CMES-Computer Modeling in Engineering & Sciences

About the Journal

This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales (picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.

Indexing and Abstracting

Science Citation Index (Web of Science): 2020 Impact Factor 1.593; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2020): 1.6; SNIP (Source Normalized Impact per Paper 2020): 0.59; RG Journal Impact (average over last three years); Engineering Index (Compendex); Applied Mechanics Reviews; Cambridge Scientific Abstracts: Aerospace and High Technology, Materials Sciences & Engineering, and Computer & Information Systems Abstracts Database; CompuMath Citation Index; INSPEC Databases; Mathematical Reviews; MathSci Net; Mechanics; Science Alert; Science Navigator; Zentralblatt fur Mathematik; Portico, etc...

  • A Survey on Machine Learning in COVID-19 Diagnosis
  • Abstract Since Corona Virus Disease 2019 outbreak, many expert groups worldwide have studied the problem and proposed many diagnostic methods. This paper focuses on the research of Corona Virus Disease 2019 diagnosis. First, the procedure of the diagnosis based on machine learning is introduced in detail, which includes medical data collection, image preprocessing, feature extraction, and image classification. Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep neural networks, and so on. What’s more, the advantages and limitations of different diagnosis methods are compared. Although the… More
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  • Estimation of Aleatory Randomness by Sa(T1)-Based Intensity Measures in Fragility Analysis of Reinforced Concrete Frame Structures
  • Abstract Based on the multiple stripes analysis method, an investigation of the estimation of aleatory randomness by Sa(T1)-based intensity measures (IMs) in the fragility analysis is carried out for two typical low- and medium-rise reinforced concrete (RC) frame structures with 4 and 8 stories, respectively. The sensitivity of the aleatory randomness estimated in fragility curves to various Sa(T1)-based IMs is analyzed at three damage limit states, i.e., immediate occupancy, life safety, and collapse prevention. In addition, the effect of characterization methods of bidirectional ground motion intensity on the record-to-record variability is investigated. It is found that the damage limit state of… More
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  • Traffic Accident Detection Based on Deformable Frustum Proposal and Adaptive Space Segmentation
  • Abstract Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving. This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively. Using 2D region proposals in an RGB image, this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network (FPS-Net) and feature extraction network (FE-Net). Subsequently, the encoder-decoder network (ED-Net) implements 3D-oriented bounding box (OBB) regression. Meanwhile, the adaptive least square regression (ALSR) method is… More
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  • Near Future Perspective of ESBL-Producing Klebsiella pneumoniae Strains Using Mathematical Modeling
  • Abstract While antibiotic resistance is becoming increasingly serious today, there is almost no doubt that more challenging times await us in the future. Resistant microorganisms have increased in the past decades leading to limited treatment options, along with higher morbidity and mortality. Klebsiella pneumoniae is one of the significant microorganisms causing major public health problems by acquiring resistance to antibiotics and acting as an opportunistic pathogen of healthcare-associated infections. The production of extended spectrum beta-lactamases (ESBL) is one of the resistance mechanisms of K. pneumoniae against antibiotics. In this study, the future clinical situation of ESBL-producing K. pneumoniae was investigated in… More
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  • Sensitivity Analysis of COVID-19 in Mediterranean Island
  • Abstract The aim of this study is to examine the progress of the worldwide pandemic Covid-19. As authors, we have decided to analyze the situation of COVID-19 on Mediterranean island with accurate data. For this purpose, a mathematical model is constructed and proposed by dividing the whole population into sensible and suitable compartments. The study captures the dates February 01 till May 15, 2021. For the control of the spread of disease, vaccination and infection rates are compared and calculated. During calculations and comparison, MatLab software is used. All of the data that are used are taken from the Ministry of… More
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  • Bioconvection Mangnetohydrodynamic Tangent Hyperbolic Nanofluid Flow with Quartic Chemical Reaction Past a Paraboloid Surface
  • Abstract In this numerical study, the effect of quartic autocatalysis type of chemical reaction, buoyancy force and thermal radiation phenomenon and magnetic effect on tangent hyperbolic nanofluid past an upper horizontal surface of a paraboloid has been studied. By considering the Buongiorno model approach, a diffusion of unequal coefficients in the presence of gyrotactic microorganism is discussed. Implementation of microorganism's idea is used to stabilize the nanoparticles through bioconvection. The modeled PDEs of the problems are converted into nonlinear ODEs with the assistant of the similarity transformations. To tackle nonlinear ODEs, MATLAB package bvp4c is used. In addition, a hallmark of… More
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  • Similarity Analytic Solutions of a 3D-Fractal Nanofluid Uncoupled System Optimized by a Fractal Symmetric Tangent Function
  • Abstract The science of strategy (game theory) is known as the optimal decision-making of autonomous and challenging players in a strategic background. There are different strategies to complete the optimal decision. One of these strategies is the similarity technique. Similarity technique is a generalization of the symmetric strategy, which depends only on the other approaches employed, which can be formulated by altering diversities. One of these methods is the fractal theory. In this investigation, we present a new method studying the similarity analytic solution (SAS) of a 3D-fractal nanofluid system (FNFS). The dynamic evolution is completely given by the concept of… More
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  • Melting Characteristics of a Phase Change Material Mixed with Nano Particles of Cobalt Oxide Bounded in a Trapezoidal Structure
  • Abstract A novel trapezoidal design for storage of heat energy through melting of phase-change material (PCM) is investigated. Latent heat thermal energy storage system (LHTES) is a promising option to diminish mis-match between energy consumption and supply. For this purpose, Paraffin: Rubitherm-35 (RT35) material is successively melted in aluminum structure which is heated from one side and the other sides are kept adiabatic. Melting of PCM is observed experimentally and melt fronts are photographed for various time lengths. The fluid-solid module in COMSOL Multiphysics 5.4 has been utilized. The transient heat conduction with enthalpy function is hired. Simulations are carried out… More
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  • A Chaos Sparrow Search Algorithm with Logarithmic Spiral and Adaptive Step for Engineering Problems
  • Abstract The sparrow search algorithm (SSA) is a newly proposed meta-heuristic optimization algorithm based on the sparrow foraging principle. Similar to other meta-heuristic algorithms, SSA has problems such as slow convergence speed and difficulty in jumping out of the local optimum. In order to overcome these shortcomings, a chaotic sparrow search algorithm based on logarithmic spiral strategy and adaptive step strategy (CLSSA) is proposed in this paper. Firstly, in order to balance the exploration and exploitation ability of the algorithm, chaotic mapping is introduced to adjust the main parameters of SSA. Secondly, in order to improve the diversity of the population… More
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  • Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network
  • Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception… More
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  • A New Rayleigh Distribution: Properties and Estimation Based on Progressive Type-II Censored Data with an Application
  • Abstract In this paper, we propose a new extension of the traditional Rayleigh distribution called the modified Kies Rayleigh distribution. The new distribution contains one scale and one shape parameter and its hazard rate function can be increasing and bathtub-shaped. Some mathematical properties of the new distribution are derived including quantiles and moments. The parameters of modified Kies Rayleigh distribution are estimated based on progressively Type-II censored data. For this purpose, we consider two estimation methods, namely maximum likelihood and maximum product of spacing estimation methods. To compare the efficiency of the proposed estimators, a simulation study is carried out. To… More
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  • Adaptive Virtual Source Imaging Using the Sequence Intensity Factor: Simulation and Experimental Study
  • Abstract Virtual source (VS) imaging has been proposed to improve image resolution in medical ultrasound imaging. However, VS obtains a limited contrast due to the non-adaptive delay-and-sum (DAS) beamforming. To improve the image contrast and provide an enhanced resolution, adaptive weighting algorithms were applied in VS imaging. In this paper, we proposed an adjustable generalized coherence factor (aGCF) for the synthetic aperture sequential beamforming (SASB) of VS imaging to improve image quality. The value of aGCF is adjusted by a sequence intensity factor (SIF) that is defined as the ratio between the effective low resolution scan lines (LRLs) intensity and total… More
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  • Design of Multi-Coupled Laminates with Extension-Twisting Coupling for Application in Adaptive Structures
  • Abstract The multiple coupling of composite laminates has a unique advantage in improving the macro mechanical properties of composite structures. A total of three hygro-thermally stable multi-coupled laminates with extension-twisting coupling were presented, which were conducive to the formation of passive adaptive structures. Then, the multi-coupled laminates were used to design the bending-twisting coupled box structure, in which the configuration of laminate and box structure could be extended to variable cross-section configuration. The optimal design of stacking sequence was realized, the optimization objectives of which were to maximize bending-twisting coupling of box structure and extension-twisting coupling of laminate, respectively. The effects… More
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  • Optimization of the Drag Forces of Shell Janus Micromotor: A Study Based on Hydrodynamical Analysis and Numerical Simulation
  • Abstract Micromotors are widely used in cell operation, drug delivery and environmental decontamination due to their small size, low energy consumption and large propelling power. Compared to traditional Janus micromotor, the shell Janus micromotor has better motion performance. However, the structural optimization of its motion performance is still unclear. The main factor restricting the motion performance of shell Janus micromotors is the drag forces. In the current work, theoretical analysis and numerical simulation were applied to analyze the drag forces of shell Janus micromotors. This study aims to design the optimum structure of shell Janus micromotors with minimum drag forces and… More
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  • Ripple+: An Improved Scheme of Ripple Consensus Protocol in Deployability, Liveness and Timing Assumption
  • Abstract Ripple acts as a real-time settlement and payment system to connect banks and payment providers. As the consensus support of the Ripple network to ensure network consistency, Ripple consensus protocol has been widely concerned in recent years. Compared with those Byzantine fault tolerant protocols, Ripple has a significant difference that the system can reach an agreement under decentralized trust model. However, Ripple has many problems both in theory and practice, which are mentioned in the previous researches. This paper presents Ripple+, an improved scheme of Ripple consensus protocol, which improves Ripple from three aspects: (1) Ripple+ employs a specific trust… More
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  • Unsupervised Binary Protocol Clustering Based on Maximum Sequential Patterns
  • Abstract With the rapid development of the Internet, a large number of private protocols emerge on the network. However, some of them are constructed by attackers to avoid being analyzed, posing a threat to computer network security. The blockchain uses the P2P protocol to implement various functions across the network. Furthermore, the P2P protocol format of blockchain may differ from the standard format specification, which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them. Therefore, the ability to distinguish different types of unknown network protocols is vital for network security. In this paper, we propose… More
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  • Topology Optimization with Aperiodic Load Fatigue Constraints Based on Bidirectional Evolutionary Structural Optimization
  • Abstract Because of descriptive nonlinearity and computational inefficiency, topology optimization with fatigue life under aperiodic loads has developed slowly. A fatigue constraint topology optimization method based on bidirectional evolutionary structural optimization (BESO) under an aperiodic load is proposed in this paper. In view of the severe nonlinearity of fatigue damage with respect to design variables, effective stress cycles are extracted through transient dynamic analysis. Based on the Miner cumulative damage theory and life requirements, a fatigue constraint is first quantified and then transformed into a stress problem. Then, a normalized termination criterion is proposed by approximate maximum stress measured by global… More
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  • Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction
  • Abstract In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by… More
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