Search Results (48)

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

This article compares selected fire safety regulatory systems in Japan, China, the United States, and the EU/UK, interpreted through the lens of responsive architecture and the implementation of digital technologies—building information modelling (BIM), digital twins (DTs), artificial intelligence (AI), and the Internet of Things (IoT). The study adopts a qualitative approach based on a structured review of legal acts, technical standards, public-sector reports, and the scientific and professional literature, organised using a common analytical framework. First, the analysis identifies shared foundations across regimes: the primacy of life safety, mandatory detection and alarm functions, fire compartmentation, requirements for protected means of exit, and the increasing importance of documenting the operational status of protection measures. Then, it contrasts key differences, including the permissibility of performance-based design (PBD), the degree to which digital documentation is formally recognised, organisational enforcement models, and cybersecurity approaches for integrated fire alarm/voice alarm/building management/IoT ecosystems. Japan and selected Chinese cities combine stringent requirements with openness to dynamic solutions and urban-scale data platforms. The USA relies on a decentralised code-based ecosystem with a strong role for professional and industry bodies, while the EU/UK continues to strengthen harmonised standards and digital building registers, reinforced by lessons after the Grenfell Tower fire. Against this background, Poland is discussed as broadly aligned in goals and baseline technical requirements yet lagging behind in implementing PBD pathways, digital registers, formal BIM/DT integration, and minimum cybersecurity requirements. The proposed directions for change aim to create a more predictable regulatory and technical framework for the development of responsive architecture and dynamic fire safety systems in Poland. The study contributes to the sustainability literature by framing regulatory readiness for digital fire safety as a lifecycle resilience strategy, directly relevant to safe, resource-efficient, and inclusive built environments. Full article

Blockchain adoption across industries has led to the emergence of multiple independent blockchain platforms, creating challenges for cross-chain data exchange and system interoperability. This paper addresses this challenge by examining interoperability frameworks that enable communication between heterogeneous blockchain networks. We adopt a platform-oriented analysis to study widely used blockchain ecosystems, focusing on the mechanisms they employ for cross-chain communication and asset transfer. To demonstrate practical applicability, we present a conceptual supply chain scenario that illustrates how interoperable architectures enable interactions among multiple blockchain entities. Finally, we identify key open research challenges, including data management, cross-chain query processing, privacy, governance, scalability, and protocol standardisation. The findings highlight both the capabilities and limitations of existing interoperability solutions and outline directions for future research. Full article

The proliferation of Internet of Things (IoT) devices has significantly expanded the threat landscape for malicious software (malware), rendering traditional signature-based detection methods increasingly ineffective in coping with the volume and evolving nature of modern threats. In response, researchers are utilising artificial intelligence (AI) for a more dynamic and robust malware detection solution. An innovative approach utilising AI is focusing on image classification techniques to detect malware on resource-constrained Single-Board Computers (SBCs) such as the Raspberry Pi. In this method the conversion of malware binaries into 2D images is examined, which can be analysed by deep learning models such as convolutional neural networks (CNNs) to classify them as benign or malicious. The results show that the image-based approach demonstrates high efficacy, with many studies reporting detection accuracy rates exceeding 98%. That said, there is a significant challenge in deploying these demanding models on devices with limited processing power and memory, in particular those involving of both calculation and time complexity. Overcoming this issue requires critical model optimisation strategies. Successful approaches include the use of a lightweight CNN architecture and federated learning, which may be used to preserve privacy while training models with decentralised data are processed. This hybrid workflow in which models are trained on powerful servers before the learnt algorithms are deployed on SBCs is an emerging field attacting significant interest in the field of cybersecurity. This paper synthesises the current state of the art, performance compromises, and optimisation techniques contributing to the understanding of how AI and image representation can enable effective low-cost malware detection on resource-constrained systems. Full article

The Unbroken Rehabilitation Center in Lviv illustrates how architectural design can support recovery in post-conflict conditions. Drawing on concepts of healing environments, evidence-based design, and trauma-informed architecture, this study aimed to identify architectural strategies that enhance physical and psychological rehabilitation in war-affected populations. A mixed-method approach was applied, combining field observations, architectural analysis, and user surveys triangulated with interviews and documentation review. Results show that decentralised layouts, daylight access, barrier-free circulation, and cross-laminated timber (CLT)-based vertical expansion contribute to therapeutic effectiveness. Survey data from 45 respondents confirmed very high ratings for accessibility (9–10/10) and strong appreciation of group therapy rooms (9.0), art therapy (8.8), and music therapy (8.7). These findings highlight the value of sensory and symbolic elements, including natural materials and culturally embedded art. While the exploratory character and uneven respondent distribution limit generalisability, the triangulated methodology enhanced reliability and revealed clear user trends. The study demonstrates that architectural design can actively support resilience and rehabilitation in war-affected contexts, offering transferable insights for future post-conflict reconstruction. Full article

Mass customisation in the automotive industry has exploded the number of wiring harness variants that must be assembled, tested and repaired on the shop floor. Existing CAD or schematic formats are too heavy and too coarse-grained to drive in-line, per-VIN validation, while supplier documentation is heterogeneous and often incomplete. This paper presents a pin-centric, two-tier graph model that converts raw harness tables into a machine-readable, wiring-aware digital twin suitable for real-time use in manufacturing plants. All physical and logical artefacts—pins, wires, connections, paths and circuits—are represented as nodes, and a dual-store persistence strategy separates attribute-rich JSON documents from a lightweight NetworkX property graph. The architecture supports dozens of vehicle models and engineering releases without duplicating data, and a decentralised validation pipeline enforces both object-level and contextual rules, reducing initial domain violations from eight to zero and eliminating fifty-two circuit errors in three iterations. The resulting platform graph is generated in 0.7 s and delivers 100% path-finding accuracy. Deployed at Ford’s Almussafes plant, the model already underpins launch-phase workload mitigation, interactive visualisation and early design error detection. Although currently implemented in Python 3.11 and lacking quantified production KPIs, the approach establishes a vendor-agnostic data standard and lays the groundwork for self-aware manufacturing: future work will embed real-time validators on the line, stream defect events back into the graph and couple the wiring layer with IoT frameworks for autonomous repair and optimisation. Full article

The integration of electric vehicles (EVs) into smart grids (SGs) is reshaping both energy systems and mobility infrastructures. This review presents a comprehensive and cross-disciplinary synthesis of current technologies, methodologies, and challenges associated with EV–SG interaction. Unlike prior reviews that address these aspects in isolation, this work uniquely connects three critical pillars: (i) the evolution of energy storage technologies, including lithium-ion, second-life, and hybrid systems; (ii) optimisation and predictive control techniques using artificial intelligence (AI) for real-time energy management and vehicle-to-grid (V2G) coordination; and (iii) cybersecurity risks and post-quantum solutions required to safeguard increasingly decentralised and data-intensive grid environments. The novelty of this review lies in its integrated perspective, highlighting how emerging innovations, such as federated AI models, blockchain-secured V2G transactions, digital twin simulations, and quantum-safe cryptography, are converging to overcome existing limitations in scalability, resilience, and interoperability. Furthermore, we identify underexplored research gaps, such as standardisation of bidirectional communication protocols, regulatory inertia in V2G market participation, and the lack of unified privacy-preserving data architectures. By mapping current advancements and outlining a strategic research roadmap, this article provides a forward-looking foundation for the development of secure, flexible, and grid-responsive EV ecosystems. The findings support policymakers, engineers, and researchers in advancing the technical and regulatory landscape necessary to scale EV–SG integration within sustainable smart cities. Full article

Autonomous Mobile Robots (AMRs) are increasingly important in Industry 4.0 intralogistics but creating path planning systems that adapt to dynamic and uncertain Flexible Manufacturing Systems (FMS), especially managing conflicts among multiple AMRs with a need for scalable decentralised solutions, remains a significant challenge. This research introduces a dynamic path planning system for AMRs designed for reactive adaptation to FMS disturbances and generalisation across factory layouts, incorporating support for multiple AMRs with integrated conflict avoidance. The system is built on a Multi-Agent Systems (MAS) architecture, where software AMR agents independently calculate their paths using a hybrid Genetic Algorithm (GA) that employs Cell-Based Decomposition (CBD) and optimises path length, smoothness, and overlap via a multi-objective fitness function. Multi-AMR conflict avoidance is implemented using the Iterative Exclusion Principle (IEP), which facilitates priority-based planning, knowledge sharing through Predictive Collision Avoidance (PCA), and iterative replanning among agents communicating via a blackboard agent. Verification demonstrated the system’s ability to successfully avoid deadlocks for up to nine AMRs and exhibit good scalability. Validation in a simulated FMS environment confirmed robust adaptation to various disturbances, including static and dynamic obstacles, while maintaining stable run times and consistent path quality. These results affirm the practical feasibility of this hybrid GA and MAS-based approach for dynamic AMR control in complex industrial settings. Full article

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers. Full article

Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent systems. Furthermore, this paper discusses future perspectives on biomimetics-driven innovations, emphasising their potential to foster resilience, efficiency, and sustainability in rapidly evolving technological landscapes. Particular attention is given to neuromorphic hardware, a biologically inspired computing paradigm that mimics neural processing through spike-based communication and analogue architectures. Key components such as memristors and neuromorphic processors enable adaptive, low-power, task-specific computation, with wide-ranging applications in robotics, AI, healthcare, and renewable energy systems. Furthermore, this paper analyses how self-organising cities, conceptualised as complex adaptive systems, embody biomimetic traits such as resilience, decentralised optimisation, and autonomous resource management. Full article

The latest research highlights the need for circularity in modern industrial supply chains, which is reflected in the decisions of European and global policymakers, as well as in the strategies of major stakeholders. Digital Twins are considered a principal catalyst in the transition to circularity, while real-world, accurate and timely data is a key factor in these supply chains. This emphasis on data highlights the central role of data analytics in extracting key insights and utilizing machine learning to propose sustainability initiatives in decentralized production ecosystems. In consequence, commercial solutions are being developed; however, a single solution might not address all requirements. In this work we present a comprehensive modular, scalable and secure analytics architecture, designed to expand the available components in commercial solutions by providing an intelligent layer to Digital Twins. Our approach integrates with the latest standards for international data spaces, interoperability and process models in distributed environments where multiple actors engage in co-opetition. The proposed architecture is implemented in a market-ready solution and demonstrated in two case studies, in Spain and in Greece. Validation results confirm that the analytics service delivers accurate, timely and actionable insights, while following open communication standards and sustainability guidelines. Our research indicates that companies implementing digital twin solutions using standardized connectors for interoperability can benefit by customizing the proposed solution and avoiding complex developments from scratch. Full article

The integration of renewable energy into residential microgrids presents significant challenges due to solar generation intermittency and variability in household electricity demand. Traditional forecasting methods, reliant on historical data, fail to adapt effectively in dynamic scenarios, leading to inefficient energy management. This paper introduces a novel adaptive energy management framework that integrates streaming machine learning (SML) with a hierarchical fractal microgrid architecture to deliver precise real-time electricity demand forecasts for a residential community. Leveraging incremental learning capabilities, the proposed model continuously updates, achieving robust predictive performance with mean absolute errors (MAE) across individual households and the community of less than 10% of typical hourly consumption values. Three battery-sizing scenarios are analytically evaluated: centralised battery, uniformly distributed batteries, and a hybrid model of uniformly distributed batteries plus an optimised central battery. Predictive adaptive management significantly reduced cumulative grid usage compared to traditional methods, with a 20% reduction in energy deficit events, and optimised battery cycling frequency extending battery lifecycle. Furthermore, the adaptive framework conceptually aligns with digital twin methodologies, facilitating real-time operational adjustments. The findings provide critical insights into sustainable, decentralised microgrid management, emphasising improved operational efficiency, enhanced battery longevity, reduced grid dependence, and robust renewable energy utilisation. Full article

Agricultural supply chains face growing scrutiny due to rising concerns over food authenticity, safety, and sustainability. These challenges stem from issues such as contamination risks, fraudulent labelling, and the absence of reliable, real-time tracking systems. Existing systems often rely on centralised databases and fragmented data flows, limiting traceability, data integrity, and end-to-end visibility. While blockchain technology offers potential, most research focuses narrowly on traceability, overlooking its role in establishing secure product identity and its integration with emerging tools. This review investigates how Decentralised Identifiers (DIDs), digital twins, and smart contracts—in conjunction with blockchain—can create verifiable digital representations of agricultural products and automate trust mechanisms. Through an analysis of over sixty recent sources and leading standards (e.g., W3C DIDs, Hyperledger Aries), the study identifies key gaps in interoperability, governance, and system maturity. A layered system architecture is proposed, and its application is demonstrated in a cold-chain case scenario. The paper concludes with a roadmap for empirical validation and policy alignment, contributing a practical and scalable framework for researchers, practitioners, and regulators advancing blockchain-enabled traceability systems. Full article

Technological evolution has driven the integration of computing devices in various domains, giving rise to heterogeneous and dynamic intelligent environments; together with market pressure, these pose challenges in formulating an architecture that takes advantage of contextual knowledge. In terms of architectural design, we are witnessing a transition from a centralised, monolithic view of systems to a decentralised view that incorporates the vertical and horizontal dimensions of the production environment. Therefore, this review aimed to (i) identify the requirements, (ii) find out about the representation models and context inference techniques, and (iii) identify architectural technologies, norms, models, and standards. The results observed in 25 articles made it possible to identify interoperability, automation, and decision-making as convergence points and observe the adoption of ontologies as a research area for context representation. In contrast, the discussion of context inference techniques remains open. Finally, this study presents recommendations for the design of a context-aware systems architecture that incorporates the principles of Industry 4.0 and facilitates the development of applications. Full article

Introduction: Ensuring vehicular cybersecurity is a critical challenge due to the increasing connectivity of modern vehicles, and traditional centralised learning approaches for intrusion detection pose significant privacy risks, as they require sensitive data to be shared from multiple vehicles to a central server. Objective: The aim of this study is therefore to develop an in-vehicle intrusion detection system (IVIDS) that integrates federated learning (FL) with neural networks, enabling decentralised and privacy-preserving detection of cyberattacks in vehicular networks. The proposed system extends previous research by detecting a broader range of attacks (eight types) and exploring different deep learning architectures. Methods: This study employs an extended version of the publicly available VeReMi dataset to train and evaluate multiple neural network architectures, including Multilayer Perceptrons (MLPs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks. Federated learning is utilised to enable collaborative model training across multiple vehicles without sharing raw data. Various data preprocessing techniques and differential privacy mechanisms are also explored. Results and Conclusions: The experimental results demonstrate that LSTM networks outperform both MLP and GRU architectures in classifying vehicular cyberattacks. The best LSTM model, trained with two previous message lags and standard normalisation, achieved a classification accuracy of 96.75% in detecting eight types of attacks, surpassing previous studies, and demonstrating the potential of applying neural networks designed to work with time series data. Full article