bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparation and tensile conductivity of carbon nanotube/polyurethane nanofiber conductive films based on the centrifugal spinning method
Authors: Luo Wei , Mei Shun-qi , Liu Teng , Yang Li-ye , Fan Ling-ling 
Published: December 2021

Abstract
Flexible conductive thin films have recently become a research area of focus in both academia and industry. In this study, a method of preparing nanofiber conductive films by centrifugal spinning is proposed. Polyurethane (PU) nanofiber films were prepared by centrifugal spinning as the flexible substrate film, and carbon nanotubes (CNTs) were used as the conducting medium, to obtain CNTs/PU nanofiber conductive films with good conductivity and elasticity. The effects of different CNT concentrations on the properties of the nanofiber films were investigated. It was found that the conductivity of the nanofiber conductive films was optimal when an impregnation concentration of 9% CNTs was used in the stretching process. Cyclic tensile resistance tests showed that the nanofiber conductive films have good durability and repeatability. Physical and structural property analysis of the CNT/PU conductive films indicate that the adsorption of the CNTs on the PU surface was successful and the CNTs were evenly dispersed on the surface of the matrix. Moreover, the CNTs improved the thermal stability of the PU membrane. The CNT/PU conductive films were pasted onto a human finger joint, wrist joint, and Adam’s apple to test the detection of movement. The results showed that finger bending, wrist bending, and laryngeal prominence movement all caused a change in resistance of the conductive film, with an approximately linear curve. The results indicate that the CNT/PU nanofiber conductive film developed in this study can be used to test the motion of human joints.
Recent advances of biomaterials in stem cell therapies
Authors: Xue Yonger , Baig Rafia , Dong Yizhou 
Published: December 2021

Abstract
Stem cells have been utilized as ‘living drugs’ in clinics for decades. Their self-renewal, differentiation, and immunomodulating properties provide potential solutions for a variety of malignant diseases and disorders. However, the pathological environment may diminish the therapeutic functions and survival of the transplanted stem cells, causing failure in clinical translation. To overcome these challenges, researchers have developed biomaterial-based strategies that facilitate in vivo tracking, functional engineering, and protective delivery of stem cells, paving the way for next-generation stem cell therapies. In this perspective, we briefly overview different types of stem cells and the major clinical challenges and summarize recent progress of biomaterials applied to boost stem cell therapies.
Facile one pot microbe-mediated in situ synthesis and antibacterial activity of reduced graphene oxide-silver nanocomposite
Authors: Patil Ashwini G 
Published: December 2021

Abstract
The present research deals with the development of a novel bioinspired in situ fabrication of reduced graphene oxide (rGO)-silver nanoparticle (AgNPs) nanocomposite (rGO@AgNCs) using microbes namely Pseudomonas aeruginosa (PA) and Staphylococcus aureus (SA). The fabricated rGO@AgNCs were characterized using Ultraviolet-visible (UV–Vis) spectroscopy, Fourier-transform infrared spectroscopy (FTIR), particle size analysis, polydispersity index (PDI), zeta potential analysis, energy dispersive x-ray analysis (EDAX), Raman spectroscopy, powder x-ray diffraction (PXRD), high-resolution transmission electron microscopy (HR-TEM) analysis, etc. Furthermore, the rGO@AgNCs-PA and rGO@AgNCs-SA interaction with serum protein, pH stability study, and in vitro dissolution of AgNPs were also performed. The research findings of the proposed study demonstrated the simultaneous reduction of graphene oxide (GO) and AgNPs and the formation of rGO@AgNCs in the presence of microbes. The in vitro dissolution studies of rGO@AgNCs composites showed better AgNPs dissolution with controlled release and offered remarkable matrix integrity throughout the dissolution period. The size and stability of rGO@AgNCs-PA and rGO@AgNCs-SA had no significant changes at physiological pH 7.4. A minimal decrease in the zeta potential of rGO@AgNCs was observed, which may be due to the weak interaction of nanocomposites and albumin. The antibacterial application of the synthesized nanocomposite was evaluated against a pathogenic mastitis-forming bacterium. The obtained results suggested an admirable antibacterial activity of synthesized nanocomposites against the tested microbes. This knowledge will assist the scientific fraternity in designing novel antibacterial agents with enhanced antibacterial activity against various veterinary pathogens in near future.
Group feature selection for enhancing information gain in MRI reconstruction
Authors: Chang Yuchou , Saritac Mert 
Published: December 2021

Abstract
Magnetic resonance imaging (MRI) has revolutionized radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside the body but also produces functional imaging. However, due to the slow imaging speed constrained by magnetic resonance physics, the MRI cost is expensive, and patients may feel not comfortable in a scanner for a long time. Parallel MRI (pMRI) has accelerated the imaging speed through a sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired. Kernel learning has been used in pMRI reconstruction to learn the interpolation weights and reconstruct the undersampled data. However, noise and aliasing artifacts still exist in the reconstructed image and a large number of auto-calibration signal lines are needed. To further improve kernel-learning-based MRI reconstruction and accelerate the speed, this paper proposes a group feature selection strategy to improve the learning performance and enhance the reconstruction quality. An explicit kernel mapping is used for selecting a subset of features which contribute most to estimating the missing k-space data. The experimental results show that the learning behaviors can be better predicted and therefore the reconstructed image quality can be improved.
Noninvasive estimation of local speed of sound by pulse-echo ultrasound in a rat model of nonalcoholic fatty liver
Authors: Telichko Arsenii V , Ali Rehman , Brevett Thurston , Wang Huaijun , Vilches-Moure Jose G , Kumar Sukumar U , Paulmurugan Ramasamy , Dahl Jeremy J 
Published: December 2021

Abstract

Objective. Speed of sound has previously been demonstrated to correlate with fat concentration in the liver. However, estimating speed of sound in the liver noninvasively can be biased by the speed of sound of the tissue layers overlying the liver. Here, we demonstrate a noninvasive local speed of sound estimator, which is based on a layered media assumption, that can accurately capture the speed of sound in the liver. We validate the estimator using an obese Zucker rat model of non-alcoholic fatty liver disease and correlate the local speed of sound with liver steatosis. Approach. We estimated the local and global average speed of sound noninvasively in 4 lean Zucker rats fed a normal diet and 16 obese Zucker rats fed a high fat diet for up to 8 weeks. The ground truth speed of sound and fat concentration were measured from the excised liver using established techniques. Main Results. The noninvasive, local speed of sound estimates of the livers were similar in value to their corresponding ‘ground truth’ measurements, having a slope ± standard error of the regression of 0.82 ± 0.15 (R
2 = 0.74 and p < 0.001). Measurement of the noninvasive global average speed of sound did not reliably capture the ‘ground truth’ speed of sound in the liver, having a slope of 0.35 ± 0.07 (R
2 = 0.74 and p < 0.001). Decreasing local speed of sound was observed with increasing hepatic fat accumulation (approximately −1.7 m s−1 per 1% increase in hepatic fat) and histopathology steatosis grading (approximately −10 to −13 m s−1 per unit increase in steatosis grade). Local speed of sound estimates were highly correlated with steatosis grade, having Pearson and Spearman correlation coefficients both ranging from −0.87 to −0.78. In addition, a lobe-dependent speed of sound in the liver was observed by the ex vivo measurements, with speed of sound differences of up to 25 m s−1 (p < 0.003) observed between lobes in the liver of the same animal. Significance. The findings of this study suggest that local speed of sound estimation has the potential to be used to predict or assist in the measurement of hepatic fat concentration and that the global average speed of sound should be avoided in hepatic fat estimation due to significant bias in the speed of sound estimate.
Variation in isocentre location of an Elekta Unity MR-linac through full gantry rotation
Authors: Hunt James R , Ebert Martin A , Rowshanfarzad Pejman , Riis Hans L 
Published: December 2021

Abstract

Objective. The objective of this study was to separately quantify the stability of the megavoltage imager (MVI) and radiation head of an Elekta Unity MRL, throughout full gantry rotation. Approach. A ball-bearing (BB) phantom was attached to the radiation head of the Unity, while a single BB was placed at isocentre. Images were acquired during rotation, using the MVI. These images were processed using an in-house developed MATLAB program to reduce the errors resulted by noise, and the positions of the BBs in the images were analysed to extract MVI and radiation head sag data. Main results. The results returned by this method showed reproducibility, with a mean standard deviation of 7 μm for the position of BBs across all gantry angles. The radiation head was found to sag throughout rotation, with a maximum course of movement of 0.59 mm. The sag pattern was stable over a period greater than a year but showed some dependence on gantry rotation direction. Significance. As MRL is a relatively new system, it is promising to have data supporting the high level of precision on one Elekta Unity machine. Isolating and quantifying the sources of uncertainty in radiation delivery may allow more sophisticated analysis of how the system performance may be improved.
Automated detection of skeletal metastasis of lung cancer with bone scans using convolutional nuclear network
Authors: Li Tongtong , Lin Qiang , Guo Yanru , Zhao Shaofang , Zeng Xianwu , Man Zhengxing , Cao Yongchun , Hu Yonghua 
Published: December 2021

Abstract
A bone scan is widely used for surveying bone metastases caused by various solid tumors. Scintigraphic images are characterized by inferior spatial resolution, bringing a significant challenge to manual analysis of images by nuclear medicine physicians. We present in this work a new framework for automatically classifying scintigraphic images collected from patients clinically diagnosed with lung cancer. The framework consists of data preparation and image classification. In the data preparation stage, data augmentation is used to enlarge the dataset, followed by image fusion and thoracic region extraction. In the image classification stage, we use a self-defined convolutional neural network consisting of feature extraction, feature aggregation, and feature classification sub-networks. The developed multi-class classification network can not only predict whether a bone scan image contains bone metastasis but also tell which subcategory of lung cancer that a bone metastasis metastasized from is present in the image. Experimental evaluations on a set of clinical bone scan images have shown that the proposed multi-class classification network is workable for automated classification of metastatic images, with achieving average scores of 0.7392, 0.7592, 0.7242, and 0.7292 for accuracy, precision, recall, and F-1 score, respectively.
Power transport efficiency during O-X-B 2nd harmonic electron cyclotron heating in a helicon linear plasma device
1
Authors: Marin J F Caneses , Lau C L , Goulding R H , Bigelow T , Biewer T M , Caughman J B O , Rapp J 
Published: December 2021

Abstract
The principal objective of this work is to report on the power coupled to a tungsten target in the prototype-material plasma exposure experiment device during oblique injection of a microwave beam (<70 kW at 28 GHz) into a high-power (∼100 kW at 13.56 MHz) over-dense (

n
e

>
1
×

10

19

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−
3

) deuterium helicon plasma column. The experimental setup, electron heating system, electron heating scheme, and IR thermographic diagnostic for quantifying the power transport is described in detail. It is demonstrated that the power transported to the target can be effectively controlled by adjusting the magnetic field profile. Using this method, heat fluxes up to 22 MW m−2 and power transport efficiencies in the range of 17%–20% have been achieved using 70 kW of microwave power. It is observed that most of the heat flux is confined to a narrow region at the plasma periphery. Ray-tracing calculations are presented which indicate that the power is coupled to the plasma electrons via an O-X-B mode conversion process. Calculations indicate that the microwave power is absorbed in a single pass at the plasma periphery via collisions and in the over-dense region via 2nd harmonic cyclotron resonance of the electron Bernstein wave. The impact of these results is discussed in the context of MPEX.
A macroscopic viscoelastic model of magnetorheological elastomer with different initial particle chain orientation angles based on fractional viscoelasticity
Authors: Fan Jiahao , Yao Jianfei , Yu Yang , Li Yan 
Published: December 2021

Abstract
In this paper, a macroscopic viscoelastic modeling method for magnetorheological elastomer (MRE) based on fractional derivative model is presented to describe the dynamic viscoelastic properties of MRE with different initial particle chain orientation angles. The angle between the particle chain and the applied magnetic field is used as an indicator to describe the directionality of the particle chain. MRE samples with different initial inclination angles have been designed and fabricated. The dynamic viscoelastic properties of different MRE samples under shear working mode were measured using a parallel plate rheometer. The dynamic viscoelastic properties of MRE with different initial inclination angles are analyzed under the test conditions of different strain amplitude, frequency and magnetic flux density. The test results show that the initial inclination angle of the particle chain in the MRE has a significant effect on the dynamic viscoelastic properties of the MRE. A polynomial function is used to describe the relationship between the initial particle chain orientation angle and the magneto-induced modulus of MRE. A phenomenological model of magneto-induced modulus is established based on the fractional derivative model. The model parameters are identified using the nonlinear least square method. The predicted values of the model are in good agreement with the experimental results, indicating that the model can well describe the dynamic viscoelastic properties of MRE.
Rotational nonlinear double-beam energy harvesting
Authors: Lu Ze-Qi , Zhang Fei-Yang , Fu Hai-Ling , Ding Hu , Chen Li-Qun 
Published: December 2021

Abstract
This paper presents an investigation of the performance of a coupled rotational double-beam energy harvester (DBEH) with magnetic nonlinearity. Two spring-connected cantilever beams are fixed on a rotating disc. Repelling magnets are attached to the frame and to the lower beam tip, and an equal-mass block is attached to the tip of the upper beam. To describe the dynamic response, a theoretical model related to the rotational motion of the coupled cantilever beam is derived from the Lagrange equations. In addition, the harmonic balance method, together with the arc-length continuation method, is applied to obtain the frequency response functions (FRFs). Parametric studies are then conducted to analyze the effect of varying the parameters on the energy harvesting performance, and numerical analysis is performed to validate the analytical solutions. Finally, the theoretical model is verified by forward- and reverse-frequency-sweeping experiments. The DBEH in rotational motion can perform effective energy harvesting over a wide range of rotational frequencies (10–35 rad s−1 ). The upper beam is found to exhibit better energy harvesting efficiency than the lower beam around the resonant frequency. This study effectively broadens the energy harvesting bandwidth and provides a theoretical model for the design of nonlinear magnet-coupled double-beam structure in rotational energy harvesting.
Modeling and validation of nonlinear voltage-current characteristics of ITER PF joint sample tested in the SULTAN facility
Authors: Huang J , Bagni T , Ilyin Y , Nijhuis A 
Published: December 2021

Abstract
The International Thermonuclear Experimental Reactor poloidal field (PF) coils are wound into double pancakes with NbTi cable-in-conduit conductors, which are connected by joints in shaking hands lap-type configuration. The coils are operating in pulsed mode with a maximum operating current of 55 kA and peak magnetic field of 6.4 T, utilizing electromagnetic load on the conductors and joints. A series of PF qualification joint samples modified in praying hands configuration is measured in the SULTAN facility. For some samples, a nonlinear voltage-current (VI) characteristic is observed during the assessment of joint resistance. The growth of joint resistance versus the B× I product is larger than what is expected from the magneto-resistant copper contribution. Two non-homogeneous contact resistance models are developed and combined to quantitatively evaluate the reason for the nonlinear VI behavior in combination with the relevant power dissipation and current redistribution in the joint. The simulations reveal that, for the particular pre-qualification PFJEU2 sample with resistance variation up to 3.5 nΩ, the most probable reason for the nonlinear VI characteristic is a widely spread defective connection between copper sole and shim. The electromagnetic force involves a separation effect on the mechanically and electrically weakly connected parts, resulting into a varying resistance depending on transport current and background field. The hypothesis and models are validated by an experiment on a similar sample PFJEU3 and a post-mortem examination of the PFJEU2 sample.
Prediction models establishment and comparison for guiding force of high-temperature superconducting maglev based on deep learning algorithms
Authors: Ke Zhihao , Liu Xiaoning , Chen Yining , Shi Hongfu , Deng Zigang 
Published: December 2021

Abstract
Due to the merits of self-stability and low energy consumption, high-temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes five different neural network models (radial basis function, deep neural network (DNN), convolution neural network, recurrent neural network, long short-term memory neural network (LSTM)) to predict HTS maglev guiding force and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptive iterative algorithms for the parameter matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. According to the results, it is revealed that the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90 000, respectively. Moreover, the K-fold cross-validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.
Stack-based grating for wideband polarization splitter in terahertz
Authors: Lin Zefan , Wang Bo , Fu Chen 
Published: December 2021

Abstract
A novel wideband terahertz polarization beam splitter with special diffraction orders working at terahertz band is described in this paper. The polarizer can achieve high diffraction efficiency and uniformity in the 2.50–2.56 THz band. Based on rigorous coupled-wave analysis (RCWA) and simulated annealing algorithm, we proposed an efficient algorithm to optimize the polarizer. After calculations, 98.45% single-port high-efficiency reflection for transverse electric (TE) polarization and 42.33%/42.57% highly uniform dual-port beam splitting for transverse magnetic (TM) polarization were finally obtained. In addition, through RCWA and simplified modal method, the electromagnetic field distributions of TE and TM polarizations are shown visually and described quantitatively. Moreover, the results displayed in section 3 prove that the grating possesses the characteristics of relatively large bandwidth and insensitivity to the incident angle. Therefore, the novel scheme in this paper has great reference value for the research of terahertz modulation devices and the integration of terahertz communication systems.
Mathematical analysis of two-phase blood flow through a stenosed curved artery with hematocrit and temperature dependent viscosity
Authors: Kumawat Chandan , Sharma B K , Mekheimer K S 
Published: December 2021

Abstract
A two-phase blood flow model is considered to analyze the fluid flow and heat transfer in a curved tube with time-variant stenosis. In both core and plasma regions, the variable viscosity model (Hematocrit and non linear temperature-dependent, respectively) is considered. A toroidal coordinate system is considered to describe the governing equations. The perturbation technique in terms of perturbation parameter ε is used to obtain the temperature profile of blood flow. In order to find the velocity, wall shear stress and impedance profiles, a second-order finite difference method is employed with the accuracy of 10−6 in the each iteration. Under the conditions of fully-developed flow and mild stenosis, the significance of various physical parameters on the blood velocity, temperature, wall shear stress (WSS) and impedance are investigated with the help of graphs. A validation of our results has been presented and comparison has been made with the previously published work and present study, and it revels the good agreement with published work. The present mathematical study suggested that arterial curvature increase the fear of deposition of plaque (atherosclerosis), while, the use of thermal radiation in heat therapies lowers this risk. The positive add in the value of λ
1 causes to increase in plasma viscosity; as a result, blood flow velocity in the stenosed artery decreases due to the assumption of temperature-dependent viscosity of the plasma region. Clinical researchers and biologists can adopt the present mathematical study to lower the risk of lipid deposition, predict cardiovascular disease risk and current state of disease by understanding the symptomatic spectrum, and then diagnose patients based on the risk.