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CONTENTS
Volume 12, Number 2, February 2022
 


Abstract
Effect of thickness stretching on free vibration, bending and buckling behavior of carbon nanotubes reinforced composite (CNTRC) laminated nanoplates rested on new variable elastic foundation is investigated in this paper using a developed four-unknown quasi-3D higher-order shear deformation theory (HSDT). The key feature of this theoretical formulation is that, in addition to considering the thickness stretching effect, the number of unknowns of the displacement field is reduced to four, and which is more than five in the other models. Two new forms of CNTs reinforcement distribution are proposed and analyzed based on cosine functions. By considering the higher-order nonlocal strain gradient theory, microstructure and length scale influences are included. Variational method is developed to derive the governing equation and Galerkin method is employed to derive an analytical solution of governing equilibrium equations. Two-dimensional variable Winkler elastic foundation is suggested in this study for the first time. A parametric study is executed to determine the impact of the reinforcement patterns, nonlocal parameter, length scale parameter, side-t-thickness ratio and aspect ratio, elastic foundation and various boundary conditions on bending, buckling and free vibration responses of the CNTRC plate.

Key Words
critical buckling load; frequencies; Galerkin method; nonlocal strain gradient theory; quasi 3D HSDT; stresses and deflection; 2D Winkler elastic foundation

Address
Mashhour A. Alazwari: Faculty of Engineering, Mechanical Engineering Department, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia

Ahmed Amine Daikh: Department of Technology, University Centre of Naama, 45000 Naama, P.O. Box 66, Algeria

Mohamed A. Eltaher: Faculty of Engineering, Mechanical Engineering Department, King Abdulaziz University, P.O. Box 80204, Jeddah, Saudi Arabia/ Faculty of Engineering, Mechanical Design and Production Department, Zagazig University, P.O. Box 44519, Zagazig, Egypt

Abstract
This paper presents sets of explicit analytical equations that compute the static displacements of nanobeams by adopting the nonlocal elasticity theory of Eringen within the framework of Euler Bernoulli and Timoshenko beam theories. Castigliano's theorem is applied to an equivalent Virtual Local Beam (VLB) made up of linear elastic material to compute the displacements. The first derivative of the complementary energy of the VLB with respect to a virtual point load provides displacements. The displacements of the VLB are assumed equal to those of the nonlocal beam if nonlocal effects are superposed as additional stress resultants on the VLB. The illustrative equations of displacements are relevant to a few types of loadings combined with a few common boundary conditions. Several equations of displacements, thus derived, matched precisely in similar cases with the equations obtained by other analytical methods found in the literature. Furthermore, magnitudes of maximum displacements are also in excellent agreement with those computed by other numerical methods. These validated the superposition of nonlocal effects on the VLB and the accuracy of the derived equations.

Key Words
analytical solution; Castigliano's theorem; Eringen's nonlocal elasticity theory; Euler-Bernoulli beam theory; static displacements; Timoshenko beam theory

Address
Indronil Devnath and Mohammad Nazmul Islam: Department of Civil and Environmental Engineering, North South University, Dhaka, Bangladesh

Minhaj Uddin Mahmood Siddique: Nippon Koei Bangladesh, Dhaka, Bangladesh

Abdelouahed Tounsi: YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea/ Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, 31261 Dhahran, Eastern Province, Saudi Arabia/ Material and Hydrology Laboratory, Faculty of Technology, Civil Engineering Department, University of Sidi Bel Abbes, Algeria

Abstract
Micro-scale serpentine structure fibers are widely used as flexible sensor in the manufacturing of micro-nano flexible electronic devices because of their outstanding non-linear mechanical properties and organizational flexibility. The use of melt electrowriting (MEW) technology, combined with the axial bending effect of the Taylor cone jet in the process, can achieve the micro-scale serpentine structure fibers. Due to the interference of the process parameters, it is still challenging to achieve the precise deposition of micro-scale and high-consistency serpentine structure fibers. In this paper, the micro-scale serpentine structure fiber is produced by MEW combined with axial bending effect. Based on the controlled deposition of MEW, applied voltage, collector speed, nozzle height and nozzle diameter are adjusted to achieve the precise deposition of micro-scale serpentine structure fibers with different morphologies in a single motion dimension. Finally, the influence mechanism of the above four parameters on the precise deposition of micro-scale serpentine fibers is explored.

Key Words
axial bending effect; melt electrowriting; precise deposition; serpentine structure

Address
Han Wang, Weicheng Ou, Jingfan He, Nian Cai, XinDu Chen, Zengxi Xue, Daohua Zhan, Jingsong Yao and Peixuan Wu: State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, P.R. China/ Guangdong Provincial Key Laboratory of Micro-Nano Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou 510006, P.R. China

Zuyong Wang: College of Materials Science and Engineering, College of Biology, Hunan University, Changsha 410072, P.R. China

Jianxiang Liao: Guangdong Foshan Nanofiberlabs Co., Ltd, Foshan 528000, P.R. China

Abstract
In this paper, the free and forced vibration analysis of rotating cantilever nanoscale cylindrical beams and tubes is investigated under the external dynamic load to examine the nonlocal effect. A couple of nonlocal strain gradient theories with different beams and tubes theories, involving the Euler-Bernoulli, Timoshenko, Reddy beam theory along with the higher-order tube theory, are assumed to the mathematic model of governing equations employing the Hamilton principle in order to derive the nonlocal governing equations related to the local and accurate nonlocal boundary conditions. The two-dimensional functional graded material (2D-FGM), made by the axially functionally graded (AFG) in conjunction with the porosity distribution in the radial direction, is considered material modeling. Finally, the derived Partial Differential Equations (PDE) are solved via a couple of the generalized differential quadrature element methods (GDQEM) with the Newmark-beta techniques for the time-dependent results. It is indicated that the boundary conditions equations play a crucial task in responding to nonlocal effects for the cantilever structures.

Key Words
beam theory; bending vibration; forced vibration; rotating; time dependent analysis; tube theory

Address
Linyuan Fan: College of Mathematics and Data Science (College of Software), Minjiang University, Fuzhou 350108, Fujian, China

Xu Zhang: Information Technology Center, Jingchu Institute of Technology, Jingmen 448023, Hubei, China

Xiaoyang Zhao: Birmingham City University, School of Computing and Digital Technology, Birmingham, B5, 5JU, U.K.

Abstract
Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer' there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancers class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

Key Words
convolutional neural network; CT image; deep learning; image processing; lung cancer

Address
Lumin Xing: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, 250014, China/ City University of Macau, Macau, 999078, China

Wenjian Liu: City University of Macau, Macau, 999078, China

Xiaoliang Liu: The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, 250014, China

Xin Li: Shandong University of Political Science and Law, Jinan, Shandong, 250014, China

Han Wang: Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, Guangdong, 519000, China

Abstract
The purpose of this study is to develop an automatic software system for bone age evaluation and to evaluate its accuracy in testing and feasibility in clinical practice. 20394 left-hand radiographs of healthy children (2-18 years old) were collected from China Skeletal Development Survey data of 1998 and China Skeletal Development Survey data of 2005. Three experienced radiologists and China-05 standard maker jointly evaluate the stages of bone development and the reference bone age was determined by consensus. 1020 from 20394 radiographs were picked randomly as test set and the remaining 19374 radiographs as training set and validation set. Accuracy of the automatic software system for bone age assessment is evaluated in test set and two clinical test sets. Compared with the reference standard, the automatic software system based on RUS-CHN for bone age assessment has a 0.04 years old mean difference, ±0.40 years old in 95% confidence interval by single reading, a 85.6% percentage agreement of ratings, a 93.7% bone age accuracy rate, 0.17 years old of MAD, 0.29 years old of RMS; Compared with the reference standard, the automatic software system based on TW3-C RUS has a 0.04 years old mean difference, a ±0.38 years old in 95% confidence interval by single reading, a 90.9% percentage agreement of ratings, a 93.2% bone age accuracy rate, a 0.16 years of MAD, and a 0.28 years of RMS. Automatic software system, AI-China-05 showed reliably accuracy in bone age estimation and steady determination in different clinical test sets.

Key Words
AI-China-05; bone age; deep learning; RUS-CHN; TW3-C RUS

Address
Chuangao Yin, Chang Wang, Huihui Lin, Gengwu Li, Lichun Zhu and Weimin Fei: Anhui Provincial Children's Hospital, Hefei 230054, China

Miao Zhang: Shijiazhuang Kid Grow Science and Technology Co. Ltd, Shijiazhuang 050000, China

Xiaoyu Wang: Anhui Medical University, Hefei 230032, China

Abstract
Silicon carbide (SiC) is a binary carbon-silicon compound. In its two-dimensional form, monolayer SiC is composed of a monolayer carbon and silicon atoms constructed as a honeycomb lattice. SiC has recently been receiving increasing attention from researchers owing to its intriguing electronic properties. In this present work, SiC nanoribbons (SiCNRs) are modelled and simulated to obtain accurate electronic properties, which can further guide fabrication processes, through bandgap engineering. The primary objective of this work is to obtain the electronic properties of monolayer SiCNRs by applying numerical computation methods using nearest-neighbour tight-binding models. Hamiltonian operator discretization and approximation of plane wave are assumed for the models and simulation by applying the basis function. The computed electronic properties include the band structures and density of states of monolayer SiCNRs of varying width. Furthermore, the properties are compared with those of graphene nanoribbons. The bandgap of ASiCNR as a function of width are also benchmarked with published DFT-GW and DFT-GGA data. Our nearest neighbour tight-binding (NNTB) model predicted data closer to the calculations based on the standard DFT-GGA and underestimated the bandgap values projected from DFT-GW, which takes in account the exchange-correlation energy of many-body effects.

Key Words
bandgap engineering; electronic properties; monolayer; nanoribbon; silicon carbide; tight-binding

Address
M.W. Chuan, Y.B. Wong, A. Hamzah, N.E. Alias, S. Mohamed Sultan, C.S. Lim and M.L.P. Tan: School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

Abstract
Object detection has always been to pursue objects with particular properties or representations and to predict details on objects including the positions, sizes and angle of rotation in the current picture. This was a very important subject of computer vision science. While vision-based object tracking strategies for the analysis of competitive videos have been developed, it is still difficult to accurately identify and position a speedy small ball. In this study, deep learning (DP) network was developed to face these obstacles in the study of tennis motion tracking from a complex perspective to understand the performance of athletes. This research has used CNN-VGG 16 to tracking the tennis ball from broadcasting videos while their images are distorted, thin and often invisible not only to identify the image of the ball from a single frame, but also to learn patterns from consecutive frames, then VGG 16 takes images with 640 to 360 sizes to locate the ball and obtain high accuracy in public videos. VGG 16 tests 99.6%, 96.63%, and 99.5%, respectively, of accuracy. In order to avoid overfitting, 9 additional videos and a subset of the previous dataset are partly labelled for the 10-fold cross-validation. The results show that CNN-VGG 16 outperforms the standard approach by a wide margin and provides excellent ball tracking performance.

Key Words
computer vision science; deep learning (DP); information entropy; unascertained measurement theory

Address
Yongfeng Zhong: College of Physical Education, Changsha University, Changsha 410022, Hunan, China

Xiaojun Liang: Ministry of Public Foundation, Zhaoqing Medical College, Zhaoqing 526020, Guangdong, China/ Graduate School, University of Perpetual Help System DALTA, Las Pinas City 1740, Manila, Philippines



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