bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time-resolved quantum beats in the fluorescence of helium resonantly excited by XUV radiation
Authors: LaForge A C , Benediktovitch A , Sukharnikov V , Krušič Š , Žitnik M , Debatin M , Falcone R W , Asmussen J D , Mudrich M , Michiels R , Stienkemeier F , Badano L , Callegari C , Di Fraia M , Ferianis M , Giannessi L , Plekan O , Prince K C , Spezzani C , Rohringer N , Berrah N 
Published: November 2020

Abstract
We report on the observation of time-resolved quantum beats in the helium fluorescence from the transition 1s3p → 1s2s, where the initial state is excited by XUV free electron laser radiation. The quantum beats originate from the Zeeman splitting of the magnetic substates due to an external magnetic field. We perform a systematic study of this effect and discuss the possibilities of studying this phenomenon in the XUV and x-ray regime.
Establishment of a Reax force field to study SF6 gas over-thermal decomposition
Authors: Zeng Fuping , Li Haotian , Zhang Mingxuan , Li Chen , Yao Qiang , Tang Ju 
Published: November 2020

Abstract
This paper focuses on the problem of missing parameters in the Reax force field containing S and F elements. First, density functional theory was used to scan SF6 and low-fluoride sulfide molecules to obtain the basic dataset for the Reax force field. The Monte Carlo method was then used to perform fitting optimization and quality verification of the established force field. Based on the established force field, molecular dynamics studies were carried out on the over-thermal decomposition of a SF6 gas-insulating medium, and infrared spectra were obtained, describing the vibration characteristics of SF6 and low-fluoride sulfide molecules. According to the vibration modes revealed by the infrared spectra, a potential-energy surface scan was performed. This paper focuses on the analysis of the total energy, potential energy and kinetic energy of SF6 and low-fluoride sulfide molecules moving at different temperatures. The results show that the energy error of the established force field is about 10% when describing the bonding and breaking processes of SF6, SF5, SF4, SF3 and SF2 molecules, which verifies the reliability of the reactive force field method when used to describe molecular behavior. The research work detailed in this paper lays the foundation for the next systematic study of the microscopic physical mechanisms of SF6 over-thermal decomposition.
Measuring how much an object has a ring-like shape
Authors: Žunić Joviša 
Published: November 2020

Abstract
An image-based algorithmic tool for measuring how much an object’s shape looks like a ring is presented. The objects whose shapes have such a structure appear in nature very often, e.g. in nanotechnology, biology, medicine, industry, etc. The method proposed has several formal desirable properties, including invariance ones. In addition, the method is easy to implement and is robust (e.g. with respect to noise).
Green synthesis as a simple and rapid route to protein modified magnetic nanoparticles for use in the development of a fluorometric molecularly imprinted polymer-based assay for detection of myoglobin
Authors: Sullivan Mark V , Stockburn William J , Hawes Philippa C , Mercer Tim , Reddy Subrayal M 
Published: November 2020

Abstract
We have developed a low-cost molecularly imprinted polymer (MIP)-based fluorometric assay to directly quantify myoglobin in a biological sample. The assay uses a previously unreported method for the development of microwave-assisted rapid synthesis of aldehyde functionalized magnetic nanoparticles, in just 20 min. The aldehyde functionalized nanoparticles have an average size of 7.5 nm ± 1.8 and saturation magnetizations of 31.8 emu g−1 with near-closed magnetization loops, confirming their superparamagnetic properties. We have subsequently shown that protein tethering was possible to the aldehyde particles, with 0.25 ± 0.013 mg of myoglobin adsorbed to 20 mg of the nanomaterial. Myoglobin-specific fluorescently tagged MIP (F-MIP) particles were synthesized and used within the assay to capture myoglobin from a test sample. Excess F-MIP was removed from the sample using protein functionalized magnetic nanoparticles (Mb-SPION), with the remaining sample analyzed using fluorescence spectroscopy. The obtained calibration plot of myoglobin showed a linear correlation ranging from 60 pg ml−1 to 6 mg ml−1 with the limit of detection of 60 pg ml−1. This method was successfully used to detect myoglobin in spiked fetal calf serum, with a recovery rate of more than 93%.
In situ probing of the thermal treatment of h-BN towards exfoliation
Authors: Yan Zhengyu , Abdelkader Amor , Day Sarah , Tang Chiu , Casiraghi Cinzia , Mirihanage Wajira 
Published: November 2020

Abstract
Two-dimensional (2D) hexagonal boron nitride (h-BN) is becoming increasingly interesting for wider engineering applications. Thermal exfoliation is being suggested as a facile technology to produce large quantities of 2D h-BN. Further optimization of the process requires fundamental understanding of the exfoliation mechanism, which is hardly realized by ex situ techniques. In this study, in situ synchrotron x-ray powder diffraction experiments are conducted while heat treating bulk h-BN up to 1273 K. During the heating process, linear expansion of c-axis is observed and the contraction of a-axis up to around 750 K is consistent with previous research. However, a changing behavior from contraction to expansion in a-axis direction is newly observed when heating over 750 K. With the consideration of previous thermally oxidation studies, a hypothesis of thermal assisted exfoliation with oxygen interstitial and substitution of nitrogen at high temperature is proposed.
Particle trajectories and its charging behaviors: effect of gas type and electric field
Authors: Sun Jixing , Zhi Qingyun , Cui Boyuan , Bian Kai , Chen Weijiang , Yan Xianglian 
Published: November 2020

Abstract
Conductive particles in the gas insulated transmission lines (GIL) induce the breakdown between electrodes or the flashover along insulators. To solve the problem of particle moving and realize the particle-moving regulation, particle trajectories should firstly be determined in air and SF6. This paper presents the results of particle trajectories and its charging behaviors at vacuum and SF6, respectively. Metal particles with different materials and sizes were introduced and the charge quantity was calculated. The results showed that the particle lift-off electric field in SF6 was higher than that in air under the same gas pressure, that is, the charge on particle in SF6 was about 0.789 times lower than that in air under the same condition. Besides, the lift-off electric field of particle increased with the pressure increase of SF6. The charge on particle was affected by the concentration of electric field near particle and the electrical negative features of SF6. The work provided the data support for development of DC GIL in particle defect suppression.
Evaluating the image quality of combined positron emission tomography-magnetic resonance images acquired in the pelvic radiotherapy position
Authors: Wyatt Jonathan J , Howell Elizabeth , Lohezic Maelene , McCallum Hazel M , Maxwell Ross J 
Published: November 2020

Abstract
Positron emission tomography-magnetic resonance (PET-MR) scanners could improve radiotherapy planning through combining PET and MR functional imaging. This depends on acquiring high quality and quantitatively accurate images in the radiotherapy position. This study evaluated PET-MR image quality using a flat couch and coil bridge for pelvic radiotherapy. MR and PET image quality phantoms were imaged in three setups: phantom on the PET-MR couch with anterior coil on top (diagnostic), phantom on a flat couch with coil on top (couch), and phantom on the flat couch with coil on a coil bridge (radiotherapy). PET images were also acquired in each setup without the anterior coil. PET attenuation correction of the flat couch and coil bridge were generated using kilovoltage computed tomography (CT) images and of the anterior coil using megavoltage CT images. MR image quality was substantially affected, with MR signal to noise ratio (SNR) relative to the diagnostic setup of 89% ± 2% (mean ± standard error of the mean, couch) and 54% ± 1% (radiotherapy), likely due to the increased distance between the patient and receive coils. The reduction impacted the low-contrast detectability score: 23 ± 1 (diagnostic), 19.7 ± 0.3 (couch) and 15 ± 1 (radiotherapy). All other MR metrics agreed within one standard error. PET quantitative accuracy was also affected, with measured activity with anterior coil being different to diagnostic without anterior coil by −16.7% ± 0.2% (couch) and −17.7 ± 0.1% (radiotherapy), without attenuation correction modification. Including the couch and coil bridge attenuation correction reduced this difference to −7.5% ± 0.1%, and including the anterior coil reduced this to −2.7% ± 0.1%. This was better than the diagnostic setup with anterior coil (difference −8.3% ± 0.2%). This translated into greater PET SNR performance for the fully corrected radiotherapy setup compared to diagnostic with coil. However contrast recovery was unchanged by the modified attenuation correction, with the diagnostic setup remaining ∼2% better. Quantitative PET in the radiotherapy setup is possible if appropriate attenuation correction is used. Pelvic radiotherapy PET-MR imaging protocols will need to consider the impact on PET-MR image quality.
Toward a general quasi-linear approach for the instabilities of bi-Kappa plasmas. Whistler instability
Authors: Moya P S , Lazar M , Poedts S 
Published: November 2020

Abstract
Kappa distributions are ubiquitous in space and astrophysical poorly collisional plasmas, such as the solar wind, suggesting that microscopic and macroscopic properties of these non-equilibrium plasmas are highly conditioned by the wave–particle interactions. The present work addresses the evolution of anisotropic bi-Kappa (or bi-κ-)distributions of electrons triggering instabilities of electromagnetic electron-cyclotron (or whistler) modes. The new quasi-linear approach proposed here includes time variations of the κ parameter during the relaxation of temperature anisotropy. Numerical results show that κ may increase for short interval of times at the beginning, but then decreases toward a value lower than the initial one, while the plasma beta and temperature anisotropy indicate a systematic relaxation. Our results suggest that the electromagnetic turbulence plays an important role on the suprathermalization of the plasma, ultimately lowering the parameter κ. Even though the variation of κ is in general negative (

Δ
κ
<
0

) this variation seems to depend of the initial conditions of anisotropic electrons, which can vary very much in the inner heliosphere.
Double explosive transitions to synchronization and cooperation in intertwined dynamics and evolutionary games
Authors: Li Xuelong , Dai Xiangfeng , Jia Danyang , Guo Hao , Li Shudong , Cooper Garth D , Alfaro-Bittner Karin , Perc MatjaŽ , Boccaletti Stefano , Wang Zhen 
Published: November 2020

AbstractCollective behavior, from murmurations to synchronized beating of heart cells, governs some of the most beautiful and important aspects of nature. Likewise, cooperation—the act of sacrificing personal benefits for the common good—is one of the pillars of social evolution, and it is the basis for the emergence of collective organized actions from single-cell organisms to modern human societies. Here we merge these two phenomena into a single model, considering an ensemble of networked oscillators, where each oscillator can be either a cooperator or a defector, and with only cooperators contributing to synchrony. At the same time, the value of the order parameter in the neighborhood of each oscillator is considered as an effective local temperature which determines the strategy updating procedure in the evolutionary game. The emergence of cooperation is thus intertwined with that of synchronization, producing a novel and fascinating dynamics which includes a double explosive transition.
Deterministic secure quantum communication with and without entanglement
Authors: Elsayed Tarek A 
Published: November 2020

Abstract
We present a protocol for sending a message over a quantum channel with different layers of security that will prevent an eavesdropper from deciphering the message without being detected. The protocol has two versions where the bits are encoded in either pairs of entangled photons or separate photons. Unlike many other protocols, it requires a one-way, rather than a two-way, quantum channel and does not require a quantum memor. A quantum key is used to encrypt the message and both the key and the message are sent over the quantum channle with the same quantum encoding technique. The key is sent only if no eavesdropper is detected.
Dirac equation in (1 + 1) dimensional curved space-time: Bound states and bound states in continuum
Authors: Ghosh P , Roy P 
Published: November 2020

Abstract
We study Dirac equation in (1 + 1) dimensional curved space-time. In particular, it is shown that bound states can be found in the presence of interactions in certain curved spaces. It has also been shown that solutions of the free particle Dirac equation when mapped to some suitable curved spaces become bound states in continuum. A number of examples have been considered and complete solutions have been obtained analytically in each case.
Impact of referencing scheme on decoding performance of LFP-based brain-machine interface
Authors: Ahmadi Nur , Constandinou Timothy G , Bouganis Christos-Savvas 
Published: November 2020

Abstract

Objective. There has recently been an increasing interest in local field potential (LFP) for brain-machine interface (BMI) applications due to its desirable properties (signal stability and low bandwidth). LFP is typically recorded with respect to a single unipolar reference which is susceptible to common noise. Several referencing schemes have been proposed to eliminate the common noise, such as bipolar reference, current source density (CSD), and common average reference (CAR). However, to date, there have not been any studies to investigate the impact of these referencing schemes on decoding performance of LFP-based BMIs. Approach. To address this issue, we comprehensively examined the impact of different referencing schemes and LFP features on the performance of hand kinematic decoding using a deep learning method. We used LFPs chronically recorded from the motor cortex area of a monkey while performing reaching tasks. Main results. Experimental results revealed that local motor potential (LMP) emerged as the most informative feature regardless of the referencing schemes. Using LMP as the feature, CAR was found to yield consistently better decoding performance than other referencing schemes over long-term recording sessions. Significance. Overall, our results suggest the potential use of LMP coupled with CAR for enhancing the decoding performance of LFP-based BMIs.
RETRACTED: Research on the Reform of Accounting Teaching Mode under the Impact of Artificial Intelligence
Authors: Yue Jingyuan 
Published: November 2020

Abstract
The widespread application of artificial intelligence (AI) has brought impact to the traditional accounting industry. Colleges and universities should clarify the reform of accounting teaching in the era of big data, innovate teaching systems, reform teaching modes, and solve the problems encountered by students to ensure them can adapt to the current development of the times. This article will discuss the reform of accounting teaching mode under the impact of AI. There are problems in the process, but the the colleges and universities need to play the guiding role.
Innovative Research on the Teaching Mode of Piano Group Lessons in Higher Vocational Preschool Education Based on AI
Authors: Gang Liang 
Published: November 2020

Abstract
The current AI technology has penetrated into many aspects of the education field (as shown in figure 1), and has made innovations in teaching models, courses and systems. Based on the background of AI, the preschool piano group teaching of higher vocational education has gradually become a mode of personalized learning, adaptive learning, deep learning, and human-computer collaborative learning. This article will analyze the current situation of piano group lesson teaching and propose innovative strategies to improve piano group lesson teaching in higher vocational preschool education.
Research on the Practice of Talent Cultivation Based on the Improvement of Innovation Ability ——Taking Electrical Engineering Automation Technology As An Example
Authors: Jin Jing , Chen Yuanyuan 
Published: November 2020

Abstract
It is in the key period of institutions of higher learning education reform now. For electrical engineering automation students in education and training, in order to further improve their innovation and practice ability, colleges and universities can develop regular way of talent training scheme and cooperation between colleges to cultivate talents. It can improve the level of colleges of electrical engineering automation teaching and promote the employment.
A WaveNet based Ion Channel prediction Method
Authors: Teng Yukun , Chen Deheng , Zhou Yichun , Vegh Nathaniel M. , Zhang Ren 
Published: November 2020

Abstract
There are many diseases associated with the dysfunction of ion channels. Ion channels are a class of proteins embedded in cell membranes that serve principle physiological functions such as memorization, learning and pain signaling. In order to help scientists learn more about ion channels and how they cause diseases, a team in Liverpool University published a public dataset about Ion channels. This paper presents a Wavenet based model to predict the number of Ion channels open at each time point. Wavenet is a type of deep neural network designed for one-dimensional signals. We used the Kalman filter to denoise the original signals and compared WaveNet with classical machine learning methods like SVM and Naïve Bayes to show the superior performance of the WaveNet model.
An Implementation Method of Zero-trust Architecture
Authors: Chuan Tao , Lv Yao , Qi Zhenfei , Xie Linjiang , Guo Wei 
Published: November 2020

Abstract
With the development of the Internet, the company network architecture is also undergoing profound changes, and the boundaries between the original internal network and external network are becoming increasingly blurred with the emergence of cloud services. More and more company businesses are deployed on the cloud server, which increases the risk of data exchange between the cloud server and the intranet. An implementation method of zero-trust architecture is proposed in this paper to apply to this scenario. The method can ensure safe and reliable data exchange when the external application server accesses the internal network, effectively protect network communication and business access while not affecting the original internal network protection measures, and make the company network safer and controllable.
Geometry Embedded Multi-Task Network for Lane Detection and Classification
Authors: Zhao Zhendong , Yue Xiaotian , Sun Pengfei , Xie Jiexin 
Published: November 2020

Abstract
Lane detection and classification are key and fundamental problems in the field of autonomous driving. Deep learning based multi-task network is commonly built with convolutional neural networks (CNNs) to handle these problems. Although existing methods have demonstrated the capability to capture the pixel-level representation and global guidance, the local geometric relationship of lanes are not taken into account to deal with the lanes with weak appearance coherence. This paper proposes geometry embedded multi-task network (GEMNet) with three branch layers, including bounding box regression, lane mask and lane classification. Convolutional operations are applied to feature maps generated by the top hidden layer of bounding box regression branch to explore the semantic context via geometric relationship. Moreover, it is able to facilitate the weight optimization in the shared layers and other branch layers simultaneously. GEMNet is evaluated on Cordova1 and Washington1 datasets, and Experimental results show that the proposed method outperforms the state-of-the art approaches. F1-Score can reach 0.906 and 0.881 with respect to Cordova1 and Washington1 datasets, respectively.
Design and Implementation of Automatic Exercises Evaluation System for Programming Courses
Authors: Liu Mingtao , Xu Rui , Wang Yunyu , Li Zhiyang , Zhang Xin 
Published: November 2020

Abstract
The evaluation of students’ exercise process is an important part of the formative evaluation for programming courses. However, it is difficult for teachers to complete the evaluation of each student’s exercise process manually. This paper proposes a design of an automatic exercises evaluation system, which can automatically analyse the amount of students’ exercise codes and help teachers to complete the evaluation. The system uses Python’s Pyhook3 library to monitor the keyboard input of the student’s computer. After text segmentation is performed on the student’s input code, the amount of the student’s exercise codes is statistically analysed, and the result can be used as an important indicator of the formative evaluation. Through the trial, it is found that there is a certain proportional relationship between the evaluation given by the system and the final test result of the students, which indicates the system can give a relatively objective evaluation of the students’ learning process.
Sentiment analysis of commodity reviews based on ALBERT-LSTM
Authors: Wang Hanqi , Hu Xiaoli , Zhang Huibing 
Published: November 2020

Abstract
Sentiment analysis of product reviews has now become one of the important research directions in the field of NLP, which is of great significance in helping merchants better understand user preferences and providing decision-making basis for other users to purchase products. Existing product review sentiment analysis models based on deep learning mostly use traditional word vector models. It is difficult to obtain the contextual semantic information of words, and there is a problem of “polysemous one word”. A product review sentiment analysis model combining ALBERT and LSTM is proposed. First, we use the ALBERT pre-training model to get the word vector containing sequence and semantic information. Then we use the LSTM model that can obtain long-distance semantic features for training. Finally, the emotional polarity of product reviews is classified and output through the emotional polarity discrimination layer. The experimental results on the data set of digital products of JD Mall show that the F1 value of the ALBERT-LSTM model is improved compared with the ALBERT and LSTM models.
A review of intelligent equipment development and and its auto-test technology
Authors: Kang Weijie , Xiao Jiyang , Chen Xin , Wen Bincheng 
Published: November 2020

Abstract
With the development of artificial intelligence technology, the intelligent level of equipment is gradually improved, and its internal structure is becoming more and more complicated. This not only facilitates users’ use, but also improves their maintenance and auto-test requirements. Taking flight simulator and other intelligent equipment as examples, this paper introduces the development history and research status of flight simulator and embedded training, and combs the development history and research status of intelligent equipment auto-test technology, which mainly involves test resource matching and fault diagnosis.