Search Results (425)

The development of Industry 4.0 has accelerated the adoption of sophisticated technologies, including Digital Twins (DTs), Artificial Intelligence (AI), and cybersecurity, within Additive Manufacturing (AM). Enabling real-time monitoring, process optimization, predictive maintenance, and secure data management can redefine conventional manufacturing paradigms. Although their individual importance is increasing, a consistent understanding of how these technologies interact and collectively improve AM procedures is lacking. Focusing on the integration of digital twins (DTs), modular AI, and cybersecurity in AM, this review presents a comprehensive analysis of over 137 research publications from Scopus, Web of Science, Google Scholar, and ResearchGate. The publications are categorized into three thematic groups, followed by an analysis of key findings. Finally, the study identifies research gaps and proposes detailed recommendations along with a framework for future research. The study reveals that traditional AM processes have undergone significant transformations driven by digital threads, digital threads (DTs), and AI. However, this digitalization introduces vulnerabilities, leaving AM systems prone to cyber-physical attacks. Emerging advancements in AI, Machine Learning (ML), and Blockchain present promising solutions to mitigate these challenges. This paper is among the first to comprehensively summarize and evaluate the advancements in AM, emphasizing the integration of DTs, Modular AI, and cybersecurity strategies. Full article

Industrial robots are undergoing a transformative shift as Artificial Intelligence (AI)-driven and bio-inspired control strategies unlock new levels of precision, adaptability, and multi-dimensional sustainability aligned with Industry 5.0 (energy efficiency, material circularity, and life-cycle emissions). This systematic review analyzes 160 peer-reviewed industrial robotics control studies (2023–2025), including an expanded bio-inspired/human-centric subset, to evaluate: (1) the dominant and emerging control methodologies; (2) the transformative role of digital twins and 5G-enabled connectivity; and (3) the persistent technical, ethical, and environmental challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the study employs a rigorous methodology, focusing on adaptive control, deep reinforcement learning (DRL), human–robot collaboration (HRC), and quantum-inspired algorithms. The key findings highlight up to 30% latency reductions in real-time optimization, up to 22% efficiency gains through digital twins, and up to 25% energy savings from bio-inspired designs (all percentage ranges are reported relative to the comparator baselines specified in the cited sources). However, critical barriers remain, including scalability limitations (with up to 40% higher computational demands) and cybersecurity vulnerabilities (with up to 20% exposure rates). The convergence of AI, bio-inspired systems, and quantum computing is poised to enable sustainable, autonomous, and human-centric robotics, yet requires standardized safety frameworks and hybrid architectures to fully support the transition from Industry 4.0 to Industry 5.0. This review offers a strategic roadmap for future research and industrial adoption, emphasizing human-centric design, ethical frameworks, and circular-economy principles to address global manufacturing challenges. Full article

This study, based on engineering education accreditation standards, addresses the supply–demand imbalance in China’s cybersecurity talent cultivation by constructing a sustainable “education-industry-society” collaborative model. Through case studies at Huaihua University and other institutions, employing methods such as literature analysis, field research, and empirical investigation, we systematically explore reform pathways for an innovative cybersecurity talent development system. The research proposes a “three-platform, four-module” practical teaching framework, where the coordinated operation of the basic skills training platform, comprehensive ability development platform, and innovation enhancement platform significantly improves students’ engineering competencies (practical courses account for 41.6% of the curriculum). Findings demonstrate that eight industry-academia practice bases established through deep collaboration effectively align teaching content with industry needs, substantially enhancing students’ innovative and practical abilities (172 national awards, 649 provincial awards). Additionally, the multi-dimensional evaluation mechanism developed in this study enables a comprehensive assessment of students’ professional skills, practical capabilities, and innovative thinking. These reforms have increased the employment rate of cybersecurity graduates to over 90%, providing a replicable solution to China’s talent shortage. The research outcomes offer valuable insights for discipline development under engineering education accreditation and contribute to implementing sustainable development concepts in higher education. Full article

Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which function as vital hubs in global trade. Methods: A hybrid methodology was employed, first using the Fuzzy Delphi Method (FDM) to validate thirteen challenges with five industry experts. Subsequently, the Relative Importance Index (RII) was used to rank these challenges based on survey data from 48 UAE port professionals. Results: The analysis revealed “Cybersecurity concerns” as the highest-ranked challenge (RII = 0.896), followed by “Engagement with external stakeholders” (RII = 0.842), and both “Process optimization” and “Technical capabilities” (RII = 0.808). Notably, factors traditionally seen as critical in other sectors, such as “Organizational readiness” (RII = 0.746), were ranked significantly lower. Conclusions: The findings indicate a strategic shift in ERP selection priorities toward digital resilience and external integration rather than internal organizational factors. This research provides a sector-specific decision-support framework and offers actionable insights for port authorities, vendors, and policymakers to enhance ERP implementation in the maritime industry. Full article

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals. Full article

Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unknown vulnerabilities, leveraging the complexity of physical and digital system interconnections, making them difficult to detect. The integration of legacy ICS networks with modern computing and networking technologies has expanded the attack surface, increasing susceptibility to cyber threats. Anomaly detection systems play a crucial role in safeguarding these infrastructures by identifying deviations from normal operations. This study investigates the effectiveness of deep learning-based anomaly detection models in revealing operational anomalies that could indicate potential cyber-attacks. We implemented and evaluated a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze ICS telemetry data. The CNN-LSTM model excels in identifying time-dependent anomalies and enables near real-time detection of cyber-attacks, significantly improving security monitoring capabilities for IIoT-integrated critical infrastructures. Full article

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and case studies—to explore how cultural principles such as whanaungatanga (collective responsibility) and manaakitanga (care and respect) influence digital safety practices. The findings demonstrate that culturally informed strategies enhance trust, resilience, and community engagement, particularly in rural and underserved Māori communities. Quantitative analysis revealed that 63% of urban participants correctly identified phishing attempts compared to 38% of rural participants, highlighting a significant urban–rural awareness gap. Additionally, over 72% of Māori respondents indicated that cybersecurity messaging was more effective when delivered through familiar cultural channels, such as marae networks or iwi-led training programmes. Focus groups reinforced this, with participants noting stronger retention and behavioural change when cyber risks were communicated using Māori metaphors, language, or values-based analogies. The study also confirms that culturally grounded interventions—such as incorporating Māori motifs (e.g., koru, poutama) into secure interface design and using iwi structures to disseminate best practices—can align with international standards like NIST CSF and ISO 27001. This compatibility enhances stakeholder buy-in and demonstrates universal applicability in multicultural contexts. Key challenges identified include a cybersecurity talent shortage in remote areas, difficulties integrating Indigenous perspectives into mainstream policy, and persistent barriers from the digital divide. The research advocates for cross-sector collaboration among government, private industry, and Indigenous communities to co-develop inclusive, resilient cybersecurity ecosystems. Based on the UTAUT and New Zealand’s cybersecurity vision “Secure Together—Tō Tātou Korowai Manaaki 2023–2028,” this study provides a model for small nations and multicultural societies to create robust, inclusive cybersecurity frameworks. Full article

In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital. Full article

The Industrial Internet of Things (IIoT) offers transformative potential for supply chain management by enabling automation, real-time monitoring, and predictive analytics. However, fragmented standardization, interoperability challenges, and cybersecurity risks hinder its sustainable adoption. This study aims to develop and validate an ISO-based framework to optimize IIoT networks for sustainable supply chain operations. A quantitative time-series research design was employed, analyzing 150 observations from 10–15 industrial firms over five years. Analytical methods included ARIMA, structural equation modeling (SEM), and XGBoost for predictive evaluation. The findings indicate a 6.2% increase in system uptime, a 4.7% reduction in operational costs, a 2.8% decrease in lead times, and a 55–60% decline in security incidents following ISO standard implementation. Interoperability improved by 40–50%, and integration cost savings ranged from 35–40%, contributing to a 25% boost in overall operational efficiency. These results underscore the critical role of ISO frameworks such as ISO/IEC 30141 and ISO 50001 in enhancing connectivity, energy efficiency, and network security across IIoT-enabled supply chains. While standardization significantly improves key performance indicators, the persistence of lead time variability suggests the need for additional optimization strategies. This study offers a structured and scalable methodology for ISO-based IIoT integration, delivering both theoretical advancement and practical relevance. By aligning with internationally recognized sustainability standards, it provides policymakers, practitioners, and industry leaders with an evidence-based framework to accelerate digital transformation, enhance operational efficiency, and support resilient, sustainable supply chain development in the context of Industry 4.0. Full article

Digitalisation can positively impact the efficiency of real-world business processes, but may also introduce new cybersecurity challenges. One area that is particularly vulnerable to cyber-attacks is the business logic embedded in processes in which flaws may exist. This is especially the case when these processes are within web-based applications and services, which is increasingly becoming the norm for many organisations. Business logic vulnerabilities (BLVs) can emerge following the software development process, which may be difficult to detect by vulnerability detection tools. Through a systematic literature review and interviews with industry practitioners, this study identifies key BLV types and the challenges in detecting them. The paper proposes an eight-stage operational framework that leverages Artificial Intelligence (AI) for enhanced BLV detection and mitigation. The research findings contribute to the rapidly evolving theory and practice in this field of study, highlighting the current reliance on manual detection, the contextual nature of BLVs, and the need for a hybrid, multi-layered approach integrating human expertise with AI tools. The study concludes by emphasizing AI’s potential to transform cybersecurity from a reactive to a proactive defense against evolving vulnerabilities and threats. Full article

The rapid expansion of the Internet of Things (IoT) ecosystem has transformed industries but also exposed significant cybersecurity vulnerabilities. Traditional centralized methods for securing IoT networks struggle to balance privacy preservation with real-time threat detection. This study presents a Federated Learning-Driven Cybersecurity Framework designed for IoT environments, enabling decentralized data processing through local model training on edge devices to ensure data privacy. Secure aggregation using homomorphic encryption supports collaborative learning without exposing sensitive information. The framework employs GRU-based recurrent neural networks (RNNs) for anomaly detection, optimized for resource-constrained IoT networks. Experimental results demonstrate over 98% accuracy in detecting threats such as distributed denial-of-service (DDoS) attacks, with a 20% reduction in energy consumption and a 30% reduction in communication overhead, showcasing the framework’s efficiency over traditional centralized approaches. This work addresses critical gaps in IoT cybersecurity by integrating federated learning with advanced threat detection techniques. It offers a scalable, privacy-preserving solution for diverse IoT applications, with future directions including blockchain integration for model aggregation traceability and quantum-resistant cryptography to enhance security. Full article

Ensuring cybersecurity and health management is a fundamental requirement in modern chemical industry plants operating under the Industry 4.0 framework. Traditionally, these two concerns have been addressed independently, despite sharing multiple underlying elements which suggest the viability of a unified detection and localization solution. This study introduces a computational intelligence framework based on fuzzy techniques, which allows for the early identification and precise localization of both faults and cyberattacks, along with the capability to recognize previously unseen events during runtime. Once new events are identified and classified, the training database is updated, creating a mechanism for continuous learning. This integrated approach simplifies the computational complexity of supervisory systems and enhances collaboration between the Operational Technology and Information Technology teams within chemical plants. The proposed methodology demonstrates strong robustness and reliability, even in complex conditions characterized by noisy measurements and disturbances, achieving outstanding performance due to its excellent discrimination capabilities. Full article

Tourism is recognized as one of the key enabling industries driving Thailand’s strategic transition toward the Thailand 4.0 economic model. This paper presents the development of a national technology roadmap to support the transition toward sustainable tourism in Thailand, conducted between January and October 2024. The primary objective is to promote tourism growth that is environmentally responsible, economically viable, and socially inclusive. The roadmap was developed through a combination of a literature review, statistical data, and group discussions with tourism professionals. The roadmapping process focused on aligning external drivers, strategic goals, and the current capabilities of Thailand’s technological ecosystem. The key drivers identified include environmental concerns, the advancement of digital platforms, the growing trends of an aging population, diversity, and inclusion, political instability, and the emergence of middle-income nations. The resulting roadmap outlines a strategic vision for Thailand’s tourism sector from 2024 to beyond 2030. It emphasizes the transition linking sustainability and eco-design principles to smart tourism, metaverse applications, and personalized travel experiences. Priority areas include the adoption of green technologies, sustainable practices, and advanced digital platforms. This study further recommends research and development (R&D) initiatives in sustainability, biodiversity conservation, Data Analytics, Cybersecurity, and E-Tourism solutions. Ultimately, this roadmap provides actionable guidance for tourism stakeholders in defining their roles, responsibilities, and contributions toward achieving a sustainable tourism future in Thailand. Full article

The On-Board Diagnostics II (OBD-II) system, driven by a wide range of embedded sensors, has revolutionized the automotive industry by enabling real-time monitoring of key vehicle parameters such as engine load, vehicle speed, throttle position, and diagnostic trouble codes. Concurrently, recent advancements in machine learning (ML) have further expanded the capabilities of OBD-II applications, unlocking advanced, intelligent, and data-centric functionalities that significantly surpass those of conventional methodologies. This paper presents a comprehensive investigation into ML-based applications that leverage OBD-II sensor data, aiming to enhance sustainability, operational efficiency, safety, and security in modern vehicular systems. To this end, a diverse set of ML approaches is examined, encompassing supervised, unsupervised, reinforcement learning (RL), deep learning (DL), and hybrid models intended to support advanced driving analytics tasks such as fuel optimization, emission control, driver behavior analysis, anomaly detection, cybersecurity, road perception, and driving support. Furthermore, this paper synthesizes recent research contributions and practical implementations, identifies prevailing challenges, and outlines prospective research directions. Full article

This study addresses the urgent challenge of safeguarding sensitive health data in today’s digital age by proposing a novel, integrated data protection framework that synthesises six critical pillars—technology, policy, cybersecurity, legal frameworks, governance, and risk assessment—into a unified socio-technical model. Unlike existing piecemeal approaches, this framework is designed to bridge the gap between regulatory requirements and practical implementation through measurable, engineering-based solutions. Healthcare organisations face persistent difficulties in aligning innovation with secure and compliant practices due to fragmented governance and reactive cybersecurity measures. This paper aims to empirically validate the effectiveness of the proposed framework by quantitatively analysing causal relationships between its components (such as between governance and compliance) using advanced statistical methods, including exploratory factor analysis (EFA) and Partial Least Squares Structural Equation Modelling (PLS-SEM). A survey of healthcare professionals across multiple countries revealed significant gaps between regulatory expectations and operational realities, underscoring the need for harmonised strategies. The results demonstrate strong causal linkages between governance, cybersecurity practices, and compliance, validating the framework’s robustness. This research contributes to the fields of digital health, information systems, industrial engineering, and electronic governance by offering a scalable, empirically tested model for socio-technical data protection. The findings provide actionable strategies for policymakers, system architects, and digital infrastructure designers. Full article