Search Results (75)

Using equivalent transformations, complicated circuits in physics that need numerous mathematical operations to analyze can be broken down into simpler equivalent circuits. It is also possible to determine the number of spanning trees—graph families in particular—using these adjustments and utilizing our knowledge of difference equations, electrically equivalent transformations, and weighted generating function rules. In this paper, we derive the exact formulas for the number of spanning trees of sequences of new graph families created by a Johnson skeleton graph 63 and a few of its related graphs. Lastly, a comparison is made between our graphs’ entropy and other graphs of average degree four. Full article

In the present study, a new Ganoderma sp. (ZHM1939) was collected from Lincang, Yunnan, China, and described on the basis of morphological characters and multigene phylogenetic analysis of rDNA-ITS, TEF1α and RPB2 sequences. This fungus is characterized by the exceptionally large basidiomata, oval shape, a pileus measuring 63.86 cm long, 52.35 cm wide, and 21.63 cm thick, and a fresh weight of 80.51 kg. The skeleton hyphae from the basidiocarp are grayish to grayish-red in color, septate, and 1.41–2.75 μm in diameter, with frequently dichotomous branched and broadly ellipsoid basidiospores. The basidiospores are monocellular, ellipsoid, with round ends or one slightly pointed end, brown–gray in color, and measured 6.52–10.26 μm × 4.68–7.17 μm (n = 30). When cultured for 9 days at 25 ± 2 °C on PDA, the colony was white, ellipsoid or oval, with slightly ragged edges, measured Φ58.26 ± 3.05 mm (n = 5), and the growth rate = 6.47 mm/day; prosperous blast-spores formed after culturing for 21 days, making the colony surface powdery-white. The mycelia were septate, hyaline, branching at near-right angles, measured Φ1.28–3.32 μm (n = 30), and had some connections. The blast-spores were one-celled, elliptic or barley-seed shaped, and measured 6.52–10.26 μm × 4.68–7.17 μm (n = 30). Its rDNA-ITS, TEF1α and RPB2 sequences amplified through PCR were 602 bp, 550 bp and 729 bp, respectively. Blast-n comparison with these sequences showed that ZHM1939 was 99.67–100% identical to related strains of Ganoderma mutabile. A maximum likelihood phylogenic tree using the concatenated sequence of rDNA-ITS, TEF1α and RPB2 was constructed and it showed that ZHM1939 clustered on the same terminal branch of the phylogenic tree with the strains Cui1718 and YUAN 2289 of G. mutabile (Bootstrap support = 100%). ZHM1939 could grow on all the 15 original inoculum substrates tested, among which the best growth was shown on substrate 2 (cornmeal 40 g, sucrose 10 g, agar 20 g), with the fastest colony growth rate (6.79 mm/day). Of the five propagation substrates tested, substrate 1 (wheat grains 500 g, gypsum powder 6.5 g and calcium carbonate 2 g) resulted in the highest mycelium growth rate (7.78 mm/day). Among the six cultivation substrates tested, ZHM1939 grew best in substrate 2 (cottonseed hulls 75 g, rice bran 12 g, tree leaves 5 g, cornmeal 5 g, lime powder 1 g, sucrose 1 g and red soil 1 g) with a mycelium growth rate of 7.64 mm/day. In conclusion, ZHM1939 was identified as Ganoderma mutabile, which is a huge mushroom and rare medicinal macrofungus resource. The original inoculum substrate 9, propagation substrate 1 and cultivation substrate 2 were the most optimal substrates for producing the original propagation and cultivation inocula of this macrofungus. This is the first report on successful growing conditions for mycelial production, but basidiocarp production could not be achieved. The results of the present work establish a scientific foundation for further studies, resource protection and application development of G. mutabile. Full article

The growing demand for real-time human motion reconstruction in Virtual Reality (VR), Augmented Reality (AR), and the Metaverse requires high accuracy with minimal hardware. This paper presents SSR-HMR, a skeleton-aware, sparse node-based method for full-body motion reconstruction from limited inputs. The approach incorporates a lightweight spatiotemporal graph convolutional module, a torso pose refinement design to mitigate orientation drift, and kinematic tree-based optimization to enhance end-effector positioning accuracy. Smooth motion transitions are achieved via a multi-scale velocity loss. Experiments demonstrate that SSR-HMR achieves high-accuracy reconstruction, with mean joint and end-effector position errors of 1.06 cm and 0.52 cm, respectively, while operating at 267 FPS on a CPU. Full article

Coffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed. This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices. Full article

The skeleton curve plays a crucial role in evaluating the seismic capacity of damaged structures. The research explored the application of data-driven machine learning approaches to predict the skeleton curves of earthquake-damaged reinforced concrete (RC) columns. Various machine learning methods, including Lasso regression, K-nearest neighbor (KNN), support vector machine (SVM), decision tree, and AdaBoost, were employed to develop a machine learning prediction model (MLPM) for seismic-damaged RC columns. A substantial dataset for the MLPM was derived from finite element (FE) analysis results. The input parameters for the machine learning models included the design specifications of the numerical column model and the damage index (DI), while the coordinates of key points on the skeleton curves served as the output parameters. The findings indicated that the K-nearest neighbor algorithm exhibited the best predictive performance, particularly for the yielding and peak points. The most influential input feature for predicting peak strength was the shear span-to-effective depth ratio, followed by the DI. The ML-based models demonstrated higher efficiency than numerical simulations and theoretical calculations in predicting the skeleton curves of damaged RC columns. Full article

The accurate measurement of a crack width in concrete infrastructure is essential for structural safety assessment and maintenance. However, existing image-based methods either suffer from overestimation in complex geometries or are computationally inefficient. This paper proposes a novel hybrid approach combining a fast skeleton-pruning algorithm and a crack-width measurement technique called edge-OrthoBoundary (EOB). The skeleton-pruning algorithm prunes the skeleton, viewed as the longest branch in a tree structure, using a depth-first search (DFS) approach. Additionally, an intersection removal algorithm based on dilation replaces the midpoint circle algorithm to segment the crack skeleton into computable parts. The EOB method combines the OrthoBoundary and edge shortest distance (ESD) techniques, effectively correcting the propagation direction of the skeleton points while accounting for their width. The validation of real cracks shows the skeleton-pruning algorithm’s effectiveness, eliminating the need for a specified threshold and reducing time complexity. Experimental results with both actual and synthetic cracks demonstrate that the EOB method achieves the smallest RMS, MAE, and R values, confirming its accuracy and stability compared to the orthogonal projection (OP), OrthoBoundary, and ESD methods. Full article

Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods. Full article

This entry gives an overview of the main data structures and approaches used for a two-dimensional representation of the terrain surface using a digital elevation model (DEM). A DEM represents the elevation of the earth surface from a set of points. It is used for terrain analysis, visualisation and interpretation. DEMs are most commonly defined as a grid where an elevation is assigned to each grid cell. Due to its simplicity, the square grid structure is the most common DEM structure. However, it is less adaptive and shows limitations for more complex processing and reasoning. Hence, the triangulated irregular network is a more adaptive structure and explicitly stores the relationships between the points. Other topological structures (contour graphs, contour trees) have been developed to study terrain morphology. Topological relationships are captured in another structure, the surface network (SN), composed of critical points (peaks, pits, saddles) and critical lines (thalweg, ridge lines). The SN can be computed using either a TIN or a grid. The Morse Theory provides a mathematical approach to studying the topology of surfaces, which is applied to the SN. It has been used for terrain simplification, multi-resolution modelling, terrain segmentation and landform identification. The extended surface network (ESN) extends the classical SN by integrating both the surface and the drainage networks. The ESN can itself be extended for the cognitive representation of the terrain based on saliences (typical points, lines and regions) and skeleton lines (linking critical points), while capturing the context of the appearance of landforms using topo-contexts. Full article

The segmentation of jujube tree branches and the estimation of primary branch inclination angles (IAs) are crucial for achieving intelligent pruning. This study presents a primary branch IA estimation algorithm for jujube trees based on an improved PointNet++ network. Firstly, terrestrial laser scanners (TLSs) are used to acquire jujube tree point clouds, followed by preprocessing to construct a point cloud dataset containing open center shape (OCS) and main trunk shape (MTS) jujube trees. Subsequently, the Chebyshev graph convolution module (CGCM) is integrated into PointNet++ to enhance its feature extraction capability, and the DBSCAN algorithm is optimized to perform instance segmentation of primary branch point clouds. Finally, the generalized rotational symmetry axis (ROSA) algorithm is used to extract the primary branch skeleton, from which the IAs are estimated using weighted principal component analysis (PCA) with dynamic window adjustment. The experimental results show that compared to PointNet++, the improved network achieved increases of 1.3, 1.47, and 3.33% in accuracy (Acc), class average accuracy (CAA), and mean intersection over union (mIoU), respectively. The correlation coefficients between the primary branch IAs and their estimated values for OCS and MTS jujube trees were 0.958 and 0.935, with root mean square errors of 2.38° and 4.94°, respectively. In summary, the proposed method achieves accurate jujube tree primary branch segmentation and IA measurement, providing a foundation for intelligent pruning. Full article

The primary objective of this research was to develop an efficient method for accurately identifying and localizing weeds in ecological tea garden environments, aiming to enhance the quality and yield of tea production. Weed competition poses a significant challenge to tea production, particularly due to the small size of weed plants, their color similarity to tea trees, and the complexity of their growth environment. A dataset comprising 5366 high-definition images of weeds in tea gardens has been compiled to address this challenge. An enhanced U-Net model, incorporating a Double Attention Mechanism and an Atrous Spatial Pyramid Pooling module, is proposed for weed recognition. The results of the ablation experiments show that the model significantly improves the recognition accuracy and the Mean Intersection over Union (MIoU), which are enhanced by 4.08% and 5.22%, respectively. In addition, to meet the demand for precise weed management, a method for determining the center of weed plants by integrating the center of mass and skeleton structure has been developed. The skeleton was extracted through a preprocessing step and a refinement algorithm, and the relative positional relationship between the intersection point of the skeleton and the center of mass was cleverly utilized to achieve up to 82% localization accuracy. These results provide technical support for the research and development of intelligent weeding equipment for tea gardens, which helps to maintain the ecology of tea gardens and improve production efficiency and also provides a reference for weed management in other natural ecological environments. Full article

Tree structural information is essential for studying forest ecosystem functions, driving mechanisms, and global change response mechanisms. Although current terrestrial laser scanning (TLS) can acquire high-precision 3D structural information of forests, mutual occlusion between trees, the scanner’s field of view, and terrain changes make the point clouds captured by laser scanning sensors incomplete, further hindering downstream tasks. This study proposes a skeleton-embedded tree point cloud completion method, termed SK-TreePCN, which recovers complete individual tree point clouds from incomplete scanning data in the field. SK-TreePCN employs a transformer trained on simulated point clouds generated by a 3D radiative transfer model. Unlike existing point cloud completion algorithms designed for regular shapes and simple structures, the SK-TreePCN method addresses structurally heterogeneous trees. The 3D radiative transfer model LESS, which can simulate various TLS data over highly heterogeneous scenes, is employed to generate massive point clouds with training labels. Among the various point cloud completion methods evaluated, SK-TreePCN exhibits outstanding performance regarding the Chamfer distance (CD) and $F1$ Score. The generated point clouds display a more natural appearance and clearer branches. The accuracy of tree height and diameter at breast height extracted from the recovered point cloud achieved ${\mathit{R}}^2$ values of 0.929 and 0.904, respectively. SK-TreePCN demonstrates applicability and robustness in recovering individual tree point clouds. It demonstrated great potential for TLS-based field measurements of trees, refining point cloud 3D reconstruction and tree information extraction and reducing field data collection labor while retaining satisfactory data quality. Full article

In recent decades, a large number of houses from the Late Yangshao period have been excavated in Zhengzhou. They are basically single-level buildings with wood skeletons and mud walls and use a huge amount of timber resources. Nevertheless, there are still a lot of questions about the uncertain relationship between plants, architecture, and human activities. In this study, we complete the reconstruction of a Holocene vegetation community around the Dahecun site via pollen analysis of the Z2 core. We take house F1 in Dahecun as an example to estimate the wood consumption of a single house and collect the published data of all houses from the Late Yangshao period in the study area to estimate the wood consumption of houses built in Zhengzhou during this period. Combining the above two approaches, this study explores the relationship between plants, architecture, and human activities in Zhengzhou in the Late Yangshao period, as well as the history of land use. The results are as follows: (1) After 4.9 ka BP, the number of trees and shrubs such as Pinus (falling from 58.8% to 46.9%) decreased rapidly, and the number of herbaceous plants increased. (2) Excluding the influence of the Holocene climate change, the large-scale decline in trees and shrubs in the region is likely to have been human-driven. The number of excavated houses in 11 of the 236 Late Yangshao sites in the Zhengzhou area reached 362, while the minimum wood consumption reached 1270.62 m³. In addition, the rapid expansion of the population size and the large-scale development of new arable land and forest clearance in the Late Yangshao period show that humans had a strong influence on the surrounding vegetation and land cover/use. The trend of regional deforestation was so obvious and irreversible that the inhabitants had to adopt techniques using less wood or no wood to build houses during the subsequent Longshan culture period. Full article

Endolichenic fungi represent an important ecological group of microorganisms that form associations with photobionts in the lichen thallus. These endofungi that live in and coevolve with lichens are known for synthesizing secondary metabolites with novel structures and diverse chemical skeletons making them an unexplored microbial community of great interest. As part of our search for new phytoprotectants, in this work, we studied the endolichenic fungus Xylaria sp. isolated from the lichen Hypogymnia tubulosa, which grows as an epiphyte on the bark of the endemic Canarian tree Pinus canariensis. From the extract of the liquid fermentation, we isolated two unreported piliformic derivatives, (+)-9-hydroxypiliformic acid (1) and (+)-8-hydroxypiliformic acid (2), along with four previously reported compounds, (+)-piliformic acid (3), hexylaconitic acid A anhydride (4), 2-hydroxyphenylacetic acid (5), and 4-hydroxyphenylacetic acid (6). Their structures were elucidated based on NMR and HRESIMS data. The extract and the isolated compounds were tested for their insect antifeedant (Myzus persicae, Rhopalosiphum padi, and Spodoptera littoralis), antifungal (Alternaria alternata, Botrytis cinerea, and Fusarium oxysporum), nematicidal (Meloidogyne javanica), and phytotoxic effects on mono- and dicotyledonous plant models (Lolium perenne and Lactuca sativa). Compounds 4, 5, and 6 were effective antifeedants against M. persicae and 4 was also active against R. padi. Moreover, 3 and 4 showed antifungal activity against B. cinerea and 4 was the only nematicidal. The extract had a strong phytotoxic effect on L. sativa and L. perenne growth, with compounds 3, 4, and 5 identified as the phytotoxic agents, while at low concentrations compounds 3 and 4 stimulated L. sativa root growth. Full article

Toll-like receptor 4 (TLR4) has been implicated in the production of uncontrolled inflammation within the body, known as the cytokine storm. Studies that employ machine learning (ML) in the prediction of potential inhibitors of TLR4 are limited. This study introduces AICpred, a robust, free, user-friendly, and easily accessible machine learning-based web application for predicting inhibitors against TLR4 by targeting the TLR4-myeloid differentiation primary response 88 (MyD88) interaction. MyD88 is a crucial adaptor protein in the TLR4-induced hyper-inflammation pathway. Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. To handle imbalance within the training data, resampling techniques such as random under-sampling, synthetic minority oversampling technique, and the random selection of 5000 instances of the majority class were employed. A 10-fold cross-validation strategy was used to evaluate model performance based on metrics including accuracy, balanced accuracy, and recall. The XGBoost model demonstrated superior performance with accuracy, balanced accuracy, and recall scores of 0.994, 0.958, and 0.917, respectively, on the test. The AdaBoost and decision tree models also excelled with accuracies ranging from 0.981 to 0.992, balanced accuracies between 0.921 and 0.944, and recall scores between 0.845 and 0.891 on both training and test datasets. The XGBoost model was deployed as AICpred and was used to screen compounds that have been reported to have positive effects on mitigating the hyperinflammation-associated cytokine storm, which is a key factor in COVID-19. The models predicted Baricitinib, Ibrutinib, Nezulcitinib, MCC950, and Acalabrutinib as anti-TLR4 compounds with prediction probability above 0.90. Additionally, compounds known to inhibit TLR4, including TAK-242 (Resatorvid) and benzisothiazole derivative (M62812), were predicted as bioactive agents within the applicability domain with probabilities above 0.80. Computationally inferred compounds using AICpred can be explored as potential starting skeletons for therapeutic agents against hyperinflammation. These predictions must be consolidated with experimental screening to enhance further optimisation of the compounds. AICpred is the first of its kind targeting the inhibition of TLR4-MyD88 binding and is freely available at http://197.255.126.13:8080. Full article

The design and preparation of advanced hybrid nanofibers with controllable microstructures will be interesting because of their potential high-efficiency applications in the environmental and energy domains. In this paper, a simple and efficient strategy was developed for preparing hybrid nanofibers of zinc oxide–molybdenum disulfide (ZnO–MoS₂) grown on polyimide (PI) nanofibers by combining electrospinning, a high-pressure hydrothermal process, and in situ growth. Unlike simple composite nanoparticles, the structure is shown in PI–ZnO to be like the skeleton of a tree for the growth of MoS₂ “leaves” as macro-materials with controlled microstructures. The surface morphology, structure, composition, and photocatalytic properties of these structures were characterized using scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and UV–vis spectroscopy. The ultra high-volume fraction of MoS₂ can be grown on the brush-shaped PI–ZnO. Decorating ZnO with nanosheets of MoS₂ (a transition metal dichalcogenide with a relatively narrow band gap) is a promising way to increase the photocatalytic activity of ZnO. The hybrid nanofibers exhibited high photocatalytic properties, which decomposed about 92% of the methylene blue in 90 min under visible light irradiation. The combination of MoS₂ and ZnO with more abundant surface-active sites significantly increases the spectral absorption range, promotes the separation and migration of carriers, and improves the photocatalytic characteristics. Full article