Variety Of Topologies Research Articles

Graph machine learning classification using architectural 3D topological models

Some architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. This paper presents a novel proof of concept workflow that enables machine learning (ML) to automatically classify three-dimensional (3D) prototypes with respect to formulating the most appropriate building/ground relationship. Here, ML, a branch of artificial intelligence (AI), can ascertain the most appropriate relationship from a set of examples provided by trained architects. Moreover, the system classifies 3D prototypes of architectural precedent models based on a topological graph instead of 2D images. The system takes advantage of two primary technologies. The first is a software library that enhances the representation of 3D models through non-manifold topology (Topologic). The second is an end-to-end deep graph convolutional neural network (DGCNN). The experimental workflow in this paper consists of two stages. First, a generative simulation system for a 3D prototype of architectural precedents created a large synthetic database of building/ground relationships with numerous topological variations. This geometrical model then underwent conversion into semantically rich topological dual graphs. Second, the prototype architectural graphs were imported to the DGCNN model for graph classification. While using a unique data set prevents direct comparison, our experiments have shown that the proposed workflow achieves highly accurate results that align with DGCNN’s performance on benchmark graphs. This research demonstrates the potential of AI to help designers identify the topology of architectural solutions and place them within the most relevant architectural canons.

A study on topology optimization of heat dissipation structures with different objective functions based on an explicit moving morphable components method

This article investigates two different objective functions (minimizing heat dissipation weakness and temperature variance) to realize topology design optimization of heat dissipation problems based on the explicit topology optimization method of moving morphable components (MMC). The advantages of explicit and clear geometric profile descriptions in dealing with heat dissipation are explored. Sensitivity derivation with respect to the explicit topological geometric variables of the MMC-based method is established for two objective functions. Numerical examples are presented, with extensive discussion and comparisons of the key factors of maximum temperature gradient, temperature variance and maximum temperature based on the two objective functions. These discussions demonstrate the scope of application of the objective functions. Compared with density-based topology design optimization, the present MMC-based method describes the geometric profile of the heat dissipation structure accurately and achieves a more accurate and efficient heat dissipation performance of the structure.

Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule

In this paper, we numerically investigate two distinct phenomena, coherence resonance (CR) and self-induced stochastic resonance (SISR), in multiplex neural networks in the presence of spike-timing-dependent plasticity (STDP). The high degree of CR achieved in one layer network turns out to be more robust than that of SISR against variations in the network topology and the STDP parameters. This behavior is the opposite of the one presented by Yamakou and Jost (Phys. Rev. E 100, 022313, 2019), where SISR is more robust than CR against variations in the network parameters but in the absence of STDP. Moreover, the degree of SISR in one layer network increases with a decreasing (increasing) depression temporal window (potentiation adjusting rate) of STDP. However, the poor degree of SISR in one layer network can be significantly enhanced by multiplexing this layer with another one exhibiting a high degree of CR or SISR and suitable inter-layer STDP parameter values. In addition, for all inter-layer STDP parameter values, the enhancement strategy of SISR based on the occurrence of SISR outperforms the one based on CR. Finally, the optimal enhancement strategy of SISR based on the occurrence of SISR (CR) occurs via long-term potentiation (long-term depression) of the inter-layer synaptic weights.

An Interleaved PFC Boost Converter With Soft Commutations and Voltage Follower Characteristics

This article presents an interleaved boost rectifier for power factor correction (PFC) that operate in the continuous conduction mode and is grounded on the switching cell with switching frequency modulation. The input current naturally follows the shape of the input voltage, which is a resistive input characteristic (voltage follower). In the proposed converter, only one output voltage control loop is required. Moreover, the proposed topology has a wide range of soft commutation and no reverse recovery when the diodes block. This article presents the operating principle, a mathematical analysis of the converter, and four topological variations. In addition, experimental results of a 1-kW rectifier prototype with four different switch technologies [ <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOSFET</small> , high voltage superjunction <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MOSFET</small> (CoolMOS), silicon carbide (SiC), and insulated gate bipolar transistor (IGBT)], are discussed and their operational characteristics are demonstrated.

Disconnection-Aware Attack Detection and Isolation With Separation-Based Detector Reconfiguration

This study addresses incident handling during an adverse event for dynamical networked control systems. Incident handling can be divided into five steps: detection, analysis, containment, eradication, and recovery. For networked control systems, the containment step can be conducted through physical disconnection of an attacked subsystem. In accordance with the disconnection, the equipped attack detection unit should be reconfigured to maintain its detection capability. In particular, separating the detection subunit associated with the disconnected subsystem is considered as a specific reconfiguration scheme in this study. This article poses the problem of disconnection-aware attack detection and isolation with the separation-based detector reconfiguration. The objective is to design an attack detection unit that preserves its detection and isolation capability even under any possible disconnection and separation. The difficulty arises from network topology variation caused by disconnection that can possibly lead to stability loss of the distributed observer inside the attack detection unit. A solution is proposed based on an existing controller design technique referred to as retrofit control. Furthermore, an application to low-voltage power distribution networks with distributed generation is exhibited. Numerical examples evidence the practical use of the proposed method through a benchmark distribution network.

Polygon-Based Random Tree Search Algorithm for a Size-Changing Robot

This paper proposes the Polygon-based Random Tree search algorithm with Variable polygon size (PRT-V) for Variable Topology Truss (VTT). This algorithm improves on our previous path planning algorithm by using VTTs ability to change its size drastically. The algorithm assigns stopover points at the key locations in environments between the initial and goal points. Stopover points are used as reference points for both path planning and size adjustment. A Voronoi diagram-based roadmap method is used to automatically find appropriate stopover points. Based on the stopover points, the desired support polygons and the corresponding nominal lengths are generated by PRT-V. Then, the locomotion of VTT is generated by a non-impact locomotion algorithm to prevent tipping over. Simulations verify the performance of PRT-V in three different workspaces with obstacles. With PRT-V, VTT can squeeze through narrow passages or move efficiently in large spaces by increasing size, a unique feature.

3D-Epigenomic Regulation of Gene Transcription in Hepatocellular Carcinoma.

The fundamental cause of transcription dysregulation in hepatocellular carcinoma (HCC) remains elusive. To investigate the underlying mechanisms, comprehensive 3D-epigenomic analyses are performed in cellular models of THLE2 (a normal hepatocytes cell line) and HepG2 (a hepatocellular carcinoma cell line) using integrative approaches for chromatin topology, genomic and epigenomic variation, and transcriptional output. Comparing the 3D-epigenomes in THLE2 and HepG2 reveal that most HCC-associated genes are organized in complex chromatin interactions mediated by RNA polymerase II (RNAPII). Incorporation of genome-wide association studies (GWAS) data enables the identification of non-coding genetic variants that are enriched in distal enhancers connecting to the promoters of HCC-associated genes via long-range chromatin interactions, highlighting their functional roles. Interestingly, CTCF binding and looping proximal to HCC-associated genes appear to form chromatin architectures that overarch RNAPII-mediated chromatin interactions. It is further demonstrated that epigenetic variants by DNA hypomethylation at a subset of CTCF motifs proximal to HCC-associated genes can modify chromatin topological configuration, which in turn alter RNAPII-mediated chromatin interactions and lead to dysregulation of transcription. Together, the 3D-epigenomic analyses provide novel insights of multifaceted interplays involving genetics, epigenetics, and chromatin topology in HCC cells.

Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks

Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associated with training several independent networks becomes expensive. We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and diverse topologies. We then briefly train each child network for a small number of epochs, which now converge significantly faster when compared to training from scratch. We explore various ways to maximize diversity in the child networks, including the use of anti-random pruning and one-cycle tuning. This diversity enables "Prune and Tune" ensembles to achieve results that are competitive with traditional ensembles at a fraction of the training cost. We benchmark our approach against state of the art low-cost ensemble methods and display marked improvement in both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100.

Power Data Quality Improvement Through PMU Bad Data Detection Based on Deep Complex Network

Phasor Measurement Units (PMUs) enable the switching of devices in various power signal modes. A jitter or glitch in a signal cause bad data and also the PMU data will spike due to a disturbance or a transmitting data mistake. As a result of these difficulties, PMU data suffer from different degrees of data quality problems. To detect the bad data, several approaches have been already utilized however it provides some disadvantages such as complexity due to the utilization of dual identical systems separately for analyzing both real and imaginary values of PMU. Likewise, the bad data due to the topology variations have not been optimally identified. To overcome these issues a Robust Bad Data Detection Technique has been proposed in which a Deep complex neural network (DCNN) is incorporated to process the complex number having both voltage magnitude and phase angle. Deep complex Networks are also proposed with the conjunction of topology processor and AC state estimator (SE). Moreover, instead of Batch normalization weight normalization is altered due to the fusion of recurrent timestamps for measuring voltage magnitude and phase angle. The comparative analysis is done in terms of accuracy , Bad data detection capability , bad data detection range and running time with existing techniques The proposed technique provides accuracy of about 99.5% which is higher than the existing techniques.

Topological dimensionality reduction-based machine learning for efficient gradient-free 3D topology optimization

Powerful gradient-free topology optimization methods are needed for structural design concerning complex responses. In this paper, a novel gradient-free optimization method is proposed by integrating the material-field series expansion topological parameterization and the deep neural networks, providing two-fold advances: firstly, it generally reduces the massive topological design variables to fewer than 200, while keeps the capability to represent relative complex 3D topologies and clear boundaries; secondly, by constructing a sequential neural network surrogate model, it sufficiently explores the reduced design space and is capable of handling multi-peak and discontinuous optimization problems. The effectiveness of this method is illustrated via several design problems, among which the optimized material effective bulk modulus achieves 98% of the H-S bound and the highly-nonlinear peak weld stress in a phone dropping process is decreased by 16.59%. This method reduces the computational time by 1–4 orders of magnitude compared with the coarse-mesh-based gradient-free methods, and it is the first time to successfully conduct gradient-free 3D topology optimization with thousands of finite elements. The method’s ease of implementation and compatibility with various simulation software, brings topology optimization into complex industrial applications and proves that gradient-free technology represents an effective optimization benchmark for improving structural performance.

A novel energy aware cluster based routing protocol for emergency message dissemination in vehicular adhoc networks

In Vehicular Adhoc Network (VANET), the typical topology variations produced by the highly mobile nature of vehicle poses major issues in data transmission. The mobility and location based data dissemination techniques could satisfy the requirements of emergency message (EM) dissemination. It faces several issues in highway as well as urban roadways like minimum latency, highly secure, and scalable, which needs to be resolved. Generally, broadcasting protocols for VANET make use of beacon signals, which are distributed amongst the vehicles to attain information about the neighboring vehicles. With this motivation, this paper presents an energy aware cluster based routing protocol for the critical message dissemination (EAC-RPCMD) technique for VANET. The goal of the EAC-RPCMD technique aims to disseminate the EMs in the cluster based VANET environment. The EAC-RPCMD technique primarily designs an equilibrium optimizer based clustering (EOC) technique for the organization of vehicles into clusters. Besides, the manta ray foraging optimization (MRFO) algorithm based route selection (RS) technique, named MRFO-RS technique for optimal choice of routes to destination. Moreover, the EM dissemination scheme is also employed in the VANET. The performance validation of the EAC-RPCMD technique takes place and the results are inspected under several aspects.

EESR: Energy efficient sector-based routing protocol for reliable data communication in UWSNs

There is a remarkable need of advancement in data communication for application of sensor and ad-hoc networks for reliable and secure delivery of data. Due to the dynamic behavior of the network, it is an emerging demand to achieve reliability in data communication. The existence of different network environment features and diverse dynamic topology leads to the need of protocol design for efficient and reliable delivery of data in underwater acoustic sensor networks. The paper proposes energy efficient and reliable protocol design for routing data packets in Underwater Wireless Sensor Networks environment using sector based forwarding mechanism. The sector based forwarding mechanism helps in routing the reliable data in an efficient and secure manner The proposal suggests to model the network in such a way that the entire network is divided into sectors to optimize the hop count that further leads towards the energy efficient delivery of data. The mechanism of data forwarding is done vertically by maintaining sector wise sequence through pivot node which is considered as sector head. The proposed sector-based network topology mechanism is useful in identifying the number of hops used in communication along with enhancement in energy for effective data communication. The experiment performed and its consecutive results suggests that the proposed protocol enriches the energy utilization of the network. The enhancement of data communication reliability in Underwater Wireless Sensor Networks environment is managed by the mechanism of reduced hop count.

Multi-stability of a planar three-limb flexible mechanism

In general, the classical multi-stable mechanisms are made of single-loop bi-stable mechanism elements, but this paper proposes a novel structure of a planar three-limb flexible mechanism whose multi-stability is dependent on the structure parameters. The structure of this flexible mechanism is ingeniously designed to have a large selection range of the original limb length, resulting in the variation of the multi-stability distribution and the possible switching paths. To analyze the variation of this flexible mechanism, the paper introduces a method avoiding computational singularity to determine the stability by contour map and a method to draw the multi-stability topology to seek for the possible switching paths. Using these methods, the paper presents the variation of the multi-stability topology of an axisymmetric and two mirror-symmetric planar three-limb flexible mechanisms as the original length of the limbs increases. The results shows the mulita-stability of this flexible mechanism can be determined by the original length of the limbs and limb arrangement. The whole statements provide a theoretical basis for designing and applying such type of flexible multi-stable mechanism.

Evolving Trends for C−C Bond Formation Using Functionalized Covalent Organic Frameworks as Heterogeneous Catalysts

AbstractCovalent organic frameworks (COFs), also called “Organic Zeolites,” are crystalline porous organic polymers with robust and pre‐designable architecture and high crystallinity. They can be structurally controlled and functionally managed, due to the availability of diverse organic functionalities, covalent linkages and topologies. The current review briefly outlines their design strategies and comprehensively discusses their applications in the carbon‐carbon bond formation reactions. The reactivity of COF‐based catalysts with their homogeneous counterparts, catalyst stability, role of matrix, tailored organocatalysts and substituent effect on the reaction parameters, chemo‐/stereo‐/regioselectivity, shape/size selectivity are also discussed. Finally, the challenges and future prospects of functionalized COF‐based catalysts are discussed. These discussions will provide a better understanding of the role of COF‐based catalysts in the C−C bond formation and will also open doors to the design and synthesis of more robust, sustainable and benign catalytic systems, which might play a key role in organic synthesis and catalysis.

Graph Theory-Based Programmable Topology Derivation of Multiport DC–DC Converters With Reduced Switches

Different from the typical topology derivation of power electronics converters (PECs) depending on manual effort, this article aims to explore a programmable method based on graph theory for multiport dc–dc converters with reduced switches. By mathematically modeling converters with graph theory and transforming their working criteria into the corresponding graph constraints, the topology derivation can be automatically and conveniently solved with the aid of computer program. In this article, first, the principle of the proposed graph theory-based programmable method is introduced in detail. Then, it is employed to derive multiport dc–dc converters with reduced switches, and a diversity of new topologies are simultaneously excavated, among which a favorable one is selected to be theoretically introduced and experimentally verified. The proposed method remains relatively simple with the increasing number of ports, as the graph constraints of working criteria are almost the same for different number of ports. More importantly, because PECs can be essentially regarded as graphs with different components and their connecting relationships, the proposed method is applicable for many various converters.

Stochastic boundary condition effects on supersonic leading edge blowing

The present manuscript studies the robustness of leading edge blowing for active control of the upstream bow shock and overall aerothermal characteristics of a supersonic airfoil subject to variability in the injection conditions. The injection boundary conditions were considered stochastic and their randomness was propagated to the flow topology and aerothermal quantities of interest by means of non-intrusive Polynomial Chaos Expansions. The evaluations needed to build these expansions were carried out via steady two-dimensional Reynolds-Averaged Navier Stokes simulations. Two different injection port sizes were considered, with the smallest promoting a Coanda effect that gives rise to non-symmetric flow topologies. The uncertain boundary conditions lead to significant variations in the flow topology, particularly at the injection exit and the bow shock position, with variations of up to 75% of the nominal value. This study identifies the key operating conditions to ensure the optimal application of leading-edge injection for active control of supersonic airfoils.

A phase-field cohesive zone model integrated with cell-based smoothed finite element method for quasi-brittle fracture simulations of concrete at mesoscale

In this work, a phase field cohesive zone model (PFCZM) combined with the cell-based smoothed finite element method (CSFEM) is presented to investigate the quasi-brittle fracture behaviour of concrete at mesoscale. An automatic quadtree decomposition technique is employed to discretise the CT image-based meso-structures including aggregates, mortar and pores, with local mesh adaptively refined at material boundaries. The resultant quadtree elements with hanging nodes are naturally treated as CSFEM polygons. In the proposed CSFEM-PFCZM, the stiffness matrices and residual vectors for displacement and damage fields are formulated by the Wachspress shape function and smoothed strain matrices. The crack driving forces are evaluated as history variables at the integration points of CSFEM subcells. Four nonlinear fracture benchmarks are presented to validate the proposed method regarding the variability of mesoscale fracture processes, crack topology and load–displacement curves. It is found that the CSFEM-PFCZM combined with the quadtree decomposition significantly improves the computational efficiency over the traditional FEM counterparts, and shows great potential for multiscale fracture evaluation of quasi-brittle composite materials. This is due to the fast mesh generation and flexible simulation of complicated nonlinear fracture using the CSFEM-PFCZM without the need for remeshing or mesh enrichments.

Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities

The future Intelligent Transport System "ITS" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle “V2V”), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network “VANET”, which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding “GF” mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced “GFA”, to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic.

A Microstructure Based Elasto-Plastic Polygonal FEM and CDM Approach to Evaluate LCF Life in Titanium Alloys

This work presents the influence of microstructure on the LCF life of two-phase titanium alloys. An elasto-plastic polygonal FEM and continuum damage mechanics approach has been used in this work. Damage coupled Chaboche-Lemaitre combined isotropic-kinematic hardening model is implemented to accurately capture the effects of deformation inhomogeneity and material plasticity. A new strain-based damage evolution law is proposed to predict the effect of mean grain size on the LCF life. The effect of different microstructural parameters i.e., topological variation, elastic anisotropy, mean grain size and shape, volume fraction of phases and initial voids is statistically analyzed on the LCF life.

Nanobody assemblies with fully flexible topology enabled by genetically encoded tetrazine amino acids.

Assembling nanobodies (Nbs) into polyvalent multimers is a powerful strategy for improving the effectiveness of Nb-based therapeutics and biotechnological tools. However, generally effective approaches to Nb assembly are currently restricted to the amino or carboxyl termini, greatly limiting the diversity of Nb multimer topologies that can be produced. Here, we show that reactive tetrazine groups—site-specifically inserted by genetic code expansion at Nb surface sites—are compatible with Nb folding and function, enabling Nb assembly at any desired point. Using two anti–SARS-CoV-2 Nbs with viral neutralization ability, we created Nb homo- and heterodimers with improved properties compared with conventionally linked Nb homodimers, which, in the case of our tetrazine-conjugated trimer, translated into enhanced viral neutralization. Thus, this tetrazine-based approach is a generally applicable strategy that greatly increases the accessible range of Nb assembly topologies, and thereby adds the optimization of topology as an effective avenue to generate Nb assemblies with improved efficacy.