Search Results (345)

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

Quantum imaging leverages entanglement and photon correlations to surpass classical limits in resolution and noise performance. However, its practical deployment is constrained by bulky optical setups and limited system adaptability. Metasurfaces—ultrathin, subwavelength-structured devices—offer a compact and reconfigurable solution for wavefront control in quantum light fields. This review presents recent advances in geometric-, propagation-, and hybrid-phase metasurface designs, showcasing their contributions to enhanced spatial resolution, improved visibility, and system miniaturization across applications such as ghost imaging, quantum holography, and single-photon microscopy. It also examines key challenges—including photon loss, fabrication-induced phase noise, and the lack of dynamic tunability—while outlining future directions for developing integrated, noise-resilient, and task-specific quantum imaging platforms. Full article

Secure and auditable data sharing in large-scale Internet of Things (IoT) environments remains a significant challenge due to weak trust coordination, limited scalability, and susceptibility to emerging quantum attacks. This study introduces a hybrid blockchain-based framework that integrates post-quantum cryptography with intelligent anomaly detection to ensure end-to-end data integrity and resilience. The proposed system utilizes Hyperledger Fabric for permissioned device lifecycle management and Ethereum for public auditability of encrypted telemetry, thereby providing both private control and transparent verification. Device identities are established using quantum-entropy-seeded credentials and safeguarded with lattice-based encryption to withstand quantum adversaries. A convolutional long short-term memory (CNN–LSTM) model continuously monitors device behavior, facilitating real-time trust scoring and autonomous revocation via smart contract triggers. Experimental results demonstrate 97.4% anomaly detection accuracy and a 0.968 F1-score, supporting up to 1000 transactions per second with cross-chain latency below 6 s. These findings indicate that the proposed architecture delivers scalable, quantum-resilient, and computationally efficient data sharing suitable for mission-critical IoT deployments. Full article

In situ preparation routes have become central to advancing lead sulfide (PbS) quantum dots (QDs) for solar-energy conversion, owing to their ability to create strongly coupled QD/oxide interfaces that are difficult to achieve with ex situ colloidal methods, along with their simplicity and potential for low-cost, scalable processing. This review systematically examines the fundamental mechanisms, processing levers, and device implications of the dominant in situ approaches successive ionic layer adsorption and reaction (SILAR), voltage-assisted SILAR (V-SILAR), and chemical bath deposition (CBD). These methods enable conformal QD nucleation within mesoporous scaffolds, improved electronic coupling, and scalable low-temperature fabrication, forming the materials foundation for high-performance PbS-based architectures. We further discuss how these in situ strategies translate into enhanced solar-energy applications, including quantum-dot-sensitized solar cells (QDSSCs) and photoelectrochemical (PEC) hydrogen production, highlighting recent advances in interfacial passivation, scaffold optimization, and bias-assisted growth that collectively suppress recombination and boost photocurrent utilization. Representative device metrics reported in recent studies indicate that in-situ-grown PbS quantum dots can deliver photocurrent densities on the order of ~5 mA cm⁻² at applied potentials around 1.23 V versus RHE in photoelectrochemical systems, while PbS-based quantum-dot-sensitized solar cells typically achieve power conversion efficiencies in the range of ~4–10%, depending on interface engineering and device architecture. These performances are commonly associated with conformal PbS loading within mesoporous scaffolds and quantum-dot sizes in the few-nanometer regime, underscoring the critical role of morphology and interfacial control in charge transport and recombination. Recent studies indicate that performance improvements in PbS-based solar-energy devices are primarily governed by interfacial charge-transfer kinetics and recombination suppression rather than QD loading alone, with hybrid heterostructures and inorganic passivation layers playing a key role in modifying band offsets and surface trap densities at the PbS/oxide interface. Remaining challenges are associated with defect-mediated recombination, transport limitations in densely loaded porous scaffolds, and long-term chemical stability, which must be addressed to enable scalable and durable PbS-based photovoltaic and photoelectrochemical technologies. Full article

Multimodal forecasting is increasingly needed to maintain energy levels, storage capacity, and compute efficiency in off-grid, renewable-powered cloud environments. Variable sensor quality, uncertain interactions with renewable energy, and rapidly changing weather patterns make real-time forecasting difficult. Current transformer, GNN, and CNN systems suffer from sensor noise instability, multimodal temporal–spectral correlation issues, and challenges in the interpretability of operational decision-making. In this research, Q-RCANeX, a quantum-guided residual convolutional attention network for off-grid cloud infrastructures, estimates battery state of charge, renewable energy sources, and microgrid efficiency to overcome these restrictions. The system uses a Hybrid Quantum–Bayesian Evolutionary Optimizer, quantum feature embedding, temporal–spectral attention, residual convolutional encoding, and signal decomposition preprocessing. These parameters reinforce features, reduce noise, and align forecasting behavior with microgrid dynamics. Q-RCANeX obtains 98.6% accuracy, 0.992 AUC, and 0.986 R³ values for REAF, WGF, SOC-F, and EEIF forecasting tasks, according to a statistical study. Additionally, it determines inference latency to 4.9 ms and model size to 18.5 MB. Even with 20% of sensor data missing or noisy, the model outperforms 12 state-of-the-art baselines and maintains 96.8% accuracy using ANOVA, Wilcoxon, Nemenyi, and Holm tests. The findings indicate that the forecasting framework has high accuracy, clarity, and resilience to failures. This makes it useful for real-time, off-grid management of renewable cloud microgrids. Full article

The environmental persistence of antibiotic residues in aquatic systems represents a critical global challenge, with photolysis serving as a primary abiotic degradation pathway. Traditional approaches to studying antibiotic photodegradation and transformation product (TP) identification face significant limitations, including complex reaction mechanisms, multiple concurrent pathways, and analytical challenges in characterizing unknown metabolites. The integration of artificial intelligence (AI) and machine learning (ML) technologies has begun to transform this field, offering new capabilities for predicting photodegradation kinetics, elucidating transformation pathways, and identifying novel metabolites. This comprehensive review examines current applications of AI/ML in antibiotic photolysis research, analyzing developments from 2020 to 2025. Key advances include quantitative structure–activity relationship (QSAR) models for photodegradation prediction, deep learning approaches for automated mass spectrometry interpretation, and hybrid computational–experimental frameworks. Machine learning algorithms, particularly Random Forests, support vector machines, and Neural Networks, have demonstrated capabilities in handling multi-dimensional environmental datasets across diverse antibiotic classes, including fluoroquinolones, β-lactams, tetracyclines, and sulfonamides. Despite progress in this field, challenges remain in model interpretability, standardization of datasets, validation protocols, and integration with regulatory frameworks. Future directions include machine-learning-enhanced quantum dynamics for improving mechanistic understanding, real-time AI-guided experimental design, and predictive tools for environmental risk assessment. Full article

We present a utilization of the magnon Kerr effect to generate nonreciprocal genuine microwave entanglement in a hybrid system consisting of a yttrium iron garnet (YIG) sphere and three microwave cavities. Based on the quantum Langevin theory and linearization method under the condition of strong magnon driving, the system dynamics and covariance evolution are deduced and then applied to determinate the quantum correlations. It is found that three microwave cavities entangle with each other at the steady state. The basic root is that the Kerr nonlinearity can not only induce the enhanced parametric amplification of magnon but also cause the magnon frequency shift. Naturally, when the direction of the externally applied bias magnetic field is changed, switching of the magnon Kerr coefficient from positive to negative occurs and nonreciprocal tripartite entanglement among three microwave photons can be achieved. This may provide a fundamental resource for practical applications in quantum information processing and quantum networks. Full article

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

In control systems, chaos is a natural dualistic phenomenon that can be both a beneficial resource to be used and a negative phenomenon to be avoided. The study examines two opposing paradigms: positive chaotic control, which aims to enhance performance, and negative chaos management, which aims to stabilize a system. More sophisticated suppression methods, including adaptive neural networks, sliding mode control, and model predictive control, can decrease convergence times. Controlled chaotic dynamics have significantly impacted the domain of embedded control systems. Specialized controller designs include fractal-based systems and hybrid switching systems that offer better control of chaotic behavior in many situations. The paper highlights the key issues that are related to chaos-based systems, such as the need to implement them in real time, parameter sensitivity, and safety. Recent research suggests an increased interdependence between artificial intelligence, quantum computing, and sustainable technology. The synthesis shows that chaos control has evolved into an engineering field, significantly impacting the industry, which was initially a theoretical concept. It also offers exclusive ideas in the design and improvement of complex control systems. Full article

The Number-Theoretic Transform is the primary performance bottleneck in hardware accelerators for post-quantum cryptography schemes like the Module-Lattice-based Key-Encapsulation Mechanism. A key design challenge is the trade-off between the massive parallelism required for low-latency computation and the prohibitive on-chip Block RAM consumption this typically entails. This paper introduces an NTT/INTT accelerator architecture that resolves this conflict, achieving a state-of-the-art latency of 40 clock cycles for a 256-point transform while utilizing only 5 BRAM blocks. Our architecture achieves this by pairing a 32-way parallel streaming datapath with a hybrid memory subsystem that strategically allocates on-chip storage resources. The core innovation is the use of distributed RAM instead of BRAM for high-bandwidth buffering of intermediate data between pipeline stages. This reserves the scarce BRAM resources for storing static twiddle factors and for system-level FIFO interfaces. By deliberately trading abundant logic fabric and dedicated DSP slices for BRAM efficiency, our work demonstrates a design point optimized for high-speed, BRAM-constrained System-on-Chip environments, proving that a focus on memory hierarchy is critical to developing PQC solutions that are both fast and practical for real-world integration. Full article

Centro Español de Metrología (CEM) is developing a quantum frequency standard based on trapped calcium ions, marking its entry into the landscape of the second quantum revolution. Optical frequency standards offer unprecedented precision by referencing atomic transitions that are fundamentally stable and immune to environmental drift. However, the challenge of developing such a system from scratch is unaffordable for a medium-sized National Metrology Institute (NMI), which seems to limit the ability of an institute such as CEM to contribute to this field of research. To overcome this, CEM has adopted a hybrid strategy, combining commercially available components with custom integration to accelerate deployment. This paper defines and implements an architecture adapted to the constraints of a medium-size NMI, where the main contribution is the systematic design, selection, and interconnection of the subsystems required to realize this standard. The rationale behind the system design is presented, detailing the integration of key elements for ion trapping, laser stabilization, frequency measurement, and system control. Current progress, ongoing developments, and future research directions are outlined, establishing the foundation for spectroscopic measurements and uncertainty evaluation. The project represents a strategic step toward strengthening national capabilities in quantum metrology for a medium-sized NMI. Full article

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions. Full article

We report a non-enzymatic electrochemical sensing platform for creatinine based on a nickel-nanoparticle/carbon-quantum-dot (NiNP–CQD) hybrid interface. In this system, the analytical signal originates from the direct electrocatalytic oxidation of creatinine mediated by the Ni(II)/Ni(III) redox couple (Ni(OH)₂/NiOOH), which forms during electrochemical activation of nickel in alkaline media. These redox centers act as catalytic sites that oxidize creatinine without requiring enzymes or biomolecular labels. The CQDs provide a conductive sp²-rich network with abundant oxygenated groups that promote homogeneous nucleation and dispersion of NiNPs, enhancing both surface area and electron-transfer efficiency. Electrochemical characterization of the modified electrodes was performed using the ferricyanide/ferrocyanide redox couple as the electron-transfer probe. Structural and microscopic characterization confirms uniform NiNP deposition on the CQD layer, while electrochemical studies demonstrates that the composite outperforms CQDs or NiNPs alone in current density, linearity, and resistance to active-site saturation. The resulting sensor exhibits a wide linear range (10–1000 µM), high area-normalized sensitivity (1.41 µA µM⁻¹ cm⁻²), and a low detection limit of 5 µM. Selectivity tests reveal minimal interference from common physiological species. By explicitly leveraging a catalyst-driven, enzyme-free oxidation pathway, this NiNP–CQD architecture provides a robust, stable, and scalable platform for clinically relevant creatinine detection. Full article

The design of multi-link vehicle suspension systems, such as the 3D double-wishbone, presents a critical challenge in automotive engineering. The process constitutes a high-dimensional, nonlinearly constrained optimization problem where traditional gradient-based methods often fail by converging to suboptimal local minima. This paper introduces a novel two-stage hybrid optimization framework designed to overcome this limitation by intelligently integrating quantum-inspired and classical techniques. The methodology explicitly defines a QUBO (Quadratic Unconstrained Binary Optimization) stage and an SQP (Sequential Quadratic Programming) stage. Stage 1 addresses the complex kinematic constraint problem by formulating it as a QUBO, which is then solved using Simulated Annealing to perform a global search, guaranteeing a physically feasible starting point. Subsequently, Stage 2 takes this feasible solution and employs an SQP algorithm to perform a high-precision local refinement, tuning the geometry to meet specific performance targets for camber and caster curves. The framework successfully converged to a design with a near-zero performance objective of 7.08 × 10⁻¹⁴. The efficacy of this hybrid approach is highlighted by the dramatic improvement from the high-error initial solution found by Simulated Annealing to the final, high-precision result from the SQP refinement. We conclude that this QUBO-SQP framework is a powerful and validated methodology for solving complex, real-world engineering design problems, effectively bridging the gap between global exploration and local precision. Full article

Thin films have become indispensable in shaping the landscape of modern and future technologies, offering versatile platforms where properties can be engineered at the atomic to microscale to deliver performance unattainable with bulk materials. Historically evolving from protective coatings and optical layers, the field has advanced into a highly interdisciplinary domain that underpins innovations in microelectronics, energy harvesting, optoelectronics, sensing, and biomedical devices. In this review, a structured approach has been adopted to consolidate the fundamentals of thin film growth and the governing principles of nucleation, surface dynamics, and interface interactions, followed by an in-depth comparison of deposition strategies such as physical vapor deposition, chemical vapor deposition, atomic layer deposition (ALD), and novel solution-based techniques, highlighting their scalability, precision, and application relevance. By critically evaluating experimental studies and technological implementations, this review identifies key findings linking microstructural evolution to device performance, while also addressing the pressing challenges of stability, degradation pathways, and reliability under operational stresses. The synthesis of evidence points to the transformative role of advanced deposition controls, in situ monitoring, and emerging AI-driven optimization in overcoming current bottlenecks. Ultimately, this work concludes that thin film technologies are poised to drive the next generation of sustainable, intelligent, and multifunctional devices, with emerging frontiers such as hybrid heterostructures, quantum materials, and bio-integrated systems charting the future roadmap. Full article