Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

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.

Research fronts and researchers of World Journal of Psychiatry in 2023: A visualization and analysis of mapping knowledge domains

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.

ISI Web of Knowledge新增分析工具Results Analysis探讨——BIOSIS Previews检索实践

针对检索结果的信息分析和信息挖掘的分析工具Results Analysis是ISI Web of Knowledge于2004年新增的服务功能。以BIOSIS Previews为检索实例,详细探讨Results Analysis分析工具的使用方法和功能,并提出改进的建议。

Effects of Health Education with Problem-Based Learning Approaches on the Knowledge, Attitude, Practice and Coping Skills of Women with High-Risk Pregnancies in Plateau Areas

Objective: Given the unique cultural background, way of life, and physical environment of the Tibetan Plateau, this study aims to investigate the effects of health education using problem-based learning (PBL) approaches on the knowledge, attitude, practice, and coping skills of women with high-risk pregnancies in this region. Methods: 76 high-risk pregnancy cases were enrolled at Tibet’s Linzhi People’s Hospital between September 2023 and April 2024. 30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education. 46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches. Two groups’ performance on their health knowledge, attitude, practice and coping skills before and after interventions were evaluated, and patient satisfaction were measured at the end of the study. Results: There was no statistical significance (P P P Conclusions: Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge, attitude and practice and healthier coping skills. Also, it can improve patient sanctification.

基于Knowledge-Analysis的军事知识联通研究

军事专业知识在军事决策系统、军事智能教学系统、军事知识查询系统等高技术产品中具有重要的作用。知识库是机器智能的重要来源,所以有必要建立一个完善、协调、具有良好联通性的大型军事知识库,而大量实践证明计算机真正具有智能不仅需要大型知识库,而且更需要知识间广泛的联通。本文主要讨论军事概念联通。所谓概念联通是指通过关系链或属性或属性值将两个概念联系起来的过程。概念联通的目的之一就是通过概念联通帮助发现不同知识源的知识之间的不一致性,尽可能保证所获取知识的完备性和精确性,建立一个大型的军事专业知识库。

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

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

Bibliometric analysis and mapping knowledge domain of pterygium:2000-2019

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.

The Influence Factors of Collective Intelligence Emergence in Knowledge Communities Based on Social Network Analysis

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.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

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.

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.

Evolution of the Socio-cognitive Structure of Knowledge Management(1986–2015): An Author Co-citation Analysis

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.

Knowledge flow analysis of knowledge co-production-based climate change adaptation for lowland rice farmers in Bulukumba Regency,Indonesia

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.

Research Progress of Garden Microclimate in Recent 15 Years in China:Knowledge Graph Analysis Based on Citespace

Taking CNKI database as the data source and measuring visual map analysis tool Citespace as auxiliary tool,domestic 843 articles in recent 15 years are analyzed,and visualization maps such as time line analysis,institutional cooperation network analysis,author collaboration network analysis,keyword co-occurrence,and emergent words analysis are drawn.Combined with the literature content analysis,four hot spots in the research field of landscape architecture microclimate in China are obtained,namely ENVI-MET,comfort,design strategy and urban green space.The research trend is thermal comfort,human body comfort and winter city.The research results can provide reference for the research on domestic garden microclimate.

Visualization Analysis of Researches on Digital Storytelling Based on Mapping Knowledge Domain

The development of the information age and globalization has challenged the training of technical talents in the 21st century, and the information media and technical skills are becoming increasingly important. As a creative sharing form of multimedia, the digital storytelling is being concerned by more and more educators because of its discipline applicability and media technology enhancing ability. In this study, the information visualization software, i.e. CiteSpace was applied to visualize and analyze the researches on digital storytelling from the aspects of key articles and citation hotspots, and make a review on the research status of the digital storytelling in the education fields, such as promoting language learning, and helping students develop the 21 st century skills.

Visualization Analysis for China’s Business Schools Accreditation Studies Based on Knowledge Mapping

The aim of the paper is to identify and explore leading thematic areas within the research field related to Business Schools Accreditation in China.Based on data from the China National Knowledge Infrastructure(CNKI)database,the keywords frequency was analyzed,and the theory of mapping knowledge domain was used to visualize the keywords co-occurrence network to make further research of the heated issues.The findings indicate that the research scope involved in business school accreditation research is broad,and research content focus on teaching,management,talent cultivation.According to the results of keywords co-occurrence analysis of different stages,AACSB,AACSB Accreditation,international accreditation,business school,AOL,internationalization,accreditation are the most important issues to business school research in China,given their position and role in the research network.The paper generates the added value mainly from the point of view of theory development.

Visualization Analysis of Research on Student Classroom Engagement by Knowledge Mapping

The aim of the paper is to identify and explore leading development situation within the research field related to student classroom engagement.Using knowledge mapping tools,this paper conducted the visualization analysis on internationalliteraturein relation to student classroom engagementfrom the Web of Science databases.By combination of the statistical data and visualizationmapping,this paper studied on the research relationship networks and status for the co-authors'countries/institutions,co-citation documents/journalsand co-occurring keywordsbased on the sample datafromliterature.The results indicate that the number ofpublished papershasincreased obviously and expanded graduallysince 2000.Main countries and institutions of researches were the UnitedStates,Australia,United Kingdom,Canada and China in order,and these countries had intense collaboration.The theoretical basis of the research come from education and teaching,psychology.The hotspots of researches fromthe global literature coveredvarious fields including achievement,motivation,behavior,education,performance.As a result of the multidisciplinary integration,many relevant internationalresearch constantly expanded the research scopes,which promoted the combination and development of theories.

Research Hotspots and Trends Analysis of Real-World Data Based on Social Network Analysis and Knowledge Graph

Objective To study the research status,research hotspots and development trends in the field of real-world data(RWD)through social network analysis and knowledge graph analysis.Methods RWD of the past 10 years were retrieved,and literature metrological analysis was made by using UCINET and CiteSpace from CNKI.Results and Conclusion The frequency and centrality of related keywords such as real-world study,hospital information system(HIS),drug combination,data mining and TCM are high.The clusters labeled as clinical medication and RWD contain more keywords.In recent 4 years,there are more articles involving the keywords of data specification,data authenticity,data security and information security.Among them,compound Kushen injection,HIS database and RWD are the top three keywords.It is a long-term research hotspot for Chinese and western medicine to use HIS to study clinical medication,clinical characteristics,diseases and injections.Besides,the research of RWD database has changed from construction to standardized collection and governance,which can make RWD effective.Data authenticity,data security and information security will become the new hotspots in the research of RWD.

Visual Analysis of Knowledge Map of Traditional Chinese Medicine in Prevention and Treatment of Pulmonary Interstitial Fibrosis Based on CiteSpace

Objective: To analyze the relevant research literature on the prevention and treatment of pulmonary interstitial fibrosis with traditional Chinese medicine (TCM), understand the current research status, hot spots and future development trend in this field, and provide basis and feasible suggestions for further research in this field. Methods: The journal literatures related to the prevention and treatment of pulmonary interstitial fibrosis with TCM in recent 20 years in CNKI database were searched and passed through CiteSpace 5.8.R3 generates the knowledge map of relevant literature authors, document issuing institutions and keywords, and makes visual analysis. Results: A total of 1,576 documents were included, and the annual number of documents showed a fluctuating upward trend, forming a relatively stable research team represented by authors such as LYU Xiaodong, PANG Lijian and LIU Chuang;According to the atlas of document issuing institutions, Shandong University of Traditional Chinese Medicine and its affiliated hospitals ranked first in the number of documents issued, and the cooperation between institutions is dominated by the University of traditional Chinese medicine and its affiliated hospitals;Keyword cluster analysis shows that a large number of studies have been carried out in the field of etiology and pathogenesis, TCM compound, clinic and experiment. Conclusion: The research on the prevention and treatment of pulmonary interstitial fibrosis with TCM has a high degree of attention, but the cooperation network between the research authors and institutions needs to be strengthened. The research on the pathogenesis and improving the quality of life of patients is the trend of development in the future.

“Products-Knowledge” Two-Dimensional Increment Model of Fashion Industry Value Chain Based on Industry Analysis

Fashion industry has a complex characteristic for it spans the first, second, and third industries. In addition, the characteristic of creative industry has high value-added for its knowledge outputting, which makes the traditional value-added analysis based on supply chain not easy and good enough to interpret its industry value-added features. From the perspective of "products-knowledge" two-dimensional analysis,a fashion industry value chain increment model is built,by simulating the process of "product flow" and "information flow" value-added. The fashion industry value chain increment model provides an effective way for the enterprise strategy formulation and production strategy adjustment.

Knowledge and skills of neonatal resuscitation of health professionals at a university teaching hospital of Northwest Ethiopia

BACKGROUND:Competency in neonatal resuscitation is critical in the delivery rooms,neonatology units and pediatrics intensive care units to ensure the safety and health of neonates. Each year,millions of babies do not breathe immediately at birth,and among them the majority require basic neonatal resuscitation. Perinatal asphyxia is a major contributor to neonatal deaths worldwide in resource-limited settings. Neonatal resuscitation is effective only when health professionals have sufficient knowledge and skills. But malpractices by health professionals are frequent in the resuscitation of neonates. The present study was to assess the knowledge and skills of health professionals about neonatal resuscitation.METHODS:An institution based cross-sectional study was conducted in our hospital from February15 to April 30,2014. All nurses,midwives and residents from obstetrics-gynecology(obsgyn),midwifery and pediatric departments were included. The mean scores of knowledge and skills were compared for sex,age,type of profession,qualification,year of service and previous place of work of the participants by using Student's t test and ANOVA with Scheffe's test. A P value <0.05 was considered statistically significant.RESULTS:One hundred and thirty-five of 150 participants were included in this study with a response rate of 90.0%. The overall mean scores of knowledge and skills of midwives,nurses and residents were 19.9(SD=3.1) and 6.8(SD=3.9) respectively. The mean knowledge scores of midwives,nurses,pediatric residents and obs-gyn residents were 19.7(SD=3.03),20.2(SD=2.94),19.7(SD=4.4) and 19.6(SD=3.3) respectively. Whereas the mean scores of skills of midwives,nurses,pediatric residents and obs-gyn residents were 7.1(SD=4.17),6.7(SD=3.75),5.7(SD=4.17) and 6.6(SD=3.97) respectively.CONCLUSIONS:The knowledge and skills of midwives,nurses and residents about neonatal resuscitation were substandardized. Training of neonatal resuscitation for midwives,nurses and residents should be emphasized.