Search Results (288)

Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. 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

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

Neuronal ceroid lipofuscinoses (NCLs) are paediatric neurodegenerative disorders that primarily affect the central nervous system (CNS). The high prevalence of gastrointestinal (GI) symptoms has prompted researchers and clinicians to move beyond an exclusively “brain-centric” perspective. At the molecular level, mutations in CLN genes lead to lysosomal dysfunction and impaired autophagy, resulting in intracellular accumulation of storage material that disrupts both central and enteric neuronal homeostasis. To systematically examine current clinical and preclinical knowledge on gut involvement in NCLs, with a focus on recent findings related to the enteric nervous system and gut microbiota. We conducted a systematic review following the PRISMA guidelines using PubMed as the sole database. Both clinical (human) and preclinical (animal) studies were included. A total of 18 studies met the inclusion criteria, focusing on gastrointestinal dysfunction, nervous system involvement, and gut microbiota. We found that the nature of GI symptoms was multifactorial in NCLs, involving not only the CNS but also the autonomic and enteric nervous systems, which were affected early by lysosomal deposits and enteric neuron degeneration. Of note, preclinical studies showed that gene therapy could improve not only CNS manifestations but also GI ones, which may have beneficial implications for patient care. While the role of the ENS seems to be clearer, that of gut microbiota needs to be further clarified. Current evidence from preclinical models highlighted alterations in the composition of the microbiota and suggested a possible influence on the progression and modulation of neurological symptoms. However, these results need to be confirmed by further studies demonstrating the causality of this relationship. GI involvement is a key feature of NCLs, with early impact on the enteric nervous system and possible links to gut microbiota. Although preclinical findings—particularly on gene therapy—are encouraging due to their dual impact on both CNS and GI manifestations, the causal role of the gut microbiota remains to be fully elucidated. In this context, the development of sensitive and specific outcome measures to assess GI symptoms in clinical trials is crucial for evaluating the efficacy of future therapeutic interventions. Full article

The evolution of Industry 4.0 and the emerging paradigm of Industry 5.0 have introduced disruptive technologies that are reshaping modern manufacturing environments. Among these, Digital Twin (DT) and Industrial Metaverse (IM) technologies are increasingly recognized for their potential to enhance Occupational Health and Safety (OHS). However, a comprehensive understanding of how these technologies integrate to support OHS in manufacturing remains limited. This study systematically explores the transformative role of DT and IM in creating immersive, intelligent, and human-centric safety ecosystems. Following the PRISMA guidelines, a Systematic Literature Review (SLR) of 75 peer-reviewed studies from the SCOPUS and Web of Science databases was conducted. The review identifies key enabling technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Internet of Things (IoT), Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and Collaborative Robots (COBOTS), and highlights their applications in real-time monitoring, immersive safety training, and predictive hazard mitigation. A conceptual framework is proposed, illustrating a synergistic digital ecosystem that integrates predictive analytics, real-time monitoring, and immersive training to enhance the OHS. The findings highlight both the transformative benefits and the key adoption challenges of these technologies, including technical complexities, data security, privacy, ethical concerns, and organizational resistance. This study provides a foundational framework for future research and practical implementation in Industry 5.0. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. 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

Semantic communication has attracted considerable interest due to its potential to support emerging human-centric services, such as holographic communications, extended reality (XR), and human-machine interactions. Different from traditional communication systems that focus on minimizing the symbol-level distortion (e.g., bit error rate, signal-to-noise ratio, etc.), semantic communication targets at delivering the intended meaning at the destination user which is often quantified by various statistical divergences, often referred to as the semantic distances. Currently, there still lacks a unified framework to quantify the rate-distortion tradeoff for semantic communication with different task-specific semantic distance measures. To tackle this problem, we propose the task-specific rate-distortion theory for semantic communication where different task-specific statistic divergence metrics can be considered. To investigate the impact of different semantic distance measures on the achievable rate, we consider two popular tasks, classification and signal generation. We present the closed-form expressions of the semantic rate-distortion functions for these two different tasks and compare their performance under various scenarios. Extensive experimental results are presented to verify our theoretical results. Full article

Sacrificial rituals provide a critical perspective for examining the fundamental characteristics and evolutionary trajectory of Chinese civilization. The Functionalist and Annales schools, through theoretical frameworks such as “natural necessity theory” and “social-centric theory”, have explored the origins of sacrificial practices and their role in social cohesion. When these schools investigate Chinese sacrificial rituals, they identify significant differences in the humanistic and ethical dimensions compared to in Western intellectual traditions, thereby revealing how these distinctions propelled China onto a unique civilizational path. The sacrificial system underwent a process of humanization and moralization during the Shang and Zhou dynasties, culminating in the recognition of benevolence–righteousness attributes within human nature, primarily characterized by affection and reverence. The interplay between intrinsic human dispositions and ritualized sacrificial practices established the foundational structure for the social order, spanning from familial units to political society in China. This synthesis ultimately shaped the distinctive philosophical characteristics of Chinese civilization, emphasizing the principles of benevolent love, reciprocal loyalty, and harmonious coexistence. Full article

Achieving high-level green technological innovation in heavily polluting enterprises is critical for advancing sustainable development, particularly in the context of both organizational and regional digitalization. This study adopts a configurational perspective grounded in the Technology–Organization–Environment (TOE) framework and integrates theoretical insights from resource orchestration, resource dependence, and IT capability theories. It investigates how different types of skilled talent, such as production, technical, sales, and managerial employees, contribute to green innovation under varying digital conditions. By applying fuzzy-set qualitative comparative analysis (fsQCA) to a sample of 96 publicly listed firms from China’s heavily polluting industries, this study identifies four distinct talent-based configurations that can lead to high levels of green innovation: production-centric, management-led, technical talent driven, and regionally enabled models. Each configuration reflects a specific system state in which a core group of skilled employees plays a leading role, supported by complementary functions, and shaped by the interaction between internal digital transformation and the external digital environment. This study contributes to the systems literature by elucidating the combinational roles of digital resources and talent deployment within the systemic TOE framework, and offers practical guidance for enterprises aiming to strategically utilize human capital to enhance green innovation performance amid ongoing digital transformations. Full article

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article

Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article

The integration of Artificial Intelligence (AI) into manufacturing is transforming the industry by advancing predictive maintenance, quality control, and supply chain optimisation, while also driving the shift from Industry 4.0 towards a more human-centric and sustainable vision. This emerging paradigm, known as Industry 5.0, emphasises resilience, ethical innovation, and the symbiosis between humans and intelligent systems, with AI playing a central enabling role. However, challenges such as the “black box” nature of AI models, data biases, ethical concerns, and the lack of robust frameworks for trustworthiness hinder its widespread adoption. This paper provides a comprehensive survey of AI trustworthiness in the manufacturing industry, examining the evolution of industrial paradigms, identifying key barriers to AI adoption, and examining principles such as transparency, fairness, robustness, and accountability. It offers a detailed summary of existing toolkits and methodologies for explainability, bias mitigation, and robustness, which are essential for fostering trust in AI systems. Additionally, this paper examines challenges throughout the AI pipeline, from data collection to model deployment, and concludes with recommendations and research questions aimed at addressing these issues. By offering actionable insights, this study aims to guide researchers, practitioners, and policymakers in developing ethical and reliable AI systems that align with the principles of Industry 5.0, ensuring both technological advancement and societal value. Full article

Amidst global efforts toward sustainable development, this research addresses underexplored academic dimensions by evaluating the transformative potential of intelligent, sustainable architecture. Employing bibliometric techniques and Citespace 6.4.R1, we analyze two decades (2005–2024) of the Web of Science literature to identify patterns and challenges. Findings demonstrate rising scholarly output, dominated by themes like energy-efficient design, Building Information Modeling integration, and circular economy principles in urban contexts. While Europe and North America lead research activity, systemic limitations persist—including duplicated methodologies, fragmented institutional networks, and incompatible smart technologies. This study advocates for three strategic priorities: fostering interdisciplinary innovation to break homogeneity, establishing cross-sector collaboration frameworks, and accelerating industry–academia knowledge transfer. Intelligent, sustainable architecture emerges as a dual solution—technologically enabling carbon-neutral construction practices while redefining human-centric spatial quality. This dual advantage positions the International Sustainability Alliance as critical infrastructure for achieving UN Sustainable Development Goals, reconciling ecological responsibility with evolving societal demands for resilient, adaptive built environments. Full article