bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vortex flow properties in simulations of solar plage region: Evidence for their role in chromospheric heating
Authors: Yadav N. , Cameron R. H. , Solanki S. K. 
Published: November 2020

Context. Vortex flows exist across a broad range of spatial and temporal scales in the solar atmosphere. Small-scale vortices are thought to play an important role in energy transport in the solar atmosphere. However, their physical properties remain poorly understood due to the limited spatial resolution of the observations.
Aims. We explore and analyze the physical properties of small-scale vortices inside magnetic flux tubes using numerical simulations, and investigate whether they contribute to heating the chromosphere in a plage region.
Methods. Using the three-dimensional radiative magnetohydrodynamic simulation code MURaM, we perform numerical simulations of a unipolar solar plage region. To detect and isolate vortices we use the swirling strength criterion and select the locations where the fluid is rotating with an angular velocity greater than a certain threshold. We concentrate on small-scale vortices as they are the strongest and carry most of the energy. We explore the spatial profiles of physical quantities such as density and horizontal velocity inside these vortices. Moreover, to learn their general characteristics, a statistical investigation is performed.
Results. Magnetic flux tubes have a complex filamentary substructure harboring an abundance of small-scale vortices. At the interfaces between vortices strong current sheets are formed that may dissipate and heat the solar chromosphere. Statistically, vortices have higher densities and higher temperatures than the average values at the same geometrical height in the chromosphere.
Conclusions. We conclude that small-scale vortices are ubiquitous in solar plage regions; they are denser and hotter structures that contribute to chromospheric heating, possibly by dissipation of the current sheets formed at their interfaces.
Altruistic punishment in modern intentional communities
Authors: Qirko Hector 
Published: November 2020

AbstractEvolutionists studying human cooperation disagree about how to best explain it. One view is that humans are predisposed to engage in costly cooperation and punishment of free-riders as a result of culture/gene coevolution via group selection. Alternatively, some researchers argue that context-specific cognitive mechanisms associated with traditional neo-Darwinian self- and kin-maximization models sufficiently explain all aspects of human cooperation and punishment. There has been a great deal of research testing predictions derived from both positions; still, researchers generally agree that more naturalistic data are needed to complement mathematical modeling and laboratory and field experiments. Most of these data have been obtained from small-scale forager and other societies, but modern intentional communities offer another productive source of information. This exploratory study describes context-specific patterns of punishment in 46 American intentional communities that cast doubt on the prediction that people are predisposed to punish free-riders in naturalistic interactions.
Effect of local dissociations in bidirectional transport of driven particles
Authors: Jindal Akriti , Kolomeisky Anatoly B , Gupta Arvind Kumar 
Published: November 2020

Abstract
Motivated by the complex processes of cellular transport when different types of biological molecular motors can move in opposite directions along protein filaments while also detaching from them, we developed a theoretical model of the bidirectional motion of driven particles. It utilizes a totally asymmetric simple exclusion process framework to analyze the dynamics of particles moving in opposite directions along the lattice of discrete sites while the particles might also dissociate from the filament in the bulk of the system. Mean-field theoretical arguments supported by extensive Monte Carlo simulations are presented in order to understand how the localized particle dissociations affect the bidirectional dynamics and spontaneous symmetry-breaking phenomena. It is found that changes in the amplitudes and in the symmetry of dissociation rates lead to significant modifications in the dynamic properties and in the stationary phase diagrams. These changes are explained using simple physical arguments. Our theoretical method clarifies some aspects of microscopic mechanisms of complex transport phenomena in biological systems.
A prediction model of buses passenger flow based on neural networks
Authors: Zhang Shuyu , Liu Zikang , Shen Fengting , Wang Shumeng , Yang Xuelin 
Published: November 2020

Abstract
With the popularization of smart buses, the ways to obtain buses operation information are becoming increasingly diversified, and passenger flow prediction based on big data information has also emerged. In this work, Principal Component Analysis (PCA) and error Back Propagation (BP) neural networks were combined to propose a prediction model of short-term buses passenger flow on the basis of PCA-BP neural networks. Firstly, the PCA method was adopted to reduce the dimensionality of the indices of buses passenger flow and improve the input nodes of the BP networks. Secondly, the fully trained network was employed to predict the buses passenger flow. More specifically, the passenger flow data set pair of the 17th bus in Guiyang on November 29, 2019 was used to carry out the PCA-BP neural networks model test. The test results indicated that the predicted passenger flow of each time period was very close, and the passenger flow prediction error in most time periods was very small. Besides, the relative error could achieve a satisfactory fitting effect, which was able to provide a reliable basis for bus dispatch. We found that the proposed PCA-BP neural networks model had high prediction accuracy and prediction performance. Therefore, it is expected to be used as a model for final short-term passenger flow prediction.
Research on the selection method of FY-3D/MWHTS clear sky observation data based on neural network
Authors: He Qiurui , Guo Xiao , Li Deguang , Jin Yanling , Zhang Lanjie , Zhang Ruanyu 
Published: November 2020

Abstract
The selection of clear sky data in space-borne remote sensing data is very important for its data application. For FY-3D satellite microwave humidity and temperature sounder (MWHTS), an inversion system of atmospheric cloud water content by MWHTS is established based on neural network. The cloud water content inversion value is used to select clear sky data from MWHTS observation data. The experimental results show that FY-3D/MWHTS clear sky data selection method based on neural network can effectively select MWHTS observation data, thus improving the simulation brightness temperatures accuracy of MWHTS by radiative transfer model. This method can be used to select clear sky data by using space-borne observation data itself. It is easy to operate and has important practical value for climate change research, numerical weather forecast, etc., based on space-borne observation data.
Deep heading estimation for pedestrian dead reckoning
Authors: Lin Jiayi , Zou Chengming , Lan Long , Gu Shanzhi , An Xinshang 
Published: November 2020

Abstract
Currently, the heading estimation could be easily achieved by many built-in direction sensors, such as the smartphone. However, the obtained heading angle is only suitable for situations where an equipped pedestrian’s movement and orientation keep the same, such as normal forward walking and turning. When the pedestrian faces a different direction from his movement, the heading angle remains the pedestrian orientation but not the movement and thus causes a heading estimation error. In this paper, we introduce several related deep learning techniques to explore their respective abilities in heading estimation of the waist-mounted Miniature Inertial Measurement Unit (MIMU). Specifically, this paper adopts two kinds of methods to analyze the data collected from MIMU, include acceleration, angular velocity, or their combination, to predict the heading angle. Firstly, considering the heading estimation is a time series prediction problem actually, this paper introduces the powerful Long Short-Time Memory (LSTM) model. On the other hand, this paper uses the Graph Convolutional Network (GCN) model to consider the relationship between the orientation and the direction of motion at different times. In experiments, we show that the accuracy of the proposed LSTM model in the test set achieved a promising 99.12%. To test our method in the real scenes, this paper designs simulation experiments and mobile terminal tests based on Tensorflow Lite. The experimental results show that the movement heading can be effectively judged based on the waist-mounted sensor data, and has a very significant accuracy.
Multi-structure non-linear semi-parametric optimal weighting parameters estimation algorithm based on adaptive sparse representation deconstruction model and integrative COD comprehensive model
Authors: Zhao Deyong , Xie Zhiying , Liu Jie , Gu Ping 
Published: November 2020

Abstract
For the combined orbit determination (COD) satellite-network system which is made of GEO-IGSO-MEO satellite-constellation and LEO satellites, the satellites-trajectory are not precise enough, because the meticulous satellite dynamics model, credible observation data model and optimal parameters estimation method are not taking into consideration. The high-level adaptive sparse-deconstruction dynamics model, non-linear semi-parametric combined orbit determination comprehensive model is put forward. By using the above models, adaptive weighting iterative wavelets decomposition algorithm of sparse-representation deconstruction model and multi-structure non-linear semi-parametric optimization weighted estimation algorithm of integrative combined orbit determination comprehensive models are designed. Simulated calculation results show that results showed that the high-precision sparse representation method and the optimization model estimation method can improve COD precision, and when comprehensive model systematic & random error parameters and adaptive sparse-representation deconstruction model parameters are estimated by integrative nonlinear semi-parametric comprehensive model synchronously, COD ultimate trajectory calculation estimation precision can get significant improvement.
Disruption management for solving FJSP with improved genetic algorithm
Authors: Sun Jinghua , Xu Li 
Published: November 2020

AbstractIn order to solve the Multi-objective flexible job shop scheduling, an improved cloud adaptive genetic annealing algorithm is proposed. Firstly, the cross-variation, improved fitness calculations, and selection operations are built. Moreover, a novel multi-objective optimization model for flexible job-shop scheduling (FJSP) is established. At the same time, the idea of gradual optimization for the target is introduced to obtain the optimal scheme of FJSP. According to the similarity test of Hamming ones, the cross operation is selected. It can improve the efficiency and convergence of the proposed algorithm. The experiment result verifies the effectiveness of the proposed improved cloud adaptive genetic annealing algorithm (CAGA), and which is compared with other algorithms experiments the existing classical algorithms.
Research on the north polar tourism resources evaluation model based on BP neural network algorithm
Authors: Jin Lili 
Published: November 2020

Abstract
Polar tourism resources have unique properties. The Polar resource evaluation is comprehensive and multi-angle based on communication value, cultural value, economic value, educational value and so on. A scientific and reasonable evaluation model can provide the basis for the exploitation and utilization of polar tourism resources. The purpose of this paper is to apply BP neural network algorithm to self-learning intelligent evaluation, especially those not fully explored polar tourism resources. The subjective random factors are avoided to objectively evaluate polar tourism resources.
The study on the index system of the smart city based on knowledge map
Authors: Liu Jialu , Ning Huicong , Bai Yuanhui , Yan Tianzi 
Published: November 2020

Abstract
Urbanization is the inevitable trend of urban development. In order to conducive to the supervision and guidance of smart city construction facts and the assessment and feedback of smart city operations, we propose a method to build the index system of the smart city based on the knowledge map. This method can effectively improve the query, visualization and reasoning ability of the index system, and provide the basis for using big data to build the index system automatically or semi-automatically, and finally give the knowledge map about the index system of the smart city.
Automated construction technology of the government agencies knowledge graph based on the topical crawler
Authors: Xiong Ziqi , Kong Dezhi , Xia Zhichao , Xue Yankai , Song Ziyu , Wang Peng 
Published: November 2020

Abstract
Government agencies are the skeleton of the knowledge graph in the field of government affairs; it is the starting and ending point of various government affairs. This paper proposes an automated construction technology of the government agencies knowledge graph based on the topical crawler, which collects government and department information from relevant government websites and constructs the knowledge graph automatically. Sampling verification shows that the knowledge precision of this government agencies knowledge graph exceeds 96%, and it covers nearly 99% of municipal government information and more than 92% of government department information across the country. Furthermore, through the tracking of government websites, the knowledge graph of government agencies can be automatically updated. As far as our understanding is concerned, this is the first time to explore the technology of automated construction of knowledge graph in the related fields of government affairs combined with the collection system of the web crawler.
Research on the construction and visualization of agricultural products circulation big data model from the perspective of regional economic integration
Authors: Zhao Hua 
Published: November 2020

Abstract
The degree of regional economic integration is one of the main factors affecting the efficiency of regional agricultural product circulation, and the two are generally positively correlated. My country’s agricultural product logistics have been developed for a long time, but it is still low in level, high in cost and large in the loss. We analyzed the current situation and existing problems of agricultural product logistics, combined with the background of big data, summarize the role of big data in agricultural product logistics, and the measures for agricultural product logistics construction in the era of big data. To effectively promote the improvement of regional agricultural product circulation efficiency, after analyzing the influencing factors, this paper proposes that the government should build a full marketing system to improve circulation efficiency. It builds a multi-channel platform to improve circulation efficiency and create an era of big data. Data integration and smooth information have further improved the circulation efficiency of China’s agricultural products.
Disease model identification methods based on maximum test and performance analysis
Authors: Yin Yaling 
Published: November 2020

Abstract
Combinatorial explosion and computational burden are always the challenges for genome-wide association study. In order to reduce the computation cost, many multi-stage methods were put forward to identify the disease models. However, one-way and two-way disease models always can be detected to leave out some SNPs for non-significance. And these SNPs are combined with other SNPs to get higher disease models. In this paper, three test statistics, Max Gtest, Max Entropy Difference and Max Relative Entropy, had been presented for the first stage to detection disease models with main effect and without main effect. Five testing methods were used for examining multiply simulation datasets and real dataset. Results were revealed that Max Entropy Difference test is the best method of recognition in five filtering methods with main-effect and max-statistic test is just right method to identify model without main-effect. Results also were showed that five statistics can get interest power for two-ways on simulation datasets and real dataset. We believe that these statistics can find strong and weak SNPs for next step in computationally and statistically.
Design of monitoring system for intelligent windows based on cloud platform
Authors: Liu Changyuan , Jia Yunxiang , Guo Penggang , Zhang Yifan 
Published: November 2020

Abstract
With the development of Internet of Things technology, the intelligent window is entering people’s lives. There are many intelligent window products in the market, but most of them are costly or functions-limited with insufficient consideration of intelligence and humanization. Therefore, an intelligent window monitoring system based on Alibaba Cloud is introduced in this paper. This product can be automatically controlled according to environmental changes or manually controlled using the APP/infrared remote control. The indoor situation is displayed on the mobile phone and OLED screen in real-time, so that people can always grasp the situation and make correct judgment and operation in time. The product has a wide scope of applications. It is suitable for most families, rich in functions, more humanized, and has certain practicality.
Research on QoS routing method based on NSGAII in SDN
Authors: Li Daoquan , Wang Xue , Jin Yingnan , Liu Haoxin 
Published: November 2020

Abstract
Software Defined Network (SDN) makes routing decisions according to the shortest path. This routing scheme does not consider link information such as link residual bandwidth, forwarding delay, packet loss rate and so on. In the case of large data flow, it is easy to cause network congestion, thus reducing the network quality of service assurance. For this reason, using the ability of SDN network controller to monitor the underlying network information and control the route forwarding, we try to set multiple objective functions according to different QoS parameters, and use the non-dominated sorting multi-objective optimization genetic algorithm (NSGA II) based on the elite strategy to make routing decisions. Compared with other routing algorithms, the proposed multi-objective optimization genetic algorithm reduces forwarding delay and packet loss rate, so can effectively prevent network congestion.
Research on motor imagery EEG signal processing algorithm
Authors: Ma Ye , Jiang Guangping , Chang Tanqing 
Published: November 2020

Abstract
To address the difficulty of extracting features from motor imagery EEG signals and the low classification accuracy, a method for feature extraction and classification of multiple types of motor imaging EEG signals based on wavelet and support vector machine (SVM) is proposed. This work first calculated the power of motor imagery EEG data and selected the scale of wavelet packet by theoretical analysis. Then, wavelet packet decomposition on power was discussed, wavelet packet entropy (WPE) of power was calculated, and wavelet packet entropy interpolation of leads C3, C4 was extracted, which composed the feature vector. Finally, this work fed the feature vector as the classifier input into a support vector machine to achieve classification. From Graz’s EEG data from the international BCI competition in 2003, the highest accuracy rate of classification was 97.56%. The feature vectors of this algorithm are low in dimension, are small in data size, and have high classification accuracy, which provides a reference method for the task of EEG feature extraction and classification.
A method for detecting weak signals using chaotic systems based on multiple correlation and phase-locked loop
Authors: He Qiurui , Li Deguang , Jin Yanling , Zhang Yang , Zhang Lanjie 
Published: November 2020

Abstract
In order to improve the detection ability of chaotic system to detect weak periodic signal frequency, phase and amplitude, a chaotic system based on multiple correlation and phase-locked loop is established. Firstly, the signal to be measured is input into the detection frequency of chaotic system. Also, the reference signal of multiple correlation technology is constructed, and the signal to be tested is processed by multiple cross-correlation. Then, the signal processed by multiple correlation is input into the phase-locked loop to obtain the phase of the signal to be measured. Finally, the signal to be measured is input into the chaotic system again, and the amplitude of the signal to be measured is detected according to the change of the chaotic oscillator in critical periodic state and large-scale periodic state. The experimental results show that the detection system can detect the frequency, phase and amplitude of weak periodic signal, which is easy to operate and has high detection ability.
Study on photometric data visualization based on IESNA LM-63 standard
Authors: Zheng G P , Guo H Y 
Published: November 2020

Abstract
The luminous intensity distribution (LID) data file is a written description of the spatial distribution characteristics of the light intensity emitted by light sources or luminaires, which is the prerequisite for the accurate lighting analysis e.g. point-by-point method. Firstly, this paper introduces the format and composition of LID file based on the widely used IESNA LM-63 standard at present. Then, three visualization methods are established to understand the luminaire in an intuitive way. The first one is the photometric curve, which is encoded and integrated into the self-developed lighting analysis software; the second one is the contour map obtained by the rectangular coordinate system in Surfer software through the data exchange file; the last one is the contour map in Origin software represented in the polar coordinate system. It is expected to provide a reference for lighting designers to develop their point-by-point calculation program.
Text line segmentation based on local baselines and connected component centroids for Tibetan historical documents
Authors: Hu Pengfei , Chen Yang , Hao Yusheng , Wang Yiqun , Wang Weilan 
Published: November 2020

Abstract
Text line segmentation is one of the key contents in document image analysis and recognition. Due to the complex situation of text images in Tibetan historical document, such as the coexistence of slanting and distortion of text lines, especially the adhesion between lines, text line segmentation has become a challenging task. In this paper, a new method of text segmentation for Tibetan historical document is proposed. This method first obtains the local baseline information of the text line, and then performs the detection and segmentation of the adhesion area. Finally, according to the barycentre coordinates of the connected component and the local baseline information, each connected component is assigned to the corresponding text line. The method avoids the effect of text line distortion on the segmentation of the stuck text line and greatly reduces the error rate of the adhesion text line segmentation and can effectively deal with the segmentation of slanted and distorted Tibetan sticky text lines.