bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of magnetic dipole on ferromagnetic hybrid nanofluid flow over a stretching cylinder
Authors: Kumar R Naveen , Gowda R J Punith , Abusorrah Abdullah M , Mahrous Y M , Abu-Hamdeh Nidal H , Issakhov Alibek , Rahimi-Gorji Mohammad , Prasannakumara B C 
Published: February 2021

Abstract
Nanofluids manage heat in the internal combustion of the engines or machines by avoiding corrosion in the cooling system as well as assist in eradicating the engine’s waste heat. Hence, they are used as coolants in many automotive industries. Inspired by these applications, the thermal and mass transfer in hybrid nanoliquid flow over a stretching cylinder on taking account of magnetic dipole is studied in this investigation. Here, we have done a comparative study on flow of two diverse combinations of hybrid nanofluids, namely

MnZnFe

2

O

4

−

NiZnFe

2

O

4

−

C

10

H

22

and

Cu
−

Al

2

O

3

−

C

10

H

22

.

The modelled equation for the assumed flow is converted to ODEs by opting appropriate similarity variables. These ODEs are solved by utilizing the Runge–Kutta Fehlberg fourth-fifth order (RKF-45) method by adopting shooting technique. Physical clarification of relevant parameters for non-dimensional discrete flow fields are discussed briefly by using graphs. Also, skin friction, Sherwood and Nusselt numbers are deliberated with the assistance of graphs. Results reveal that, the upsurge in ferromagnetic interaction parameter declines the velocity in both fluids but converse trend is detected in temperature and concentration of the liquids. The heightening of ferromagnetic interaction parameter declines the rate of heat and mass transfer.
The electromagnetic waves propagating parallel to the magnetic field in a uniform magnetized plasma with a transverse electric field
Authors: Zhou Deng , Ming Yue , Wang Jinfang , Gan Chenxiao 
Published: February 2021

Abstract
In natural and laboratory magnetized plasmas, an equilibrium electric field may exist perpendicular to the background magnetic field. In such a situation all the plasma species experience a common drift and the equilibrium distribution functions have a common shift in velocity space. The susceptibility tensor is derived for such a situation using the characteristics and then the Lorentz transformation method is adopted to verify the results. As an application we give an analysis of the waves propagating parallel to the background magnetic field for a simple electron plus single charged ion plasma. It is predicted that the presence of a transverse equilibrium electric field can drastically change the wave propagation only if the wave frequency is close to the plasma Langmuir frequency.
Temporal and spatial chaos in the Kerr-AdS black hole in an extended phase space *
Authors: Tang Bing 
Published: February 2021

Abstract
Based on the Melnikov method, we investigate chaotic behaviors in the extended thermodynamic phase space for a slowly rotating Kerr-AdS black hole under temporal and spatial perturbations. Our results show that the temporal perturbation coming from a thermal quench of the spinodal region in the phase diagram may cause temporal chaos only when the perturbation amplitude is above a critical value, which involves the angular momentum J. Under the spatial perturbation, however, it is found that spatial chaos always occurs, independent of the perturbation amplitude.
Improving the performance of multisubject motor imagery-based BCIs using twin cascaded softmax CNNs
Authors: Luo Jing , Shi Weiwei , Lu Na , Wang Jie , Chen Hao , Wang Yaojie , Lu Xiaofeng , Wang Xiaofan , Hei Xinhong 
Published: February 2021

Abstract

Objective. Motor imagery (MI) EEG signals vary greatly among subjects, so scholarly research on motor imagery-based brain–computer interfaces (BCIs) has mainly focused on single-subject systems or subject-dependent systems. However, the single-subject model is applicable only to the target subject, and the small sample number greatly limits the performance of the model. This paper aims to study a convolutional neural network to achieve an adaptable MI-BCI that is applicable to multiple subjects. Approach. In this paper, a twin cascaded softmax convolutional neural network (TCSCNN) is proposed for multisubject MI-BCIs. The proposed TCSCNN is independent and can be applied to any single-subject MI classification convolutional neural network (CNN) model. First, to reduce the influence of individual differences, subject recognition and MI recognition are accomplished simultaneously. A cascaded softmax structure consisting of two softmax layers, related to subject recognition and MI recognition, is subsequently applied. Second, to improve the MI classification precision, a twin network structure is proposed on the basis of ensemble learning. TCSCNN is built by combining a cascaded softmax structure and twin network structure. Main results. Experiments were conducted on three popular CNN models (EEGNet and Shallow ConvNet and Deep ConvNet from EEGDecoding) and three public datasets (BCI Competition IV datasets 2a and 2b and the high-gamma dataset) to verify the performance of the proposed TCSCNN. The results show that compared with the state-of-the-art CNN model, the proposed TCSCNN obviously improves the precision and convergence of multisubject MI recognition. Significance. This study provides a promising scheme for multisubject MI-BCI, reflecting the progress made in the development and application of MI-BCIs.
Resource allocation method based on mobile edge computing in smart grid
Authors: Qin Ningli , Li Bo , Li Da , Jing Xiaosong , Du Changyu , Wan Chunyi 
Published: February 2021

Abstract
Mobile edge computing(MEC) refers to offloading the collected data to the mobile edge server, which can calculate data efficiently, further improve the operating efficiency of the smart grid and reduce energy consumption. In the face of increasing data, the spectrum overhead and energy consumption of the network are becoming increasingly severe. Based on this, this paper proposes to use massive multiple input multiple output (MIMO) technology to improve the spectrum and energy efficiency of the system, and proposes a massive MIMO-MEC smart grid system framework. In addition, considering that it is easy to be eavesdropped in the process of offloading data, we propose to use physical layer security technology to ensure user information security. In order to minimize the energy consumption of applications such as smart meters, this paper proposes a sequential iterative optimization algorithm to jointly optimize the offloading rate and transmission power. The simulation results prove that as the number of antennas increases, the transmission rate of the system increases, which reduces the total energy consumption of the system.
Numerical simulation of scale deposition in boiler water wall with different structures
Authors: xing Wu , yangyan Zheng , yining Wang , chuanyang Xie 
Published: February 2021

Abstract
The scale produced by the boiler water wall will affect the heat exchange and shorten its service life. In this paper, based on the Euler-Lagrangian method, the numerical simulation of the accretion rate of the water wall under different dip angles and different flow conditions is carried out. In this paper, the accretion rate is almost power-distributed with the dip angles. From 0° to 60°, accretion rate from 6.55×10”2kg/m2.-s increases to 0.38kg/m2.-s As the Re number increases from about 6×104 to 7×104, the accretion rate declines rapidly and then flattens out. Compared with dip Angle, the influence of velocity on accretion rate is less.
Eigensensitivitity analysis of large-scale structures by substructuring method
Authors: Song Xiaodong , Weng Shun , Yang Guojing , Yan Yongyi , Li Jiajing 
Published: February 2021

Abstract
In sensitivity-based model updating, the eigensolutions and the associated derivatives of the analytical model need to be calculated iteratively to achieve an optimal solution, which is very time-consuming for a large-scale structure. To overcome this shortcoming, this paper proposes a new method to calculate the eigensensitivity via substructuring approach. When the design parameters in one particular substructure need to update, the sensitivity matrices to the updating parameters at this substructure are calculated while they are zeros at other substructures. The eigensensitivity at the global level is assembled by performing some constraints on the sensitivity matrices at the substructure level. The proposed method is applied in a frame and a practical bridge structure. Numerical results show that the present substructuring approach is more efficient than the traditional global method, at the satisfactory precision.
Experiment study of vibration isolation by multi-rows of piles
Authors: Wang Zhiying , Gui Zunyao , Meng Qingjuan 
Published: February 2021

Abstract
Pile barriers have been broadly used to decrease the effect of ground vibration generated by machines or traffic. However, so far, the research about the model test of vibration isolation of pile barriers is very few. In the present paper, the basic properties of the soil in the concrete model box are determined by the basic properties test of the soil firstly. Secondly, through the model tests with two types of piles under different vibration frequencies, the influences of different factors on the vibration isolation effect of pile barriers are studied. The results show that the multi-row pile barriers can effectively isolate the vibration. The main factors influencing the isolation effect are the pile diameter, the number of rows of piles and the vibration wave frequency.
Vibration characteristics and vibration isolation design of oil-immersed power transformer
Authors: Qian Zhijie , Cao Meigen , Ying Jianlie , Zhuang Weide , Zhang Hong , Shi Jin , Zhou Dongcheng 
Published: February 2021

Abstract
Transformer vibration and noise has become a prominent problem in power grid construction and operation. Firstly, on the basis of expounding the vibration mechanism of iron core and winding, the transmission path of vibration and noise of transformer is analyzed. Secondly, it is pointed out that it is not cost-effective to control transformer vibration and noise only from the selection of core and winding materials, manufacturing process and structural optimization. It is necessary to start from the transmission path of transformer sound and vibration, and adopt auxiliary vibration reduction and noise reduction measures such as vibration isolation of the body to reduce the transmission of vibration of the body to the oil tank wall, foundation and adjacent structures. Then, the design method of oil immersed power transformer body vibration isolation is discussed, and the relevant parameters such as frequency ratio, damping ratio and the arrangement of vibration isolation support are proposed. The reasonable vibration isolation design of transformer body can achieve remarkable vibration isolation effect and the vibration isolation efficiency can reach more than 96%. Finally, the vertical design stiffness of 81 kinds of oil immersed power transformer vibration isolation layers with 4 voltage levels of 10 kV, 35 kV, 110 kV and 220 kV are calculated. Taking a typical 110 kV transformer as an example, the corresponding vibration isolation design scheme is proposed. The research provides a good reference for the vibration isolation design of transformer body in the future.
HMM speech recognition study of an Improved Particle Swarm Optimization Based on Self-Adaptive Escape (AEPSO)
Authors: Liangpan Ye , Tao He 
Published: February 2021

Abstract
Aiming at the single evaluation content of the current train simulation training system, the process of voice interaction in the daily work of train conductors cannot be effectively and comprehensively reflected, and the coordination between dispatching terminology and train running equipment are not considered. In order to realize the objective evaluation of whether the terms are clear and the semantics accurate and the consistency check of “schedule terms-equipment operation”, the HMM speech recognition subsystem of an Improved Particle Swarm Optimization Based on Self-Adaptive Escape (AEPSO) for railway dispatching was constructed. Compared with the traditional Baum-Welch algorithm, the experiment result shows that AEPSO-BW algorithm has certain advantages in speech recognition compared with traditional Baum-Welch algorithm. It provides a more comprehensive evaluation index for the training and assessment of vehicle service personnel.
BIM-based ventilation shaft construction schedule simulation and plan optimization
Authors: Shen Jiao , Wu Qing , Peng Chen , Zhao Suya 
Published: February 2021

Abstract
In order to solve the problems of multiple construction links, mutual influence and restriction, and tight construction period of the ventilation shaft under complex geological conditions, the BIM technology is used to realize the three-dimensional parametric modeling of the Nanhu ventilation shaft project from geology, main structure and main construction equipment, etc. Simulating the construction schedule, formulating a reasonable schedule and optimizing the construction plan are of great significance for accelerating the construction schedule, controlling construction risks, and striving to make the Nanhu air shaft project put into use as soon as possible.
Dynamic Analysis of Seventh-Class ColumnFrame with Cushion
Authors: Yang Songye , Wang Jianliang , Wang Yifan , Li Tieying 
Published: February 2021

Abstract
In accordance with the relevant regulations of Building Method, this paper uses the finite element model to establish a two-column seven-grade dovetail column frame model. 4 kinds of thin cushions are designed with different elasticity and shear modulus at the bottom of the column. The elastic modulus of the cushion is 1/5, 1/10, 1/15, 1/30 of the column frame modulus. A simple harmonic wave with an amplitude of 50 gal and a frequency of 1Hz-2.5Hz was applied to the wooden frame model with cushion and without cushion. There are a total of 20 models and their natural vibration period is obtained and the relative displacement time history curve of the top of the column was extracted to obtain the displacement angle between each model. The results show that: the natural vibration period of the wooden frame with cushion is greater than that without cushion, and the period increases with the decrease of the elastic modulus of the cushion. As the harmonic frequency increases, the layer displacement angle of each wood frame model decreases, and as the cushion modulus decreases, the decreasing amplitude becomes more gentle, indicating that the cushioned wood structure has better resistance to deformation.
Effect of Alternating Electromagnetic Activated Water on Properties of Cement Mortar
Authors: Zhang Zhifeng , Li Borong , Song Nan , Zhao Lei 
Published: February 2021

Abstract
For cement mixture, water is indispensable and directly affects the performance of cement mixture of raw materials. Based on the research conclusion of alternating electromagnetic field, square wave excitation signal and 50-turn excitation coil were used to activate water through alternating electromagnetic field with different magnetic induction intensity (52.6mT, 97.8mT, 151.2mT, 193.6mT) at different flow rates (0.69m/s, 0.85m/s, 1.05m/s) to explore the effect of activated water on the performance of cement mortar. Through tests, the workability and compressive strength of cement mortar are improved, the water retention is better, and the apparent density is increased. The maximum increase of consistency is 34%, the layering degree is within 20mm, and the compressive strength of 7th day, 14th day and 28th day can be increased by 27.5%, 23.8% and 20.3%.
Health For You
Authors: Surapur Pooja , Reddy Vishnu Preetham , Hussain Munavar , Satya Kumar C 
Published: February 2021

Abstract
Health for you is an application which acts like interface between patients and doctors. All the patients can fill the application regarding the disease they had been suffering with. Any doctor who is interested in serving people for free can register using their license during the sign up process and give solution to the patient’s disease. The doctor can examine the patient’s details, write the prescription or second opinion and mail it to the patient. The problem was to create an application which efficiently does one of the most strenuous and huge tasks like connecting poor people with well experienced doctors at free of cost and gain very valuable suggestions. Finally, the app should act like a natural hospital where the treatment is available free of cost from your living room. To avoid certain doubts to our doctors we have made a bunch of clarifications to our patients and users that we validate for a unique like license of doctor and make a search in our national database we register him as a doctor after having some typical steps of registration.
High and Low Temperature Resistance of Phenyl silicone rubber
Authors: Huang Yanhua , Mu Qiuhong , Su Zhengtao 
Published: February 2021

Abstract
Methyl Phenyl and diphenyl silicone rubbers were synthesized from octamethyl cyclotetrasiloxane (D4), methylphenyl cyclosiloxane, octaphenyl cyclotetrasiloxane, etc. Compared with the commercial methylvinyl silicone rubber, the high and low temperature resistances of methyl vinyl silicone rubber were studied in this paper. The results showed that the introduction of phenyl groups can improve the thermal properties of silicone rubber, and the thermal stability of diphenyl silicone rubber is better than that of methyl phenyl silicone rubber. But there is little difference of mechanical properties in high temperature environment for three silicone rubber. DMTA analysis showed that there were still a small amount of crystals in diphenyl silicone rubber, while methylphenyl silicone rubber was amorphous rubber. The mechanical properties at low temperature indicated that the tensile strength of diphenyl silicone rubber was 4.1Mpa higher than that of methylphenyl silicone rubber at -70 °C due to the existence of low-temperature crystallization.
Preparation and characterization of magnetic α-Fe2O3/Fe3O4 heteroplasmon nanorods via the ethanol solution combustion process of ferric nitrate
Authors: Yang Xigang , Jin Baosheng , Yu Lulu , Zhu Fahua , Xu Yueyang , Liu Ruijiang 
Published: February 2021

Abstract
An ethanol solution combustion process of ferric nitrate for preparing magnetic α-Fe2O3/Fe3O4 heteroplasmon nanorods was introduced. The influencing factors, including the solvent type and the calcination conditions, were discussed. Anhydrous ethanol was considered to be the most suitable solvent for the preparation of α-Fe2O3/Fe3O4 heteroplasmon nanorods, and the optimal calcination time was determined to be 2 h. By changing the calcination temperature, α-Fe2O3/Fe3O4 heteroplasmon nanorods with different phase compositions could be obtained, and the mechanism was explained in detail. The results indicated that the rapid combustion method could achieve the controlled preparation of α-Fe2O3/Fe3O4 heteroplasmon nanorods, which provided a general preparation approach for α-Fe2O3/Fe3O4 heteroplasmon nanomaterials.
Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning
Authors: Li Wen , Kazemifar Samaneh , Bai Ti , Nguyen Dan , Weng Yaochung , Li Yafen , Xia Jun , Xiong Jing , Xie Yaoqin , Owrangi Amir , Jiang Steve 
Published: February 2021

Abstract

Background and purpose. Replacing CT imaging with MR imaging for MR-only radiotherapy has sparked the interest of many scientists and is being increasingly adopted in radiation oncology. Although many studies have focused on generating CT images from MR images, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task. Materials and methods. Brain T2 MR and corresponding CT images were collected from SZSPH (source domain dataset), brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from The University of Texas Southwestern (UTSW) (target domain dataset). To investigate the model’s generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset. Results. The adapted model achieved best quantitative results of 74.56 ± 8.61, 193.18 ± 17.98, 28.30 ± 0.83, and 0.84 ± 0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89 ± 15.64, 195.73 ± 31.29, 27.72 ± 1.43, and 0.83 ± 0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance. Conclusions. This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.
Review on Ruddlesden–Popper perovskites as cathode for solid oxide fuel cells
Authors: Ding Peipei , Li Wenlu , Zhao Hanwen , Wu Congcong , Zhao Li , Dong Binghai , Wang Shimin 
Published: February 2021

Abstract
Solid oxide fuel cells (SOFC) are highly efficient energy conversion device, but its high operating temperature (800∼1000 °C) restricts industrial commercialization. Reducing the operating temperature to <800 °C could broaden the selection of materials, improve the reliability of the system, and lower the operating cost. However, traditional perovskite cathode could not both attain the high catalytic activity towards the oxygen reduction reaction and good durability at medium and low temperature range. In contrast to the conventional perovskites, Ruddlesden–Popper perovskites exhibit fast oxygen surface exchange kinetic and excellent stability at medium and low temperatures, and excel both in oxide-conducting fuel cells (O-SOFC) and proton-conducting fuel cells (H-SOFC). In this paper, we try to relate its prominent performance with the crystal structure, main physical properties, and transport mechanism of oxygen ions and protons. We also summarize the current strategy in improving its application in O-SOFC and H-SOFC. Finally, we discuss the challenges and outlook for the future development of RP perovskites in SOFC.
Photonic pattern reconstruction enabled by on-chip online learning and inference
Authors: Marquez Bicky A , Guo Zhimu , Morison Hugh , Shekhar Sudip , Chrostowski Lukas , Prucnal Paul , Shastri Bhavin J 
Published: February 2021

Abstract
Recent investigations in neuromorphic photonics exploit optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing. Integrated solutions enabled by silicon photonics enable high-bandwidth, low-latency and low switching energy, making it a promising candidate for special-purpose artificial intelligence hardware accelerators. Here, we experimentally demonstrate a silicon photonic chip that can perform training and testing of a Hopfield network, i.e. recurrent neural network, via vector dot products. We demonstrate that after online training, our trained Hopfield network can successfully reconstruct corrupted input patterns.