Specific Topology Research Articles

Design of Stator-Magnet Moving-Iron Transverse-Flux Linear Oscillatory Machine Considering Asymmetric Saturation

Stator-magnet moving-iron transverse-flux linear oscillatory machine (SMTLOM) is a promising reciprocating drive for linear actuator due to its merits of simple structure, high reliability, low fabrication cost, etc. However, for the special topology and operation principle of SMTLOM, the asymmetric saturation would occur during its operation, which has significant influence on the electromagnetic performance. Hence, in this article, the design method for SMTLOM is presented with the full consideration on saturation effect. First, the axial-asymmetric saturation is described in detail, and consequently, the influence of such effect on the main parameters selection is investigated based on the introduced saturation coefficient. Furthermore, the demagnetization calibration is also carried out to illustrate how the axial-asymmetric saturation impacts the operation point of the magnets on flux-weaken-side stator. Finally, a prototype is manufactured and tested by both static and dynamic experiments, which have validated the theoretical analysis and simulation.

Harnessing the ubiquitin code to respond to environmental cues.

Ubiquitination is an essential post-translational signal that allows cells to adapt and respond to environmental stimuli. Substrate modifications range from a single ubiquitin molecule to complex polyubiquitin chains, where diverse chain topologies constitute a code that is utilized to modify the functions of proteins in numerous cellular signalling pathways. Diverse ubiquitin chain topologies are generated by linking the C-terminus of ubiquitin to one of seven lysine residues or the N-terminal methionine 1 residue of the preceding ubiquitin. Cooperative action between a large array of E2 conjugating and E3 ligase enzymes supports the formation of not only homotypic ubiquitin chains but also heterotypic mixed or branched chains. This complex array of chain topologies is recognized by proteins containing linkage-specific ubiquitin-binding domains and regulates numerous cellular pathways. Although many functions of the ubiquitin code in plants remain unknown, recent work suggests that specific chain topologies are associated with particular molecular processes. Deciphering the ubiquitin code and how plants utilize it to cope with the changing environment is essential to understand the regulatory mechanisms that underpin myriad stress responses and establishment of environmental tolerance.

Flow-by-flow traffic matrix prediction methods: Achieving accurate, adaptable, low cost results

Traffic matrix (TM) prediction methods aim to accurately and efficiently predict future network traffic demands by using previous traffic matrices. These methods are critical for network operation and management. The recent research focuses on designing complex models to extract spatiotemporal correlations from TMs and network topologies using traffic volume datasets that consist of all the origin–destination (OD) flows sampled from the network. These entire-matrix models are confined to a specific network topology and are difficult to apply to other networks. To solve such problems, we investigate a flow-by-flow prediction method in this paper which utilizes the intra-flow temporal correlations. State-of-the-art results have been achieved by our method on Abilene and GÉANT datasets. Using only 10% of the OD flows as the training samples, the flow-by-flow models can outperform the previously achieved state-of-the-art prediction performance. This small sample greatly reduces the end-to-end traffic monitoring costs. In addition, the flow-by-flow models can achieve outstanding cross-dataset evaluation performance. We suggest that leveraging a large external dataset with a flow-by-flow method can be a better choice when the measurement environment is difficult on the target network. The source code for our prediction method is publicly available at https://github.com/FreeeBird/Flow-By-Flow-Prediction.

Interactions Between Flame Topology and Turbulent Transport in High-Pressure Premixed Combustion

Direct numerical simulations of a turbulent premixed stoichiometric methane-oxygen flame were conducted. The chosen combustion pressure was 20 bar, to resemble conditions encountered in modern rocket combustors. The chemical reactions followed finite rate detailed mechanism integrated into the EBI-DNS solver within the OpenFOAM framework. Flame geometry was thoroughly investigated to assess its interaction with the transport of turbulent properties. The resulting flame front was remarkably thin, with high density gradients and moderate Karlovitz and Damköhler numbers. At mid-flame positions, the variable-density transport mechanisms dominated, leading to the generation of both vorticity and turbulence. A reversion of this trend towards the products was observed. For intermediate combustion progress, vorticity transport is essentially a competition between the baroclinic torque and vortex dilatation. The growth of turbulent kinetic energy is strongly correlated to this process. A geometrical analysis reveals that the generation of enstrophy and turbulence is restricted to specific topologies. Convergent and divergent flame propagation promote turbulence creation due to pressure fluctuation gradients through different physical processes. The possibility of modeling turbulence transport based on curvature is discussed along with the inherent challenges.

INVESTIGATING THE IMPACT OF TREE-BASED NETWORK TOPOLOGY ON THE SDN CONTROLLER PERFORMANCE

Software Defined Networking (SDN) is an important technology that enables a new approach to how we develop and manage networks. SDN divides the data plane and control plane and promotes logical centralization of network control so that the controller can schedule the data in the network effectively through the OpenFlow protocol. The performance and capabilities of the controller itself are important. The impact of network topology type on controller performance can be very significant. In order to have better communication in SDN, it is essential to have an analysis of the performance of specific network topologies. In this paper, we simulate ONOS and RYU controllers and compare their different network parameters under the proposed complex custom Tree-based topology. A network topology has been designed using a Mininet emulator, and the code for topology is executed in Python. From the throughput, packet transmission rate, and latency analysis, the ONOS controller displayed better results than RYU, showing that it can respond to requests more efficiently under complex SDN topologies and traffic loads. On the contrary, the RYU controller provides better results for the less complex SDN networks.

Texturing of nanocoatings for surface acoustic wave-based sensors for volatile organic compounds

Abstract An approach for texturing of gas-sensitive nanocoatings by using surface acoustic waves (SAW) is presented in this article. The objective of the work is to enhance the performance of precise SAW-based gas sensors due to the increased specific area of the sensitive nanocoating, induced during its growth and to replace the expensive lithographic techniques for nanopatterning, typically used for this purpose. The technique can be used for tuneable alignment of nanoparticles or nanowires and it is scale-independent. To control the texture of the sensitive nanocoating, a specific electrode topology was used to generate waves with a specific space distribution, which in turn caused assembling of the nanoparticles increasing the adsorption capacity. In this way, a broader dynamic range of 7,000 ppm was achieved (three times extended as compared to the non-textured sensing film), measurement error of 0.6% against 4% for the non-patterned, faster response time in the sub-seconds range (970 ms vs 1.1 s), negligible hysteresis of 10 mV (against >100 mV), and very good sensitivity of 5 µV per ppm, which are in line with the current standards for ethanol sensors. The enhanced sensor parameters were achieved by implementation of conventional patterning technologies without the need for nanolithographic techniques for the texturing the nanocoating. The method is low-cost, and applicable in a variety of sensing structures despite the sensing coating (optical, biological, etc.).

Co-Precipitation Synthesis of Co3[Fe(CN)6]2·10H2O@rGO Anode Electrode for Lithium-Ion Batteries.

Rechargeable lithium-ion batteries (LIBs) are known to be practical and cost-effective devices for storing electric energy. LIBs have a low energy density, which calls for the development of new anode materials. The Prussian blue analog (PBA) is identified as being a candidate electrode material due to its facile synthesis, open framework structures, high specific surface areas, tunable composition, designable topologies and rich redox couples. However, its poor electrical conductivity and mechanical properties are the main factors limiting its use. The present study loaded PBA (Co3[Fe(CN)6]·10H2O) on graphene oxide (Co-Fe-PBA@rGO) and then conducted calcination at 300 °C under the protection of nitrogen, which reduced the crystal water and provided more ion diffusion pathways. As a result, Co-Fe-PBA@rGO showed excellent performance when utilized as an anode in LIBs, and its specific capacities were 546.3 and 333.2 mAh g−1 at 0.1 and 1.0 A g−1, respectively. In addition, the electrode also showed excellent performance in the long-term cycle, and its capacity reached up to 909.7 mAh g−1 at 0.1 A g−1 following 100 cycles.

Frame-level refinement networks for skeleton-based gait recognition

Gait is considered as a promising biometric feature due to its support for long-distance and non-contact recognition. Most existing skeleton-based gait recognition methods deliver the same topology of skeleton graph for each frame and treat them equally in the process of temporal feature extraction and fusion, which undoubtedly limits the expressive capability of the model. In this paper, we propose a frame-level refinement network to adaptively learn specific topology in different frames and capture long-range dependencies between frames through transformer self-attention. Specifically, we design a frame-level topology refinement graph convolution (FTR-GC) to dynamically model different correlations between joints for each frame. In addition, we introduce transformer self-attention at the frame level, which can learn inter-frame long-range relations between the same joint. Finally, an attention-based frame-level feature aggregation module (FFAM) is presented to produce discriminative global features of input gait sequences. Experiments on the popular public dataset CASIA-B and OUMVLP-Pose show that our method notably surpasses state-of-the-art model-based methods, verifying the effectiveness of the proposed modules.

Negative Sequence Compensation Method for High-Speed Railway With Integrated Photovoltaic Generation System

The serious negative sequence (NS) issues and energy shortage problems are aroused by the single-phase 25-kV traction power supply system (TPSS) of high-speed railways. To achieve NS compensation and alleviation of energy demand, a TPSS with an integrated PV generation system is proposed. According to the structural characteristics of the TPSS and the PV system, a special topology and an NS compensation method are presented. Moreover, taking the NS control as the primary goal, three operation modes are classified by considering the load conditions of the TPSS and the output state of the PV system. After that, a coordinated control system with a central controller and multiple local controllers is proposed. The central controller can realize the flexible selection of operation modes and calculate the reference power. Meanwhile, the local controllers achieve the accurate tracking of the reference current and the stability of the DC link voltage by switching the control modes. Finally, the feasibility and effectiveness of the proposed system and the NS compensation method are verified by simulations.

Neuromorphic adaptive spiking CPG towards bio-inspired locomotion

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability through a sCPG. This sCPG generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a FSR sensor to provide feedback. The sCPG consists of a network of five populations of LIF neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, eventually, the locomotion of an end robotic platform could be adapted to the terrain by using any sensor as input. The sCPG with adaptation has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the sCPG while the input stimulus varies. To validate the robustness and adaptability of the sCPG, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.

Programmable Skyrmion Logic Gates Based on Skyrmion Tunneling

Magnetic skyrmions are promising candidates as elementary nanoscale bits in logic-in-memory devices, intrinsically merging high density memory and computing capabilities. Here we exploit the dynamics of skyrmions interacting with anisotropy energy barriers patterned by ion irradiation to design programmable logic gates. Using micromagnetic simulations with experimental parameters, we show that a fine tuning of the barrier height and width allows the selective tunneling of skyrmions between parallel nanotracks triggered by skyrmion-skyrmion interaction. This can be leveraged to design skyrmion De-multiplexer (DMux) logic gate which works solely using skyrmions as logic inputs. By cascading and connecting demultiplexer gates with a specific topology, we develop a fully programmable logic gate capable of producing any possible logic output as a sum of all minterms generated by a given set of inputs without requiring any complex additional electric/magnetic interconversion. The proposed design is fully conservative and cascadable and paves a new pathway for full skyrmionic-based logic-in-memory devices.

Improved implicit potential method for incompressible flows on a fully collocated mesh

AbstractIn the present work, we present a new version of the pressure‐based implicit potential (IPOT) method for incompressible flows, which can be applied on a fully collocated mesh. The new version combines the IPOT algorithm with the Rhie and Chow (RC) technique, to produce solutions on collocated grids that are free of spurious pressure modes. The IPOT‐RC method retains all the benefits of the original algorithm, i.e. explicit velocity–pressure coupling, easy implementation and reduced iteration time, without requiring a special grid topology. The presentation of the IPOT‐RC method is accompanied by an extensive discussion on the cause of the spurious oscillations in zero‐div problems in general, and a possible cure that is linked to the RC technique. The IPOT‐RC method is validated through several benchmark problems including the lid‐driven cavity flow, flow over a backward facing step and direct numerical simulation of turbulent channel flow.

Triple-leptoquark interactions for tree- and loop-level proton decays

We study the impact of triple-leptoquark interactions on matter stability for two specific proton decay topologies that arise at the tree- and one-loop level if and when they coexist. We demonstrate that the one-loop level topology is much more relevant than the tree-level one when it comes to the proton decay signatures despite the usual loop-suppression factor. We subsequently present detailed analysis of the triple-leptoquark interaction effects on the proton stability within one representative scenario to support our claim, where the scenario in question simultaneously features a tree-level topology that yields three-body proton decay p → e+e+e− and a one-loop level topology that induces two-body proton decays p → π0e+ and p → π+ overline{nu} . We also provide a comprehensive list of the leading-order proton decay channels for all non-trivial cubic and quartic contractions involving three scalar leptoquark multiplets that generate triple-leptoquark interactions of our interest, where in the latter case one of the scalar multiplets is the Standard Model Higgs doublet.

The Role of Parent Phase Topology in Double Young–Kurdjumow–Sachs Variant Selection during Phase Transformation in Low-Carbon Steels

The present paper investigates the role of parent phase topology on a crystallographic variant selection rule. This rule assumes that product phase nuclei appear at certain grain boundaries in the parent structure, such that a specific crystallographic orientation relationship is observed with both parent grains at either side of the grain boundary. The specific crystallographic orientation correspondence considered here is the Young–Kurdjumow–Sachs (YKS) orientation relationship <112>90° (which exhibits 24 symmetrical equivalents). The aforementioned relationship is characteristic of phase transformations in low-carbon steel grades. It is shown that, for different parent phase textures, ~20% of the grain boundaries comply with the double YKS condition allowing for a tolerance of 5°, ignoring the presence of topology in the parent phase microstructure. The presented model allows for connecting the presence of a specific parent phase topology with the condition of the double YKS variant selection rule in a number of practical cases: (i) for hot rolled Ti–Interstitial Free (IF) steel with and without Mn addition, (ii) for cold rolled IF steel exhibiting very strong texture memory after forward and reverse α ⇌ γ phase transformation and (iii) for a martensitic transformation in a Fe–8.5% Cr steel. It is shown that the double YKS variant selection criterion may explain several specific features of the observed transformation textures, while assuming a non-correlated arbitrary pair topology of the parent austenite structure (implying that for N parent orientations N/2 pairs are selected in an arbitrary manner).

An Energy-Efficient Routing Scheduling Based on Fuzzy Ranking Scheme for Internet of Things

Internet of Things (IoT) is a wireless network of various battery-powered sensing units. Due to the limited battery capacity, the nonaccessible/abandoned nodes demand more energy to be reached or reintegrated to keep the network connected. When a specific tree topology for routing, called destination-oriented directed acyclic graphs (DODAGs), is used, isolated nodes spend maximum energy on the assigned task during data transformation from the sensor field to the DODAG root. The nodes closer to the DODAG root need to rely on faraway nodes and resource-burden-constrained nodes to lead to the quick energy drain. It brings an idea of IoT network nodes in which an extra amount of energy provide to the longer time alive nodes. This article proposes an ARFOR-adaptive ranking fuzzy-based energy-efficient opportunistic routing protocol for sustainable IoT applications. The proposed protocol consists of a parent node (PN) that acts as a head node in a cluster to aggregate the packets to DODAG root; and a volunteer node (VN) acts as a forwarder to transfer the packets to PN with threshold energy limits to increase network lifetime during the transmission cycle. The proposed VN selection is based on fuzzy parameters, such as Canberra distance, residual energy, and threshold. The simulation outcomes depict that the ARFOR fairly justifies the network timeline requirement with maximum percentage area coverage. The percentage gain in terms of network lifetime is comparatively significant for a lower number of VN.

Three-dimensional Magnetic and Thermodynamic Structures of Solar Microflares

Microflares, one of the small-scale solar activities, are believed to be caused by magnetic reconnection. Nevertheless, their three-dimensional (3D) magnetic structures, thermodynamic structures, and physical links to reconnection are unclear. In this Letter, based on a high-resolution 3D radiative magnetohydrodynamic simulation of the quiet Sun spanning from the upper convection zone to the corona, we investigate the 3D magnetic and thermodynamic structures of three homologous microflares. It is found that they originate from localized hot plasma embedded in the chromospheric environment at the height of 2–10 Mm above the photosphere and last for 3–10 minutes with released magnetic energy in the range of 1027–1028 erg. The heated plasma is almost cospatial with the regions where the heating rate per particle is maximal. The 3D velocity field reveals a pair of converging flows with velocities of tens of km s−1 moving toward and outflows with velocities of about 100 km s−1 moving away from the hot plasma. These features support magnetic reconnection playing a critical role in heating the localized chromospheric plasma to coronal temperature, giving rise to the observed microflares. The magnetic topology analysis further discloses that the reconnection region is located near quasi-separators where both current density and squashing factors are maximal although the specific topology may vary from a tether-cutting to fan-spine-like structure.

A distributed energy-efficient opportunistic routing accompanied by timeslot allocation in wireless sensor networks

Sensed data can be forwarded only in one direction to the base station in one-dimensional queue wireless sensor networks different from mesh structure, so the network lifetime will be shortened if some continuous neighboring nodes have run out of their energy. So designing routing protocols for balancing energy consumption is a challenging problem. However, traditional and existing opportunistic routing protocols for one-dimensional queue wireless sensor network proposed so far have not yet addressed this problem to prolong the network lifetime by introducing sleep mode. In this article, we propose a distributed energy-efficient opportunistic routing algorithm accompanied by timeslot allocation by using specific network topology of one-dimensional queue wireless sensor network. In our new algorithm, clustering and routing tree construction is performed while introducing the optimal relay transmission distance achieved by using opportunistic routing principle, and at the same time, interference-free wake up time is scheduled, which may optimize energy consumption and decrease the number of various control messages as possible to prolong the network lifetime. Furthermore, this improves energy efficiency by introducing the operation mode giving up cluster head role. Simulation results show that the proposed protocol can significantly improve the network performance such as energy consumption and network connectivity, when compared with other existing protocols.

Analysis of interpenetrating metal ceramic composite structures using an enhanced random sequential absorption microstructure generation algorithm

In this paper, we present the analysis of an interpenetrating metal ceramic composite structure. We introduce a new generation algorithm for the modeling of interpenetrating composite microstructures with connected, spherical cavities embedded into an open-porous foam structure. The method uses a geometric ansatz and is designed to create structures of special topology, as the investigated metal ceramic composite structures consisting of a connected AlSi10Mg phase showing spherical shapes embedded into an Al2O3 preform. Based on the introduced enhanced random sequential absorption approach, the generated microstructures yield numerical insights into the material that are not accessible by experimental techniques. The generated microstructures are compared to structures reconstructed from experimental CT scan data considering microstructural features and mechanical behavior. We show that the proposed method is able to generate statistically equivalent microstructures by using only a small number of statistical descriptors. The numerical formulation is validated using compression tests including plastic yielding in the aluminum and damage progression in the ceramic phase. Both the composite material and the pure ceramic preform are considered in this analysis, and good agreement is found between reconstructed and generated microstructures. Furthermore, the observations reveal the importance of the local geometrical sphere arrangement with respect to the mechanical behavior. A validation with experimental results is presented and it is shown that the model predicts microstructural properties and gives meaningful insights into the structural and material interplay. Finally, we discuss the potential of the method for the investigation of failure mechanisms.

A Generalized Method for Comprehension of Switched-Capacitor High Step-Up Converters Including Coupled Inductors and Voltage Multiplier Cells

High step-up converters are crucial in many power electronic interfaces, including for renewable energy sources. As the result of topological variation of high step-up converters, many topologies share similar characteristics. In order to have a clear understanding of an optimized design that makes the best use of components to achieve high gain, it is necessary to devise a generalized comprehension method for high step-up converters. This article presents a novel generalized method for analyzing single-switch step-up converters that can include switched capacitor (SC) cells, a coupled inductor (CI), and/or voltage multiplier cells (VMCs). The proposed method is neither dependent on the position of the CI nor the structure of the VMC, and is not tied to a specific topology. Thus, the proposed generalized method uniquely reveals the unifying theory underlying high step-up converters with any variation of SC/CI/VMC. In order to verify the theoretical analysis, many examples from the literature are investigated. Then, using design tips from the generalized method, a new high step-up converter is designed. A 150-W prototype of the converter shows 97.5% peak efficiency. The proposed converter also compares favorably to other topologies in both a power loss breakdown analysis and a component stress factor analysis.

On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules.