期刊,仿生工程学报（英文版） 2024年卷2期_国家学术搜索

期刊信息/Journal information

仿生工程学报（英文版）

主　　编：任露泉

出版周期：季刊

国际刊号：1672-6529

电子邮箱：fsxb@jlu.edu.cn

电　　话：0431-85095180，85094074

邮政编码：130022

地　　址：吉林省长春市人民大街5988号

仿生工程学报（英文版）/Journal Journal of Bionic EngineeringCSCDCSTPCDEISCI

查看更多>>本刊办刊宗旨是为仿生科学与工程领域中的新思想、新发现、新理论和新技术提供交流的平台。主要报道涉及仿生科学与工程所有方面的原始论文和综述，包括动植物仿生工程方面的基础研究，以及这些基础研究在工程技术和设计方面的应用。

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The Effect of Spironolactone Loading on the Properties of 3D-Printed Polycaprolactone/Gold Nanoparticles Composite Scaffolds for Myocardial Tissue Engineering

Sharareh GhaziofShahrokh ShojaeiMehdi MehdikhaniMohammad Khodaei...

924-937页

查看更多>>摘要：Engineered cardiac constructs(ECC)aid in the progression of regenerative medicine,disease modeling and targeted drug delivery to adjust and aim the release of remedial combination as well as decrease the side effects of drugs.In this research,polycaprolactone/gold nanoparticles(PCL/GNPs)three-dimensional(3D)composite scaffolds were manufactured by 3D printing using the fused deposition modeling(FDM)method and then coated with gelatin/spironolactone(GEL/SPL).Scan-ning electron microscopy(SEM)and Fourier transform-infrared spectroscopy(FTIR-ATR)were applied to characterize the samples.Furthermore,drug release,biodegradation,behavior of the myoblasts(H9C2)cell line,and cytotoxicity of the 3D scaffolds were evaluated.The microstructural observation of the scaffolds reported interconnected pores with 150-300 pm in diameter.The 3D scaffolds were degraded significantly after 28 days of immersion in stimulated body fluid(SBF),with the maximum rate of GEL-coated 3D scaffolds.SPL release from cross-linked GEL coating demonstrated the excess of drug release over time,and according to the control release systems,the drug delivery systems(DDS)went into balance after the 14th day.In addition,cell culture study showed that with the addition of GNPs,the proliferation of(H9C2)was enhanced,and with GEL/SPL coating the cell attachment and viability were improved significantly.These findings suggested that PCL/GNPs 3D scaffolds coated with GEL/SPL can be an appropriate choice for myocardial tissue engineering.

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Modelling and Characterization of Basalt/Vinyl Ester/SiC Micro-and Nano-hybrid Biocomposites Properties Using Novel ANN-GA Approach

Yesudhasan ThooyavanLakshmi Annamali KumaraswamidhasRobinson Dhas Edwin RajJoseph Selvi Binoj...

938-952页

查看更多>>摘要：Basalt fiber reinforcement in polymer matrix composites is becoming more and more popular because of its environmental friendliness and mechanical qualities that are comparable to those of synthetic fibers.Basalt fiber strengthened vinyl ester matrix polymeric composite with filler addition of nano-and micro-sized silicon carbide(SiC)element spanning from 2 weight percent to 10 weight percent was studied for its mechanical and wear properties.The application of Artificial Neu-ral Network(ANN)to correlate the filler addition composition for optimum mechanical properties is required due to the non-linear mechanical and tribological features of composites.The stuffing blend and composition of the composite are optimized using the hybrid model and Genetic Algorithm(GA)to maximize the mechanical and wear-resistant properties.The predicted and tested ANN-GA optimal values obtained for the composite combination had a tensile,flexural,impact resilience,hardness and wear properties of 202.93 MPa,501.67 MPa,3.460 J/s,43 HV and 0.196 g,respectively,for its optimum combination of filler and reinforcement.It can be noted that the nano-sized SiC filler particle enhances most of the properties of the composite which diversifies its applications.The predicted mechanical and wear values of the developed ANN-GA model were in closer agreement with the experimental values which validate the model.

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Advances in Manta Ray Foraging Optimization:A Comprehensive Survey

Farhad Soleimanian GharehchopoghShafi GhafouriMohammad NamaziBahman Arasteh...

953-990页

查看更多>>摘要：This paper comprehensively analyzes the Manta Ray Foraging Optimization(MRFO)algorithm and its integration into diverse academic fields.Introduced in 2020,the MRFO stands as a novel metaheuristic algorithm,drawing inspiration from manta rays'unique foraging behaviors—specifically cyclone,chain,and somersault foraging.These biologically inspired strategies allow for effective solutions to intricate physical challenges.With its potent exploitation and exploration capabili-ties,MRFO has emerged as a promising solution for complex optimization problems.Its utility and benefits have found traction in numerous academic sectors.Since its inception in 2020,a plethora of MRFO-based research has been featured in esteemed international journals such as IEEE,Wiley,Elsevier,Springer,MDPI,Hindawi,and Taylor & Francis,as well as at international conference proceedings.This paper consolidates the available literature on MRFO applications,covering various adaptations like hybridized,improved,and other MRFO variants,alongside optimization challenges.Research trends indicate that 12％,31％,8％,and 49％of MRFO studies are distributed across these four categories respectively.

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Image-Based Flow Prediction of Vocal Folds Using 3D Convolutional Neural Networks

Yang ZhangTianmei PuJiasen XuChunhua Zhou...

991-1002页

查看更多>>摘要：In this work,a three dimensional(3D)convolutional neural network(CNN)model based on image slices of various normal and pathological vocal folds is proposed for accurate and efficient prediction of glottal flows.The 3D CNN model is composed of the feature extraction block and regression block.The feature extraction block is capable of learning low dimensional features from the high dimensional image data of the glottal shape,and the regression block is employed to flatten the output from the feature extraction block and obtain the desired glottal flow data.The input image data is the condensed set of 2D image slices captured in the axial plane of the 3D vocal folds,where these glottal shapes are synthesized based on the equa-tions of normal vibration modes.The output flow data is the corresponding flow rate,averaged glottal pressure and nodal pressure distributions over the glottal surface.The 3D CNN model is built to establish the mapping between the input image data and output flow data.The ground-truth flow variables of each glottal shape in the training and test datasets are obtained by a high-fidelity sharp-interface immersed-boundary solver.The proposed model is trained to predict the concerned flow variables for glottal shapes in the test set.The present 3D CNN model is more efficient than traditional Computational Fluid Dynamics(CFD)models while the accuracy can still be retained,and more powerful than previous data-driven prediction models because more details of the glottal flow can be provided.The prediction performance of the trained 3D CNN model in accuracy and efficiency indicates that this model could be promising for future clinical applications.

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Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson's Disease Diagnosis

Liuyan FengYongquan ZhouQifang Luo

1003-1021页

查看更多>>摘要：Parkinson's disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson's patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accu-racy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson's disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson's disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

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Balancing Exploration-Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models

Yan HanWeibin ChenAli Asghar HeidariHuiling Chen...

1022-1054页

查看更多>>摘要：Extracting photovoltaic(PV)model parameters based on the measured voltage and current information is crucial in the simu-lation and management of PV systems.To accurately and reliably extract the unknown parameters of different PV models,this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder-Mead simplex method,INMVO.Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation.Further,to verify the feasibility and com-petitiveness of the proposal,this paper employed INMVO to extract the unknown parameters on single-diode,double-diode,three-diode,and PV module four well-known PV models,and the high-performance techniques are selected for comparison.In addition,the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically.Various evaluation metrics,such as root means square error,relative error,absolute error,and statistical test,demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques.In addition,the capability of INMVO to stably and accurately extract unknown parameters was also veri-fied on three commercial PV modules under different irradiance and temperatures.In conclusion,the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models.Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.

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Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

Yupeng LiDong ZhaoAli Asghar HeidariShuihua Wang...

1055-1091页

查看更多>>摘要：In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

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An Improved Golden Jackal Optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering Optimization Problems

Jun WangWen-chuan WangKwok-wing ChauLin Qiu...

1092-1115页

查看更多>>摘要：Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the Golden Jackal Optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively bal-ance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four con-strained industrial applications.The outcomes of the experiment reveal that the proposed algorithm can solve challenging,constrained problems and is very competitive compared with other optimization algorithms.This article provides a new approach to solving real-world optimization problems.

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