Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

Efficiency analysis of numerical integrations for finite element substructure in real-time hybrid simulation

Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.

Global SH-wave propagation in a 2D whole Moon model using the parallel hybrid PSM/FDM method

We present numerical modeling of SH-wave propagation for the recently proposed whole Moon model and try to improve our understanding of lunar seismic wave propagation. We use a hybrid PSM/FDM method on staggered grids to solve the wave equations and implement the calculation on a parallel PC cluster to improve the computing efficiency. Features of global SH-wave propagation are firstly discussed for a 100-km shallow and900-km deep moonquakes, respectively. Effects of frequency range and lateral variation of crust thickness are then investigated with various models. Our synthetic waveforms are finally compared with observed Apollo data to show the features of wave propagation that were produced by our model and those not reproduced by our models. Our numerical modeling show that the low-velocity upper crust plays significant role in the development of reverberating wave trains. Increasing frequency enhances the strength and duration of the reverberations.Surface multiples dominate wavefields for shallow event.Core–mantle reflections can be clearly identified for deep event at low frequency. The layered whole Moon model and the low-velocity upper crust produce the reverberating wave trains following each phases consistent with observation. However, more realistic Moon model should be considered in order to explain the strong and slow decay scattering between various phases shown on observation data.

Optimized quantum singular value thresholding algorithm based on a hybrid quantum computer

Quantum singular value thresholding(QSVT) algorithm,as a core module of many mathematical models,seeks the singular values of a sparse and low rank matrix exceeding a threshold and their associated singular vectors.The existing all-qubit QSVT algorithm demands lots of ancillary qubits,remaining a huge challenge for realization on nearterm intermediate-scale quantum computers.In this paper,we propose a hybrid QSVT(HQSVT) algorithm utilizing both discrete variables(DVs) and continuous variables(CVs).In our algorithm,raw data vectors are encoded into a qubit system and the following data processing is fulfilled by hybrid quantum operations.Our algorithm requires O [log(MN)] qubits with0(1) qumodes and totally performs 0(1) operations,which significantly reduces the space and runtime consumption.

Designing an automated FAQ answering system for farmers based on hybrid strategies

Purpose: The purpose of this study is to develop an automated frequently asked question(FAQ) answering system for farmers. This paper presents an approach for calculating the similarity between Chinese sentences based on hybrid strategies.Design/methodology/approach: We analyzed the factors influencing the successful matching between a user's question and a question-answer(QA) pair in the FAQ database. Our approach is based on a combination of multiple factors. Experiments were conducted to test the performance of our method.Findings: Experiments show that this proposed method has higher accuracy. Compared with similarity calculation based on TF-IDF,the sentence surface forms and the semantic relations,the proposed method based on hybrid strategies has a superior performance in precision,recall and F-measure value.Research limitations: The FAQ answering system is only capable of meeting users' demand for text retrieval at present. In the future,the system needs to be improved to meet users' demand for retrieving images and videos.Practical implications: This FAQ answering system will help farmers utilize agricultural information resources more efficiently.Originality/value: We design the algorithms for calculating similarity of Chinese sentences based on hybrid strategies,which integrate the question surface similarity,the question semantic similarity and the question-answer similarity based on latent semantic analysis(LSA) to find answers to a user's question.

From Digitalized to Intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service applications.The main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and models.In order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and algorithm-based.This paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering production.Further research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&M.Finally,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

SCStore： Managing Scientific Computing Packages for Hybrid System with Containers

Managing software packages in a scientific computing environment is a challenging task, especially in the case of heterogeneous systems. It is error prone when installing and updating software packages in a sophisticated computing environment. Testing and performance evaluation in an on-the-fly manner is also a troublesome task for a production system. In this paper, we discuss a package management scheme based on containers. The newly developed method can ease the maintenance complexity and reduce human mistakes. We can benefit from the self-containing and isolation features of container technologies for maintaining the software packages among intricately connected clusters. By deploying the Super Computing application Strore（SCStore） over the WAN connected world-largest clusters, it proved that it can greatly reduce the effort for maintaining the consistency of software environment and bring benefit to achieve automation.

Mapping cloud computing in university e-governance system

Purpose–The purpose of this paper is to propose a model to map the on-premise computing system of the university with cloud computing for achieving an effective and reliable university e-governance(e-gov)system.Design/methodology/approach–The proposed model incorporates the university’s internal e-gov system with cloud computing in order to achieve better reliability,accessibility and availability of e-gov services while keeping the recurring expenditure low.This model has been implemented(and tested on a university e-gov system)in the University of Kashmir(UOK);case study of this implementation has been chosen as the research methodology to discuss and demonstrate the proposed model.Findings–According to the results based on practical implementation,the proposed model is ideal for e-governed systems as it provided adequate cost savings and high availability(HA)with operational ease,apart from continuing to have the necessary security in place to maintain confidential information such as student details,grades,etc.Practical implications–The implication of this study is to achieve HA and to reduce the cost from using external clouds,mapping internal IT servers of the university with the external cloud computing services.Originality/value–Because no established mapping model for universities has been provided for effective,low-cost,highly available university e-gov system,the proposed mapping model through this paper closes this gap and provides guidelines to implement a hybrid-mapped e-gov model for universities while keeping the recurring expenditure on cloud computing minimal.The paper provides the perceptions of its adoption at UOK for achieving high reliability,accessibility and uptime of its e-gov applications while keeping the recurring expenditure on cloud computing minimal.

An efficient large-scale mesh deformation method based on MPI/Open MP hybrid parallel radial basis function interpolation

An efficient MPI/OpenMP hybrid parallel Radial Basis Function (RBF) strategy for both continuous and discontinuous large-scale mesh deformation is proposed to reduce the computational cost and memory consumption.Unlike the conventional parallel methods in which all processors use the same surface displacement and implement the same operation,the present method employs different surface points sets and influence radius for each volume point movement,accompanied with efficient geometry searching strategy.The deformed surface points,also called Control Points (CPs),are stored in each processor.The displacement of spatial points is interpolated by using only 20-50 nearest control points,and the local influence radius is set to 5-20 times the maximum displacement of control points.To shorten the searching time for the nearest control point clouds,an Alternating Digital Tree (ADT) algorithm for 3D complex geometry is designed based on an iterative bisection technique.Besides,an MPI/OpenMP hybrid parallel approach is developed to reduce the memory cost in each High-Performance Computing (HPC) node for large-scale applications.Three 3D cases,including the ONERA-M6 wing and a commercial transport airplane standard model with up to 2.5 billion hybrid elements,are used to test the present mesh deformation method.The robustness and high parallel efficiency are demonstrated by a wing deflection case with a maximum bending angle of 450 and more than 80% parallel efficiency with 1024 MPI processors.In addition,the availability for both continuous and discontinuous surface deformation is verified by interpolating the projecting displacement with opposite directions surface points to the spatial points.

Numerical simulation of a dense solid particle flow inside a cyclone separator using the hybrid Euler-Lagrange approach

This paper presents a numerical simulation of the flow inside a cyclone separator at high particle loads. The gas and gas–particle flows were analyzed using a commercial computational fluid dynamics code. The turbulence effects inside the separator were modeled using the Reynolds stress model. The two phase gas–solid particles flow was modeled using a hybrid Euler–Lagrange approach, which accounts for the four-way coupling between phases. The simulations were performed for three inlet velocities of the gaseous phase and several cyclone mass particle loadings. Moreover, the influences of several submodel parameters on the calculated results were investigated. The obtained results were compared against experimental data collected at the in-house experimental rig. The cyclone pressure drop evaluated numerically underpredicts the measured values. The possible reason of this discrepancies was disused.

A 3D hybrid grid generation technique and a multigrid/parallel algorithm based on anisotropic agglomeration approach

A hybrid grid generation technique and a multigrid/parallel algorithm are presented in this paper for turbulence flow simulations over three-dimensional （3D） complex geometries. The hybrid grid generation technique is based on an agglomeration method of anisotropic tetrahedrons. Firstly, the complex computational domain is covered by pure tetrahedral grids, in which anisotropic tetrahedrons are adopted to discrete the boundary layer and isotropic tetrahedrons in the outer field. Then, the anisotropic tetrahedrons in the boundary layer are agglomerated to generate prismatic grids. The agglomeration method can improve the grid quality in boundary layer and reduce the grid quantity to enhance the numerical accuracy and efficiency. In order to accelerate the convergence history, a multigrid/parallel algorithm is developed also based on anisotropic agglomeration approach. The numerical results demonstrate the excellent accelerating capability of this multigrid method.

Neuro-optimized numerical treatment of HIV infection model

In this paper,a neuro-optimized numerical method is presented for approximation of HIV virus progression model in the human body.The model is composed of coupled nonlinear system of differential equations(DEs)containing healthy and infected T-Cells and HIV free virus particles.The coupled system is transformed into feedforward artificial neural network(ANN)with Mexican hat wavelet function in the hidden layers.Two meta-heuristic algorithms based on chaotic particle swarm optimization(CPSO)and its hybrid version with local search technique are exploited to tune the parameters of ANN in an unsupervised manner of error function.A comprehensive testbed is established to observe the virus growth per day with performance metric containing fitness value,computational time complexity and convergence.The proposed solutions are compared with state of art Runge-Kutta method and Legendre Wavelet Collocation Method(LWCM).The core advantages of the proposed scheme are getting the solution on continuous grid,consistent convergence,simplicity in implementation and handling strong nonlinearity effectively.