Search

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]

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]

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]

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]

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]

Due to the lack of data quality in real production environment, the traditional line loss calculation method cannot be applied, thus through the investigation of various information systems' operation in power supply enterprises, a short-term low-voltage distribution network line loss prediction algorithm based on Kmeans-LightGBM is proposed. Operating data quality evaluation system of low-voltage distribution network was set up based on Hadoop platform, the feature dimensions were expanded by feature engineering, then those with no multicollinearity and high correlation with the line loss were selected, data normalization was again performed, Kmeans clustering algorithm was used to cluster the area and then, LightGBM algorithm was used to predict the classes within the area of line loss. Finally, the line loss of the numerical inverse normalization was found and validated with Beijing Power Grid of a low-voltage distribution network. By comparison, the model's prediction accuracy is found to be higher than BPNN, FOA-SVR and traditional LightGBM. [ABSTRACT FROM AUTHOR]

Environment model is an important component of an artificial society. To level up the resolution of residence-building environment models in artificial Beijing, we divide residential buildings in Beijing into two categories, which are noncommunity residential buildings and community residential buildings, and statistically analyze the numbers of floors and areas of the real residential buildings. In this paper, we have exhibited the distribution of numbers of floors in noncommunity residential buildings and community residential buildings. Furthermore, we analyze the distribution of areas of noncommunity residential buildings and residence-use proportions of community zones by Nonlinear Regression. Results indicate that areas of noncommunity residential buildings in Beijing obey the log-normal distribution and residence-use proportions of community zones obey normal distribution. According to the statistical models, the virtual residential buildings in artificial Beijing can be endowed with rational attribute values. [ABSTRACT FROM AUTHOR]

An analytical framework for conceptualizing the issue of international competitiveness at the firm level is presented taking into account that the firms are increasingly being exposed to international competition. The model identifies three interdependent innovative capability dimensions, which offer insight into the sources of sustainable international competitive advantages over time: Technological capability, managerial capability and resource exploiting capability. The paper presents the challenges of these three components of innovative capability in an international context, and describes how a firm can develop and sustain competitiveness through the operation in the environment of globalization. Finally, based on data from 213 firms in the Beijing area, the paper proves that these three innovative capability components are closely related to international competitiveness. The study reinforces the importance of innovative capability composition in internationalization decision-making and suggests further research in this context. [ABSTRACT FROM AUTHOR]

An important part of industrial policy in China has been directed towards improving the degree and effectiveness of knowledge transfer between key firms in China's innovation system. Amongst these policies, the creation of regional science parks that encourage labour mobility and inter-firm collaboration on innovation projects have been central. Learning through inter-firm knowledge transfer focuses the attention on at least two key factors, improving absorptive capability (Cohen and Levinthal, 1990), which relies on the development of specialised skills in the firm and the establishment of inter-organisational networks through which knowledge is transferred. This paper contributes to this analysis through a detailed study of the relationship between learning and knowledge transfer of knowledge workers working on innovation projects in Chinese ICT companies located in Beijing's Zhongguancun (ZGC) high-technology park. A major advantage of analysing knowledge transfer through the activities of R'D employees is that it highlights the process by which specific competencies and network relations are built. A skills profile of R&D employees is developed that, amongst other features, includes three different networks Chinese knowledge workers use to access and share knowledge: formal organisational networks, personal networks and scanning networks. Empirical data based on two unique surveys in China of senior R&D managers and R&D employees was collected and analysed. This suggests that a skills profile combining knowledge within and outside of the company and scanning activity positively impact both the innovation projects and the labour market position of the knowledge workers. Policy recommendations in terms of training and development in R&D follow. [ABSTRACT FROM AUTHOR]

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China's large cities. Currently, most of the underground utility tunnels' risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the K2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses. [ABSTRACT FROM AUTHOR]

Objective: With data drawn from Beijing's New Rural Cooperative Medical System (NRCMS), the rule characteristics of disease risks are mined in terms of risk factors and risk measurements aiming to discover valuable knowledge within the vast amounts of Beijing's NRCMS data and provide administrators with a more scientific basis for decision making. Methodology:The association rule algorithm is utilized to recover both potentially valuable knowledge and decision-making information from Beijing's NRCMS data. Results: The main objects of healthcare in Beijing from 2012 to 2014 include: circulatory diseases in patients 41 years of age or older, pediatric respiratory disease prevention, reproductive healthcare for women of childbearing age, and the prevention and treatment of diabetes in female patients; in county-level hospitals with a relatively low average level of consumption, injuries still resulted in high expenses; the primary post-NRCMS reimbursement level-3 and level-4 high-risk groups were patients of 41-65 years of age. Conclusion: According to the ranking of rule supports, the highest support rule in Beijing is the circulatory diseases of middle-aged patients, especially patients that are hospitalized in county-level medical institutions; the second highest support comes from the utilization of fertility services for women of childbearing age. Suggestions: It is recommended that the rule support rankings should be combined to actively and implement with emphasis major prevention and healthcare services, lower disease risk factors, control NRCMS reimbursement costs, promote the sustainable development of NRCMS, adopt a classified management of mined rules, and establish a decision-making knowledge base. [ABSTRACT FROM AUTHOR]

Urban region functions are the types of potential activities in an urban region, such as residence, commerce, transportation, entertainment, etc. A service which mines urban region functions is of great value for various applications, including urban planning and transportation management, etc. Many studies have been carried out to dig out different regions' functions, but few studies are based on social media text analysis. Considering that the semantic information embedded in social media texts is very useful to infer an urban region's main functions, we design a service which extracts human activities using Sina Weibo (www.weibo.com ; the largest microblog system in Chinese, similar to Twitter) with location information and further describes a region's main functions with a function vector based on the human activities. First, we predefine a variety of human activities to get the related activities corresponding to each Weibo post using an urban function classification model. Second, urban regions' function vectors are generated, with which we can easily do some high-level work such as similar place recommendation. At last, with the function vectors generated, we develop a Web application for urban region function querying. We also conduct a case study among the urban regions in Beijing, and the experiment results demonstrate the feasibility of our method. [ABSTRACT FROM AUTHOR]

A framework for technological and organizational capability audits is proposed, and the relevance of integrating technology and organization and product competitiveness of Chinese firms is then examined. Empirical data were acquired through a recent study of innovative firms from Beijing, China. An organization-technology map and statistical analyses are used to verify five hypotheses in order to identify the relationship amongst product competitiveness and integration of technology and organization. The results effectively verify the positive relationship among technological innovation, organizational innovation and product competitiveness. The findings also reveal that integrated innovation between technology and organization is critical for the improvement of product competitiveness of Chinese firms. [ABSTRACT FROM AUTHOR]