Search Results (17)

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. 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

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

The growing deployment of the Internet of Things (IoT) across various sectors introduces significant security and privacy challenges. Although numerous individual solutions exist, comprehensive frameworks that effectively combine advanced technologies to address evolving threats are lacking. This paper presents the Integrated Adaptive Security Framework for IoT (IASF-IoT), which integrates artificial intelligence, blockchain technology, and quantum-resistant cryptography into a unified solution tailored for IoT environments. Central to the framework is an adaptive AI-driven security orchestration mechanism, complemented by blockchain-based identity management, lightweight quantum-resistant protocols, and Digital Twins to predict and proactively mitigate threats. A theoretical performance model and large-scale simulation involving 1000 heterogeneous IoT devices were used to evaluate the framework. Results showed that IASF-IoT achieved detection accuracy between 85% and 99%, with simulated energy consumption remaining below 1.5 mAh per day and response times averaging around 2 s. These findings suggest that the framework offers strong potential for scalable, low-overhead security in resource-constrained IoT environments. Full article

Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact. Full article

Quantum information has emerged as a frontier in scientific research and is transitioning to real-world technologies and applications. In this work, we explore the integration of quantum secure direct communication (QSDC) with time-sensitive networking (TSN) for the first time, proposing a novel framework to address the security and latency challenges of Ethernet-based networks. Because our QSDC-TSN protocol inherits all the advantages from QSDC, it will enhance the security of the classical communications both in the traditional TSN- and QKD-based TSN by the quantum principle and reduce the communication latency by transmitting information directly via quantum channels without using keys. By analyzing the integration of QSDC and TSN in terms of time synchronization, flow control, security mechanisms, and network management, we show how QSDC enhances the real-time performance and security of TSN. These advantages enable our QSDC-TSN to keep the balance between and meet the requirements of both high security and real-time performance in industrial control, in a digital twin of green power and green hydrogen systems in distributed energy networks, etc., showing its potential applications in future quantum-classical-hybrid systems. Full article

The Quantum Internet (QI) necessitates a complete revision of the classical protocol stack and the technologies used, whereas its operating principles depend on the physical laws governing quantum mechanics. Recent experiments demonstrate that Optical Fibers (OFs) allow connections only in urban areas. Therefore, a novel Quantum Satellite Backbone (QSB) composed of a considerable number of Quantum Satellite Repeaters (QSRs) deployed in Low Earth Orbit (LEO) would allow for the overcoming of typical OFs’ attenuation problems. Nevertheless, the dynamic nature of the scenario represents a challenge for novel satellite networks, making their design and management complicated. Therefore, we have designed an ad hoc QSB considering the interaction between Digital Twin (DT) and Software-Defined Networking (SDN). In addition to defining the system architecture, we present a DT monitoring protocol that allows efficient status recovery for the creation of multiple End-to-End (E2E) entanglement states. Moreover, we have evaluated the system performance by assessing the path monitoring and configuration time, the time required to establish the E2E entanglement, and the fidelity between a couple of Ground Stations (GSs) interconnected through the QSB, also conducting a deep analysis of the created temporal paths. Full article

The fifth generation (5G) of wireless communication is in its finalization stage and has received favorable reception in many nations. However, research is now geared towards the anticipated sixth-generation (6G) wireless network. The new 6G promises even more severe performance criteria than the current 5G generation. New sophisticated technologies and paradigms are expected to be incorporated into the 6G network designs and procedures to meet the ever-dynamic user needs and standards. These 6G-enabling technologies include digital twin (DT), intelligent reflecting surface (IRS), visible light communication (VLC), quantum computing (QC), blockchain, unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA), among others. Optimal network performance requires that machine learning (ML) techniques be integrated over the 6G wireless network to provide solutions to highly complex networking problems, massive users, high overhead, and computational complexity. Consequently, we have provided a state-of-the-art overview of wireless network generations leading to the future 6G, and huge emphases have been laid on ML’s role in optimization applications for different enabling 6G technologies. Several key performance indicators for the different application scenarios have been highlighted. ML has proved to significantly improve the performance of the existing 6G-enabling technologies, and choosing the appropriate approach can ultimately yield optimal results. Full article

The job shop scheduling problem (JSSP) is a popular NP-hard problem in combinatorial optimization, due to its theoretical appeal and its importance in applications. In practical applications, the online version is much closer to the needs of smart manufacturing in Industry 4.0 and 5.0. Here, the online version of the job shop scheduling problem is solved by a heuristics that governs local queues at the machines. This enables a distributed implementation, i.e., a digital twin can be maintained by local processors which can result in high speed real time operation. The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. Major advances of the approach are the inherent parallelism and its robustness, promising natural and likely successful application to other variations of JSSP. Experimental results show that the heuristics, although designed for solving the online version, can provide near-optimal and often even optimal solutions for many benchmark instances of the offline version of JSSP. It is also demonstrated that the best solutions of the new heuristics clearly improve over the results obtained by heuristics based on standard dispatching rules. Of course, there is a trade-off between better computational time and the quality of the results in terms of makespan criteria. Full article

The ongoing industrial revolution emphasizes the importance of precise machinery monitoring. Among these machines, induction motors (IMs) stand out due to their large numbers, which imply a significant part of industrial energy consumption. To achieve accurate in-service IM monitoring, robust modelling is required, with a particular emphasis on in situ constraints. In this study, we create a precise digital model for squirrel cage induction motors (SCIMs) that can be used in Industry 4.0 digital twin applications. To achieve this, we survey the existing literature, describe the main modelling techniques, identify the best models in terms of ease of implementation, and ensure the accuracy of our digital representation. We develop four methods, namely finite element analysis (FEA), thermal modelling, circuit-based models, and quantum-based fuzzy logic control, as a crucial first step in implementing digital twin (DT) technology for IMs. The quantum fuzzy logic is based on the transition from classical equations to the quantum equation determining the speed of the motor in the quantum world by passing through the Schrödinger equation. We propose the DT level of integration architecture for IMs based on the industry 4.0 reference architecture model. Finally, the main tools used to successfully implement DT for IMs are revealed. Full article

Digital society strategies in healthcare include the rapid development of digital twins (DTs) for patients and human organs in medical research and the use of artificial intelligence (AI) in clinical practice to develop effective treatments in a cheaper, quicker, and more effective manner. This is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from wearable devices). DTs can use AI models to create predictions of future health outcomes for an individual patient in the form of an AI-generated digital twin to support the rapid assessment of in silico intervention strategies. DTs are gaining the ability to update in real time in relation to their corresponding physical patients and connect to multiple diagnostic and therapeutic devices. Support for this form of personalized medicine is necessary due to the complex technological challenges, regulatory perspectives, and complex issues of security and trust in this approach. The challenge is also to combine different datasets and omics to quickly interpret large datasets in order to generate health and disease indicators and to improve sampling and longitudinal analysis. It is possible to improve patient care through various means (simulated clinical trials, disease prediction, the remote monitoring of apatient’s condition, treatment progress, and adjustments to the treatment plan), especially in the environments of smart cities and smart territories and through the wider use of 6G, blockchain (and soon maybe quantum cryptography), and the Internet of Things (IoT), as well as through medical technologies, such as multiomics. From a practical point of view, this requires not only efficient validation but also seamless integration with the existing healthcare infrastructure. Full article

Quantum technologies promise major advances in different areas. From computation to sensing or telecommunications, quantum implementations could bring significant improvements to these fields, arousing the interest of researchers, companies, and governments. In particular, the deployment of Quantum Key Distribution (QKD) networks, which enable the secure dissemination of cryptographic keys to remote application entities following Quantum Mechanics Principles, appears to be one of the most attractive and relevant use cases. Quantum devices and equipment are still in a development phase, making their availability low and their price high, hindering the deployment of physical QKD networks and, therefore, the research and experimentation activities related to this field. In this context, this paper focuses on providing research stakeholders with an open-access testbed where it is feasible to emulate the deployment of QKD networks, thus enabling the execution of experiments and trials, where even potential network attacks can be analyzed, without the quantum physical equipment requirement, nor compromising the integrity of an already built QKD network. The designed solution allows users to automatically deploy, configure, and run a digital twin environment of a QKD network, offering cost-effectiveness and great flexibility in the study of the integration of quantum communications in the current network infrastructures. This solution is aligned with the European Telecommunications Standard Institute (ETSI) standardized application interface for QKD, and is built upon open-source technologies. The feasibility of this solution has been validated throughout several functional trials carried out in the 5G Telefónica Open Network Innovation Centre (5TONIC), verifying the service performance in terms of speed and discarded qubits when generating the quantum keys. Full article

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing. Full article

Quantum digital signatures (QDS) are able to verify the authenticity and integrity of a message in modern communication. However, the current QDS protocols are restricted by the fundamental rate-loss bound and the secure signature distance cannot be further improved. We propose a twin-field quantum digital signature (TF-QDS) protocol with fully discrete phase randomization and investigate its performance under the two-intensity decoy-state setting. For better performance, we optimize intensities of the signal state and the decoy state for each given distance. Numerical simulation results show that our TF-QDS with as few as six discrete random phases can give a higher signature rate and a longer secure transmission distance compared with current quantum digital signatures (QDSs), such as BB84-QDS and measurement-device-independent QDS (MDI-QDS). Moreover, we provide a clear comparison among some possible TF-QDSs constructed by different twin-field key generation protocols (TF-KGPs) and find that the proposed TF-QDS exhibits the best performance. Conclusively, the advantages of the proposed TF-QDS protocol in signature rate and secure transmission distance are mainly due to the single-photon interference applied in the measurement module and precise matching of discrete phases. Besides, our TF-QDS shows the feasibility of experimental implementation with current devices in practical QDS system. Full article

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of “uncertain knowledge”. Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes. Full article