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

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

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

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

New density clustering-based approach for failure mode and effect analysis considering opinion evolution and bounded confidence

Failure mode and effect analysis(FMEA)is a preven-tative risk evaluation method used to evaluate and eliminate fail-ure modes within a system.However,the traditional FMEA method exhibits many deficiencies that pose challenges in prac-tical applications.To improve the conventional FMEA,many modified FMEA models have been suggested.However,the majority of them inadequately address consensus issues and focus on achieving a complete ranking of failure modes.In this research,we propose a new FMEA approach that integrates a two-stage consensus reaching model and a density peak clus-tering algorithm for the assessment and clustering of failure modes.Firstly,we employ the interval 2-tuple linguistic vari-ables(I2TLVs)to express the uncertain risk evaluations provided by FMEA experts.Then,a two-stage consensus reaching model is adopted to enable FMEA experts to reach a consensus.Next,failure modes are categorized into several risk clusters using a density peak clustering algorithm.Finally,the proposed FMEA is illustrated by a case study of load-bearing guidance devices of subway systems.The results show that the proposed FMEA model can more easily to describe the uncertain risk information of failure modes by using the I2TLVs;the introduction of an endogenous feedback mechanism and an exogenous feedback mechanism can accelerate the process of consensus reaching;and the density peak clustering of failure modes successfully improves the practical applicability of FMEA.

Analysis of Patents Related to COVID-19-Based on Patent Clustering Model in Specific Fields

Objective To improve the efficiency of patent clustering related to COVID-19 through the topic extraction algorithm and BERT model,and to help researchers understand the patent applications for novel corona virus.Methods The weights of topic vector and BERT model vector were adjusted by cross-entropy loss algorithm to obtain joint vector.Then,k-means++algorithm was used for patent clustering after dimension reduction.Results and Conclusion The model was applied to patents for corona virus drugs,and five clustering topics were generated.Through comparison,it is proved that the clustering results of this model are more centralized and the differentiation between clusters is significant.The five clusters generated are visually analyzed to reveal the development status of patents for corona virus drugs.

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.

Genetic Diversity and Clustering Analysis of 48Cultivars of Olea euyopaea L.

Inter-simple sequence repeat（ISSR） molecular markers were applied to analyze the genetic diversity and clustering of 48 introduced and bred cultivars of Olea euyopaea L. Totally 106 DNA bands were amplified by 11 screened primers, including 99 polymorphic bands; the percentage of polymorphic loci was 93.40%, indicating a rich genetic diversity in Olea euyopaea L. germplasm resources. Based on Nei＇s genetic distances between various cultivars, a dendrogram of 48 cultivars of Olea euyopaea L. was constructed using unweighted pair-group（UPMGA）method,which showed that 48 cultivars were clustered into four main categories; 84.6% of native cultivars were clustered into two categories; most of introduced cultivars were clustered based on their sources and main usages but not on their geographic origins. This study will provide references for the utilization and further genetic improvement of Olea euyopaea L. germplasm resources.

Clustering analysis algorithm for security supervising data based on semantic description in coal mines

In order to mine production and security information from security supervising data and to ensure security and safety involved in production and decision-making,a clustering analysis algorithm for security supervising data based on a semantic description in coal mines is studied.First,the semantic and numerical-based hybrid description method of security supervising data in coal mines is described.Secondly,the similarity measurement method of semantic and numerical data are separately given and a weight-based hybrid similarity measurement method for the security supervising data based on a semantic description in coal mines is presented.Thirdly,taking the hybrid similarity measurement method as the distance criteria and using a grid methodology for reference,an improved CURE clustering algorithm based on the grid is presented.Finally,the simulation results of a security supervising data set in coal mines validate the efficiency of the algorithm.

Clustering Analysis on Large Grained Brassica napus Materials Based on the Optimized ACGM Markers

[Objective] This study aimed to develop ACGM markers for the clustering analysis of large grained Brassica napus materials. [Method] A total of 44 pairs of ACGM primers were designed according to 18 genes related to Arabidopsis grain development and their homologous rape EST sequences. After electrophoresis, 18 pairs of ACGM primers were selected for the clustering analysis of 16 larger grained samples and four fine grained samples of rapeseed. [Result] PCR result showed that 2-6 specific bands were respectively amplified by each pair of primes, and all the bands were polymorphic and repeatable, suggesting that the optimized ACGM markers were useful for clustering analysis of B. napus species. Clustering analysis revealed that the 20 rapeseed samples were divided into three clusters A, B, and C at similarity coefficient 0.6. Then, the clusters A and B were further divided into five sub clusters A1, A2, A3, B1 and B2 at similarity coefficient 0.67. [Conclusion] This study will provide theoretical and practical values for rape breeding.

Study on Trace Elements in Rehmannia glutinosa Libosch. by Principal Component Analysis and Clustering Analysis

[Objective] This study aimed to investigate the trace elements in Rehman- nia glutinosa Libosch. by using principal component analysis and clustering analysis. [Method] Principal component analysis and clustering analysis of R. glutinosa medicinal materials from different sources were conducted with contents of six trace elements as indices. [Result] The principal component analysis could comprehen- sively evaluate the quality of R. glutinosa samples with objective results which was consistent with the results of clustering analysis. [Conclusion] Principal component analysis and clustering analysis methods can be used for the quality evaluation of Chinese medicinal materials with multiple indices.

Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs

Efficient iterative unsupervised machine learning involving probabilistic clustering analysis with the expectation-maximization(EM)clustering algorithm is applied to categorize reservoir facies by exploiting latent and observable well-log variables from a clastic reservoir in the Majnoon oilfield,southern Iraq.The observable well-log variables consist of conventional open-hole,well-log data and the computer-processed interpretation of gamma rays,bulk density,neutron porosity,compressional sonic,deep resistivity,shale volume,total porosity,and water saturation,from three wells located in the Nahr Umr reservoir.The latent variables include shale volume and water saturation.The EM algorithm efficiently characterizes electrofacies through iterative machine learning to identify the local maximum likelihood estimates(MLE)of the observable and latent variables in the studied dataset.The optimized EM model developed successfully predicts the core-derived facies classification in two of the studied wells.The EM model clusters the data into three distinctive reservoir electrofacies(F1,F2,and F3).F1 represents a gas-bearing electrofacies with low shale volume(Vsh)and water saturation(Sw)and high porosity and permeability values identifying it as an attractive reservoir target.The results of the EM model are validated using nuclear magnetic resonance(NMR)data from the third studied well for which no cores were recovered.The NMR results confirm the effectiveness and accuracy of the EM model in predicting electrofacies.The utilization of the EM algorithm for electrofacies classification/cluster analysis is innovative.Specifically,the clusters it establishes are less rigidly constrained than those derived from the more commonly used K-means clustering method.The EM methodology developed generates dependable electrofacies estimates in the studied reservoir intervals where core samples are not available.Therefore,once calibrated with core data in some wells,the model is suitable for application to other wells that lack core data.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Bibliometric analysis of ChatGPT and plastic surgery research:Insights from diverse search strategies and co-word analysis

Background:The rise of artificial intelligence in healthcare,particularly the development of large language models like ChatGPT,has opened new avenues for innovation in medical fields,including plastic surgery.ChatGPT offers potential applications in patient education,surgical planning,and decision-making support,making it an important research subject.However,there has been limited investigation into its impact on plastic surgery.The objective of this study was to investigate the progress of research on ChatGPT and plastic surgery,focusing on key contributors and emerging topics within the field.Methods:Five distinct search strategies were employed to analyze relevant publications from the Scopus database.Results:The analysis identified and presented the top authors,universities,countries,sponsors,and journals(within each search strategy).The co-authorship networks of authors,universities,and countries are graphically presented.The authors’performance was depicted by various indicators,such as total publications,citations,hindex,g-index,and m-index.A co-word analysis revealed the focus of the papers,which were presented in 15 groups.This multifaceted approach provides a detailed understanding of key themes in the field.Conclusion:This report offers a comprehensive overview of the current state of research at the intersection of ChatGPT and plastic surgery.

Comparative Analysis of Differences among Northern,Jiangnan,and Lingnan Classical Private Gardens Using Principal Component Cluster Method

This paper investigates the design essence of Chinese classical private gardens,integrating their design elements and fundamental principles.It systematically analyzes the unique characteristics and differences among classical private gardens in the Northern,Jiangnan,and Lingnan regions.The study examines nine classical private gardens from Northern China,Jiangnan,and Lingnan by utilizing the advanced tool of principal component cluster analysis.Based on literature analysis and field research,273 variables were selected for principal component analysis,from which four components with higher contribution rates were chosen for further study.Subsequently,we employed clustering analysis techniques to compare the differences among the three types of gardens.The results reveal that the first principal component effectively highlights the differences between Jiangnan and Lingnan private gardens.The second principal component serves as the key to defining the types of Northern private gardens and distinguishing them from the other two types,and the third principal component indicates that Lingnan private gardens can be categorized into two distinct types as well.

Clustering Structure Analysis in Time-Series Data With Density-Based Clusterability Measure

Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.

Identifying a competency improvement strategy for infection prevention and control professionals:A rapid systematic review and cluster analysis

Remarkable progress has been made in infection prevention and control(IPC)in many countries,but some gaps emerged in the context of the coronavirus disease 2019(COVID-19)pandemic.Core capabilities such as standard clinical precautions and tracing the source of infection were the focus of IPC in medical institutions during the pandemic.Therefore,the core competences of IPC professionals during the pandemic,and how these contributed to successful prevention and control of the epidemic,should be studied.To investigate,using a systematic review and cluster analysis,fundamental improvements in the competences of infection control and prevention professionals that may be emphasized in light of the COVID-19 pandemic.We searched the PubMed,Embase,Cochrane Library,Web of Science,CNKI,WanFang Data,and CBM databases for original articles exploring core competencies of IPC professionals during the COVID-19 pandemic(from January 1,2020 to February 7,2023).Weiciyun software was used for data extraction and the Donohue formula was followed to distinguish high-frequency technical terms.Cluster analysis was performed using the within-group linkage method and squared Euclidean distance as the metric to determine the priority competencies for development.We identified 46 studies with 29 high-frequency technical terms.The most common term was“infection prevention and control training”(184 times,17.3%),followed by“hand hygiene”(172 times,16.2%).“Infection prevention and control in clinical practice”was the most-reported core competency(367 times,34.5%),followed by“microbiology and surveillance”(292 times,27.5%).Cluster analysis showed two key areas of competence:Category 1(program management and leadership,patient safety and occupational health,education and microbiology and surveillance)and Category 2(IPC in clinical practice).During the COVID-19 pandemic,IPC program management and leadership,microbiology and surveillance,education,patient safety,and occupational health were the most important focus of development and should be given due consideration by IPC professionals.

Optimization of constitutive parameters of foundation soils k-means clustering analysis

The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits： raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un- confined compression strengths and other parameters of the different soils.

Unsupervised seismic facies analysis using sparse representation spectral clustering

Traditional unsupervised seismic facies analysis techniques need to assume that seismic data obey mixed Gaussian distribution.However,fi eld seismic data may not meet this condition,thereby leading to wrong classifi cation in the application of this technology.This paper introduces a spectral clustering technique for unsupervised seismic facies analysis.This algorithm is based on on the idea of a graph to cluster the data.Its kem is that seismic data are regarded as points in space,points can be connected with the edge and construct to graphs.When the graphs are divided,the weights of the edges between the different subgraphs are as low as possible,whereas the weights of the inner edges of the subgraph should be as high as possible.That has high computational complexity and entails large memory consumption for spectral clustering algorithm.To solve the problem this paper introduces the idea of sparse representation into spectral clustering.Through the selection of a small number of local sparse representation points,the spectral clustering matrix of all sample points is approximately represented to reduce the cost of spectral clustering operation.Verifi cation of physical model and fi eld data shows that the proposed approach can obtain more accurate seismic facies classification results without considering the data meet any hypothesis.The computing efficiency of this new method is better than that of the conventional spectral clustering method,thereby meeting the application needs of fi eld seismic data.

Evaluation and classification of residential greenbelt quality based on factor analysis & clustering analysis:An example of Xinxiang City,China

Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

AN ANALYSIS OF THE APPLICABILITY OF FUZZY CLUSTERING IN ESTABLISHING AN INDEX FOR THE EVALUATION OF METEOROLOGICAL SERVICE SATISFACTION

An evaluation index is a prerequisite for the scientific evaluation of a public meteorological service.This paper aims to explore a technical method for determining and screening evaluation indicators.Based on public satisfaction survey data obtained in Wafangdian,China in 2010,this study investigates the suitability of fuzzy clustering analysis method in establishing an evaluation index.Through quantitative analysis of multilayer fuzzy clustering of various evaluation indicators,correlation analysis indicates that if the results of clustering were identical for two evaluation indicators in the same sub-evaluation layer,then one indicator could be removed,or the two indicators merged.For evaluation indicators in different sub-evaluation layers,although clustering reveals attribute correlations,these indicators may not be substituted for one another.Analysis of the applicability of the fuzzy clustering method shows that it plays a certain role in the establishment and correction of an evaluation index.