Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelli...Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.展开更多
In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Exis...In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.展开更多
Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-...Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.展开更多
文章基于CNKI、Web of Science两大数据库2005—2024年收录的图书馆学习空间研究文献,运用CiteSpace、Bicomb、SPSS软件进行可视化分析。结果显示,国外研究以技术驱动创新为主导,聚焦智能化技术与跨学科应用;而国内研究呈现理论创新与...文章基于CNKI、Web of Science两大数据库2005—2024年收录的图书馆学习空间研究文献,运用CiteSpace、Bicomb、SPSS软件进行可视化分析。结果显示,国外研究以技术驱动创新为主导,聚焦智能化技术与跨学科应用;而国内研究呈现理论创新与实践探索并重,通过大数据、AI等技术形成本土化解决方案。文章提出通过数字技术与人文关怀的平衡发展、国际化视野与本土化创新的有机结合、理论研究与实践应用的深度融合,为我国图书馆学习空间的研究提供借鉴与参考。展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditi...[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditional Chinese medicine shampoos,aiming to reveal the shifting patterns of core technological focuses at different stages and identify key future research directions.[Methods]Using a sample of 515 publications from CNKI journals between 2005 and 2025,CiteSpace 6.3.R1 was employed to construct multi-dimensional networks of authors,institutions,and keywords.Burst detection,keyword and cluster analyses,and time zone mapping were applied to track disciplinary dynamics.[Results]The analysis on the annual number of publications indicated that research on shampoo products underwent multiple phases including initial development,growth,fluctuation,peak,and adjustment from 2005 to 2025,with an overall upward trend.The significant differences between the collaboration network density of core authors and the strength of institutional cooperation indicated a need for academia to enhance cross-institutional collaborative innovation mechanisms.Keyword clustering analysis and co-occurrence mapping revealed that the research on traditional Chinese medicine shampoo products,known for their natural,mild,and non-irritating characteristics,demonstrates notable advantages in dandruff removal,anti-hair loss,hair growth,and scalp health management,which have become current research hotspots,providing data-driven decision-making support for technological upgrading and industry-academia-research integration in the field.[Conclusions]The bibliometric analysis based on 515 publications indicates that the overall development direction of shampoo products will comprehensively advance toward refined efficacy and personalized customization,greener and more natural ingredients,technological innovation and industry-academia-research collaboration,market segmentation and diversified development,as well as safety and efficacy evaluation.This study provides theoretical support for the innovation of specialized traditional Chinese medicine shampoo products.展开更多
The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“archit...The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“architectural space narrative”from the China National Knowledge Infrastructure(CNKI)over the past two decades,alongside an analysis and content identification of high-frequency keywords,clustering,timelines,and other knowledge graphs generated by CiteSpace,this study summarizes the evolution path of spatial narrative theory.This approach offers diverse perspectives for examining emerging trends and key issues within spatial narrative research.展开更多
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 a...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.展开更多
With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the resea...With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the research knowledge on this novel area,there is a need to have a bibliometric analysis on composite marine risers.A research methodology was developed whereby the data retrieval was from SCOPUS database from 1977–2023.Then,VOSviewer was used to visualize the knowledge maps.This study focuses on the progress made by conducting knowledge mapping and scientometric review on composite marine risers.This scientometric analysis on the subject shows current advances,geographical activities by countries,authorship records,collaborations,funders,affiiliations,co‑occurrences,and future research areas.It was observed that the research trends recorded the highest publication volume in the U.S.A.,but less cluster affiiliated,as it was followed by countries like the U.K.,China,Nigeria,Australia and Singapore.Also,thisfiield has more conference papers than journal papers due to the challenge of adaptability,acceptance,qualifiication,and application of composite marine risers in the marine industry.Hence,there is a need for more collaborations on composite marine risers and more funding to enhance the research trend.展开更多
Objective To study the key technologies in the field of ginsenosides and to offer a guide for the future development ginsenosides through the main path identification method based on genetic knowledge persistence algo...Objective To study the key technologies in the field of ginsenosides and to offer a guide for the future development ginsenosides through the main path identification method based on genetic knowledge persistence algorithm(GKPA).Methods The global ginsenoside invention authorized patents were used as the data source to construct a ginsenoside patent self-citation network,and to identify high knowledge persistent patents(HKPP)of ginsenoside technology based on the GKPA,and extract its high knowledge persistence main path(HKPMP).Finally,the genetic forward and backward path(GFBP)was used to search the nodes on the main path,and draw the genetic forward and backward main path(GFBMP)of ginsenoside technology.Results and Conclusion The algorithm was applied to the field of ginsenosides.The research results show the milestone patents in ginsenosides technology and the main evolution process of three key technologies,which points out the future direction for the technological development of ginsenosides.The results obtained by this algorithm are more interpretable,comprehensive and scientific.展开更多
Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal an...Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal and government priorities include retaining residents,preventing skilled worker outflow,and meeting the evolving needs of urban populations.The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk.Using advanced geoinformatics,GIS techniques,and an expert knowledge base,the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize the Scenario-based Urban Environment Quality Index across 87,905 standardized unit areas.The methodology incorporates comprehensive criteria aligned with existing urban planning frameworks and includes demographic targeting to address the city’s heterogeneous population.Validation against expert evaluations demonstrates high accuracy and consistency,while dynamicmodeling capabilities facilitate monitoring the effects of planned urban development initiatives.This approach bridges a critical gap in urban planning by providing granular,data-driven insights that reflect residents’real needs and spatial inequalities.The tool greatly benefits municipal authorities by enabling evidence-based prioritization of interventions,fostering inclusive and sustainable urban growth,and enhancing transparency and participatory governance.Its implementation as a no-code/low-code QGIS plugin ensures wide accessibility and practical application in strategic urban development,marking a significant advancement in urban environment quality assessment science and practice.展开更多
AIM:To track the knowledge structure,topics in focus,and trends in emerging research in pterygium in the past 20 y.METHODS:Base on the Web of Science Core Collection(Wo SCC),studies related to pterygium in the past 20...AIM:To track the knowledge structure,topics in focus,and trends in emerging research in pterygium in the past 20 y.METHODS:Base on the Web of Science Core Collection(Wo SCC),studies related to pterygium in the past 20 y from 2000-2019 have been included.With the help of VOSviewer software,a knowledge map was constructed and the distribution of countries,institutions,journals,and authors in the field of pterygium noted.Meanwhile,using cocitation analysis of references and co-occurrence analysis of keywords,we identified basis and hotspots,thereby obtaining an overview of this field.RESULTS:The search retrieved 1516 publications from Wo SCC on pterygium published between 2000 and 2019.In the past two decades,the annual number of publications is on the rise and fluctuated a little.Most productive institutions are from Singapore but the most prolific and active country is the United States.Journal Cornea published the most articles and Coroneo MT contributed the most publications on pterygium.From cooccurrence analysis,the keywords formed 3 clusters:1)surgical therapeutic techniques and adjuvant of pterygium,2)occurrence process and pathogenesis of pterygium,and 3)epidemiology,and etiology of pterygium formation.These three clusters were consistent with the clustering in co-citation analysis,in which Cluster 1 contained the most references(74 publications,47.74%),Cluster 2 contained 53 publications,accounting for 34.19%,and Cluster 3 focused on epidemiology with 18.06%of total 155 cocitation publications.CONCLUSION:This study demonstrates that the research of pterygium is gradually attracting the attention of scholars and researchers.The interaction between authors,institutions,and countries is lack of.Even though,the research hotspot,distribution,and research status in pterygium in this study could provide valuable information for scholars and researchers.展开更多
The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discove...The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.展开更多
In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF...In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.展开更多
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 ...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.展开更多
Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can a...Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.展开更多
Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submi...Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.展开更多
To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strate...To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strategy that is considered appropriate in the context of the increasing frequency of disasters caused by climate change.Previous research of knowledge co-production on climate change adaptation in Indonesia is insufficient,particularly at local level,so we examined the flow of climate change adaptation knowledge in the knowledge co-production process through climate field school(CFS)activities in this study.We interviewed 120 people living in Bulukumba Regency,South Sulawesi Province,Indonesia,involving 12 crowds including male and female farmers participated in CFS and not participated in CFS,local government officials,agriculture extension workers,agricultural traders,farmers’family members and neighbors,etc.In brief,the 12 groups of people mainly include two categories of people,i.e.,people involved in CFS activities and outside CFS.We applied descriptive method and Social network analysis(SNA)to determine how knowledge flow in the community network and which groups of actors are important for knowledge flow.The findings of this study reveal that participants in CFS activities convey the knowledge they acquired formally(i.e.,from TV,radio,government,etc.)and informally(i.e.,from market,friends,relatives,etc.)to other actors,especially to their families and neighbors.The results also show that the acquisition and sharing of knowledge facilitate the flow of climate change adaptation knowledge based on knowledge co-operation.In addition,the findings highlight the key role of actors in the knowledge transfer process,and key actors involved in disseminating information about climate change adaptation.To be specific,among all the actors,family member and neighbor of CFS actor are the most common actors in disseminating climate knowledge information and closest to other actors in the network;agricultural trader and family member of CFS actor collaborate most with other actors in the community network;and farmers participated in CFS,including those heads of farmer groups,agricultural extension workers,and local government officials are more willing to contact with other actors in the network.To facilitate the flow of knowledge on climate change adaptation,CFS activities should be conducted regularly and CFS models that fit the situation of farmers’vulnerability to climate change should be developed.展开更多
BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM ...BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023.By delving into these publications,the aim is to deter-mine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry.METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023,forming the dataset for the analysis.Employing advanced visualization techniques,this study mapped the knowledge domains represented in these papers.RESULTS The findings revealed a prevalent focus on key topics such as depression,mental health,anxiety,schizophrenia,and the impact of coronavirus disease 2019.Additionally,through keyword clustering,it became evident that these papers were predominantly focused on exploring mental health disorders,depression,anxiety,schizophrenia,and related factors.Noteworthy contributions hailed authors in regions such as China,the United Kingdom,United States,and Turkey.Particularly,the paper garnered the highest number of citations,while the American Psychiatric Association was the most cited reference.CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry.Additionally,there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.展开更多
基金support from the Scientific Funding for the Center of National Railway Intelligent Transportation System Engineering and Technology,China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ354)。
文摘Knowledge graphs,which combine structured representation with semantic modeling,have shown great potential in knowledge expression,causal inference,and automated reasoning,and are widely used in fields such as intelligent question answering,decision support,and fault diagnosis.As high-speed train systems become increasingly intelligent and interconnected,fault patterns have grown more complex and dynamic.Knowledge graphs offer a promising solution to support the structured management and real-time reasoning of fault knowledge,addressing key requirements such as interpretability,accuracy,and continuous evolution in intelligent diagnostic systems.However,conventional knowledge graph construction relies heavily on domain expertise and specialized tools,resulting in high entry barriers for non-experts and limiting their practical application in frontline maintenance scenarios.To address this limitation,this paper proposes a fault knowledge modeling approach for high-speed trains that integrates structured logic diagrams with knowledge graphs.The method employs a seven-layer logic structure—comprising fault name,applicable vehicles,diagnostic logic,signal parameters,verification conditions,fault causes,and emergency measures—to transform unstructured knowledge into a visual and hierarchical representation.A semantic mapping mechanism is then used to automatically convert logic diagrams into machine-interpretable knowledge graphs,enabling dynamic reasoning and knowledge reuse.Furthermore,the proposed method establishes a three-layer architecture—logic structuring,knowledge graph transformation,and dynamic inference—to bridge human-expert logic with machinebased reasoning.Experimental validation and system implementation demonstrate that this approach not only improves knowledge interpretability and inference precision but also significantly enhances modeling efficiency and system maintainability.It provides a scalable and adaptable solution for intelligent operation and maintenance platforms in the high-speed rail domain.
基金supported by the Global Research and Innovation Platform Fund for Scientific Big Data Transmission(Grant No.241711KYSB20180002)National Key Research and Development Project of China(Grant No.2019YFB1405801).
文摘In the age of information explosion and artificial intelligence, sentiment analysis tailored for the tobacco industry has emerged as a pivotal avenue for cigarette manufacturers to enhance their tobacco products. Existing solutions have primarily focused on intrinsic features within consumer reviews and achieved significant progress through deep feature extraction models. However, they still face these two key limitations: (1) neglecting the influence of fundamental tobacco information on analyzing the sentiment inclination of consumer reviews, resulting in a lack of consistent sentiment assessment criteria across thousands of tobacco brands;(2) overlooking the syntactic dependencies between Chinese word phrases and the underlying impact of sentiment scores between word phrases on sentiment inclination determination. To tackle these challenges, we propose the External Knowledge-enhanced Cross-Attention Fusion model, CITSA. Specifically, in the Cross Infusion Layer, we fuse consumer comment information and tobacco fundamental information through interactive attention mechanisms. In the Textual Attention Enhancement Layer, we introduce an emotion-oriented syntactic dependency graph and incorporate sentiment-syntactic relationships into consumer comments through a graph convolution network module. Subsequently, the Textual Attention Layer is introduced to combine these two feature representations. Additionally, we compile a Chinese-oriented tobacco sentiment analysis dataset, comprising 55,096 consumer reviews and 2074 tobacco fundamental information entries. Experimental results on our self-constructed datasets consistently demonstrate that our proposed model outperforms state-of-the-art methods in terms of accuracy, precision, recall, and F1-score.
基金The National Natural Science Foundation of China (No. 52302373, 52472317)the Natural Science Foundation of Beijing (No. L231023)the Beijing Nova Program (No. 20230484443)。
文摘Connected and autonomous vehicle formation(CAVF)technology is considerably important for improving transportation efficiency,optimizing traffic flow,and reduc-ing energy consumption.Despite the extensive research con-ducted on trajectory tracking control and other aspects of CAVF,the quality of the extant literature varies consider-ably,and research content remains scattered.To better pro-mote the sustainable and healthy development of the CAVF field,this paper employs the mapping knowledge domain(MKD)methodology to comprehensively review and visual-ize the current research status in this domain.Based on this review,research themes,hotspots,research challenges,and future development directions are proposed.The findings suggest that the research on CAVF can be categorized into three primary developmental stages.China and the United States are the primary countries conducting CAVF research.There is a positive correlation between economic develop-ment and the generation of scientific research outcomes.Re-search institutions are predominantly concentrated in univer-sities.The field exhibits significant interdisciplinary and inte-gration characteristics,forming key research personnel and teams.It is expected that future research will concentrate on topics such as deep learning,trajectory optimization,energy management strategy,mixed vehicle platoon,and other re-lated subjects.Research on cognition-driven intelligent for-mation decision-making mechanisms,resilience-oriented for-mation safety assurance systems,multiobjective collabora-tive formation optimization strategies,and digital twin-driven formation system validation platforms represents key future development directions.
文摘文章基于CNKI、Web of Science两大数据库2005—2024年收录的图书馆学习空间研究文献,运用CiteSpace、Bicomb、SPSS软件进行可视化分析。结果显示,国外研究以技术驱动创新为主导,聚焦智能化技术与跨学科应用;而国内研究呈现理论创新与实践探索并重,通过大数据、AI等技术形成本土化解决方案。文章提出通过数字技术与人文关怀的平衡发展、国际化视野与本土化创新的有机结合、理论研究与实践应用的深度融合,为我国图书馆学习空间的研究提供借鉴与参考。
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金Supported by Undergraduate Innovation and Entrepreneurship Program of Faculty of Chinese Medicine Science of Guangxi University of Chinese Medicine (Autonomous Region Level) (S202413643051)Guangxi First-class Discipline Construction Project (GJKY[2022]1).
文摘[Objectives]Based on bibliometric methods,the evolution path and knowledge structure of shampoo product research in China were systematically analyzed,with a focus on the composition,efficacy,and mechanisms of traditional Chinese medicine shampoos,aiming to reveal the shifting patterns of core technological focuses at different stages and identify key future research directions.[Methods]Using a sample of 515 publications from CNKI journals between 2005 and 2025,CiteSpace 6.3.R1 was employed to construct multi-dimensional networks of authors,institutions,and keywords.Burst detection,keyword and cluster analyses,and time zone mapping were applied to track disciplinary dynamics.[Results]The analysis on the annual number of publications indicated that research on shampoo products underwent multiple phases including initial development,growth,fluctuation,peak,and adjustment from 2005 to 2025,with an overall upward trend.The significant differences between the collaboration network density of core authors and the strength of institutional cooperation indicated a need for academia to enhance cross-institutional collaborative innovation mechanisms.Keyword clustering analysis and co-occurrence mapping revealed that the research on traditional Chinese medicine shampoo products,known for their natural,mild,and non-irritating characteristics,demonstrates notable advantages in dandruff removal,anti-hair loss,hair growth,and scalp health management,which have become current research hotspots,providing data-driven decision-making support for technological upgrading and industry-academia-research integration in the field.[Conclusions]The bibliometric analysis based on 515 publications indicates that the overall development direction of shampoo products will comprehensively advance toward refined efficacy and personalized customization,greener and more natural ingredients,technological innovation and industry-academia-research collaboration,market segmentation and diversified development,as well as safety and efficacy evaluation.This study provides theoretical support for the innovation of specialized traditional Chinese medicine shampoo products.
基金Sponsored by General Project of the Modern Design and Culture Research Center,Sichuan Provincial Key Research Base for Social Sciences(MD24E019)General Project of the Digital Culture and Media Research Base,Sichuan Provincial Key Research Base for Philosophy and Social Sciences(SC23DCMB006).
文摘The theory of spatial narrative enhances the connotation of architectural design and broadens the interaction between architecture and its users.Drawing upon a literature review of relevant keywords related to“architectural space narrative”from the China National Knowledge Infrastructure(CNKI)over the past two decades,alongside an analysis and content identification of high-frequency keywords,clustering,timelines,and other knowledge graphs generated by CiteSpace,this study summarizes the evolution path of spatial narrative theory.This approach offers diverse perspectives for examining emerging trends and key issues within spatial narrative research.
基金supported by the National Natural Science Foundation of China,No.81471308(to JL)the Stem Cell Clinical Research Project in China,No.CMR-20161129-1003(to JL)the Innovation Technology Funding of Dalian in China,No.2018J11CY025(to JL)
文摘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.
基金support of the School of Engineering,Lancaster University,UK,for the Engineering Department Studentship as well as the Engineering and Physical Sciences Research Council(EPSRC)’s Doctoral Training Centre(DTC)。
文摘With the increasing exploration of oil and gas into deep waters,the necessity for material development increases for lighter conduits such as composite marine risers,in the oil and gas industry.To understand the research knowledge on this novel area,there is a need to have a bibliometric analysis on composite marine risers.A research methodology was developed whereby the data retrieval was from SCOPUS database from 1977–2023.Then,VOSviewer was used to visualize the knowledge maps.This study focuses on the progress made by conducting knowledge mapping and scientometric review on composite marine risers.This scientometric analysis on the subject shows current advances,geographical activities by countries,authorship records,collaborations,funders,affiiliations,co‑occurrences,and future research areas.It was observed that the research trends recorded the highest publication volume in the U.S.A.,but less cluster affiiliated,as it was followed by countries like the U.K.,China,Nigeria,Australia and Singapore.Also,thisfiield has more conference papers than journal papers due to the challenge of adaptability,acceptance,qualifiication,and application of composite marine risers in the marine industry.Hence,there is a need for more collaborations on composite marine risers and more funding to enhance the research trend.
文摘Objective To study the key technologies in the field of ginsenosides and to offer a guide for the future development ginsenosides through the main path identification method based on genetic knowledge persistence algorithm(GKPA).Methods The global ginsenoside invention authorized patents were used as the data source to construct a ginsenoside patent self-citation network,and to identify high knowledge persistent patents(HKPP)of ginsenoside technology based on the GKPA,and extract its high knowledge persistence main path(HKPMP).Finally,the genetic forward and backward path(GFBP)was used to search the nodes on the main path,and draw the genetic forward and backward main path(GFBMP)of ginsenoside technology.Results and Conclusion The algorithm was applied to the field of ginsenosides.The research results show the milestone patents in ginsenosides technology and the main evolution process of three key technologies,which points out the future direction for the technological development of ginsenosides.The results obtained by this algorithm are more interpretable,comprehensive and scientific.
基金funded by theMinistry of Science and Higher Education of Russia,R&D project number FEFS-2026-0003.
文摘Urban environmental quality research is crucial,as cities become competitive centers concentrating human talent,industrial activity,and financial resources,contributing significantly to national economies.Municipal and government priorities include retaining residents,preventing skilled worker outflow,and meeting the evolving needs of urban populations.The study presents the development and application of a scenario-based spatial analysis tool for assessing urban environmental quality at a detailed spatial scale within the city of Novosibirsk.Using advanced geoinformatics,GIS techniques,and an expert knowledge base,the tool integrates diverse thematic data layers with user-defined scenarios to compute and visualize the Scenario-based Urban Environment Quality Index across 87,905 standardized unit areas.The methodology incorporates comprehensive criteria aligned with existing urban planning frameworks and includes demographic targeting to address the city’s heterogeneous population.Validation against expert evaluations demonstrates high accuracy and consistency,while dynamicmodeling capabilities facilitate monitoring the effects of planned urban development initiatives.This approach bridges a critical gap in urban planning by providing granular,data-driven insights that reflect residents’real needs and spatial inequalities.The tool greatly benefits municipal authorities by enabling evidence-based prioritization of interventions,fostering inclusive and sustainable urban growth,and enhancing transparency and participatory governance.Its implementation as a no-code/low-code QGIS plugin ensures wide accessibility and practical application in strategic urban development,marking a significant advancement in urban environment quality assessment science and practice.
基金the National Natural Science Foundation of China(No.81870644)。
文摘AIM:To track the knowledge structure,topics in focus,and trends in emerging research in pterygium in the past 20 y.METHODS:Base on the Web of Science Core Collection(Wo SCC),studies related to pterygium in the past 20 y from 2000-2019 have been included.With the help of VOSviewer software,a knowledge map was constructed and the distribution of countries,institutions,journals,and authors in the field of pterygium noted.Meanwhile,using cocitation analysis of references and co-occurrence analysis of keywords,we identified basis and hotspots,thereby obtaining an overview of this field.RESULTS:The search retrieved 1516 publications from Wo SCC on pterygium published between 2000 and 2019.In the past two decades,the annual number of publications is on the rise and fluctuated a little.Most productive institutions are from Singapore but the most prolific and active country is the United States.Journal Cornea published the most articles and Coroneo MT contributed the most publications on pterygium.From cooccurrence analysis,the keywords formed 3 clusters:1)surgical therapeutic techniques and adjuvant of pterygium,2)occurrence process and pathogenesis of pterygium,and 3)epidemiology,and etiology of pterygium formation.These three clusters were consistent with the clustering in co-citation analysis,in which Cluster 1 contained the most references(74 publications,47.74%),Cluster 2 contained 53 publications,accounting for 34.19%,and Cluster 3 focused on epidemiology with 18.06%of total 155 cocitation publications.CONCLUSION:This study demonstrates that the research of pterygium is gradually attracting the attention of scholars and researchers.The interaction between authors,institutions,and countries is lack of.Even though,the research hotspot,distribution,and research status in pterygium in this study could provide valuable information for scholars and researchers.
文摘The advent of the age of Information shifts the environment we live in from off-line to on-line. The prospect of Collective Intelligence (CI) is promising. Based on this background, the aim of this paper is to discover the emergence mechanism and influence factors of CI in knowledge communities using the method of quantitative and qualitative analysis. On the basis of the previous research work, our model theorizes that the two dimensions of social network (i.e., interactive network structure and participant’s characteristics) affect two references of effectiveness (i.e. group knowledge production and participation of group decision). And this hypothetical model is validated with simulation data from “Zhihu” community. Our model has been useful since it offers some inspirations and directions to promote the level of CI in knowledge communities.
文摘In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic.
文摘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.
基金financial supports from National Natural Science Foundation of China(No.62205172)Huaneng Group Science and Technology Research Project(No.HNKJ22-H105)Tsinghua University Initiative Scientific Research Program and the International Joint Mission on Climate Change and Carbon Neutrality。
文摘Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.
文摘Purpose: The evolution of the socio-cognitive structure of the field of knowledge management(KM) during the period 1986–2015 is described. Design/methodology/approach: Records retrieved from Web of Science were submitted to author co-citation analysis(ACA) following a longitudinal perspective as of the following time slices: 1986–1996, 1997–2006, and 2007–2015. The top 10% of most cited first authors by sub-periods were mapped in bibliometric networks in order to interpret the communities formed and their relationships.Findings: KM is a homogeneous field as indicated by networks results. Nine classical authors are identified since they are highly co-cited in each sub-period, highlighting Ikujiro Nonaka as the most influential authors in the field. The most significant communities in KM are devoted to strategic management, KM foundations, organisational learning and behaviour, and organisational theories. Major trends in the evolution of the intellectual structure of KM evidence a technological influence in 1986–1996, a strategic influence in 1997–2006, and finally a sociological influence in 2007–2015.Research limitations: Describing a field from a single database can offer biases in terms of output coverage. Likewise, the conference proceedings and books were not used and the analysis was only based on first authors. However, the results obtained can be very useful to understand the evolution of KM research.Practical implications: These results might be useful for managers and academicians to understand the evolution of KM field and to(re)define research activities and organisational projects.Originality/value: The novelty of this paper lies in considering ACA as a bibliometric technique to study KM research. In addition, our investigation has a wider time coverage than earlier articles.
文摘To increase the resilience of farmers’livelihood systems,detailed knowledge of adaptation strategies for dealing with the impacts of climate change is required.Knowledge co-production approach is an adaptation strategy that is considered appropriate in the context of the increasing frequency of disasters caused by climate change.Previous research of knowledge co-production on climate change adaptation in Indonesia is insufficient,particularly at local level,so we examined the flow of climate change adaptation knowledge in the knowledge co-production process through climate field school(CFS)activities in this study.We interviewed 120 people living in Bulukumba Regency,South Sulawesi Province,Indonesia,involving 12 crowds including male and female farmers participated in CFS and not participated in CFS,local government officials,agriculture extension workers,agricultural traders,farmers’family members and neighbors,etc.In brief,the 12 groups of people mainly include two categories of people,i.e.,people involved in CFS activities and outside CFS.We applied descriptive method and Social network analysis(SNA)to determine how knowledge flow in the community network and which groups of actors are important for knowledge flow.The findings of this study reveal that participants in CFS activities convey the knowledge they acquired formally(i.e.,from TV,radio,government,etc.)and informally(i.e.,from market,friends,relatives,etc.)to other actors,especially to their families and neighbors.The results also show that the acquisition and sharing of knowledge facilitate the flow of climate change adaptation knowledge based on knowledge co-operation.In addition,the findings highlight the key role of actors in the knowledge transfer process,and key actors involved in disseminating information about climate change adaptation.To be specific,among all the actors,family member and neighbor of CFS actor are the most common actors in disseminating climate knowledge information and closest to other actors in the network;agricultural trader and family member of CFS actor collaborate most with other actors in the community network;and farmers participated in CFS,including those heads of farmer groups,agricultural extension workers,and local government officials are more willing to contact with other actors in the network.To facilitate the flow of knowledge on climate change adaptation,CFS activities should be conducted regularly and CFS models that fit the situation of farmers’vulnerability to climate change should be developed.
基金Supported by Philosophy and Social Science Foundation of Hunan Province,China,No.23YBJ08China Youth&Children Research Association,No.2023B01Research Project on the Theories and Practice of Hunan Women,No.22YB06.
文摘BACKGROUND In the rapidly evolving landscape of psychiatric research,2023 marked another year of significant progress globally,with the World Journal of Psychiatry(WJP)experiencing notable expansion and influence.AIM To conduct a comprehensive visualization and analysis of the articles published in the WJP throughout 2023.By delving into these publications,the aim is to deter-mine the valuable insights that can illuminate pathways for future research endeavors in the field of psychiatry.METHODS A selection process led to the inclusion of 107 papers from the WJP published in 2023,forming the dataset for the analysis.Employing advanced visualization techniques,this study mapped the knowledge domains represented in these papers.RESULTS The findings revealed a prevalent focus on key topics such as depression,mental health,anxiety,schizophrenia,and the impact of coronavirus disease 2019.Additionally,through keyword clustering,it became evident that these papers were predominantly focused on exploring mental health disorders,depression,anxiety,schizophrenia,and related factors.Noteworthy contributions hailed authors in regions such as China,the United Kingdom,United States,and Turkey.Particularly,the paper garnered the highest number of citations,while the American Psychiatric Association was the most cited reference.CONCLUSION It is recommended that the WJP continue in its efforts to enhance the quality of papers published in the field of psychiatry.Additionally,there is a pressing need to delve into the potential applications of digital interventions and artificial intelligence within the discipline.
