Tree Geometry Research Articles

Impact of Tree Geometry on the Growth and Yield Performance of Black Gram (Vigna mungo L.) under Kadam (Neolamarckia cadamba Roxb.) based Agroforestry System

Agroforestry is an environmentally conscious and comprehensive method of land management that entails cultivating agricultural crops, forest trees, and animals in conjunction on the same parcel of land through the implementation of suitable management strategies. The present study entitled “Impact of tree geometry on the growth and yield performance of Black Gram (Vigna mungo L.) under Kadam (Neolamarckia cadamba Roxb.) based Agroforestry System” was conducted during the Kharif season of 2022-23 within an already established 2-year-old plantation situated at Forestry research farm, RLBCAU, Jhansi. The study encompasses three different spacings, i.e., S1- 5 m x 5 m, S2- 5 m x 4 m and S3- 5 m x 3m. A randomized block design having four treatments each replicated five times was laid out, Outcomes of research revealed that sole cropping of black gram exhibited higher plant height (77.44 cm), leaf and branch numbers (6.38 and 5.32, respectively), dry matter accumulation (17.05 g/plant for above-ground and 2.12 g/plant for below-ground), leaf area (2183.06 cm2), root nodules (11.54), shorter phenological duration viz., 50% flowering, 50% pod setting and maturity (40.11 days, 54.49 days, and 70.84 days, respectively). It also demonstrated significantly higher yield attributes such as number of pods and seeds per pod (27.28), number of seeds per pod (5.82), test weight (36.73 g), grain yield (0.79 t ha-1), straw yield (1.78 t ha-1), biological yield (2.57 t ha-1), harvest index (30.63 %), and grain-to-straw ratio (0.44) compared to the intercropped. Among all the agroforestry treatments Neolamarckia cadamba at 5 m × 5 m with Vigna mungo, found to perform better than closer spacing (i.e., 5 m x 4 m and 5 m x 3 m) spatial arrangements. The study encountered challenges in achieving an optimal balance between tree-crop interactions and resource competition, which are critical factors in agroforestry systems. Nevertheless, it provides significant contributions by offering insights into the optimization of tree geometry, enhancing both yield performance and the sustainability of such integrated systems.

A Critical Analysis of the CFD-DEM Simulation of Pharmaceutical Aerosols Deposition in Upper Intra-Thoracic Airways: Considerations on Aerosol Transport and Deposition.

The reliability and accuracy of numerical models and computer simulations to study aerosol deposition in the human respiratory system is investigated for a patient-specific tracheobronchial tree geometry. A computational fluid dynamics (CFD) model coupled with discrete elements methods (DEM) is used to predict the transport and deposition of the aerosol. The results are compared to experimental and numerical data available in the literature to study and quantify the impact of the modeling parameters and numerical assumptions. Even if the total deposition compares very well with the reference data, it is clear from the present work how local deposition results can depend significantly upon spatial discretization and boundary conditions adopted to represent the respiratory act. The modeling of turbulent fluctuations in the airflow is also found to impact the local deposition and, to a minor extent, the flow characteristics at the inlet of the computational domain. Using the CFD-DEM model, it was also possible to calculate the airflow and particles splitting at bifurcations, which were found to depart from the assumption of being equally distributed among branches adopted by some of the simplified deposition models. The results thus suggest the need for further studies towards improving the quantitative prediction of aerosol transport and deposition in the human airways.

Aerosol dosimetry in the whole conducting zone of a murine left-lung using CF-PD and LSFM images

Aerosol dosimetry in respiratory airways is relevant for pulmonary drug delivery and inhalation toxicology. Consequently, computational fluid-particle dynamics (CF-PD) modelling of pulmonary aerosol delivery is an active research field. Additionally, mice are the most commonly used animals in medical research. Technological advances have provided information on whole mice lung morphologies with unprecedented high resolution. Therefore, in this study, we used high-resolution light sheet fluorescent microscopy (LSFM) images of a healthy C57BL/6 mouse lung with a constant air flow rate of 72 ml/min, to extract an anatomical 3-dimensional (3D) geometry of the entire airway tree of the left lung from the primary bronchi to the most distal bronchioles excluding the trachea. The airways were segmented based on an order- and generation-based method. Also, to compare the morphological data and regional deposition, a generation-based investigation including 25 generations was employed in the present model. One-way coupling of CF-PD modeling was applied to model an intubated and mechanically-ventilated mouse. Maximum values of the velocity and vorticity magnitude of 3.2 m/s and 200,000 1/s were reached in the second order, respectively, and maximum pressure and wall shear stress levels were 30 Pa and 3.5 Pa, respectively. Finally, order- and generation-based particle deposition efficiency and dose per lung area were obtained for the particle size range of 1 μm ≤ dp ≤ 10 μm yielding pronounced hotspot deposition patterns mainly near the proximal bifurcations. The results showed a positive correlation between deposition efficiency and particle size due to a size-dependent increase in inertial and gravitational effects. Maximum regional deposition and normalized dose was seen for 10 μm particles in the 1st order of the murine left lung. Smaller peak sizes of deposition efficiency were seen in the third and fourth orders of the mouse left lung due to almost complete loss of the largest particles in lower order airways. It also justifies the close to zero deposition efficiency in the highest orders (fifth to sixth). Both lung morphology as well as total and regional aerosol deposition showed reasonably good agreement with empirical data from the literature. The present CF-PD model with accurate realistic lung morphology, improves our knowledge of airway aerosol deposition hotspots. The obtained modeling method and the qualitative results can be implemented on human airways.

Development of a Precise Tree Structure from LiDAR Point Clouds

Abstract. A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%.

Realistic reconstruction of trees from sparse images in volumetric space

The realistic reconstruction of real-world trees is a challenging task in the community of computer graphics because natural trees have complex structures of branches and leaves. Existing terrestrial laser scanning (TLS) system is able to capture dense and precise tree point clouds, yet the TLS system is expensive and not easy to carry around. An alternative low-cost and portable way is the reconstruction of tree point cloud from multiple view images. However, it is usually difficult to reconstruct a complete tree point cloud because of the texture similarity of branches and leaves as well as the lack of a sufficient number of images. Thus, we propose a new approach for reconstructing tree point clouds and geometries from sparse images. We first infer the camera parameters of each image, and then calculate the bounding volume of a tree from the camera parameters. Next, we set the mask of each image and the resolution of voxel, and then project each voxel in 3D space to all the mask images to determine the validity of the voxel. To alleviate the miss deletion of valid voxel, we utilize a boundary threshold and adjust mask resolution for robust point cloud reconstruction. Finally, an efficient tree reconstruction method is proposed to generate plausible tree geometries. We tested 6 different tree species that contain deciduous and evergreen trees, and the results showed that our approach is able to generate complete tree point cloud and realistic tree models even from a few number of images.

Integrated drag coefficient formula for estimating the wave attenuation capacity of Rhizophora sp. mangrove forests

Recently, bulk drag coefficient (C˜D) formulations used to quantify wave energy dissipation by Rhizophora mangroves were developed from laboratory data; however, these formulations have not yet been validated with field data. Additionally, due to the complex geometry of mangrove trees within forests and spatial variability, common criteria for determining the adequate geometric characteristics of mangrove forests are lacking and are required to obtain accurate definitions for C˜D. This paper addresses these knowledge gaps by proposing a newly integrated C˜D formulation based on the comprehensive characterization of a Rhizophora mangle forest combined with wave measurements in field, and by using numerical modeling for the calibration process. The field campaign consisted of 23 continuous days of recorded wave data and spatial distribution observations of the geometric characteristics of the mangrove forest. The variation in frontal area per unit height per square meter (Ahm) along the mangrove forest was reported for three zones with different densities identified along the study transect, with decreasing root density from the vegetation edge to the forest interior. On average, the incident wave height decreased by 34% at 63 m in mangrove forests, and the wave attenuation ratios (r) varied between 0.001 and 0.01 m-1. To estimate the C˜D values associated with these wave height attenuation ratios, the Simulating Waves Nearshore (SWAN) numerical model was used to calibrate the model results with the field observations. The variation in the tree frontal area along the mangrove forest and the wave conditions at the site are considered during the calibration process. To further characterize C˜D for this type of mangrove species, the C˜D values acquired from the calibration together with the values reported in the literature from laboratory experiments are presented as a function of the Keulegan-Carpenter number (KC). Root diameter is defined as the characteristic length according to the inherent geometric characteristics of a Rhizophora sp. forest. The new formulation allows us to predictably estimate C˜D values that can be used as inputs in drag force-based models to estimate the attenuation of wave energy produced by Rhizophora sp. forests.

Umbel convexity and the geometry of trees

For every p∈(0,∞), a new metric invariant called umbel p-convexity is introduced. The asymptotic notion of umbel convexity captures the geometry of countably branching trees, much in the same way as Markov convexity, the local invariant which inspired it, captures the geometry of bounded degree trees. Umbel convexity is used to provide a “Poincaré-type” metric characterization of the class of Banach spaces that admit an equivalent norm with Rolewicz's property (β). We explain how a relaxation of umbel p-convexity, called infrasup-umbel p-convexity, plays a role in obtaining compression rate bounds for coarse embeddings of countably branching trees. Local analogues of these invariants - fork p-convexity and infrasup-fork p-convexity - are introduced, and their relationship to Markov p-convexity and relaxations of the p-fork inequality is discussed. The metric invariants are estimated for a large class of Heisenberg groups, and in particular a parallelogram p-convexity inequality is proved for Heisenberg groups over p-uniformly convex Banach spaces. Finally, a new characterization of non-negative curvature is given.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of “cell tree age,” it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

Should we care about the level of detail in trees when running urban microscale simulations?

Due to lack of information and long geometry generation times, tree geometries are usually oversimplified or even ignored in Computational Fluid Dynamic (CFD) simulations that predict wind and pollutant dispersion in urban areas. Nevertheless, trees are known to impact local wind patterns and air quality levels. Thus, in this paper we explore the effects that tree models automatically reconstructed at diverse Level of Detail (LoD) (1, 2 and 3) have in numerical wind predictions. We address this by comparing the non-dimensional velocity magnitude differences between simulations with multiple tree LoDs. To further understand these differences in changing environmental contexts we use three morphologies: an isolated tree, an idealized street, canyon, and a real urban geometry from Rotterdam, The Netherlands The numerical results show that the velocity magnitude differences between the cases with LoD1 tree models and those with LoD2 tree models can be over 1.0m/s while the differences between LoD2 and LoD3 cases are rather limited, usually lower than 0.2m/s. Consequently, through this study we highlight the importance of using tree models in LoD2 or LoD3 at least for CFD simulations of wind flows in urban areas. To further support this conclusion we also analyze the impact of changing wind directions and tree Leaf Area Density (LAD) values in the impact of tree LoDs on wind. The differences found in this work linked to the level of realism in your tree models can support future studies where researchers want to make an informed choice.

Design and fabrication of a three-dimensional asymmetric neonate lung model to study surfactant transport in airways

Neonatal respiratory distress syndrome is a potentially life-threatening condition that is often treated with the delivery of exogenous surfactants through a process called surfactant replacement therapy. This therapy includes the administration of the liquid surfactant through an endotracheal tube and mechanical ventilation. Due to the difficulty of imaging neonate lungs during this therapy, the success of surfactant delivery is often determined by observational techniques and evaluation of blood oxygen levels. The limitation of imaging creates challenges in evaluating the distribution of surfactant in airways. To address this limitation, we designed a computational, eight-generation, asymmetric neonate lung model using morphometric data to mimic the geometric structure of the human airway tree and fabricated it using an additive manufacturing technique. We used our model to study two-aliquot delivery of a clinically rated liquid surfactant under two different orientations to evaluate its distribution in airways. Our study offers a complex lung airway tree design that mimics the native geometry of the human airway tree to enable studies of therapeutics transport in airways.

Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario.

We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.

Modeling of the 3D Tree Skeleton using Real-World Data: A Survey.

Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The rapid development of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. To summarize the most relevant contributions, these have been classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, which is also divided into three groups (thinning, clustering and spanning tree refinement), and (3) image-based modeling. In addition, we describe other approaches aimed at improving the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.

Grow‐contact simulation of hybrid joints for living architectures

AbstractHybrid joints consist of living trees and industrial materials are the solutions to realize the living architectures. However, the mechanism of grow‐contact‐swallow behavior remains ambiguous. Some computational graphical methods have struggled to generate the tree geometry. However, physical features such as stiffness were not considered. In this paper, we proposed a grow‐contact simulation method for describing the swallowing process to generate hybrid joints. Our temporal evolution approach is implemented using the Non‐Uniform Rational B‐Spline curves, and the genetic algorithms. We formulate the grow‐contact process as a solution for optimization problem that leads to the state with minimal total generalized potential energy. We conclude that the simulation results explain 4 general contact behaviors, concerning stiffness, and the age of the tree as the mechanical metrics. Especially for swallowing behavior, the three‐dimensional geometry prediction shows potential for the design of hybrid joints for living architectures.

Modal properties of fractal trees as recursive analytical solutions

This study analyzes the modal properties of two-dimensional fractal trees under the assumptions of linear-elastic vibrations and axially inextensible branches. The examined fractal trees are of a generalized sympodial architecture with possibly non-zero and non-uniform branching angles. The study first presents the construction of tree geometry via iterated functions and then formulates the trees’ dynamic matrices from a renormalization viewpoint. The study proposes a novel recursive analytical solution to capture the trees’ modal properties. The proposed solution perceives the modal analysis of the fractal trees as an equivalent static analysis of trees with a modified configuration. These “modified” trees are made from the original trees but with all branches connected to the ground via negative stiffness springs. The analysis shows that identifying the lateral trunk displacements of the modified trees (under a static unit force) is associated with the recursive construction of a new family of functions that are introduced in the study, called the auxiliary P-functions. The results reveal that the modal frequencies of the original fractal trees are given by the roots of all auxiliary P-functions. Crucially, these auxiliary P-functions allow the graphical construction of the trees’ mode shapes. Illustrative examples demonstrate how the proposed recursive analytical solutions can be used to determine the trees’ modal properties. Lastly, the study concludes that the repetition of branches, even without geometrical symmetry, is a necessary condition for the existence of a unique vibration mitigation mechanism in fractal trees, namely the self-similar modes.

Latent L-systems: Transformer-based Tree Generator

We show how a Transformer can encode hierarchical tree-like string structures by introducing a new deep learning-based framework for generating 3D biological tree models represented as Lindenmayer system (L-system) strings. L-systems are string-rewriting procedural systems that encode tree topology and geometry. L-systems are efficient, but creating the production rules is one of the most critical problems precluding their usage in practice. We substitute the procedural rules creation with a deep neural model. Instead of writing the rules, we train a deep neural model that produces the output strings. We train our model on 155k tree geometries that are encoded as L-strings, de-parameterized, and converted to a hierarchy of linear sequences corresponding to branches. An end-to-end deep learning model with an attention mechanism then learns the distributions of geometric operations and branches from the input, effectively replacing the L-system rewriting rule generation. The trained deep model generates new L-strings representing 3D tree models in the same way L-systems do by providing the starting string. Our model allows for the generation of a wide variety of new trees, and the deep model agrees with the input by 93.7% in branching angles, 97.2% in branch lengths, and 92.3% in an extracted list of geometric features. We also validate the generated trees using perceptual metrics showing 97% agreement with input geometric models.

Tree Architecture & Blockchain Integration: An off-the-shelf Experimental Approach

Temporally sensitive tree modeling and urban park spatially explicit simulation offer advantages to large-scale landscape planning and design, especially in the context of smart applications for virtual parks and forests, while Blockchain technology provides collaborative engineering, data integrity, and information confidence. A proof-of-concept 2.5D tree architecture and Blockchain integration technique (distributed Internet-of-Trees images, “IoTr-images”) was presented as a low-cost metaverse case study that affects the forest monitoring and digital landscape architecture design infrastructures. At the core of the proposed feature-based parametric modeling methodology is a 2.5D tree CAD model composed of two perpendicular 2D tree frames on which recorded tree texture has been assigned. A “Batch command-line programming” technique has been implemented, as a user-defined routine at the top of a commercial CAD platform, to describe the proposed off-the-self method and to create tangible tree-image NFT tokens (Internet-of-Trees-images Blockchain). As important findings were recorded, the add-in planning intelligence, the superior data integrity, and confidence, the offline relaxed error-free CAD design, and the superiority in terms of time and cost compared to traditional 3D tree modeling methods (laser scanning, close-range photogrammetry, etc.); as well as the satisfactory tree modeling accuracy for smart forest monitoring and landscape architecture applications. The proposed 2.5D parametric tree model added new value to the CAD-Blockchain integration industry because a plain “Blockchain/Merkle hash tree” tracks tree geometry growth and texture change temporarily with simple parametric transactions (i.e. controlled hash tree magnification/scaling). So, metaverse functionality (decentralized, autonomous, coordinated, and parallel design; same-data sharing; data validation), modification and redesign ability, and planning intelligence are effectively supported by the proposed technique. Main contributions are regarded as the ability for smart forest distributed surveillance and collaborative parallel landscape architecture design, open-source Web-based educational simulations, as well as the potential for off-the-shelf contractual collaborative frameworks (smart contracts between designers and clients). Stratification based on forest types improved above-ground biomass (AGB) estimation, especially when AGB was greater than 500 Mg/ha, using the proposed “IoTr-images” technique. So, this research provides new insight into AGB modeling and monitoring. Finally, the proposed method’s robustness has been validated by performance evaluation testing.

A novel simulation model to predict photosynthetic active radiation interception in micro-irrigated citrus production orchards based on tree spacing, canopy geometry, and row orientation

Diurnal and seasonal patterns of photosynthetically active radiation (PAR) intercepted by tree canopies in production orchards have an important role for eco-physiological processes that determine tree water use and productivity. Under well-watered conditions, PAR intercepted by trees is a reliable predictor of crop consumptive water use, which is regulated by tree spacing, tree row orientation, and by the tree canopy dimensions and geometry. Despite its advantages, only rare attempts have been made by the scientific and fruit production communities to develop canopy PAR interception simulators that rely on analytical principles to inform irrigation planning and management decisions. In this article, a novel model is presented that simulates PAR interception by citrus trees based on the canopy geometry and its shading patterns. The article also illustrates preliminary field testing and validation of the model with data collected in commercial citrus production orchards located in the San Joaquin Valley of California. The testing and validation could enable citrus growers and orchard managers to predict the net radiation for mature, micro-irrigated Navel orange and Page mandarin orchards grown with north–south and east–west tree row orientations and different tree densities. Results from the simulations and field validation showed that the canopy PAR light interception contributed 58.8 (± 0.02) and 71.1 (± 1.60) percent to the overall net radiation for the Navel orange orchards during the periods around summer solstice and autumnal equinox, respectively, whereas for the Page mandarin orchards, PAR light interception contributed 63.0 (±5.60) and 85.7 (±18.87) percent to the overall net radiation during the same time periods. Good correlations were found between daily values of PAR and net radiation for these citrus production orchards grown with the two alternative row orientations, which suggest that the proposed model could be used to inform irrigation planning and management decisions for different site-specific citrus orchard features and growing conditions.

Using Green Atlas Cartographer to investigate orchard-specific relationships between tree geometry, fruit number, fruit clustering, fruit size and fruit colour in commercial apples and pears

Using Green Atlas <i>Cartographer</i> to investigate orchard-specific relationships between tree geometry, fruit number, fruit clustering, fruit size and fruit colour in commercial apples and pears

Hierarchical Fine Extraction Method of Street Tree Information from Mobile LiDAR Point Cloud Data

The classification and extraction of street tree geometry information in road scenes is crucial in urban forest biomass statistics and road safety. To address the problem of 3D fine extraction of street trees in complex road scenes, this paper designs and investigates a method for extracting street tree geometry and forest parameters from vehicle-mounted LiDAR point clouds in road scenes based on a Gaussian distributed regional growth algorithm and Voronoi range constraints. Firstly, a large number of non-tree and other noise points, such as ground points, buildings, shrubs and vehicle points, are filtered by applying multi-geometric features; then, the main trunk of the street tree is extracted based on the vertical linear features of the tree and the region growth algorithm based on Gaussian distribution; secondly, a Voronoi polygon constraint is established to segment the single tree canopy region with the main trunk center of mass; finally, based on the extracted locations of the street trees and their 3D points, the tree growth parameters of individual trees are obtained for informative management and biomass estimation by combining geometric statistical methods. In this paper, the experimental data from vehicle-borne LiDAR point clouds of different typical areas were selected to verify that the proposed Gaussian-distributed regional growth algorithm can achieve fine classification and extraction of tree growth parameters for different types of roadside trees, with accuracy, recall and F1 values reaching 96.34%, 97.22% and 96.45%, respectively. This research method can be used for the extraction of 3D fine classification of street trees in complex road environments, which in turn can provide support for the safety management of traffic facilities and forest biomass estimation in urban environments.

Biology, Physics and Genetics of Intracranial Aneurysm Formation: A Review.

Intracranial aneurysms (IAs) are persistent, localised dilatations of the arterial wall that are found in approximately 3% of the general population. The most severe complication of IAs is rupture, which results in devastating consequences such as subarachnoid haemorrhage and brain damage with serious neurological sequelae. Numerous studies have characterised the mechanisms underlying IA development and growth and identified a number of environmental modifiable (smoking, hypertension) and nonmodifiable risk factors (related to the histology of cerebral arteries and genetic factors) in its pathogenesis. Haemodynamic stress also likely plays a crucial role in the formation of IAs and is conditioned by the geometry and morphology of the vessel tree, but its role in the natural history of unruptured IAs remains poorly understood; it is believed that changes in blood flow might generate the haemodynamic forces that are responsible for damage to the vascular wall and vessel remodelling that lead to IA formation. This review summarises the most relevant data on the current theories on the formation of IAs, with particular emphasis on the roles of special conditions resulting from the microscopic anatomy of intracranial arteries, haemodynamic factors, bifurcation morphometry, inflammatory pathways, and the genetic factors involved in IA formation.