Determining future high speed rail review topics through bibliometric analysis

As High Speed Rail(HSR)has proliferated globally,so has a related research field dedicated to exploring and addressing its unique issues.Yet,studies to understand and classify the HSR research domain are limited.This paper addresses the gap,using bibliometric analysis to identify future research areas and 20 candidate topics for literature review based on keyword analysis through VOSviewer.Article and review papers related to HSR published in the last 20 years(2003-2022)were retrieved from Scopus,and then analyzed to determine the split in knowledge between languages,the collaboration between countries and institutions,highly productive and cited journals,and research topics which have and have not been reviewed.Approximately 30%of the search results were published exclusively in Chinese,highlighting the importance of extending the evaluation to cover both languages.This is a novel aspect of the work,which has enabled the recognition of potential knowledge gaps.It is recommended that future reviews incorporate works in both languages,possibly through international collaboration.Institutions in China and other countries that are strong collaborators have been identified,as well as relevant,highly cited journals.

End-to-end aspect category sentiment analysis based on type graph convolutional networks

For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.

A knowledge discovery method based on analysis of multiple co-occurrence relationships in collections of journal papers

Purpose: This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach: A variety of methods such as the model construction,system analysis and experiments are used. The author has improved Morris' crossmapping technique and developed a technique for directly describing,visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Findings: The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. It can reveal more and in-depth information than analyzing co-occurrence relations between two entities. Therefore,this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.Research limitations: The technique could only be used to analyze co-occurrence relations of less than three entities at present.Practical implications: This research has expanded the study scope of co-occurrence analysis.The research result has provided a theoretical support for co-occurrence analysis.Originality/value: There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. This research defines multiple co-occurrence and the research scope,develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.

The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.

Spatio-Temporal Variations in Co-Occurrence Patterns of Fish Communities in Haizhou Bay, China: Null Model Analysis

Co-occurrence pattern of fish species plays an important role in understanding the spatio-temporal structure and the stability of fish community.Species coexistence may vary with time and space.The co-occurrence patterns of fish species were examined using the C-score under fixed-fixed null model for fish communities in spring and autumn over different years in the Haizhou Bay,China.The results showed that fish assemblages in the whole bay had non-random patterns in spring and autumn over different years.However,the fish co-occurrence patterns were different for the northern and southern fish assemblages in spring and autumn.The northern fish assemblage showed structured pattern,whereas the southern assemblage were randomly assembled in spring.The co-occurrence patterns of fish communities were relatively stable over different years,and the number of significant species pairs in northern assemblage was more than that in the southern assemblage.Environmental heterogeneity played an important role in determining the distributions of fish species that formed significant species pairs,which might affect the co-occurrence patterns of northern and southern assemblages further in the Haizhou Bay.

Visual Exploration Research in the Field of Library and Information Based on Co-occurrence Analysis

Towards current modern society,our country’s library and information science is not only rapid development,but it also has won the favor of many researchers in more than a decade of development.Due to new technology elements and methods are widely used in the discipline of library and information science,cause society our country’s library and information science has entered a new stage with the development of information industry.This paper is based on the research theory of co-occurrence analysis,clustering co-occurrence analysis of references,social network co-occurrence analysis from researchers and research institutions of the field of library and information science,to have more precise and in depth research on the research focus of the library and information science,the authors themselves and the research institutions circumstances over the past few years.At the same time,the theory is used to analyze the effects and analysis of accurate data,to ensure that researchers can analyze the theory of library and information science for a long time.

Keyword Co-occurrence Analysis of Research on Innovation Ecosystem Studies

The aim of the paper is to identify and explore leading thematic areas within the research field related to innovation ecosystem.Based on data from the Web of Science database,the keywords frequency and its co-occurrence frequency pair were analyzed,and the theory of mapping knowledge domains was used to visualize the keywords co-occurrence network in innovation ecosystem to make further research of the heated issues.The findings indicate that the research scope involved in innovation ecosystem research is broad,and research content focus on micro and macro levels.According to the results of keywords co-occurrence analysis of different stages,innovation,network,knowledge,strategy,open innovation,value creation are the most important issues to innovation ecosystem research,given their position and role in the research network.The paper generates the added value mainly from the point of view of theory development.

Research on Subject Areas of Innovation Ecosystem: A Discipline Co-occurrence Analysis

Based on data from the Web of Science database,co-occurrence analysis and network visualization technique were conducted to explore the structure and patterns of interdisciplinary collaborations in innovation ecosystem research.This study presented the descriptive statistics of disciplines involved in relative research and interdisciplinary network over time.The results suggested that the scope of disciplines involved in innovation ecosystem research is broad and the distribution is unbalanced.Management and business are the core subject area while Regional&Urban Planning,Environmental Studies,and Environmental Sciences are playing key role in research and practices.

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.

Learner Language Analysis of the Use of“Past Tense”

The study focuses on uses of different category of verbs in two learners’writing about the events happened in the past.The results suggest that errors of past tense include:present tense used instead of past tense,overuse of past tense,misuse of verb’s past tense form,perfect used instead of past,and be added before verb instead of past.The results of errors for each verb category are that states and activities are made more errors in past tense expressions,and there was little difference between states and activities.

Assembly and co-occurrence patterns of rare and abundant bacterial sub-communities in rice rhizosphere soil under short-term nitrogen deep placement

Nitrogen(N)deep placement has been found to reduce N leaching and increase N use efficiency in paddy fields.However,relatively little is known how bacterial consortia,especially abundant and rare taxa,respond to N deep placement,which is critical for understanding the biodiversity and function of agricultural ecosystem.In this study,lllumina sequencing and ecological models were conducted to examine the diversity patterns and underlying assembly mechanisms of abundant and rare taxa in rice rhizosphere soil under different N fertilization regimes at four rice growth stages in paddy fields.The results showed that abundant and rare bacteria had distinct distribution patterns in rhizosphere samples.Abundant bacteria showed ubiquitous distribution;while rare taxa exhibited uneven distribution across all samples.Stochastic processes dominated community assembly of both abundant and rare bacteria,with dispersal limitation playing a more vital role in abundant bacteria,and undominated processes playing a more important role in rare bacteria.The N deep placement was associated with a greater influence of dispersal limitation than the broadcast N fertilizer(BN)and no N fertilizer(NN)treatments in abundant and rare taxa of rhizosphere soil;while greater contributions from homogenizing dispersal were observed for BN and NN in rare taxa.Network analysis indicated that abundant taxa with closer relationships were usually more likely to occupy the central position of the network than rare taxa.Nevertheless,most of the keystone species were rare taxa and might have played essential roles in maintaining the network stability.Overall,these findings highlighted that the ecological mechanisms and co-occurrence patterns of abundant and rare bacteria in rhizosphere soil under N deep placement.

Image texture indices and trend analysis for forest disturbance assessment under wood harvest regimes

Effective disturbance indices for Hyrcanian forests in Kheyroud,Nowshahr,Iran were determined.The study area was divided into landscape mosaics based on ecosystem parameters including profile type,slope and elevation.Co-occurrence texture indices were derived as forest disturbance factors on the first five bands of Landsat TM,ETM+and OLI images for the prevailing wood harvest disturbance regimes.These indices were screened using ten types of trend analyses and used for modeling disturbance of the harvesting regime through artificial neural networks.The results show that the selected indices can be useful in distinguishing areas with different disturbance intensities and as such,used in the context of health assessment through the health distance method.The accuracy of the health maps derived from the indices[increasing disturbance]led to give rise higher disturbance classification accuracy.

A study on clinical nursing research trends and hotspots based on bibliometric analysis

Objective: This study was conducted to understand the trends and research hotspots of clinical nursing in China and abroad, thereby to provide guidance for nursing practice and studies.Methods: We retrieved clinical nursing literature(excluding literature related to psychological nursing and traditional Chinese medicine nursing) cited in Wanfang Data(www.wanfangdata.com.cn) and PubMed between 2007 and 2015 and subsequently performed bibliometric analyses on article volume,journal, clustering of high-frequency keywords, and co-occurrence of keywords.Results: A total of 10374 publications, excluding those related to psychological nursing, were retrieved via a PubMed search. The literature displayed a rising trend in terms of annual publication volume. The journal distribution of clinical nursing publications overall was consistent with Bradford's Law. The main contents of clinical nursing literature between 2007 and 2009 included:(1) studies on effects of the midwife and midwifery methods for natural childbirth;(2) studies on emergency nursing care;(3)studies on dementia care;(4) studies on bandage dressing in trauma nursing; and(5) studies on quality of life in cancer patients nursing. The main contents of clinical nursing literature between 2010 and 2012 included:(1) studies on obstetric nurse-patient relationships;(2) studies on patients' medical team in stroke nursing;(3) studies on cancer nursing methods;(4) studies on nursing patients with trauma related to urinary incontinence; and(5) studies on the quality of life of caregivers of dementia patients.The main contents of clinical nursing literature between 2013 and 2015 included(1) studies on skin care and bandaging in trauma nursing;(2) studies on evidence-based nursing in AIDS care;(3) studies on nurses' role in obstetric nurse-patient relationships; studies on cancer nursing methods;(4) studies on nursing for Alzheimer's disease and dementia; and(5) studies on caregivers and nursing of stroke patients. A Wanfang Data search retrieved a total of 85570 publications, whose volume also exhibited an annual rising trend, in particular with regard to those in core journals. However, percentage of total publication volume, mean cited frequency per article, and total ranking of clinical nursing publications overall displayed a decreasing trend. The main contents of clinical nursing literature between 2007 and 2009 included:(1) diabetes nursing;(2) nursing for cancer;(3) nursing for senility-related diseases;(4)nursing in the event of complications;(5) nursing for hypertension;(6) studies on quality of life after nursing interventions;(7) chemotherapy nursing;(8) studies on compliance after nursing interventions;(9) constipation nursing; and(10) nursing for stroke patients. The main contents of clinical nursing literature between 2010 and 2012 included:(1) diabetes nursing;(2) nursing for cancer;(3) nursing for senility-related diseases;(4) studies on quality of life after nursing interventions;(5) nursing in the event of complications;(6) nursing for hypertension;(7) prevention nursing;(8) perioperative nursing;(9)pain nursing; and(10) studies on compliance after nursing interventions. The main contents of clinical nursing literature between 2013 and 2015 included:(1) diabetes nursing;(2) nursing in the event of complications;(3) cancer nursing;(4) studies on quality of life after nursing interventions;(5) nursing for stroke patients;(6) nursing for the elderly;(7) studies on compliance after nursing interventions;(8)nursing for hypertension;(9) constipation nursing; and(10) prevention nursing.Conclusions: Clinical nursing research in China is in a rapid development stage as defined by Price's law whereby there is a rapid growth in publication volume. By comparison, clinical nursing research in developed countries is in the stable development stage or maturation phase, which is characterized by a flat curve of publication volume and stable development rate. There is a gap of 25-30 years between China and these countries in terms of the growth phases of clinical nursing literature.

Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some sentiment.Therefore,sentiment analysis is currently a hot topic of research.In particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of polarity.It is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or neutral.Currently,the available lexicon-based method accuracy is affected by limited coverage.Several of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different topics.This paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify ABSA-PER.Experimental outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

Current Situation and Prospect of Sports Tourism Research Based on CiteSpace Bibliometric Analysis

Based on CNKI database,the paper takes journal literatures with the theme of “sports tourism” as the research objects to draw knowledge map through bibliometrics method and CiteSpace software.Moreover,the paper draws knowledge map and analyses for 1,208 periodical literatures with the theme of “sports tourism” in CNKI from 2002 to 2022.The results show that the sports tourism research from 2002 to 2022 can be divided into three stages:The first is the accelerated development stage,which focuses on the industrialization development of sports tourism;The second is the rapid prosperity stage,which focuses on how to promote economic development,and constantly deepen and enrich the connotation of sports tourism industry;The third is the rising stage of fluctuation,which begins to explore new development opportunities of sports tourism from multiple perspectives.Based on keywords co-occurrence analysis and cluster analysis,the research hotspots and changes of sports tourism in China are obtained.Five key research hotspots and three main directions are extracted to provide reference for the development of sports tourism research hotspots in the future.

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.

Bibliometric analysis of acupuncture research through the Web of Science database from 1990 to 2019

Background:The main points of focus of bibliometric analysis of acupuncture treatment of diseases include pain(headache,low back pain),insomnia,and knee osteoarthritis,for example.In this paper,we analyze the frontiers,hotspots,and research trends of acupuncture over the past 30 years and compare them for each of three 10-year periods.Methods:All the studies on acupuncture research in three different periods(1990–1999,2000–2009,2010–2019)were collected from the Web of Science database.The evolution of the research,hotspots,and trends in acupuncture were explored intuitively by analyzing the frequency,betweenness centrality,and subject word clustering of the three periods.Results:(1)1990–1999,the main content relating to research was the mechanism of research of acupuncture treatment of pain.Naloxone was the high-frequency subject word,and centrality included,for example,the spinal cord,enkephalin,smoking cessation,and detoxification.The results of keyword cluster analysis showed that the main research content included capsaicin-induced neurogenic edema,chemical dependency treatment,afferent fiber,and sufferers from xerostomia.(2)2000–2009,during this stage,the frequency of keywords appeared in new research content such as randomized controlled trials of acupuncture and low back pain,but pain still dominated the main research content.From the perspective of intermediary centrality,along with the rise in randomized controlled trials,there were many important meta-analyses,as well as the management of acupuncture treatment.The main elements of the keyword cluster analysis included,for example,systematic review,randomized controlled pilot study,add-on therapy,brief overview,and ovarian morphology.(3)2010–2019,during this period,compared with the previous two stages,there was increased frequency of keywords,a growth in clinical randomized controlled trials,and distribution of centrality was evident in the emergence of acupuncture in care,osteoarthritis treatment,and breast cancer research.The keyword clustering covered,for example,neural specificity,inflammatory reaction,chronic pain,sleep pattern,and consort statement.Conclusion:This article summarizes the trend of development of acupuncture from 1990 to 2019 and compares the main research categories and hotspots in each of three different 10-year periods within this span,thereby helping elucidate the research direction within the field.

Using Fuzzy Theory in %GR&R and NDC of Measurement System Analysis

ISO9001:2000 and TS 16949 have become the major quality system management models in present traditional and Hi-tech industries. The Measurement System Analysis (MSA) Reference Manual, on the other hand, is one of the core tools in ISO/TS 16949. MSA aims to evaluate Gauge Repeatability and Reproducibility (GR&R) where the control, monitoring, and maintenance of the measurement process are required in measurement systems so that the measurement capability could be ensured under statistical control. An ideal measurement system should present the statistical characteristic of zero error on any measured product. Nevertheless, such an ideal measurement system hardly exists. Managers therefore have to adopt such measurement systems with unsatisfactory statistical characteristics. Traditional MSA indexes are constructed with definite observed values. Nevertheless, measurements with observed values are not entirely error-free. For this reason, this study proposes to research three cases in a case company and apply the integration of Fuzzy Theory and GR&R to discuss the differences in the evaluation index GR&R and the Number of Distinct Categories (NDC). Substituting fuzzy numbers for definite numbers found that the data of %GR&R were increased and NDC was decreased after fuzzification. Such results verify that the fuzzified %GR&R and NDC become stricter in the determination criterion. The research outcomes could assist the case company in improving the reference data of measurement systems and promoting the measurement quality.

Statistical Analysis of Network Based Issues and Their Impact on Social Computing Practices in Pakistan

Social computing and online groups have accompanied in a new age of the network, where information, networking and communication technologies are enabling systematized human efforts in primarily innovative ways. The social network communities working on various social network domains face different hurdles, including various new research studies and challenges in social computing. The researcher should try to expand the scope and establish new ideas and methods even from other disciplines to address the various challenges. This idea has diverse academic association, social links and technical characteristics. Thus it offers an ultimate opportunity for researchers to find out the issues in social computing and provide innovative solutions for conveying the information between social online groups on network computing. In this research paper we investigate the different issues in social media like users’ privacy and security, network reliabilities, and desire data availability on these social media, users’ awareness about the social networks and problems faced by academic domains. A huge number of users operated the social networks for retrieving and disseminating their real time and offline information to various places. The information may be transmitted on local networks or may be on global networks. The main concerns of users on social media are secure and fast communication channels. Facebook and YouTube both claimed for efficient security mechanism and fast communication channels for multimedia data. In this research a survey has been conducted in the most populated cities where a large number of Facebook and YouTube users have been found. During the survey several regular users indicate the certain potential issues continuously occurred on these social web sites interfaces, for example unwanted advertisement, fake IDS, uncensored videos and unknown friend request which cause the poor speed of channel communication, poor uploading and downloading data speed, channel interferences, security of data, privacy of users, integrity and reliability of user communication on these social sites. The major issues faced by active users of Facebook and YouTube have been highlighted in this research.