bluuebunny/crossref_metadata_2025_split huggingface dataset:
Screen-printed conductive pattern on spandex for stretchable electronic textiles
Authors: Lim Taehwan , Kim Hee Jin , Zhang Huanan , Lee Sohee 
Published: April 2021

Abstract
Current stretchable conductive fibers used for electronic textiles have received significant attention, but the fiber-based electronic textiles restrict the elaborate patterning due to the limited weaving process. To fabricate electrical circuits on textiles directly to ultimately combine various electronic devices, different approaches would be required to allow the pattern printing onto the stretchable fabric. Here, screen-printing was taken to prepare a highly durable, stretchable conductive pattern on spandex with elastic recovery and high washing durability. A zigzag pattern with the right angle was selected as an optimal shape to prevent the failure of conductive paste from the spandex after stretching cycles. The pattern can successfully diverge the stress by stretching to the

1

/

2

of applied one, which enables the conductive characteristic to maintain from the stress under the range of elastic recovery. Another key issue of wearable electronic textiles is the ability to operate under mechanical stress. Encapsulation is an easy modification method to allow the electrical characteristic of the conductive pattern on the spandex when stretched. We confirmed the pattern after encapsulation could act as conductive wires under the stretching deformation of 18% that was much higher than that of the pattern without encapsulation (7%). Consequently, the results suggest potential and further modification methods to fabricate high durable, stretchable electronic textiles.
Theoretical analysis and experimental validation of frequency-moldable electrostatic energy harvesters biased with a high elastic electret film
Authors: Ma Xingchen , Yang Xiaoya , Ding Chuan , Zhang Xiaoqing , Dai Ying , He Pengfei 
Published: April 2021

Abstract
It is an effective strategy to improve the power output of energy harvesters by adjusting resonance frequency of the devices to adapt to complex frequency distribution of vibrations in environments. This paper presents mechanical analysis and experimental validation of a kind of light-weighted, flexible electrostatic vibrational energy harvesters with a customizable resonance frequency range. Such energy harvester consists of a stretchable electret beam, whose high elasticity was achieved by introducing a corrugated structure, an arc-shaped counter electrode, and a light seismic mass. The resonance behavior of the device can be simulated by the corrugated beam with flexible support at both ends. Mechanical analytical modeling allows one to predict resonance frequency of energy harvesters, making the devices potentially interesting for customizable resonance harvesting and broad bandwidth energy harvesting, and thus to satisfy actual application scenarios. The experimental results are in agreement with the theoretical prediction. For a device with an initial size of 15 × 10 × 9 mm3 and a seismic of 0.06 g, by modulating the length of the fluorinated polyethylene propylene electret beam from 15 to 35 mm, a customizable resonance frequency ranging 14–60 Hz, and the normalized output power up to several 100 μW was achieved, demonstrating great superiority.
Fiber-solid hybrid end-pumped Nd:YVO4 picosecond amplifier with high beam quality
Authors: Liu Minzhe , Liu Menglin , Yan Wei , Yang Dewang , Wang Lisha , Sun Songsong , Jia Zhongqing , Zhai Ruizhan , Wang Yong , Zhao Kun 
Published: April 2021

Abstract
In recent years, high-brightness picosecond lasers have been widely used in high-end precision processing, spectroscopy and high harmonic generation. To obtain higher power and excellent beam quality, it is an ideal solution to add a high-gain amplifier to the back end of the high-stability picosecond fiber or solid seed. In this article, a fiber-solid hybrid end-pumped Nd:YVO4 picosecond laser system with high beam quality is demonstrated. The influence of the self-developed picosecond fiber seed output characteristics on solid amplifiers has been studied in detail, including the energy, repetition rate and spectrum. After two-stage traveling wave amplifiers, the maximum 155 μJ single pulse energy is obtained with 20 kHz repetition rate, and the corresponding peak power reaches 14.9 MW with 10.4 ps pulse duration. Meanwhile, the beam quality factors under the maximum 18.4 W average power are measured to be M
x
2 = 1.27 and M
y
2 = 1.25, respectively.
Research on magnetic fields of galaxies using RZ-model
Authors: Mikhailov Evgeny Alexandrovich , Pushkarev Vasilii Vladimirovich 
Published: April 2021

Abstract
The generation of magnetic fields of galaxies is usually described by the dynamo mechanism. This process is characterized by the Steenbeck – Krause – Rädler equation, which is the result of averaging the magnetohydrodynamics equations by distances which are associated with the size of turbulent cells in the interstellar medium. This equation is quite difficult to solve both from an analytical and numerical point of view. For galaxies, the no-z approximation is widely used. It describes the magnetic fields in thin discs. For such objects, where it is important to study the vertical structure of the field, it is not very applicable, so it is quite useful to adopt the RZ-model, which takes into account the dependence of the distance from the equatorial plane. During our research we have obtained the critical values of the dynamo number for galaxies with large half-thickness. We have also described typical z-structure for the magnetic field. Moreover, we have demonstrated that it is possible to generate dipolar magnetic fields.
FUV and NIR size of the HI selected low surface brightness galaxies
Authors: Cheng Cheng , Du Wei , Xu Cong Kevin , Cao Tian-Wen , Zhang Hong-Xin , Huang Jia-Sheng , He Chuan , Li Zi-Jian , Wu Shu-Mei , Xu Hai , Dai Y. Sophia , Shao Xu , Musin Marat 
Published: April 2021

Abstract
How low surface brightness galaxies (LSBGs) form stars and assemble stellar mass is one of the most important questions related to understanding the LSBG population. We select a sample of 381 HI bright LSBGs with both far ultraviolet (FUV) and near infrared (NIR) observations to investigate the star formation rate (SFR) and stellar mass scales, and the growth mode. We measure the FUV and NIR radii of our sample, which represent the star-forming and stellar mass distribution scales respectively. We also compare the FUV and H band radius-stellar mass relation with archival data, to identify the SFR and stellar mass structure difference between the LSBG population and other galaxies. Since galaxy HI mass has a tight correlation with the HI radius, we can also compare the HI and FUV radii to understand the distribution of HI gas and star formation activities. Our results show that most of the HI selected LSBGs have extended star formation structure. The stellar mass distribution of LSBGs may have a similar structure to disk galaxies at the same stellar mass bins, but the star-forming activity of LSBGs happens at a larger radius than the high surface density galaxies, which may help to identify the LSBG sample from the wide-field deep u band image survey. The HI is also distributed at larger radii, implying a steeper (or not) Kennicutt-Schmidt relation for LSBGs.
Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients
Authors: Sand Daniel , Rappel Pnina , Marmor Odeya , Bick Atira S , Arkadir David , Lu Bao-Liang , Bergman Hagai , Israel Zvi , Eitan Renana 
Published: April 2021

Abstract

Objective. Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson’s disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients. Approach. Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3–12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states. Main results. Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models. Significance. We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.
A point plasma model for linear plasma devices based on SOLPS-ITER equations: application to helium plasma
Authors: Tonello E. , Formenti A. , Alberti G. , Uccello A. , Passoni M. 
Published: April 2021

Abstract
Linear plasma devices represent an essential tool for nuclear fusion research, whereby understanding crucial aspects related to plasma-wall interactions or edge plasma behaviour. Simplified models are of great importance to complement and integrate experimental and simulation results of complex systems such as plasmas in linear machines, because they are fast and simple to employ. In this work, we present a global volume-averaged (0D) model for plasma investigation in linear machines. The 0D model equations are based on the space integration of the state of the art edge plasma model implemented in the SOLPS-ITER code. Comparisons between helium plasmas described with 2D simulations performed with SOLPS-ITER and with the 0D model highlight that contributions often neglected in tokamak edge models, e.g. electron-neutral excitation, may be relevant when describing weakly ionized plasmas in linear devices. The model is used to perform sensitivity studies with respect to several parameters and to analyse the time evolution of the system, leading to the identification of two relevant time scales governing the system. Lastly, a comparison of 0D results with experimental data from the linear device GyM is performed, showing satisfactory agreement. Our methods and results provide crucial interpretative keys in the investigation of the physics of edge plasmas.
3D radiation, density, and MHD structures following neon shattered pellet injection into stable DIII-D Super H-mode discharges
Authors: Sweeney R. , Baylor L.R. , Eidietis N.W. , Granetz R.S. , Herfindal J.L. , Hollmann E.M. , Hu D. , Lehnen M. , Moyer R. , Raman R. , Shiraki D. , Strait E.J. , DIII-D Team  
Published: April 2021

Abstract
Six nominally repeat neon shattered pellet injection (SPI) shutdowns of stable DIII-D Super H-modes are studied to understand the 3D properties of the radiation and impurity transport. The radiation efficiency and radiation peaking determine whether first wall melting is expected following disruption mitigation in ITER. Previous studies make use of axisymmetric approximations to infer radiation efficiencies, but validating the high efficiency required by ITER necessitates improved accuracy, and this work contributes by exploring the 3D radiation and density structures that will inform forward modeling. When the neon shatter plume produced by the SPI reaches the plasma edge, m/n = 3/1 and 2/1 island O-points are observed to align with the injection trajectory in five out of six cases, suggesting that the injected material seeds the island O-points. Field aligned neon structures emitting Ne-I line radiation drift at 1 km s−1 in the ion diamagnetic drift direction during the pre-thermal quench, tracking the motion of the m/n = 2/1 island O-point. Neon fragments penetrate to the q = 2 surface by the time of the thermal quench. Techniques to constrain the 3D emissivity are explored, and one method constrains a 3D flux tube that is consistent with the radiation data, and when mapped to the interferometers, intersects the lasers that measure the highest density. The resulting structure derived from the radiation measurements exists near the 2/1 island X-point. In five repeatable discharges, the peak of the radiation in the toroidal direction exists in a 120° toroidal sector where the injection occurs, in contrast with the outlier discharge where the toroidal peak exists in the complementary 240° toroidal sector far from the injector, and where a 50% lower density rise is observed. The n = 1 phase behavior is markedly different in the outlier discharge, suggesting a possible dependence of the radiation structure and the assimilation efficiency on MHD.
The competency of vocational graduates in the fields of mechanics and workshop management according to the needs of the automotive motorcycle industry
Authors: Wahyudi K S , Sulistyo B , Budiman A , Arifin Z 
Published: April 2021

Abstract
This study aims to determine the mechanics competence and workshop management needs of the competencies in the vocational education engineering curriculum and the motorcycle business according to the automotive motorcycle industry. Research subjects were eight motorcycle automotive companies in the field of production and services. The data of the study were collected by using a questionnaire survey and analyzed by quantitative descriptive statistics with percentages. The results of the study showed that the needed competencies according to the Automotive Motorcycle Industry were mechanics competence and workshop management competence. The vocational education curriculum produced 39 mechanics competencies and 18 workshop management competencies. There were 37 basic competencies in mechanics competence in the very needed category and 2 basic competencies in the sufficiently needed category. Workshop management competencies found 18 basic competencies that were all needed by the industry. Competency from the mechanics field and workshop management had competencies in the very needed category relatively. In total there were 15 competencies that not exist yet in the curriculum which taught in vocational education.
Detection of ibuprofen and aspirin on silver nets by surface enhanced Raman scattering (SERS) spectroscopy
Authors: Yarantseva N D , Belyatsky V N , Shleiko E V , Osotskaya E S , Burko A A , Dolgiy A L , Girel K V , Bandarenka H V 
Published: April 2021

Abstract
In this work, surface enhanced Raman scattering (SERS) spectra of aspirin and ibuprofen molecules adsorbed on the surface of silver nets sputtered on porous silicon were collected and analyzed. The bands in the SERS-spectra were correlated in accordance with the type of molecular vibration. Predominantly chemical adsorption of the indicated drugs on the SERS-active substrate through oxygen was observed. It was established that the Raman spectroscopy combined with the SERS-active silver nets makes it possible to detect the aspirin and ibuprofen at 10-6 M concentration.
Simulation of Evacuation in Crowded Places Based on BIM and Pathfinder
Authors: Zhang Hui , Long Hao-cheng 
Published: April 2021

Abstract
Densely-populated public areas with high concentration of people have many potential hazards for its high difficulty in evacuation. Once an emergency occurs, it is easy to cause crowd panic and casualties. Therefore, research on the building characteristics and evacuation methods for densely-populated areas can provide reference and the emergency plan preparation for emergencies. Taking the central building of the Women and Children’s Health Hospital in a certain city as the research object, the simulation and analysis of the evacuation process in the three-dimensional building scene are carried out based on BIM modeling and Pathfinder simulation software. The results show that due to the special proportion of people in the building, appropriately increasing the width of the evacuation doors near the evacuation stairs and fire elevators, clarifying the responsibilities of medical staff, and controlling the escape speed of people in different areas can significantly improve the evacuation efficiency.
A Fast Traffic Sign Detection Algorithm Based on Modified YOLOv3
Authors: Dou Huazhou , Zhang Haiyun , Li Bo 
Published: April 2021

Abstract
Traffic sign detection is an important component of both automatic driving systems and driver assistance systems. However, it is still a big challenge to detect traffic signs quickly and accurately. In this paper, in order to solve this problem, a lightweight algorithm based on YOLOv3 is proposed, which can achieve real-time and accurate traffic sign detection. Firstly, a new lightweight backbone network is proposed by modifying the darknet53 network, which is the original backbone network of YOLOv3. Secondly, in order to further speed up the detection time, depthwise separable convolution is introduced into the proposed backbone network to further reduce the amount of parameters and computations of the model. Thirdly, dense connection is adopted to maintain the accuracy. Experiments on CCTSDB dataset illustrate that the algorithm has great real-time performance and accuracy, which is better than other state-of-the-art algorithms. The algorithm can detect a 416*416 image in only 3.4 milliseconds, which is 44.3% faster than YOLOv3. In addition, the mAP and F1 value are 97.2% and 91.6% respectively, which are better than YOLOv3.
A Traffic Interval Prediction Method Based on ARIMA
Authors: Lv Tiantian , Wu Yanqin , Zhang Le 
Published: April 2021

Abstract
The purpose of this paper is to construct an autoregressive integrated moving average model (ARIMA) for forecasting the traffic interval, which is helpful for the mobile industry to forecast the change of requirements for the peak of customer traffic, adjust the bandwidth dynamically, and improve the ability of active service. In this paper, the lateral time series analysis method is applied to analyze the data of the peak traffic of the uplink and downlink network from August to October of 2017 and 2018 to establish ARIMA prediction model for determining the parameters of it. MAPE method is used for model assessment and model diagnosis. Then, the optimal forecasting model is selected and the forecast error rate is calculated as the adjustment parameter of the forecast range. The model is used to forecast the customer flow range in the first three days of October 2019. Finally, the MIA method is proposed to compare with the LSTM algorithm and linear regression method for interval cumulative error comparison of traffic interval prediction. The result shows that the ARIMA (0,1,0) model has the lowest mean interval error rate of 15.5%, which proves the reliability of the model in predicting peak traffic interval.
Cross-Visual Attention Fusion Network with Dual-Constrained Marginal-Ranking for Visible-Infrared Person Re-Identification
Authors: Su Fang , Qi Meibin , Chen Cuiqun , Bo Tan , Jiang Jianguo 
Published: April 2021

Abstract
Visible-Infrared Person re-identification(VI-REID) is extremely important for night-time surveillance applications. It is a challenging problem due to large cross-modality discrepancies and intra-modality variations caused by different illuminations, human poses, viewpoints, etc. In this paper, we propose a cross visual attention fusion dual-path neural network with dual-constrained marginal ranking(DCAF) to solve the problem. First, we utilize cross-visual attention to learn discriminative feature of high-level semantic information in their respective modals. Second, in order to establish the relationship between modals, we fuse attentional weight of two modals and add it into backpropagation to obtain those regions that are distinctive for classification. Third, a dual-constrained marginal-ranking loss is introduced to narrow the gap between different networks and to learn strongly the similarities between two modals. Extensive experiments demonstrate that the proposed approach effectively improves the performance of VI-REID task and remarkably outperforms the state-of-the-art methods.
Multi-scene Representation Learning with Neural Radiance Fields
Authors: Fu Bofeng , Wang Zheng 
Published: April 2021

Abstract
Getting representations of multiple objects or scenes is a raising research topic in Machine Learning (ML) community. Here, we propose a multi-scene representation model that can learn the representation of complex scenes and reconstruct them in high resolution given novel viewing directions. Our method represents a single scene with fully-connected layers. Each set of fully-connected layers are controlled by hyper-networks for multiple scenes modeling. For each scene, we take 3D coordinates (x, y, z) and 2D view-point orientations (θ, ɸ) as inputs. A set of fully-connected layers output volume density and RGB values at given 3D spatial positions. Then, we render the output volume density and RGB values along the camera rays into images using volume density rendering techniques. During training process, we optimize a continuous volume scene function with a small amount of input viewing directions. By designing versatile embedding module and multi-scene representation networks, our model can render photographic images with novel viewing directions for different complex scenes. Experiment results demonstrate the neural rendering and multi-scene representation abilities of our model. Several thorough experiments show that our method outperforms previous model on both reconstruction precision and scenes generation ability from novel viewing directions.
Application of Artificial Intelligence in Cultural Heritage Protection
Authors: Li Junli 
Published: April 2021

Abstract
With the continuous development of science and technology, artificial intelligence has become the subject of extensive discussion in recent years. Artificial intelligence technology uses deep learning to apply this, it shows the trend of high development, such as the previous Go game that shocked the world, the fact that artificial intelligence “Alphago” defeated the world Go champion, people realized that the development of artificial intelligence exceeded people Imagination. Various social industries are gradually introducing artificial intelligence technology to give them new vitality for development. This is exactly how the cultural heritage protection process should be combined with artificial intelligence technology. Cultural protection should enter the digital era, using virtual reality technology and applying segmentation algorithms to segment the prospects of cultural heritage. Complete all aspects of cultural heritage protection without applications. This article introduces the process of artificial intelligence in the protection of cultural heritage using virtual reality technology to realize the digitization of cultural heritage, realizing that the protection of cultural heritage keeps pace with the times. The experimental results of this paper show that the virtual reality technology combined with artificial intelligence is applied to the importance of cultural heritage protection, which improves the vitality of cultural heritage and more perfectly shows the charm of cultural heritage application.