Search Results (1,464)

The management and exchange of electronic health records (EHRs) remain critical challenges in healthcare, with fragmented systems, varied standards, and security concerns hindering seamless interoperability. These challenges compromise patient care and operational efficiency. This paper proposes a novel solution to address these issues by leveraging distributed ledger technology (DLT), including blockchain, to enhance data security, integrity, and transparency in healthcare systems. The decentralized and immutable nature of DLT enables more efficient and secure information exchange across platforms, improving decision-making and coordination of care. This paper outlines a strategic implementation approach, detailing timelines, resource requirements, and stakeholder involvement while addressing crucial privacy and security concerns like encryption and access control. In addition, it explores standards and protocols necessary for achieving interoperability, offering case studies that demonstrate the framework’s effectiveness. This work contributes by introducing a DLT-based solution to the persistent issue of EHR interoperability, providing a novel pathway to secure and efficient health data exchanges. It also identifies the standards and protocols essential for integrating DLT with existing health information systems, thereby facilitating a smoother transition toward enhanced interoperability. Full article

The past two years saw a rapid proliferation of information and communication technologies (ICTs) in higher education. Digital technologies and environments offer many affordances. New digital literacy practices in universities have implications for teaching and learning. E-textbooks, in particular, act as mediating tools that can facilitate teaching and learning through developing students’ understandings of scientific concepts. This paper positions e-textbooks as mediators of learning, rather than merely objects of learning. There is thus a need to understand the mediating role of e-textbooks that lecturers draw on in their teaching. While much research was conducted on students’ use of e-textbooks, relatively little was conducted on lecturers’ use of e-textbooks in engineering education. The current study aimed to answer the following research question: What are lecturers’ perspectives on the use of e-textbooks to facilitate learning in engineering? To address this question, data were collected through five individual interviews conducted with engineering lecturers working in the Extended Curriculum Programme (ECP) of first-year students from three engineering departments (chemical engineering, mechanical engineering, and nautical science) at a university of technology in South Africa. The data were analysed using thematic content analysis with the help of ATLAS.ti. Data analysis was guided by a theoretical framework that drew on the cultural-historical activity theory (CHAT). In this study, the focus was on e-textbooks as pedagogical tools within engineering teaching and learning. The findings provide insight into how lecturers incorporate e-textbooks into their teaching, but also reveal the extent to which new digital literacy reading practices remain unfamiliar to engineering lecturers. CHAT enabled the identification of a critical insight, namely, the tension between mediation and division of labour. This highlights important aspects of the discourse surrounding seamless technology integration in higher education. The discussion points to the need for an expansive transformation regarding the use of e-textbooks as important mediating tools for teaching and learning. Full article

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings demonstrated that integrating IoT, blockchain, and XBRL is technically feasible, with significant potential to enhance accounting systems. Additionally, the findings identified key challenges of the proposed system, including the complexity of integration, data validation across technologies, costs, user adoption, and scalability concerns. However, the results showed that this system offers substantial benefits, such as real-time data capture from IoT devices, secure data storage and immutability through blockchain, standardized financial reporting via XBRL, accounting process automation, improved data accuracy, and enhanced security and transparency in financial reporting. The study also identified an optimal mechanism for ensuring seamless data transmission between these technologies. The study makes a valuable contribution to the accounting field by providing a new framework for automating data collection, enhancing data security, and streamlining financial reporting, with significant potential to advance accounting systems and improve transparency, accuracy, and efficiency in financial reporting. The study’s potential to impact accounting systems and financial reporting research and practice emphasizes its importance. Full article

As a new engine of China’s economic development, the digital economy is playing an important role in rural revitalization. Due to the impact of the novel coronavirus epidemic, there is a lack of recent empirical research on the digital economy and rural revitalization and development of the Yangtze River Economic Belt. Based on the provincial panel data of 11 provinces in the Yangtze River Economic Belt from 2014 to 2022, this paper calculates the comprehensive development level of the digital economy and rural revitalization and conducts a benchmark regression on their relationship. The results of the heterogeneity analysis show that the impact of the digital economy on rural revitalization in the upper and middle reaches of the Yangtze River is stronger than that in the lower reaches. Then we adopt the Moran index and spatial Durbin model for further regression analysis, and find that there is a spatial autocorrelation between the digital economy and rural revitalization. The digital economy of the Yangtze River Economic Belt has a spatial spillover effect on rural revitalization. To effectively harness the digital economy’s role in advancing rural revitalization, it is crucial to tailor resource allocation to local conditions, implement targeted policies, and establish a robust monitoring and evaluation system. This strategy aims to facilitate the seamless integration of the digital economy with rural revitalization, thereby achieving synergistic effects and promoting comprehensive, sustainable development in both economic and social dimensions. Full article

The Industrial Internet of Things (IIoT) is pivotal in advancing industrial automation, offering significant improvements in connectivity and efficiency. However, the integration of heterogeneous devices within IIoT systems presents substantial challenges, primarily due to the diversity in device hardware, protocols, and functionalities. In this paper, we propose a new pattern language specifically designed to enhance interoperability and operational efficiency across industrial settings. Drawing from a case study of the State Company for Automotive Industry (S.C.A.I.) in Iraq, this study details the development and integration of eleven interrelated patterns. These patterns were carefully combined based on identified relationships, forming a comprehensive pattern language that addresses key aspects of system heterogeneity, including device communication, data security, and system scalability. The pattern language was validated using the Delphi process theory, engaging industry experts to refine and optimize the framework for practical application. The implementation of this pattern language led to significant improvements in system integration, enabling seamless communication between diverse devices and enhancing operational workflows. The case study demonstrates the practical viability of the proposed pattern language in enhancing interoperability within real-world Industrial Internet of Things (IIoT) applications. Furthermore, the replicable nature of this framework makes it a valuable resource for other industrial environments seeking to harness the power of IIoT technologies. Full article

This study explores manipulator control using reinforcement learning, specifically targeting anthropomorphic gripper-equipped robots, with the objective of enhancing the robots’ ability to safely exchange diverse objects with humans during human–robot interactions (HRIs). The study integrates an adaptive HRI hand for versatile grasping and incorporates image recognition for efficient object identification and precise coordinate estimation. A tailored reinforcement-learning environment enables the robot to dynamically adapt to diverse scenarios. The effectiveness of this approach is validated through simulations and real-world applications. The HRI hand’s adaptability ensures seamless interactions, while image recognition enhances cognitive capabilities. The reinforcement-learning framework enables the robot to learn and refine skills, demonstrated through successful navigation and manipulation in various scenarios. The transition from simulations to real-world applications affirms the practicality of the proposed system, showcasing its robustness and potential for integration into practical robotic platforms. This study contributes to advancing intelligent and adaptable robotic systems for safe and dynamic HRIs. Full article

Traditional door mechanisms in public spaces, such as knob locks and standard handles, require manual contact, making them prone to contamination and posing significant health risks. To address the critical need for a safer and more hygienic solution, this study aimed to develop an innovative foot-operated door mechanism that is accessible and intuitive for all users. The study applies the Theory of Inventive Problem Solving (TRIZ), ergonomic principles, and universal design to develop the foot-operated mechanism, while using Importance–Performance Analysis (IPA) and the Kano model to evaluate user satisfaction and identify design improvements. The foot-operated mechanism developed in this study features internal and external pedals for seamless door operation, a secure locking system, and color-coded indicators for clear occupancy status communication, ensuring both ease of use and privacy. The design significantly enhances hygiene by minimizing manual contact and improves user convenience, as confirmed through the IPA-Kano analysis. This mechanism not only provides a practical and effective solution to contamination risks but also demonstrates versatility, making it suitable for various public spaces and accessible to a wide range of users. This study represents a significant contribution to public infrastructure by providing a safer, more hygienic, and sustainable solution for door operation in public spaces. Full article

Fifth-generation (5G) sensor networks are critical enablers of Industry 4.0, facilitating real-time monitoring and control of industrial processes. However, significant challenges to their deployment in industrial settings remain, such as a lack of support for interoperability and manageability with existing industrial applications and the specialized technical expertise required for the management of private 5G sensor networks. This research proposes a solution to achieve interoperability between private 5G sensor networks and industrial applications by mapping Yet Another Next Generation (YANG) models to Open Platform Communication Unified Architecture (OPC UA) models. An OPC UA pyang plugin, developed to convert YANG models into OPC UA design model files, has been made available on GitHub for open access. The key finding of this research is that the proposed solution enables seamless interoperability without requiring modifications to the private 5G sensor network components, thus enhancing the efficiency and reliability of industrial automation systems. By leveraging existing industrial applications, the management and monitoring of private 5G networks are streamlined. Unlike prior studies that explored OPC UA’s integration with other protocols, this work is the first to focus on the YANG–OPC UA integration, filling a critical gap in Industry 4.0 enablement research. Full article

In this study, a 4D trajectory weather (4DT-Wx) prototype system was developed and evaluated for effective weather information integration in trajectory-based operation (TBO) environments. The system has two key distinguishing features: multi-model-based trajectory services and buffer zone information provision. We constructed a distributed processing system using Apache Spark, enabling the efficient processing of large-scale weather data. The performance evaluation demonstrated excellent scalability and efficiency in processing large-scale data. An analysis of the buffer configurations highlighted that buffer zone information is valuable in decision-making processes and has the potential to enhance the system performance. The system’s practical applicability is presented through visualizations of the extracted weather information. This system is expected to enhance aviation safety and operational efficiency, providing a foundation for addressing increasingly complex weather conditions and flight scenarios in the future. The approach presented in this study marks a significant step toward effective TBO implementation and the advancement of future air traffic management. The evaluation of the 4DT-Wx system analyzed the accuracy of weather data processing and the performance of distributed processing, finding that the temperature (T) estimation had the highest accuracy, and that the parallel processing using Apache Spark was most effectively modeled by Ahmed et al.’s model. The findings suggest the potential for further optimization in integrating various weather models and developing algorithms to enhance their utilization. Full article

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. Full article

Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information—a crucial factor for seamless synchronisation, optimisation, and, consequently, for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations, and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key methodological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning, and NCO. Finally, we critically discuss and outline methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow’s transport systems. Full article

Introduction: Rabies remains a significant global threat, yet accurate estimations of its impact are hindered by the lack of confirmatory diagnoses. Postmortem diagnosis of rabies traditionally involves invasive brain tissue testing, a process met with resistance from deceased patients’ families, impeding consent. This paper presents and evaluates an innovative yet unpublished transnasal approach for postmortem brain tissue collection, offering a minimally invasive, easier, faster, and safer method. This method preserves the cadaver’s integrity, potentially easing family reluctance towards autopsies. The limited testing of both human and animal rabies in Ghana highlights the challenges in diagnosing this fatal disease. Scarce diagnostic resources and the complexity of obtaining brain tissue samples exacerbate the issue. Cultural and religious beliefs surrounding autopsies contribute to familial hesitation, as families view these procedures as disruptive and disfiguring, further complicating consent. Methodology: The transnasal technique involves approaching the brain tissue through the nostrils and cribriform plate without any superficial manipulation of the patient’s head and face, thereby preserving the aesthetics and natural features of the person. Results: Technological advancements and seamless One Health collaboration among governmental, non-governmental, and research entities locally and globally have culminated in Ghana’s first confirmed rabies diagnosis using this method of brain tissue collection. This success emphasizes the efficiency and feasibility of the transnasal brain collection approach and the invaluable role of the One Health approach and collaborative efforts in overcoming diagnostic challenges in rabies control. Full article

In industrial production, the identification and characterization of micron-sized second phases, such as carbonitrides in alloys, hold significant importance for optimizing alloy compositions and processes. However, conventional methods based on threshold segmentation suffer from drawbacks, including low accuracy, inefficiency, and subjectivity. Addressing these limitations, this study introduced a carbonitride instance segmentation model tailored for various nickel-based superalloys. The model enhanced the YOLOv8n network structure by integrating the SPDConv module and the P2 small target detection layer, thereby augmenting feature fusion capability and small target detection performance. Experimental findings demonstrated notable improvements: the mAP50 (Box) value increased from 0.676 to 0.828, and the mAP50 (Mask) value from 0.471 to 0.644 for the enhanced YOLOv8n model. The proposed model for carbonitride detection surpassed traditional threshold segmentation methods, meeting requirements for precise, rapid, and batch-automated detection in industrial settings. Furthermore, to assess the carbonitride distribution homogeneity, a method for quantifying dispersion uniformity was proposed and integrated into a data processing framework for seamless automation from prediction to analysis. Full article

The problems of achieving the UN’s sustainable development goals related to providing both developed and developing countries with cheap and accessible energy, as well as in the context of taking climate action, cannot be solved, on the one hand, without a transition to Energy 5.0, within the framework of the upcoming Fifth Industrial Revolution. On the other hand, it cannot be carried out without ensuring a “seamless” Fourth Energy Transition, which poses new challenges for the technological modernization of power production from non-renewables. Along with this, the expected transition to a human-centric Industry 5.0 challenges researchers to identify obstacles to the diffusion of technologies within hydrocarbon production industries and ways to overcome them in regard to the upcoming Mining 5.0 and Oil and Gas 5.0 environment. In this regard, the purpose of this review is to analyze the structure of scientific publications in this field of research on the human-centric development of technologies in terms of these platforms in order to outline the basis for further research. To achieve this goal, this review provides a multifaceted overview of the main technologies of Industry 5.0, embodied within Energy 5.0, Mining 5.0, and Oil and Gas 5.0, such as collaborative artificial intelligence and co-bots, digital tees, the industrial Internet of Everything, smart cities, and industry; their human-centric nature is revealed as the basis for achieving significant sustainable development goals. This review concludes that there is a need for further analysis of certain areas of the transition to Energy 5.0, such as the human-centric development of digital technologies of Industry 5.0 in the fuel and energy sector, and the revision of its role in terms of achieving the sustainable development goals in the future. Full article

Wearable technologies represent a significant advancement in facilitating communication between humans and machines. Powered by artificial intelligence (AI), human gestures detected by wearable sensors can provide people with seamless interaction with physical, digital, and mixed environments. In this paper, the foundations of a gesture-recognition framework for the teleoperation of infrared consumer electronics are established. This framework is based on force myography data of the upper forearm, acquired from a prototype novel soft pressure-based force myography (pFMG) armband. Here, the sub-processes of the framework are detailed, including the acquisition of infrared and force myography data; pre-processing; feature construction/selection; classifier selection; post-processing; and interfacing/actuation. The gesture recognition system is evaluated using 12 subjects’ force myography data obtained whilst performing five classes of gestures. Our results demonstrate an inter-session and inter-trial gesture average recognition accuracy of approximately 92.2% and 88.9%, respectively. The gesture recognition framework was successfully able to teleoperate several infrared consumer electronics as a wearable, safe and affordable human–machine interface system. The contribution of this study centres around proposing and demonstrating a user-centred design methodology to allow direct human–machine interaction and interface for applications where humans and devices are in the same loop or coexist, as typified between users and infrared-communicating devices in this study. Full article