Original Topology Research Articles

Analysis of Epidemic Models in Complex Networks and Node Isolation Strategie Proposal for Reducing Virus Propagation

Many models of virus propagation in Computer Networks inspired by epidemic disease propagation mathematical models that can be found in the epidemiology field (SIS, SIR, SIRS, etc.) have been proposed in the last two decades. The purpose of these models has been to determine the conditions under which a virus becomes rapidly extinct in a network. The most common models of virus propagation in networks are SIS-type models or their variants. In such models, the conditions that lead to a rapid extinction of the spread of a computer virus have been calculated and its dependence on some parameters inherent to the mathematical model has been observed. In this article, we will try to analyze a particular SIS-type model proposed in the past by Chakrabarti as well as an SIRS-type variation of this model proposed in the past by myself. I will show through simulations the influence that the topology of a network has on the dynamics of the spread of a virus in different network types. In the recent past, there have been interesting articles that demonstrate the relationship between the eigenvalue λ1 of the adjacency matrix and the reduction in the spread of a virus in a network. From this, the minimization of the spectral radius strategies by edge suppression has been proposed. This problem is NP-complete in its general case and for this reason, heuristic algorithms have been proposed. In this article, I will perform simulations of an SIS-type model in topologies with the same number of nodes but with different structures to compare their epidemic behavior. The simulations will show that regular topologies with small node degrees, i.e., of degree 4, as is the case of the topology that I call Lattice4, have favorable behavior in terms of the fast extinction property, with respect to other denser and less regular topologies such as the binomial topologies as well as Power law topologies. Based on the results of the simulations, my contribution will consist of proposing, as a node isolation strategy, a transformation of the original topology into an approximately regular topology by edge elimination. Although such a transformed topology is not optimal in terms of reducing the propagation of a virus, it induces the rapid extinction of the virus in the network.

A Dual-channel Progressive Graph Convolutional Network via subgraph sampling

<p>Graph Convolutional Networks (GCNs) demonstrate an excellent performance in node classification tasks by updating node representation via aggregating information from the neighbor nodes. Note that the complex interactions among all the nodes can produce challenges for GCNs. Independent subgraph sampling effectively limits the neighbor aggregation in convolutional computations, and it has become a popular method to improve the efficiency of training GCNs. However, there are still some improvements in the existing subgraph sampling strategies: 1) a loss of the model performance caused by ignoring the connection information among different subgraphs; and 2) a lack of representation power caused by an incomplete topology. Therefore, we propose a novel model called Dual-channel Progressive Graph Convolutional Network (DPGCN) via sub-graph sampling. We construct subgraphs via clustering and maintain the connection information among the different subgraphs. To enhance the representation power, we construct a dual channel fusion module by using both the geometric information of the node feature and the original topology. Specifically, we evaluate the complementary information of the dual channels based on the joint entropy between the feature information and the adjacency matrix, and effectively reduce the information redundancy by reasonably selecting the feature information. Then, the model convergence is accelerated through parameter sharing and weight updating in progressive training. We select 4 real datasets and 8 characteristic models for comparison on the semi-supervised node classification task. The results verify that the DPGCN possesses superior classification accuracy and robustness. In addition, the proposed architecture performs excellently in the low labeling rate, which is of practical value to label scarcity problems in real cases.</p>

Synthesis of a mixed-linker Ce-UiO-67 metal-organic framework.

Ce-based metal-organic frameworks (MOFs) have recently gained scientific interest, since Ce is the most abundant rare-earth element in the Earth's crust and since their synthesis has some advantages, including first of all their redox activity, the high porosity of these crystalline materials, and Ce availability. In particular, Ce(iv)-based MOFs, such as Ce-UiO-66 and Ce-UiO-67, are synthesised under mild conditions. For most applications, the presence of functional groups in the frameworks is needed; in this context, linkers containing N-functionalities have been highlighted, as they allow for the incorporation of a large variety of metal cations. In order to insert N-functionalities for the sake of successive metal-functionalization of materials as Ce-UiO-67, we have successfully synthesised a mixed-linker version of this MOF, by incorporating 2,2'-bipyridine-5,5'-dicarboxylic acid together with the conventional biphenyl-4,4'-dicarboxylic acid linker; we have worked on a reproducible and upscalable procedure using benzoic acid as the modulator, without altering the original framework topology. Mixed-linker Ce-UiO-67 MOFs exhibit good thermal stability, high Brunauer-Emmett-Teller (BET) SSAs and microporosity. The pristine samples are highly stable if stored in a desiccator, as demonstrated by the preservation of their high crystallinity for at least 18 months.

Adaptive multi-channel contrastive graph convolutional network with graph and feature fusion

The domain of multi-view semi-supervised classification is an appealing topic in real-world applications. Due to the powerful capability of gathering information from neighbors, Graph Convolutional Network (GCN) has become a hotspot in the classification task. However, most of multi-view classification works based on GCN only assign weights for feature fusion, and directly consider the weighted sum of the adjacency matrices, ignoring the interaction and correlation among features. These may be problematic since aggregating the matrices from less relevant views may destroy the original topology space, leading to undesired performance. To tackle the aforementioned challenges, this paper presents an Adaptive Multi-Channel Graph Convolutional Network (AMC-GCN). To extract the interactive information, AMC-GCN designs a deep interactive feature integration network to incorporate consensus and complementary information. To fuse the graph structures, AMC-GCN exploits the relevance between views and imposes an adjacency matrix fusion network on constructing multiple GCN channels, thereby delivering discriminative information on graphs. To enhance the homogeneity of the framework, AMC-GCN applies a contrastive loss to joint learning during the optimization for classification. With these considerations, AMC-GCN exploits relevant and interactive information between views to promote graph and feature fusion. Substantial experimental results on real-world datasets verify the superiority of AMC-GCN.

PRINCIPAL COMPONENTS VERSUS AUTOENCODERS FOR DIMENSIONALITY REDUCTION: A CASE OF SUPER-RESOLVED OUTPUTS FROM PRISMA HYPERSPECTRAL MISSION DATA

Abstract. This study attempts to solve these issues associated with hyperspectral (HS) data, i.e., coarse spatial resolution and high volume, by understanding the effect of deep learning and traditional dimensionality reduction on super-resolved products generated from the recently launched PRecursore IperSpettrale della Missione Applicativa (PRISMA) HS mission. Four single-frame super-resolution (SR) algorithms have been used to super-resolve a 30 m PRISMA scene of Ahmedabad, India and generate 15 m spatial resolution images with both spatial and spectral fidelity. Iterative back projection (IBP) and sparse representation (SIS) are the best and worst-performing SR algorithms following a comparative assessment and validation protocol. Next, denoising autoencoders and PCT computed using singular and eigenvalue decompositions have been executed on the original PRISMA, IBP and SIS-based super-resolved datasets. The resulting low-dimensional representations have been assessed to preserve the original dataset's topology using label-independent Lee and Verleysen's co-ranking matrix and loss of quality measure. Findings suggest that autoencoders are computationally expensive and require a higher neighbourhood size than PCT and its variants to produce a high-quality encoding. These insights remain significant for urban information extraction as there are few direct comparative assessments between machine learning-based linear and non-linear data compression methods in earlier studies.

Primal Structure with Closure Operators and Their Applications

Acharjee et al. have created a new structure in mathematics called a primal. Therefore, the primary goal of this research was to introduce and explore more primal space features. Additionally, we studied some of the fundamental characteristics of two novel operators that we define using primal spaces. Using these new operators, we were able to create a weaker version of the original topology. Finally, we provide some examples to further illustrate our discussion of some of their characteristics.

Micro-Grid Line Protection Method Based on Current Frequency Variability

Due to the access of multiple distributed power sources (DG) in the Micro-Grid, the original radiation topology is changed, making the traditional distribution network line protection methods no longer applicable; and most of the existing Micro-Grid line protection methods discuss their grid-connected and Islanded operation separately, resulting in a complex and variable line protection configuration. By analysing the transient current characteristics provided by the DG side and the network side of the Micro-Grid during grid-connected operation and the transient current characteristics provided by different types and numbers of DG on both sides during Islanded operation, it is concluded that there are significant differences in the frequency characteristics of the first-mode components of the current provided on both sides of the protected section of the Micro-Grid line during a fault. As a result, a Micro-Grid line longitudinal differential protection method based on the frequency similarity of the current first-mode quantities is proposed to achieve the identification and removal of faults inside and outside the protected section. A 380 V Micro-Grid simulation model is set up in MATLAB software and the proposed method is tested in a large number of experiments to enable it to operate correctly under different operating modes, different fault locations, and different transition resistances of the Micro-Grid, and to adapt to different operating conditions.

A novel method for intersecting machining feature segmentation via deep reinforcement learning

Machining feature segmentation is a primary task in machining feature recognition, as it directly impacts downstream activities such as feature type identification and process planning. However, the intersecting and overlapping of multiple machining features within intersecting machining features disrupt the original geometries and topologies, resulting in complex structures and varied forms. Therefore, segmenting intersecting machining features is highly challenging. Existing methods for intersecting machining feature segmentation often suffer from issues related to poor effectiveness and the difficulty of applying segmentation results to subsequent process planning. To address these concerns, this paper presents a novel framework for segmenting intersecting machining features using deep reinforcement learning. The framework takes intersecting machining features represented by attributed adjacency graphs as input and generates isolated machining features as output. Leveraging the robust feature representation, decision-making, and automatic learning capabilities of deep reinforcement learning, this framework enhances the effectiveness of intersecting machining feature segmentation. Moreover, the framework takes into account the machining process when designing the reward mechanism, thereby ensuring that the results are beneficial to subsequent process planning. In the experimental studies, plenty of intersecting machining features from 2.5D computer numerical control parts are used as examples to verify the feasibility and effectiveness of the proposed approach. The experimental results demonstrate that the proposed approach successfully addresses some existing challenges faced by several state-of-the-art methods in intersecting machining feature segmentation. The findings highlight the robustness of the proposed approach and its potential to address the challenges associated with intersecting machining feature segmentation. Consequently, this study contributes to the advancement of machining feature recognition and holds practical implications for process planning.

Scene context‐aware graph convolutional network for skeleton‐based action recognition

AbstractSkeleton‐based action recognition methods commonly employ graph neural networks to learn different aspects of skeleton topology information However, these methods often struggle to capture contextual information beyond the skeleton topology. To address this issue, a Scene Context‐aware Graph Convolutional Network (SCA‐GCN) that leverages potential contextual information in the scene is proposed. Specifically, SCA‐GCN learns the co‐occurrence probabilities of actions in specific scenarios from a common knowledge base and fuses these probabilities into the original skeleton topology decoder, producing more robust results. To demonstrate the effectiveness of SCA‐GCN, extensive experiments on four widely used datasets, that is, SBU, N‐UCLA, NTU RGB + D, and NTU RGB + D 120 are conducted. The experimental results show that SCA‐GCN surpasses existing methods, and its core idea can be extended to other methods with only some concatenation operations that consume less computational complexity.

Development of Respiratory Tract Organs for ICRP Pediatric Mesh-type Reference Computational Phantoms.

As part of the activities of the International Commission on Radiological Protection (ICRP) Task Group 103, the present study developed a new set of respiratory tract organs consisting of the extrathoracic, bronchial, bronchiolar, and alveolar-interstitial regions for newborn, 1-, 5-, 10-, and 15-y-old males and females for use in pediatric mesh-type reference computational phantoms. The developed respiratory tract organs, while preserving the original topologies of those of the pediatric voxel-type reference computational phantoms of ICRP Publication 143, have improved anatomy and detailed structure and also include μm-thick target and source regions prescribed in ICRP Publication 66. The dosimetric impact of the developed respiratory tract organs was investigated by calculating the specific absorbed fraction for internal electron exposures, which were then compared with the ICRP Task Group 96 values. The results showed that except for the alveolar-interstitial region as a source region, the pediatric mesh phantoms showed larger specific absorbed fractions than the Task Group 96 values. The maximum difference was a factor of ~3.5 for the extrathoracic-2 basal cell and surface as target and source regions, respectively. These results reflect the differences in the target masses and geometry caused by the anatomical enhancement of the pediatric mesh phantoms. For the alveolar-interstitial region as a source region, the pediatric mesh phantoms showed larger values for low energy ranges and lower values with increasing energies, owing to the differences in the size and shape of the alveolar-interstitial region.

TABASCO—Topology Algorithm That Benefits from Adaptation of Sorted Compliances Optimization

Although structural topology optimization has been developing for decades, it still plays a leading role within the area of engineering design. Solving contemporary design problems coming from industry requires the implementation of efficient methods and approaches. This stimulates research progress in the development of novel and versatile topology optimization algorithms. To follow these modern trends, an original topology generator has been elaborated and finally built as a Cellular Automaton with original update rules. The motivation for building the algorithm in this way came from the idea of utilizing the benefits of local compliances sorting. This is conducted on two levels: on the global level, the monotonic function mapping local compliances distribution is defined based on their sorted values; on the local level, for each cell, the compliances are sorted within the cell neighborhood. The three largest absolute values are selected, and these are the basis from which to formulate Cellular Automata update rules. These original rules can efficiently control the generation of structural topologies. This technique is somewhat inspired by the grey wolf optimizer strategy, wherein the process of updating design variables refers to the positions of the three best fitted wolves. It is proposed that we refer to the topology algorithm that benefits from the adaptation of sorted compliances optimization as TABASCO. The developed algorithm is a modified version of the flexible Cellular Automata one presented previously. The implemented extension, regarding the local level cell sorting, allows us to improve the resulting compliance values. The advantages of the algorithm, both from numerical and practical engineering points of view, as compared to the others developed within the field, may be gathered as follows: the algorithm works based on simple update rules, i.e., its numerical implementation is not complicated; it does not require gradient computations; filtering techniques are not needed; and it can easily be combined with professional structural analysis programs which allow engineering applications. The developed topology generator has been linked with ANSYS to show that it can be incorporated into a commercial structural analysis package. This is especially important with respect to the engineering implementations.

Active power decoupling scheme of symmetrical LCL structure in single‐phase grid‐connected voltage source inverter for ultra‐high voltage transmission

SummaryThe voltage source converter (VSC) is an important component of ultra‐high voltage (UHV) technology, where the former is responsible for converting new energy sources into electricity and the latter enables the transmission of electricity over long distances. However, the VSC has a serious problem of oscillation power under three‐phase unbalance, which comes from the single‐phase voltage source inverter (VSI) that is the basic structure of VSC. So there is always the requirement for active power decoupling (APD). Conventional passive solutions increase the overall size significantly, and existing active solutions either require the addition of a decoupling topology or rely strongly on their own original topology and more complex control. To overcome this drawback, this paper proposes an improved APD method with a combination of topology‐type and control‐type decoupling features for single‐phase VSI based on the symmetrical LCL filter for solving the power oscillation problem of VSC at the source. The proposed structure can decrease common mode interference and can absorb the oscillation power by the original filter capacitors of the LCL filter. Besides, the original circuit components can be reused, and the decoupling is simple enough to achieve APD without affecting the full bridge control. Next, the control is optimized to reduce the compensation error and input voltage. Finally, the experimental device is carried out to verify the effectiveness of the proposed method.

On countably perfectly meager and countably perfectly null sets

We study a strengthening of the notion of a universally meager set and its dual counterpart that strengthens the notion of a universally null set.We say that a subset A of a perfect Polish space X is countably perfectly meager (respectively, countably perfectly null) in X, if for every perfect Polish topology τ on X, giving the original Borel structure of X, A is covered by an Fσ-set F in X with the original Polish topology such that F is meager with respect to τ (respectively, for every finite, non-atomic, Borel measure μ on X, A is covered by an Fσ-set F in X with μ(F)=0).We prove that if 2ℵ0≤ℵ2, then there exists a universally meager set in 2N which is not countably perfectly meager in 2N (respectively, a universally null set in 2N which is not countably perfectly null in 2N).

Crystal chemistry and ab initio investigations of new hard tetragonal C9 and C12 allotropes with edge- and corner-sharing C4 tetrahedra and diamond-related properties

Stable tetragonal C9 and C12 with original topologies have been devised based on crystal chemistry rationale and unconstrained geometry optimization calculations within the density functional theory (DFT). The two new carbon allotropes characterized by corner- and edge-sharing tetrahedra, are mechanically (elastic constants) and dynamically (phonons) stable and exhibit thermal and mechanical properties close to diamond. The electronic band structures show insulating behavior with band gaps close to 5 eV, like diamond.

An enriched mixed finite element model for the simulation of microseismic and acoustic emissions in fractured porous media with multi‐phase flow and thermal coupling

AbstractA novel mixed enriched finite element model is developed for coupled non‐linear thermo‐hydro‐mechanical simulation of fractured porous media with three‐phase flow and thermal coupling. Simulation of induced acoustic emission (AE) and microseismic emission (ME) due to tensile fracturing and shear slip instability of pre‐existing fracture interfaces is carried out and the numerical results of the emitted signals are analysed. The mathematical model is based on the generalized Biot's theory for coupled interaction of solid and fluid phases. A computationally robust non‐linear solver is developed to handle the severe non‐linearities arising from fluid saturations, relative permeabilities of fluids, constitutive models of interfaces and convective thermal coupling. To model pre‐existing natural fractures and faults, discrete fracture propagation and nucleation of cracks (micro‐cracking) independently of the original mesh topology, a local Partition‐of‐Unity (PU) finite element method, namely, the Phantom Node Method (PNM) is implemented. The cohesive fracture modelling scheme is implemented to account for the non‐linear behaviour of fracturing and localization, and to rectify the non‐physical stress singularity condition at the fracture tip. Effects of different system parameters on fracturing, shear‐slip instability and the associated induced AEs and MEs are investigated through various numerical results.

Energy-Efficient Topology Control Mechanism for IoT-Oriented Software-Defined WSNs

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet-of-Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this paper, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay sub-networks of SDWSN, respectively. In the cluster sub-network, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay sub-network, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay sub-network is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay sub-network. Finally, simulation results show that both two TC algorithms can improve the corresponding sub-network EE of time-varying SDWSN.

High Proton Conductivity of Magnetically Ordered 2D Oxalate-Bridged [MnIICrIII] Coordination Polymers with Irregular Topology.

Two heterometallic coordination polymers {[NH(CH3)2(C2H5)]8[Mn4Cl4Cr4(C2O4)12]}n (1) and {[NH(CH3)-(C2H5)2]8[Mn4Cl4Cr4(C2O4)12]}n (2) were obtained by slow evaporation of an aqueous solution containing the building block [A]3[Cr(C2O4)3] [A = (CH3)2(C2H5)NH+ or (CH3)(C2H5)2NH+] and MnCl2·2H2O. The isostructural compounds comprise irregular two-dimensional (2D) oxalate-bridged anionic layers [Mn4Cl4Cr4(C2O4)12]n8n- with a Shubnikov plane net fes topology designated as (4·82), interleaved by the hydrogen-bonded templating cations (CH3)2(C2H5)NH+ (1) or (CH3)(C2H5)2NH+ (2). They exhibit remarkable humidity-sensing properties and very high proton conductivity at room temperature [1.60 × 10-3 (Ω·cm)-1 at 90% relative humidity (RH) of 1 and 9.6 × 10-4 (Ω·cm)-1 at 94% RH of 2]. The layered structure facilitates the uptake of water molecules, which contributes to the enhancement of proton conductivity at high RH. The better proton transport observed in 1 compared to that in 2 can be tentatively attributed to the higher hydrophilicity of the cations (CH3)2(C2H5)NH+, which is closely related to their affinity for water molecules. The original topology of the anionic networks in both compounds leads to the development of interesting magnetic phases upon cooling. The magnetically ordered ground state can be described as the coupling of ferromagnetic spin chains in which Mn2+ and Cr3+ ions are bridged by bis(bidentate) oxalate groups into antiferromagnetic planes through monodentate-bidentate oxalate bridges in the layers, which are triggered to long-range order below temperature 4.45 K via weaker interlayer interactions.

Core‐Shell‐Type All‐Inorganic Heterometallic Nanoclusters: Record High‐Nuclearity Cobalt Polyoxoniobates for Visible‐Light‐Driven Photocatalytic CO2 Reduction

AbstractOnly rarely have polyoxometalates been found to form core–shell nanoclusters. Here, we succeeded in isolating a series of rare giant and all‐inorganic core–shell cobalt polyoxoniobates (Co−PONbs) with diverse shapes, nuclearities and original topologies, including 50‐nuclearity {Co12Nb38O132}, 54‐nuclearity {Co20Nb34O128}, 62‐nuclearity {Co26Nb36O140} and 87‐nuclearity {Co33Nb54O188}. They are the largest Co−PONbs and also the polyoxometalates containing the greatest number of Co ions and the largest cobalt clusters known thus far. These molecular Co−PONbs have intriguing and atomically precise core–shell architectures comprising unique cobalt oxide cores and niobate oxide shells. In particular, the encapsulated cobalt oxide cores with different nuclearities have identical compositions, structures and mixed‐valence Co3+/Co2+ states as the different sized Co−O moieties of the bulk cubic‐spinel Co3O4, suggesting that they can serve as various molecular models of the cubic‐spinel Co3O4. The successful construction of the series of the Co−PONbs reveals a feasible and versatile synthetic method for making rare core–shell heterometallic PONbs. Further, these new‐type core–shell bimetal species are promising cluster molecular catalysts for visible‐light‐driven CO2 reduction.

Core-Shell-Type All-Inorganic Heterometallic Nanoclusters: Record High-Nuclearity Cobalt Polyoxoniobates for Visible-Light-Driven Photocatalytic CO2 Reduction.

Only rarely have polyoxometalates been found to form core-shell nanoclusters. Here, we succeeded in isolating a series of rare giant and all-inorganic core-shell cobalt polyoxoniobates (Co-PONbs) with diverse shapes, nuclearities and original topologies, including 50-nuclearity {Co12 Nb38 O132 }, 54-nuclearity {Co20 Nb34 O128 }, 62-nuclearity {Co26 Nb36 O140 } and 87-nuclearity {Co33 Nb54 O188 }. They are the largest Co-PONbs and also the polyoxometalates containing the greatest number of Co ions and the largest cobalt clusters known thus far. These molecular Co-PONbs have intriguing and atomically precise core-shell architectures comprising unique cobalt oxide cores and niobate oxide shells. In particular, the encapsulated cobalt oxide cores with different nuclearities have identical compositions, structures and mixed-valence Co3+ /Co2+ states as the different sized Co-O moieties of the bulk cubic-spinel Co3 O4 , suggesting that they can serve as various molecular models of the cubic-spinel Co3 O4 . The successful construction of the series of the Co-PONbs reveals a feasible and versatile synthetic method for making rare core-shell heterometallic PONbs. Further, these new-type core-shell bimetal species are promising cluster molecular catalysts for visible-light-driven CO2 reduction.

Transformer-Less Soft-Switching High-Gain PWM Boost Converter With Reduced Components and Increased Effective Duty Cycle

A transformer-less soft-switching high-gain pulsewidth-modulation (PWM) converter is proposed in this article. The proposed converter is derived from a single-switch dual-inductor transformer-less boost converter by replacing the complementary diode connected to the switch with a synchronous rectifier switch. Moreover, the peak value of the reverse current of the rear-end inductor in continuous bidirectional conduction mode (CBCM) is regulated to exceed the input inductor valley current. Based on these two measures, the zero-voltage switching (ZVS) for all switches and the natural turn-off for the output diode are realized, and the effective duty cycle is enhanced. Besides, the proposed ZVS converter has the merits of fewer power components count, reduced voltage stress, and lower cost. Furthermore, the ringing issue in the original topology under light load or high input voltage conditions is eliminated. The operation principle, steady-state characteristics, dynamic model, soft-switching operation, and realization conditions are analyzed in detail, and subsequently, the parameter design method is elaborated. The proposed topology demonstrates an enhanced efficiency performance over wide operating operations with a maximum efficiency of 97.1%.