Search Results (2,141)

Sm₂TM₁₇ sintered magnets, (where TM = Co, Fe, Cu, Zr), are typically utilised in high temperature magnetic applications due to their magnetic properties being very stable at 200–350 °C. Sm and Co are critical materials and need to be recycled to reduce reliance on virgin material supply chains. This work explored HD processing of Sm₂TM₁₇ sintered magnet production scrap as a potential recycling technique. Sintered magnet scrap was initially analysed compositionally, microstructurally and magnetically to determine issues with magnet quality. Scrap material was then HD processed at 18 bar and 2 bar at temperatures between 25–300 °C. The resultant material was characterised in terms of hydrogen content, particle size, degassing behaviour and unit cell expansion. Production scrap magnets exhibited irregular demagnetisation traces with poor domain wall pinning behaviour. Non-magnetic ZrC inclusions likely prevented cell structure formation locally and hence were poor domain wall pinning sites. Scrap material processed at 18 bar and 2 bar required temperatures of 100 °C to allow for the greatest extent of HD reaction, reaching 0.299 Wt.% and 0.323 Wt.% hydrogen respectively. The HD behaviour of production scrap material was comparable to commercial grade magnets. Therefore, HD is a potentially viable technique for recycling Sm₂TM₁₇ sintered magnet production scrap. Full article

Background: This study evaluated morphological and functional differences among eyes with retinitis pigmentosa (RP) classified according to fundus autofluorescence (FAF) patterns. Methods: A total of 146 eyes from 73 patients with RP were analysed. Based on FAF imaging, eyes were classified as having regular hyperautofluorescent rings (n = 23), irregular rings (n = 76), or absent rings (n = 47). Participants underwent best-corrected visual acuity (BCVA), contrast sensitivity, 10–2 and 30–2 static perimetry, multifocal electroretinography (mfERG), and optical coherence tomography (OCT). FAF morphometrics included ring diameters and area. Results: Eyes with a regular FAF ring demonstrated significantly better visual function than those with irregular or absent rings, including higher BCVA (p < 0.001 and p = 0.001) and greater contrast sensitivity (both p < 0.001). The mfERG amplitude density in the first ring was higher in regular than irregular FAF patterns (p = 0.034). Eyes with irregular FAF showed more advanced visual field loss, with lower mean deviation on 10–2 (p = 0.042) and 30–2 perimetry (p = 0.027). In the regular-ring group, the ellipsoid zone was predominantly intact (p = 0.012). The hyperautofluorescent ring area correlated positively with mfERG amplitude density in the first and second rings (Rs = +0.573, p = 0.016; Rs = +0.736, p = 0.001) and with macular volume (Rs = +0.667, p = 0.003). Conclusions: FAF patterns reflect central retinal functional and structural impairment in RP. Therefore, incorporating FAF imaging into the diagnostic algorithm is valuable for monitoring disease progression. Full article

Cloud detection in remote sensing imagery is a research hotspot in the field of image processing, and accurately detecting and segmenting cloud regions is crucial for improving the utilization efficiency of remote sensing data. However, standard convolutional neural networks face limitations in modeling the complex spatial structures of clouds. To address these challenges, this paper proposes a cloud detection method based on DDS-DeeplabV3+. First, a lightweight design of the Xception network is adopted to control model complexity, and part of its standard convolutional layers are replaced with Deformable Convolutional Networks (DCN), which enhances the capability of the model to capture geometric features of irregular cloud formations. Second, a Dual-Branch Collaborative Mechanism (DCM) that integrates global context modeling with local detail perception is designed to reconstruct the Atrous Spatial Pyramid Pooling (ASPP) module, thereby improving performance in handling complex scenes and fine boundary delineation. Finally, the SimAM (Simple, Parameter-Free Attention Module) is incorporated into the decoder module, enhancing thin cloud detection capability. Experimental results on the Landsat-8 and GF-1 datasets show that the proposed model achieves Mean Intersection over Union (MIoU) values of 92.61% and 94.04%, respectively, outperforming other comparative methods and demonstrating its superior performance in cloud detection tasks. Full article

We present a supervised learning method to estimate two local descriptors of time-series dynamics, the mean-reversion rate $\theta$ and a heavy-tail estimate $\alpha$, from short windows of data. These parameters summarize recovery behavior and tail heaviness and are useful for interpreting stochastic signals in sensing applications. The method is trained on synthetic, dimensionless Ornstein–Uhlenbeck processes with $\alpha$-stable noise, ensuring robustness for non-Gaussian and heavy-tailed inputs. Gradient-boosted tree models (CatBoost) map window-level statistical features to discrete $\alpha$ and $\theta$ categories with high accuracy and predominantly adjacent-class confusion. Using the same trained models, we analyze daily financial returns, daily sunspot numbers, and NASA POWER climate fields for Austria. The method detects changes in local dynamics, including shifts in the financial tail structure after 2010, weaker and more irregular solar cycles after 2005, and a redistribution in clear-sky shortwave irradiance around 2000. Because it relies only on short windows and requires no domain-specific tuning, the framework provides a compact diagnostic tool for signal processing, supporting the characterization of local variability, detection of regime changes, and decision making in settings where long-term stationarity is not guaranteed. Full article

Accurate identification of concealed coal seam structures, such as folds or faults, is crucial for safe and effective production in the coal mining industry. In-seam seismic exploration serves as a promising technique for advanced detection of coal seam structures, but traditional numerical simulation methods easily produce errors when coping with irregular interfaces. This study uses the curvilinear grid finite-difference method (FDM) for modeling the 3D channel wave propagation. The body-fitted grids are utilized to conform to undulating interfaces, while the DRP/opt MacCormack difference scheme and the fourth-order Runge–Kutta algorithm are applied for the spatial and temporal derivative approximation, in that order. The forward and backward extrapolation for in-seam waves are implemented in the curvilinear coordinates. The roofs and floors of coal seams and special structures are imaged by reverse-time migration (RTM) using an excitation amplitude imaging condition. Numerical results show that compared with conventional methods, the curvilinear grid method effectively reduces spurious scattering caused by the staircase approximation, improves the modeling accuracy of channel waves, and enhances the continuity and interpretability of imaged coal-seam interfaces and structural boundaries. The proposed method has the potential to enhance the accuracy of channel wave exploration under complex geological conditions, supporting advanced hazard detection in coal mines. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

Object detection in UAV aerial imagery presents significant challenges, including large-scale variations, complex background interference, object occlusion, and a high density of small targets. These factors restrict the generalization and localization capabilities of existing detectors. To address these issues, we propose YOLO-DMA, an efficient detection framework for aerial images. The framework incorporates three key improvements. First, we designed a Hierarchical Deformable Block (HDB), which uses adaptive sampling grids and a progressive multi-branch structure to capture features of irregular objects while preserving network depth, enabling richer hierarchical feature representation. Second, we proposed a Dual-Path Linear-complexity Perception (DPLP) module. One path employs a linear-complexity attention mechanism to model the global context efficiently, while the other utilizes lightweight convolutions to extract local details. This design effectively fuses shallow details with mid-level semantics, improving detection and localization accuracy. Third, we adopted the Wise-IoU v3 loss function, which dynamically adjusts optimization objectives, suppressing harmful gradients from low-quality samples and emphasizing small objects during training. Comprehensive experiments on the VisDrone dataset show that YOLO-DMA achieves 42.8% mAP50 and 25.7% mAP50:95. These correspond to improvements of 4.8% and 3.1% over YOLOv10. Experimental results demonstrate the effectiveness and practicality of the proposed framework. Full article

Premature skid resistance deterioration is a critical issue limiting the long-term performance of thin asphalt overlays. To elucidate the meso-scale degradation mechanisms, this study employs the Discrete Element Method (DEM) implemented in Particle Flow Code (PFC) to compare a conventional Stone Matrix Asphalt (SMA-10) mixture with an optimized skeleton-reinforced design, termed Optimized Gradation-10. The optimized gradation was developed by introducing supplementary sieve sizes of 5.6, 6.7, and 8.0 mm within the critical range of 4.75–9.5 mm following the V–S gradation framework. Cross-sectional images of actual mixtures were vectorized using Python (Version: 3.11.5) and MATLAB (Version: R2024a) to reconstruct irregular aggregate clump models that accurately capture particle morphology and spatial arrangement. Meso-scale parameters were calibrated and validated through uniaxial compression tests, and the evolution of contact number, contact force, and stress transmission was analyzed under 2.3 × 10⁵ wheel load cycles. Compared with SMA-10, the optimized mixture increased effective aggregate contacts by 41.8%, enhanced stress transfer efficiency by 19.8%, and reduced rut depth by 10%. These findings confirm that synergistic gradation optimization through supplementary sieves and the V–S method markedly improves structural stability and deformation resistance, providing a meso-mechanical foundation for prolonging skid resistance in thin overlays. Full article

In modern survey sampling, particularly when using stratified random sampling (StRS), the existence of outliers and model mis-specifications is a daunting challenge to the conventional parametric and nonparametric methods of estimating parameters. This research presents a new type of predictive estimator that is synergistic to both robust regression and nonparametric local polynomial kernel regression. It aims to offer more resistant and efficient estimators of the average parameter in the areas where supplementary information is known, but irregularity in the data is usual. The proposed estimators use dual calibration methods based on both auxiliary variable means and coefficients of variation, which improves efficiency. This framework enhances predictive performance by integrating the adaptability of kernel-based smoothing with the outlier resistance of robust regression. The accuracy of the suggested estimators is measured by using large scales of simulation experiments on artificial populations with structural heterogeneity and outlier contamination. An empirical comparison, based on percentage relative efficiency (PRE), indicates that the new estimators are superior to classical methods based on the use of a kernel regression in most bandwidth selection strategies. In addition to bringing methodological innovation as it connects distribution theory, regression models, and robust estimation strategies, this work also offers the usefulness of survey practitioners who work with complicated and imperfect real-life data of fisheries and radiations. Full article

Path planning for large-scale agricultural fields faces challenges such as irregular field shapes, uncertain boundaries, and the need to balance path efficiency, energy consumption, and coverage quality. To address these problems, this research introduces a strategy-aware hierarchical hybrid optimization framework (HANS) for autonomous agricultural operations. This framework introduces a global principal axis extraction method based on Principal Component Analysis (PCA), utilizing the statistical distribution of field boundaries to guide path direction, thereby improving robustness against boundary noise and irregular geometries. The framework integrates Adaptive Large Neighborhood Search (ALNS) for global exploration and Tabu Search (TS) for local optimization, forming a tightly coordinated hybrid structure. The framework further employs a Pareto-set-based decision support selection strategy to solve a multi-objective optimization model encompassing machine kinematics, turning patterns, and energy-aware cost evaluation. This strategy provides three methods: weighted preference-based compromise solution selection, crowding distance-based diversified solution selection, and single-objective extreme value-based dedicated optimization solution selection. To balance the impact of path length, energy consumption, and coverage rate, we assigned equal or nearly equal weights to them (i.e., (0.33, 0.33, 0.34)). Furthermore, the framework incorporates operators and feedback learning mechanisms specific to agricultural coverage path problems to enable adaptive operator selection and reduce reliance on manual parameter tuning. Simulation results under three representative field scenarios show that compared to fixed-direction planning, HANS improves the average coverage rate by 0.51 percentage points and reduces fuel consumption by 4.34%. Compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Tabu Search (TS), and Simulated Annealing (SA), the proposed method shortens the working path length by 0.37–0.83%, improves coverage rate by 0.34–1.11%, and reduces energy consumption by 0.61–1.03%, while maintaining competitive computational costs. These results demonstrate the effectiveness and practicality of HANS in large-scale autonomous farming operations. Full article

Accurate fingerprinting of Internet of Things (IoT) devices is essential for network security, management, and anomaly detection. Existing machine-learning-based approaches can be broadly classified into two categories. The first are time-domain-based approaches that infer device identity from aggregated traffic statistics, while effective in dense communication environments, they perform poorly for devices that generate sparse, low-volume, or irregular traffic, which restricts behavioral visibility. The second, radio frequency fingerprinting (RFF), extracts hardware-specific traits from radio frequency signals but is limited in wired or mixed-connectivity IoT networks and lacks behavioral or functional insights. To overcome these limitations, this paper proposes a hybrid fingerprinting framework that integrates network traffic analysis with frequency-domain representations using wavelet transform techniques. This approach captures both temporal and spectral characteristics, combining behavioral and structural perspectives to enable robust and accurate IoT device identification. The proposed system is evaluated on three real-world datasets under multiple experimental scenarios, including (1) device identification, (2) device type classification, (3) scalability with dataset size and complexity, and (4) performance under Distributed Denial-of-Service (DDoS) attack conditions. Experimental results show that wavelet-based features consistently outperform conventional time-domain features across all evaluation metrics, achieving higher accuracy, resilience, and generalization. Full article

To enhance public safety and safeguard lives and property, the automatic detection of anomalous and violent behaviors in video has become a key task in intelligent surveillance systems. Violent actions are often abrupt, rapid, and irregular, posing considerable challenges to conventional approaches. Existing methods based on hand-crafted features and convolutional neural networks still exhibit limitations in spatiotemporal feature extraction, recognition accuracy, and model robustness. To address these issues, this paper proposes HSTNet, a hybrid neural architecture that integrates Spiking Neural Networks (SNNs) with Transformers. The framework adopts a dual-branch design: the SNN branch models temporal dynamics in video, while the Transformer branch extracts spatial structural information. A feature interaction module is further introduced to enable deep cross-modal fusion. Experiments on multiple datasets including UCF101, HMDB51, Hockey Fight, and Movies Fight demonstrate that HSTNet achieves significantly higher accuracy than state-of-the-art baselines, indicating strong performance and promising application potential. Full article

Semantic segmentation of metallographic micrographs is a key task for quantitative microstructural analysis in additive manufacturing, yet it remains challenging due to phase heterogeneity, complex morphologies, and the scarcity of annotated data. The MetalDAM dataset, composed of 42 labeled scanning electron microscopy images of steel microstructures, has been widely adopted as a benchmark, with U-Net commonly reported as the strongest supervised baseline. Nevertheless, the encoder–decoder structure of U-Net imposes architectural constraints that hinder the precise delineation of heterogeneous and irregular phase boundaries under severe data limitations. To address this limitation, this paper investigates a Fully Convolutional Network (FCN)-based architecture as an alternative approach for semantic segmentation on the MetalDAM dataset. The FCN is trained and evaluated under the same experimental protocol as the U-Net baseline, enabling a direct and fair comparison. Performance is assessed using multiple evaluation metrics, including Intersection over Union (IoU), precision, recall, and mean Average Precision at an IoU threshold of 0.5. The results show that the FCN achieves comparable overall IoU values (0.75) while delivering substantial improvements at the class level, particularly for minority and morphologically complex phases, with gains of up to 25–30% in class-specific IoU. Additional metrics confirm enhanced robustness, with consistently higher precision, recall, and mAP@0.5 values. These findings demonstrate that FCN-based architectures constitute a competitive and robust alternative to U-Net for metallographic segmentation in additive manufacturing scenarios characterized by limited annotated data. Full article

Image-based detection of concrete water tank damage in mining areas holds promise for practical applications. However, current deep learning-based detection algorithms often face challenges in balancing accuracy with computational complexity for real-world deployment. This paper presents an improved YOLOv5 detection method for concrete water tank damage. Firstly, the conventional convolution module in the CSPDarknet backbone is optimized with GSConv (Grouped Shuffle Convolution) to enhance feature extraction while reducing the number of parameters. Secondly, a weight transformation attention mechanism is integrated into the C3 structure to strengthen the feature representation of crack regions. Finally, the Minimum Point Distance IoU (MPDIoU) is employed for precise localization of irregular damage. On a dataset of over 11,000 images, the proposed method achieves a mean average precision (mAP@0.5) of 84.3% (precision: 88.7%; recall: 85.9%). It outperforms the original YOLOv5s, with a 6.5% higher mAP and an 11.1% faster inference speed, while maintaining a compact model size of 7.5M parameters and running at 86 FPS. Ablation studies confirm the individual contributions of each proposed module to these improvements. The algorithm thus provides an efficient and accurate solution that is suitable for deployment on resource-constrained devices. Full article

Accurate cropland parcel mapping is essential for food security and sustainable land management in arid Africa, yet it remains challenging in Egypt due to edge blurring, spectral confusion, and fragmented fields in medium-resolution imagery. A novel dual-encoder deep learning method that integrates multi-scale axial attention and boundary constraints (MAA-BCNet) is proposed for the precise extraction of croplands in Egypt from Sentinel-2 multispectral images. A dual-path encoder is designed to fuse CNN-based local textures with an RMT global branch using spatial decay attention for complementary feature extraction. A multi-scale axial attention module is introduced to capture anisotropic parcel structures for improved spectral–spatial discrimination, and a multi-directional gradient edge enhancement module is developed for explicitly preserving boundary integrity. A U-Net++ decoder is employed for dense multi-scale aggregation. Experimental results in Egypt demonstrate that MAA-BCNet achieves superior performance in delineating cropland parcels, particularly for irregular or fragmented croplands with complex landscapes and fuzzy boundaries. Compared with the widely used segmentation models such as DeepLabV3_plus, PSPnet, Link_net, FCN_resnet101, and U-Net++ under the same training and evaluation settings, our model has the best performance, with Recall, Precision, IoU, and F1-Score reaching 94.92%, 90.77%, 86.57%, and 92.80%, respectively. These advancements make MAA-BCNet suitable for cropland mapping of large areas of Egypt, with applications in precision agriculture and sustainable land management. Full article