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.

A Research on Competitiveness of Guangxi City——Based on System Clustering Method and Principal Component Analysis Method

A total of 10 indices of regional economic development in Guangxi are selected.According to the relevant economic data,regional economic development in Guangxi is analyzed by using System Clustering Method and Principal Component Analysis Method.Result shows that System Clustering Method and Principal Component Analysis Method have revealed similar results analysis of economic development level.Overall economic strength of Guangxi is weak and Nanning has relatively high scores of factors due to its advantage of the political,economic and cultural center.Comprehensive scores of other regions are all lower than 1,which has big gap with the development of Nanning.Overall development strategy points out that Guangxi should accelerate the construction of the Ring Northern Bay Economic Zone,create a strong logistics system having strategic significance to national development,use the unique location advantage and rely on the modern transportation system to establish a logistics center and business center connecting the hinterland and the Asean Market.Based on the problems of unbalanced regional economic development in Guangxi,we should speed up the development of service industry in Nanning,construct the circular economy system of industrial city,and accelerate the industrialization process of tourism city in order to realize balanced development of regional economy in Guangxi,China.

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.

A New Algorithm for Black-start Zone Partitioning Based on Fuzzy Clustering Analysis

On the process of power system black start after an accident, it can help to optimize the resources allocation and accelerate the recovery process that decomposing the power system into several independent partitions for parallel recovery. On the basis of adequate consideration of fuzziness of black-start zone partitioning, a new algorithm based on fuzzy clustering analysis is presented. Characteristic indexes are extracted fully and accurately. The raw data matrix is made up of the electrical distance between every nodes and blackstart resources. Closure transfer method is utilized to get the dynamic clustering. The availability and feasibility of the proposed algorithm are verified on the New-England 39 bus system at last.

A New Approach to Investigate Students’ Behavior by Using Cluster Analysis as an Unsupervised Methodology in the Field of Education

The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.

Kernel method-based fuzzy clustering algorithm

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

Mathematical Tools of Cluster Analysis

The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.

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

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

Time Slice Analysis Method Based on OTCA Used in fMRI Weak Signal Function Extraction

The original temporal clustering analysis (OTCA) is an effective technique for obtaining brain activation maps when the timing and location of the activation are completely unknown, but its deficiency of sensitivity is exposed in processing brain activation signal which is relatively weak. The time slice analysis method based on OTCA is proposed considering the weakness of the functional magnetic resonance imaging (fMRI) signal of the rat model. By dividing the stimulation period into several time slices and analyzing each slice to detect the activated pixels respectively after the background removal, the sensitivity is significantly improved. The inhibitory response in the hypothalamus after glucose loading is detected successfully with this method in the experiment on rat. Combined with the OTCA method, the time slice analysis method based on OTCA is effective on detecting when, where and which type of response will happen after stimulation, even if the fMRI signal is weak.

Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

Supervised learning methods（eg.PLS-DA,SVM,etc.） have been widely used with laser-induced breakdown spectroscopy（LIBS） to classify materials;however,it may induce a low correct classification rate if a test sample type is not included in the training dataset.Unsupervised cluster analysis methods（hierarchical clustering analysis,K-means clustering analysis,and iterative self-organizing data analysis technique） are investigated in plastics classification based on the line intensities of LIBS emission in this paper.The results of hierarchical clustering analysis using four different similarity measuring methods（single linkage,complete linkage,unweighted pair-group average,and weighted pair-group average） are compared.In K-means clustering analysis,four kinds of choosing initial centers methods are applied in our case and their results are compared.The classification results of hierarchical clustering analysis,K-means clustering analysis,and ISODATA are analyzed.The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS.

Principal component analysis and cluster analysis based orbit optimization for earth observation satellites

This paper proposes a design optimization method for the multi-objective orbit design of earth observation satellites, for which the optimality of orbit performance indices with different units, such as: total coverage time, the frequency of coverage, average time per coverage and maximum coverage gap, etc. is required simultaneously. By introducing index normalization method to convert performance indices into dimensionless variables within the range of [0, 1], a design optimization method based on the principal component analysis and cluster analysis is proposed, which consists of index normalization method, principal component analysis, multiple-level cluster analysis and weighted evaluation method. The results of orbit optimization for earth observation satellites show that the optimal orbit can be obtained by using the proposed method. The principal component analysis can reduce the total number of indices with a non-independent relationship to save computing time. Similarly, the multiple-level cluster analysis with parallel computing could save computing time.

Source analysis and risk evaluation of heavy metal in the river sediment of polymetallic mining area:Taking the Tonglüshan skarn type Cu-Fe-Au deposit as an example,Hubei section of the Yangtze River Basin,China

In this paper,25 sampling points of overlying deposits in Tonglushan mining area,Daye City,Hubei Province,China were tested for heavy metal content to explore pollution characteristics,pollution sources and ecological risks of heavy metals in sediments.A geo-accumulation index method was used to evaluate the degree of heavy metal pollution in the sediment.The mean sediment quality guideline quotient was used for evaluating the ecological risk level of heavy metal in the sediment.And a method of correlation analysis,clustering analysis,and principal component analysis was used for preliminary analysis on the source of heavy metal in the sediment.It was indicated that there was extremely heavy metal pollution in the sediment,among which Cd was extremely polluted,Cu strongly contaminated,Zn,As,and Hg moderately contaminated,and Pb,Cr,and Ni were slightly contaminated.It was also indicated by the mean sediment quality guideline-quotient result that there was a high ecological risk of heavy metals in the sediment,and 64%of the sample sites had extremely high hidden biotoxic effects.For distribution,the contamination of branches was worse than that of the main channel of Daye Dagang,and the deposition of each heavy metal was mainly influenced by the distance from this sample site to the sewage draining exit of a tailings pond.The source analysis showed that the heavy metals in the sediment come from pollution discharging of mining and beneficiation companies,tailings ponds,smelting companies,and transport vehicles.In the study area,due to the influence of heavy metal discharging from these sources,the ecotoxicity of heavy metals in the sediment was extremely high,and Cd was the most toxic pollutant.The research figured out the key restoration area and elements for ecological restoration in the sediment of the Tonglüshan mining area,which could be referenced by monitoring and governance of heavy metal pollution in the sediment of the polymetallic mining area.

Photometric solution and period analysis of the contact binary system AH Cnc

Photometric observations of AH Cnc, a W UMa-type system in the open cluster M67, were car- fled out by using the 50BIN telescope. About 100h of time-series/3- and V-band data were taken, based on which eight new times of light minima were determined. By applying the Wilson-Devinney method, the light curves were modeled and a revised photometric solution of the binary system was derived. We con- firmed that AH Cnc is a deep contact （f = 51%）, low mass-ratio （q - 0.156） system. Adopting the distance modulus derived from study of the host cluster, we have re-calculated the physical parameters of the binary system, namely the masses and radii. The masses and radii of the two components were estimated to be respectively 1.188（4-0.061） Me, 1.332（4-0.063） RQ for the primary component and 0.185（4-0.032） Me, 0.592（4-0.051） Re for the secondary. By adding the newly derived minimum timings to all the available data, the period variations of AH Cnc were studied. This shows that the orbital period of the binary is con- tinuously increasing at a rate of dp/dt = 4.29 x 10-10 d yr-1. In addition to the long-term period increase, a cyclic variation with a period of 35.26 yr was determined, which could be attributed to an unresolved tertiary component of the system.

The Superconductivity in Fe-Based Family of Superconductors and Its Electronic Structure Analysis in Presence of Dopants Rh and Pd

The discovered in 2008 Fe-based superconductors (SC) are a paramagnetic semimetal at ambient temperature and in some cases they become superconductor upon doping. In spite of so many years since its discovery it is still not known the mechanism that leads to superconductivity. The electronic structure study is used for determining key features of the SC mechanism in these materials. The calculations were performed using the modern suite of programs MOLPRO 2021. We performed quantum calculations of a cluster embedded in a background charge distribution that represents the infinite crystal. The Natural Population Analysis was used for determining the charge and spin distribution in the studied materials. As follows from our results, the possible mechanism for superconductivity corresponds to the RVB theory proposed by Anderson for high Tc superconductivity in cuprates.

基于因子分析法与聚类分析法的河南省农产品物流发展水平研究

2023年中央一号文件提出“坚持不懈地把解决好‘三农’问题作为全党工作重中之重,举全党全社会之力全面推进乡村振兴,加快农业农村现代化”,推动农业发展,助力乡村振兴是我国的重点工作。为了研究河南省农产品物流情况,助力农业发展,以河南省的18个省辖市为研究样本,借助SPSS.23软件进行因子分析,以评价各市农产品物流不同的发展水平。利用聚类分析,按照农产品物流发展水平的综合得分将河南省的18个省辖市分为4类,最后有针对性的提出相关建议以推进各地区农产品物流高效发展。

大凉山地区不同马铃薯品种产量和营养品质的综合评价

为筛选出适宜四川大凉山地区种植的马铃薯品种,对14个马铃薯品种的12个产量与营养品质指标进行测定,并结合主成分分析、隶属函数法、系统聚类分析对马铃薯产量和品质表现进行综合评价。结果表明,单株结薯数、单薯鲜质量、单株块茎鲜质量、单产、还原糖含量的变异系数均超过30%;单株块茎鲜质量与单薯鲜质量、单产和蛋白质含量呈极显著正相关,干物质含量与淀粉含量、蛋白质含量呈极显著正相关,单薯鲜质量与VC含量、锌含量呈显著负相关,蛋白质含量与钾含量呈极显著负相关;主成分分析结果表明,12个指标可用4个主成分来表示,方差累积贡献率达到86.040%。进一步采用隶属函数法和系统聚类分析将14个品种分为3类,筛选出6个综合表现较优的品种,分别为川凉薯10号、青薯9号、川芋50、川凉芋13、云薯108、川芋22号。

基于空间投影和聚类划分的SVR加速算法

数据不仅能产生价值,还对统计学的科学发展提供了动力。随着科技的飞速发展,海量数据得以涌现,但大规模的数据会导致很多传统处理方法很难满足各领域对数据分析的需求。面对海量数据时代学习算法的低效性,分治法通常被认为是解决这一问题最直接、最广泛使用的策略。SVR是一种强大的回归算法,在模式识别和数据挖掘等领域有广泛应用。然而在处理大规模数据时,SVR训练效率低。为此,该文利用分治思想提出一种基于空间投影和聚类划分的SVR加速算法(PKM-SVR)。利用投影向量将数据投影到二维空间;利用聚类方法将数据空间划分为k个互不相交的区域;在每个区域上训练SVR模型;利用每个区域的SVR模型预测落入同一区域的待识别样本。在标准数据集上与传统的数据划分方法进行对比实验,实验结果表明该算法训练速度较快,并表现出更好的预测性能。

管制员个体工作负荷多维量化研究

为提高空管系统高效运行,聚焦管制员个体工作负荷建立量化模型;首先设计试验采集一线16名区域管制员的岗前与岗后各项指标数据,根据测试数据变化,选择出敏感变量,描述个体工作负荷;其次建立包含心理感知负荷、生理反应负荷与脑力工作负荷3个维度的综合评估指标体系,构建管制员个体工作负荷指数模型;然后通过熵权-客观组合法求解个体工作负荷指数最优权重,最终得出管制员个体工作负荷量化模型;最后进一步根据管制员个体工作负荷综合指数进行K-Means聚类分析,结果表明:管制员因个体不同岗后工作负荷存在差异。依据个体工作负荷指数大小,管制员可分为3类,A类管制员数量占总人数50%,岗后个体工作负荷增长最小;B类管制员数量占总人数43.75%,岗后负荷增长居中;C类管制员数量占总人数6.25%,岗后负荷增长最大,与教员对管制员能力的评分结果一致。

基于多维标度和聚类分析的“物质结构和性质”模块情境素材分类研究

不同类别情境素材具有不同的教学功能.在“物质结构与性质”模块教学中,合理选用情境素材能促进学生理解与辨析、逻辑与推理等能力的培养.为系统探察“物质结构与性质”模块情境素材的类别,以课程标准和人教版选择性必修2为源,通过文本研究法和问卷调查法筛选出35个情境素材.以中学化学一线教师以及某师范大学本科生、研究生为研究对象,通过系统聚类法确定情境素材的类别.研究发现,“物质结构和性质”模块情境素材分为社会生活、学术探究、科技前沿、模拟联想、科学史实、实验探索6类.研究可为学者和一线教师高效开展情境教学研究与实践提供参考.

数字经济背景下智慧供应链发展水平测度——以长江经济带为例

物联网、云计算及大数据等技术的深度融合促进了数字经济的发展,也促进了区域智慧供应链的发展。长江经济带是连接东西部的重要区域,其智慧供应链的发展水平是判断其良性发展的重要依据。为此文章基于数字经济背景,从资源、输出和柔性三个维度构建了长江经济带智慧供应链发展水平评价体系,采用博弈论组合赋权法确定各指标权重,测算了长江经济带11省(市)2016年至2021年智慧供应链发展水平,运用系统聚类分析法和马尔科夫链统计分析方法探索了长江经济带智慧供应链的时空特征及未来发展趋势,并根据分析结果对其智慧供应链发展提出相应的建议。