Search Results (5,293)

As nanofabrication advances toward atom-by-atom control of surface morphology, plasmonic electrodes and nanogap devices are being pushed into a regime where atomic-scale protrusions and sub-nanometer separations become accessible. In this extreme limit, classical electrodynamics becomes unreliable because it cannot capture quantum effects. To this end, we compute the optical response of metallic sub-nanometer nanogaps containing atomic-scale protrusions by employing quantum hydrodynamic theory (QHT), and benchmark the predictions against the classical local-response approximation (LRA). We revealed that atomic-scale variations in protrusion can leave the far-field scattering spectrum nearly unchanged while profoundly reshaping tnear-field nanofocusing. Upon a continuous decrease in the nanogap, QHT successfully predicts non-monotonic spectral evolution with a redshift-to-blueshift deflection point accompanied via a suppression of field enhancement, whereas LRA yields a continuous redshift and a monotonic increase in field enhancement. We further demonstrated that such an inflection point is tunable, as determined by the atomic morphology of the electrodes, which provide a theoretical foundation for the experimental observation of varied inflection points. These results provide a practical route to optically diagnose and engineer tunneling-enabled charge exchange and quantum-regulated nanofocusing in extreme plasmonic nanogaps, and offer design guidance for molecular-scale optoelectronic and nanophotonic devices. Full article

Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is proposed. When sparse PU activity is masked by irregular interference bursts, traditional sensing algorithms misclassify weak transmissions as noise, leading to low detection reliability. To resolve this, the first hidden layer employs Discrete Wavelet Sparse Bayesian Kernel Analysis (DW-SBK), integrating Discrete Wavelet Packet Transform (DWPT), Sparse Bayesian Learning (SBL), and Kernel PCA. This restores the true sparse pattern of the spectrum, separates interference from actual PU signals, and enhances detection of weak channels. Additionally, PU signals are fragmented due to cross-scale activity drift, where dynamic bandwidth switching and variable burst durations disrupt temporal continuity. Therefore, the second layer incorporates Gradient Boosted Multi-Head Fuzzy Clustering (GB-MHFC), where Gradient Boosted Decision Trees (GBDT) model nonlinear spectral–temporal patterns, Multi-Head Self-Attention (MHSA) captures long- and short-range temporal dependencies, and Fuzzy C-Means Clustering (FCM) groups feature representations into stable PU activity modes, thereby reducing misclassifications and enhancing robustness under highly dynamic CRN conditions. The proposed method demonstrates superior performance with a maximum detection probability of 0.98, classification accuracy of 98%, lowest sensing error of 5.412%, and the fastest sensing time of 3.65 s. Full article

This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The framework integrates Multiplicative Intrinsic Component Optimization (MICO_2D) for bias field correction, followed by a distance-regularized multiphase Vese–Chan level-set model for coarse global tumor segmentation. To achieve precise boundary delineation, a localized refinement stage is employed using Localized Active Contours (LAC) with Local Image Fitting (LIF) energy, supported by Gaussian regularization to ensure smooth and coherent boundaries in regions with ambiguous tissue transitions. Building upon the refined semantic tumor mask, the framework further incorporates a panoptic-style tumor instance segmentation stage, enabling the decomposition of connected tumor regions into distinct anatomical instances, which were evaluated on both MIAS and INBreast mammography datasets to demonstrate generalizability. This extension facilitates detailed structural analysis of tumor multiplicity and spatial organization, enhancing interpretability beyond conventional pixel wise segmentation. Experiments conducted on Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) mammographic views demonstrate competitive performance relative to baseline U-Net and advanced deep learning fusion architectures, including multi-scale and multi-view networks, while offering improved interpretability and robustness. Quantitative evaluation using overlap-related metrics shows strong spatial agreement between predicted and reference segmentations, with per-image Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) distributions reported to ensure reproducibility. Descriptive per-image analysis, supported by bootstrap-based confidence intervals and paired comparisons, indicates consistent performance improvements across images. Robustness analysis under realistic perturbations, including noise, contrast degradation, blur, and rotation, demonstrates stable performance across varying imaging conditions. Furthermore, feature space visualizations using t-SNE and UMAP reveal clear separability between cancerous and non-cancerous tissue regions, highlighting the discriminative capability of the proposed framework. Overall, the results demonstrate the effectiveness, robustness, and clinical motivation of this hybrid panoptic framework for comprehensive dense breast tumor analysis in mammography, while emphasizing reproducibility and conservative statistical assessment. Full article

Remote sensing scene classification (RSSC) plays a crucial role in Earth observation. Current deep learning methods, while accurate, tend to focus on high-level semantic features and overlook complementary shallow details such as edges and textures. Moreover, conventional CNNs are limited by fixed receptive fields, whereas transformers incur high computational costs. To address these limitations, we propose the Lie Group lightweight multi-scale network (LGLMNet), a lightweight multi-scale network that integrates Lie Group covariance features. It employs a dual-branch architecture combining Lie Group machine learning (LGML) for shallow feature extraction and a deep learning branch for high-level semantics. In the deep branch, we design a parallel depthwise separable convolution block (PDSCB) for multi-scale perception and a spatial-channel collaborative attention mechanism (SCCA) for efficient global–local modeling. A cross-layer feature fusion block (CLFFB) effectively merges the two branches. Compared with state-of-the-art methods, the proposed LGLMNet achieves accuracy improvements of 2.14%, 2.32%, and 1.12% on UCM-21, AID, and NWPU-45 datasets, respectively, while maintaining a lightweight structure with only 2.6 M parameters. Full article

This study presents and systematizes a high-reliability measurement and technological dataset suitable for prediction-based validation of the Spent Fuel Interim Storage Facility (SFISF) of the Paks Nuclear Power Plant. The primary objective of this dataset is not the validation of a general-purpose software tool, but to establish a reproducible experimental basis for the objective and quantitative validation of a three-dimensional, facility-scale heat transfer and buoyancy-driven flow model of the SFISF, developed using the finite difference method (FDM), in a passively cooled system where heat conduction, thermal radiation, and natural convection simultaneously occur. The applied measurement systems (SMAS, CTRS, and the in-house developed CFEPR), their spatial arrangement, accuracy characteristics, as well as data post-processing and the generation of model execution inputs are described in detail. Special emphasis is placed on the functional separation of the available data into initialization data, model execution data, and independent validation datasets, ensuring that model assessment does not rely on calibration or parameter fitting. Furthermore, the estimation of decay heat generated by the stored fuel assemblies is presented using both a standard correlation method (ANSI/ANS-5.1) and isotope inventory-based calculations, and the discrepancies between these approaches are treated as input uncertainties and sensitivity analysis factors. The spectral solar load is considered based on the ASTM G-173 reference spectrum, while during cloudy periods an effective irradiance estimation derived from on-site lux measurements is applied. The results indicate that the available measurement and technological information is sufficient for supporting reproducible, transparent, and quantitative validation studies of the three-dimensional numerical model of the SFISF, as well as for assessing the impact of dominant input uncertainties. Full article

Renewable Energy Certificates (RECs) have emerged as critical market-based policy instruments to promote renewable energy development worldwide. This comprehensive review examines the theoretical foundations, market mechanisms, policy effectiveness, and challenges of global REC systems based on extensive international experiences spanning over two decades. RECs function by separating the environmental attributes of renewable electricity from its physical energy, creating flexible trading mechanisms that effectively channel private investment toward renewable energy projects while providing compliance tools for renewable portfolio standards. Our analysis reveals significant variations in design and implementation across major markets, including the United States, European Union, China, India, Australia, and emerging economies. Despite their widespread adoption with over 50 countries implementing various forms of REC mechanisms, these markets face persistent challenges including price volatility, limited liquidity, regulatory inconsistencies, and ongoing debates about their environmental additionality. Recent technological developments, particularly blockchain-enabled tracking systems and digital platforms, are reshaping REC markets by enhancing transparency, reducing transaction costs, and enabling smaller-scale participation. This review proposes corresponding recommendations from the dimensions of optimizing market design, promoting digital transformation and product diversification, and establishing international coordination mechanisms. Full article

Object detection in UAV aerial imagery plays a pivotal role across a wide spectrum of applications. However, existing detection models continue to face significant challenges stemming from small object scales, dense spatial distributions, and highly complex backgrounds. To address these challenges, this paper proposes a novel dual-backbone network model named WCDB-YOLO. The core innovation of this work lies in introducing a “target-context decoupled perception” paradigm, which utilizes two structurally complementary backbone networks to separately process local object features and global background information: one backbone focuses on extracting fine-grained local features of objects, while the other innovatively incorporates a wavelet convolution module to efficiently model the global contextual semantics of complex scenes with minimal computational cost by constructing a large receptive field. To further enhance the scale adaptability for small objects, a Dilation-wise Residual (DWR) module is designed, which employs parallel convolutional branches with different dilation rates to achieve dynamic adaptation to multi-scale small object features. Additionally, the model optimizes the feature pyramid structure by integrating high-resolution P2/4 features into the detection head, significantly improving the localization accuracy of tiny objects. Experimental results on the VisDrone dataset show that the proposed method achieves an 8.4% improvement in mAP50 over the baseline YOLOv11s model and outperforms current state-of-the-art (SOTA) approaches. This work presents a highly accurate and robust solution for small object detection from UAV platforms in complex environments. Full article

Key parameters were estimated separately for the European eel, Anguilla anguilla (Linnaeus, 1758) subpopulations across freshwater and brackish environments within the Eastern Adriatic Eel Management Unit (EMU: EA). Between 2023 and 2024, European eel sampling was carried out at 23 locations along the Croatian coast (N = 678). Ages ranged from 1 to 13 years in freshwater and 1 to 8 years in brackish waters. The population structure was dominated by undifferentiated (42.8%) in freshwater and females (46.3%) in brackish habitats. Eels in freshwater exhibited a significantly higher b-coefficient in their length–weight relationship and better condition. Based on the otolith annuli patterns, age classes 3 to 5 predominated in both groups. A slightly longer asymptotic length and lower growth rate were found for the freshwater group compared to a shorter length and higher growth rate in the brackish habitat. Natural mortality was estimated at 0.174 ± 0.09 year⁻¹ and 0.191 ± 0.133 year⁻¹ for brackish and freshwater habitats, respectively. Total mortality was higher in freshwater (0.86 year⁻¹) in comparison with the brackish (0.83 year⁻¹) habitat. According to obtained results, more than 50% of eels aged three years are under exploitation. The maximum Yield per Recruit (Y/R) was 0.082 g/recruit in brackish at L_c = 44.88 cm, and a current L_c is 19.4 cm in the samples. In freshwater, Y/R peaked at 0.042 g/recruit at L_c = 55.49 and a current L_c 11.1 cm. It is recommended, following a precautionary approach to management, that the current fishing practices change in order to increase the minimum landing size (MLS), at least to 45 cm (above the current official MLS of 35 cm), to increase the fishing yield, and directly enhance population resilience. Findings emphasise the need for sustainable eel management policies considering different subpopulation parameters along the freshwater/brackish gradient at a small spatial scale when proposing and implementing stock management measures. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

The safety of belt conveyor operation is of great importance during coal conveyance. This paper proposes a multi-task-based GSSA-YOLOM algorithm for monitoring the state of belt conveyors, which utilizes segmentation head to detect foreign objects and belt deviation, thereby balancing the trade-offs among multiple tasks. The detection neck is responsible for multi-scale feature fusion by incorporating the Asymptotic Feature Pyramid Network (AFPN) to achieve enhanced spatial perception. Then, Groupwise Separable Convolution (GSConv) is further introduced to simplify the network architecture, reducing computational complexity while maintaining sufficient detection accuracy for edge device deployment. Moreover, the SlideLoss and Soft-NMS functions are integrated to reduce the rate of false positives and missed detections. Comparison experiments were conducted, and the results indicate that the proposed GSSA-YOLOM model can improve mAP@50 by 3.4% compared with the baseline model while reducing the number of parameters by 27%, thereby satisfying coal mine safety monitoring requirements. Full article

Rural transportation corridors remain weakly instrumented for continuous low-altitude airspace monitoring. At the same time, Vehicle-to-Everything (V2X) roadside units (RSUs) are increasingly deployed for transportation safety services. This work investigates whether existing RSUs can be extended with passive, cooperative RF sensing to detect small UAVs without modifying standards-compliant ITS communications in the protected 5.9 GHz band. A calibrated simulation study evaluates corridor-scale operation under realistic propagation conditions, including terrain masking and narrowband interference. All results reported in this paper are derived from simulation and do not include field measurements or hardware prototyping. False alarm performance under diverse ISM emitters is not quantified. The results show that cooperative processing across neighboring RSUs improves epoch-level verified detection coverage compared with single-RSU sensing. Bearing variability is reduced for weak or partially masked signals. These gains result from feature-level validation across spatially separated receivers rather than deterministic signal combining. RF calibration constrains detections to physically plausible kilometer-scale ranges. The resulting angular accuracy is sufficient for early warning and track initiation, but not for precise localization. Overall, the findings indicate that existing V2X infrastructure can support supplementary early warning capability for corridor-scale airspace monitoring while preserving primary V2X safety functions. Full article

Stormwater and greywater are increasingly recognized as freshwater resources, and the effective separation and reutilization of nitrogen (N) and phosphorus (P) from these streams is vital for water quality improvement and urbanization sustainability. In this study, we constructed a pilot-scale hydroponic green roof wetland system planted with two economically important Chinese herbal plant species (Mentha spicata L. (ML) and Basella alba L. (BL)) to separate and reutilize N and P from synthetic stormwater/greywater. The results reveal that the highest plant biomass was obtained at an ML:BL ratio of 1:3, indicating their superior adaptation to rooftop hydroponics with synthetic stormwater/greywater. This configuration also achieved the strongest water purification, with substantial separation and reutilization efficiency of N (82.09%) and P (81.90%). Furthermore, the lowest microbial richness in the ML roots at this specific plant ratio suggested that increasing BL may enhance ML’s allelopathic effects. An increase in the BL proportion was further associated with a gradual shift in the dominant ML root-associated microorganisms toward microeukaryotic taxa. The green vegetation of the two plant species also effectively suppressed algal blooms (especially diatoms) in the hydroponic rooftop system. This study demonstrates that a Chinese herb-based green roof wetland system can effectively separate and reuse N and P from stormwater/greywater while concurrently purifying water and producing economic crops. Full article

We perform a systematic analysis of the nuclear dependence of two-particle–two-hole meson-exchange current contributions to inclusive lepton-nucleus scattering within the relativistic mean-field framework. We present microscopic calculations of nuclear responses for a set of 17 nuclei, ranging from helium to uranium, using a model with different Fermi momenta for protons and neutrons. We propose a novel scaling prescription based on the two-particle phase space and key nuclear parameters. The resulting description is accurate over a wide range of nuclear targets, with typical deviations below 10%, and allows for a separate treatment of the different emission channels. In addition, a consistent benchmark against electron-scattering data is provided. The parametrization presented provides a practical framework for extending the responses to different nuclear targets in neutrino event generators. Full article

Accurate derivative information is central to sensitivity analysis and optimization, yet standard finite differences can lose many digits when the step size is small because of subtractive cancellation. Complex-step differentiation largely resolves this issue for first derivatives, but robust second derivatives and mixed partials remain delicate: several practical complex-step variants for $f^{″}$ still subtract nearly equal quantities, and quaternion-step rules are often presented as separate constructions. We develop a unified slice-based framework that extracts first and second derivatives from a single evaluation by projecting algebraic coefficients in commutative subalgebras of the complexified quaternions. First, we formulate a directional quaternion-steprule parameterized by an arbitrary unit pure quaternion u and provide an explicit projection operator that makes the underlying complex slice ${\mathbb{C}}_u\simeq \mathbb{C}$ transparent; the resulting first-derivative formula is rotation invariant and recovers classical j-step and planar $(j,k)$-step rules as special cases. Second, we construct a bicomplex double-step calculus in the commuting imaginary units $\mathrm{i}$ and u and show that one evaluation at $z+(\mathrm{i}+u)h$ separates derivative information into distinct coefficients, with the $\mathrm{i}u$-component equal to $h^2{f}^{″}\left(z\right)+\mathcal{O}\left(h^4\right)$, giving a subtraction-free $\mathcal{O}\left(h^2\right)$ approximation of $f^{″}$. For bivariate analytic functions we additionally derive one-shot identities for $f_x$, $f_y$, and $f_{xy}$ from $f(x+uh,y+\mathrm{i}h)$ and supply practical extraction identities, step-size guidance for $h^2$-scaled coefficients, and branch-consistency diagnostics for non-entire functions. The “cancellation-free” property here refers to avoiding the subtraction of nearly equal real quantities at the level of the differentiation formula; in floating-point arithmetic, coefficient extraction and the $1/h^2$ scaling for second-order quantities still interact with roundoff, and we quantify the resulting stable regimes numerically. Full article

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article