Search Results (330)

Hot isostatic pressing (HIP) was employed to fabricate 3 mol% Y₂O₃-stabilized ZrO₂ ceramics with nearly pore-free microstructures. Zirconia ceramics containing residual pores (size: ~0.3 μm, <0.1%) exhibited a four-point bending strength of 1.11 GPa. In contrast, pore-free specimens achieved significantly higher strengths of 1.74 GPa for samples containing a small fraction of cubic grains and 2.29 GPa for specimens composed solely of the tetragonal phase. At the moment of fracture in the high-strength specimens, intense electrical discharges (visible sparks) were observed near the fracture origin. Post-fracture observations revealed that zirconia containing residual pores fractured into two pieces with relatively smooth fracture surfaces, whereas pore-free zirconia exhibited extensive fragmentation, producing highly irregular fracture surfaces. This behavior is likely associated with extensive rupture of Zr–O bonds within the crystal lattice during catastrophic fracture. These results demonstrate that the elimination of residual pores by HIP markedly enhances the attainable strength of zirconia ceramics and significantly alters their fracture behavior. Full article

Object detection in unstructured agricultural environments remains challenging due to large scale variations, complex backgrounds, irregular obstacle shapes, and limited computational resources. To address these challenges, this paper proposes Agri-DETR, an efficient end-to-end detection framework based on the Real-Time Detection Transformer (RT-DETR), with coordinated improvements in feature perception, multi-scale representation, spatial reconstruction, and bounding box regression. Specifically, a lightweight backbone with a high-resolution feature branch is introduced to enhance the representation of small and fine-grained targets. A large selective feature fusion module is designed to strengthen multi-scale contextual modeling and improve feature discrimination under complex backgrounds. In addition, an attention-enhanced dynamic upsampling module refines high-resolution feature reconstruction, while a scale–shape–geometry-aware Intersection over Union (SSGIoU) loss improves localization stability for irregular and elongated objects. Experimental results show that Agri-DETR achieves 66.0% Average Precision (AP) on the self-constructed Agricultural Obstacle Dataset (AO-Dataset), outperforming representative detectors while reducing the parameter count by approximately 25% compared with RT-DETR-R18 baseline. In particular, small-object AP increases by 1.4%, demonstrating improved detection capability for small obstacles. Cross-dataset evaluation on COCO2017 further shows that Agri-DETR achieves 48.3% AP, demonstrating favorable generalization capability beyond the agricultural domain. These results indicate that Agri-DETR achieves an effective balance among detection accuracy, model complexity, and practical efficiency, making it a promising solution for real-world agricultural obstacle detection. Full article

The new LOAC2 optical aerosol counter is designed to detect liquid and solid particulates across 19 to 30 size classes within the 0.15–90 µm size range, and to provide their main typology. The instrument can be used at ground level and on all kinds of balloons, including weather balloons, up to an altitude of about 35 km. The measurements are based on principles established for the previous version of LOAC, now incorporating improved electronics and detection geometry. Counting is performed at small scattering angles in the diffraction domain, making it insensitive to the refractive indices and the porosity of the particles, thus allowing a direct relationship between scattered intensity and aerosol size. Typology identification is now performed at three additional scattering angles, where the scattered flux is highly sensitive to the refractive index of the different aerosol families present in the atmosphere. The calibration was conducted using calibrated spherical and irregular grains, as well as different types of solid particles. Several intercomparison sessions with other counters and with reference mass-concentration air quality monitoring stations were carried out indoors, in an atmospheric simulation chamber, and in outdoor ambient air. The agreement between LOAC2 and the other instruments is good, confirming the ability of LOAC2 to be used for scientific studies and for monitoring atmospheric aerosols. Full article

Metallographic microstructure analysis is essential for understanding the evolution of steel microstructures during heat treatment and mechanical processing. However, accurate analysis of optical micrographs remains difficult because of blurred grain boundaries, grayscale inhomogeneity within grains, and irregular grain morphologies. To address these issues, this work proposes an automated metallographic image-processing method based on superpixels, DPSS (dual-phase steel segmentation), with the main contribution focused on microstructure segmentation. First, image contrast and boundary visibility are enhanced by edge detection and sharpening. Then, superpixel segmentation is combined with extracted edge information to improve boundary localization and preserve irregular grain morphology, enabling more complete extraction of grain or particle regions from optical images. The proposed method is validated on optical micrographs of Mn-Si low-alloy steel, and the results show that it provides more accurate and complete segmentation than conventional ImageJ (Version: 1.54f)-based processing. Based on the segmented regions, a lightweight neural network is further used for phase identification. The final classification recognition accuracy can reach 99.91%. This classification result serves to demonstrate that the improved segmentation results can provide more reliable inputs for subsequent microstructure recognition. Overall, the proposed method offers an effective and automated solution for metallographic image segmentation and supports more accurate downstream phase analysis. Full article

Background/Objectives: In Croatia, national data on adolescents’ dietary habits are limited, resulting in a lack of evidence-based food-based dietary guidelines and public health interventions. This study aims to conduct an in-depth evaluation of dietary habits in a national sample of Croatian adolescents stratified by region, sex and age, from the National food consumption survey on adolescents and adults (NIPNOD 2018–2023). Methods: This cross-sectional study included 258 adolescents (50.4% boys; aged 10 to < 18) from the NIPNOD 2018–2023 survey (OC/EFSA/DATA/2017/01), conducted according to the EU Menu methodology. For analysis, the sample was divided into two age groups (10–13 and 14–17 years). To assess dietary intake, two 24 h recalls were analyzed using NutriCro^® v. 3.0 software. Dietary intake was compared with European Food Safety Authority dietary reference values (DRV). The contribution of 14 food groups to daily energy intake was analyzed. Diet quality was assessed using the Diet Quality Index for Adolescents (DQI-A). Results: The mean daily energy intake was 1820 ± 529 kcal, consisting of 45.5 ± 7.0% carbohydrates, 37.8 ± 6.3% fats, and 15.1 ± 3.1% protein. The observed two-day mean intake suggested that 51.6% of adolescents had carbohydrate intake within the EFSA DRV range, while 5.4% and 32.2% had protein and fat intake within the EFSA DRVs, respectively. The main contributors to daily energy intake were grains and grain products (31.5%), meat, poultry, fish, and eggs (18.1%), and cakes, confectionery, sweets, and sugar (14.9%). Frequent breakfast skipping and snack consumption were common, particularly among older adolescents. Adolescents had moderate overall diet quality (57.4 ± 11.6% DQI-A), with no differences between age groups. Conclusions: Analysis of the dietary habits of adolescents in Croatia indicates that most have inadequate macronutrient intake, irregular meal frequency, and moderate overall diet quality. These results highlight the need to develop public health strategies and interventions to improve dietary habits among adolescents in Croatia. Full article

Volcanic ash preserves critical information on eruption dynamics, magma evolution, and fragmentation processes, yet its small size and fragile structure pose challenges for conventional analytical methods. Advances in SEM-based automated mineralogy combined with X-ray mapping (GXMAP) provide high-resolution characterization of ash textures, particle morphology, and mineral assemblages, offering a more robust basis for interpreting pyroclastic deposits. This study applies an integrated GXMAP workflow alongside sieve-based granulometry to the Early Pleistocene trachyandesite to rhyolitic pyroclastic sequences at the Kurtan quarry (Kechut Volcanic Province, Armenia), a key regional stratigraphic marker associated with early human occupation. GXMAP-based granulometry minimizes preparation-induced fragmentation and yields more consistent and reliable grain-size and morphological data for fine ash deposits than dry sieving. The three stratigraphic units at Kurtan display distinct combinations of grain size, mineral assemblages, and particle morphologies, reflecting contrasting magma evolution, fragmentation conditions, and depositional regimes. Shape-parameter fields derived from BSE images reveal clear differences between the highly irregular, concave compound fragments dominating TP-13-1 and the smoother, more compact particles characteristic of TP-13-2 and TP-13-3. Most particles fall within the ductile domain of established shape-morphology diagrams, indicating that ductile deformation of bubble walls was a major component of fragmentation, accompanied by heterogeneous brittle breakage. These results demonstrate the effectiveness of the combined SEM-based automated mineralogy and GXMAP approach for resolving primary fragmentation, sorting characteristics, and depositional processes in fragile pyroclastic deposits. The Kurtan sequence provides new constraints on explosive volcanism in the Lesser Caucasus Mts. region. At the same time, the methodological framework offers broad applicability to tephra studies worldwide and underscores the potential of imaging-based techniques in volcanology. Full article

Underwater detection is crucial for the autonomous operation of Autonomous Underwater Vehicles (AUVs). However, underwater environments pose significant challenges, including severe image degradation, complex target deformation, and densely distributed small objects. Most existing methods treat image enhancement as an independent preprocessing module and rely on fixed-shape convolution kernels for feature extraction, which often leads to inconsistent optimization objectives and limited capability in handling irregular targets and fine-grained small-object details. To address these issues, we propose an End-to-End Adaptive Underwater Detection framework (E2E-AUD). Specifically, a lightweight image enhancement module, UnitModule, is embedded into the detection network so that enhancement can be jointly optimized with detection and directly serve downstream feature learning. In addition, linear deformable convolution (LDConv) is introduced into the backbone to adaptively model polymorphic targets, while Haar wavelet downsampling (HWD) is adopted to preserve boundary and texture information through frequency-domain analysis. Experiments on the DUO and URPC datasets demonstrate that E2E-AUD achieves superior performance over both general-purpose and underwater-specific detectors. Specifically, on the DUO dataset, our model reaches 86.2% mAP50 and 67.8% mAP50-95, outperforming the recent YOLOv12 by 3.0% and 2.7%, respectively. On the highly turbid URPC dataset, it achieves 84.3% mAP50 and 50.8% mAP50-95, surpassing the competitive underwater-specific detector LEFEN by notable margins in strict localization metrics. Furthermore, E2E-AUD maintains a real-time inference speed of 21.8 FPS with highly constrained computational complexity (9.4 GFLOPs), proving its exceptional balance between detection accuracy and deployment efficiency compared to previous methods. Full article

Vehicle damage detection is an important task in intelligent transportation systems and insurance assessment, yet it remains challenging due to the subtle appearance, irregular shapes, and spatial dispersion of damage regions in complex environments. We propose a specialized structural synergy that organically integrates a GSConv-based Slim-Neck, a dynamic Bi-Level Routing Attention mechanism, and an orientation-aware SIoU loss. Rather than a superficial architectural combination, this cooperative design introduces a novel methodological framework engineered specifically to resolve the fundamental conflict between edge-deployment efficiency and fine-grained feature preservation in vehicle inspection. The method is evaluated on the publicly available Car Damage Detection dataset and compared with representative two-stage and one-stage detectors, including DETR, Faster R-CNN, YOLOv5n, YOLOv8n, and YOLO11n. Experimental results show that the proposed approach achieves a mAP50 of 67.9% and mAP50–95 of 53.8%, outperforming the baseline YOLO11n and other lightweight YOLO variants with only a moderate increase in computational cost. These results indicate that the proposed framework offers a favorable trade-off between detection accuracy and efficiency, showing potential for vehicle damage inspection under resource-constrained conditions. Full article

Accurate and automatic segmentation of the liver and liver tumors from computed tomography (CT) images is essential for computer-assisted diagnosis, treatment planning, and clinical decision-making. Although deep learning-based segmentation models, particularly U-Net and its variants, have achieved promising results in medical image analysis, many existing approaches mainly focus on local pixel-level feature extraction and may have limited ability to explicitly model long-range spatial relationships among anatomically meaningful regions. In addition, liver tumor segmentation remains challenging due to low contrast, irregular tumor boundaries, heterogeneous tumor appearances, and noise or artifacts in CT images. To address these limitations, this study proposes a hybrid ensemble neural network architecture that integrates an improved U-Net and a Graph U-Net for automatic liver and liver tumor segmentation. The improved U-Net is designed to capture fine-grained local features and preserve detailed spatial information through an encoder–decoder structure with skip connections, while the Graph U-Net uses Simple Linear Iterative Clustering (SLIC)-based superpixels to construct a graph representation of CT images and model spatial dependencies between adjacent image regions. By combining these complementary representations through an ensemble learning strategy, the proposed framework enhances both pixel-level segmentation accuracy and robustness against noisy imaging conditions. The proposed method was evaluated on the LiTS17 dataset, where CT images were preprocessed using intensity filtering, resizing, data augmentation, and normalization. Experimental results demonstrate that the proposed ensemble architecture achieves 99.2% accuracy for liver segmentation and 98.1% accuracy for liver tumor segmentation, outperforming representative segmentation models such as MultiresUnet and R2U-Net. Furthermore, robustness experiments under different signal-to-noise ratio conditions show that the proposed model maintains stable performance in noisy CT images, achieving 85% accuracy even under severe noise at −4 dB SNR. This result highlights the advantage of integrating convolutional feature learning with graph-based spatial relationship modeling for improving segmentation stability when image quality is degraded by noise or artifacts. These findings indicate that the integration of improved U-Net, SLIC-based graph construction, and Graph U-Net provides an effective and noise-robust solution for liver and liver tumor segmentation, with potential applicability as a computer-assisted tool in clinical image analysis after further validation on larger and external datasets. Full article

Texture anomaly segmentation aims to localize irregularities on textured surfaces, a task critical for industrial quality control. Supervised methods require extensive labeled data, while unsupervised approaches often struggle to generalize to unseen target domains. Recent zero-shot methods based on vision-language models such as Contrastive Language-Image Pretraining (CLIP) enable anomaly detection through text prompts without target-domain training data. However, existing approaches typically rely on generic prompts and show limited sensitivity to fine-grained texture variations. To address these limitations, we propose TextureCLIP, a cross-dataset zero-shot framework with auxiliary training for texture anomaly segmentation. The framework is trained on source texture data from the MVTec AD texture subset using annotated source-domain samples and directly evaluated on six unseen target datasets without access to target-domain training images, annotations, or fine-tuning. The proposed Triadic Descriptive Prompting (TriDP) integrates normal prompts, generic anomaly prompts, and descriptive anomaly prompts to provide complementary semantic cues for improved cross-domain generalization. To enhance spatial sensitivity, Dual Attention Modules (DAMs) are incorporated into the CLIP image encoder to refine local feature representations. In addition, Softmax-Weighted Averaging (SMWA) aggregates multiple anomaly cues by emphasizing the prompt responses with higher similarity scores. Experimental results demonstrate that TextureCLIP achieves strong and consistent performance across diverse texture datasets, attaining 67.06% AP and 65.69% F1-max, with improvements of 5.17 and 2.66 percentage points over the competitive baselines, respectively. Full article

Wind turbine blade defect detection requires accurate identification of small and irregular defects while maintaining low computational cost for practical inspection scenarios. However, lightweight detectors often suffer from insufficient local feature extraction, limited multiscale feature fusion, and weak responses to critical defect regions. To address these issues, this study proposes a Receptive-Field-Enhanced You Only Look Once model (RFE-YOLO), a lightweight defect detection model based on You Only Look Once version 10 nano (YOLOv10n).The proposed model introduces three task-oriented improvements. First, C2f-RFAConv is embedded into the backbone to enhance receptive field aware local feature representation for fine grained defects. Second, a Compact Cross-scale Feature Fusion Module, termed CCFM, is designed in the neck to improve the integration of low-level detail information and high-level semantic features with reduced computational complexity. Third, an Efficient Local Attention module is inserted before the detection head to strengthen defect-related spatial responses after feature fusion. Experiments were conducted on a wind turbine blade defect dataset containing three categories, namely Crack, Oil leakage, and Peel. The results show that RFE-YOLO achieves 89.9% mean Average Precision at an Intersection over Union threshold of 0.5, namely mAP@0.5, and 64.73% mAP@0.5:0.95. Compared with YOLOv10n, RFE-YOLO improves mAP@0.5 by 2.8 percentage points while reducing the number of parameters from 2.70M to 1.91M and giga floating point operations from 8.4 to 5.3. The inference speed reaches 88.8 frames per second on an NVIDIA GeForce RTX 3090 GPU. These results indicate that RFE-YOLO achieves a favorable balance between detection accuracy and model efficiency under the current experimental setting. Full article

To address the dual challenges of discriminating weeds from rice seedlings for precision weed management operations, such as targeted spraying and robotic weeding, in complex paddy-field scenes and deploying high-precision models on resource-limited edge devices, we propose DGS-Net, a deformable attention, GSConv-based feature fusion, and SEAM-enhanced lightweight network based on YOLOv11n. The backbone incorporates a convolutional block with parallel split attention and deformable attention transformer (C2PSA_DAT) module to improve the extraction of irregular and fine-grained weed features, the neck integrates a VoV-GSCSP module to enable lightweight multi-scale feature fusion for small and densely distributed targets, and a separated and enhancement attention module (SEAM) is placed before the detection head to enhance robustness under leaf occlusion and complex paddy-field background interference. In comparative experiments conducted on the paddy-field dataset under unified training and evaluation settings, DGS-Net achieved 91.7% precision, 86.8% recall, and 92.4% mean average precision (mAP), with a model size of 5.8 MB and a computational cost of 6.2 giga floating-point operations (GFLOPs). Compared with representative lightweight baseline detectors, DGS-Net showed a more favorable balance between detection accuracy and deployment efficiency. In additional edge-device deployment tests using the test set, the model sustained real-time inference at 32.5 FPS and achieved mAP@0.5, precision, and recall of approximately 0.928, 0.919, and 0.867, respectively. Overall, DGS-Net improves irregular feature extraction, enables lightweight multi-scale feature fusion, and increases robustness to occlusion while retaining strong deployability. The method therefore provides practical visual-perception support for precise, real-time crop–weed discrimination and precision weed management in complex paddy-field environments. Full article

Unsupervised anomaly detection in industrial fabric inspection remains a formidable challenge due to the complexity of background textures and the subtle, irregular nature of real-world defects. Although the teacher-student distillation paradigm has demonstrated promising performance without reliance on anomalous data, existing methods still struggle in the presence of complex textures, largely due to limited semantic guidance, insufficient frequency modeling, and inadequate multi-scale representation. To address these limitations, we propose a novel reverse distillation framework tailored for fabric defect detection. The core of our method is the frequency decoupling Feature fusion module (FDFM), which achieves frequency domain alignment between teacher and student features through spatially adaptive and learnable filter banks, namely the adaptive high-pass filter (AHPF) and the adaptive low-pass filter (ALPF). Specifically: (1) the high-frequency pathway employs deconvolutional residual enhancement to emphasize boundary details; (2) the low-frequency pathway leverages the CARAFE operator to Handle these normal fluctuations to prevent the model from mistakenly identifying background changes as abnormal areas. This design not only maintains a lightweight architecture but also significantly improves sensitivity to fine-grained anomalies. Furthermore, we introduce a cross-layer residual alignment mechanism that guides the student network in reconstructing deep semantic representations from the teacher-student feature pairs. To balance detection accuracy and deployment efficiency, we develop two model variants: a high-capacity version optimized for precision, and a lightweight version tailored for real-time industrial applications. Compared with other methods from recent years, the experimental results of FAD-RNet validate its superiority in relevant metrics. It should be noted that this study is conducted based on the data organization and processing protocol of the ZJU-Leaper dataset, which may introduce certain dataset-specific characteristics. Full article

Glaze flaking is widespread in Hongzhou kiln celadon dating from the Eastern Han to the Tang Dynasty, yet its underlying mechanism cannot be attributed to a single factor. In this study, 11 Hongzhou kiln celadon specimens from the Eastern Han, Southern Dynasties, and Sui–Tang periods were examined using microscopic observation, SEM–EDS, Raman spectroscopy, crack-width measurements, glaze-area analysis, water-absorption tests, and burial environment analysis to investigate the characteristics and causes of glaze flaking. The results show that crazing-crack width is significantly and positively correlated with the extent of glaze flaking. The body–glaze interlayer generally exhibited heterogeneous features, including anorthite crystallization, unmelted quartz grains, bubbles, and locally phase-separated droplets. Anorthite crystals and adjacent regions were frequently associated with crystal-shaped corrosion pits, irregular voids, and localized structural loosening; degraded areas showed depletion of Ca and Si and relative enrichment of Al and Fe. The burial soils were generally neutral to slightly alkaline and showed no evident salt accumulation, suggesting that high salinity was not the primary direct cause of glaze flaking in these samples. These findings suggest that glaze flaking in Hongzhou kiln celadon results from the interaction between firing-induced heterogeneity at the body–glaze interface and prolonged post-burial corrosion. Crazing and interconnected cracks acted as pathways for moisture and soluble ions to penetrate the body–glaze interlayer, triggering selective corrosion of Ca-rich crystalline phases and adjacent glassy phases and ultimately causing interfacial destabilization and glaze loss. Full article

This paper proposes an efficient algorithm that integrates a variable neighborhood search (VNS) framework with an adaptive voxel discretization for the three-dimensional irregular packing problem. The problem arises in additive manufacturing, logistics loading, and other fields, especially in strip packing scenarios where the filling length in a virtual container with a fixed cross-section and infinite length is to be minimized. The algorithm first discretizes continuous three-dimensional geometric models into Boolean voxel matrices, thereby transforming complex geometric interference detection into efficient logical operations. An initial solution is generated using a greedy “largest-volume-first” strategy. An innovative adaptive voxel precision adjustment mechanism is introduced to dynamically modify the discretization granularity according to the current filling rate, realizing a hierarchical solution strategy of “coarse-grained fast search + fine-grained precise optimization”. On this basis, a variable-neighborhood iterative framework based on tabu search (TS-VNS) is constructed. Three complementary neighborhood operators are designed: single-item reinsertion, block exchange, and rotation perturbation, together with an adaptive operator selection mechanism driven by historical contributions. Experiments on multiple standard instances of varying scales and complexities (e.g., miniature chess pieces and engine components) show that the proposed algorithm outperforms comparative methods in both packing height and average height, achieving a favorable balance between solution efficiency and stability. Thus, it provides a reliable and efficient approach for the practical engineering application of three-dimensional irregular packing. Full article