Spatial Structure Research Articles

Helicase HELQ: Molecular Characters Fit for DSB Repair Function.

The protein sequence and spatial structure of DNA helicase HELQ are highly conserved, spanning from archaea to humans. Aside from its helicase activity, which is based on DNA binding and translocation, it has also been recently reconfirmed that human HELQ possesses DNA-strand-annealing activity, similar to that of the archaeal HELQ homolog StoHjm. These biochemical functions play an important role in regulating various double-strand break (DSB) repair pathways, as well as multiple steps in different DSB repair processes. HELQ primarily facilitates repair in end-resection-dependent DSB repair pathways, such as homologous recombination (HR), single-strand annealing (SSA), microhomology-mediated end joining (MMEJ), as well as the sub-pathways' synthesis-dependent strand annealing (SDSA) and break-induced replication (BIR) within HR. The biochemical functions of HELQ are significant in end resection and its downstream pathways, such as strand invasion, DNA synthesis, and gene conversion. Different biochemical activities are required to support DSB repair at various stages. This review focuses on the functional studies of the biochemical roles of HELQ during different stages of diverse DSB repair pathways.

Geostatistical Simulation of Daily Rainfall Fields - Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé river basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like Ordinary Kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Analysis of the spatial association network structure of China’s green innovation efficiency

Improving the green innovation efficiency (GIE) is the main approach to high-quality development in China. Identifying the spatial distribution and influencing factors of GIE is conducive to sustainable and balanced regional development. In this research, the slack based measure-data envelopment analysis model (SBM-DEA) is used to measure the GIE of 30 provinces in China from 2013 to 2023. The gravity model and social network analysis methods are used to explore the spatial correlation of GIE in the provinces. The influencing factors of GIE are analyzed by the Tobit model. The results show that: (1) During the study period, the GIE of China’s provinces presented a high level, the spatial accumulation characteristics were obvious and the spatial correlation showed a complex network structure; (2) The association network is still in the primary stage, and the network strength and network association degree are low; (3) Provinces with strong innovation ability do not affect the green innovation of other provinces. In addition, these regions have obtained higher innovation linkage benefits from other provinces; (4) The amount of patent, education and government support have a significant positive impact on GIE, while total energy consumption and the secondary industry’s share of the gross domestic product have a significant negative impact on GIE. This study will reveal the characteristics of green innovation behavior of China from a new perspective, and provide a scientific basis for the formulation of green innovation behavior and related policies of China.

The Changsha historic urban area: a study on the evolution characteristics and influencing factors of the connectivity of construction land

This study aims to understand the connectivity characteristics of construction land during the spatial evolution of historic urban areas. The complex network analysis method is employed to examine the evolution of the spatial structure of construction land in the four periods of the Changsha historic urban area. The results indicate that the spatial network morphology of construction land follows a repeated development pattern resembling a cluster–mother–child relationship. Additionally, the vulnerability in the evolution of land use spatial relationships is very low, while the overall stability of the land use network structure decreases. The spatial equilibrium of land use shows a downwards trend, the accessibility of land where existing cultural relics and historic sites are located is generally moderate to low, and some plots require improvement. In the evolution of connectivity in the construction land of historic urban areas, the main development challenges are the uneven growth caused by imbalanced land value influenced by capital and the need to balance the preservation of land use patterns with improved accessibility to cultural relics and historic sites. Therefore, it is highly important to dynamically detect and optimize the network structure of construction land to protect and develop historic urban areas.

Pseudomonas aeruginosa surface motility and invasion into competing communities enhance interspecies antagonism.

The polymicrobial nature of many chronic infections makes their eradication challenging. Particularly, coisolation of Pseudomonas aeruginosa and Staphylococcus aureus from airways of people with cystic fibrosis and chronic wound infections is common and associated with severe clinical outcomes. The complex interplay between these pathogens is not fully understood, highlighting the need for continued research to improve management of chronic infections. Our study unveils that P. aeruginosa is attracted to S. aureus, invades into neighboring colonies, and secretes anti-staphylococcal factors into the interior of the colony. Upon inhibition of P. aeruginosa motility and thus invasion, S. aureus colony architecture changes dramatically, whereby S. aureus is protected from P. aeruginosa antagonism and responds through physiological alterations that may further hamper treatment. These studies reinforce accumulating evidence that spatial structuring can dictate community resilience and reveal that motility and chemotaxis are critical drivers of interspecies competition.

Experimental study on coherent structures by small particles suspended in high aspect-ratio (Γ=\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Gamma =$$\\end{document} 2.5) thermocapillary liquid bridges of high Prandtl number

In time-dependent traveling-wave convection, it has been confirmed that small particles of low Stokes number (St≪\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{St}\\ll$$\\end{document} 1) form three-dimensional closed structures known as the particle accumulation structures (PASs). We focus on coherent structures formed in high aspect-ratio thermocapillary liquid bridges (Γ=\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Gamma =$$\\end{document} 2.5), where oscillatory convection due to the so-called hydrothermal wave instability of mHTW=\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m_{\ extrm{HTW}}=$$\\end{document} 1 stably emerges, and carry out experimental explorations into the volume ratio dependence of the emergence of coherent structures. Variation in the volume ratio induces the presence of coherent structures with different azimuthal wave numbers, that is, different spatial structures. Among the particles added to the liquid bridge, we perform spatio-temporal tracking of particles to quantify the correlation between the behavior of particles and the spatial structures of coherent structures with different rational wave numbers.

G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design.

A fundamental computer vision task called semantic segmentation has significant uses in the understanding of medical pictures, including the segmentation of tumors in the brain. The G-Shaped Net architecture appears in this context as an innovative and promising design that combines components from many models to attain improved accuracy and efficiency. In order to improve efficiency, the G-Shaped Net architecture synergistically incorporates four fundamental components: the Self-Attention, Squeeze Excitation, Fusion, and Spatial Pyramid Pooling block structures. These factors work together to improve the precision and effectiveness of brain tumor segmentation. Self-Attention, a crucial component of G-Shaped architecture, gives the model the ability to concentrate on the image's most informative areas, enabling accurate localization of tumor boundaries. By adjusting channel-wise feature maps, Squeeze Excitation completes this by improving the model's capacity to capture fine-grained information in the medical pictures. Since the G-Shaped model's Spatial Pyramid Pooling component provides multi-scale contextual information, the model is capable of handling tumors of various sizes and complexity levels. Additionally, the Fusion block architectures combine characteristics from many sources, enabling a thorough comprehension of the image and improving the segmentation outcomes. The G-Shaped Net architecture is an asset for medical imaging and diagnostics and represents a substantial development in semantic segmentation, which is needed more and more for accurate brain tumor segmentation.

Early warning signals for bifurcations embedded in high dimensions

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about some properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.

The Photophysics and Photochemistry of Phenylalanine, Tyrosine, and Tryptophan: A CASSCF/CASPT2 Study.

Among all amino acids, the three aromatic systems including phenylalanine, tyrosine, and tryptophan are known to manifest UV light absorption at the higher wavelengths. Their fluorescent properties are important for protein detection and determination of the spatial structure. Based on ab initio quantum chemical calculations, the absorption spectra in the 0-12 eV UV range of these aromatic amino acids were interpreted, and the photochemistry of the lowest-lying excited states was studied. For that, molecular ground-state geometries were determined using both the coupled-cluster single and double (CCSD) and complete active space self-consistent field (CASSCF) methods. The vertical electronic transition energies, associated oscillator strengths, and electric dipole moments of the lowest excited states were computed at the CASPT2//CASSCF level. The decay mechanisms of the lowest-energy excited states were investigated by performing minimum energy path (MEPs) computations. The results showed that the CCSD and CASSCF computations yielded similar structures. The lowest-energy S1 state in phenylalanine and tyrosine, and S1 and S2 in tryptophan are mainly described by the π → π* excitation localized in the aromatic ring. Upon light absorption, the main photoresponse of the molecules is driven by the benzene, phenol, and indole chromophoric units.

A novel mutation Trp-2027-Gly in acetyl-CoA carboxylase confers resistance to cyhalofop-butyl in Chinese sprangletop (Leptochloa chinensis).

Chinese sprangletop [Leptochloa chinensis (L.) Nees] control is threatened by resistance to acetyl-CoA carboxylase (ACCase)-inhibiting herbicides. In this study, a L. chinensis population, HFLJ18, that survived cyhalofop-butyl [aryloxyphenoxypropionate (APP) herbicide, CyB] treatment was collected from a rice field in Lujiang County, Anhui Province, China. This study aimed to evaluate the susceptibility of HFLJ18 to herbicides with different modes-of-action and investigate the potential mechanisms of resistance to CyB. The HFLJ18 population exhibited high levels of resistance to CyB (10.92-fold) and showed resistance to the ACCase inhibitors metamifop (4.63-fold) and fenoxaprop-P-ethyl (8.39-fold), but was susceptible to clethodim, pinoxaden, florpyrauxifen-benzyl, oxadiazon and pretilachlor. Target gene sequencing revealed a novel Trp-to-Gly substitution at codon position 2027 of ACCase in the resistant plants. Molecular docking revealed that the spatial structure of ACCase changed significantly following the substitution, as indicated by reduced H-bonds. A newly derived cleaved amplified polymorphic sequence (dCAPS) marker was subsequently developed to detect the Trp-2027-Gly mutation in the ACCase of L. chinensis. Additionally, pretreatment with the cytochrome P450 (P450) inhibitor piperonyl butoxide (PBO) and the glutathione S-transferase (GST) inhibitor 4-chloro-7-nitrobenzoxadiazole (NBD-Cl) did not reverse resistance to CyB, suggesting that nontarget-site resistance mechanisms were not involved in CyB resistance in the HFLJ18 population. Overall, the resistance to CyB in the HFLJ18 population derived from the mutation of ACCase gene, and to the best of our knowledge, this is the first report of the ACCase Trp-2027-Gly mutation conferring resistance to ACCase-inhibiting herbicides in grass species. © 2024 Society of Chemical Industry.

Dynamic changes of food environment: In and out of COVID-19 pandemic

The outbreak of the COVID-19 pandemic has precipitated food crises worldwide, prompting a re-examination of the resilience of the urban food environment. While previous research on the urban food environment has predominantly focused on Western contexts, scant attention has been given to China. This study takes Shenzhen, China as an example to establish a food environment evaluation framework centered on accessibility, diversity, and healthiness factors, aiming to analyze the dynamic changes of the food environment during normal and pandemic periods. By using the GA optimization algorithm, some convenience stores are transformed into self-pickup points (SPPs), which is expected to eliminate the deserts risk areas (DRAs) with low cost and high efficiency. The findings reveal a distinctive “center-periphery” spatial structure characterizing the food environment in Shenzhen, and the improvement of healthiness plays a crucial role in sustaining food oases and ameliorating food swamps. This research provides methods for improving the resilience of the food environment during the pandemic across diverse nations, bolstering the security of urban lifeline systems.

Flexible Modeling of Nonstationary Extremal Dependence using Spatially-Fused LASSO and Ridge Penalties

Statistical modeling of a nonstationary spatial extremal dependence structure is challenging. Parametric max-stable processes (MSPs) are common choices for modeling spatially-indexed block maxima, where an assumption of stationarity is usual to make inference feasible. However, this assumption is unrealistic for data observed over a large or complex domain. We develop a computationally-efficient method for estimating extremal dependence using a globally nonstationary but locally-stationary MSP construction, with the spatial domain divided into a fine grid of subregions, each with its own dependence parameters. We use LASSO (L 1) or ridge (L 2) penalties to obtain spatially-smooth parameter estimates. We then develop a novel data-driven algorithm to merge homogeneous neighboring subregions. The algorithm facilitates model parsimony and interpretability. To make our model suitable for high-dimensional data, we exploit a pairwise likelihood to perform inference and discuss its computational and statistical efficiency. We apply our proposed method to model monthly maximum temperature data at over 1400 sites in Nepal and the surrounding Himalayan and sub-Himalayan regions; we show significant improvements in model fit compared to a stationary model. Furthermore, we demonstrate that the estimated merged partition is interpretable from a geographic perspective and leads to better model diagnostics by adequately reducing the number of parameters.

Multi-dimensional attention-aided transposed ConvBiLSTM network for hyperspectral image super-resolution

Hyperspectral (HS) image always suffers from the deficiency of low spatial resolution, compared with conventional optical image types, which has limited its further applications in remote sensing areas. Therefore, HS image super-resolution (SR) techniques are broadly employed in order to observe finer spatial structures while preserving the spectra of ground covers. In this paper, a novel multi-dimensional attention-aided transposed convolutional long-short term memory (LSTM) network is proposed for single HS image super-resolution task. The proposed network employs the convolutional bi-directional LSTM for the purpose of local and non-local spatial–spectral feature explorations, and transposed convolution for the purpose of image amplification and reconstruction. Moreover, a multi-dimensional attention module is proposed, aiming to capture the salient features on spectral, channel, and spatial dimensions, simultaneously, to further improve the learning abilities of network. Experiments on four commonly-used HS images demonstrate the effectiveness of this approach, compared with several state-of-the-art deep learning-based SR methods.

Enhancement of sludge dewaterability using combined technology of bioleaching and Fenton: Microscopic structure and hydrophilic/hydrophobic properties of sludge particles

Bioleaching and Fenton technology are commonly used preconditioning techniques for sludge dewatering. This study compared the dewatering mechanisms of different conditioning technologies. The results showed that bound water, specific resistance to filtration (SRF), and capillary suction time decreased from 3.95 g/g, 6.16 × 1012 m/kg, and 130.6 s to 3.15 g/g, 2.81 × 1011 m/kg, and 33 s, respectively, under combined treatment condition. Moreover, the free radicals, including ·OH, O2−·and Fe (Ⅳ), further damaged the cell structure, thus increasing the concentration of DNA in the S-EPS layer. This intense degradation sludge particle size decreased by 15.6% and significantly increased zeta potential. Under the combined technology, the α-helix and β-sheet decreased by 42.2% and 56.5%, respectively, destabilizing the spatial structure of proteins and promoting the release of bound water. In addition, the combined technology decreased (Ala/Lys) ratio in the TB-EPS layer by 67.6%, indicating the weakening of protein water-holding capacity. Moreover, the conversion of oxygen-containing compounds to nonpolar hydrocarbons increased the hydrophobicity of the sludge under a combined treatment, thus enhancing dewatering performance.

3D printing for constructing biocarriers using sodium alginate/ε-poly-l-lysine ink: Enhancing microbial enrichment for efficient nitrogen removal in wastewater

The microbial enrichment of traditional biocarriers is limited due to the inadequate consideration of spatial structure and surface charging characteristics. Here, capitalizing on the ability of 3D printing technology to fabricate high-resolution materials, we further designed a positively charged sodium alginate/ε-poly-l-lysine (SA/ε-PL) printing ink, and the 3D printed biocarriers with ideal pore structure and rich positive charge were constructed to enhance the microbial enrichment. The rheological and mechanical tests confirmed that the developed SA/ε-PL ink could simultaneously satisfy the smooth extrusion for printing process and the maintenance of 3D structure. The utilization of the ε-PL secondary cross-linking strategy reinforced the 3D mechanical structure and imparted the requisite physical properties for its application as a biocarrier. Compared with traditional sponge carriers, 3D printed biocarrier had a faster initial attachment rate and a higher biomass of 14.58 ± 1.18 VS/cm3, and the nitrogen removal efficiency increased by 53.9 %. Besides, due to the superior electrochemical properties and biocompatibility, the 3D printed biocarriers effectively enriched the electroactive denitrifying bacteria genus Trichococcus, thus supporting its excellent denitrification performance. This study provided novel insights into the development of new functional biocarriers in the wastewater treatment, thereby providing scientific guidance for practical engineering.

Resilience of regional container port network: based on projection correlation and dynamic spatial Markov Chain

ABSTRACT Ports are interconnected by shipping routes within the global logistics network. Shocks and risks stemming from financial crises, epidemics, ecology, and so on have prompted shifts in cross-border labor division towards regional cooperation, reflecting an ‘anti-globalization’ trend. Despite these dynamics, few studies have analyzed the spatial structure and dynamic evolution of resilience within regional container port network. Addressing this gap, this study considers spatial and dynamic attributes to identify influential factors of resilience and introduces the concept of spatial lag-multidimensional resistance (SL-MR) to strengthen the understanding and cognition of regional container port network resilience (RCPNR). By ports in Tianjin and Hebei, China, it constructs a dynamic spatial Markov chain (DSMC) based on the projection correlation (PC index) to dissect transition trajectories amid financial crises and other conflicts. The findings indicate: (1) The regional container port network often experiences a low-level path dependence like ‘poverty trap.’ (2) Hierarchical spatial lags significantly influence the transition probability of container port network resilience degradation, with regional port relationships being unidirectional rather than cooperative. (3) The impact of the 2008 financial crisis and the COVID-19 pandemic on regional port resilience varies, serving as key determinants in shaping diverse resilience types.

Exhaustive Enumeration of Spatial Prime Structures

Prime structures are link chains with 0 DoF (degrees of freedom), not including subchains with 0 or fewer DoF, which are expected to be used in systematic kinematic and dynamic analyses of link mechanisms. This paper describes the exhaustive enumeration of spatial prime structures with three–five links. There will be more types of spatial prime structures than planar prime structures due to the variety in the DoF of kinematic pairs and the existence of prime structures with idle DoF. In the enumeration, the graphs which represent the connection of links and pairs are used. The vertices of the graphs represent the links of structures, and the edges represent the kinematic pairs. To consider the pair DoF, weights are set on edges. First, the numbers of pairs are calculated using Grübler’s equation. Second, the graphs corresponding to structures are enumerated, without considering pair DoF, and the isomorphic graphs and other inappropriate graphs are eliminated. Then, the combinations of pair DoF arrangement are enumerated, and the isomorphic graphs and graphs which correspond to non-prime structures are eliminated. Finally, prime structures with idle DoF are considered. As a result, 3, 13, and 97 kinds of spatial prime structures for 3, 4, and 5 links, respectively, are obtained.

Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

Study on the stressing state features of basalt fibre concrete lining structure under sulphate erosion

Basalt fibre (BF) is an environmentally friendly material with excellent corrosion resistance. When tunnels cross gypsum rock strata, the gypsum rock will dissolve sulphate and erode the supporting structure, thus affecting the safe operation of the tunnels in the long term. Adding BF to the supporting structure of gypsum rock tunnels can improve its resistance to sulphate erosion. This paper took Wuzhishan Tunnel as a case, and first determined the sulphate concentration of the gypsum rock stratigraphic environment at the site; then, analysed the change of mechanical properties of basalt fibre concrete (BFC) before and after sulphate erosion by mechanical tests; finally, examined the deformation and force characteristics of the BFC secondary lining structure by numerical simulation. The results showed that the bridging effect of BF fibres and the stable spatial network structure formed inside the concrete enhanced the mechanical properties of BFC and its ability to resist sulphate erosion. However, due to the agglomeration effect of BF fibres, too much BF would reduce the mechanical properties and erosion resistance of BFC. At a BF content of 6 kg/m³, BFC had the strongest ability to resist sulphate erosion, and the cube compressive strength, axial compressive strength, and modulus of elasticity of BFC were the highest, with 47.8 MPa, 33.6 MPa, and 38.7 GPa, respectively. The dense calcium silicate hydrate (C-S-H) gel, generated near the BF, enhanced the connection between the cementitious and the aggregate, inhibited the development of microcracks within the concrete, and reduced the erosion channels for sulphate ions. The maximum displacement of the BFC secondary lining structure was reduced by 20.15 % compared to plain concrete (PC) after sulphate erosion. The secondary lining structures consisting of both BFC and PC were stressed the most at footing. The secondary lining structure composed of BFC had better deformation resistance and higher strength than PC.

Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds.

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.