Search Results (442)

Graphene quantum dots (GQDs) obtained by microwave-induced pyrolysis of glutamic acid and triethylenetetramine (trien) are fairly stable, emissive, water-soluble, and positively charged nano-systems able to interact with negatively charged meso-tetrakis(4-sulfonatophenyl) porphyrin (TPPS₄). The stoichiometric control during the preparation affords a supramolecular adduct, GQDs@TPPS₄, that exhibits a double fluorescence emission from both the GQDs and the TPPS₄ fluorophores. These supramolecular aggregates have an overall negative charge that is responsible for the condensation of cations in the nearby aqueous layer, and a three-fold acceleration of the metalation rates of Cu²⁺ ions has been observed with respect to the parent porphyrin. Addition of various metal ions leads to some changes in the UV/Vis spectra and has a different impact on the fluorescence emission of GQDs and TPPS₄. The quenching efficiency of the TPPS₄ emission follows the order Cu²⁺ > Hg²⁺ > Cd²⁺ > Pb²⁺ ~ Zn²⁺ ~ Co²⁺ ~ Ni²⁺ > Mn²⁺ ~ Cr³⁺ >> Mg²⁺ ~ Ca²⁺ ~ Ba²⁺, and it has been related to literature data and to the sitting-atop mechanism that large transition metal ions (e.g., Hg²⁺ and Cd²⁺) exhibit in their interaction with the macrocyclic nitrogen atoms of the porphyrin, inducing distortion and accelerating the insertion of smaller metal ions, such as Zn²⁺. For the most relevant metal ions, emission quenching of the porphyrin evidences a linear behavior in the micromolar range, with the emission of the GQDs being moderately affected through a filter effect. Deliberate pollution of the samples with Zn²⁺ reveals the ability of the GQDs@TPPS₄ adduct to detect sensitively Cu²⁺, Hg^2+, and Cd²⁺ ions. Full article

Background/Objectives: In digital radiography imaging, dual-layer flat-panel detectors (DFDs), in which two flat-panel detector layers are stacked with a minimal distance between the layers and appropriate alignment, are commonly used in material decompositions as dual-energy applications with a single x-ray exposure. DFDs also enable more efficient use of incident photons, resulting in x-ray images with improved noise power spectrum (NPS) and detection quantum efficiency (DQE) performances as single-energy applications. Purpose: Although the development of DFD systems for material decomposition applications is actively underway, there is a lack of research on whether single-energy applications of DFD can achieve better performance than the single-layer case. In this paper, we experimentally observe the DFD performance in terms of the modulation transfer function (MTF), NPS, and DQE with discussions. Methods: Using prototypes of DFD, we experimentally measure the MTF, NPS, and DQE of the convex combination of the images acquired from the upper and lower detector layers of DFD. To optimize DFD performance, a two-step image registration is performed, where subpixel registration based on the maximum amplitude response to the transform based on the Fourier shift theorem and an affine transformation using cubic interpolation are adopted. The DFD performance is analyzed and discussed through extensive experiments for various scintillator thicknesses, x-ray beam conditions, and incident doses. Results: Under the RQA 9 beam conditions of 2.7 μGy dose, the DFD with the upper and lower scintillator thicknesses of 0.5 mm could achieve a zero-frequency DQE of 75%, compared to 56% when using a single-layer detector. This implies that the DFD using 75 % of the incident dose of a single-layer detector can provide the same signal-to-noise ratio as a single-layer detector. Conclusions: In single-energy radiography imaging, DFD can provide better NPS and DQE performances than the case of the single-layer detector, especially at relatively high x-ray energies, which enables low-dose imaging. Full article

The advent of quantum computing has introduced new opportunities for enhancing classical machine learning architectures. In this study, we propose a novel hybrid model, the HQDNN (Hybrid Quantum–Deep Neural Network), designed for the automatic detection of fake news. The model integrates classical fully connected neural layers with a parameterized quantum circuit, enabling the processing of textual data within both classical and quantum computational domains. To assess its effectiveness, we conducted experiments on the widely used LIAR dataset utilizing Term Frequency–Inverse Document Frequency (TF-IDF) features, as well as transformer-based DistilBERT embeddings. The experimental results demonstrate that the HQDNN achieves a superior recall performance—92.58% with TF-IDF and 94.40% with DistilBERT—surpassing traditional machine learning models such as Logistic Regression, Linear SVM, and Multilayer Perceptron. Additionally, we compare the HQDNN with SetFit, a recent CPU-efficient few-shot transformer model, and show that while SetFit achieves higher precision, the HQDNN significantly outperforms it in recall. Furthermore, an ablation experiment confirms the critical contribution of the quantum component, revealing a substantial drop in performance when the quantum layer is removed. These findings highlight the potential of hybrid quantum–classical models as effective and compact alternatives for high-sensitivity classification tasks, particularly in domains such as fake news detection. Full article

Secure communication is critically dependent on high-speed and high-security quantum random number generation (QRNG). In this work, we present a responsive approach to enhance the efficiency and security of QRNG by leveraging polarization-controlled heterodyne detection to simultaneously measure the quadrature amplitude and phase fluctuations of vacuum shot noise. To address the practical non-idealities inherent in QRNG systems, we investigate the critical impacts of imbalanced heterodyne detection, amplitude–phase overlap, finite-size effects, and security parameters on quantum conditional min-entropy derived from the entropy uncertainty principle. It effectively mitigates the overestimation of randomness and fortifies the system against potential eavesdropping attacks. For a high-security parameter of ${10}^{-20}$, QRNG achieves a true random bit extraction ratio of 83.16% with a corresponding real-time speed of 37.25 Gbps following a 16-bit analog-to-digital converter quantization and 1.4 GHz bandwidth extraction. Furthermore, we develop a deep convolutional neural network for rapid and accurate entropy evaluation. The entropy evaluation of 13,473 sets of quadrature data is processed in 68.89 s with a mean absolute percentage error of 0.004, achieving an acceleration of two orders of magnitude in evaluation speed. Extracting the shot noise with full detection bandwidth, the generation rate of QRNG using dual-quadrature heterodyne detection exceeds 85 Gbps. The research contributes to advancing the practical deployment of QRNG and expediting rapid entropy assessment. Full article

With increasing global challenges related to water scarcity and phosphorus depletion, the recovery and reuse of wastewater-derived nutrients offer a sustainable path forward. This study evaluates the dual role of lanthanides (Ce³⁺ and La³⁺) in recovering phosphorus from municipal wastewater and supporting corn (Zea mays) cultivation through lanthanide phosphate (Ln-P) and lanthanide-reclaimed wastewater (LRWW, wastewater spiked with lanthanide). High-purity precipitates of CePO₄ (98%) and LaPO₄ (92%) were successfully obtained without pH adjustment, as confirmed by X-ray photoelectron spectroscopy (XPS) and energy-dispersive spectroscopy (EDS). Germination assays revealed that lanthanides, even at concentrations up to 2000 mg/L, did not significantly alter germination rates compared to traditional coagulants, though root and shoot development declined above this threshold—likely due to reduced hydrogen peroxide (H₂O₂) production and elevated total dissolved solids (TDSs), which induced physiological drought. Greenhouse experiments using desert-like soil amended with Ln-P and irrigated with LRWW showed no statistically significant differences in corn growth parameters—including plant height, stem diameter, leaf number, leaf area, and biomass—when compared to control treatments. Photosynthetic performance, including stomatal conductance, quantum efficiency, and chlorophyll content, remained unaffected by lanthanide application. Metal uptake analysis indicated that lanthanides did not inhibit phosphorus absorption and even enhanced the uptake of calcium and magnesium. Minimal lanthanide accumulation was detected in plant tissues, with most retained in the root zone, highlighting their limited mobility. These findings suggest that lanthanides can be safely and effectively used for phosphorus recovery and agricultural reuse, contributing to sustainable nutrient cycling and aligning with the United Nations’ Sustainable Development Goals of zero hunger and sustainable cities. Full article

In this paper, the first demonstration of a highly strained In_0.8Ga_0.2As/In_0.2Ga_0.8Sb type-II superlattice structure grown on InAs substrates by molecular beam epitaxy (MBE) for long-wavelength infrared detection was reported. Novel methodologies were developed to optimize the As and Sb flux growth conditions. The quality of the epitaxial layer was characterized using multiple analytical techniques, including differential interference contrast microscopy, atomic force microscopy, high-resolution X-ray diffraction, and high-resolution transmission electron microscopy. The high-quality superlattice structure, with a total thickness of 1.5 μm, exhibited exceptional surface morphology with a root-mean-square roughness of 0.141 nm over a 5 × 5 μm² area. Single-element devices with PIN architecture were fabricated and characterized. At 77 K, these devices demonstrated a 50% cutoff wavelength of approximately 12.1 μm. The long-wavelength infrared PIN devices exhibited promising performance metrics, including a dark current density of 7.96 × 10⁻² A/cm² at −50 mV bias and a high peak responsivity of 4.90 A/W under zero bias conditions, both measured at 77 K. Furthermore, the devices achieved a high peak quantum efficiency of 65% and a specific detectivity (D*) of 2.74 × 10¹⁰ cm·Hz^1/2/W at the peak responsivity wavelength of 10.7 µm. These results demonstrate the viability of this material system for long-wavelength infrared detection applications. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

This paper starts with an updated review and analyzes recent developments in quantum-inspired algorithms for cybersecurity, with specific attention to possible perspectives of optimization. The enhancement of classical computing capabilities with quantum principles is transforming fields such as machine learning, optimization, and cybersecurity. Evolutionary algorithms are one example where progress has already been made using quantum techniques through increased efficiency, generalization, and problem-solving techniques exploited by quantum principles. Quantum-inspired evolutionary algorithms (QIEAs) and quantum kernel methods are prime examples of such approaches. Quantum techniques are also used in the field of cybersecurity: QML-based identification systems for intrusion detection strengthen threat detection and encoding through quantum techniques with advanced cryptographic security, while quantum-secure hashing (QSHA) offers sophisticated means of protecting sensitive information. More specifically, QGANs are known for their integration into adversarial generative networks that increase efficiency by replacing classical models in adversarial defense through the generation of synthetic attack models. In this work, a set of benchmarks is provided for comparison with classical and other quantum-inspired technologies. The results demonstrate that these methods far outperform others in terms of computational efficiency and satisfactory scalability. Although fully functional models are still awaited, quantum computing benefits greatly from quantum-inspired technologies, as the latter enable the development of frameworks that bring us closer to the quantum era. Consequently, the work takes the form of an updated systematic review enriched with optimized perspectives. Full article

Smart cities necessitate ultra-secure and scalable communication frameworks to manage billions of interconnected IoT devices, particularly in the face of the emerging quantum computing threats. This paper proposes the QESIF, a novel Quantum-Enhanced Secure IoT Framework that integrates Quantum Key Distribution (QKD) with classical IoT infrastructures via a hybrid protocol stack and a quantum-aware intrusion detection system (Q-IDS). The QESIF achieves high resilience against eavesdropping by monitoring quantum bit error rate (QBER) and leveraging entropy-weighted key generation. The simulation results, conducted using datasets TON IoT, Edge-IIoTset, and Bot-IoT, demonstrate the effectiveness of the QESIF. The framework records an average QBER of 0.0103 under clean channels and discards over 95% of the compromised keys in adversarial settings. It achieves Attack Detection Rates (ADRs) of 98.1%, 98.7%, and 98.3% across the three datasets, outperforming the baselines by 4–9%. Moreover, the QESIF delivers the lowest average latency of 20.3 ms and the highest throughput of 868 kbit/s in clean scenarios while maintaining energy efficiency with 13.4 mJ per session. Full article

Pinus radiata D. Don is planted in South Central Chile on a wide range of sites using genetically improved genotypes for timber production. As drought events are expected to increase with ongoing climatic change, the variability in gas exchange, which could impact growth and water use, needs to be evaluated. In this study, we assessed the genotypic variability of leaf-level light-saturated photosynthesis (A_sat), stomatal conductance (g_s), transpiration (E), intrinsic water use efficiency (iWUE), and Chlorophyll a fluorescence (OJIP-test parameters) among 30 P. radiata genotypes (i.e., full-sib families) from third-cycle parents at age 6 years on three sites in Central Chile. We also evaluated tree height (HT), diameter at breast height (DBH), and stem index volume (VOL). Families were ranked for HT as top-15 and bottom-15. In the OJIP-test parameters we observed differences at the family level for the maximum quantum yield of primary PSII photochemistry (Fv/Fm), the probability that a photon trapped by the PSII reaction center enters the electron transport chain (ψ_Eo), and the potential for energy conservation from photons captured by PSII to the reduction in intersystem electron acceptors (PI_ABS). Fv/Fm, PI_ABS, and ψ_Eo ranged from 0.82 to 0.87, 45 to 95, and 0.57 to 0.64, respectively. Differences among families for growth and not for leaf-level physiology were detected. DBT, H, and VOL were higher in the top-15 families (12.6 cm, 8.4 m, and 0.10 m³, respectively) whereas A_sat, g_s, E, and iWUE were similar in both the top-15 and bottom-15 families (4.0 μmol m⁻² s⁻¹, 0.023 mol m⁻² s⁻¹, 0.36 mmol m⁻² s⁻¹, and 185 μmol mol m⁻² s⁻¹, respectively). However, no family by site interaction was detected for growth and leaf-level physiology. The results of this study suggest that highly improved genotypes of P. radiata have uniformity in leaf-level physiological rates, which could imply uniform water use at the stand-level. The family variation found in PI_ABS suggests that this parameter could be incorporated to select genotypes tolerant to environmentally stressful conditions. 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

This research introduces a comprehensive AI-driven framework for secure and efficient load management in multi-station electric vehicle (EV) charging networks, responding to the increasing demand and operational difficulties associated with widespread EV adoption. The suggested architecture has three main parts: a Smart Load Balancer (SLB), an AI-driven intrusion detection system (AIDS), and a Real-Time Analytics Engine (RAE). These parts use advanced machine learning methods like Support Vector Machines (SVMs), autoencoders, and reinforcement learning (RL) to make the system more flexible, secure, and efficient. The framework uses federated learning (FL) to protect data privacy and make decisions in a decentralized way, which lowers the risks that come with centralizing data. The framework makes load distribution 23.5% more efficient, cuts average wait time by 17.8%, and predicts station-level demand with 94.2% accuracy, according to simulation results. The AI-based intrusion detection component has precision, recall, and F1-scores that are all over 97%, which is better than standard methods. The study also finds important gaps in the current literature and suggests new areas for research, such as using graph neural networks (GNNs) and quantum machine learning to make EV charging infrastructures even more scalable, resilient, and intelligent. Full article

Glaucoma is a leading cause of irreversible blindness globally, with early diagnosis being crucial to preventing vision loss. Traditional diagnostic methods, including fundus photography, OCT imaging, and perimetry, often fall short in sensitivity and fail to integrate structural and functional data. This study proposes a novel multi-modal diagnostic framework that combines convolutional neural networks (CNNs), vision transformers (ViTs), and quantum-enhanced layers to improve glaucoma detection accuracy and efficiency. The framework integrates fundus images, OCT scans, and clinical biomarkers, leveraging their complementary strengths through a weighted fusion mechanism. Datasets, including the GRAPE and other public and clinical sources, were used, ensuring diverse demographic representation and supporting generalizability. The model was trained and validated using cross-entropy loss, L2 regularization, and adaptive learning strategies, achieving an accuracy of 96%, sensitivity of 94%, and an AUC of 0.97—outperforming CNN-only and ViT-only approaches. Additionally, the quantum-enhanced architecture reduced computational complexity from O(n²) to O (log n), enabling real-time deployment with a 40% reduction in FLOPs. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. The proposed system addresses key limitations of previous methods in terms of computational cost, data integration, and interpretability. This framework offers a scalable and clinically viable tool for early glaucoma detection, supporting personalized care and improving diagnostic workflows in ophthalmology. Full article

Belle II is a particle physics experiment working at an high luminosity collider within a hard irradiation environment. The Time-Of-Propagation detector, aimed at the charged particle identification, surrounds the Belle II tracking detector on the barrel part. This detector is composed by 16 modules, each module contains a finely fused silica bar, coupled to microchannel plate photomultiplier tube (MCP-PMT) photo-detectors and readout by high-speed electronics. The MCP-PMT lifetime at the nominal collider luminosity is about one year, this is due to the high photon background degrading the quantum efficiency of the photocathode. An alternative to these MCP-PMTs is multi-pixel photon counters (MPPC), known as silicon photomultipliers (SiPM). The SiPMs, in comparison to MCP-PMTs, have a lower cost, higher photon detection efficiency and are unaffected by the presence of a magnetic field, but also have a higher dark count rate that rapidly increases with the integrated neutron flux. The dark count rate can be mitigated by annealing the damaged devices and/or operating them at low temperatures. We tested SiPMs, with different dimensions and pixel sizes from different producers, to study their time resolution (the main constraint that has to satisfy the photon detector) and to understand their behavior and tolerance to radiation. For these studies we irradiated the devices to radiation up to $5\times {10}^{11}1$ MeV neutrons equivalent (neq) per cm² fluences; we also started studying the effect of annealing on dark count rates. We performed several measurements on these devices, on top of the dark count rate, at different conditions in terms of overvoltage and temperatures. These measurements are: IV-curves, amplitude spectra, time resolution. For the last two measurements we illuminated the devices with a picosecond pulsed laser at very low intensities (with a number of detected photons up to about twenty). We present results mainly on two types of SiPMs. A new SiPM prototype developed in collaboration with FBK with the aim of improving radiation hardness, is expected to be delivered in September 2025. Full article