System error iterative identification for underwater positioning based on spectral clustering

The observation error model of the underwater acous-tic positioning system is an important factor to influence the positioning accuracy of the underwater target.For the position inconsistency error caused by considering the underwater tar-get as a mass point,as well as the observation system error,the traditional error model best estimation trajectory(EMBET)with little observed data and too many parameters can lead to the ill-condition of the parameter model.In this paper,a multi-station fusion system error model based on the optimal polynomial con-straint is constructed,and the corresponding observation sys-tem error identification based on improved spectral clustering is designed.Firstly,the reduced parameter unified modeling for the underwater target position parameters and the system error is achieved through the polynomial optimization.Then a multi-sta-tion non-oriented graph network is established,which can address the problem of the inaccurate identification for the sys-tem errors.Moreover,the similarity matrix of the spectral cluster-ing is improved,and the iterative identification for the system errors based on the improved spectral clustering is proposed.Finally,the comprehensive measured data of long baseline lake test and sea test show that the proposed method can accu-rately identify the system errors,and moreover can improve the positioning accuracy for the underwater target positioning.

Mapping theme trends and knowledge structures for human neural stem cells:a quantitative and co-word biclustering analysis for the 2013-2018 period

Neural stem cells,which are capable of multi-potential differentiation and self-renewal,have recently been shown to have clinical potential for repairing central nervous system tissue damage.However,the theme trends and knowledge structures for human neural stem cells have not yet been studied bibliometrically.In this study,we retrieved 2742 articles from the PubMed database from 2013 to 2018 using "Neural Stem Cells" as the retrieval word.Co-word analysis was conducted to statistically quantify the characteristics and popular themes of human neural stem cell-related studies.Bibliographic data matrices were generated with the Bibliographic Item Co-Occurrence Matrix Builder.We identified 78 high-frequency Medical Subject Heading(MeSH)terms.A visual matrix was built with the repeated bisection method in gCLUTO software.A social network analysis network was generated with Ucinet 6.0 software and GraphPad Prism 5 software.The analyses demonstrated that in the 6-year period,hot topics were clustered into five categories.As suggested by the constructed strategic diagram,studies related to cytology and physiology were well-developed,whereas those related to neural stem cell applications,tissue engineering,metabolism and cell signaling,and neural stem cell pathology and virology remained immature.Neural stem cell therapy for stroke and Parkinson’s disease,the genetics of microRNAs and brain neoplasms,as well as neuroprotective agents,Zika virus,Notch receptor,neural crest and embryonic stem cells were identified as emerging hot spots.These undeveloped themes and popular topics are potential points of focus for new studies on human neural stem cells.

Co-word clustering analysis for nursing safety management research focuses by PubMed

Objective： To analyze hot research areas and the present research status of nursing safety management in PubMed. Methods： PubMed was searched using ＂safety management＂ for the literature on nursing safety management. BICOMB 2.0 and SPSS 20.0 software were used to analyze high-frequency keywords and conduct co-word clustering analysis. Results： We searched for totally 2353 articles related to our topic and extracted 19 high-frequency keywords （27.50%）. Five research focuses were concluded, including： study on nursing safety culture; team work to promote nursing safety; practice of nursing safety management; workplace violence against nursing staffs; nursing safety and quality evaluation standard. Conclusion： Analysis of the hotspots of nursing safety management in the past 10 years will contribute to understanding the research emphases and trend of development, and provide reference for the study and practice of nursing safety management.

The hot issues of studies in China on digital information resources: Based on co-word analysis

With the SPSS and the help of factor method and hierarchical clustered method,journal articles on digital information resources(DIR) from CNKI in the past ten years are analyzed with a co-word analytical method in this paper. The hot issues of studies on DIR and the relationship between those subjects are analyzed in this investigation as well.

Structures and dynamics of scientific knowledge networks:An empirical analysis based on a co-word network

Co-word networks are constructed with author-provided keywords in academic publications and their relations of co-occurrence.As special form of scientific knowledge networks,they represent the cognitive structure of scientific literature.This paper analyzes the complex structure of a co-word network based on 8,190 author-provided keywords extracted from 3,651 papers in five Chinese core journals in the field of management science.Small-world and scale-free phenomena are found in this network.A large-scale co-word network graph,which consists of one major giant component and many small isolated components,has been generated with the GUESS software.The dynamic growth of keywords and keyword co-occurrence relationships are described with four new informetrics measures.The results indicate that existing concepts always serve as the intellectual base of new ideas as represented by keywords.

Research status and hotspots of economic evaluation in nursing by co-word clustering analysis

Objective:The aim of this study is to discover research status and hotspots of economic evaluation(EE)in nursing area using co-word cluster analysis.Methods:Medical Subject Heading(MeSH)term“cost–benefit analysis”was searched in PubMed and nursing journals were limited by the function of filter.The information of author,country,year,journal,and keywords of collected paper was extracted and exported to Bicomb 2.0 system,where high-frequency terms and other data could be further mined.SPSS 19.0 was used for cluster analysis to generate dendrogram.Results:In all,3,020 articles were found and 10,573 MeSH terms were detected;among them,1,909 were MeSH major topics and generated 42 high-frequency terms.The consequence of dendrogram showed seven clusters,representing seven research hotspots:skin administration,infection prevention,education program,nurse education and management,EE research,neoplasm patient,and extension of nurse function.Conclusions:Nursing EE research involved multiple aspects in nursing area,which is an important indicator for decision-making.Although the number of papers is increasing,the quality of study is not promising.Therefore,further study may be required to detect nurses’knowledge of economic analysis method and their attitude to apply it into nursing research.More nursing economics course could carry out in nursing school or hospitals.

基于改进Co-word分析法的脆弱性数据分析技术研究

针对脆弱性信息系统中脆弱性数据质量保证问题,研究基于改进Co-word分析法的脆弱性数据分析技术。依据脆弱性分类法分类特征确定分析关键字,通过对Co-word分析法的改进设计了脆弱性数据分析算法,实现了脆弱性信息系统中脆弱性数据的验证、完善和补充。

Big data in healthcare:Conceptual network structure,key challenges and opportunities

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.

对当前引文分析研究中若干问题的思考

对引文分析的理论前提进行了再认识，讨论了引文分析的时效性、引文行为与ｃｏ－ｗｏｒｄ分析，并指出了我国引文分析研究中存在的问题。

基于可靠性屋的维修配置方法

将故障树分析(FTA)、失效模式影响分析(FMEA)整合到质量功能配置(QFD)中,建立了维修配置流程。首先对维修影响因素进行了重要度分析;然后建立了以维修性能目标函数最大和配置成本最低为目标的模型,将维修指标配置到具体的工程技术特征上;最后,以数控车床电主轴为例,验证了配置方法的有效性。

Forestry big data platform by Knowledge Graph

Using the advantages of web crawlers in data collection and distributed storage technologies,we accessed to a wealth of forestry-related data.Combined with the mature big data technology at its present stage,Hadoop's distributed system was selected to solve the storage problem of massive forestry big data and the memory-based Spark computing framework to realize real-time and fast processing of data.The forestry data contains a wealth of information,and mining this information is of great significance for guiding the development of forestry.We conducts co-word and cluster analyses on the keywords of forestry data,extracts the rules hidden in the data,analyzes the research hotspots more accurately,grasps the evolution trend of subject topics,and plays an important role in promoting the research and development of subject areas.The co-word analysis and clustering algorithm have important practical significance for the topic structure,research hotspot or development trend in the field of forestry research.Distributed storage framework and parallel computing have greatly improved the performance of data mining algorithms.Therefore,the forestry big data mining system by big data technology has important practical significance for promoting the development of intelligent forestry.

Axial Compression Behavior and Analytical Method of L-Shaped Column Composed of Concrete-Filled Square Steel Tubes

Based on the characteristics of an L-shaped column composed of concrete-filled square steel tubes, the axial compression experiment and nonlinear finite element analysis were carried out to study the mechanical property of the L-shaped column. The load-displacement curve for the L-shaped column, the deflection and load-strain curves for the mono columns were obtained by the axial compression experiment. The results show that the L-shaped column exhibits a flexural-torsional buckling failure mode. The numerical simulation by the finite element analysis shows that the bearing capacity and failure mode are in accordance with those of the axial compression experiment and the feasi- bility of the finite element analysis is proved. For the calculation of the bearing capacity of the L-shaped column com- posed of concrete-filled square steel tubes, an analytical method is proposed based on the theory of the elastic stability and spatial truss model. The results of the analytical method are in good agreement with those of the axial compression experiment and the finite element analysis.

Bibliometric analysis of subject trends and knowledge structures of gut microbiota

BACKGROUND Gut microbiota is an emerging field of research,with related research having breakthrough development in the past 15 years.Bibliometric analysis can be applied to analyze the evolutionary trends and emerging hotspots in this field.AIM To study the subject trends and knowledge structures of gut microbiota related research fields from 2004 to 2018.METHODS The literature data on gut microbiota were identified and downloaded from the PubMed database.Through biclustering analysis,strategic diagrams,and social network analysis diagrams,the main trend and knowledge structure of research fields concerning gut microbiota were analyzed to obtain and compare the research hotspots in each period.RESULTS According to the strategic coordinates and social relationship network map,Clostridium Infections/microbiology,Clostridium Infections/therapy,RNA,Ribosomal,16S/genetics,Microbiota/genetics,Microbiota/immunology,Dysbiosis/immunology,Infla-mmation/immunology,Fecal Microbiota Transplantation/methods,Fecal Microbiota Transplantation can be used as an emerging research hotspot in the past 5 years(2014-2018).CONCLUSION Some subjects were not yet fully studied according to the strategic coordinates;and the emerging hotspots in the social network map can be considered as directions of future research.

Failure analysis of unmanned autonomous swarm considering cascading effects

In this paper, we focus on the failure analysis of unmanned autonomous swarm(UAS) considering cascading effects. A framework of failure analysis for UAS is proposed.Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simulation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents.The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm.The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.

Identification of distant co-evolving residues in antigen 85C from Mycobacterium tuberculosis using statistical coupling analysis of the esterase family proteins

A fundamental goal in cellular signaling is to understand allosteric communication, the process by which sig-nals originating at one site in a protein propagate reliably to affect distant functional sites. The general principles of protein structure that underlie this process remain unknown. Statistical coupling analysis （SCA） is a statistical technique that uses evolutionary data of a protein family to measure correlation between distant functional sites and suggests allosteric communication. In proteins, very distant and small interactions between collections of amino acids provide the communication which can be important for signaling process. In this paper, we present the SCA of protein alignment of the esterase family （pfam ID： PF00756） containing the sequence of antigen 85C secreted by Mycobacterium tuberculosis to identify a subset of interacting residues. Clustering analysis of the pairwise correlation highlighted seven important residue positions in the esterase family alignments. These resi-dues were then mapped on the crystal structure of antigen 85C （PDB ID： 1DQZ）. The mapping revealed corre-lation between 3 distant residues （Asp38, Leu123 and Met125） and suggests allosteric communication between them. This information can be used for a new drug against this fatal disease.

Contributing Factors for Train Delays during Morning Rush Hour in Japanese Metropolitan Areas

The present study aims to reveal the contributing factors for train delays in Tokyo metropolitan area by conducting statistical analyses, focusing on passenger trains, and using a variety of information by including data concerning train cars, stations, passengers, tracks and working timetables as explanatory variables. The present study conducted 2 types of statistical analyses including the standard multiple regression analysis and the logistic regression analysis by setting “average delay time” which indicates the quantitative conditions of delays, and “occurrence of delays” which indicates the qualitative condition, as objective variables. According to the results of the logistic regression analysis, the possibility of direct operations increasing the delay occurrence rate was quantitatively indicated. Therefore, direct operations are regarded as a contributing factor for train delays concerning metropolitan areas in recent years. Additionally, it was confirmed that the concentration of demand on terminal stations is also a contributing factor for train delays. On the other hand, it is certain that direct operations contribute to improving the convenience of passengers as well as the operational efficiency of train cars. Therefore, it would be ideal to resolve delays by easing the concentration of demands which may be accomplished by recommending off-peak commuting as well as adjustments to the working timetables.

The Novel Pyruvated Glucogalactan Sulfate Isolated from the Red Seaweed, Hypnea pannosa

The polysaccharide was isolated from Hypnea pannosa which was grown in Okinawa, Japan. The yield of the polysaccharide was 17.2%, and the total carbohydrates, pyruvic acid, sulfuric acid and ash contents were 55.2%, 3.8%, 35.2% and 24.3%, respectively. 3,6-Anhydro-α-D-galactose, β-D-galactose, α-D-galactose and D-glucose were identified by liquid and thin-layer chromatography. Fourier transform infrared (FTIR) spectra of the polysaccharide resembled that of ι-carrageenan. From the 1H- and 13C-NMR spectra, 1,3-linked β-D-galactose, 1,4-linked anhydro-α-D-galactose, 1,4-linked α-D-galactose, 1,4-linked β-D-glucose and pyruvic acid (carboxyl acetal, methyl proton and methyl carbon) were assigned. Methylation analysis revealed terminal D-galactose 0.1 mol), 1,4-linked D-glucose (1.0 mol) and 1,2,3,4,6-linked D-galactose (3.7 mol) for native polysaccharide, and terminal D-galactose, 1,4-linked D-galactose (1.9 mol), 1,4-linked D-glucose (1.0 mol), 1,3- linked D-galactose (1.7 mol), and 1,3,4,6-linked D-galactose (0.3 mol) which substituted with pyruvate group at 4 and 6 positions for desulfated polysaccharide. The polysaccharide was the novel pyruvated glucogalactan sulfate, the structure of which was proposed.

RECl_3-CaCl_2-MgCl_2 TERNARY PHASE DIAGRAMS(RE:La,Ce,Pr,Nd)

Nine sub-binary phase diagrams of the RECl3-CaCl2,RECl3-MgCl2 and CaCl2-MgCl2 systems,andthermodynamic data for these systems are critically assessed and optimized.Using Hillert model and takingMgCl2 as an asymmetric component,the ternary phase diagrams or the RECl3-CaCl2-MgCl2 systems are predtcted.As well,the determination of asymmetric component in the asymmetric model is investigated.

Content analysis of documents using neural networks: A study of Antarctic science research articles published in international journals

Content analysis of scientific papers emanating from Antarctic science research during the 25 years period （1980-- 2004） has been carried out using neural network based algorithm-CATPAC. A total of 10 942 research articles published in Science Citation Indexed （SCI） journals were used for the study. Normalized co-word matrix from 35 most-used significant words was used to study the semantic association between the words. Structural Equivalence blocks were constructed from these 35 most-used words. Four-block model solution was found to be optimum. The density table was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent the thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with the blocks, characterizing thematic composition of Antarctic science research. The analysis has provided an analytical framework for carrying out studies on the con- tent of scientific articles. The paper has shown the utility of co-word analysis in highlighting the important areas of research in Antarctic science.

Design and Maxwell 3D simulation of small permanent magnetic actuator

According to the magnetic circuit design theory and performance requirements of magnetic field, an H-type permanent magnetic actuator that generates uniform magnetic field larger than 0.4 T in the interested re- gion has been designed in this paper. The static magnetic field simulation analysis was done by Ansoft＇ s Max- well three-dimensional （3D） software. The simulation results showed that the magnetic field of this system can meet the requirements, and this permanent magnetic actuator designed in this paper can be used in small nuclear magnetic resonance （NMR） svstem.