Irregular Network Research Articles

Modeling Crash Risk on Roadway Networks using Bayesian Regression Trees

Statistical modeling of vehicle crashes leads to a better understanding of how and why such crashes occur. Due to the irregular network structure of roadways, analyses are typically confined to a single roadway rather than considering the entire network collectively. Here, we present methodology to model crash risk of vehicle crashes on irregular roadway networks and estimate how that risk varies with road characteristics. We model vehicle crashes observed on a road network as a Poisson point pattern with a piecewise linear intensity surface. Further, we combine Bayesian additive regression trees (BART) and spatial data analysis to accurately explain the intensity surface allowing inference on the effect of road characteristics on crash risk. We illustrate the methodology using a dataset of vehicle crashes on Interstate highways in Utah.

Hydrological drought and floodplain disconnectivity: quantifying flow thresholds in a large coastal plain river

ABSTRACT Hydrological drought and river disconnectivity worsen from dams and human activities. This research investigates how dam constructions exacerbate hydrological drought and river disconnectivity in Apalachicola River system. Data from two hydrological stations (1928-2022), a hydrographic survey, high-resolution LiDAR, and a triangular irregular network (TIN) were used to identify three connectivity flow (141.5 m3/s, 169.8 m3/s, and 226.4 m3/s) categories, each impacting floodplain differently. A relative elevation model (REM) was also developed for floodplain connectivity assessment. Findings show a significant increase in the frequency, duration, and magnitude of connectivity low flow events from pre-dam to post-dam periods, especially at the 169.8 m³/s connectivity flow threshold. A 20th percentile variable threshold shows increasing hydrological drought. Drought increases have led to decreased connectivity between tributaries and the main river system, and an increase in the moderately disconnected floodplain area from 37.1% to 46.0%. Adaptive management strategies are needed to address drought-induced floodplain disconnectivity

Implicit sensing self-supervised learning based on graph multi-pretext tasks for traffic flow prediction

AbstractIn recent years, spatio-temporal graph neural networks (GNNs) have successfully been used to improve traffic prediction by modeling intricate spatio-temporal dependencies in irregular traffic networks. However, these approaches may not capture the intrinsic properties of traffic data and can suffer from overfitting due to their local nature. This paper introduces the Implicit Sensing Self-Supervised learning model (ISSS), which leverages a multi-pretext task framework for traffic flow prediction. By transforming data into an alternative feature space, ISSS effectively captures both specific and general representations through self-supervised tasks, including contrastive learning and spatial jigsaw puzzles. This enhancement promotes a deeper understanding of traffic features, improved regularization, and more accurate representations. Comparative experiments on six datasets demonstrate the effectiveness of ISSS in learning general and discriminative features in both supervised and unsupervised modes. ISSS outperforms existing models, demonstrating its capabilities in improving traffic flow predictions while addressing challenges associated with local operations and overfitting. Comprehensive evaluations across various traffic prediction datasets, have established the validity of the proposed approach. Unsupervised learning scenarios have shown the improvements in RMSE for the METR-LA and PEMSBAY datasets of 0.39 and 0.35 for location-dependent and location-independent tasks, respectively. In supervised learning scenarios, for the same datasets, the improvements were 1.16 for location-dependent tasks and 0.55 for location-independent tasks.

MFCA-MICNN: a convolutional neural network with multiscale fast channel attention and multibranch irregular convolution for noise removal in dMRI

Diffusion magnetic resonance imaging (dMRI) currently stands as the foremost noninvasive method for quantifying brain tissue microstructure and reconstructing white matter fiber pathways. However, the inherent free diffusion motion of water molecules in dMRI results in signal decay, diminishing the signal-to-noise ratio (SNR) and adversely affecting the accuracy and precision of microstructural data. In response to this challenge, we propose a novel method known as the Multiscale Fast Attention-Multibranch Irregular Convolutional Neural Network for dMRI image denoising. In this work, we introduce Multiscale Fast Channel Attention, a novel approach for efficient multiscale feature extraction with attention weight computation across feature channels. This enhances the model's capability to capture complex features and improves overall performance. Furthermore, we propose a multi-branch irregular convolutional architecture that effectively disrupts spatial noise correlation and captures noise features, thereby further enhancing the denoising performance of the model. Lastly, we design a novel loss function, which ensures excellent performance in both edge and flat regions. Experimental results demonstrate that the proposed method outperforms other state-of-the-art deep learning denoising methods in both quantitative and qualitative aspects for dMRI image denoising with fewer parameters and faster operational speed.

An Improved Adaptive Grid-Based Progressive Triangulated Irregular Network Densification Algorithm for Filtering Airborne LiDAR Data

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

On the Issue of Constructing Spatial Isolines for Irregular Monitoring Networks

The article examines spatial isolines construction based on data on an irregular grid. In particular, a possible solution to the problem of contour breaks is explored. Measurements of the magnetic field parameters taken from variation stations are used as data. The paper presents a description of the algorithm for constructing isolines and describes the software system that implements this algorithm. The results of system performance testing are given in the last chapter.

Intelligent processing of UAV remote sensing data for building high-precision DEMs in complex terrain: A case study of Loess Plateau in China

The Loess Plateau in China is renowned for its dense gullies and complex terrain, with drastic changes primarily due to soil erosion and human activities, significantly affecting the evolution of the ecological environment. The complex terrains and dense vegetation make precise terrain measurement and modeling challenging. Although the development of Unmanned Aerial Vehicle (UAV) light detection and ranging (LiDAR) scanning and photogrammetry technologies has improved data acquisition precision, relying solely on one remote sensing technology struggles with accurately extracting bare earth information. This study adopted a method that fuses UAV lidar scanning with aerial photogrammetric imagery, generating detailed lidar point cloud data that includes coordinate, reflectance, true color, and texture information to enhance data classifiability and interpretability. Subsequently, a point cloud classification model based on the Transformer architecture (Stratified Transformer) is introduced to intelligently complete the initial ground point cloud extraction in complex gully terrains. Further, to address residual non-ground noise in the initial ground point clouds, a new point cloud classification optimization algorithm (MDD, Multi-scale C2M Distance Difference) is proposed. This algorithm, based on the characteristics of discrete and non-continuous with the ground surface of the noisy point clouds, effectively eliminates the discrete noisy point clouds by analyzing the distances between the point clouds and TINs (Triangular Irregular Networks) of different scales and their differences. This study effectively addresses the technical challenges of ground point cloud extraction in the mixed environment of complex terrain and vegetation, solving the problem of precise terrain measurement and intelligent data processing in complex gully terrains, and offering new technical pathways for detecting geomorphological changes.

A reverse tracing of the water flow path algorithm for slope length extraction based on triangulated irregular network

Terrain factor is an important factor affecting soil erosion, in which slope length is an important indicator of terrain factor. In this paper, we model the regional topographic relief with triangulated irregular network TIN, use the slope aspect of the TIN triangular surface to determine the flow direction, and propose an algorithm (RT-WFP) for extracting the slope length by tracing the water flow trajectory in reverse direction. The slope cutoff point rule is set in the algorithm to improve the accuracy of the slope length extraction results. We calculated the slope length of the experimental area of the small watershed of Golden Hook-shaped collapsing hill in Bailu Township, Ganxian District, and the rationality of the algorithm proposed in this paper is verified through the comparison and analysis with the traditional slope length extraction algorithm. The experimental results show that, compared with the traditional D8 algorithm, the water flow path extracted by this algorithm in the experimental area more closely matches the water flow path based on contour mapping, and the slope length extracted by this algorithm has a lower sensitivity to the resolution of the data, and the percentage of cells with a slope length value of no more than 300 m (the limit standard of the RUSLE model) at a resolution of 30 m reaches 94.19%.

A status digital twin approach for physically monitoring over-and-under excavation in large tunnels

Accurate assessments of over-and-under-excavation volumes during tunnel construction are critical for stability and structural safety. Over-and-under-excavation assessment is typically time-consuming as it is undertaken manually and is prone to inaccuracies that adversely impact quality, safety, and costs. To overcome this problem, a status digital twin (to monitor physical conditions) is developed to assess over-and-under excavation during tunneling, enabling a robust and automated assessment of volumes of earth to be determined. The development of the status digital twin comprises three stages: (1) creating an as-designed building information model and as-built point cloud model; (2) producing an automated status update with newly added data; and (3) calculating the of over-and-under excavation volume by building a Triangulated Irregular Network (TIN). The status digital twin is tested in a large-scale tunnel project to evaluate its feasibility and effectiveness. The average over-and-under excavation thickness is 0.05 m, and the mean error of volume calculation is 4.54 %. The results demonstrate that the status digital twin can accurately assess over-and-under excavation during the construction of large-scale tunnel projects by providing instant feedback concerning over/under excavation, thus enabling quality to be effectively managed and costs to be controlled.

The Influence of Local Constraints on Solvent Motion in Polymer Materials.

The influence of obstacles in the form of polymer chains on the diffusion of a low-molecular-weight solvent was the subject of this research. Studies were performed by computer simulations. A Monte Carlo model-the Dynamic Lattice Liquid algorithm-based on the idea of cooperative movements was used. The tested materials were polymer networks with an ideal structure (with a uniform mesh size) and real, irregular networks (with a non-uniform mesh size) obtained numerically by copolymerization. The diffusion of the solvent was analyzed in systems with a polymer concentration that did not exceed 16%. The influence of the polymer concentration and macromolecular architecture structure on the mobility and character of the motion of the solvent was discussed. The influence of irregular network morphology on solvent dynamics appeared to be significantly stronger than that of regular networks and star-like polymers.

Pore-scale study of coupled heat and mass transfer during primary freeze-drying using an irregular pore network model

This study presents a pore network (PN) model with transient heat transfer and quasi-steady transitional vapor transport that is for the first time applied to irregular porous structures that are obtained by reconstruction of X-ray tomography image data. In contrast to previous studies, the irregular pores are not approximated by spheres but implemented in their original shape. Secondly, instead of assuming cylindrical throats as pore connections, the actual distance between pore centers as well as the pore cross sections are used for the computation of the vapor transport coefficient. The control volume elements of the computational model are matched with the cells obtained by Voronoi tessellation. The improvements have clear advantages over former approaches where the reconstructed void space is usually strongly simplified by balls and sticks confined in a regular lattice structure. A freeze-dried sample of maltodextrin DE12 with 20% (w/w) solid content is used for benchmarking the new methodology. Its morphological and thermal properties are determined by the novel PN model. The simulation results of primary freeze-drying (FD) are compared to reference cases in two ways. First, the differences in heat and mass transfer kinetics as compared to regular PNs are emphasized. Secondly, the PN simulation results are confronted with a simple literature model that neglects pore size distribution (PSD) and transient heat transfer. It is shown that already in small domains with relatively narrow PSD, the variation of the mass transfer coefficient affects the computed sublimation fluxes and yields a significant deviation from the simpler literature model. Moreover, it is revealed in this study that the structure has a significant impact on the sublimation front temperature. This is demonstrated by the comparison of FD in the irregular PN to a regular PN with almost identical PSD but different porosity. The development, verification, and benchmarking of the new PN model can be seen as an important step for studies of the structure dependence of FD.

Magnetic porous functional material from coal mine waste for simultaneous removal of cadmium and arsenic in water and soil: Synergistic effect of iron-containing minerals and porous structure

Heavy metals such as cadmium and arsenic (Cd/As) can cause harmful effects on human health. Coal mine waste (coal gangue) is a solid waste residue without commercial values produced abundantly in the coalfield. Hence, treating Cd and As concurrently with reutilizing coal gangue (CG) is critical to ecologically sustainable growth. This work successfully synthesized the magnetic porous functional materials (MPCG) based on CG using a two-step method for the simultaneous removal of Cd and As in water and soil. First, MPCG exhibited an irregular network porous structure. Then, it consisted of iron (Fe), mainly in magnetite, hematite, and pyrite forms. Such properties also exhibited excellent pH adaptability and coadsorption capacity by removing up to 103.79 mg g−1 and 579.43 mg g−1 for Cd(II) and As(V), respectively. There is a synergistic effect between the iron-containing minerals and porous structure to enhance the adsorption capacity of MPCG for Cd(Ⅱ) and As(Ⅴ). Several primary potential adsorption mechanisms by MPCG for Cd(Ⅱ) and As(Ⅴ) were identified. The potential mechanisms of Cd(II) adsorption mainly include ion exchange, electrostatic attraction, surface complexation and precipitation/co-precipitation, and the potential mechanisms of As(V) adsorption mainly include electrostatic attraction, surface complexation and precipitation. In addition, the removal rates of MPCG for Cd and As in soil were as high as 44.78 and 38.44 %, respectively. In conclusion, MPCG has excellent potential as an efficient and environmentally compatible functional material, which provides a potential way to realize efficient heavy metal removal from our ecosystems.

Video Color OCT Angiography for Myopic Choroidal Neovascularization

To investigate the myopic macular neovascularization (mMNV) features on dynamic video-color optical coherence tomography (OCT) angiography (OCTA) and the diagnostic rate versus the static, four-segmentations visualization mode. Retrospective cohort study PARTICIPANTS: Fifty-four patients with mMNV METHODS: Sixty-two eyes with high myopia complicated by mMNV were included. Clinical charts, fluorescein angiography and structural OCT were used as standard reference to assess lesion activity. Static and video-color OCTA were then analysed and compared by two independent reviewers. Morphology description of mMNV on video-color OCTA and differences in the proportion of diagnosis between video-colour and static OCTA. 62 eyes from 54 patients (mean age 63,22 years) were enrolled. Thirty-four (55%) mMNV were active and 28 (45%) inactive. Twenty-two (65%) active mMNV presented on video-color OCTA as an interlacing vascular network in the outer retina and the choriocapillaris. A tapered form was the prevalent size (72,7%). In 3 eyes (9%) an abnormal and irregular vascular network (AVN) was disclosed and in 5 (15%) only some blood flow alteration. All the lesions extended both in the outer retinal and the choriocapillaris. Eleven (39%) inactive mMNV presented on video-color OCTA as an interlacing vascular network too, in the outer retina and the choriocapillaris. Eight (29%) had some AVN and 6 (21%) only some blood flow alteration. The diagnostic rate of video-color vs static OCTA was 95% (IC 95% 86% to 99%) vs 77% (IC 95% 86% to 99%, p= 0.0009), and shows an advantage in favour of video-colour OCTA of 15% (CI 95%, 3%-27%) and 22% (CI 95%, 7%-38%) in active and inactive lesions, respectively (p<0.026). Lesion extension within both the outer retina and the choriocapillaris was present in 90% and 69% of cases on dynamic OCTA and static OCTA, respectively, with a proportion difference of 20% (CI 95%, 10%-31%, p= 0.0005). Concordance between the two examiners was high: 0.95 (95%, CI 0.88 to 1.00) and 0.96 (0.91 to 1.00) for active and inactive lesions, respectively. Video color-enhanced OCTA may help in diagnosing mMNV and should be considered by clinicians in addition to structural OCT and static OCTA.

An Irregular Pupil Localization Network Driven by ResNet Architecture

An Irregular Pupil Localization Network Driven by ResNet Architecture

Bridging Macroscopic Diffusion and Microscopic Cavity Escape of Brownian and Active Particles in Irregular Porous Networks.

While irregular and geometrically complex pore networks are ubiquitous in nature and industrial processes, there is no universal model describing nanoparticle transport in these environments. 3D super-resolution nanoparticle tracking was employed to study the motion of passive (Brownian) and active (self-propelled) species within complex networks, and universally identified a mechanism involving successive cavity exploration and escape. In all cases, the long-time ensemble-averaged diffusion coefficient was proportional to a quantity involving the characteristic length scale and time scale associated with microscopic cavity exploration and escape (D ∼ r2/ttrap), where the proportionality coefficient reflected the apparent porous network connectivity. For passive nanoparticles, this coefficient was always lower than expected theoretically for a random walk, indicating reduced network accessibility. In contrast, the coefficient for active nanomotors, in the same pore spaces, aligned with the theoretical value, suggesting that active particles navigate "intelligently" in porous environments, consistent with kinetic Monte Carlo simulations in networks with variable pore sizes. These findings elucidate a model of successive cavity exploration and escape for nanoparticle transport in porous networks, where pore accessibility is a function of motive force, providing insights relevant to applications in filtration, controlled release, and beyond.

Preparing Cadastral Map Databases for Mosul District

Investigates the development and implementation of digital cadastral map databases for Mosul District, Iraq, addressing the transition from traditional paper-based systems to modern geospatial technologies. The research explores the integration of Geographic Information Systems (GIS), remote sensing, and Global Positioning System (GPS) technologies to enhance cadastral mapping processes. Encompass data collection from diverse sources, including high-resolution satellite imagery, aerial photography, and existing cadastral maps. A comprehensive analysis of data types— vector, raster, Triangulated Irregular Network (TIN), and tabular—is conducted to establish an optimal database structure. The study delineates the technical capabilities of GIS in data processing, spatial analysis, and model preparation, emphasizing the importance of data accuracy and consistency. A systematic approach to data entry, including map scanning, georeferencing, and digitization, is presented. The research outlines the creation of a multi-layered geographic database, incorporating both spatial and descriptive data. Protocols for database updating using satellite imagery and field surveys are developed, ensuring the currency and reliability of cadastral information. The results demonstrate significant improvements in data management, spatial analysis capabilities, and information accessibility compared to traditional methods. The study concludes that digital cadastral databases offer enhanced decision-making support for land administration and urban planning in Mosul District. Contributes to the body of knowledge on modernizing cadastral systems in developing regions, providing insights into technological integration, data standardization, and the challenges associated with transitioning to digital cadastral management systems. The findings have implications for policy makers and land administrators seeking to implement similar systems in comparable urban contexts.

Interactive effects of elevation difference, slope variation, and terrain formation on road traffic crashes occurrences using triangular irregular network

AbstractNumerous factors contribute to traffic crashes, which result in fatalities and injuries. Road traffic crashes and their severity level are likely caused by variations in elevation, slope, and terrain formation. As far as our knowledge is concerned, the triangular irregular network (TIN) was not used to analyze dangerous zones of the road network with respect to traffic crashes' location. The main purpose of this study was to investigate a dangerous road network that was affected by traffic crashes using TIN. To address the goal, this study used elevation differences slope variation, and terrain between road traffic crash locations. The study used the intersection zone road network coordinates of traffic crashes in the city of Budapest that were recorded from 2017 to 2021. Using tools like ArcGIS and MS Excel, the study organized, modeled, and analyzed the data. Geometrical interval classification was used to classify the data in accordance with the characteristics of elevation deference and slope variation. Both inferential and descriptive statistics were engaged to explain, analyze, and summarize the findings. The results of this study demonstrated that the majority of traffic crashes occurred in locations with lower elevations and their differences, steeper/cliff slopes between points, and flat terrain. The finding was in contrast to what was observed in other studies. This study advocates that TIN is the optimal method and substitute for analyzing the risky zone of the road network at macroscopic level.

Enhanced resource optimization with the unevenness coefficients use in regular and irregular graph-based networks

The paper presents a simulation-based study of the impact of transmission resource reallocation on the performance of a communication network. The study considers the unevenness coefficients use to control network resources allocation. The results show that adjusting the transmission resource allocation based on the above-mentioned parameter can significantly improve network performance, leading to a equalization of network characteristics, and all of this without increasing the resources pool. The study’s findings provide valuable insights for network designers and operators seeking to optimize network performance in communication systems.

Reinforcement learning-based pinning control for synchronization suppression in complex networks

Synchronization in complex networks is a ubiquitous and important phenomenon with implications in various fields. Excessive synchronization may lead to undesired consequences, making desynchronization techniques essential. Exploiting the Proximal Policy Optimization algorithm, this work studies reinforcement learning-based pinning control strategies for synchronization suppression in global coupling networks and two types of irregular coupling networks: the Watts-Strogatz small-world networks and the Barabási-Albert scale-free networks. We investigate the impact of the ratio of controlled nodes and the role of key nodes selected by the LeaderRank algorithm on the performance of synchronization suppression. Numerical results demonstrate the effectiveness of the reinforcement learning-based pinning control strategy in different coupling schemes of the complex networks, revealing a critical ratio of the pinned nodes and the superior performance of a newly proposed hybrid pinning strategy. The results provide valuable insights for suppressing and optimizing network synchronization behavior efficiently.

Multi-perspective feature collaborative perception learning network for non-destructive detection of pavement defects

Pavement defects detection has made significant progress with the development of convolutional neural networks. Due to the topological complexity of pavement defects regions, most existing detection methods are limited to a limited number of types of defects, which results in unsatisfactory detection results for multi-classified defects. Several studies have used enhanced data to address this issue, however, few have considered the inefficiency caused by imbalanced differences between classes from the perspective of the network model itself. In light of this issue, our goal is to fill this gap by better exploring the fusion of different feature information to improve the performance of the network. As a result, we propose a multi-perspective feature collaborative perception learning network (MFCPLNet), which uses the model's contextual information to guide high-quality multi-classified defects detection and that can be adapted to various pavement conditions. Specifically, for adaptive feature extraction, an irregular perceptual extraction network (IPENet) with enhanced edge information is first designed to draw upon hybrid deformable convolution for efficient network block hybridization. Additionally, a multi-balanced collaborative learning mechanism (MCLM) consisting of auxiliary axial information and 3D feature guidance is constructed to enhance deep semantic features, improve the localization of defects in defective regions, and compensate for weak category supervision. Lastly, in the detection head, an iterative soft aggregation module has been designed to enhance scalable spatial interactions. The results of extensive testing were based on publicly available pavement defects datasets. Based on the proposed network, the mAP index reaches 29.6% and 33.8%, respectively, an increase of 8.3% and 4.6% over the baseline, which is qualitatively and quantitatively superior to mainstream methods.