Coverage Conditions Research Articles

Three-dimensional spectrum coverage gap map construction in cellular networks: A non-linear estimation approach

Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.

Frog tongue-inspired wettable microfibers for particles capture

Fibers have been of great significance in our daily lives, especially in the industrial production of masks. Research in this area has been focused on developing microfibers with superior functions to enhance the filtration performances of the masks. Herein, inspired by the frog’s predation mechanism using its tongues to swiftly grab flying insects, we propose novel porous wettable microfibers from microfluidics to efficiently capture particles in the air for filtration. Upon pre-dispersing LP emulsions into polyurethane (PU), porous microfibers dispersed with oil droplets could be continuously spun from a co-flow microfluidic device based on the quick phase inversion of PU. To design an optimal system with frog-tongue-like interfacial adhesion properties, the wettability performances of the porous microfibers are investigated under full, partial, and no oil coverage conditions. When implemented in a mask, the 3D patterned networks based on the frog-tongue-inspired microfibers have been proven with remarkable particle capture performances while maintaining good air permeability. Based on these features, we believe that frog-tongue-inspired microfibers and their derived masks are of practical significance in multiple applications.

From manual to UAV-based inspection: Efficient detection of levee seepage hazards driven by thermal infrared image and deep learning

Levee failures are caused mainly by river water seepage erosion inside the levee, manifesting as slope leakage and foundation piping phenomena. To address the urgent need for levee safety monitoring during flood seasons and extreme rainfall events, unmanned aerial vehicles (UAVs) equipped with thermal infrared imagers can quickly detect leakage and piping hazards based on temperature differences between damaged areas and their surroundings. In this study, we collected 5995 UAV thermal infrared images of leakage and piping on levees in four floodplains under various weather, time, and surface coverage conditions to evaluate the applicability of combining thermal infrared imaging with a deep learning model in complex natural environments. We categorized levee hazards into water piping, ground piping, and slope leakage, and developed a Mask R-CNN segmentation model. The results revealed that thermal infrared levee inspection was affected by vegetation occlusion and subtle temperature differences caused by continuous rainstorms and water body depth. The Mask R-CNN demonstrated strong generalizability to hazards with significant variability in shape, size, and temperature difference, making it suitable for detecting evolving and expanding damaged area. The mean average precision, recall, and precision of the Mask R-CNN were 0.977, 0.982, and 0.897, respectively, and the detection time was 0.015 s per image. Moreover, Eigenvector-based class activation mapping (Eigen-CAM) was used to visualize the decision basis and failure modes of the Mask R-CNN to improve interpretability. Seepage damage is a progressive process, so timely hazard identification can provide valuable time for levee repair.

Understanding the Enhanced Separation Mechanism of C2H4/C2H6 at Low Pressure by HKUST−1

The production of ethylene (C2H4) is typically accompanied by the formation of impurities like ethane (C2H6), making the separation of C2H4 and C2H6 crucial in industrial processes. Here, we investigated the S-shaped adsorption phenomenon of C2H6 on the metal–organic framework HKUST−1. The virial equation is used to fit the C2H6 and C2H4 adsorption isotherms under low coverage. The results showed that the repulsion energy between neighboring C2H6 molecules was significantly higher than that between neighboring C2H4 molecules, which was an important reason for the lower adsorption of C2H6 by HKUST−1 at low coverage. As more molecules are adsorbed, gas molecules aggregate within pores, leading to more hydrogen bonds formed between HKUST−1 and larger-sized C2H6 under high coverage conditions. This phenomenon plays a crucial role in the S-shaped adsorption behavior of HKUST−1 on C2H6. Additionally, this unique adsorption behavior allows for the efficient separation of C2H4/C2H6 mixtures at low pressures. The ideal adsorbed solution theory (IAST) selectivity of HKUST−1 for C2H4/C2H6 mixtures was 3.78 at 283 K and 1 bar, but increased significantly to 7.53 under low pressure. This unique mechanism provides a theoretical basis for the low-pressure separation of C2H4/C2H6 by HKUST−1 and establishes a solid foundation for future practical research applications.

How does lexical coverage affect the processing of L2 texts?

Abstract Lexical coverage, i.e. the extent to which words in a text are known, is considered an important predictor of reading comprehension, with studies suggesting 98% lexical coverage leads to adequate comprehension. However, no studies to date have examined how the various lexical coverage percentages suggested in the literature are reflected by the cognitive effort involved in processing text and the attention that is devoted to the unknown vocabulary. This study used eye-tracking to examine how lexical coverage affects the processing of text (global measures) and unknown vocabulary (word-level measures), as well as the relationship between processing time on unknown vocabulary and learning. Advanced L2 learners of English read a text in one of four lexical coverage conditions (90%, 95%, 98%, 100%) while their eye movements were recorded. Knowledge of unknown pseudowords in the texts was assessed via an immediate, meaning recall post-test. Results showed that only one of the three global measures examined showed a processing advantage for the 98% condition, reflected by longer saccades and less effortful reading than the 90% and 95% conditions. Crucially, lexical coverage did not have a significant impact on the amount of attention spent on unknown vocabulary. Processing times were found to significantly predict vocabulary gains.

Monitoring of Vegetation Drought Index in Laibin City Based on Landsat Multispectral Remote Sensing Data

Due to the impact of global warming, drought has caused serious damage to China’s ecological environment and social status. This article selects Laibin City in the Guangxi Zhuang Autonomous Region as the research area, utilizing multispectral remote sensing data as the data source and Landsat series image data for relevant preprocessing. It calculates the monthly normalized vegetation index (NDVI) and surface temperature (LST) data for Laibin City. Based on the ecological environment and surface coverage conditions of the research area, the ratio vegetation index (RVI), normalized vegetation moisture index (NDWI), temperature vegetation drought index (TVDI), and conditional vegetation temperature drought index (VTCI) were selected to calculate and invert the drought monitoring results of Laibin City. The drought monitoring results were obtained and overlaid with the vegetation area map to generate the vegetation drought monitoring results of Laibin City. Based on the climate, geography, and ecological characteristics of the monitored area in Laibin City, a specific analysis will be conducted to develop an appropriate TVDI index drought level, and generate vegetation drought level result maps for Laibin City in 2021, 2022, and 2023. Then, a detailed analysis of the vegetation drought situation in Laibin City is conducted according to time and space. Among them, in the past three years, the vegetation areas in Laibin City have experienced drought seasons mostly in summer and autumn. The interannual drought is mainly mild drought, and the proportion of areas with mild drought shows a relatively stable trend. In conclusion, TVDI proves to be a valuable tool for monitoring vegetation drought in Laibin City, offering insights for efficient water resource management strategies.

Promoting subsurface Sn incorporation at Nb(100) oxide surface sites leading to homogeneous Nb3Sn film growth for superconducting radiofrequency applications

For next-generation superconducting radiofrequency (SRF) cavities, the interior walls of existing Nb SRF cavities are coated with a thin Nb3Sn film to improve the superconducting properties for more efficient, powerful accelerators. The superconducting properties of these Nb3Sn coatings are limited due to inhomogeneous growth resulting from poor nucleation during the Sn vapor diffusion procedure. To develop a predictive growth model for Nb3Sn grown via Sn vapor diffusion, we aim to understand the interplay between the underlying Nb oxide morphology, Sn coverage, and Nb substrate heating conditions on Sn wettability, intermediate surface phases, and eventual Nb3Sn nucleation. In this work, Nb-Sn intermetallic species are grown on a single crystal Nb(100) in an ultrahigh vacuum chamber equipped with in situ surface characterization techniques including scanning tunneling microscopy, Auger electron spectroscopy, and x-ray photoelectron spectroscopy. Sn adsorbate behavior on oxidized Nb was examined by depositing Sn with submonolayer precision on a Nb substrate held at varying deposition temperatures (Tdep). Experimental data of annealed intermetallic adlayers provide evidence of how Nb substrate oxidization and Tdep impact Nb-Sn intermetallic coordination. The presented experimental data contextualize how vapor and substrate conditions, such as the Sn flux and Nb surface oxidation, drive homogeneous Nb3Sn film growth during the Sn vapor diffusion procedure on Nb SRF cavity surfaces. This work, as well as concurrent growth studies of Nb3Sn formation that focus on the initial Sn nucleation events on Nb surfaces, will contribute to the future experimental realization of optimal, homogeneous Nb3Sn SRF films.

An Enhanced Particle Swarm Optimization-Based Node Deployment and Coverage in Sensor Networks.

Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6-120 m with the existing works.

Windthrow Impact on Alpine Forest Humipedon: Soil Microarthropod Communities and Humus Dynamics Five Years after an Extreme Windstorm Event

The ecological impact of windthrow disturbance on humipedons and soil microarthropod communities is examined in two areas of the Italian Alps (Val di Fassa and Cansiglio) five years after the Vaia Storm. The following soil coverage conditions were identified: herbaceous vegetation (G), decaying wood (W), no vegetation (B) in windthrow areas; and these were compared with conditions in adjacent undisturbed intact forests (IF) and, only in Val di Fassa, with permanent meadows (M). Soil pH, soil organic matter content (SOM), humus systems and microarthropod communities were analyzed. In Val di Fassa, SOM loss was observed in windthrow areas vs. IF, moving toward a Mull humus system, while G evolved toward M-like conditions, W maintained a thicker O horizon and lower pH and B exhibited severe soil erosion and the lowest SOM. In Cansiglio, windthrow areas showed a slower transition to a Mull system, with a trend toward increasing pH and decreasing SOM. A clear relationship between microarthropod communities and humus systems could not be established because the consistency and biological origins of the humus diagnostic horizons were not considered. Microarthropod communities under different conditions exhibited significant dissimilarity, with varying responses across groups; Shannon and QBS-ar indices remained stable except for a significant decrease in B. Community dissimilarity thus appears to be enhanced by post-windthrow disturbance, suggesting that destructive windstorms may also present an opportunity for enriched microarthropod diversity.

Selective Oxidation of Methanol to Methyl Formate on Gold: The Role of Low-Coordinated Sites Revealed by Isothermal Pulsed Molecular Beam Experiments and AIMD Simulations.

To elucidate the role of low-coordinated sites in the partial methanol oxidation to methyl formate (MeFo), the isothermal reactivity of flat Au(111) and stepped Au(332) in pulsed molecular beam experiments was compared for a broad range of reaction conditions. Low-coordinated step sites were found to enhance MeFo selectivity, especially at low coverage conditions, as found at higher temperatures. The analysis of the transient kinetics provides evidence for the essential role of Au x O y phases for MeFo formation and the complex interplay of different oxygen species for the observed selectivity. Ab initio molecular dynamic simulations yielded microscopic insights in the formation of Au x O y phases on flat and stepped gold surfaces emphasizing the role of low-coordinated sites in their formation. Moreover, associated surface restructuring provides atomic-scale insights which align with the experimentally observed transient kinetics in MeFo formation.

An analytical model for predicting residual stress in shot peening with strain energy method

In this study a model by novel analytical approach is developed and experimentally verified for shot peening residual stress distribution. The residual stress field induced by single shot impact is calculated by using Glinka–Molski energy-based method and kinematic hardening model. The formulation of the compressive residual stress (CRS) distribution is often based on plane strain or plane stress. It can be determined from the derived relation presented in this paper, the final residual stress in the full coverage conditions is the average of the two strain and stress plane expressions proposed by previous researchers. The distribution of residual stress is one of the key differences between the profiles produced by the results of the current model. There is a significant distinction between surface residual stress and maximum CRS, because the CRS profile near the surface is more curved compared to profiles obtained in earlier analytical models. The experimental data obtained by XRD analysis indicate the correctness and precision of the current model. Another goal of this study is to increase the fatigue life of GTD-450 stainless steel by shot peening at two different peening intensities. The fatigue life of samples were obtained by rotary bending test. Analytical results that confirmed by experimental findings shown bigger maximum compressive residual stresses occurred in higher shot peening intensities. This incident can improved fatigue life by deeper plastically deformed layer.

Utilizing deep learning towards real-time snow cover detection and energy loss estimation for solar modules

Conversion of solar energy using photovoltaic (PV) panels faces challenges due to snow accumulation on PV surface in cold regions. Despite existing methods to assess this impact, there remains a gap in real-time detection and accurate quantification of the energy loss. This study introduces a novel deep learning-based method for detecting snow coverage on PV panels for maximizing solar energy conversion. The model achieved a Dice score of 0.81 when trained on a diverse dataset of PV images, achieving a 44% improvement over conventional computer vision methods. Energy losses from snow on solar panels showed the model's predictions align closely with ground-truth data, achieving an error under 5% in snow coverage prediction. Six PV arrays were analyzed for energy loss estimation under varying snow coverage conditions. The analysis showed that the model could reliably predict energy losses due to snow accumulation with a mean error of 0.05 kWh/m2/month. The maximum energy loss was 0.23 kWh from a large PV array system covering 117 m2 area. Analysis of the impact of snow coverage duration on energy loss showed a saving potential of 0.13 kWh/m2 using timely clearing of snow coverage. This study highlights the effectiveness of CNN-based models in the early detection and measurement of snow coverage for improving the management and maintenance of PV systems.

Covalent surface modification of single-layer graphene-like BC6N nanosheets with reactive nitrenes for selective ammonia sensing via DFT modeling

Novel graphene-like nanomaterials with a non-zero bandgap are important for the design of gas sensors. The selectivity toward specific targets can be tuned by introducing appropriate functional groups on their surfaces. In this study, we use first-principles simulations, in the form of density functional theory (DFT), to investigate the covalent functionalization of a single-layer graphitized BC6N with azides to yield aziridine-functionalized adducts and explore their possible use to realize ammonia sensors. First, we determine the most favorable sites for physical adsorption and chemical reaction of methylnitrene, arising from the decomposition of methylazide, onto a BC6N monolayer. Then, we examine the thermodynamics of the [1 + 2]–cycloaddition reaction of various phenylnitrenes and perfluorinated phenylnitrenes para-substituted with (R = CO2H, SO3H) groups, demonstrating favorable energetics. We also monitor the effect of the functionalization on the electronic properties of the nanosheets via density of states and band structure analyses. Finally, we test four dBC6N to gBC6N substrates in the sensing of ammonia. We show that, thanks to their hydrogen bonding capabilities, the functionalized BC6N can selectively detect ammonia, with interaction energies varying from −0.54 eV to −1.37 eV, even in presence of competing gas such as CO2 and H2O, as also confirmed by analyzing the change in the electronic properties and the values of recovery times near ambient temperature. Importantly, we model the conductance of a selected substrate alone and in presence of NH3 to determine its effect on the integrated current, showing that humidity and coverage conditions should be properly tuned to use HO2C-functionalized BC6N-based nanomaterials to develop selective gas sensors for ammonia.

Data-driven forced oscillation localization using inferred impulse responses

Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferring the sources of forced oscillation (FO) using solely synchrophasor measurements. During normal grid operations, fast-rate ambient data are collected to recover the impulse responses in the small-signal regime, without requiring the system model. When FO events occur, the source is estimated based on the frequency domain analysis by fitting the least-squares (LS) error for the FO data using the impulse responses recovered previously. Although the proposed framework is purely data-driven, the result has been established theoretically via model-based analysis of linearized dynamics under a few realistic assumptions. Numerical validations demonstrate its applicability to realistic power systems including nonlinear, higher-order dynamics with control effects using the IEEE 68-bus system, and the 240-bus system from the IEEE-NASPI FO source location contest. The generalizability of the proposed methodology has been validated using different types of measurements and partial sensor coverage conditions.

Cyclic olefin copolymer (COC) as a promising biomaterial for affecting bacterial colonization: investigation on Vibrio campbellii

Cyclic olefin copolymer (COC) has emerged as an interesting biocompatible material for Organ-on-a-Chip (OoC) devices monitoring growth, viability, and metabolism of cells. Despite ISO 10993 approval, systematic investigation of bacteria grown onto COC is a still not documented issue.This study discusses biofilm formations of the canonical wild type BB120 Vibrio campbellii strain on a native COC substrate and addresses the impact of the physico-chemical properties of COC compared to conventional hydroxyapatite (HA) and poly(dimethylsiloxane) (PDMS) surfaces.An interdisciplinary approach combining bacterial colony counting, light microscopy imaging and advanced digital image processing remarks interesting results. First, COC can reduce biomass adhesion with respect to common biopolymers, that is suitable for tuning biofilm formations in the biological and medical areas. Second, remarkably different biofilm morphology (dendritic complex patterns only in the case of COC) was observed among the examined substrates. Third, the observed biofilm morphogenesis was related to the interaction of COC with the conditioning layer of the planktonic biological medium. Fourth, Level Co-occurrence Matrix (CGLM)-based analysis enabled quantitative assessment of the biomass textural fractal development under different coverage conditions. All of this is of key practical relevance in searching innovative biocompatible materials for pharmaceutical, implantable and medical products.

High-precision analytical solutions for Earth coverage of constellation equipped with reentry glide vehicles

To efficiently and accurately design satellite constellations equipped with Reentry Glide Vehicles (RGVs), new analytical solutions are developed for calculating their coverage performance. Specifically, a new coverage model is established by approximating the Reentry Reachable Domain (RRD). However, the computation of real-time relative distances between satellites and targets, which is essential for coverage analysis based on this model, imposes a significant computational burden. To address this challenge, a coverage analysis method based on two-dimensional map theory is proposed. This method represents the coverage conditions of a target as a fixed area on a two-dimensional map and transforms the satellite trajectory into a series of parallel lines. By determining the intersection points between these lines and the area boundaries, the coverage analytical solutions for a target point are derived. On this basis, coverage theorems are presented for rapid calculation of the constellation coverage performance for an area. Simulation results demonstrate the effectiveness and high precision of the proposed analytical solutions.

First implementation of an innovative infra-red camera system integrated into a mobile CBCT scanner for applicator tracking in brachytherapy-Initial performance characterization.

To enable a real-time applicator guidance for brachytherapy, we used for the first time infra-red tracking cameras (OptiTrack, USA) integrated into a mobile cone-beam computed tomography (CBCT) scanner (medPhoton, Austria). We provide the first description of this prototype and its performance evaluation. We performed assessments of camera calibration and camera-CBCT registration using a geometric calibration phantom. For this purpose, we first evaluated the effects of intrinsic parameters such as camera temperature or gantry rotations on the tracked marker positions. Afterward, calibrations with various settings (sample number, field of view coverage, calibration directions, calibration distances, and lighting conditions) were performed to identify the requirements for achieving maximum tracking accuracy based on an in-house phantom. The corresponding effects on camera-CBCT registration were determined as well by comparing tracked marker positions to the positions determined via CBCT. Long-term stability was assessed by comparing tracking and a ground-truth on a weekly basis for 6 weeks. Robust tracking with positional drifts of 0.02±0.01mm was feasible using the system after a warm-up period of 90min. However, gantry rotations affected the tracking and led to inaccuracies of up to 0.70mm. We identified that 4000 samples and full coverage were required to ensure a robust determination of marker positions and camera-CBCT registration with geometric deviations of 0.18±0.03mm and 0.42±0.07mm, respectively. Long-term stability showed deviations of more than two standard deviations from the initial calibration after 3 weeks. We implemented for the first time a standalone combined camera-CBCT system for tracking in brachytherapy. The system showed high potential for establishing corresponding workflows.

Tuning Optical Properties of Plasmonic Aerosols through Ligand-Solvent Interactions.

Plasmonic nanoparticles are highly tunable light-harvesting materials with a wide array of applications in photonics and catalysis. More recently, there has been interest in using aerosolized plasmonic nanoparticles for cloud formation, airborne photocatalysts, and molecular sensors, all of which take advantage of the large scattering cross sections and the ability of these particles to support intense local field enhancement ("hot spots"). While extensive research has investigated properties of plasmonic particles in the solution phase, surfaces, and films, aerosolized plasmonics are relatively unexplored. Here, we demonstrate how the capping ligand, suspension solvent, and atomization conditions used for aerosol generation control the steady-state optical properties of aerosolized Silica@Au plasmonic nanoshells. Our experimental results, supported with spectral simulations, illustrate that ligand coverage and atomization conditions control the degree of solvent retention and thus the spectral characteristics and potential access to surfaces for catalysis in the aerosol phase, opening a new regime for tunable applications of plasmonic metamaterials.

Greenspace Exposure with Chronic Obstructive Pulmonary Disease: A Systematic Review

Although a growing number of studies have suggested the relevance of greenspace exposure to respiratory health, most have explored single greenfield indicators or selected disease outcomes. This systematic review aimed to comprehensively assess the evidence that greenspace exposure is associated with chronic obstructive pulmonary disease (COPD) incidence, prevalence, mortality, and related lung function indicators and to explore the impact of greenspace exposure on COPD-related disease outcomes. We searched the literature published before 28 September 2023, using six electronic databases and snowball searches. After screening 3689 studies, 16 eligible studies were included. These studies examined various greenspace indicators such as the Normalized Difference Vegetation Index (NDVI), canopy coverage, greenspace coverage, and land cover conditions. Most studies have reported the positive effects of greenspace exposure. However, the findings had significant heterogeneity, with large differences in the greenness assessment, COPD identification methods, and population characteristics. The mechanisms of this impact have not been conclusively determined, but we propose several hypotheses to explain the pathways by which greenspace exposure affects COPD. Overall, our findings contribute valuable knowledge for understanding the relationship between greenspace exposure and COPD, informing future research in this field.

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.