Topological Model Research Articles

Topological models of abstract commensurators

The full solenoid over a topological space X is the inverse limit of all finite covers. When X is a compact Hausdorff space admitting a locally path-connected universal cover, we relate the pointed homotopy equivalences of the full solenoid to the abstract commensurator of the fundamental group \pi_{1}(X) . The relationship is an isomorphism when X is an aspherical CW complex. If X is additionally a geodesic metric space and \pi_{1}(X) is residually finite, we show that this topological model is compatible with the realization of the abstract commensurator as a subgroup of the quasi-isometry group of \pi_{1}(X) . This is a general topological analog of work of Biswas, Nag, Odden, Sullivan, and others on the universal hyperbolic solenoid, the full solenoid over a closed surface of genus at least two.

Research on a Critical Link Discovery Method for Network Security Situational Awareness.

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

The topological model of NS4B and its TMD3 in duck TMUV proliferation

Duck Tembusu virus (DTMUV) belongs to the Flaviviridae family and mainly infects ducks. Duck Tembusu virus genome encodes one polyprotein that undergoes cleavage to produce 10 proteins. Among these, NS4B, the largest transmembrane protein, plays a crucial role in the viral life cycle. In this study, we investigated the localization of NS4B and found that it is located in the endoplasmic reticulum, where it co-localizes with DTMUV dsRNA. Subsequently, we confirmed 5 different transmembrane domains of NS4B and discovered that only its transmembrane domain 3 (TMD3) can traverse ER membrane. Then mutations were introduced in the conserved amino acids of NS4B TMD3 of DTMUV replicon and infectious clone. The results showed that V111G, V117G, and I118G mutations enhanced viral RNA replication, while Q104A, T106A, A113L, M116A, H120A, Y121A, and A122G mutations reduced viral replication. Recombinant viruses with these mutations were rescued and studied in BHK21 cells. The findings demonstrated that A113L and H120A mutations led to higher viral titers than the wild-type strain, while Q104A, T106A, V111G, V117G, and Y121A mutations attenuated viral proliferation. Additionally, H120A, M116A, and A122G mutations enhanced viral proliferation. Furthermore, Q104A, T106A, V111G, M116A, V117G, Y121A, and A122G mutants showed reduced viral virulence to 10-d duck embryos. Animal experiments further indicated that all mutation viruses resulted in lower genome copy numbers in the spleen compared to the WT group 5 days postinfection. Our data provide insights into the topological model of DTMUV NS4B, highlighting the essential role of NS4B TMD3 in viral replication and proliferation.

Space classification for indoor pedestrian navigation with morphological and functional characteristics

AbstractIndoor navigation networks serve as the foundation for indoor pedestrian navigation services. However, current graph‐based topological network models cannot easily represent complex indoor structures in a manner consistent with user behaviour and cognitive patterns, and thus cannot support fine‐grained indoor navigation analysis services. To enhance the rationality of the model, we considered the influences of morphological and functional characteristics of indoor spaces on pedestrian movement and proposed a method for classifying navigation spaces. This method, based on convex space segmentation, defines morphological and functional characteristic functions, classifying indoor navigation spaces as either corridors or open spaces, which suitable for representing as median axis models or visual graphs, respectively. These categories supported the creation of topological network models for route planning analysis. Indoor map experiments confirmed the effectiveness of our method in identifying various types of corridors and open spaces, that addressing the limitations of width‐based parameters, even in complex indoor settings.

Fundamental aspects of the molecular topology of fuchsine acid dye with connection numbers.

Fuchsine acid serves as a supramolecular dye in Masson's trichrome stain, finding extensive applications in histology. It is also utilized with picric acid in Van Gieson's method to reveal red collagen fibers and in Masson's trichrome to highlight smooth muscle in contrast to collagen. Beyond these applications, it plays a crucial role in electronic fields and photonic devices as an organic semiconductor. Therefore, investigating and predicting the complex molecular structure of fuchsine acid becomes essential, serving as the foundation for understanding its physicochemical features. This article employs topological modeling, specifically a connection number edge partition, to explore the supramolecular nature of fuchsine acid. Closed formulae for key degree-based molecular descriptors are derived, aiming to illuminate the effectiveness of these descriptors for QSAR and QSPR analyses.

A bio-inspired model for robust navigation assistive devices

This paper proposes a new implementation and evaluation in a real-world environment of a bio-inspired predictive navigation model for mobility control, suitable especially for assistance of visually impaired people and autonomous mobile systems. This bio-inspired model relies on the interactions between formal models of three types of neurons identified in the mammals’ brain implied in navigation tasks, namely place cells, grid cells, and head direction cells, to construct a topological model of the environment under the form of a decentralized navigation graph. Previously tested in virtual environments, this model demonstrated a high tolerance to motion drift, making possible to map large environments without the need to correct it to handle such drifts, and robustness to environment changes. The presented implementation is based on a stereoscopic camera, and is evaluated on its possibilities to map and guide a person or an autonomous mobile robot in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map, localize and guide a person through this path. The model predictions remain robust to environment changes, and allow to estimate traveled distances with an error rate below 3% over test paths, up to 100m. The tests performed on a robotic platform also demonstrated the pertinence of navigation data produced by this navigation model to guide an autonomous system. These results open the way toward efficient wearable assistive devices for visually impaired people independent navigation.

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.

Inverse design of three-dimensional multicellular biobots with target functions

Hybrid living biobots consisting of active cells hold promise for significant applications as, for example, intelligent devices in medical engineering and organisms with specific functions in synthetic biology. However, the design and creation of living biobots with various cells remain a challenge. In this paper, we propose a three-dimensional inverse optimization strategy based on the pixel topology optimization method, to design self-propelled living biobots with the function of biomechanical actuations. For illustration, we design several biobots composed of active and passive elements that mimic cardiomyocytes and passive epidermal cells sourced from such as Xenopus laevis, human induced pluripotent stem cells or neonatal rats. Their topologies are optimized by implementing the active constitutive relations of cells into the multicellular topological interpolation model. Effects of nutrient concentrations, elasticity, and anisotropic contraction of cardiomyocytes on the topologies and functionalities of the biobots are examined. In addition, we unveil the living topological interfaces generated by the collective actuations of the optimized biobots. We show a potential of collective biobots for high-throughput drug screening owing to their distinct biomechanical responses under healthy and sick conditions. The proposed inverse optimization method can be extended to design various functional multicellular biological systems, which impacts the studies of organ development, synthetic biology, and medical engineering.

Identification of Key Risk Hotspots in Mega-Airport Surface Based on Monte Carlo Simulation

The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.

Observer‐based event‐triggered consensus control of nonlinear cyber‐physical systems under backlash‐like hysteresis and denial‐of‐service attacks

AbstractIn this article, we investigate the problem of event‐triggered distributed consensus control for a class of complex cyber‐physical systems (CPSs), where unknown backlash‐like hysteresis inputs and denial‐of‐service (DoS) attacks are considered in actuators and communication channels, respectively. In the first place, when communication networks are subjected to malicious DoS attacks, the connection weights of network communication topologies experience disruptions. To solve this issue, this work proposes a solution by constructing topological models for communication networks employing switching topologies and utilizing observers to estimate the combined measurement errors during the time interval of DoS attacks. Secondly, to address the repercussions of DoS attacks on control units and to optimize the utilization of communication resources, a distributed control protocol based on an event‐triggered scheme and employing locally deployed estimators is designed to significantly decrease the update frequency of control signals. Meanwhile, utilizing Lyapunov stability theory, the boundedness of all signals in the closed‐loop system is validated, thereby accomplishing the goal of consensus control. At last, the efficacy of the proposed control scheme is demonstrated through two simulation examples.

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences

Learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies among the event sequences. Fortunately, in practice, we find these dependencies can be modeled by a topological network, suggesting a potential solution to the non-i.i.d. problem by introducing the prior topological network into Granger causal discovery. This observation prompts us to tackle two ensuing challenges: 1) how to model the event sequences while incorporating both the prior topological network and the latent Granger causal structure, and 2) how to learn the Granger causal structure. To this end, we devise a unified topological neural Poisson auto-regressive model with two processes. In the generation process, we employ a variant of the neural Poisson process to model the event sequences, considering influences from both the topological network and the Granger causal structure. In the inference process, we formulate an amortized inference algorithm to infer the latent Granger causal structure. We encapsulate these two processes within a unified likelihood function, providing an end-to-end framework for this task. Experiments on simulated and real-world data demonstrate the effectiveness of our approach.

Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights

Ensemble clustering learns more accurate consensus results from a set of weak base clustering results. This technique is more challenging than other clustering algorithms due to the base clustering result set's randomness and the inaccessibility of data features. Existing ensemble clustering methods rely on the Co-association (CA) matrix quality but lack the capability to handle missing connections in base clustering. Inspired by the neighborhood high-order and topological similarity theories, this paper proposes a topological ensemble model based on high-order information. Specifically, this paper compensates for missing connections by mining neighborhood high-order connection information in the CA matrix and learning optimal connections with adaptive weights. Afterward, the learned excellent connections are embedded into topology learning to capture the topology of the base clustering. Finally, we incorporate adaptive high-order connection representation and topology learning into a unified learning framework. To our knowledge, this is the first ensemble clustering work based on topological similarity and high-order connectivity relations. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed method. The source code of the proposed approach is available at https://github.com/ltyong/awec.

Modeling surface structure of tribotechnical materials

Modern tribology makes it possible to correctly calculate, diagnose, predict and select appropriate materials for friction pairs, to determine the optimal mode of operation of the tribo-joint. The main parameter for solving friction problems and other problems of tribology is the topography of the surface. The main purpose of the models in these tasks is to display the tribological properties of engineering surfaces. In the framework of the classical approach, the topography of the surface is studied on the basis of its images from the point of view of functional and statistical characteristics: the evaluation of the functional characteristics is based on the maximum roughness along the height and the average roughness along the center line, and the statistical characteristics are estimated using the power spectrum or the autocorrelation function. However, these characteristics are not only surface properties. They depend on the resolution of the device for measuring the surface geometry and the length of the scan. However, the degree of complexity of a surface shape can be represented by a parameter called the fractal dimension: a higher degree of complexity has a larger value of this parameter. Fractal dimensionality is a characteristic of surface relief and makes it possible to explain tribological phenomena without the influence of resolution. This article provides an overview of mathematical approaches to the description of the relief of engineering surfaces, in particular statistical, stochastic and topological modeling, their limitations, advantages and disadvantages. The implementation of the principles of the theory of fractal structures is discussed, which makes it possible to introduce the degree of imbalance of the tribological system into the analysis of structure formation in the surface and near-surface layers of materials and to describe the development of friction and wear processes. This is the basis for controlling the structure of the surface layers of materials with given properties. The concept of fractals, used for the quantitative description of the dissipative structure of the tribojunction zone, makes it possible to establish a connection between its fractal dimension and mechanical properties, as well as critical states of deformation of metals and alloys. The course of research and stages of fractal modeling, the classification of methods of fractal analysis of the structure of engineering contact surfaces are considered. A critical analysis of modern models based on the energy-spectral density function, which are quite similar to fractal models, is presented. Readers are expected to gain an overview of research developments in existing modeling methods and directions for future research in the field of tribology

Intelligent Extraction of Multi-style and Multi-template Title Block Information Based on Fuzzy Matching

ABSTRACT Title blocks are crucial primary data on drawings necessary for the operation of modern manufacturing enterprises. However, their uniform export poses a challenge due to the heterogeneity across multiple sources and the variety of export templates. Therefore, this paper proposes an intelligent extraction method for title block information (TBI) based on fuzzy matching. This method evaluates the geometrical composition of the title block, the logical relationship between information cells, and the diversification of the title block. It establishes a weighted intersection point matrix for the title block as the topological structure model. The fuzzy matching, based on the intersection matrix points, disregards the variations in the size and position of the title block and focuses only on the style that impacts the extraction of information. The matched title blocks are categorized, and the base table of each type determines the information distribution of the title block. A hash table is then created between the description cell and the value cell of information to accumulate the TBI. The method is integrated into the AutoCAD software and presented with an interactive program interface. Finally, an example is presented to further demonstrate the stability, reliability, and efficiency of the method and the system.

A synthesis method for epicyclic chains based on new topological model and similarity

The structural design is a prerequisite for the creation of mechanical equipment. This study focuses on the structural synthesis of epicyclic chains (ECs), and proposes a new topological model and synthesis method of ECs to address the existing research problems (i.e. synthesis dependence on multiple graphic transformations and the generation of pseudo-isomorphism). First, the structural characteristics of ECs are analyzed to explore the relation between their axles, gears, and planet carriers, and the corresponding topological model and the adjacency matrix expression of ECs are provided. Then, the topological elements corresponding to the axles, gears, and gear pairs are added sequentially to the topological graph corresponding to the planet carriers, and the constraints are established based on similarity to inhibit the generation of isomorphism. Finally, all the databases of feasible ECs are obtained through gear loop discrimination. All processes are carried out with computer assistance. The synthesis results of the ECs with 1–3 degrees of freedom and up to 10 components are presented. The logic of this paper is to simplify (or optimize) the synthesis process to improve efficiency of structural synthesis, which will break through the threshold of computing power and provide more possibilities for the design of mechanical equipment.

Reactive power capacity allocation technology of shore‐based power supply system of offshore oil production platform under different network topology reliability measures

AbstractTo improve the economy of offshore oil production platform shore‐based power transmission mode, the reactive power capacity allocation technology of offshore oil production platform shore‐based power supply system based on different network topology reliability measures was proposed. The optimization model of power supply system network topology and the structure model of voltage source converter high voltage direct current transmission system were established, the start and stop control scheme of flexible direct current (DC) transmission system was designed, and the operation parameters of converter station and DC line were monitored in real time. The topological structure of offshore oil production platform shore‐based power supply system is constructed, the mathematical model of flexible DC transmission system is established when the alternating current side voltage is balanced, the reactive power capacity configuration of offshore oil production platform shore‐based power supply system is optimized, and the life cycle cost results are calculated. The experimental results show that the algorithm proposed in this paper can obtain the optimal solution only after 26 calculations, with high optimization efficiency and good convergence effect. Reactive capacity configuration of shore‐based power supply system for offshore production platform can reduce failure rate and life cycle cost.

Assessment and solutions for vulnerability of urban rail transit network based on complex network theory: A case study of Chongqing

As a typical complex network system, the operating environment of rail transit network (RTN) is complex and demanding. This study aims to accurate assess the weaknesses and vulnerability of RTN, which is crucial for ensuring its smooth operation. Taking Chongqing Rail Transit (CRT) as an example, this study developed a network topology model using the spatial L method and analyzed the network structure characteristics, along with the importance of key nodes under different indicators, based on complex network theory. Additionally, this study analyzed the geographical spatial distribution characteristics of nodes based on the topography and urban spatial structure of Chongqing. Then, this study classified the nodes in the RTN according to basic topological indicators, namely degree, betweenness centrality, network efficiency, and passenger flow volume (PFV). The results indicated six cluster of nodes, reflecting the variability in node vulnerability concerning overall influence (providing alternative paths, reducing path length), regional aggregation capacity, and transportation capacity. Finally, this study proposed targeted management strategies for different clusters of nodes and their respective geographical locations, providing necessary references for rational planning, safety protection, and sustainable construction of RTN.

Critical scaling of a two-orbital topological model with extended neighboring couplings

Extended-range models are the interesting systems, which has been widely used to understand the non-local properties of the fermions at quantum scale. We aim to study the interplay between criticality and extended range couplings under various symmetry constraints. Here, we consider a two orbital Bernevig–Hughes–Zhang model in one dimension with longer (finite neighbor) and long-range (infinite neighbor) couplings. We study the behavior of model using scaling laws and universality class for models with Hermitian, parity-time (PT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr{P}\\mathscr{T}$$\\end{document}) symmetric and broken time-reversal symmetries. We observe the interesting results on multi-criticalities, where the universality class of critical exponent is different than the normal criticalities. Also, the results can be generalized by considering the interplay between criticalities and different symmetry classes of Hamiltonian. Also, with the introduction of extended-range of coupling, there occurs different criticalities, and we provide the analogy to characterize their universality classes. We also show the violation of Lorentz invariance at multi-criticalities and evaluation of short-range limit in long-range models as the highlights of this work.

AVERAGED MODEL OF DCMGS WITH PV ARRAYS FOR TRACTION SUBSTATION IN HEAVY RAIL SYSTEMS

This paper presents the mathematical model of DCMGs with PV arrays for traction substation in heavy rail systems. The considered power system consists of many power converters in which the dynamic model is time-varying due to the switching actions inside the power converters. The time-varying model is not useable for the system analysis and design. Hence, this paper describes the way how to derive the time-invariant model using the combination between DQ and GSSA methods. The simulation results using the SimPowerSystem® block set on MATLAB are used to validate the proposed model. The results show that the proposed model can provide the accurate transient and steady-state responses. Moreover, simulations using the proposed model can reduce the simulation time by 72% in comparison with the exact topological model of MATLAB. The resulting averaged models are also very useful for studies of dynamic behavior of complex power systems

Using a simple radial basis function neural network to predict the glass transition temperature of alkali borate glasses

Artificial Neural Networks (ANN) are powerful machine learning algorithms. In the context of glass science, ANNs have been successfully used to induce composition-property regression models. These models are already influencing the way that we design new glasses, moving away from the traditional “cook and look” approach to computer-aided design. In this work, we induced an ANN model for the glass transition temperature Tg for glasses of the family xM2O⋅(100−x)B2O3, where M = Li, Na, K, Rb, and Cs, using simple radial basis function neural networks (RBF) with only two neurons. Statistically, the results of our methodologies are comparable to other complex models (empirical or physical) available in the literature with more neurons, such as the recent GlassNet and the topological model proposed by Mauro and co-authors (2009). This result supports the idea that a simpler specialized model can perform better than a complex general model for a restricted training domain.