Global Topology Research Articles

Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain.

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration. However, limited by factors such as imaging hardware, scanning time, and cost, it is challenging to acquire high-resolution MR images clinically. In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed to produce super-resolution (SR) MR images from the low-resolution counterparts. By adopting the divide-and-conquer scheme, FP-GANs are designed to deal with the low-frequency (LF) and high-frequency (HF) components of MR images separately and parallelly. Specifically, FP-GANs first decompose an MR image into LF global approximation and HF anatomical texture subbands in the wavelet domain. Then, each subband generative adversarial network (GAN) simultaneously concentrates on super-resolving the corresponding subband image. In generator, multiple residual-in-residual dense blocks are introduced for better feature extraction. In addition, the texture-enhancing module is designed to trade off the weight between global topology and detailed textures. Finally, the reconstruction of the whole image is considered by integrating inverse discrete wavelet transformation in FP-GANs. Comprehensive experiments on the MultiRes_7T and ADNI datasets demonstrate that the proposed model achieves finer structure recovery and outperforms the competing methods quantitatively and qualitatively. Moreover, FP-GANs further show the value by applying the SR results in classification tasks.

EPCS: Endpoint-based part-aware curve skeleton extraction for low-quality point clouds

The curve skeleton is an important shape descriptor which has been utilized in various applications in computer graphics, machine vision, and artificial intelligence. In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed. The novel random center shift (RCS) method is first proposed for detecting the endpoints on point clouds. The endpoints are used as the initial seed points for dividing each part into layers, and then the skeletal points are obtained by computing the center points of the oriented bounding box (OBB) of the layers. Subsequently, the skeletal points are connected, thus forming the branches. Furthermore, the multi-vector momentum-driven (MVMD) method is also proposed for locating the junction points which connect the branches. Due to the shape differences between different parts on point clouds, the global topology of the skeleton is finally optimized by removing the redundant junction points, re-connecting some branches using the proposed MVMD method, and applying an interpolation method based on the splitting operator. Consequently, a complete and smooth curve skeleton is achieved. The proposed EPCS method is compared with several state-of-the-art methods, and the experimental results verify its robustness and effectiveness. Furthermore, the skeleton extraction and model segmentation results on challenging point clouds of broken Terracotta also highlight the utility of the proposed method.

Topology optimization of periodic structures for crash and static load cases using the evolutionary level set method

Assembly complexity and manufacturing costs of engineering structures can be significantly reduced by using periodic mechanical components, which are defined by combining multiple identical unit cells into a global topology. Additionally, the superior energy-absorbing properties of lattice-based periodic structures can potentially enhance the overall performance in crash-related applications. Recent research developments in periodic topology optimization (PTO) have shown its efficacy for tackling new design problems and finding advanced novel structures. However, most of these methods rely on gradient information in the optimization process, which poses difficulties for crash problems where analytical sensitivities are usually not directly applicable. In this paper, we present an effective periodic evolutionary level set method (P-EA-LSM) for the optimization of periodic structures. P-EA-LSM uses a low-dimensional level-set representation based on moving morphable components to parametrize a single unit cell, which is replicated in the design domain according to a predefined pattern. The unit cell is optimized using an evolutionary algorithm and the structural responses are calculated for the entire system. We initially assess the performance of P-EA-LSM using three 2D minimum compliance test cases with varying periodicities. Our results demonstrate that our approach produces solutions comparable to other state-of-the-art methods for PTO while keeping a low dimensionality of the optimization problem. Subsequently, we effectively evaluate the capabilities of P-EA-LSM in a crashworthiness scenario. This particular application highlights the significant potential of the method, which does not rely on analytical sensitivities.

An improved gravity centrality for finding important nodes in multi-layer networks based on multi-PageRank

How to identify important nodes in multi-layer networks is still an unresolved issue in network science, which has aroused the interest of many researchers. In addition, the relationships between entities in many real-world systems are diverse and can be modeled as multi-layer networks. In the past few decades, scholars have defined various centrality methods from different perspectives to find influential nodes in multi-layer networks, but they only utilize the local or global topology information. Recently, various gravity centralities that utilize both the local and global topological structure information have been defined for identifying key nodes in single-layer networks. In the gravity model, the interaction between two nodes is related to their mass and distance. In consideration of the advantages of gravity model, in this paper, we define an improved gravity centrality for identifying key nodes in multi-layer networks based on multi-PageRank centrality, referred to as the PRGC. Unlike the existing gravity centralities that treat each node degree as its mass, our proposed centrality views the multi-PageRank centrality value of each node as its mass. Furthermore, PRGC weights the shortest path distance between any two nodes across all network layers to define their distance in multi-layer networks. Finally, to illustrate the effectiveness and superiority of the proposed centrality approach, numerical experiments are conducted on six real-world multi-layer networks show that our proposed centrality method outperforms state-of-the-art centralities.

Retracted: Evaluation Method of Public Physical Training Quality Based on Global Topology Optimization Deep Learning Model.

[This retracts the article DOI: 10.1155/2022/4043876.].

Impact of artificial topological changes on flow and transport through fractured media due to mesh resolution

We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network.

Observer-based adaptive controllers for Lur'e multi-agent systems with a dynamic leader

This paper deals with the leader-following consensus problem of Lur'e multi-agent systems (MASs) under undirected topologies, in which the leader has an unknown input. First, based only on the relative outputs among neighbouring agents, a distributed unknown input observer (UIO) is introduced to estimate the relative state of each follower, and the observation errors can exponentially converge to zero. Then, we propose an observer-based discontinuous controller with adaptive gains and demonstrate that the tracking errors can converge to zero. Subsequently, we further propose an observer-based continuous controller with adaptive gains under which chattering phenomenon can be avoided. Unlike the related works, both controllers can achieve leader-following consensus in a fully distributed manner, meaning that they do not depend on the global communication topology structure and the upper bound of the leader's input. Furthermore, two tractable algorithms are devised for computing the gain matrices. Finally, some simulation examples are presented on Chua's circuit system to validate the theoretical results.

A Study of the Global Topological Map Construction Algorithm Based on Grid Map Representation for Multirobot

In some large environments where humans and machines coexist, such as smart factories, restaurants, and hotels, multiple mobile robots system have certain advantages over single mobile robot in terms of task complexity, execution efficiency, and system robustness. However, path conflicts and blockages may occur among robots without effective global coordination method, especially in some special areas like narrow straight roads (N-S-R) and crossroads (C-R). To solve this problem, using the global topology map might be a good solution. Therefore, this paper proposes a method called Surplus Topology Generation Algorithm (<i>STGA</i>) for constructing a global topological map based on grid map representation. On the basis of grid maps, we designed <i>Node-Filter</i> to generate enough topological nodes, and simplified the node set by <i>Node-Selection</i>. In special areas such as N-S-R and C-R, which are prone to path conflicts and blocking scenarios, we extracted the Harris corner point and combined them to select the corner point combinations that characterize some special features like passage entrance, etc. Then, feature nodes that can characterize the special features were obtained by establishing mapping relationships between nodes and corner point combinations. Finally, the global topology map was obtained after establishing the reachability relationships between nodes. The simulation experiment results showed that the topological maps generated by <i>STGA</i> had obvious advantages in the richness of topological relationships. Besides, the topological maps also showed certain advantages compared to grid maps for the planning efficiency and security of global paths. Furthermore, the rich topological nodes on the global topological map were feasible in solving path conflicts of multiple robots. <i>Note to Practitioners</i>&#x2014;Multi-robot system has better efficiency and system robustness, but how to solve the path conflicts among robots is a problem. We propose a method to solve multi-robot conflicts with global topological maps. This topological map is constructed by STGA. The grid map environment is discretized into a set of topological nodes, which have been used to maintain the spatial transitions of each robot in the environment. When robots encounter unavoidable path conflicts, they are scheduled with the help of topological nodes to achieve orderly and efficient collaboration.

Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease.

This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.

Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in dynamic networks

Link prediction in dynamic(temporal) networks refers to predicting future edges by analyzing the available network information. Among the existing temporal link prediction approaches, non-negative matrix factorization(NMF) is a kind of competitive algorithm and has attracted extensive attention. However, traditional NMF-based prediction methods are shallow methods and cannot fully mine the dynamic network, which may lead to a decrease in performance of algorithms. To overcome these shortcomings, inspired by deep Autoencoder, we propose two novel deep Autoencoder-like NMF with graph regularized prediction methods for dynamic networks. By fusing encoder component with deep structure into deep NMF model, our algorithms can sufficiently exploit the complex hierarchical information hidden in dynamic networks. To further extract the abundant information hidden in dynamic networks, graph regularization and PageRank are utilized to exploit the local and global topology information of each snapshot, respectively. By jointly optimizing them in deep Autoencoder-like NMF model, our model is able to preserve the local and global information hidden in dynamic networks, simultaneously. Moreover, an effective alternating iterative method with convergence guarantee is developed for minimizing the established model. Finally, we test our proposed prediction methods on several synthetic and real world datasets to demonstrate that our approaches outperform the state-of-the-art prediction approaches.

Double Optimization Design of the Formula Racing Car Frame Based on the Variable Density Method and the Joint Variable Method

In the Formula Student Racing Car, the frame is a fundamental component that supports the body. The frame’s anti-deformation ability will impact the four-wheel positioning parameters of the car, which subsequently affect the car’s stability. The quality of the frame directly determines the power and efficiency of the racing car, making the frame crucial to the overall race performance. Therefore, researching lightweight frame design is particularly important to enhance frame performance and reduce its total weight. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient utilization of the material, and improve the torsional stiffness of the frame. Compared with the previous local topology optimization, the global topology optimization involves a wider range, and the results are more accurate. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out to obtain the influence level of each design variable. The size of the frame is optimized according to the variables with high influence levels. After optimization, the total mass of the frame is reduced by 12.8%; the performance in terms of maximum displacement and maximum stress is improved; and the lightweight design of the frame is realized as a whole.

MTGDC: A Multi-Scale Tensor Graph Diffusion Clustering for Single-Cell RNA Sequencing Data.

Single-cell RNA sequencing (scRNA-seq) is a new technology that focuses on the expression levels for each cell to study cell heterogeneity. Thus, new computational methods matching scRNA-seq are designed to detect cell types among various cell groups. Herein, we propose a Multi-scale Tensor Graph Diffusion Clustering (MTGDC) for single-cell RNA sequencing data. It has the following mechanisms: 1) To mine potential similarity distributions among cells, we design a multi-scale affinity learning method to construct a fully connected graph between cells; 2) For each affinity matrix, we propose an efficient tensor graph diffusion learning framework to learn high-order information among multi-scale affinity matrices. Firstly, the tensor graph is explicitly introduced to measure cell-cell edges with local high-order relationship information. To further preserve more global topology structure information in the tensor graph, MTGDC implicitly considers the propagation of information via a data diffusion process by designing a simple and efficient tensor graph diffusion update algorithm. 3) Finally, we mix together the multi-scale tensor graphs to obtain the fusion high-order affinity matrix and apply it to spectral clustering. Experiments and case studies showed that MTGDC had obvious advantages over the state-of-art algorithms in robustness, accuracy, visualization, and speed. MTGDC is available at https://github.com/lqmmring/MTGDC.

Fully Distributed Control of Multiagent Networks With Edge-Based Event-Triggered Communication

This paper addresses the synchronization problem of linear multi-agent systems with intermittent communication. To solve this problem, we propose an adaptive distributed control algorithm with edge-based event-triggering mechanisms that differs fundamentally from existing approaches in that it does not employ any form of global topology information and absolute state information. Instead, agents update their control signals and check triggering conditions based on relative state information. As our main contribution, we prove that all agents achieve global asymptotic synchronization with strictly positive minimum inter-event times by using the proposed event-triggered control method when the communication graph is connected. The main result is illustrated with an example, and through which we demonstrate the effectiveness of the proposed algorithm.

Altered Metabolic Networks in Mesial Temporal Lobe Epilepsy with Focal to Bilateral Seizures.

This study was designed to identify whether the metabolic network changes in mesial temporal lobe epilepsy (MTLE) patients with focal to bilateral tonic-clonic seizures (FBTCS) differ from changes in patients without FBTCS. This retrospective analysis enrolled 30 healthy controls and 54 total MTLE patients, of whom 27 had FBTCS. Fluorodeoxyglucose positron emission tomography (FDG-PET) data and graph theoretical analyses were used to examine metabolic connectivity. The differences in metabolic networks between the three groups were compared. Significant changes in both local and global network topology were evident in FBTCS+ patients as compared to healthy controls, with a lower assortative coefficient and altered betweenness centrality in 15 brain regions. While global network measures did not differ significantly when comparing FBTCS- patients to healthy controls, alterations in betweenness centrality were evident in 13 brain regions. Significantly altered betweenness centrality was also observed in four brain regions when comparing patients with and without FBTCS. The study revealed greater metabolic network abnormalities in MTLE patients with FBTCS as compared to FBTCS- patients, indicating the existence of distinct epileptogenic networks. These findings can provide insight into the pathophysiological basis of FBTCS.

Eddy-resolving Reynolds-stress modelling of thermal mixing in cross-stream type T-junction configurations

The present work is concerned with the numerical simulation study of turbulent thermal mixing involving hot main and cold branch streams at high Reynolds numbers in two T-junction configurations that differ in flow rate ratio. This results in two topologically very different flow scenarios, classified as “wall-jet” and “impinging-jet” mixing processes. The underlying reference database originates from the well-known experimental WATLON study[16], dealing with the phenomena of thermal stripping in piping systems, and provides details on the configuration geometry and the associated boundary and operating conditions. The turbulence model used, implemented in the finite-volume-based numerical code OpenFOAM® [30], is a near-wall Reynolds stress model formulation appropriately sensitized by applying the scale-adaptive modeling strategy to enable its eddy-resolving capability [14]. The flow analysis includes the first- and second-order statistics, including both the flow and thermal fields, with the model results closely following the experimental reference. This refers to the global flow topology in terms of the flow separation pattern, both in terms of its shape and size, as well as to the individual mean velocity and temperature profiles and their fluctuating components. In addition, the spectral features of the flow are studied in detail using the proper orthogonal decomposition (POD) method to identify the low-rank structures whose frequency signatures contribute most to the phenomena of thermal stripping phenomena.

Rolling bearing fault diagnosis based on multi-scale weighted visibility graph and multi-channel graph convolution network

Current data-driven fault diagnosis methods are prone to overfitting and a decrease in accuracy when working with only a limited number of labeled samples. Additionally, existing graph neural network-based fault diagnosis methods often fail to comprehensively utilize both global and local features. To address these challenges, we propose a rolling bearing fault diagnosis method based on multi-scale weighted visibility graph and a multi-channel graph convolutional network (MCGCN). Our approach converts vibration signals into multiple weighted graphs from the perspective of geometric meaning and extracts local node feature information and global topology information of graphs using MCGCN. Experimental results demonstrate that our method achieves excellent performance under both sufficient and limited data conditions, providing a promising approach for real-world industrial bearing fault diagnosis.

Lake-TopoCat: a global lake drainage topology and catchment database

Abstract. Lakes and reservoirs are ubiquitous across global landscapes, functioning as the largest repository of liquid surface freshwater, hotspots of carbon cycling, and sentinels of climate change. Although typically considered lentic (hydrologically stationary) environments, lakes are an integral part of global drainage networks. Through perennial and intermittent hydrological connections, lakes often interact with each other, and these connections actively affect water mass, quality, and energy balances in both lacustrine and fluvial systems. Deciphering how global lakes are hydrologically interconnected (or the so-called “lake drainage topology”) is not only important for lake change attribution but also increasingly critical for discharge, sediment, and carbon modeling. Despite the proliferation of river hydrography data, lakes remain poorly represented in routing models, partially because there has been no global-scale hydrography dataset tailored to lake drainage basins and networks. Here, we introduce the global Lake drainage Topology and Catchment database (Lake-TopoCat), which reveals detailed lake hydrography information with careful consideration of possible multifurcation. Lake-TopoCat contains the outlet(s) and catchment(s) of each lake; the interconnecting reaches among lakes; and a wide suite of attributes depicting lake drainage topology such as upstream and downstream relationship, drainage distance between lakes, and a priori drainage type and connectivity with river networks. Using the HydroLAKES v1.0 (Messager et al., 2016) global lake mask, Lake-TopoCat identifies ∼ 1.46 million outlets for ∼ 1.43 million lakes larger than 10 ha and delineates 77.5×106 km2 of lake catchments covering 57 % of the Earth's landmass except Antarctica. The global lakes are interconnected by ∼ 3 million reaches, derived from MERIT Hydro v1.0.1 (Yamazaki et al., 2019), stretching a total distance of ∼10×106 km, of which ∼ 80 % are shorter than 10 km. With such unprecedented lake hydrography details, Lake-TopoCat contributes towards a globally coupled lake–river routing model. It may also facilitate a variety of limnological applications such as attributing water quality from lake scale to basin scale, tracing inter-lake fish migration due to changing climate, monitoring fluvial–lacustrine connectivity, and improving estimates of terrestrial carbon fluxes. Lake-TopoCat is freely accessible at https://doi.org/10.5281/zenodo.7916729 (Sikder et al., 2023).

Preterm-birth alters the development of nodal clustering and neural connection pattern in brain structural network at term-equivalent age.

Preterm-born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole-brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term-equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi-shell diffusion-weighted MRI of 28 preterm-born scanned a short time after birth (PB-AB) and at TEA (PB-TEA), and 28 matched term-born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more "connector" hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within-module degree, and participation coefficient) in the limbic/paralimbic, default-mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth-related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.

Distributed Data-Driven Secondary Regulation for the Conflict Between Voltage Recovery and Accurate Current Sharing in DC Microgrids

The problem of voltage restoration and current balancing has been a hot topic in the study of DC microgrids (MGs), and how to engineer a secondary controller to solve the conflict between these two is the concern of this paper. With the above consideration, an optimal distributed data-driven secondary control strategy is proposed in this paper, which relies entirely on the input and output (current measurements) data of DC MGs. The proposed framework completes the design of a secondary control strategy for DC MGs based on a data model for the first time, which incorporates the physical coupling relationship between voltage and current into the controller design and is independent of the global circuit parameters and topology information. As a result of the proposed cost function with future current prediction, the designed secondary control strategy not only retains the advantages of consensus control, such as facilitating the plug-and-play of generation units, but also enables DC MGs to achieve better output current quality at steady state, fast dynamic response and improves robustness to external disturbances. Unlike the existing schemes based on average voltage observer, this paper resorts to a combination of limiting the voltage reference value generated by the secondary controller and a current cooperative strategy based on the learned data model to achieve an easy-to-use trade-off between the two objectives of voltage regulation and current sharing in DC MGs. From the design and implementation point of view, the solution proposed in this paper is going to be simpler. Then, an analytical model of a closed-loop DC MG system under the proposed control scheme is developed, and the analysis of stability and consensus of weighted output current is given accordingly. Finally, the proposed control strategy is tested on a hardware DC MG system with solar panels and an MPPT controller to verify its feasibility.

Early development of the functional brain network in newborns.

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.