Exploring profiles of varied types of achievement goals, emotions and digital insight problem solving through cluster analysis

Yeh, Yu‐chu; Wu, Hsiao-an; Huang, Frank

doi:10.1016/j.tsc.2022.101170

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…Thus, the monthly spring discharge data from January 1959 to December 2015 were analyzed by cluster analysis to examine the impact of human disturbance on spring discharge, and the rationality of the section was analyzed by a Wilcoxon rank-sum test. Briefly, cluster analysis aims to categorize a collection of observational samples based on their similarities, in such a way that samples within the same group are more comparable to each other than to those outside the group [34]. In other words, the purpose of cluster analysis is to divide samples into groups, where samples of the same group have some properties in common.…”

Section: Cluster Analysis and The Significance Testmentioning

confidence: 99%

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

2023

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This study focused on the impact of anthropogenic activity on magnitude, frequency, and minima of spring discharge. Niangziguan Springs (NS), China, was selected as an example, as its discharge is decreasing due to the combined effects of climate variation and human activity. For exploring the impact of human activity on the spring discharge from climate change, the spring discharges from 1959 to 2015 were divided into two periods: pre-development period (i.e., 1959–1980) and post-development period (i.e., 1981–2015). A polynomial regression model of the spring discharge was developed for the pre-development period. We deduced the model in the post-development period, compared the results with the observed spring discharge, and concluded that the climate variation and human activity caused 6.93% and 32.38% spring discharge decline, respectively. The relationships of spring discharge with Indian Summer Monsoon (ISM), East Asian Summer Monsoon (EASM), E1 Niño Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO) were analyzed by wavelet analysis during the two periods. The results illustrated that the monsoons (i.e., ISM and EASM) were dominated by climate factors that affect the NS discharge versus climate teleconnections (i.e., ENSO and PDO). According to different time scales, human activities have had an impact on the periodicity of NS discharge, which altered the periodicities of the spring discharge at inter-annual time scales, but the periodicities at intra-annual and annual time scales have remained the same between the two periods. Under the effects of human activity, the local parameter of non-stationary general extreme value (NSGEV) distribution varied with time. The predicted spring discharge minimum value is supposed to be 4.53 m3/s with a 95% confidential interval with an upper boundary of 6.06 m3/s and a lower boundary of 2.80 m3/s in 2020. The results of this study would benefit the management of spring discharge and water resources.

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Section: Cluster Analysis and The Significance Testmentioning

confidence: 99%

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

2023

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“…Cluster analysis is a common method in bibliometrics, and in statistics, it is a multivariate statistical analysis method for studying the "clustering of things" [66,67]. In this study, hierarchical clustering was applied to first take the keyword of each cluster as a category; subsequently, the keywords were merged into a higher-level cluster based on similarity, and finally, all individuals were grouped into categories.…”

Section: Cluster Analysis and Multiple Correspondence Analysis Of Hig...mentioning

confidence: 99%

Progress of Ecological Restoration Research Based on Bibliometric Analysis

Wei

Song

Shao

et al. 2022

IJERPH

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With the deterioration of the global/regional ecological environment, ecological restoration plays an important role in sustainable development. However, due to the differences in research methods, objectives, and perspectives, the research results are highly diverse. This makes it necessary to sort the publications related to ecological restoration, clarify the research status, grasp the research hotspots, and predict the future research trends. Here, 23,755 articles from the core database of Web of Science were retrieved, and bibliometric analysis was carried out to understand the global ecological restoration research progress from 1990 to 2022 from a macro perspective, with the aim to determine the future development direction. The results are as follows. (1) From 1990 to 2022, the number of publications in the field of ecological restoration constantly increased, and the fluctuation of the average annual citations increased. The most important articles were published in high-ranking journals. (2) Ecological restoration covers a wide range of research areas, including biodiversity, ecosystem services, climate change, land use, and ecological restoration theories and technologies. The four main hotspots in this field are heavy metal removal, soil microbial biomass carbon and nitrogen concentrations, grassland ecological restoration, and evaluation framework and modeling of ecological restoration’s effects. Currently, studies focus on river basin remediation, heavy metal removal, and forest restoration. (3) Future ecological restoration research should strengthen the multi-object aspect and multi-scale ecological restoration research, improve the ecological restoration effect evaluation system, and incorporate social and economic issues. This study identified current research hotspots and predicted potential future research directions, providing a scientific reference for future studies in the field of ecological restoration.

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“…The clustering analysis method in unsupervised learning is being increasingly applied to various fields [ 19 , [20] , [21] , [22] , [23] , [24] , [25] ], which helps us make full use of a large number of unmarked samples and saves a lot of manual work and time. There are also many applications that combine clustering analysis with machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on fast marking method for indicator diagram of pumping well based on K-means clustering

Wang,

Shao,

Shen

et al. 2023

Heliyon

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Exploring profiles of varied types of achievement goals, emotions and digital insight problem solving through cluster analysis

Cited by 3 publications

References 43 publications

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

The Effects of Climate Variation and Anthropogenic Activity on Karst Spring Discharge Based on the Wavelet Coherence Analysis and the Multivariate Statistical

Progress of Ecological Restoration Research Based on Bibliometric Analysis

Research on fast marking method for indicator diagram of pumping well based on K-means clustering

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