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): 2019 Impact Factor 0.805; Current Contents: Engineering, Computing & Technology; Scopus Citescore (Impact per Publication 2019): 1.0; SNIP (Source Normalized Impact per Paper 2019): 0.499; 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...

  • Nanomechanics and Ultrastructure of Bone: A Review
  • Abstract In this review, a brief presentation is first given to the hierarchical structure and mechanical behavior of bone. Then, the recent advancements in nanoscale characterization of bone ultrastructure and ingredients are discussed based on an extensive quantity of references in the literature.Moreover, computational and analytical bone mechanics at ultrastructure levels are critically reviewed with the growing body of knowledge in the field. The computational and analytical models are summarized in several categories for ease of understanding bone nanomechanics and their applicability/limitations. This review is expected to provide a well-informed foundation for the researchers interested in interrogating the complex biomechanical response… More
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  • Online AUV Path Replanning Using Quantum-Behaved Particle Swarm Optimization with Selective Differential Evolution
  • Abstract This paper presents an online AUV (autonomous underwater vehicle) path planner that employs path replanning approach and the SDEQPSO (selective differential evolution-hybridized quantum-behaved particle swarm optimization) algorithm to optimize an AUV mission conducted in an unknown, dynamic and cluttered ocean environment. The proposed path replanner considered the effect of ocean currents in path optimization to generate a Pareto-optimal path that guides the AUV to its target within minimum time. The optimization was based on the onboard sensor data measured from the environment, which consists of a priori unknown dynamic obstacles and spatiotemporal currents. Different sensor arrangements for the forward-looking sonar… More
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  • Modelling Strategy and Parametric Study of Metal Gaskets for Automotive Applications
  • Abstract This paper is focused on finite element simulation of cylinder head gaskets. Finite element codes support several methodologies, each of which has its own strengths and weaknesses. One of the key points lies in the influence of the gasket geometry on its final behaviour. Such a contribution can come from the detailed modelling of the gasket or by defining a global non-linear behaviour in which material and geometry non-linearities are summarised. Two approaches were used to simulate the gasket behaviour. The first one consists in using a 2D approach, which allows to model through-thickness non-linear behaviour of gasket. The second… More
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  • Ziegler–Nichols Customization for Quadrotor Attitude Control under Empty and Full Loading Conditions
  • Abstract An aircraft quadrotor is a complex control system that allows for great flexibility in flight. Controlling multirotor aerial systems such as quadrotors is complex because the variables involved are not always available, known, and accurate. The inclusion of payload changes the dynamic characteristics of the aircraft, making it necessary to adapt the control system for this situation. Among the various control methods that have been investigated, proportional-integralderivative (PID) control offers good results and simplicity of application; however, achieving stability and high performance is challenging, with the most critical task being tuning the controller gains. The Ziegler–Nichols (ZN) theory was used… More
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  • Periodic Lattice Porous Structure Produced by Selective Laser Melting: Process, Experiment and Numerical Simulation Analysis
  • Abstract To accurately perform the coupled simulation of temperature field and stress field of complex parts and porous structures under the optimal manufacturing process parameters, three kinds of porous structures with different complexity were designed in this paper. Firstly, ANSYS additive software was used to conduct the stress/deformation simulation of the whole structure under different scanning strategies. Secondly, the optimal scanning strategy for different porous structures was determined, then the experimental preparation was performed, and mechanical properties of compression were tested and studied. The results show that the elastic modulus and yield strength increase with the increase of pole diameter/wall thickness.… More
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  • Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model
  • Abstract According to the time series characteristics of the trajectory history data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. It can improve the traffic flow prediction effect, achieve efficient traffic guidance and traffic control. The model combined the characteristics of LSTM (Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting) algorithms. First, we used the LSTM model that increases dropout layer to train the data set after preprocessing. Second, we replaced the… More
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  • Prediction of Intrinsically Disordered Proteins with a Low Computational Complexity Method
  • Abstract The prediction of intrinsically disordered proteins is a hot research area in bio-information. Due to the high cost of experimental methods to evaluate disordered regions of protein sequences, it is becoming increasingly important to predict those regions through computational methods. In this paper, we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence, which includes the Shannon entropy, the topological entropy, the sample entropy and three amino acid preferences including Remark 465, Deleage/Roux, and Bfactor(2STD). Particularly, we introduced the sample entropy for calculating time series complexity by mapping the amino… More
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  • Deep Residual Network Based on Image Priors for Single Image Super Resolution in FFA Images
  • Abstract Diabetic retinopathy, aged macular degeneration, glaucoma etc. are widely prevalent ocular pathologies which are irreversible at advanced stages. Machine learning based automated detection of these pathologies facilitate timely clinical interventions, preventing adverse outcomes. Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images (FFA) which capture retinal components featuring diverse morphologies such as retinal vasculature, macula, optical disk etc. However, these images have low resolutions, hindering the accurate detection of ocular disorders. Construction of high resolution images from these images, by super resolution approaches expedites the diagnosis of pathologies with better accuracy. This paper presents a deep learning network for Single… More
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  • Simulation Analysis on Mechanical Property Characterization of Carbon Nanotubes Reinforced Epoxy Composites
  • Abstract Carbon nanotube (CNT)-reinforced composites have ultra-high elastic moduli, low densities, and fibrous structures. This paper presents the multi-scale finite element modeling of CNT-reinforced polymer composites from micro- to macro-scales. The nanocomposites were modeled using representative volume elements (RVEs), and finite element code was written to simulate the modeling and loading procedure and obtain equivalent mechanical properties of the RVEs with various volume fractions of CNTs, which can be used directly in the follow-up simulation studies on the macroscopic model of CNT-reinforced nanocomposites. When using the programming to simulate the deformation and fracture process of the CNT-reinforced epoxy composites, the mechanical… More
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  • The Efficient Finite Element Methods for Time-Fractional Oldroyd-B Fluid Model Involving Two Caputo Derivatives
  • Abstract In this paper, we consider the numerical schemes for a timefractional Oldroyd-B fluid model involving the Caputo derivative. We propose two efficient finite element methods by applying the convolution quadrature in time generated by the backward Euler and the second-order backward difference methods. Error estimates in terms of data regularity are established for both the semidiscrete and fully discrete schemes. Numerical examples for two-dimensional problems further confirm the robustness of the schemes with first- and second-order accurate in time. More
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  • A Novel Binary Firey Algorithm for the Minimum Labeling Spanning Tree Problem
  • Abstract Given a connected undirected graph G whose edges are labeled, the minimum labeling spanning tree (MLST) problem is to find a spanning tree of G with the smallest number of different labels. The MLST is an NP-hard combinatorial optimization problem, which is widely applied in communication networks, multimodal transportation networks, and data compression. Some approximation algorithms and heuristics algorithms have been proposed for the problem. Firey algorithm is a new meta-heuristic algorithm. Because of its simplicity and easy implementation, it has been successfully applied in various fields. However, the basic firefly algorithm for the MLST problem is proposed in this… More
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  • Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Techniques
  • Abstract Schizophrenia (SZ) is one of the most common mental diseases. Its main characteristics are abnormal social behavior and inability to correctly understand real things. In recent years, the magnetic resonance imaging (MRI) technique has been popularly utilized to study SZ. However, it is still a great challenge to reveal the essential information contained in the MRI data. In this paper, we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data, in which biomarkers represent both abnormal brain functional connectivity and… More
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  • Performance Analysis of Intelligent CR-NOMA Model for Industrial IoT Communications
  • Abstract Aiming for ultra-reliable low-latency wireless communications required in industrial internet of things (IIoT) applications, this paper studies a simple cognitive radio non-orthogonal multiple access (CR-NOMA) downlink system. This system consists of two secondary users (SUs) dynamically interfered by the primary user (PU), and its performance is characterized by the outage probability of the SU communications. This outage probability is calculated under two conditions where, a) the transmission of PU starts after the channel state information (CSI) is acquired, so the base station (BS) is oblivious of the interference, and b) when the BS is aware of the PU interference, and… More
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  • An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks
  • Abstract As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from the information they wrote. In… More
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  • Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identi
  • Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More
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  • Robust Design Optimization and Improvement by Metamodel
  • Abstract The robust design optimization (RDO) is an effective method to improve product performance with uncertainty factors. The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables. There are some important issues in RDO, such as how to judge robustness, deal with multi-objective problem and black-box situation. In this paper, two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment. The robustness measure based on maximum entropy is proposed. Weighted sum method is improved to deal with the objective function, and the basic framework of… More
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  • Multiple Images Steganography of JPEG Images Based on Optimal Payload Distribution
  • Abstract Multiple images steganography refers to hiding secret messages in multiple natural images to minimize the leakage of secret messages during transmission. Currently, the main multiple images steganography algorithms mainly distribute the payloads as sparsely as possible in multiple cover images to improve the detection error rate of stego images. In order to enable the payloads to be accurately and efficiently distributed in each cover image, this paper proposes a multiple images steganography for JPEG images based on optimal payload redistribution. Firstly, the algorithm uses the principle of dynamic programming to redistribute the payloads of the cover images to reduce the… More
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  • Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets
  • Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with three parameters based on the… More
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