Branching Characteristics Research Articles

Identification of New Cultivar and Different Provenances of Dendrocalamus brandisii (Poaceae: Bambusoideae) Using Simple Sequence Repeats Developed from the Whole Genome.

Dendrocalamus brandisii is a high-quality bamboo species that can be used for both bamboo shoots and wood. The nutritional components and flavors of D. brandisii vary from different geographical provenances. However, the unique biological characteristics of bamboo make morphological classification methods unsuitable for distinguishing them. Although the new cultivar 'Manxie No.1' has significant differences in the branch characteristics and the color of shoot sheaths compared to the D. brandisii, it still lacks precise genetic information at the molecular level. This study identified 231,789 microsatellite markers based on the whole genome of D. brandisii and analyzed their type composition and distribution on chromosomes in detail. Then, using TP-M13-SSR fluorescence-labeling technology, 34 pairs of polymorphic primers were screened to identify the new cultivar 'Manxie No.1' and 11 different geographical provenances of D. brandisii. We also constructed DNA fingerprinting profiles for them. At the same time, we mapped six polymorphic SSRs to the gene of D. brandisii, among which SSR673 was mapped to DhB10G011540, which is related to plant immunity. The specific markers selected in this study can rapidly identify the provenances and the new cultivar of D. brandisii and help explore candidate genes related to some important traits.

Analyzing the Anatomical Characteristics of the Facial Artery Branches: A Human Cadaveric Study

Analyzing the Anatomical Characteristics of the Facial Artery Branches: A Human Cadaveric Study

Classification and extraction method of hidden dangers along railway lines based on semantic segmentation network

ABSTRACT Foreign objects invading high-speed railway lines can cause danger. One existing solution is to use remote sensing images to analyse the dangerous areas along the railway line, thereby providing a certain amount of investigation time. Considering the spatial and temporal resolution characteristics of existing remote sensing technologies in identifying floating objects and the reality of rapid land use changes, this paper identifies areas on the ground where floating objects may be generated by using semantic segmentation techniques oriented to remotely sensed imagery and provides early warnings to staff along the route. However, these regions that need to be analysed have different semantics and scales. To address these challenges, this paper proposes a Dual-branch Parallel Fusion Network (DPFNet) based on Transformer, aimed at enhancing multi-class semantic segmentation in remote sensing images. To leverage global contextual information, we introduce a Swin Transformer-based backbone network, which employs self-attention to capture a comprehensive scene context, facilitating better segmentation by considering the entire scene’s context. For multi-scale semantic features, we propose one approach that involves independent branching feature expression and a Multi-scale Feature Space Fusion Module (MFSFM). The former enriches multi-scale information, while the latter fuses features across different levels to capture diverse semantic features. Experimental results demonstrate that DPFNet can effectively identify the hidden danger area, and the fusion of multi-scale features makes the network more accurately identify and segment the risk area of different sizes, improving the segmentation accuracy and robustness, and is of great significance to the formation of the ‘prevention’ as the core of the railway safety operation.

Предпосылки создания отраслевых индустриальных парков в Тульско-Новомосковской агломерации

The tendencies of the industrial parks formation in the Russian agglomerations are considers in the article. The branch and territorial features of industrial parks foundation in the Tula region are determined on basis of the experience study of the special economic zone (SEZ) «Uzlovaya». The positive influence of the manufacturing SEZ on the branch clusters in agglomerations is confirmed.

GLIC: Underwater target detection based on global–local information coupling and multi-scale feature fusion

With the rapid development of object detection technology, underwater object detection has attracted widespread attention. Most of the existing underwater target detection methods are built based on convolutional neural networks (CNNs), which still have some limitations in the utilization of global information and cannot fully capture the key information in the images. To overcome the challenge of insufficient global–local feature extraction, an underwater target detector (namely GLIC) based on global–local information coupling and multi-scale feature fusion is proposed in this paper. Our GLIC consists of three main components: spatial pyramid pooling, global–local information coupling, and multi-scale feature fusion. Firstly, we embed spatial pyramid pooling, which improves the robustness of the model while retaining more spatial information. Secondly, we design the feature pyramid network with global–local information coupling. The global context of the transformer branch and the local features of the CNN branch interact with each other to enhance the feature representation. Finally, we construct a Multi-scale Feature Fusion (MFF) module that utilizes balanced semantic features integrated at the same depth for multi-scale feature fusion. In this way, each resolution in the pyramid receives equal information from others, thus balancing the information flow and making the features more discriminative. As demonstrated in comprehensive experiments, our GLIC, respectively, achieves 88.46%, 87.51%, and 74.94% mAP on the URPC2019, URPC2020, and UDD datasets.

Nearshore optical video object detector based on temporal branch and spatial feature enhancement

The computing power of nearshore and ship-borne devices is limited, posing significant challenges for accurately detecting objects in real-time on such devices. We propose a nearshore video object detector (NVID) to tackle these challenges. Considering the abundance of dynamic entities in the nearshore environment, we have developed you can look more (YCLM) to perceive the temporal characteristics of these objects. Furthermore, to improve the ability to detect objects of different sizes of networks, we designed parallel deformable attention (PDA) based on the spatial features of objects. More importantly, we developed fast re-parameterization convolution (FREConv) and faster conv (FConv). Building on these innovations, we proposed a fast re-parameterization network (FRENet) specifically tailored to produce low-parameter, multi-scale feature outputs. With end-to-end training, our pipeline outperforms other state-of-the-art (SOTA) methods on the nearshore objects (NearshoreObjects) dataset (90.4 average precision (AP) 50 (＋4.7), 9.3 parameters (Params) (−1.0M), 24.8 frames per second (FPS) (Jetson Nano) (＋0.6)). In addition, NVID also achieved excellent results in the on board (OnBoard) dataset (90.3 AP50 (＋2.8), 9.3 params (−1.0M), 26.5 FPS (Jetson Nano) (＋0.8)). The source code can be accessed at https://github.com/Yuanlin-Zhao/NVID.

Characteristic of fluoroether gum via non-linear rheological approaches

Abstract Large amplitude oscillatory shear (LAOS) tests were conducted on four types of fluoroether elastomer gum, namely VPL-85540, VPL-75545, VPL-65455, and VPL-55540 in order to study their molecular weight, chain structure, and branching degree from the perspective of rubber rheology. For the setting of shear rate in varied LAOS measurements, frequency and angular velocity controlling modes were attempted and compared. Non-linear rheological parameters showed that VPL-65455 possessed significant polymeric branching characteristics. VPL-55540 possessed the lowest molecular weight, and VPL-75545 was detected with higher long-pendant monomer content in its polymer chain. Rheological properties concluded from the analysis of two shear rate controlling modes of LAOS tests exhibited great consistency, which confirmed the reliability of LAOS tests in the characterization of fluoroether gum.

AMTrack:Transformer tracking via action information and mix-frequency features

Nowadays, Transformer-based visual tracking algorithms have been developing quickly because of the self-attention mechanism of Transformer, which has the capability to model global information. Although the self-attention mechanism in Transformer can effectively capture long-range dependencies in feature space, they only use flattened two-dimensional features and are unable to capture long-range temporal dependencies. Furthermore, since the self-attention in Transformer functions as a low-pass filter, it picks up on low-frequency features of the target while ignoring high-frequency features. This research suggests a Transformer tracker based on action information and mix-frequency features (AMTrack) to address these problems. Specifically, to address the lack of temporal remote dependencies, we introduce the target action aware module and the target action offset module. The target action aware module sets up several pathways to extract spatio-temporal, channel, and motion feature independently. In contrast, the target action offset module computes the target’s offset information by computing relative feature maps. Furthermore, in order to address the imbalance between high and low frequency features, we propose a mix-frequency attention and multi-frequency self-attention convolutional block. The mix-frequency attention uses high-frequency features within partitioned local windows as input for the high-frequency branch and average-pooled low-frequency features as the input for the low-frequency branch, calculating attention scores in both branches respectively. The multi-frequency self-attention convolutional block uses self-attention to capture low-frequency features and convolution to capture high-frequency features. Extensive experiments are carried out on eight challenging tracking datasets (e.g., OTB100 (Object Tracking Benchmark 100), NFS (Need For Speed), UAV123 (Unmanned Aerial Vehicles 123), TC128 (Temple Color 128), VOT2018 (Visual Object Tracking 2018), LaSOT (Large-scale Single Object Tracking), TrackingNet (Tracking Network), GOT-10k (Generic Object Tracking-10k)), and the experimental results show that our tracker achieves excellent tracking performance when compared with several state-of-the-art tracking algorithms. The experimental results show that on LaSOT, the success rates AUC (Area Under Curve), PNorm, and P reach 65.8%, 69.2%, and 68.0%, respectively, where the AUC value is 2.1% higher than the baseline algorithm TrDiMP (Transformer Discriminative Model Prediction). On other datasets, our tracker also achieves excellent tracking performance.

Thermal convection in a Brinkman–Darcy and Kelvin–Voigt fluid of order one with coupled stresses effect

AbstractOur paper thoroughly examines a model for the thermal convection of specific viscoelastic fluids within a porous medium characterized by Brinkman–Darcy properties. These fluids demonstrate first‐order Kelvin–Voigt behavior as they permeate the medium. This investigation considers the influence of couple stresses on the system. We calculate the critical Rayleigh number required for linear instability theory, focusing on both stationary and oscillatory types of convection. Additionally, we conduct the nonlinear stability theory. In the latter cases, it is demonstrated that oscillatory convection can occur. A detailed investigation is conducted into the characteristics of both stationary and oscillatory convection branches, including the points at which the transition occurs.

Superficial temporal artery branching pattern in single stage auricular reconstruction: A novel classification

IntroductionThe two most severe complications of single-stage, porous polyethene microtia reconstruction are flap necrosis/framework exposure and frontal nerve paralysis. To reduce these risks, require a temporoparietal fascia (TPF) flap that includes both the parietal and frontal branches of the superficial temporal artery (STA) while sparing the nerve. We propose a classification that helps minimize said complications. Material and methodsFifty-five TPF flaps of 54 microtia patients who underwent single-stage auricular reconstruction from May 2018 to July 2021 were studied. Flaps were harvested using endoscopic techniques. The parietal and frontal branch characteristics and measurements were obtained using a microscope/endoscope. ResultsThe frontal artery might have 1 to 4 branches. If they were close to Pitanguy's line (≤5 mm), there would be a high risk of nerve damage. Parietal (P) and frontal (F) artery diameters <0.5 mm were risk factors for partial flap necrosis. Based on this observation, we proposed 0.5 mm as the diameter threshold to determine whether an arterial branch is hypoplasia or sufficient.From this study, a new classification of STA branching pattern was proposed with five types: PF1 (23.6 %), PF2 (43.6 %), pF1 (3.6 %), pF2 (12.8 %), and Pf (16.4 %); where P/F indicates sufficient branches, p/f indicates absent or hypoplasia ones, and the number indicates single or multiple frontal artery branching. ConclusionThe risk of flap necrosis and frontal nerve damage is due to abnormalities of the frontal artery of the STA in the TPF flap. Understanding the anatomical classification with clear visualization during flap harvest ensures a successful outcome.

Dual cross-enhancement network for highly accurate dichotomous image segmentation

The existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, such as salient, camouflaged, and meticulous objects, etc. However, the research of highly accurate Dichotomous Image Segmentation (DIS) combining these tasks has just started and still faces problems such as insufficient information interaction between layers and incomplete integration of high-level semantic information and low-level detailed features. In this paper, a new dual cross-enhancement network (DCENet) for highly accurate DIS is proposed, which mainly consists of two new modules: a cross-scaling guidance (CSG) module and a semantic cross-transplantation (SCT) module. Specifically, the CSG module adopts the adjacent-layer cross-scaling guidance method, which can efficiently interact with the multi-scale features of the adjacent layers extracted; the SCT module uses dual-branch features to complement each other. Moreover, in the way of transplantation, the high-level semantic information of the low-resolution branch is used to guide the low-level detail features of the high-resolution branch, and the features of different resolution branches are effectively fused. Finally, experimental results on the challenging DIS5K benchmark dataset show that the proposed network outperforms the 9 state-of-the-art (SOTA) networks in 5 widely used evaluation metrics. In addition, the ablation experiments also demonstrate the effectiveness of the cross-scaling guidance module and the semantic cross-transplantation module.

Semantic Segmentation of Point Cloud Scene via Multi-Scale Feature Aggregation and Adaptive Fusion

Point cloud semantic segmentation is a key step in 3D scene understanding and analysis. In recent years, deep learning–based point cloud semantic segmentation methods have received extensive attention from researchers. Multi-scale neighborhood feature learning methods are suitable for inhomogeneous density point clouds, but different scale branching feature learning increases the computational complexity and makes it difficult to accurately fuse different scale features to express local information. In this study, a point cloud semantic segmentation network based on RandLA-Net with multi-scale local feature aggregation and adaptive fusion is proposed. The designed structure can reduce computational complexity and accurately express local features. The mean intersection-over-union is improved by 1.1% on the SemanticKITTI data set with an inference speed of nine frames per second, while the mean intersection-over-union is improved by 0.9% on the S3DIS data set, compared with RandLA-Net. We also conduct ablation studies to validate the effectiveness of the proposed key structure.

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

Order of magnitude increase in power from flow-induced vibrations

Natural hydro environments such as rivers and ocean currents provide abundant and essentially free sources of kinetic energy. Although significant research has been devoted to harnessing this power through traditional turbine systems, there are also studies on the novel use of flow-induced vibration of a bluff body. The large oscillations of an elliptical cylinder resulting from the newly-discovered hyper branch have raised interesting questions about its potential as a source of effective renewable energy. This study investigates the power extraction characteristics of the hyper branch over a range of flow velocities. A factor-of-ten increase in the maximum time-mean power coefficient relative to an established flow-induced vibration system — the vortex induced vibration for aquatic clean energy converter is demonstrated. This suggests the possibility of a new and effective strategy to utilise flow-induced vibrations for energy generation and could help drive the commercial implementation and uptake of oscillating energy converters.

Low-light enhancement method with dual branch feature fusion and learnable regularized attention

Restricted by the lighting conditions, the images captured at night tend to suffer from color aberration, noise, and other unfavorable factors, making it difficult for subsequent vision-based applications. To solve this problem, we propose a two-stage size-controllable low-light enhancement method, named Dual Fusion Enhancement Net (DFEN). The whole algorithm is built on a double U-Net structure, implementing brightness adjustment and detail revision respectively. A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation. We also design a learnable regularized attention module to balance the enhancement effect on different regions. Besides, we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process. The proposed DFEN is tested on several low-light datasets, and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters. It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×1024 in an RTX 3090 GPU.Graphical

Neurosurgical Importance on Temporal Branch of the Facial Nerve.

We aimed to determine anatomic features of the temporal branch (TB) of the facial nerve and its relation to various anatomic landmarks on the face in order to prevent damage in the surgical approach after head trauma or in planned neurosurgical interventions. Nine male cadavers preserved with formalin, were bilaterally dissected under a microscope at the laboratory of anatomy department of the university. The anatomical features of the branch and branching pattern of facial nerve and its relationships with adjacent neurovascular structures were evaluated using descriptive statistical data. The mean distance at the lateral canthus level between the far most anterior branch and far most posterior branch of the TB (APD) was 14.93 mm. The mean distance to the tragus of the entrance point of the TB to the orbicularis oculi muscle (ETT) was 74.72 mm. The mean distance between the origin point of the TB to the tragus (TT) was 24.50 mm. The angle between the far most anterior branch and far most posterior branch at the level of the lateral canthus (APA) was minimum 15°, maximum 40° and the mean value of APA was 24.61°. The number of branches originating from the TB (NB) were one to three branches which lay under the zygomatic arch and two to five branches upper the zygomatic arch, respectively. The number of anastomosis between the TB and zygomatic branch (AN) varies between 1 and 4; AN was found to be less intense in 10 sides and very intense in eight sides. The APA and APD value is mentioned for the first time in this study and nerve angle is considered to be as important as temporal branching in directing facial neurosurgical operations. We found that the unsafety zone may be exist for a 40 degree and 1.5 cm long area at the lateral canthus level, especially on the zygomatic arc where branching pattern is greater and 2.5 cm in front of the tragus. The data is significant for neurosurgeons to reach exactly this area without causing postoperative complications such as facial paralysis.

Structure and position-aware graph neural network for airway labeling

We present a novel graph-based approach for labeling the anatomical branches of a given airway tree segmentation. The proposed method formulates airway labeling as a branch classification problem in the airway tree graph, where branch features are extracted using convolutional neural networks and enriched using graph neural networks. Our graph neural network is structure-aware by having each node aggregate information from its local neighbors and position-aware by encoding node positions in the graph.We evaluated the proposed method on 220 airway trees from subjects with various severity stages of Chronic Obstructive Pulmonary Disease (COPD). The results demonstrate that our approach is computationally efficient and significantly improves branch classification performance than the baseline method. The overall average accuracy of our method reaches 91.18% for labeling 18 segmental airway branches, compared to 83.83% obtained by the standard CNN method and 87.37% obtained by the existing method. Furthermore, the reader study done on an additional set of 40 subjects shows that our algorithm performs comparably to human experts in labeling segmental-airways. We published our source code at https://github.com/DIAGNijmegen/spgnn. The proposed algorithm is also publicly available at https://grand-challenge.org/algorithms/airway-anatomical-labeling/.

Crashworthiness analysis of bio-inspired hierarchical multi-cell circular tubes under axial crushing

Drawing inspiration from nature’s hierarchical organization, we propose a novel bio-inspired hierarchical multi-cellular circular tubes (BHMC) with a fractal structure. Using an experimentally validated LS-DYNA finite element model, we investigated the impact resistance of this structure under axial loading. Numerical analysis was conducted to explore the energy absorption performance of BHMC tubes with varying numbers of branches, fractal orders, and tube masses. The numerical findings indicate that specific energy absorption (SEA) increases with higher numbers of fractal orders. Specifically, the SEA of 2nd order BHMC tubes surpasses that of conventional multicellular circular and single-layer tubes by approximately 35.07% and 17.10%, respectively. Optimal performance is achieved with the proposed structure featuring tree branching (N = 6) and 2nd order fractal characteristics, yielding the highest specific absorption energy across different parameters. These results offer valuable insights for designing multicellular bilayer tubes with superior energy absorption efficiency, providing an effective guideline for structural optimization.

Boundary-Aware Gradient Operator Network for Medical Image Segmentation.

Medical image segmentation is a crucial task in computer-aided diagnosis. Although convolutional neural networks (CNNs) have made significant progress in the field of medical image segmentation, the convolution kernels of CNNs are optimized from random initialization without explicitly encoding gradient information, leading to a lack of specificity for certain features, such as blurred boundary features. Furthermore, the frequently applied down-sampling operation also loses the fine structural features in shallow layers. Therefore, we propose a boundary-aware gradient operator network (BG-Net) for medical image segmentation, in which the gradient convolution (GConv) and the boundary-aware mechanism (BAM) modules are developed to simulate image boundary features and the remote dependencies between channels. The GConv module transforms the gradient operator into a convolutional operation that can extract gradient features; it attempts to extract more features such as images boundaries and textures, thereby fully utilizing limited input to capture more features representing boundaries. In addition, the BAM can increase the amount of global contextual information while suppressing invalid information by focusing on feature dependencies and the weight ratios between channels. Thus, the boundary perception ability of BG-Net is improved. Finally, we use a multi-modal fusion mechanism to effectively fuse lightweight gradient convolution and U-shaped branch features into a multilevel feature, enabling global dependencies and low-level spatial details to be effectively captured in a shallower manner. We conduct extensive experiments on eight datasets that broadly cover medical images to evaluate the effectiveness of the proposed BG-Net. The experimental results demonstrate that BG-Net outperforms the state-of-the-art methods, particularly those focused on boundary segmentation.

BMCS-Net: A Bi-directional multi-scale cascaded segmentation network based on transformer-guided feature Aggregation for medical images

convolutional neural networks (CNNs) show great potential in medical image segmentation tasks, and can provide reliable basis for disease diagnosis and clinical research. However, CNNs exhibit general limitations on modeling explicit long-range relation, and existing cures, resorting to building deep encoders along with aggressive downsampling operations, leads to loss of localized details. Transformer has naturally excellent ability to model the global features and long-range correlations of the input information, which is strongly complementary to the inductive bias of CNNs. In this paper, a novel Bi-directional Multi-scale Cascaded Segmentation Network, BMCS-Net, is proposed to improve the performance of medical segmentation tasks by aggregating these features obtained from Transformers and CNNs branches. Specifically, a novel feature integration technique, termed as Two-stream Cascaded Feature Aggregation (TCFA) module, is designed to fuse features in two-stream branches, and solve the problem of gradual dilution of global information in the network. Besides, a Multi-Scale Expansion-Aware (MSEA) module based on the convolution of feature perception and expansion is introduced to capture context information, and further compensate for the loss of details. Extensive experiments demonstrated that BMCS-Net has an excellent performance on both skin and Polyp segmentation datasets.