Specific Topology Research Articles

Design and Analysis of a Hybridly Excited Asymmetric Stator Pole Doubly Salient Machine

This article presents the design and analysis of a 12/7 (numbers of stator/rotor poles) hybridly excited asymmetric stator pole doubly salient machine (HEASPDSM) with a main focus on the special topology and operational principle by using the analytical and 2-D finite element analysis. A full comparison is conducted on the average torque, torque ripples, no-load back electromotive force and flux regulation capacity under the condition of different structural parameters, which can provide good references to the machine design. A 12/7 HEASPDSM is designed and the main performance indexes are analyzed in detail. Based on the parameters, the performance of 12/7 HEASPDSM is compared with traditional 6/7 doubly salient permanent magnet machine (DSPMM) with the same volume and thermal load. Finally, a prototype of the 12/7 HEASPDSM is manufactured and tested to validate the theoretical analysis. The results fully demonstrate that the HEASPDSM possesses the merits of strong flux regulation capability, higher torque density, and wider constant power speed range benefitted from the dc field winding than the traditional DSPMM.

Aptamers Which Target Proteins: What Proteotronics Suggests to Pharmaceutics

Aptamers represent a challenging field of research, relevant for diagnosis in macular degeneration, cancer, thrombosis and many inflammatory diseases, and promising in drug discovery and development. Their selection is currently performed by a stable in vitro technology, namely, SELEX. Furthermore, computationalstatistical tools have been developed to complement the SELEX selection; they work both in the preliminary stage of selection, by designing high affinity aptamers for the assigned target, and also in the final stage, analyzing the features of the best performers to implement the selection technique further. A massive use of the in silico approach is, at present, only restricted by the limited knowledge of the specific aptamer-target topology. Actually, only about fifty X-ray structures of aptamer-protein complexes have been experimentally resolved, highlighting how this knowledge has to be improved. The structure of biomolecules like aptamer-protein complexes can be represented by networks, from which several parameters can be extracted. This work briefly reviews the literature, discussing if and how general network parameters in the framework of Proteotronics and graph theory (such as electrical features, link number, free energy change, and assortativity), are important in characterizing the complexes, anticipating some features of the biomolecules. To better explain this topic, a case-study is proposed, constituted by a set of anti-angiopoietin (Ang2) aptamers, whose performances are known from the experiments, and for which two different types of conformers were predicted. A topological indicator is proposed, named Möbius (M), which combines local and global information, and seems able to discriminate between the two possible types of conformers, so that it can be considered as a useful complement to the in vitro screening for pharmaceutical aims.

Deep learning architecture to predict daily hospital admissions

Air pollution and airborne pollen play a key role in respiratory and circulatory disorders and thus have a direct relation to hospital admissions for these causes. Knowing in advance the influx of patients to emergency services allows clinical institutions to optimize resources and to improve their service. Since the variables influencing respiratory and circulatory-related hospital admissions belong to fields such aerobiology or meteorology, we aim for a data-based system which is able to predict admissions without a priori assumptions. Given the number and distribution of observation stations (meteorological, pollen and chemical pollution stations and hospital), previous approaches generate many model-dependent systems that need to be combined in order to obtain the full representation of future environmental conditions. A unified approach able to extract all temporal dynamics as well as all spatial relations would allow a better representation of the aforementioned conditions and consequently a more precise hospital admissions forecast. The proposed system is based on a specific neural network topology of long short-term memories and convolutional neural networks to obtain the spatio-temporal relations between all independent and target variables. It was applied to forecast daily hospital admissions due to respiratory- and circulatory-related disorders. The proposal outperforms the benchmark approaches by reducing as an average the prediction error by 28% and 20% for the circulatory and respiratory cases, respectively. Consequently, the system extracts all relevant information without specific field knowledge and provides accurate hospital admissions forecasts.

Identifying Link Layer Home Network Topologies Using HTIP

In this article, we propose a method to identify the link layer home network topology, motivated by applications to cost reduction of support centers. If the topology of home networks can be identified automatically and efficiently, it is easier for operators of support centers to identify fault points. We use MAC address forwarding tables (AFTs) which can be collected from network devices via HTIP (ITU-T G.9973, Home network Topology Identifying Protocol). There are a couple of existing methods for identifying a network topology using AFTs, but they are insufficient for our purpose; they are not applicable to some specific network topologies that are typical in home networks. Our method proposed in this paper can handle such topologies. Furthermore, our method is faster because, for detecting a leaf node at each round, the existing methods use a result of set inclusion operations, while our method only needs to check the sizes of sets, which is much less costly. We also give experimental evaluations to show the advantages of our method.

Self-Assembly and Photoinduced Spindle-Toroid Morphology Transition of Macromolecular Double-Brushes with Azobenzene Pendants.

Asymmetric macromolecular double-brushes (MDBs) are composed of two different side chains grafted on a linear backbone, possessing distinct assembly behaviors in comparison with conventional amphiphiles, owing to the Janus architecture and combined effects of backbone and hetero double-brushes. Additionally, the introduction of unique functionalities and responsiveness into the self-assembly system of MDBs endows extra opportunities to pursue morphologic diversity and intriguing properties. Herein, we report the synthesis of Janus-like MDBs of polyacrylate-g-poly(6-(4-butyl-4'-oxyazobenzene) hexyl acrylate)/poly(ethylene oxide) (PA-g-PAzo/PEO), in which hydrophilic PEO and hydrophobic PAzo brushes were grafted using the combination of concurrent ATRP and click reaction. Due to the special Janus topology and inter/intramolecular association of pendant azobenzene groups, amphiphilic PA-g-PAzo/PEO self-assembled into multimolecular rod and spindle-like aggregates. It is interesting that a transition of spindle-toroid-spindle was observed upon the alternative irradiation between UV and visible light, which is ascribed to the trans-to-cis isomerization of azobenzene molecular brushes. To our best knowledge, this is the first time that azobenzene-containing MDBs enable the fabrication of distinctive self-assembled morphologies and photoinduced toroid formation. The controlled synthesis of MDBs with unique functionalities and subsequent development of their structure-property relationships would shed light on the design and optimization of bottlebrush-based nanomaterials.

Benchmarking seeding strategies for spreading processes in social networks: an interplay between influencers, topologies and sizes

The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.

Experimental twelve degree of freedom rubber isolator models for use in substructuring assemblies

Commercial off-the-shelf rubber isolators often come with no additional information other than the static stiffness in three translational directions. Hydraulic testing machines can be used to obtain frequency dependent dynamic stiffnesses of rubber isolators in translational degrees of freedom (DoF). Alternatively, dynamic substructuring based methods can be used, which can additionally identify the dynamic stiffness in rotational DoF while requiring only standard vibration testing equipment. Results of two substructuring methods will be compared to those from a hydraulic machine. Both of the presented methods use locally rigid fixtures, mounted to the bottom and top of the isolators. Frequency based substructuring (FBS) requires knowing the fixtures dynamics to decouple them. Inverse substructuring, also called in-situ decoupling, does not require knowing the fixtures dynamics, but is assuming negligible mass and a special stiffness matrix topology of the rubber isolator. Both methods produce accurate results for translational DoF up to the kilo Hertz range, which is confirmed by comparison to measurements on the hydraulic machine. However, FBS does not rely on specific assumptions about the isolator, like inverse substructuring. The limits of inverse substructuring's underlying assumptions are shown theoretically and in the measurements presented here. We propose two extensions to compensate for the assumptions and present their results. Nevertheless, the rubber model obtained with the FBS decoupling can provide better results when used in an assembly. This is illustrated by testing the experimental rubber element models, obtained with either method, in a substructuring prediction of coupled frequency response functions (FRFs) and comparing that to reference measurements.

Fault diagnosis in low voltage smart distribution grids using gradient boosting trees

In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies. Particularly, as it is shown, the method can be estimated in a specific grid topology and be employed in a different one. To test the algorithm, the method is evaluated in a simulated real LV distribution grid of Portugal. In this case study, different fault resistances, fault locations and hours of the day are considered. In detail, the algorithm is evaluated at eighteen fault resistance values between 0.1 and 1000Ω; similarly, nine fault locations are considered within each one of the 32 sectors of the grid and the faults are simulated across different hours of a day. The developed algorithm showed promising results in both out-of-sample branch and fault resistance data especially for fault detection, demonstrating a maximum fault detection error of 0.72%.

GC ends control topology of DNA G-quadruplexes and their cation-dependent assembly.

GCn and GCnCG, where n = (G2AG4AG2), fold into well-defined, dimeric G-quadruplexes with unprecedented folding topologies in the presence of Na+ ions as revealed by nuclear magnetic resonance spectroscopy. Both G-quadruplexes exhibit unique combination of structural elements among which are two G-quartets, A(GGGG)A hexad and GCGC-quartet. Detailed structural characterization uncovered the crucial role of 5′-GC ends in formation of GCn and GCnCG G-quadruplexes. Folding in the presence of 15NH4+ and K+ ions leads to 3′–3′ stacking of terminal G-quartets of GCn G-quadruplexes, while 3′-GC overhangs in GCnCG prevent dimerization. Results of the present study expand repertoire of possible G-quadruplex structures. This knowledge will be useful in DNA sequence design for nanotechnological applications that may require specific folding topology and multimerization properties.

PSNet: Reconfigurable network topology design for accelerating parameter server architecture based distributed machine learning

The bottleneck of Distributed Machine Learning (DML) has shifted from computation to communication. Lots of works have focused on speeding up communication phase from perspective of Parameter Server (PS) architecture, for example resource scheduling. Nonetheless, the performance improvement of these schemes is limited due to the agnostic of the physical topology to the communication pattern of the applications. Concurrently, some articles have also pointed out the impact of topology on DML performance. Besides, our analysis and experimental results also indicate that the general topologies cannot match well with the communication characteristics of DML based on PS architecture. However, to the best of our knowledge, no special topology is tailored for DML. Therefore, in this paper, we propose PSNet, a reconfigurable modular network topology for DML with consideration of the communication characteristics of PS architecture. The main idea of PSNet is that servers are firstly divided into two categories, namely workers configured with high-performance computing capability and parameter servers equipped with multiple Network Interface Cards (NICs). Then Electrical Circuit Switch (ECS) is exploited to connect workers and Top of Rack (ToR) switches for flexibility and reconfigurability in each module. Our theoretical analysis proves that PSNet not only provides high performance for DML tasks, but also achieves high fault tolerance and flexibility. In order to validate the performance of PSNet, we conduct large-scale simulations and small-scale testbed experiments, and the results of experiments demonstrate that PSNet performs 1.89× and 1.92× faster than FatTree for VGG-16 and ResNet50, respectively.

Topologies of G-quadruplex: Biological functions and regulation by ligands

G-Quadruplex (G4) is one of the higher-order structures occurring in guanine-rich sequences of nucleic acids, and plays critical roles in biological processes. The G4-forming sequences can generate three kinds of topologies, i.e., parallel, anti-parallel, and hybrid, and these polymorphic structures have an important influence on G4-related biological functions. In this review, we highlight variety of structures generated by G4s containing various sequences and under diverse conditions. We also discuss the G4 ligands which induce specific topologies and/or conversion between different topologies.

A hybrid symmetry–PSO approach to finding the self-equilibrium configurations of prestressable pin-jointed assemblies

For pin-jointed assemblies with many members or self-stress states, the form-finding problem using conventional methods generally involves considerable computational complexities due to the large size of the solution spaces. Here, we propose an improved form-finding method for prestressable pin-jointed structures by combining symmetry-based qualitative analysis with particle swarm optimization. Expressed in the symmetry-adapted coordinate system, the nodal coordinate vectors of a structure with specific symmetry and topology are independently extracted from the key blocks of the small-sized force density matrices associated with rigid-body translations. Then, the first block of the equilibrium matrix is computed, in which the null space reveals integral self-stress states. Particle swarm optimization is introduced and adapted to find feasible prestress modes, where the uniformity and unilaterality conditions of the members are considered. Besides, the QR decomposition with column pivoting is adopted for efficient computations on the null space of these blocks. The QR decompositions of the small-sized blocks of the force density matrix and the equilibrium matrix are performed iteratively, to simultaneously find a stable self-equilibrium configuration and a feasible prestress mode. Representative examples show the presented method is computationally efficient and accurate for the form-finding of symmetric tensegrities and prestressed cable–strut structures.

Infective flooding in low-duty-cycle networks, properties and bounds

Flooding information is an important function in many networking applications. In some networks, as wireless sensor networks or some ad-hoc networks it is so essential as to dominate the performance of the entire system. Exploiting some recent results based on the distributed computation of the eigenvector centrality of nodes in the network graph and classical dynamic diffusion models on graphs, this paper derives a novel theoretical framework for efficient resource allocation to flood information in mesh networks with low duty-cycling without the need to build a distribution tree or any other distribution overlay. Furthermore, the method requires only local computations based on each node neighborhood. The model provides lower and upper stochastic bounds on the flooding delay averages on all possible sources with high probability. We show that the lower bound is very close to the theoretical optimum. A simulation-based implementation allows the study of specific topologies and graph models as well as scheduling heuristics and packet losses. Simulation experiments show that simple protocols based on our resource allocation strategy can easily achieve results that are very close to the theoretical minimum obtained building optimized overlays on the network.

Parameterized model checking of networks of timed automata with Boolean guards

Parameterized model checking is a formal verification technique for verifying that some specifications hold in systems consisting of many similar cooperating but indistinguishable processes. The problem is known to be undecidable in general, even when restricted to reachability properties. To overcome this limitation, several techniques have been explored to address specific system families, logical formulas or topologies of process networks. Some use the notion of cutoff, i.e. if a certain property is verified for systems up to a certain size (the cutoff) then it is verified for systems of any size. Here we analyze the case of networks consisting of an arbitrary number of timed automata that can synchronize by looking at which state the neighbors are currently. We show that cutoffs exist independently from the checked formula, with or without a distinguished process acting as controller. We show how, exploiting the cutoffs, we can obtain upper bounds on complexity of the parameterized model-checking problem. Finally, we show how to use the theoretical results in order to model and verify a distributed algorithm for clock synchronization based on gossip techniques.

Robust Architecture for Programmable Universal Unitaries.

The decomposition of large unitary matrices into smaller ones is important because it provides ways to the realization of classical and quantum information processing schemes. Today, most of the methods use planar meshes of tunable two-channel blocks; however, the schemes turn out to be sensitive to fabrication errors. We study a novel decomposition method based on multichannel blocks. We have shown that the scheme is universal even when the block's transfer matrices are chosen at random, making it virtually insensitive to errors. Moreover, the placement of the variable elements can be arbitrary, so that the scheme is not bound to specific topologies. Our method can be beneficial for large-scale implementations of unitary transformations by techniques, which are not of wide proliferation today or have yet to be developed.

First-principles study of magnetism in some novel MXene materials.

Magnetic two-dimensional materials have gained considerable attention in recent years due to their special topologies and promising applications in electronic and spintronic devices. As a new family of two-dimensional materials, MXene materials may have unusual magnetic properties. In this work, the structural stabilities and electronic properties of 1H and 1T type pristine M2C (M = Sc, Ti, Fe, Co, Ni, Cu, Zn) MXenes with different magnetic configurations were calculated and compared. The critical temperatures of the magnetic MXenes were evaluated through Monte Carlo simulations using the spin–exchange coupling parameters. The results suggest that the ground-state 1T-Ti2C and 1T-Fe2C, 1H-Co2C MXenes are antiferromagnetic or ferromagnetic materials with high Néel or Curie temperatures. Different from the other pristine M2C MXenes with metallic properties, indirect band gaps were found for the 1T-Ti2C and 1T-Ni2C MXenes, which may be useful for their application in information storage or sensors. The findings are expected to promote the development of novel devices based on MXenes and their magnetic properties.

Design of a Multiple-beam Cassegrain Antenna with Quasi-optical Isolator at 200 GHz for Target Tracking

This letter proposes a multiple-beam Cassegrain antenna with quasi-optical isolator at 200 GHz for target tracking. The proposed antenna is designed with a special topology, in which a multichannel transceiver feed integrated with a quasi-optical isolator is designed and placed in the back of the main reflector, aiming to isolate the transmitting and receiving channels, and to avoid the aperture blockage. Four receiving feeds are laterally defocused to produce offset beams and each of them is received and sampled individually without comparators, which simplifies the structure and provides flexibility for signal processing. An optimization is performed to achieve the best compromise design considering the conflicts induced by its special structure. The fabricated antenna is measured and the results demonstrate an excellent radiation performance of the proposed antenna. Four well-performed offset beams are obtained as expected and the antenna works stably within a bandwidth of 10 GHz. The measured 3 dB beamwidths vary around 0.6° and the sidelobe levels are lower than −12.5 dB for all the beams. The superiorities of the proposed antenna show its potential application in high-performance tracking and imaging systems.

Analysis of Maximal Topologies and Their DoFs in Topological Interference Management

Topological interference management (TIM) can obtain degrees of freedom (DoF) gains with no channel state information at the transmitters (CSIT) except topological information of network in the interference channel. It was shown that TIM achieves symmetric DoF 1/2 when internal conflict does not exist among messages [7]. However, it is difficult to assure whether a specific topology can achieve symmetric DoF 1/2 without scrutinizing internal conflict. It is also hard to derive a specific topology directly from the conventional condition for symmetric DoF 1/2. Even except for the topology achieving symmetric DoF 1/2, topology achieving specific DoF less than 1/2 is not well known. With these problems in mind, we propose a method to derive all maximal topologies directly in TIM, named as alliance construction in K-user interference channel. That is, it is proved that a topology is maximal if and only if it is derived from alliance construction. Further we translate a topology design by alliance construction in the alignment-conflict graph into topology matrix and propose conditions for maximal topology matrix (MTM). Moreover, we propose a generalized alliance construction that derives topologies achieving DoF 1/n for n ≥ 3 by generalizing sub-alliances. A topology matrix can also be used to analyze topologies with DoF 1/n .

Service Embedding in IoT Networks

The Internet of Things (IoT) is the cornerstone of smart applications such as smart buildings, smart factories, home automation, and healthcare automation. These smart applications express their demands in terms of high-level requests. Application requests in service-oriented IoT architectures are translated into a business process (BP) workflow. In this paper, we model such a BP as a virtual network containing a set of virtual nodes and links connected in a specific topology. These virtual nodes represent the requested processing and locations where sensing and/or actuation are needed. The virtual links capture the requested communication requirements between nodes. We introduce a framework, optimized using mixed integer linear programming (MILP), that embeds the BPs from the virtual layer into a lower-level implementation at the IoT physical layer. We formulate the problem of finding the optimal set of IoT nodes and links to embed BPs into the IoT layer considering three objective functions: i) minimizing network and processing power consumption only, ii) minimizing mean traffic latency only, iii) minimizing a weighted combination of power consumption and traffic latency to study the trade-off between minimizing the power consumption and minimizing the traffic latency. We have established, as reference, a scenario where service embedding is performed to meet all the demands with no consideration to power consumption or latency. Compared to this reference scenario, our results indicate that the power savings achieved by our energy efficient embedding scenario is 42% compared with the energy-latency unaware service embedding (ELUSE) reference scenario, while our low latency embedding reduced the traffic latency by an average of 47% compared to the ELUSE scenario. Our combined energy efficient low latency service embedding approach achieved high optimality by jointly realizing 91% of the power and latency reductions obtained under the single objective of minimizing power consumption or latency.

The Extracellular Juncture Domains in the Intimin Passenger Adopt a Constitutively Extended Conformation Inducing Restraints to Its Sphere of Action

Enterohemorrhagic and enteropathogenic Escherichia coli are among the most important food-borne pathogens, posing a global health threat. The virulence factor intimin is essential for attachment of pathogenic E. coli to the intestinal host cell. Intimin consists of four extracellular bacterial immunoglobulin-like (Big) domains, D00-D2, extending into the fifth lectin subdomain (D3) that binds to the Tir-receptor on the host cell. Here, we present the crystal structures of the elusive D00-D0 domains at 1.5 A and D0-D1 at 1.8 A resolution that confirm that the passenger of intimin has five distinct domains. We describe that D00-D0 exhibits a higher degree of rigidity and D00 likely functions as a juncture domain at the outer membrane-extracellular medium interface. We conclude that D00 is a unique Big domain with a specific topology likely found in a broad range of other inverse autotransporters. The accumulated data allows us to model the complete passenger of intimin and propose functionality to the Big domains, D00-D0-D1, extending directly from the membrane.