Search Results (213)

The integration of Digital Twin (DT), Internet of Things (IoT), and Long Range Wireless (LoRa) technology in industrial automation increases efficiency, flexibility, and real-time monitoring. This study proposes a decentralized automation architecture for SCARA robots, leveraging wireless sensor networks to improve scalability, reduce the number of infrastructure components, and optimizing data-driven decision-making. Experimental validation demonstrated a 74.9% reduction in cycle time, decreasing from 55.42 s to 13.91 s across all test scenarios. The system achieved a 98.6% packet delivery success rate, ensuring reliable communication, while latency remained between 1 and 2 s, maintaining synchronization between the real robot and its digital twin. The main contributions include the following: (i) a decentralized control framework for SCARA robots, (ii) an evaluation of LoRa-based wireless communication, and (iii) experimental validation of feasibility. The results confirm the effectiveness of the system in stable real-time data transmission and precise robotic movements, offering a cost-effective alternative to conventional structures. Despite the advantages, challenges such as data security, interoperability, and real-time synchronization require further research. This study provides insights into the practical implementation of DT, IoT, and LoRa in industrial robotics, paving the way for advancements in smart manufacturing and Industry 4.0. Full article

Cloud computing has the potential to accelerate the digital journey of railways. Railway systems are big and complex, involving a lot of parts, like trains, tracks, signaling systems, and control systems, among others. The application of cloud computing technologies in the railway industry has the potential to enhance operational efficiency, data management, and overall system performance. Cloud management is essential for complex systems, and the automation of management services can speed up the provisioning, deployment, and maintenance of cloud infrastructure and applications by enabling visibility across the environment. It can provide consistent and unified management over resource allocation, streamline security processes, and automate the monitoring of key performance indicators. Key railway cloud management challenges include the lack of open interfaces and standardization, which are related to the vendor lock-in problem. In this paper, we propose an approach to design the railway cloud resource management as a service. Based on typical use cases, the requirements to fault and performance management of the railway cloud resources are identified. The main functionality is designed as RESTful services. The approach feasibility is proved by formal verification of the cloud resource management models supported by cloud management application and services. The proposed approach is open, in contrast to any proprietary solutions and feature scalability and interoperability. Full article

The rapid growth of the Internet of Things (IoT) has introduced critical challenges related to security, scalability, and data integrity. Blockchain technology, with its decentralized, immutable, and tamper-resistant framework, presents a transformative solution to address these challenges. This study explores blockchain applications in the IoT, focusing on security, automation, scalability, and data sharing. Industry-specific applications, including supply chain management, smart cities, and healthcare, highlight the potential of blockchains to optimize operations, ensure compliance, and foster innovation. Additionally, blockchain technology enables robust audit trails, enhances accountability, and reduces fraud in sensitive IoT applications, such as finance and healthcare. The synergy between blockchains and the IoT creates a secure and transparent platform for managing device interoperability and data exchange, fostering seamless communication between diverse IoT components. Furthermore, this paper discusses layer 2 scaling techniques and tokenization to address scalability, ownership, monetization, and cost challenges, providing practical solutions for real-world deployments. Future directions emphasize integrating blockchain systems with artificial intelligence (AI), machine learning (ML), and edge computing, offering groundbreaking capabilities to further revolutionize IoT ecosystems. By merging these advanced technologies, organizations can build secure, scalable, and intelligent systems to drive innovation and trust. Full article

Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS platforms, evaluating their functionalities, strengths, and limitations. Through an extensive literature review, the survey explores their adoption and utilization across key research domains. Additionally, it identifies emerging trends shaping the field. The main contributions of this survey include (1) a detailed overview of leading open-source platforms, highlighting their strengths and weaknesses; (2) an examination of their impact on research; and (3) a synthesis of current trends, particularly in interoperability with emerging technologies such as AI/ML solutions and edge computing. This study aims to provide researchers and practitioners with a holistic understanding of open-source ADS platforms, guiding them in selecting the right platforms for future innovation. Full article

This review is focused on FC-BENTEN, an advanced synchrotron X-ray experimental database developed at SPring-8 with support from Japan’s New Energy and Industrial Technology Development Organization (NEDO). Designed to advance polymer electrolyte fuel cells (PEFCs) research, FC-BENTEN addresses challenges in improving efficiency, durability, and cost-effectiveness through data-driven approaches informed by materials informatics (MI). Through standardization of protocols for sample preparation, data acquisition, analysis, and formatting, the database ensures high-quality, reproducible data essential for reliable scientific outcomes. FC-BENTEN streamlines metadata creation using automated processes and template-based tools, enhancing data management, accessibility, and interoperability. Security measures include two-factor authentication, safeguarding sensitive information and maintaining controlled user access. Planned integration with MI platforms will broaden data cross-referencing capabilities, facilitate PEFC applications expansion, and guide future research. This review discusses FC-BENTEN’s architectural framework, metadata standardization efforts, and role in advancing PEFC research through a high-throughput experimental workflow. It illustrates how data-driven methods and standardized practices contribute to innovation, underscoring databases’ potential to accelerate next-generation PEFC technologies development. Full article

This review analyzes the role of artificial intelligence (AI) and automation in optimizing vegetable production within hydroculture systems. Methods: Following the PRISMA methodology, this study examines research on IoT-based monitoring and AI techniques, particularly Deep Neural Networks (DNNs), K-Nearest Neighbors (KNNs), Fuzzy Logic (FL), Convolutional Neural Networks (CNNs), and Decision Trees (DTs). Additionally, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models were analyzed due to their effectiveness in processing temporal data and improving predictive capabilities in nutrient optimization. These models have demonstrated high precision in managing key parameters such as pH, temperature, electrical conductivity, and nutrient dosing to enhance crop growth. The selection criteria focused on peer-reviewed studies from 2020 to 2024, emphasizing automation, efficiency, sustainability, and real-time monitoring. After filtering out duplicates and non-relevant papers, 72 studies from the IEEE, SCOPUS, MDPI, and Google Scholar databases were analyzed, focusing on the applicability of AI in optimizing vegetable production. Results: Among the AI models evaluated, Deep Neural Networks (DNNs) achieved 97.5% accuracy in crop growth predictions, while Fuzzy Logic (FL) demonstrated a 3% error rate in nutrient solution adjustments, ensuring reliable real-time decision-making. CNNs were the most effective for disease and pest detection, reaching a precision rate of 99.02%, contributing to reduced pesticide use and improved plant health. Random Forest (RF) and Support Vector Machines (SVMs) demonstrated up to 97.5% accuracy in optimizing water consumption and irrigation efficiency, promoting sustainable resource management. Additionally, LSTM and RNN models improved long-term predictions for nutrient absorption, optimizing hydroponic system control. Hybrid AI models integrating machine learning and deep learning techniques showed promise for enhancing system automation. Conclusion: AI-driven optimization in hydroculture improves nutrient management, water efficiency, and plant health monitoring, leading to higher yields and sustainability. Despite its benefits, challenges such as data availability, model standardization, and implementation costs persist. Future research should focus on enhancing model accessibility, interoperability, and real-world validation to expand AI adoption in smart agriculture. Furthermore, the integration of LSTM and RNN should be further explored to enhance real-time adaptability and improve the resilience of predictive models in hydroponic environments. Full article

In recent years, innovative Non-Destructive Testing (NDT) techniques, applicable for the assessment of existing civil structures, have become available for in situ analysis on Reinforced Concrete (RC) and masonry structures, but they are still not established for regular inspections, especially after seismic events. The damage assessment of RC buildings after seismic events is a very relevant issue in Italy, where most of the structures built in the last 50 years are RC structures. Furthermore, there is also a growing interest in being able to monitor structural health aspects by storing them on the building’s digital twin. For these reasons, it is necessary to develop an affordable and ready-to-use NDT procedure that provides more accurate indications on the real state of damage of reinforced concrete buildings after seismic events and to integrate these data into an interoperable digital twin for automated, optimized building performance monitoring, management, and preventive maintenance. To this end, a case study was conducted on a building in the Marche region in Italy, damaged by the 2016 earthquake. Non-destructive tests were performed and inserted into the LIS platform for the creation of a digital twin of the building. This platform seamlessly manages, visualizes, and analyzes the collected data and integrates various sensor nodes deployed throughout the building. The paper also presents a methodology to simplify the work of the test operator and make the entire process of knowledge of the building faster and more sustainable through a QR-code interface. Full article

In the realm of sustainable construction, Life Cycle Assessment (LCA) plays a key role as a tool for quantifying the environmental impacts of building materials and products. The integration of LCA and Building Information Modeling (BIM) makes it possible to evaluate the environmental performance of buildings at the design stage. This integration can help to improve the LCA process for buildings thanks to the potential for automation and interoperability. The goal of this study is to establish a BIM-based LCA workflow using the Industry Foundation Classes (IFC) open file format. The interoperability of BIM data exchange is achieved by applying IFC. The steps of the assessment process are described in accordance with the LCA phases outlined in the ISO 14040 standard. The impact assessment and results interpretation phases are automated by means of a program code for IFC file processing. The proposed BIM-based LCA is validated for a case study of a BIM model constructed for a three-story educational building. The GWP of the building materials and products of envelope and load-bearing structures at the A1–A3 life cycle stages are calculated for the purpose of proposed workflow testing. The resulting workflow allows for the calculation of negative environmental impacts to be agile, depending on the goal and scope set. Full article

Enterprise networking optimization has become crucial recently due to increasing demand for a secure, adaptable, reliable, and interoperable network infrastructure. Novel techniques to optimize network security and toimprove scalability and efficiency are constantly being developed by network enablers, particularly in more challenging multi-cloud and edge scenarios. This paper, therefore, presents a comprehensive review of the traditional and most recent developments in enterprise networking. We structure the paper with particular emphasis on the adoption of state of-the-art technologies, such as software-defined wide area network(SD-WAN), secure access service edge (SASE) architecture, and network automation, driven by artificial intelligence (AI). The review also identifies various challenges associated with the adoption of the aforementioned technologies. These include operational complexity, cybersecurity threats, and trade-offs between cost-effectiveness and high performance requirements. Furthermore, the paper examines how different organizations are addressing a plethora of challenges by exploiting these technological innovations to drive robust and agile business interconnectivity. The review is concluded with an outline of possible solutions and future prospects, capable of promoting digital transformation and enhancing seamless connectivity within the enterprise networking environment. Full article

The conversion of unstructured clinical data into structured formats, such as Fast Healthcare Interoperability Resources (FHIR), is a critical challenge in healthcare informatics. This study explores the potential of large language models (LLMs) to automate this conversion process, aiming to enhance data interoperability and improve healthcare outcomes. The effectiveness of various LLMs in converting clinical reports into FHIR bundles was evaluated using different prompting techniques, including iterative correction and example-based prompting. The findings demonstrate the critical role of prompt engineering, with the two-step approach shown to significantly improve accuracy and completeness. While few-shot learning enhanced performance, it also introduced a risk of overreliance on examples. The performance of the LLMs is assessed based on the precision, hallucination rate, and resource mapping accuracy across mammography and dermatological reports from two clinics, providing insights into effective strategies for reliable FHIR data conversion and highlighting the importance of tailored prompting strategies. Full article

The evolution of Protection and Control (P&C) systems has developed though analogue and digital generations, and is presently advancing towards the utilization of Virtualization of Protection, Automation and Control environments (VPAC). This article focuses on redefining the features of traditional and modern P&C systems, Centralized Protection Automation and Control (CPAC), and VPAC, focusing on the integration of Intelligent Electronic Devices (IEDs) with secure communication that is time-effective in the centralized distribution of power and prevention of network vulnerability. Though standards such as IEC 61850-9-2 LE have been adopted, the actualization of full interoperability between diverse IED manufacturers remains elusive. With the digitization of technologies, P&C systems are naturally transitioning to virtual environments, with timing precision, redundancy and security being imperative. Latency and resource management and allocation in VPAC systems are considerable global issues. This paper discusses the issues of maintaining low operational performance in virtual substation environments while satisfying the requirements for performance in real time. The impacts of large volumes of data and artificial intelligence on the management of the grid are studied, and AI-based analytics that predict system failures and automatically change load flows are shown, as they have the potential to increase the flexibility and stability of the grid. The use of big data enables electric power utilities to enhance their protection systems, anticipate disturbances and improve energy management methods. The paper presents a comparative analysis between traditional P&C and its virtualized counterparts, with strong emphasis placed on the flexibility and scaling of VPAC resources. Full article

Complex dynamics and nonlinear systems play a critical role in industrial processes, where complex interactions, high uncertainty, and external disturbances can significantly impact efficiency, stability, and safety. In sectors such as mining, manufacturing, and energy networks, even small perturbations can lead to unexpected system behaviors, operational inefficiencies, or cascading failures. Understanding and controlling these dynamics is essential for developing robust, adaptive, and resilient industrial systems. This study conducts a systematic literature review covering 2015–2025 in Scopus and Web of Science, initially retrieving 2628 (Scopus) and 343 (WoS) articles. After automated filtering (Python) and applying inclusion/exclusion criteria, a refined dataset of 2900 references was obtained, from which 89 highly relevant studies were selected. The literature was categorized into six key areas: (i) heat transfer with magnetized fluids, (ii) nonlinear control, (iii) big-data-driven optimization, (iv) energy transition via SOEC, (v) fault detection in control valves, and (vi) stochastic modeling with semi-Markov switching. Findings highlight the convergence of robust control, machine learning, IoT, and Industry 4.0 methodologies in tackling industrial challenges. Cybersecurity and sustainability also emerge as critical factors in developing resilient models, alongside barriers such as limited data availability, platform heterogeneity, and interoperability gaps. Future research should integrate multiscale analysis, deterministic chaos, and deep learning to enhance the adaptability, security, and efficiency of industrial operations in high-complexity environments. Full article

This study explores the integration of the JADE (Java Agent Development Framework) platform with deep reinforcement learning (DRL) to enhance governmental interoperability and optimize administrative workflows in municipal settings. The proposed approach combines the JADE’s robust multi-agent system (MAS) capabilities with the adaptive decision-making power of DRL to address prevalent challenges faced by government agencies, such as fragmented operations, incompatible data formats, and rigid communication protocols. By enabling seamless communication between agents across departments such as the Treasury, the Event Management department, and the Public Safety department, the hybrid system fosters real-time collaboration and supports efficient, data-driven decision making. Agents leverage historical and real-time data to adapt to environmental changes and make optimized decisions that align with overarching governmental objectives, such as resource allocation and emergency response. The result is a system capable of managing intricate administrative duties using structured agent communication and the integration of DRL-driven learning models, improving governmental interoperability. Key performance indicators highlight the system’s effectiveness, achieving a task completion rate of 95%, decision accuracy of 96%, and a communication latency of just 120 ms. Additionally, the framework’s flexibility ensures seamless scalability, accommodating complex and large-scale tasks across multiple governmental units. This research presents a scalable, automated, and resilient framework for optimizing governmental processes, offering a pathway to more efficient, transparent, and adaptive public sector operations. Full article

Industry 4.0 represents the main paradigm currently bringing great innovation in the field of automation and data exchange among production technologies, according to the principles of interoperability, virtualization, decentralization and production flexibility. The Fourth Industrial Revolution is driven by structural changes in the manufacturing sector, such as the demand for customized products, market volatility and sustainability goals, and the integration of artificial intelligence and Big Data. This work aims to analyze, from a bibliometric point of view of journal papers on Scopus, with no time limitation, the existing literature on the application of AI in SMEs, which are crucial elements in the industrial and economic fabric of many countries. However, the adoption of modern technologies, particularly AI, can be challenging for them, due to the intrinsic structure of this type of enterprise, despite the positive effects obtained in large organizations. Full article

Modern manufacturing systems are influenced by the growing complexity of mechatronics, control systems, IIoT, and communication technologies integrated into cyber-physical systems. These systems demand flexibility, modularity, and rapid project execution, making digital tools critical for their design. Virtual commissioning, based on digital twins, enables the testing and validation of control systems and designs in virtual environments, reducing risks and accelerating time-to-market. This research explores the development of digital twin models to bridge the gap between simulation and real-world validation. The models identify design flaws, validate the PLC control code, and ensure interoperability across software platforms. A case study involving a modular Festo manufacturing system modelled in Tecnomatix Process Simulate demonstrates the ability of digital twins to detect inefficiencies, such as collision risks, and to validate automation systems virtually. This study highlights the advantages of virtual commissioning for optimizing manufacturing systems. Communication testing showed compatibility across platforms but revealed limitations with certain data types due to software constraints. This research provides practical insights into creating robust digital twin models, improving the flexibility, efficiency, and quality of manufacturing system design. It also offers recommendations to address current challenges in interoperability and system performance. Full article