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The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

To exactly study the riding comfort of passengers in urban rail transit trains, a coupling vibration model, including passengers, train, and bridge is proposed in this paper. According to the Lagrange Equation, the differential equations of motion of each part are derived, which are coupled with each other through displacement and dynamic interaction forces. A corresponding calculation program is compiled by the Fortran language. A representative steel-concrete composite girder bridge on Beijing Metro Line 5 is selected as the research background, the dynamic responses of passengers and train are simulated in detail, and the dynamic response differences between the passenger and carriage are compared. The passengers' riding comfort is evaluated according to ISO-2631 Standard based on the simulated data. The result shows that passengers in the middle carriages of the train feel better than those in the head and tail ones. While in the same carriage, passengers on the middle seats feel better than those on both ends of it. If only vibration is considered, passengers will feel better in a fully loaded carriage than those with few passengers. The riding comfort of passengers will be gradually reduced with the increase of the train speed. [ABSTRACT FROM AUTHOR]

The application of deep learning in time-series prediction has developed gradually. In this paper, we propose a deep generative network model for feature extraction of multivariate time series, namely, mutual information variational autoencoders (MI-VAE). In the architecture of the proposed model, we use the latent space of VAE for feature learning, which can extract the essential features of multivariate time-series data effectively. The latent space employed directly as a feature extractor can avoid poor interpretability of model. In addition, we introduce a mutual information term into the loss function, which improves the expression capability and accuracy of model. The proposed model, combining the merits of VAE and mutual information, extracts features for multivariate time-series data from a new perspective. The Lorenz system and Beijing air quality time series are used to test performance of the proposed model and comparative models. Results show that the proposed model is superior to other similar models in terms of accuracy and expression capability of latent space. [ABSTRACT FROM AUTHOR]

There is a coupling relationship between the development of urban transportation and cities: Urban growth leads to increase in the demand for urban transportation and consequently, a lot of transportation emissions. Therefore, an in-depth understanding of the mechanism behind the driving effect of urban development on transportation emissions is a crucial prerequisite for coordinated development of low-carbon urban transportation and cities. Based on the oil product allocation method, this paper estimates the transportation emission in Beijing from 1995 to 2016. Then based on the understanding of the driving mechanism, this paper applies the urban allometric scaling law to analyze the relationship between city size and transportation emission. Finally, the driving mechanism is analyzed using the STRIPAT model. The results reveal a superlinear relationship between transportation emission in Beijing and the expansion of the city, as the former outgrew the latter. Population size, urbanization, economic size, industrial structure, spatial scale and infrastructure construction are positive driving factors of transportation emission, whereas progress of energy technologies as a negative driving factor can restrain the growth of transportation emission. Urbanization has the most significant impact on urban transportation emission, and economic size contributes the most to the growth of transportation emission. Based on the results, we make a few policy recommendations for low-carbon urban transportation of Beijing, which include: improving transportation efficiency in the process of urbanization; promoting energy conservation and emission reduction while pursuing economic development so as to decouple transportation emission from urban development; restricting unordered urban expansion and updating the concept of transportation infrastructure supply; and developing energy technologies to improve energy efficiency. [ABSTRACT FROM AUTHOR]

In rail transit systems, improving transportation efficiency has become a research hotspot. In recent years, a new method of train control system based on virtual coupling has attracted the attention of many scholars. Virtual coupled train is a typical nonlinear system. How to control the nonlinear system with high precision is the key to realize the safe and efficient operation of virtual coupled train. Therefore, this paper proposes a virtual coupling scheme based on local leader–follower method. On this basis, the controller based on artificial potential field with variable parameter mixed is applied to carry out the collaborative and anti-collision control for the virtual coupled train system with nonlinear dynamics. Finally, a section of the Beijing–Shanghai high-speed railway is taken as the background to verify the effectiveness of artificial potential field with variable parameter mixed controller. The results show that the variable parameter artificial potential field controller with changing the weight can reduce the average error of the stop by 0.4304 m compared with the traditional controller. [ABSTRACT FROM AUTHOR]

Robustness studies on integrated urban public transport networks have attracted growing attention in recent years due to the significant influence on the overall performance of urban transport system. In this paper, topological properties and robustness of a bus–subway coupled network in Beijing, composed of both bus and subway networks as well as their interactions, are analyzed. Three new models depicting cascading failure processes on the coupled network are proposed based on an existing binary influence modeling approach. Simulation results show that the proposed models are more accurate than the existing method in reflecting actual passenger flow redistribution in the cascading failure process. Moreover, the traffic load influence between nodes also plays a vital role in the robustness of the network. The proposed models and derived results can be utilized to improve the robustness of integrated urban public transport systems in traffic planning. [ABSTRACT FROM AUTHOR]

In recent years, the grey-based models with fractional accumulation have received extensive attention by scholars and have been widely used in various fields. However, the existing rough construction of the background value in the fractional grey model impairs its predictive performance to some extent. To address this problem, this paper reconstructs a dynamic background value for the fractional grey model by the composite integral median theorem, as a result, a novel fractional grey model-based variable background value (denoted as OFAGM(1,1) for short) is proposed. In particular, the Particle Swarm Optimization algorithm (PSO) is then employed to determine the optimum for the fractional order and the background value coefficients. Based on electricity consumption of two regions of China (i.e. Beijing, Inner Mongolia), the superiority of the proposed model has been verified in comparison with other benchmark models, the electricity consumption of Beijing is predicted to reach 1 3 8 5. 3 5 × 1 0 8 Kwh in 2022 and that of Inner Mongolia will reach 5 0 6 3. 3 9 × 1 0 8 Kwh in 2023. This study also takes the relative growth rate and the doubling time into account for these two regions that facilities comparison of the regions' performance. [ABSTRACT FROM AUTHOR]

In order to predict more accurately the structural vibration and noise of elevated tracks induced by moving trains, a new prediction method based on the scaled model test is proposed in this paper. A 32-m simply supported box girder bridge used in the Beijing–Shanghai high-speed railway is selected as the prototype for designing and constructing a scaled model test with 10:1 geometric similarity ratio. Both experimental tests and finite element analyses were carried out to verify the similarity relationship between the model and prototype. The test result shows that the scaled model can predict the structural vibration and noise of the prototype, as long as the similarity constants between the prototype and scaled model are correctively determined. Furthermore, a standard finite element analysis model for the scaled model is built. Based on the sensitivity analysis, the model parameters for finite element analysis are updated by minimizing the errors between the measured and calculated modes. The computational results show that the updated model based on the local parameters partitioning works best, and the precision of the modal frequency calculated is noticeably improved after updating, with the average relative error reduced from 5.46% to 3.09%, and the difference of the peak values reduced from 0. 3 5 8 × 1 0 3 m/s2 to 0. 1 8 9 × 1 0 3 m/s2. The calculated dynamic response of the finite element model after updating is basically in line with experimental results, indicating that the updated model can better reflect the dynamic properties of the scaled box girder model. The updated finite element model is useful both for verification with the model test result and for reliable prediction of the dynamic characteristics of the prototype. [ABSTRACT FROM AUTHOR]

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Flower plants have become a major difficulty in virtual plant research because of their rich external morphological structure and complex physiological processes. Computer vision simulation provides powerful tools for exploring powerful biological systems and operating laws. In this paper, Chrysanthemum and Chinese rose, double flowers as the symbolic flowers of Beijing, are chosen as the study subject. On the basis of maximizing the protection of flower growth structure, an effective method based on laser scanning for three-dimensional (3D) reconstruction and visual simulation of flower plants is proposed. This method uses laser technology to scan the sample and store it as point cloud data. After applying a series of image analysis and processing techniques such as splicing, denoising, repairing and color correction, the digital data optimized by the sample is obtained accurately and efficiently, and a highly realistic 3D simulation model of the plant is formed. The results of the research indicate that it is a convenient research method for the 3D reconstruction of flower plants and computer vision simulation of virtual plants. It also provides an effective way for in-depth study of scientific experiments and digital protection of rare and endangered plants. [ABSTRACT FROM AUTHOR]

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights - are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable. [ABSTRACT FROM AUTHOR]

Traffic state evaluation is a basic and critical work in the research on road traffic congestion. It can provide basic data support for the improvement measures and information release in traffic management and service. The aim of this research is to obtain a comprehensive value to describe traffic state accurately based on the evaluation index system. In this paper, it is carried out using fuzzy c-means (FCM) algorithm and fuzzy entropy weight method. In the framework, traffic flow was classified into six different states to determine the fuzzy range of indices using the improved FCM clustering analysis. Besides, fuzzy entropy weight method is proposed to compute the evaluation result of traffic state for section, road and road network, respectively. The experiments based on the traffic information in a subset of Beijing's road network prove that the findings of traffic evaluation are in accordance with the actual situation and people's sense of traffic state. [ABSTRACT FROM AUTHOR]

In this paper, we present the empirical investigation results on the urban public transport networks (PTNs) and propose a model to understand the results obtained. We investigate some urban public traffic networks in China, which are the urban public traffic networks of Beijing, Guangzhou, Wuhan and etc. The empirical results on the big cities show that the accumulative act-degree distributions of PTNs take neither power function forms, nor exponential function forms, but they are described by a shifted power function, and the accumulative act-degree distributions of PTNs in medium-sized or small cities follow the same law. In the end, we propose a model to show a possible evolutionary mechanism for the emergence of such network. The analytic results obtained from this model are in good agreement with the empirical results. [ABSTRACT FROM AUTHOR]

Short-term traffic forecasting has played a key role in supporting the need of proactive and dynamic traffic control system. K-nearest neighbor (KNN) nonparametric regression models have been widely used in traffic prediction. KNN models give predictions based on the future state of traffic speed that is completely determined by the current state, but with no dependence on the past sequences of traffic speed that produced the current state. In fact, traffic speed is not completely random in nature, and some patterns repeat in the traffic stream. In this paper, we proposed a methodology called bi-pattern recognition KNN model (BKNN) which uses pattern recognition technique twice in the searching process to predict the future traffic state. Then the proposed BKNN model is applied to predict one day real traffic speed series of two sites, which are located near the North 2nd and 3rd Ring Road in Beijing, respectively. With the optimal neighbor and pattern size, the BKNN model provides good predictions. Moreover, in comparison with the KNN model, PKNN model (a modified model based on KNN), seasonal autoregressive integrated moving average (SARIMA) and the artificial neural networks (ANN), the BKNN model appears to be the most promising and robust of the five models to provide better short-term traffic prediction. [ABSTRACT FROM AUTHOR]

Focuses on the collection of research papers on numerical acoustics during the International Congress of Theoretical and Computational Acoustics in Beijing, China. Discussion of the problems of contemporary research; Assessment of the accuracy problems of the boundary-element collocation method; Description of the appearance of viscoelastic material.