Convergence Of Technologies Research Articles

Multi‐Objective Federated Averaging Algorithm

ABSTRACTThe recent global trend is the convergence of information and communications technology (ICT). By applying ICT in various fields such as the humanities, new types of products and services are created, and new values that help people's lives can be created. AI can be selected as a representative technology in such convergence ICT. However, applying AI technology to actual production requires ensuring data security. Federated learning (FL) can achieve secure sharing of data, where all parties participate in model training locally and upload it to the server for aggregation. The data never leaves the parties involved, thus solving the problems of data privacy and data silos. However, FL faces issues such as high communication cost, imbalanced performance distribution among participants, and low privacy protection. To achieve a balance between model accuracy, communication cost, fairness, and privacy, this paper proposes a multi‐objective optimization‐based FL algorithm (M‐FedAvg). The multi‐objective optimization problem of maximising the accuracy of the global model, minimising the communication cost, minimising the variance of the accuracy, and minimising the privacy budget is solved by NSGA‐III. The experimental results show that the algorithm proposed can effectively reduce the communication cost of FL and achieve privacy protection for participants without affecting the accuracy of the global model.

Advanced cryopreservation as an emergent and convergent technological platform

Advanced cryopreservation technologies have the potential to transform organ transplants, biomedical research, food storage, aquaculture, biodiversity repositories, ecological restoration, and numerous other applications. These surpass the capability of existing cryopreservation technologies to extend the life and viability of biological materials at various scales from cells to tissues, organs, and entire organisms. In this article, we demonstrate why innovations in advanced cryopreservation, which we analyze as emergent, convergent platform technologies, raise novel concerns for research ethics and coordination, governance, and equitable access to benefits. As emerging technologies, they may disrupt markets or destabilize social institutions, including the systems that govern the distribution of organs for transplant. As convergent technologies, their impact will be heightened through interaction with other technologies. The technologies that may intensify the social and ethical effects of advanced cryopreservation include information technologies that permit the administration of complex logistics of storage and transport, biotechnologies for the management of floral and faunal species and populations, and 3D printing technologies that may enable the development and distribution of customizable peripheral components of this platform technology. The speed of development among diverse applications of the core platform is likely to vary between sectors in ways that are responsive to public support as well as to ethical constraints, and advancements in any sector will affect the achievement of reliability for the core technology across sectors. We recommend that societal benefits and risks be assessed both in the specific contexts for which peripheral components are developed and for the core technology.

Development of a cutting-edge ensemble pipeline for rapid and accurate diagnosis of plant leaf diseases

Selecting techniques is a crucial aspect of disease detection analysis, particularly in the convergence of computer vision and agricultural technology. Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security. Deep learning is a viable answer to meet this need. To proceed with this study, we have developed and evaluated a disease detection model using a novel ensemble technique. We propose to introduce DenseNetMini, a smaller version of DenseNet. We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning. Another unique proposition involves utilizing Gradient Product (GP) as an optimization technique, effectively reducing the training time and improving the model performance. Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements. Test accuracy rates of 99.65 %, 98.96 %, and 98.11 % are seen in the Plantvillage, Tomato leaf, and Appleleaf9 datasets, respectively. One of the research's main achievements is the significant decrease in processing time, which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency. Beyond quantitative successes, the study highlights Explainable Artificial Intelligence (XAI) methods, which are essential to improving the disease detection model's interpretability and transparency. XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification, which promotes confidence and understanding of the model's functionality.

Future Technological Directions for Hydrogen Internal Combustion Engines in Transport Applications

The paper discusses some of the requirements, drivers, and resulting technological paths for manufacturers to develop hydrogen combustion engines for use in two types of market application – on-road heavy- and light-duty. One of the main requirements is legislative certainty, and this has now been afforded – at least in the major market of Europe – by the European Union's recent adoption into law of tailpipe emissions limits specifically designed to encourage the uptake of hydrogen engines in heavy-duty vehicles, giving manufacturers the confidence they need to invest in productionized solutions to offer to customers.It then discusses combustion systems and boosting systems for the two market types, emphasizing that heavy-duty vehicles need best efficiency throughout their operating map while light-duty ones, since they are rarely operated at full load, will mainly primarily need efficiency in the part-load region. This difference will likely cause a divergence in solutions, with heavy-duty engines running very lean everywhere and light-duty ones likely operating at the stoichiometric air-fuel ratio, at least for most of the map. The impacts of the strategies on engine systems and vehicle integration are discussed.It is postulated that due to reasons of preignition avoidance and efficiency hydrogen engines will rapidly adopt direct injection and that the long-term heavy-duty types will migrate towards the typical current spark-ignition-type cylinder head architecture where tumble, rather than swirl, will ultimately be needed for air motion in the cylinder for these reasons. They may also adopt active pre-chamber technology to ignite extremely lean mixtures for maximum efficiency and minimum emissions of oxides of nitrogen.It is suggested that light-duty engines will evolve less from their current gasoline architectural norm since they already contain all of the necessary fundamentals for hydrogen combustion. However, since part-load efficiency will be important, some new strategies may become desirable. Developing dual-fuel light-duty engines could accelerate their uptake as the heavy-duty market simultaneously accelerates the creation of the fuel supply infrastructure.The likely technological evolution suggests that variable valve trains, and specifically cam profile switching technology, would be extremely useful for all types of hydrogen engine, especially since they are readily available in different gasoline engines now. New operating strategies afforded by variable valve trains would benefit both heavy- and light-duty engines, and these strategies will become more sophisticated. There will therefore likely be a convergence of technologies for the two markets, albeit with some key differences maintained due to their vehicle applications and their differing operation in the field.

Optimizing Wing-In-Ground Effect UAVS for Enhanced Search and Rescue Operations: A Comprehensive Review

Abstract: Wing-In-Ground Effect (WIG) Unmanned Aerial Vehicles (UAVs) represent a unique convergence of aerodynamic principles and unmanned systems technology, offering promising solutions for maritime operations. This paper explores the historical development, theoretical foundations, and potential applications of WIG UAVs. The phenomenon of ground effect, which enhances lift and reduces drag when flying close to water surfaces, has been observed since the early 20th century. Soviet engineer Rostislav Alexeyev's pioneering work on the Lun-class ekranoplan in the 1960s demonstrated the viability of WIG technology for military logistics. Advancements in materials science, propulsion systems, and computer-aided design have further refined WIG vehicle performance. The integration of autonomous capabilities has expanded the operational scope of WIG UAVs, enabling missions in challenging maritime environments. The aerodynamic investigation of WIG vehicles reveals the complex interplay between the vehicle's wings and the water surface, leading to enhanced lift-to-drag ratios and improved efficiency. Computational Fluid Dynamics (CFD) simulations and wind tunnel experiments have provided valuable insights into the optimization of WIG UAV designs. The potential applications of WIG UAVs span maritime surveillance, coastal patrolling, search and rescue operations, and cargo transportation. Their ability to collaborate with other unmanned systems, such as aerial drones and surface vessels, enhances overall mission effectiveness. As the demand for sophisticated maritime capabilities grows, the development of WIG UAVs presents a promising frontier for innovation, offering unique solutions to address evolving challenges in maritime security and operational effectiveness

Got milk? Got cybersecurity risks! Unraveling ransomware threats in the German dairy industry

PurposeThis study aims to explore the vulnerabilities of the dairy industry to ransomware threats, focusing particularly on the upstream supply chain and applying routine activity theory (RAT) to understand the evolving dynamics of cybercrime in critical infrastructure sectors.Design/methodology/approachUtilizing expert interviews and network analysis, this research investigates the exploitation of complex supply chain vulnerabilities by motivated offenders. It delves into the intricate interplay between digital threats and physical supply continuity.FindingsThe study uncovers that ransomware threats transcend digital boundaries, manifesting in disruptions to physical operations and presenting significant risks to food security. It underscores the threat posed by the convergence of information technology (IT) and operational technology (OT), emphasizing the urgent need for heightened awareness and robust defenses against this substantial menace.Practical implicationsAddressing cyber vulnerabilities in critical sectors like dairy ensures not only the security of operations but also safeguards broader societal interests such as food security. Collaboration and proactive measures are essential to mitigate potential social and economic disruptions caused by cyber incidents.Originality/valueThis research fills a knowledge gap by shedding light on the nexus between cyber threats and supply chain resilience. It emphasizes the need for industries to adapt traditional defense mechanisms in the face of sophisticated digital adversaries.

The Role of Sentiment Analysis in Machine Translation- A Review on the Example of ChatGPT

In today’s globalization, machine translation technology has become an indispensable bridge for cross-lingual communication. The integration of sentiment analysis makes machine translation not only able to convey information, but also able to accurately express and understand the emotional color of the original text, which is of great significance in the fields of cross-cultural communication, customer service, and social media interaction. This study overviews the development of machine translation technology, from early rule-based models to modern statistical models to Transformer-based neural network models, with a special focus on the integrated application of ChatGPT in sentiment analysis and machine translation. First, this study introduces the background and research significance of machine translation, and discusses the role of sentiment analysis in improving translation quality and user satisfaction. Subsequently, the historical development of machine translation technology is reviewed in detail, and the basic concepts and methodologies of sentiment analysis and its applications in different fields are elaborated. On this basis, this study analyzes in depth the technical architecture, language model and interaction capability of ChatGPT, as well as its specific application and performance in sentiment analysis. Further, this study explores the importance of sentiment analysis in machine translation, including sentiment recognition, translation strategies for sentiment expression, and the impact of sentiment analysis on improving translation quality and cultural adaptability. Meanwhile, the advantages and limitations of ChatGPT in sentiment analysis are assessed, and future research directions are proposed, including technology convergence, construction of datasets and evaluation criteria, and ethical and bias issues. Finally, this study summarizes the practical application value of sentiment analysis in machine translation and looks forward to the potential areas of future research, aiming to provide reference and inspiration for the further development of machine translation and sentiment analysis.

Usability evaluation of a smart upper limb rehabilitation exercise device for individuals with disabilities.

User-friendly rehabilitation exercise devices can enhance health and quality of life through the convergence of information communication and medical technology. The development of rehabilitation exercise devices accessible to individuals with disabilities is important. We aimed to investigate the convenience, safety, and effectiveness of the developed rehabilitation exercise device for individuals with disabilities. A usability evaluation of the developed rehabilitation exercise device was conducted with five rehabilitation exercise experts and six athletes with disabilities using a questionnaire, the reliability of the revised questionnaire was r=0.87. Qualitative analysis was conducted using semistructured interviews following the testing of the upper limb rehabilitation exercise device by the two user groups. In the usability evaluation survey of the developed rehabilitation exercise device, the results were very positive, with an overall score of 4.25 out of 5. The highest score was assigned to the convenience of the device, followed by the effectiveness of the exercise and the safety of the device. The developed rehabilitation exercise device allows for three types of strength improvement exercises, including isometric, isokinetic, and isotonic exercises, all within a single device. Given that it is constructed with a wire system, it can be adapted to various exercise methods, depending on the type of disability. The device can be used by both nondisabled and disabled individuals, allowing for various exercise methods. The evaluation results indicate that the proposed device is highly versatile and practical.

The role of green and digital technology convergence on carbon emission reduction: evidence from China

The role of green and digital technology convergence on carbon emission reduction: evidence from China

The Influence of the Convergence of Digital and Green Technologies on Regional Total Factor Productivity: Evidence from 30 Provinces in China

This paper explores the development of digital and green collaboration, empirically examining both the linear and nonlinear impacts of the convergence of digital and green technologies on regional total factor productivity (TFP). Using data from 30 provinces and cities in China from 2010 to 2022, the study constructs a panel threshold model with business environment and intellectual property protection as threshold variables to investigate their roles in mediating the effects of digital–green technology convergence on regional TFP. The key findings are as follows: (1) The linear analysis reveals that the convergence of digital and green technologies significantly enhances regional TFP. (2) The nonlinear analysis demonstrates a nonlinear relationship between the convergence of these technologies and regional TFP. (3) The threshold effect test identifies a single-threshold effect for the business environment, showing that once the threshold is surpassed, the positive influence of the convergence of digital and green technologies on TFP increases. Additionally, a double-threshold effect is found with intellectual property protection; as intellectual property protection surpasses the first and second thresholds, the positive impact initially strengthens but then weakens. (4) A heterogeneity analysis shows that the convergence of digital and green technologies significantly contributes to TFP in the eastern regions, while the effects in central and western regions are not significant.

High-Efficiency Microwave Wireless Power Transmission via Reflective Phase Gradient Metasurfaces and Surface Wave Aggregation.

Microwave Wireless Power Transfer (MWPT) technology is crucial for emergency power supply during natural disasters and powering off-grid equipment. Traditional antenna arrays, however, suffer from low energy capture efficiency, difficult impedance matching, complex synthetic networks, and intricate manufacturing processes. This paper introduces a microwave energy receiver design utilizing Reflective Phase Gradient Metasurfaces (R-PGMs) and surface wave energy convergence technology. The design leverages the effective plane wave-to-surface wave conversion capability of R-PGMs to transform incident microwave energy into a surface wave mode, which is then efficiently harvested using a circular energy convergence array before being output to a coupling port. By optimizing R-PGM parameters, an ideal 60° phase gradient distribution is achieved, facilitating the focus of surface wave energy via dispersion characteristics. These components are integrated into a hybrid antenna array, complemented by a matched energy output port structure. Numerical simulations show that this array can efficiently convert microwave energy from plane waves to surface waves, achieving a conversion efficiency of 85.32% and a collection efficiency of 68.26%. Experimental results corroborate these findings, with peak energy collection efficiency reaching 64.68% at 5.8 GHz and an RF-DC conversion efficiency of 42%, confirming the design's efficacy. Compared to conventional methods, this design simplifies the system by avoiding complex combining networks and significantly enhances the efficiency of microwave MWPT.

A novel integrated approach for quantifying the convergence of disruptive technologies from science to technology

With rapid developments in science and technology, knowledge transfer from science to technology and technology convergence from different fields are accelerating. Technology convergence has become a main source of disruptive technologies (DTs). To facilitate enterprise R&D strategic decision-making and government innovation policies formulation, it is necessary to quantify the convergence processes of DTs and understand the DTs' emergence characteristics from science to technology. Existing research on technology convergence measurement mainly used patent citation information, patent co-classification analysis, and text mining. However, since these studies have limited analysis of the sources and causes of technology convergence from the perspective of knowledge memes, resulting in insufficient revelation of the processes and characteristics of DTs' emergence. Knowledge meme theory helps to reveal the relationships between knowledge diffusion, knowledge convergence, and technology convergence. Therefore, in this paper, we proposed a research framework for quantifying the convergence of DTs from science to technology. In this framework, we analyzed the knowledge diffusion and technology convergence of DTs from science to technology based on knowledge meme theory. We also integrated patent citation analysis, text mining, and cascade network models to quantitatively measure knowledge diffusion and technology convergence characteristics. We tried to understand the generation mechanisms of DTs from the perspective of technology convergence. We took smartphones as a case study to verify the framework's validity and flexibility. This paper provides a novel approach for quantifying the convergence of DTs from science to technology, which can help us to understand the emergence and development trends of DTs. This paper will also be of interest to smartphone technology R&D experts.

Enhancing Data Processing Efficiency : The Synergy of Edge Computing and Hybrid Cloud Storage

This comprehensive article explores the transformative integration of edge computing and hybrid cloud storage, a technological convergence that is reshaping data processing architectures in the era of exponential data growth. The research delves into the fundamental principles of edge computing and hybrid cloud storage, examining their synergistic relationship in addressing the limitations of traditional centralized cloud computing. By bringing computational resources closer to data sources, this integrated approach significantly reduces latency, enhances processing efficiency by up to 50%, and improves overall system reliability. The article presents detailed case studies in autonomous driving and smart city infrastructure, showcasing real-world applications and benefits. It critically analyzes the challenges inherent in this integration, including security concerns in decentralized architectures, data consistency issues, and cost implications. Furthermore, the article explores future directions, discussing emerging technologies such as AI-powered edge devices, evolving hybrid cloud solutions, and the potential for further optimization. This research provides valuable insights for organizations and researchers navigating the complex landscape of distributed computing, offering a roadmap for leveraging edge computing and hybrid cloud storage to achieve unprecedented levels of performance, scalability, and flexibility in data management and processing.

The impact of digital transformation on labour demand quantity and structure: Evidence from China

While the impact of digital transformation on firms’ labour demand has attracted considerable attention, no studies have examined how different types of digital transformation impact different workforce labour demand. Using data from Chinese A-share listed manufacturing companies from 2013 to 2022, this study employs a two-way fixed effects model, the instrumental variable method and multi-temporal difference-in-differences analysis. The findings reveal that digital convergence technologies significantly increase manufacturing firms’ labour demand, whereas e-commerce technologies have no significant effect on employment. Furthermore, digitalisation has a negative effect on the demand for operational employees and a positive effect on the demand for skilled and service employees. The mechanism test reveals that digital transformation primarily substitutes labour through productivity effects and generates labour demand via financing constraints and business scope effects, ultimately increasing manufacturing firms’ labour demand. These results further enhance the understanding of how digital technology development impacts labour demand.

Oyster shell based indirect carbonation integrated with probiotic encapsulation.

Recycling oyster shells-an abundant industrial waste-is essential to reduce marine pollution. Indirect carbonation is promising; however, is cost-prohibitive. This study is a pioneering endeavor to merge indirect carbonation and probiotic encapsulation technologies using oyster shells. Probiotics were encapsulated in the CaCO3 produced through indirect carbonation with oyster shells, and the performance was evaluated. Confocal laser scanning microscopy certified the survival of a substantial proportion of the encased probiotics. Importantly, the majority of the enveloped probiotics demonstrated robust survivability while passing through gastrointestinal and bile fluids. These findings underscore the applicability of oyster shells as an optimal precursor for probiotic encapsulation which is eco-friendly and addresses the challenges faced in industrial waste recycling. This novel approach overcomes the economic limitations associated with indirect carbonation and mitigates the shortcomings of existing probiotic encapsulation methods. Convergence of indirect carbonation and probiotic encapsulation technologies can chart new routes for the environmental sector.

Strategizing Originality for Technology Convergence: An Engineering Management Approach

This study examines how the integration of internal expertise with external research partnerships influences the confluence of technologies. Analyzing a decade’s patents, it clarifies the efficacy of varied knowledge acquisition strategies on innovation. The research quantifies the contribution of such synergies to innovation across technological, industrial, and geographic realms. Methodologically, it employs patent analytics to dissect originality into technology scope, assignee variety, industry diversity, and country-specific innovation trends from 2010 to 2019. Findings reveal a positive link between comprehensive technological and industrial knowledge and enhanced technological convergence. Significantly, it accentuates the role of global collaboration in unifying new technologies and industries. This interdisciplinary approach and in-depth organizational learning understanding are vital for spurring innovation. The results offer pivotal insights for academia and policy formulation in technology governance, advocating for integrated knowledge strategies to drive sector-spanning technology unification. The study provides a blueprint for fostering innovation through inter-sectoral and interdisciplinary exchange.

Towards real-time myocardial infarction diagnosis: a convergence of machine learning and ion-exchange membrane technologies leveraging miRNA signatures.

Rapid diagnosis of acute myocardial infarction (AMI) is crucial for optimal patient management. Accurate diagnosis and time of onset of an acute event can influence treatment plans, such as percutaneous coronary intervention (PCI). PCI is most beneficial within 3 hours of AMI onset. MicroRNAs (miRNAs) are promising biomarkers, with potential of early AMI diagnosis, since they are released before cell death and subsequent release of larger molecules [e.g., cardiac troponins (cTn)], and have greater sensitivity and stability in plasma versus cTn regardless of timing of AMI onset. However, miRNA-based AMI diagnosis can result in false positives due to miRNA content overlap between AMI and stable coronary artery disease (CAD). Accordingly, we explored the possibility of using a miRNA profile, rather than a single miRNA, to distinguish between CAD and AMI, as well as different stages following AMI onset. First we screened a library of 800 miRNA using plasma samples from 4 patient cohorts; no known CAD, CAD, ST-segment elevation myocardial infarction (STEMI) and STEMI followed by PCI, using Nanostring miRNA profiling technology. From this screening, based on machine learning SCAD and Lasso algorithms, we identified 9 biomarkers (miR-200b, miR-543, miR-331, miR-3605, miR-301a, miR-18a, miR-423, miR-142, and miR-132) that were differentially expressed in CAD, STEMI and STEMI-PCI and explored them to identify a miRNA profile for rapid and accurate AMI diagnosis. These 9 miRNAs were selected as the most frequently identified targets by SCAD and Lasso, as indicated in the "drum-plot" model in the machine learning approach. We used age-matched patient samples to validate selected 9 miRNA biomarkers using a multiplexed ion-exchange membrane-based miRNA sensor platform, which measures specific miRNAs, and cTn as a control, simultaneously as a point-of-care device. Findings from this study will inform timely and accurate diagnosis of AMI and its stages, which are essential for effective management and optimal patient outcomes.

Bridging the Gap: The Synergy of AI, Data Integration, and Data Science in Driving Innovation

The intersection of Artificial Intelligence (AI), Data Integration, and Data Science is fundamentally rewriting the definition of how businesses collaborate and differentiate. In this article, we will discuss how the triple convergence of these three fields facilitates one another to provide valuable insights, decision-making optimization, and any high-level process automation. In turn, AI relies on data to deliver predictive analytics and automate decision-making, while Data Integration ensures diverse raw data is brought together in a way that makes it possible. Data Science: Utilizing sophisticated, statistical, and computational methods to extract actionable insights. Collectively they deliver a compelling stack that enables organizations to unleash the power of their data, ensure operational efficiency and drive competitive advantage in the market. Outlining the convergence of both technologies, this paper describes core challenges, advantages and future impact, presenting a pathway for organizations planning to leverage the two digitally transformative phenomena.

MetaBlock: A Revolutionary System for Healthcare Industry Fusing Metaverse and Blockchain

The Metaverse is like another world functioning parallel to the actual physical world. It merges physical reality with digital virtuality by the convergence of different technologies that enable interactions with virtual objects and environment. The complete immersive experience with metaverse generates huge data which comes with its own set of challenges, a major one being the security and storage of user centric data. Considerably, blockchain offers a significant solution due to its exceptional features of decentralization, immutability, and transparency. For a detailed insight into the role of blockchain in the metaverse, the paper explores its usage in the healthcare industry. The paper then proposes a healthcare system named MetaBlock that is a fusion of both the metaverse and blockchain techniques. The paper discusses the components of the system from a detailed technical interpretation, such as data acquisition, data storage, virtual object and space simulation, data sharing and data privacy protection. Finally, an important research direction in terms of distributed consensus mechanism is discussed.

Implementación de un Dispositivo Inteligente para la Asistencia de Personas con Discapacidad Visual en Entornos Universitarios

This project arises in response to the growing global need for inclusive technological solutions aimed at improving the quality of life of people with visual impairments. The motivation is based on addressing the specific barriers these individuals face, with a particular focus on university environments. The methodology adopted focuses on the convergence of web services and networked communication technologies, seeking to promote the autonomy and safety of visually impaired people. A distinctive aspect of this project is the implementation of low power consumption and long-range technology, marking a pioneering approach in this field, which not only addresses the immediate need for inclusive solutions; but contributes to technological advancement by introducing efficient and long-range solutions. Through testing, very short response times are achieved in terms of identifying dangerous movements such as falls and detecting nearby objects, while character recognition still requires further refinement. The key innovation lies in the effective convergence of technologies, setting a new standard for inclusive and sustainable solutions. In other words, this project not only responds to a pressing need, but also marks a significant milestone in improving inclusion for the visually impaired, highlighting its relevance at the local level and contributing to global progress in this crucial field.