Institute, in a whitepaper titled “The Five ICS Cybersecurity Critical Controls”, present five critical controls for an ICS cybersecurity strategy. This paper discusses ICS and the increased convergence of IT and OT. The paper also outlines significant cyberattacks directed at ICS. The paper then follows an exploratory research methodology done in response to the Five ICS Cybersecurity Critical Controls to determine the state of ICS literature that can help ICS operators secure their environments in accordance with the framework. Additionally, the ICS Cybersecurity Critical Controls are mapped to the NERC CIP standards, which provide guidance on the security of the Bulk Electric System (BES) and associated critical assets in North America.

Quantity or quality: The roles of technology and science convergence on firm innovation performance

While firms in science-based sectors have multiple convergence opportunities, few empirical studies have explored how technology and science convergence can improve innovation performance. This study offered a novel approach by distinguishing between novel technology convergence (NTC) and reinforced technology convergence (RTC), investigated their different effects on firm innovation quantity and quality, and examined the moderating role of science convergence. We analyzed data on published papers, patents, and other panel data of firms in China's organic chemical industry between 1998 and 2015. The results show that (a) NTC and RTC positively affect firm innovation quantity, but NTC's effect is weaker than that of RTC; (b) NTC positively affects firm innovation quality, whereas RTC negatively affects it; (c) NTC's positive effect on firm innovation quantity is strengthened by science convergence, whereas RTC's positive effect on firm innovation quantity is weakened by it; and (d) NTC's positive effect on firm innovation quality is strengthened by science convergence, while RTC's negative effect on firm innovation quality is weakened by it. By shedding light on these relationships, this study contributes to the literature on technology convergence and recombinant innovation theory, and offers valuable management implications, particularly in terms of selecting appropriate convergence strategies for different innovation orientations.

Intelligent grid load forecasting based on BERT network model in low-carbon economy

In recent years, the reduction of high carbon emissions has become a paramount objective for industries worldwide. In response, enterprises and industries are actively pursuing low-carbon transformations. Within this context, power systems have a pivotal role, as they are the primary drivers of national development. Efficient energy scheduling and utilization have therefore become critical concerns. The convergence of smart grid technology and artificial intelligence has propelled transformer load forecasting to the forefront of enterprise power demand management. Traditional forecasting methods relying on regression analysis and support vector machines are ill-equipped to handle the growing complexity and diversity of load forecasting requirements. This paper presents a BERT-based power load forecasting method that leverages natural language processing and image processing techniques to enhance the accuracy and efficiency of transformer load forecasting in smart grids. The proposed approach involves using BERT for data preprocessing, analysis, and feature extraction on long-term historical load data from power grid transformers. Multiple rounds of training and fine-tuning are then conducted on the BERT architecture using the preprocessed training datasets. Finally, the trained BERT model is used to predict the transformer load, and the predicted results are compared with those obtained based on long short-term memory (LSTM) and actual composite values. The experimental results show that compared with LSTM method, the BERT-based model has higher short-term power load prediction accuracy and feature extraction capability. Moreover, the proposed scheme enables high levels of accuracy, thereby providing valuable support for resource management in power dispatching departments and offering theoretical guidance for carbon reduction initiatives.

Information and communication technologies (ICT) access indicators in the LGBTQ+ population with a high risk of contagion by acquired immunodeficiency virus (HIV) in Honduras

In all societies, some populations are prioritized to be served by civil organizations and governments; access to technologies is typically left aside, especially in developing countries. The sample was for intentional contributions through the snowball method to reach 107 study participants. A survey-type instrument has been carried out where access, the use, and knowledge about convergent devices have been measured in a sample of individuals belonging to the lesbian, gay, bisexual, transgender, queer, and more (LGBTQ+) community. Basic statistics have been carried out to describe the frequencies of the sociodemographic variables and access to information and communication technologies (ICT). The LGBTQ+ community has access to convergent technologies, with a regular quality of service, with a preference for mobile devices and high daily use of them; there's an incongruence between income and spending on telecommunications services since it was perceived that this is a prioritized expense for all of them, even for those with the lowest economic income. The results of this study indicate that the LGBTQ+ community has significantly greater access to information technology and telecommunications services than the general population that hosts them; this study gives us a vision of how to offer technological solutions to prevent HIV in this community with a high incidence of this disease. Keywords: ICT; Digital divide; LGBTQ+; Convergency

What can accelerate technological convergence of hydrogen energy: a regional perspective

Focusing on technological innovation and convergence is crucial for utilizing hydrogen energy, an emerging infrastructure area. This research paper analyzes the extent of technological capabilities in a region that could accelerate the occurrence of technological convergence in the fields related to hydrogen energy through the use of triadic patents, their citation information, and their regional information. The results of the Bayesian spatial model indicate that the active exchange of diverse original technologies could facilitate technological convergence in the region. On the other hand, it is difficult to achieve regional convergence with regard to radical technology. The findings could shed light on the establishment of an R&D strategy for hydrogen technologies. This study could contribute to the dissemination and utilization of hydrogen technologies for sustainable industrial development.

Construction of a refined people management model for SMEs based on discrete regression algorithm

Abstract Management innovation is particularly important today when technological convergence is becoming increasingly evident. Furthermore, as employees are the most important resource in work dynamic scheduling, the mobility of employees will bring extremely discrete regression particle swarm algorithm uncertainty to work projects, making its scheduling problem more complex. This paper first proposes to apply a discrete regression particle swarm algorithm based on refinement management as an enterprise management innovation to optimize three indicators of time, cost, and stability by implementing refinement personnel management. Secondly, we set constraints for dynamic tasks to implement management rescheduling to avoid overworking employees and thus maximize profits for the enterprise. Finally, the effectiveness of the discrete regression particle swarm algorithm is verified by selecting 27 benchmark examples to compare from three indicators of contribution and time and cost. The results show that the discrete regression particle swarm algorithm (S-PSO-VNS) has an average time reduction of 22.65%, a cost reduction of 15.21%, and a stability improvement of 35.74% compared to the other two control algorithms in terms of management algorithm quality and performance analysis. The research data illustrates that the discrete regression algorithm proposed in this paper can have high efficiency and effectiveness, and the discrete regression particle swarm algorithm can provide managers of SMEs with more informed decisions on work schedules, thus maximizing profits and achieving more profitability.

Predicting and analyzing technology convergence for exploring technological opportunities in the smart health industry

Technology convergence (TC) represents a prevailing innovation pattern in various industries, as it enables the integration of two or more existing technologies to create new hybrid technologies or even a new technological domain, transforming the way firms compete with each other. The advent of smart health can be attributed to the convergence of various technologies, including wireless communication, embedded systems, artificial intelligence (AI), medical technologies, and information security. It is essential for smart health firms to systematically comprehend TC patterns and anticipate emerging convergent technologies at an early stage to leverage potential technological opportunities. This paper proposes a machine learning methodology that integrates network analysis to predict and analyze changing TC patterns from patent data related to smart health. A machine learning-based link prediction method is developed to forecast convergent technologies from a comprehensive set of link prediction measures. Network centrality analysis is then used to understand and evaluate the convergent patterns in the predicted technological knowledge interaction network and identify critical convergent technologies that may play significant roles in the future. The research not only demonstrates that convergent innovation is a continually growing trend in the smart health industry but also identifies several important emerging convergent patterns.

Catching Up of Latecomer Economies in ICT for Sustainable Development: An Analysis Based on Technology Life Cycle Using Patent Data

In the digital economy era, ICT plays a vital role in supporting the sustainable and high-quality development of latecomer economies. Using technology life cycle analysis and patent data from the United States Patent and Trademark Office (1960–2014), this study analyzed the catching-up characteristics of latecomer economies (with the US as the first mover), including take-off time, growth time, growth rate, and ceiling values in nine sub-fields of information and communication technology (ICT). We applied the logistic and bi-logistic model to reveal the sequence of technological development and growth speed of different economies in different ICT sub-fields. The results show that European economies (Great Britain, France, and Germany) and the US developed first, followed by Japan, Korea, and Taiwan, with China (Mainland) coming later; Asian economies (Japan, Korea, Taiwan, and China (Mainland)) displayed synchronous development strategies, while European economies displayed non-synchronous development strategies. Asian economies are catching up with the US, whereas European economies are standing still both in imitation and indigenous processes. Korea and Taiwan prioritized catching up with a few sub-fields in the indigenous process. Finally, we analyzed the technological convergence among economies in their catching-up processes and proposed policy implications for the sustainable development of ICT latecomers.