Search Results (136)

Additive manufacturing via stereolithography (SLA) enables the fabrication of highly customized lattice structures, yet the interplay between geometry and graded density in defining mechanical behavior remains underexplored. This research investigates the mechanical behavior and failure mechanisms of cylindrical lattice structures considering uniform, linear, and quadratic density variations. Various configurations, including IsoTruss, face-centered cubic (FCC)-type cells, Kelvin structures, and Tet oct vertex centroid, were examined under a complete factorial design that allowed a thorough exploration of the interactions between lattice geometry and density variation. A 3D printer working with SLA was used to fabricate the models. For the analysis, a universal testing machine, following ASTM D638-22 Type I and ASTM D1621-16 standards, was used for tension and compression tests. For microstructural analysis and surface inspection, a scanning electron microscope and a digital microscope were used, respectively. Results indicate that the IsoTruss configuration with linear density excelled remarkably, achieving an impressive energy absorption of approximately 15 MJ/m³ before a 44% strain, in addition to presenting the most outstanding mechanical properties, with a modulus of elasticity of 613.97 MPa, a yield stress of 22.646 MPa, and a maximum stress of 49.193 MPa. On the other hand, the FCC configuration exhibited the lowest properties, indicating lower stiffness and mechanical strength in compression, with an average modulus of elasticity of 156.42 MPa, a yield stress of 5.991 MPa, and the lowest maximum stress of 14.476 MPa. The failure modes, which vary significantly among configurations, demonstrate the substantial influence of the lattice structure and density distribution on structural integrity, ranging from localized bending in IsoTruss to spalling in FCC and shear patterns in Kelvin. This study emphasizes the importance of selecting fabrication parameters and structural design accurately. This not only optimizes the mechanical properties of additively manufactured parts but also provides essential insights for the development of new advanced materials. Overall, the study demonstrates that both lattice geometry and density distribution play a crucial role in determining the structural integrity of additively manufactured materials. Full article

Quantitative seismic characterization of transitional shale gas resources in the Da Ning–Ji Xian area, Ordos Basin, is severely hampered by complex coal-measure stratigraphy and rapid lithological variations. These challenges are critically exacerbated by severe signal attenuation from a thick loess overburden and multiple coal seams, which significantly degrades vertical resolution and undermines the reliability of quantitative interpretation. To surmount these obstacles, this study proposes an integrated, attenuation-centric inversion workflow that systematically rectifies attenuation effects as a foundational pre-conditioning step. The novelty of this study lies in establishing a systematic workflow where a data-driven, spatially variant Q-estimation is used as a crucial pre-conditioning step to guide a robust inverse Q-filtering, enabling a high-fidelity quantitative inversion for shale gas parameters in a geological setting with severe attenuation. The proposed workflow begins with a data-driven estimation of a spatially variant quality factor (Q) volume using the Local Centroid Frequency Shift (LCFS) method. This crucial Q-volume then guides a robust post-stack inverse Q-filtering process, engineered to restore high-frequency signal components and correct phase distortions, thereby substantially broadening the effective seismic bandwidth. With the seismic data now compensated for attenuation, high-resolution shale gas parameters, including Total Organic Carbon (TOC), are quantitatively derived through post-stack simultaneous inversion. Application of the workflow to field data yields an inverted volume characterized by improved structural clarity, sharply defined stratigraphic boundaries, and more robust lithological discrimination, highlighting its practical effectiveness. This attenuation-compensated inversion framework thus establishes a robust and transferable methodology for unlocking high-fidelity quantitative interpretation in geological settings previously deemed intractable due to severe seismic attenuation. Full article

The segmentation and classification of color are crucial stages in image processing, computer vision, and pattern recognition, as they significantly impact the results. The diverse, hand-labeled datasets in the literature are applied for monochromatic or color segmentation in specific domains. On the other hand, synthetic datasets are generated using statistics, artificial intelligence algorithms, or generative artificial intelligence (AI). This last one includes Large Language Models (LLMs), Generative Adversarial Neural Networks (GANs), and Variational Autoencoders (VAEs), among others. In this work, we propose VitralColor-12, a synthetic dataset for color classification and segmentation, comprising twelve colors: black, blue, brown, cyan, gray, green, orange, pink, purple, red, white, and yellow. VitralColor-12 addresses the limitations of color segmentation and classification datasets by leveraging the capabilities of LLMs, including adaptability, variability, copyright-free content, and lower-cost data—properties that are desirable in image datasets. VitralColor-12 includes pixel-level classification and segmentation maps. This makes the dataset broadly applicable and highly variable for a range of computer vision applications. VitralColor-12 utilizes GPT-5 and DALL·E 3 for generating stained-glass images. These images simplify the annotation process, since stained-glass images have isolated colors with distinct boundaries within the steel structure, which provide easy regions to label with a single color per region. Once we obtain the images, we use at least one hand-labeled centroid per color to automatically cluster all pixels based on Euclidean distance and morphological operations, including erosion and dilation. This process enables us to automatically label a classification dataset and generate segmentation maps. Our dataset comprises 910 images, organized into 70 generated images and 12 pixel segmentation maps—one for each color—which include 9,509,524 labeled pixels, 1,794,758 of which are unique. These annotated pixels are represented by RGB, HSL, CIELAB, and YCbCr values, enabling a detailed color analysis. Moreover, VitralColor-12 offers features that address gaps in public resources such as violin diagrams with the frequency of colors across images, histograms of channels per color, 3D color maps, descriptive statistics, and standardized metrics, such as ΔE76, ΔE94, and CIELAB Chromacity, which prove the distribution, applicability, and realistic perceptual structures, including warm, neutral, and cold colors, as well as the high contrast between black and white colors, offering meaningful perceptual clusters, reinforcing its utility for color segmentation and classification. Full article

Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management of bearings has become an important issue. Although deep learning has shown remarkable advantages, its performance still relies on the assumption that the training and testing data share the same distribution, which often deteriorates in real applications due to variations in load and rotational speed. This study focused on the scenario of domain generalization (DG) and proposed a Meta-Learning with Gradient Alignment and Data Augmentation (MGADA) method for cross-domain bearing fault diagnosis. Within the meta-learning framework, Mixup-based data augmentation was performed on the support set in the inner loop to alleviate overfitting under small-sample conditions and enhanced task-level data diversity. In the outer loop optimization stage, an arithmetic gradient alignment constraint was introduced to ensure consistent update directions across different source domains, thereby reducing cross-domain optimization conflicts. Meanwhile, a centroid convergence constraint was incorporated to enforce samples of the same class from different domains to converge to a shared centroid in the feature space, thus enhancing intra-class compactness and semantic consistency. Cross-working-condition experiments conducted on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves high classification accuracy across different target domains, with an average accuracy of 98.89%. Furthermore, ablation studies confirm the necessity of each module (Mixup, gradient alignment, and centroid convergence), while t-SNE and confusion matrix visualizations further illustrate that the proposed approach effectively achieves cross-domain feature alignment and intra-class aggregation. The proposed method provides an efficient and robust solution for bearing fault diagnosis under complex working conditions and offers new insights and theoretical references for promoting domain generalization in practical industrial applications. Full article

In Russia, Western Siberia, Anopheles from maculipennis subgroup comprises three vector species: An. messeae, An. daciae, An. beklemishevi, and the hybrid between An. messeae and An. daciae (Anopheles m-d), which exhibit complex cryptic morphological traits. Traditional morphological methods, such as egg morphology and exochorion coloration, have proven insufficient for reliably distinguishing these closely related species due to overlapping characteristics and high intra-species variability. To overcome these limitations, geometric morphometrics (GM) has emerged as a powerful tool for analyzing cryptic morphology. This article focuses on wing venation patterns, where GM provides precise, quantitative data based on defined anatomical landmarks, enabling detailed assessment of size and shape variation among species. Procrustes ANOVA, principal component analysis (PCA), and canonical variate analysis (CVA) were employed to assess shape variation and species differentiation. Centroid size and its relationship to shape variation were examined using multivariate regression. Despite significant morphological differences, the overlap observed in hybrids (An. m-d) reflects their intermediate position between the parental species. Our analyses revealed significant differences in wing shape and size among An. messeae, An. daciae, An. beklemishevi, and their hybrids, with hybrids showing intermediate morphologies. Landmarks on radial and medial veins were the most consistent contributors to species separation. No evidence of static allometry was detected, and wing shape differences were not explained by size. These findings demonstrate that wing morphometrics, combined with molecular identification, provides a reliable framework for species delimitation and surveillance of malaria vectors in temperate regions. Full article

The outburst of Zonag Lake in the permafrost region of the Qinghai-Tibet Plateau (QTP) has significantly altered the local environment, particularly affecting surface conditions and permafrost dynamics. By employing remote sensing and GIS tools, this study analyzed the spatial and temporal variations in surface environmental changes (surface temperature, vegetation, and dryness) within the Zonag–Salt Lake basin. The results indicate that the outburst caused higher surface temperatures and reduced vegetation cover around Zonag Lake. Analysis using the Temperature–Vegetation Dryness Index (TVDI) reveals higher dryness levels in downstream areas, especially from Kusai Lake to Salt Lake, compared to the upstream Zonag Lake. Temporal trends from 2000 to 2023 show a decrease in average Land Surface Temperature (LST) and an increase in the Normalized Difference Vegetation Index (NDVI). Geographical centroid shifts in environmental indices demonstrate migration patterns influenced by seasonal climate changes and the outburst event. Desertification around Zonag Lake accelerates permafrost development, while the wetting environment around Salt Lake promotes permafrost degradation. The Zonag Lake region is also an ecologically significant area, serving as a key calving ground for the Tibetan antelope (Pantholops hodgsonii), a nationally protected species. Thus, the environmental changes revealed in this study carry important implications for biodiversity conservation on the Tibetan Plateau. These findings highlight the profound impact of the Zonag Lake outburst on the surface environment and permafrost dynamics in the region, providing critical insights for understanding environmental responses to lake outbursts in high-altitude regions. Full article

Objectives: This study aimed to evaluate the reproducibility of artificial intelligence (AI) in identifying cephalometric landmarks, comparing its performance with manual tracing by an experienced orthodontist. Methods: A high-quality lateral cephalogram of a 26-year-old female patient, meeting strict inclusion criteria, was selected. Eighteen cephalometric landmarks were identified using the WebCeph software (version 1500) in three experimental settings: AI tracing without image modification (AInocut), AI tracing with image modification (AI-cut), and manual tracing by an orthodontic expert. Each evaluator repeated the procedure 10 times on the same image. X and Y coordinates were recorded, and reproducibility was assessed using the coefficient of variation (CV) and centroid distance analysis. Statistical comparisons were performed using one-way ANOVA and Bonferroni post hoc tests, with significance set at p < 0.05. Results: AInocut achieved the highest reproducibility, showing the lowest mean CV values. Both AI methods demonstrated greater consistency than manual tracing, particularly for landmarks such as Menton (Me) and Pogonion (Pog). Gonion (Go) showed the highest variability across all groups. Significant differences were found for the Posterior Nasal Spine (PNS) point (p = 0.001), where AI outperformed manual tracing. Variability was generally higher along the X-axis than the Y-axis. Conclusions: AI demonstrated superior reproducibility in cephalometric landmark identification compared to manual tracing by an experienced operator. While certain points showed high consistency, others—particularly PNS and Go—remained challenging. These findings support AI as a reliable adjunct in digital cephalometry, although the use of a single radiograph limits generalizability. Broader, multi-image studies are needed to confirm clinical applicability. Full article

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field weed management, presents a novel solution to the weed challenges in lily fields. The accurate localization of weed regions and the optimal selection of laser targeting points are crucial technologies for successful laser weeding implementation. In this study, we propose a two-stage weed detection and localization method specifically designed for lily fields. In the first stage, we introduce an enhanced detection model named YOLO-Morse, aimed at identifying and removing lily plants. YOLO-Morse is built upon the YOLOv8 architecture and integrates the RCS-MAS backbone, the SPD-Conv spatial enhancement module, and an adaptive focal loss function (ATFL) to enhance detection accuracy in conditions characterized by sample imbalance and complex backgrounds. Experimental results indicate that YOLO-morse achieves a mean Average Precision (mAP) of 86%, reflecting a 3.2% improvement over the original YOLOv8, and facilitates stable identification of lily regions. Subsequently, a ResNet-based segmentation network is employed to conduct semantic segmentation on the detected lily targets. The segmented results are utilized to mask the original lily areas in the image, thereby generating weed-only images for the subsequent stage. In the second stage, the original RGB field images are first converted into weed-only images by removing lily regions; these weed-only images are then analyzed in the HSV color space combined with morphological processing to precisely extract green weed regions. The centroid of the weed coordinate set is automatically determined as the laser targeting point.The proposed system exhibits superior performance in weed detection, achieving a Precision, Recall, and F1-score of 94.97%, 90.00%, and 92.42%, respectively. The proposed two-stage approach significantly enhances multi-weed detection performance in complex environments, improving detection accuracy while maintaining operational efficiency and cost-effectiveness. This method proposes a precise, efficient, and intelligent laser weeding solution for weed management in lily fields. Although certain limitations remain, such as environmental lighting variation, leaf occlusion, and computational resource constraints, the method still exhibits significant potential for broader application in other high-value crops. Full article

Understanding morphological variation and asymmetry in avian limbs provides essential insights into functional anatomy, locomotor behavior, and developmental stability. In this study, we investigated shape and size variation in the feet of quails (Coturnix coturnix) using two-dimensional geometric morphometric methods. A total of 233 animals were analyzed, representing both the left and right feet of male and female individuals. Nine homologous fixed landmarks were digitized on each foot, and configurations were subjected to Generalized Procrustes Analysis, followed by mirroring of right-side landmarks to ensure consistent orientation. Statistical analyses revealed no significant sexual dimorphism in either foot shape or centroid size. Principal Component Analysis indicated that the main shape variation was distributed individually rather than by sex and primarily affected the relative positions of toes and claws. Procrustes ANOVA confirmed that differences between sexes were not greater than expected by chance. Directional and fluctuating asymmetry were evaluated using a bilateral symmetry model to assess bilateral asymmetry. Directional asymmetry indicated consistent left–right differences, while fluctuating asymmetry reflected individual-level developmental instability and comprised the main source of variation. These findings provide a detailed morphological baseline for quail foot structure and highlight the importance of considering asymmetry in studies of avian functional morphology. The approach may also be a reference for future research into developmental stress, locomotor adaptation, or species-specific anatomical patterns. Full article

The survival status of Lumnitzera littorea is near threatened globally and critically endangered in China. Clarifying its global distribution pattern and its changing trends under different future climate models is of great significance for the protection and restoration of its endangered status. To build a model for this purpose, this study selected 73 actual distribution points of Lumnitzera littorea worldwide, combined with 12 environmental factors, and simulated its potential suitable habitats in six periods: the Last Interglacial (130,000–115,000 years ago), the Last Glacial Maximum (27,000–19,000 years ago), the Mid-Holocene (6000 years ago), the present (1970–2000), and the future 2050s (2041–2060) and 2070s (2061–2080). The results show that the optimal model parameter combination is the regularization multiplier RM = 4.0 and the feature combination FC (Feature class) = L (Linear) + Q (Quadratic) + P (Product). The MaxEnt model has a low omission rate and a more concise model structure. The AUC values in each period are between 0.981 and 0.985, indicating relatively high prediction accuracy. Min temperature of the coldest month, mean diurnal range, clay content, precipitation of the warmest quarter, and elevation are the dominant environmental factors affecting its distribution. The environmental conditions for min temperature of the coldest month at ≥19.6 °C, mean diurnal range at <7.66 °C, clay content at 34.14%, precipitation of the warmest quarter at ≥570.04 mm, and elevation at >1.39 m are conducive to Lumnitzera littorea’s survival and distribution. The global potential distribution areas are located along coasts. Starting from the paleoclimate, the plant’s distribution has gradually expanded, and its adaptability has gradually improved. In China, the range of potential highly suitable habitats is relatively narrow. Hainan Island is the core potential habitat, but there are fragmented areas in regions such as Guangdong, Guangxi, and Taiwan. The modern centroid of Lumnitzera littorea is located at (109.81° E, 2.56° N), and it will shift to (108.44° E, 3.22° N) in the later stage of the high-emission scenario (2070s (SSP585)). Under global warming trends, it has a tendency to migrate to higher latitudes. The development of the aquaculture industry and human deforestation has damaged the habitats of Lumnitzera littorea, and its population size has been sharply and continuously decreasing. The breeding and renewal system has collapsed, seed abortion and seedling establishment failure are common, and genetic variation is too scarce. This may indicate why Lumnitzera littorea is near threatened globally and critically endangered in China. Therefore, the protection and restoration strategies we propose are as follows: strengthen the legislative guarantee and law enforcement supervision of the native distribution areas of Lumnitzera littorea, expanding its population size outside the native environment, and explore measures to improve its seed germination rate, systematically collecting and introducing foreign germplasm resources to increase its genetic diversity. Full article

Climate change poses significant threats to urban tree health and survival worldwide. This study evaluates climate suitability risks for 12 common tree species in Wuhan, a Chinese metropolis facing escalating climate challenges. We analyzed risk dynamics and interspecific variations across three periods, the baseline (1981–2022), near future (2023–2050), and distant future (2051–2100), quantifying climate risk as differences between local climate conditions and species’ climatic niches. We further examined how species’ geographic distribution and functional traits influence these climate risks. The results revealed significant warming trends in Wuhan during the baseline period (p < 0.05), with projected increases in temperature and precipitation under future scenarios (p < 0.05). The most prominent risk factors included the precipitation of the driest month (PDM), annual mean temperature (AMT), and maximum temperature of the warmest month (MTWM), indicating intensifying drought–heat stress in this region. Among the studied species, Cedrus deodara (Roxb.) G. Don, Platanus acerifolia (Aiton) Willd., Metasequoia glyptostroboides Hu & W.C.Cheng, and Ginkgo biloba L. faced significantly higher hydrothermal risks (p < 0.05), whereas Koelreuteria bipinnata Franch. and Osmanthus fragrans (Thunb.) Lour. exhibited lower current risks but notable future risk increases (p < 0.05). Regarding the factors driving these interspecific variation patterns, the latitude of species’ distribution centroids showed significant negative correlations with the risk values of the minimum temperature of the coldest month (MTCM) (p < 0.05). Among functional traits, the wood density (WD) and xylem vulnerability threshold (P50) were negatively correlated with precipitation-related risks (p < 0.05), while the leaf dry matter content (LDMC) and specific leaf area (SLA) were positively associated with temperature-related risks (p < 0.05). These findings provide scientific foundations for developing climate-adaptive species selection and management strategies that enhance urban forest resilience under climate change in central China. Full article

Migratory flyways sustain waterbird populations by linking critical habitats across their annual cycle. However, stage-specific impacts of climate change on these habitats remain poorly understood. We integrated species distribution models with annual migration data from 30 Greater White-fronted Geese (Anser albifrons frontalis) to assess changes in habitat suitability, distributional shifts, and suitability fluctuations across breeding, stopover, and wintering stages under mid-century (2040–2060) climate scenarios. Suitability fluctuations were quantified as the coefficient of variation ($C_V$) in habitat suitability between current and future projections. Projected habitat responses varied markedly across stages: breeding areas contracted by 29.9%, wintering areas expanded by 62.7%, and stopover sites showed minimal net change. Centroids of all habitats are projected to shift northward by mean distances of 125–492 km under future climate scenarios. Breeding habitats exhibited the greatest suitability fluctuations ($C_V=3$0–45; ~50% area affected under SSP585), followed by stopover and wintering grounds ($C_V$ ≈ 11), with 35.8% and 23.3% of their areas falling within high-fluctuation zones. These findings highlight the urgent need to prioritize breeding habitats, implement stage-specific conservation strategies, and enhance international cooperation to ensure the protection of waterbirds along the East Asian Flyway. Full article

Geometric morphometrics is performed on different species in different contexts. Here, the aim was to investigate morphological differences in the head of the Sanmartinero Creole bovine to examine head shape variations between sexes using geometric morphometric methods. A sample of cranial pictures of 43 animals (13 males and 30 females) was obtained, and form (size + shape) was studied by means of geometric morphometric techniques using a set of 14 landmarks. This approach eliminated potential dietary effects, ensuring that the observed shape variations were primarily due to intrinsic morphological differences. Sexual dimorphism was found in form (for both size and shape) of the head of the Sanmartinero Creole bovine breed. Males had significantly larger heads based on centroid size (U = 714, p = 0.0004), confirming true sexual size differences, and Principal Component Analysis revealed overlapping head shapes with sexual dimorphism concentrated at midline sagittal landmarks (between the most rostral and caudal orbit points) and paired lateral points, indicating that males have broader and longer heads. The two evaluated characters (head size and shape) are of special interest for the conservation of the breed, especially in those cases whose objectives are to maintain the uniqueness, distinctiveness, and uniformity of the populations. This study analyzed animals subjected to the same feeding program, ensuring the elimination of additional variables. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

The precise identification of hydrocarbon-rich zones is crucial for optimizing exploration and production processes in the oil industry. Magnetotelluric (MT) surveys play a fundamental role in mapping subsurface geological structures. This study presents a novel methodology for automatically delineating resistivity contrasts in MT models by employing advanced machine learning and computer vision techniques. This approach commences with data augmentation to enhance the diversity and volume of resistivity data. Subsequently, a bilateral filter was applied to reduce noise while preserving edge details within the resistivity images. To further improve image contrast and highlight significant resistivity variations, contrast-limited adaptive histogram equalization (CLAHE) was employed. Finally, k-means clustering was utilized to segment the resistivity data into distinct groups based on resistivity values, enabling the identification of color features in different centroids. This facilitated the detection of regions with significant resistivity contrasts in the reservoir. From the clustered images, color masks were generated to visually differentiate the groups and calculate the area and proportion of each group within the pictures. Key features extracted from resistivity profiles were used to train unsupervised learning models capable of generalizing across different geological settings. The proposed methodology improves the accuracy of detecting zones with oil potential and offers scalable applicability to different datasets with minimal retraining, applicable to different subsurface environments. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. During initial analyses using only k-means, the resulting optimal value of the silhouette coefficient K was 2. After using bilateral filtering together with contrast-limited adaptive histogram equalization (CLAHE) and validation by an expert, the results were more representative, and six clusters were identified. Ultimately, this study seeks to improve the efficiency of petroleum exploration by providing a high-precision automated framework with segmentation and contrast delineation for resistivity analysis, integrating advanced image processing and machine learning techniques. Full article