Virtual Layer Research Articles

A Novel 3-D DGTD-FDTD Hybrid Method withOne Overlapping Virtual Layer

Compared with the traditional finite difference time-domain (FDTD) method, the discontinuous Galerkin time-domain (DGTD) method may face the issue of intense computation. In this paper, a novel 3-D DGTD-FDTD hybrid method is proposed to dramatically reduce the unknowns of the DGTD method. Instead of the common mass-lumped elements, this virtual layer of the Yee grid is implemented on the intersecting boundary, which simplifies the mesh generation and reduces the number of unknowns. To validate the proposed method, two examples of sphere scattering and horn antenna are considered. The simulation results demonstrate the effectiveness of the proposed method.

Building a Flexible and Resource-Light Monitoring Platform for a WLCG-Tier2

Software development projects at Edinburgh identified a desire to build and manage our own monitoring platform. This better allows us to support the developing and varied physics and computing interests of our Experimental Particle Physics group. This production platform enables oversight of international experimental data management, local software development projects and active monitoring of lab facilities within our research group. Larger sites such as CERN have access to many resources to support generalpurpose centralised monitoring solutions such as MONIT. At a WLCG Tier2 we only have access to a fraction of these resources and manpower. Recycling nodes from grid storage and borrowed capacity from our Tier2 Hypervisors has enabled us to build a reliable monitoring infrastructure. This also contributes back to our Tier2 management improving our operational and security monitoring. Shared experiences from larger sites gave us a head-start in building our own service monitoring (FluentD) and multi-protocol (AMQP/STOMP/UDP datagram) messaging frameworks atop both our Elasticsearch and OpenSearch clusters. This has been built with minimal hardware and software complexity, maximising maintainability, and reducing manpower costs. A secondary design goal has also been the ability to migrate and upgrade individual components with minimal service interruption. To achieve this, we made heavy use of different layers of containerisation (Podman/Docker), virtualization and NGINX web proxies. This presentation details our experiences in developing this platform from scratch with a focus on minimal resource use. This includes lessons learnt in deploying and comparing both an Elasticsearch and OpenSearch clusters, as well as designing various levels of automation and resiliency for our monitoring framework. This has culminated in us effectively indexing, parsing and storing >200GB of logging and monitoring data per day.

RESEARCH OF CYBER SECURITY TECHNOLOGIES OF CLOUD SERVICES IAAS, PAAS AND SAAS

Cybersecurity threats are constantly evolving, and cloud computing is no exception. Attackers are improving attack techniques aimed at identifying vulnerabilities in IaaS, PaaS, and SaaS. The work examines the following problematic issues: insufficient analytical tools; data privacy and security; financial and organizational costs to ensure cyber security of IaaS, PaaS and SaaS cloud technologies. The aim of the work is to investigate how the common models of cloud computing: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS) need to properly implement adequate and appropriate protection measures to ensure cyber security. For this purpose, the paper examined models of cloud technologies, it was determined that cloud services include various models that allow users to access various types of resources via the Internet. It has been found that there are three generally accepted models of cloud services: infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS), and two main players: the cloud provider and the cloud subscriber. The set of levels over which each of these players has control depends on the model of the cloud service or environment. For each of these cloud services, its description was given, recommendations were given for access control, privacy assurance, terms of use were defined, advantages and disadvantages were given, and market trends of these services were considered. Proposed approaches to the formation of a secure application development environment in cloud services. Features such as wide network access, resource pooling, fast elasticity, service metering, and data sharing are also summarized. Guidelines for designing access control for IaaS, PaaS, and SaaS according to their different characteristics are proposed. In addition, the security policy rules for each cloud system are summarized. Technologies are proposed to protect security at each of the three main layers of cloud services: the application, middleware, and virtual machine layers, due to differences in the organization that controls each of these layers. It was found that for any level it is possible to provide more effective protection if the same subject controls the level below. Since the network, hardware, and resource abstraction layer in all cloud service models are controlled by the cloud provider, it has more effective protections at its disposal.

Quantification and visualization of US racial geography using the National Racial Geography Dataset 2020.

Racial geography studies the spatial distributions of multiracial populations. Technical challenges arise from the fact that US Census data, upon which all US-based studies rely, is only available in the form of spatial aggregates at a few levels of granularity. This negatively affects spatial analysis and, consequently, the quantification of racial segregation, especially on a smaller length scale. A recent methodology called the Racial Landscape (RL) stochastically disaggregates racial data at the level of census block aggregates into a grid of monoracial cells. RL-transformed racial data makes possible pattern-based, zoneless analysis, and visualization of racial geography. Here, we introduce the National Racial Geography Dataset 2020 (NRGD2020)-a collection of RL-based grids calculated from the 2020 census data and covering the entire conterminous US. It includes a virtual image layer for a bird's-eye-like view visualization of the spatial distribution of racial sub-populations, numerical grids for calculating racial diversity and segregation within user-defined regions, and precalculated maps of racial diversity and segregation on various length scales. NRGD2020 aims to facilitate and extend spatial analyses of racial geography and to make it more interpretable by tightly integrating quantitative analysis with visualization (mapping).

GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks

In recent years, the Internet of Things (IoT) devices utilization with Information Communication Technology (ICT) has grown exponentially in various Smart City applications, including Smart Homes, Smart Enterprises, and others. The fusion of IoT, ICT, and Smart Home delivers interactive solutions to reduce costs and resource consumption, enhance performance, and engage people's needs more virtually and proactively. A smart home has numerous advantages with the integration of emerging advanced technologies. Big data, centralization, data and resource allocation, security, and privacy issues persist as challenges in IoT-enabled Smart Home Networks. To address these challenges, in this paper, we propose a GRU-based Digital Twin Framework for Data Allocation in IoT-enabled Smart Home Networks. Data and resource allocation of smart home applications are completed at the virtual twin layer using Gated Recurrent Unit (GRU)-based Digital Twin Networks. Low-priority data is stored and processed at the Macro-based Stations (MBSs), and high-priority data is transferred to the upper (Security) layer for authentication and validation. Blockchain-based distributed networks are utilized for Smart Home Data authentication at the security layer with a Proof of Authentication (PoAh) Consensus Algorithm; Data is stored at the cloud layer after validation. The validation results of the proposed framework demonstrate superior performance as the quantitative analysis with accuracy 0.9412, Root Mean Square Error (RMSE) 0.0588 for IoT-enable Smart Home compared to existing works as LSTM-based Digital Twin network and provide a secure environment in IoT-enabled Smart Home.

An improved acoustic model for mixed lubrication contact interface

Mixed lubrication interfaces are widespread in engineering. The measurement of contact stiffness is a challenge for device performance evaluation. The ultrasonic reflection method is an effective method, but the difficulty of accurate construction of the acoustic model limits the measurement accuracy. In this study, an improved acoustic model is proposed to analyze the contact stiffness of the mixed lubrication interface. The model is constructed using a quasi-static spring model, virtual material model, statistical microcontact model, and multilayer acoustic model. The mixed lubrication interface is equivalent to a homogeneous and isotropic virtual material layer. Then, the mechanical, geometric, and acoustic parameters of the virtual layer are determined by introducing thickness coefficients. The contact stiffness is obtained with the proposed model and compared with the quasi-static spring model and the virtual material layer model. The reflection coefficient of the interface can also be calculated using a multilayer acoustic model based on the calculated parameters. The proposed model is verified by comparing the predicted reflection coefficients with the published experimental results.

Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation.

Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.

Using FlowVisor and evolutionary algorithms to improve the switch migration in SDN

The switch migration (SM) issue has been widely addressed in distributed Software Defined Networks (SDN). Most of researches have focused on achieving SM with balanced loads among controllers but little efforts are devoted to equilibrate the service offered to switches when limited resources in control plane are experienced. This paper deals with the problem of dynamic switch migration in multi-controller SDN. Basically, it aims to achieve a trade-off between the whole flow setup delay, the load balancing among controllers and the service fairness among switches by efficiently splitting requests of a switch between multiple controllers. The trade-off is solved using a combination of the Unbalanced Transportation Problem optimization (UTP) and Genetic algorithms (GA). Henceforth, a new genetic algorithm denoted Reference-based Non-Dominated Sorting Algorithm (R-NSGA-II) is proposed to achieve this trade-off. The proposed approach uses the FlowVisor framework as a virtualization layer between switches and controllers. FlowVisor will relay requests coming from switches toward controllers based on current network status with the aim of achieving the above trade-off. The proposed approach is associated with no switch migration cost since assignment is done logically by FlowVisor. Simulation results show that the proposed approach outperforms several existing approaches in terms of the whole flow-setup time, the load balancing among controllers and the request service fairness among switches.

Optimal Design and Analysis of a Hybrid Hydrogen Energy Storage System for an Island-Based Renewable Energy Community

Installations of decentralised renewable energy systems (RES) are becoming increasing popular as governments introduce ambitious energy policies to curb emissions and slow surging energy costs. This work presents a novel model for optimal sizing for a decentralised renewable generation and hybrid storage system to create a renewable energy community (REC), developed in Python. The model implements photovoltaic (PV) solar and wind turbines combined with a hybrid battery and regenerative hydrogen fuel cell (RHFC). The electrical service demand was derived using real usage data from a rural island case study location. Cost remuneration was managed with an REC virtual trading layer, ensuring fair distribution among actors in accordance with the European RED(III) policy. A multi-objective genetic algorithm (GA) stochastically determines the system capacities such that the inherent trade-off relationship between project cost and decarbonisation can be observed. The optimal design resulted in a levelized cost of electricity (LCOE) of 0.15 EUR/kWh, reducing costs by over 50% compared with typical EU grid power, with a project internal rate of return (IRR) of 10.8%, simple return of 9.6%/year, and return on investment (ROI) of 9 years. The emissions output from grid-only use was reduced by 72% to 69 gCO2e/kWh. Further research of lifetime economics and additional revenue streams in combination with this work could provide a useful tool for users to quickly design and prototype future decentralised REC systems.

The virtual capital of the Middle Urals: To the 300th anniversary of Yekaterinburg

The article presents one of the virtual layers of modern architecture of Yekaterinburg, the capital of the Middle Urals, which has been created by the author for two decades and offered to investors and city authorities for realization. The article touches the problem of stylistic and large-scale juxtaposition of modern and historical layers of the urban architecture in the context of intensive development of the urban structure.

3D process simulation-assisted device failure analysis with virtual defect injection in IC layout

The development and use of nanotechnology has enabled the creation of submicron electronic devices with unprecedented levels of functionality, speed, and efficiency. While most of the semiconductor industry and its consumers are becoming increasingly dependent on nanoelectronics, these devices are becoming more susceptible to defects and transient faults. Non-Visual Defects (NVD) is a category of semiconductor material and process induced defect that cause electrical failures but are not detected with visual wafer inspection tools (Findlay et al., 2016 [1] and Kim et al., 2009 [10]) or with fault localization tools. Devices with NVD may fail at any stage of their life cycle and may benefit from complex Failure Analysis (FA) investigations including software support to analyze design and diagnostic data. For a higher FA success rate, these investigations can be further supplemented with Technology Computer-Aided Design (TCAD) Process Simulation software to simulate a failure prior to actual physical analysis on limited device samples. In this work, we will showcase an innovative technique for simulating NVD using a 3D process model to pinpoint the exact process step where the defect was introduced. By injecting a new virtual defect layer (which can be autonomously introduced by an FA engineer) in the original device data, we can emulate the failing device. We also present a real-life case study where defect simulation correlates well with the actual Transmission Electron Microscope (TEM) cross-section results.

High-velocity penetration impact characteristics of fiber metal hybrid cylindrical shells: Theoretical and experimental investigation

The high-velocity penetration impact characteristics of fiber metal hybrid (FMH) cylindrical shells are investigated. A finite element (FE) model of such a shell subjected to an internal high-velocity projectile impact is established based on ABAQUS software to predict impact parameters, in which the impact damage failures of FRP, metal, and virtual cohesive layers are considered, respectively. Both literature and experimental results are utilized to verify the current model, with the influences of critical parameters on the residual velocity and specific energy absorption properties being discussed. The outputs provide valuable references for evaluating the high-velocity impact resistance of such structures.

A layer-based random SGD for distributed training of RNNs

Since frequent data communication leads to increased time consumption in distributed training, compression of communication data is one of the effective ways to speed up the whole training process. Many existing compression methods introduce extra time overhead in data compression and decompression, for instance, Top-K and Random-k. There are also methods that can reduce the extra time overhead but suffer from the shortcoming of insufficient generalization. For example, the layer-based compression LR-SGD (Layer-based Random Stochastic Gradient Descent) cannot be applied to the training of recurrent neural networks. In this paper, an improved method based on LR-SGD is proposed. By being divided into virtual layers for compression, this improved method is able to train various networks efficiently. To validate the proposed method, experiments were conducted using different neural networks on a stimulative cluster. The results demonstrate that the proposed method applies well on different networks with low communication overhead and a nice convergence effect.

A digital twin-based green construction management method for prefabricated buildings

Background: Site pollution in construction can be reduced by using high levels of prefabrication and industrialization. However, the lack of green concepts and methods during the prefabrication assembly process hinders its environmental benefits. Digital twin technology can monitor sites in real-time and provide data visualization for decision support, which has been used in construction management and risk control. Methods: We propose a six-dimensional digital twin framework that includes physical and virtual spaces, project management and service layers, twin data, and component connections. The framework integrates green factors of prefabricated construction into a model evolution framework and mechanism that enables real-time green services throughout the process. Results: The proposed framework, modeling method, and evolution method were tested in prefabrication projects in Tianjin. By applying these methods, inadequate management measures were promptly identified and strengthened. Energy consumption and pollution were reduced by comparing with the plan before construction. In addition, the model evolution method optimized green management measures and improved the level of green construction management on site. Conclusions: The application results demonstrate the effectiveness of our proposed framework, the model building method, and the evolution method in improving the green level of prefabricated construction.

Accurate adventitia reconstruction; significant or not in atherosclerotic plaque growth simulationsƒ A comparative study.

A reform in the diagnosis and treatment process is urgently required as carotid artery disease remains a leading cause of death in the world. To this purpose, all computational techniques are now being applied to enhancing the most cutting-edge diagnosis techniques. Computational modeling of plaque generation and evolution is being refined over the past years to forecast the atherosclerotic progression and the corresponding risk in patient-specific carotid arteries. A prerequisite to their implementation is the reconstruction of the precise three-dimensional models of patient-specific main carotid arteries. Even with the most sophisticated algorithms, accurate reconstruction of the arterial vessel is frequently difficult. Furthermore, there are several works of plaque growth modeling that ignore the reconstruction of the artery's outer layer in favor of a virtual one. In this paper, we investigate the importance of an accurate adventitia layer in plaque growth modeling. This is done as a comparative study by implementing a novel plaque growth model in two reconstructed carotid arterial segments using either their realistic or virtual adventitia layer as input. The results indicate that accurate adventitia reconstruction is of minor importance regarding species distributions and plaque growth in carotid segments, which initially did not contain any plaque regions.Clinical Relevance- The findings of this comparative study emphasize the importance of precise adventitia geometry in plaque growth modeling. As a result, this work sets a higher standard for publishing new plaque growth models.

Cyber resilience and cyber security issues of intelligent cloud computing systems

It is necessary to provide the cyber security of cloud computing according to the components that constitute its structure. The first step in advancing the cyber security of this technology is to accurately identify its threats. In this paper, a new cyber security reference model of the cloud system, which consists of components making up separate layers of cloud computing is proposed. Available reference models of cloud computing security do not describe the virtualization and service layers and the important components for providing the cyber security of cloud computing in detail, do not consider the social media IoT sensor layer, which collects the text data typed by attackers to carry out cyber attacks on the cloud infrastructure, and the cyber resilience issues of the cloud computing at all In addition, this paper studies the cyber security issues of cloud computing service models, and constructs an attack model to provide security of cloud systems. It gives an interpretation of standards and legislative acts on the cyber security of cloud computing. According to security aspects, clarification of the cyber security and cyber resilience concepts of cloud systems is provided. The cyber resilience architecture of intelligent cloud systems is developed. The advantage of developed cyber resilience model over available one is that, it determines the information security and cyber security aspects of cloud computing and combines them to form the cyber resilience aspects of cloud systems.

Performance Evaluation of Communication Infrastructure for Peer-to-Peer Energy Trading in Community Microgrids

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

Granular media deformation and fluid flow as overlapping, concurrent, coupled multilayered depth-averaged framework

A novel coupled depth-averaged formulation for the combination of multilayered fluid and monolayer granular flows as a set of interpenetrating continua has been developed in this study. Granular and fluid phases are coupled through an interaction force pair comprised of fluid stresses, and drag forces. Fluid phase is discretized in the vertical direction through a set N of virtual layer interfaces to obtain a variation of flow properties along depth. A novel algorithm for mass and momentum exchange between fluid layers for flow through and over porous media has been proposed. Eigenstructure analysis of the coupled system shows that the system may be discretized in N+1 separate yet coupled Riemann problems. An improved version of well-known HLL method is developed for solution of the model (Finite Volume Method) in a regular grid. The resistive source–sink terms are embedded implicitly within the interfacial flux. Based on the choices of state variables and layer-interface porosity, four variants of the coupled system are developed. A set of benchmark experimental test-cases and a representative test involving both rigid and deformable granular media has been simulated using the four variants to establish the robustness of the model and the coupling procedure.

Epidemic trajectories and awareness diffusion among unequals in simplicial complexes

The interplay between awareness diffusion and epidemic spreading has been an active topic of research in recent years. Studies have shown that group interactions are an important consideration in contagion processes, and that thus higher-order interactions should be introduced into epidemic modeling. Research has also shown that individual responses to an unfolding epidemic are often strongly heterogeneous. We therefore present a two-layer network model, where the diffusion of awareness unfolds over 2-simplicial complexes in one layer, and the actual epidemic spreading unfolds over pairwise physical contacts in the other layer. The model takes into account individual differences in the degree of acceptance of information and self-protection measures once the epidemic is perceived. We use the micro Markov chain approach to determine the epidemic threshold of the model, which agrees well with the results obtained by Monte Carlo simulations. We show that the synergistic reinforcement due to 2-simplicial complexes in the virtual layer can restrain epidemic spreading by facilitating awareness diffusion, and moreover, that individual heterogeneity in the physical layer can increase the epidemic threshold and decrease the size of epidemic transmission. However, heterogeneity in the perception can also have the opposite effect because it inhibits the diffusion of awareness. Our results reveal the intricate interplay between awareness diffusion and epidemic spreading, and we hope they can help determine effective control measures.

Connection force measurement of precision small interference components using ultrasound

Precision small interference components are mainly assembled using the press-fit method. Interface wear is inevitable in the press-fit process, which will reduce the connection strength. Pull-out experiments are the main means of measuring the connection force, but they are not available in the production process for its destructive characteristics. In this study, a nondestructive testing method for connection force based on ultrasound is proposed. An acoustic model was constructed based on the virtual layer model and statistical microcontact theory, which can be used to obtain the R- Kn- P mapping relationship. An ultrasonic measurement device was built manufactured and calibrated to achieve accurate alignment of the ultrasonic transducer with the interference component and accurate measurement of contact stress. The coaxiality error between the fixture and the turntable is less within ±5.9 μm. The parallelism errors between the turntable and Z-axis stage in the xoz- and yoz-plane are 0.0894° and 0.0662°, respectively. The measurement error due to the transducer positioning error is less than 1.5%. The equivalent static friction coefficient was proposed to compensate R- Kn- P calibration error and measurement error. The integral expression of the connection force was given. Finally, the connection force of the interference components was measured and verified by pull-out experiments. The measurement error is within ±10%.