Search Results (984)

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts. Full article

High-Intensity Focused Ultrasound (HIFU) is a non-invasive technology widely used in aesthetic dermatology for skin tightening and facial rejuvenation. This study aimed to evaluate the safety and efficacy of a modified HIFU device, More Young, compared to the standard 7D HIFU system through a randomized, single-blinded clinical trial. The More Young device features enhanced focal depth precision and energy delivery algorithms, including nine pre-programmed stabilization checkpoints to minimize treatment risks. A total of 100 participants with facial wrinkles and skin laxity were randomly assigned to receive either More Young or 7D HIFU treatment. Skin improvements were assessed at baseline and one to six months post-treatment using the VISIA^® Skin Analysis System (7th Generation), focusing on eight key parameters. Patient satisfaction was evaluated through the Global Aesthetic Improvement Scale (GAIS). Data were analyzed using paired and independent t-tests, with effect sizes measured via Cohen’s d. Both groups showed significant post-treatment improvements; however, the More Young group demonstrated superior outcomes in wrinkle reduction, skin tightening, and texture enhancement, along with higher satisfaction and fewer adverse effects. No significant differences were observed in five of the eight skin parameters. Limitations include the absence of a placebo group, limited sample diversity, and short follow-up duration. Further studies are needed to validate long-term outcomes and assess performance across varied demographics and skin types. Full article

This paper proposes a novel automatic recognition method for distinguishing voids and severe loosening in road structures based on features of ground-penetrating radar (GPR) B-scan images. By analyzing differences in image texture, the intensity and clarity of top reflection interfaces, and the regularity of internal waveforms, a set of discriminative features is constructed. Based on these features, we develop the FKS-GPR dataset, a high-quality, manually annotated GPR dataset collected from real road environments, covering diverse and complex background conditions. Compared to datasets based on simulations, FKS-GPR offers higher practical relevance. An improved ACF-YOLO network is then designed for automatic detection, and the experimental results show that the proposed method achieves superior accuracy and robustness, validating its effectiveness and engineering applicability. Full article

The effects of trace Ce on the microstructure and texture of non-oriented silicon steel during recrystallization and grain growth were examined using X-ray diffraction and electron backscatter diffraction. Additionally, this study focused on investigating the mechanisms by which trace Ce influences the evolution of the {114} <481> and γ-fiber textures. During the recrystallization process, as the recrystallization fraction of annealed sheets increased, the intensity of α-fiber texture decreased, while the intensities of α*-fiber and γ-fiber textures increased. The {111} <112> grains preferentially nucleated in the deformed γ-grains and their grain-boundary regions and tended to form a colony structure with a large amount of nucleation. In addition, the {100} <012> and {114} <481> grains mainly nucleated near the deformed α-grains, which were evenly distributed but found in relatively small quantities. The hindering effect of trace Ce on dislocation motion in cold-rolled sheets results in a 2–7% lower recrystallization ratio for the annealed sheets, compared to conventional annealed sheets. Trace Ce suppresses the nucleation and growth of γ-grains while creating opportunities for α*-grain nucleation. During grain growth, trace Ce reduces γ-grain-boundary migration rate in annealed sheets, providing growth space for {114} <418> grains. Consequently, the content of the corresponding {114} <481> texture increased by 6.4%, while the γ-fiber texture content decreased by 3.6%. Full article

The aim of this study was to evaluate the influence of genotype and diet on geese from crossbreeding meat lines Tapphorn (T) and Eskildsen (E). This study was conducted on 240 crossbred geese assigned to two dietary groups: an SBM diet group fed a standard soybean-based diet and an LPS diet group fed a yellow lupin-based diet. Birds were reared under identical management conditions and slaughtered at 17 weeks of age. The following traits were recorded: meat colour (CIELab), pH₂₄, cooking loss, breast and thigh muscle texture (shear force and energy), and sensory traits. The results showed a significant effect of both genotype and diet on meat quality. The LPS diet lowered shear force and energy (by ~11%, p < 0.001), reduced cooking loss in breast muscles (by ~5%, p < 0.001), and improved the juiciness and flavour of thigh muscles. The ET genotype positively influenced the meat colour intensity (lower L*, higher a*), while the lupin-based diet improved technological parameters, especially the water-holding capacity. The results confirm that replacing soybean meal with yellow lupin protein is an effective nutritional strategy that can improve goose meat quality and sustainability without compromising the sensory quality. These outcomes support developing soy-free feeding strategies in goose production to meet consumer expectations and reduce reliance on imported feed. Full article

The increasing interest in fibre-enriched and functional bakery products has led to the exploration of novel plant-based ingredients with both technological functionality and consumer acceptance. This study evaluates the effects of incorporating flours derived from apple (Malus domestica cv. Oberländer Himbeerapfel), sweet chestnut (Castanea sativa), horse chestnut (Aesculus hippocastanum), and red, sessile, and pedunculate oak (Quercus rubra, Q. petraea, and Q. robur) into wheat bread at 5%, 10%, and 15% substitution levels. The impact on crumb structure, crust colour, textural parameters (hardness, adhesiveness, springiness), and sensory attributes was assessed. The inclusion of apple and sweet chestnut flours resulted in a softer crumb, lower adhesiveness, and higher sensory scores related to flavour, aroma, and crust appearance. In contrast, higher levels of oak- and horse-chestnut-derived flours increased crumb hardness and reduced overall acceptability due to bitterness or excessive density. Apple flour preserved crumb brightness and contributed to warm tones, while oak flours caused more intense crust darkening. These findings suggest that selected non-traditional flours, especially apple and sweet chestnut, can enhance the sensory and physical properties of wheat bread, supporting the development of fibre-rich, clean-label formulations aligned with consumer trends in sustainable and functional baking. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Purpose: To investigate if radiomics signatures generated from longitudinal CT scans could predict IRE treatment effectiveness and outcomes in patients with locally advanced pancreatic cancer (LAPC). Methods: A cohort of 50 (60% male, mean [SD] age 60.7 [8.7] years) LAPC patients treated with IRE were retrospectively selected. Preoperative and 12-week follow-up CT scans were reviewed by two radiologists for tumor segmentation. A total of 2078 features were extracted: shape (n = 16), texture (n = 68), filter (n = 1892), intensity (n = 18), and local texture (n = 84). Principal component analysis (PCA) was applied to develop composite radiomics features. Composite signatures and clinically relevant radiomics features were correlated with time to recurrence (TTR), time to local recurrence (TTLR), time to distant recurrence (TTDR), recurrence-free survival (RFS) and overall survival (OS). Risk stratification performance was evaluated using hazard ratios (HRs), and significance was evaluated using the log-rank test. Results: Statistically significant separation between high and low patient TTR risk groups was observed in the following: gray-level co-occurrence matrix (HR = 2.65, p < 0.01, median survival difference = 6.6 mo); composite radiomics features derived from the following feature groups: all radiomics features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo), intensity features (HR = 3.13, p < 0.01, median survival difference = 14.0 mo), and filter features (HR = 2.27, p = 0.01, median survival difference = 6.4 mo). Conclusions: Pre-treatment radiomics signatures were significantly associated with LAPC patient outcomes. The observed correlations used pre-treatment CT scans, implying that the features predict the individual risk of disease recurrence. Full article

The objective of this study was to analyze the impact of solar and hybrid dryers on the physicochemical characteristics, fingerprints, and cognitive-sensory perceptions of Mexican consumers of traditional tostadas made with corn of different races. Corn tostadas from different native races were evaluated with solar and hybrid (solar-photovoltaic solar panels) dehydration methods. Proximal chemical quantification, instrumental analysis (color, texture), fingerprint by Fourier transform infrared spectroscopy (FTIR), and sensory-cognitive profile (emotions and memories) and its relationship with the level of pleasure were carried out. The data were evaluated using analysis of variance models, Cochran Q, and an external preference map (PREFMAP). The results showed that the drying method and corn race significantly (p < 0.05) affected only moisture content, lipids, carbohydrates, and water activity. Instrumental color was influenced by the corn race effect, and the dehydration type influenced the fracturability effect. FTIR fingerprinting results revealed that hybrid samples exhibited higher intensities, particularly associated with higher lime concentrations, indicating a greater exposure of glycosidic or protein structures. Race and dehydration type effects impacted the intensity of sensory attributes, emotions, and memories. PREFMAP vector model results revealed that consumers preferred tostadas from the Solar-Chiquito, Hybrid-Pepitilla, Hybrid-Cónico, and Hybrid-Chiquito races for their higher protein content, moisture, high fracturability, crunchiness, porousness, sweetness, doughy flavor, corn flavor, and burnt flavor, while images of these tostadas evoked positive emotions (tame, adventurous, free). In contrast, the Solar-Pepitilla tostada had a lower preference because it was perceived as sour and lime-flavored, and its tostada images evoked more negative emotions and memories (worried, accident, hurt, pain, wild) and fewer positive cognitive aspects (joyful, warm, rainy weather, summer, and interested). However, the tostadas of the Solar-Cónico race were the ones that were most rejected due to their high hardness and yellow to blue tones and for evoking negative emotions (nostalgic and bored). Full article

Background/Objective: The approach to the clinical management of metastatic melanoma patients is undergoing a significant transformation. The availability of a large amount of data from medical images has made Artificial Intelligence (AI) applications an innovative and cutting-edge solution that could revolutionize the surveillance and management of these patients. In this study, we develop and validate a machine-learning model based on radiomic data extracted from a computed tomography (CT) analysis of patients with metastatic melanoma (MM). This approach was designed to accurately predict prognosis and identify the potential key factors associated with prognosis. Methods: To achieve this goal, we used radiomic pipelines to extract the quantitative features related to lesion texture, morphology, and intensity from high-quality CT images. We retrospectively collected a cohort of 58 patients with metastatic melanoma, from which a total of 60 CT series were used for model training, and 70 independent CT series were employed for external testing. Model performance was evaluated using metrics such as sensitivity, specificity, and AUC (area under the curve), demonstrating particularly favorable results compared to traditional methods. Results: The model used in this study presented a ROC-AUC curve of 82% in the internal test and, in combination with AI, presented a good predictive ability regarding lesion outcome. Conclusions: Although the cohort size was limited and the data were collected retrospectively from a single institution, the findings provide a promising basis for further validation in larger and more diverse patient populations. This approach could directly support clinical decision-making by providing accurate and personalized prognostic information. Full article

Background: Radiographic evaluation of bone regeneration following maxillary sinus floor elevation commonly emphasizes volumetric gains. However, the qualitative microarchitecture of the regenerated bone, particularly when assessed via two-dimensional imaging modalities, such as panoramic radiographs, remains insufficiently explored. This study aimed to evaluate early trabecular changes in grafted maxillary sinus regions using fractal dimension, first-order statistics, and gray-level co-occurrence matrix analysis. Methods: This retrospective study included 150 patients who underwent maxillary sinus floor augmentation with bovine-derived xenohybrid grafts. Postoperative panoramic radiographs were analyzed at 6 months to assess early healing. Four standardized regions of interest representing grafted sinus floors and adjacent tuberosity regions were analyzed. Image processing and quantitative analyses were performed to extract fractal dimension (FD), first-order statistics (FOS), and gray-level co-occurrence matrix (GLCM) features (contrast, homogeneity, energy, correlation). Results: A total of 150 grafted sites and 150 control tuberosity sites were analyzed. Fractal dimension (FD) and contrast values were significantly lower in grafted areas than in native tuberosity bone (p < 0.001 for both), suggesting reduced trabecular complexity and less distinct transitions. In contrast, higher homogeneity (p < 0.001) and mean gray-level intensity values (p < 0.001) were observed in the grafted regions, reflecting a more uniform but immature trabecular pattern during the early healing phase. Energy and correlation values also differed significantly between groups (p < 0.001). No postoperative complications were reported, and resorbable collagen membranes appeared to support graft stability. Conclusions: Although the grafted sites demonstrated radiographic volume stability, their trabecular architecture remained immature at 6 months, implying that volumetric measurements alone may be insufficient to assess biological bone maturation. These results support the utility of advanced textural and fractal analysis in routine imaging to optimize clinical decision-making regarding implant placement timing in grafted sinuses. Full article

Human Pose Estimation (HPE) aims to accurately locate the positions of human key points in images or videos. However, the performance of HPE is often significantly reduced in practical application scenarios due to environmental interference. To address this challenge, we propose a ladder side-tuning method for the Vision Transformer (ViT) pre-trained model based on multi-path feature fusion to improve the accuracy of HPE in highly interfering environments. First, we extract the global features, frequency features and multi-scale spatial features through the ViT pre-trained model, the discrete wavelet convolutional network and the atrous spatial pyramid pooling network (ASPP). By comprehensively capturing the information of the human body and the environment, the ability of the model to analyze local details, textures, and spatial information is enhanced. In order to efficiently fuse these features, we devise an adaptive symmetric feature fusion strategy, which dynamically adjusts the intensity of feature fusion according to the similarity among features to achieve the optimal fusion effect. Finally, a multi-graph feature aggregation method is developed. We construct graph structures of different features and deeply explore the subtle differences among the features based on the dual fusion mechanism of points and edges to ensure the information integrity. The experimental results demonstrate that our method achieves 4.3% and 4.2% improvements in the AP metric on the MS COCO dataset and a custom high-interference dataset, respectively, compared with the HRNet. This highlights its superiority for human pose estimation tasks in both general and interfering environments. Full article

The extensive use of copper in thermal and electrical systems calls for constant performance enhancement by means of innovative material treatments. The effects on the microstructural, mechanical, and electrical characteristics of copper in deep cryogenic treatment (DCT) and deep cryogenic treatment followed by heat treatment (DCT + HT) are investigated in this work. Copper samples were treated for various soaking durations ranging from 6 to 24 h. Mechanical properties such as tensile strength, hardness, and wear rate were analyzed. In the DCT-treated samples, tensile strength increased, reaching a peak of 343 MPa at 18 h, alongside increased hardness (128 HV) and a refined grain size of 9.58 µm, primarily due to elevated dislocation density and microstrain. At 18 h of soaking, DCT + HT resulted in improved structural stability, high hardness (149 HV), a fine grain size (7.42 µm), and the lowest wear rate (7.73 × 10⁻¹⁰ mm³/Nm), consistent with Hall–Petch strengthening. Electrical measurements revealed improved electron mobility (52.08 cm²/V·s) for samples soaked for 24 h in DCT + HT, attributed to increased crystallite size (39.9 nm), reduced lattice strain, and higher (111) texture intensity. SEM–EBSD analysis showed a substantial increase in low-angle grain boundaries (LAGBs) in DCT + HT-treated samples, correlating with enhanced electrical conductivity. Overall, an 18 h soaking duration was found to be optimal for both treatments. However, the strengthening mechanism in DCT + HT is influenced by grain boundary stabilization and thermal recovery and is different to DCT, which is strain-induced enhancement. Full article

Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non-linear intensity distortion, which pose significant challenges to image traditional matching. This paper presents a robust feature matching method based on crater neighborhood structure, which is particularly robust to changes in illumination. The method integrates deep-learning based crater detection, Crater Neighborhood Structure features (CNSFs) construction, CNSF similarity-based matching, and outlier removal. To evaluate the effectiveness of the proposed method, we created an evaluation dataset, comprising Multi-illumination Lunar Orbiter Images (MiLOIs) from different latitudes (a total of 321 image pairs). And comparative experiments have been conducted using the proposed method and state-of-the-art image matching methods. The experimental results indicate that the proposed approach exhibits greater robustness and accuracy against variations in illumination. Full article