Dongyang Ou, Yongjian Ren, Congfeng Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.029987

Abstract Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on random forest. On-line service… More >

Shujiang Xu^1,2,*, Ziye Wang^1,2, Lianhai Wang^1,2, Miodrag J. Mihaljević^1,2,3, Shuhui Zhang^1,2, Wei Shao^1,2, Qizheng Wang^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.046164

Abstract Blockchain technology, with its attributes of decentralization, immutability, and traceability, has emerged as a powerful catalyst for enhancing traditional industries in terms of optimizing business processes. However, transaction performance and scalability has become the main challenges hindering the widespread adoption of blockchain. Due to its inability to meet the demands of high-frequency trading, blockchain cannot be adopted in many scenarios. To improve the transaction capacity, researchers have proposed some on-chain scaling technologies, including lightning networks, directed acyclic graph technology, state channels, and sharding mechanisms, in which sharding emerges as a potential scaling technology. Nevertheless, excessive cross-shard transactions and uneven shard… More > Graphic Abstract

Yu Cheng^1,2,5, Yajun Huang³, Shuai Li⁴, Zhongbin Zhou⁵, Xiaohui Yuan^1,2,*, Yanming Xu^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045668

Abstract A new approach for flexoelectric material shape optimization is proposed in this study. In this work, a proxy model based on artificial neural network (ANN) is used to solve the parameter optimization and shape optimization problems. To improve the fitting ability of the neural network, we use the idea of pre-training to determine the structure of the neural network and combine different optimizers for training. The isogeometric analysis-finite element method (IGA-FEM) is used to discretize the flexural theoretical formulas and obtain samples, which helps ANN to build a proxy model from the model shape to the target value. The effectiveness… More >

Lanze Zhang, Yijun Gu^*, Jingjie Peng

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045129

Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for… More >

Shijun Zhao¹, Qing Zhang², Yusong Miao¹, Weizhao Zhang³, Xinbo Zhao¹, Wei Xu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045015

Abstract The surrounding rock of roadways exhibits intricate characteristics of discontinuity and heterogeneity. To address these complexities, this study employs non-local Peridynamics (PD) theory and reconstructs the kernel function to represent accurately the spatial decline of long-range force. Additionally, modifications to the traditional bond-based PD model are made. By considering the micro-structure of coal-rock materials within a uniform discrete model, heterogeneity characterized by bond random pre-breaking is introduced. This approach facilitates the proposal of a novel model capable of handling the random distribution characteristics of material heterogeneity, rendering the PD model suitable for analyzing the deformation and failure of heterogeneous layered… More >

Qianqi Sun, Fenhua Bai^*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044558

Abstract Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development. Nevertheless, scalability challenges, characterized by diminished broadcast efficiency, heightened communication overhead, and escalated storage costs, have significantly constrained the broad-scale application of blockchain. This paper introduces a novel Encode-and CRT-based Scalability Scheme (ECSS), meticulously refined to enhance both block broadcasting and storage. Primarily, ECSS categorizes nodes into distinct domains, thereby reducing the network diameter and augmenting transmission efficiency. Secondly, ECSS streamlines block transmission through a compact block protocol and robust RS coding, which not only reduces the size of broadcasted blocks but also ensures transmission… More >

Abidhan Bardhan^1,*, Raushan Kumar Singh², Mohammed Alatiyyah³, Sulaiman Abdullah Alateyah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044467

Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results featuring five influencing factors was… More >

Yujie Duan¹, Jianguo Liang^1,*, Jianglin Liu¹, Haifeng Gao¹, Yinhui Li², Jinzhu Zhang¹, Xinyu Wen³

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044400

Abstract In the fiber winding process, strong disturbance, uncertainty, strong coupling, and fiber friction complicate the winding constant tension control. In order to effectively reduce the influence of these problems on the tension output, this paper proposed a tension fluctuation rejection strategy based on feedforward compensation. In addition to the bias harmonic curve of the unknown state, the tension fluctuation also contains the influence of bounded noise. A tension fluctuation observer (TFO) is designed to cancel the uncertain periodic signal, in which the frequency generator is used to estimate the critical parameter information. Then, the fluctuation signal is reconstructed by a… More >

Qianqian Shao¹, Min Wang^2,*, Nuo Xu¹

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044313

Abstract To investigate the influences of co-flow and counter-flow modes of reactant flow arrangement on a proton exchange membrane fuel cell (PEMFC) during start-up, unsteady physical and mathematical models fully coupling the flow, heat, and electrochemical reactions in a PEMFC are established. The continuity equation and momentum equation are solved by handling pressure-velocity coupling using the SIMPLE algorithm. The electrochemical reaction rates in the catalyst layers (CLs) of the cathode and anode are calculated using the Butler-Volmer equation. The multiphase mixture model describes the multiphase transport process of gas mixtures and liquid water in the fuel cell. After validation, the influences… More >

Wei Zhou¹, Xincong Jiang^2,*, Chuan Qin²

CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.043248

Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict the code coverage of test… More >