XIAOJUN ZHOU^*, XIAOYU LU, XUBO WANG

BIOCELL, Vol.46, No.11, pp. 2489-2495, 2022, DOI:10.32604/biocell.2022.020311 - 07 July 2022

Abstract Euptelea pleiosperma is a characteristic species of East Asian flora with both ornamental and scientific values. Based on the reduced-representation sequencing (RRS) technology of RAD-Seq, this study conducted high-throughput Illumina paired-end sequencing to find SSR marker information in the genome of E. pleiosperma, and to screen and verify polymorphism of SSR markers. We obtained 5.5G of high-quality data using RAD-Seq. The total number of contigs of the RAD tags was 299,376, with the maximum contig length of 2,062 bp and the average length of 445 bp. From these sequences, we identified 20,718 SSR loci, with a distribution… More >

Kun Du, Zhen Wei^*

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.3, pp. 751-760, 2022, DOI:10.32604/fdmp.2022.019577 - 22 February 2022

Abstract Improving drilling efficiency is the best way to reduce drilling costs and the choice of the drilling mode is instrumental in doing so. At present, however, a standard approach for the optimization of these processes does not exists yet. Through a comparative statistical analysis of the rock-breaking mechanisms and the characteristics of different drilling methods, this research proposes a set of cues to achieve this objective. Available statistical data are classified by means of a fuzzy cluster analysis according to the anti-drilling characteristic parameters of formation. The results show that different drilling methods rely on More >

Simon Crase^1,2, Benjamin Hall², Suresh N. Thennadil^3,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2435-2458, 2022, DOI:10.32604/cmc.2022.022414 - 07 December 2021

Abstract Cluster analysis in spectroscopy presents some unique challenges due to the specific data characteristics in spectroscopy, namely, high dimensionality and small sample size. In order to improve cluster analysis outcomes, feature selection can be used to remove redundant or irrelevant features and reduce the dimensionality. However, for cluster analysis, this must be done in an unsupervised manner without the benefit of data labels. This paper presents a novel feature selection approach for cluster analysis, utilizing clusterability metrics to remove features that least contribute to a dataset's tendency to cluster. Two versions are presented and evaluated:… More >

Tossapon Boongoen, Natthakan Iam-On^*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1993-2011, 2022, DOI:10.32604/cmc.2022.019776 - 07 September 2021

Abstract A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, and that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different… More >

Ting Wang¹, Zhiqiang Wang^1,*, Yin Yang¹, Jianhui Zhou^2,*

Journal of Renewable Materials, Vol.10, No.2, pp. 527-540, 2022, DOI:10.32604/jrm.2022.017955 - 30 August 2021

Abstract The acoustic emission (AE) technique can perform non-destructive monitoring of the internal damage development of bamboo and wood materials. In this experiment, the mechanical properties of different bamboo and wood (bamboo scrimber, bamboo plywood and SPF (Spruce-pine-fir) dimension lumber) during four-point loading tests were compared. The AE activities caused by loadings were investigated through the single parameter analysis and K-means cluster analysis. Results showed that the bending strength of bamboo scrimber was 3.6 times that of bamboo plywood and 2.7 times that of SPF dimension lumber, respectively. Due to the high strength and toughness of… More > Graphic Abstract

Simon Crase^1,2, Benjamin Hall², Suresh N. Thennadil^3,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1945-1965, 2021, DOI:10.32604/cmc.2021.018517 - 21 July 2021

Abstract Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the More >

Pengfei Gong^1,2, Qun Wang^2,*, Junjun Zhu³

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 31-42, 2021, DOI:10.32604/iasc.2021.017695 - 12 May 2021

Abstract This study analyzed in-depth investigation reports for 418 traffic accidents with at least five deaths (TALFDs) in China from 2016 to 2019. Statistical analysis methods including hierarchical cluster analysis were employed to examine the distribution characteristics of these accidents. Accidents were found to be concentrated in July and August, and the distribution over the seven days of the week was relatively uniform; only Sunday had a higher number of accidents and deaths. In terms of 24-hour distribution, the one-hour periods with the most accidents and deaths were 8:00–9:00, 10:00–11:00, 14:00–15:00, and 18:00–19:00. Tibet, Qinghai, and… More >

Haoliang Cui¹, Shaozhang Niu¹, Keyue Li^1,*, Chengjie Shi², Shuai Shao³, Zhenguang Gao⁴

Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 277-291, 2021, DOI:10.32604/iasc.2021.016408 - 17 March 2021

Abstract At present, the research on the classification of e-commerce users is relatively mature, but with the rise of mobile social networks, the combination of social networks and e-commerce networks has become a trend and is developing rapidly. Traditional e-commerce user classification methods are not suitable for social e-commerce users. Therefore, based on the research on traditional e-commerce user classification methods, according to the characteristics of social e-commerce users, we improved data preprocessing and parameter tuning methods, and proposed a clustering method of social e-commerce users based on the K-means++ algorithm. The test on the actual More >

Wei Tan¹, Mingxiu Xu¹, Siqi Xie¹, Yan Zhang¹, Shuai Wu¹, Qinyan Zou¹, Qifeng Zhao², Qingliang Li^3,*

Phyton-International Journal of Experimental Botany, Vol.90, No.2, pp. 553-570, 2021, DOI:10.32604/phyton.2021.014082 - 07 February 2021

Abstract To understand the anthocyanin characteristics of wine grape varieties, the anthocyanin composition and content of 31 wine grape varieties were analyzed to explore the use of anthocyanins as chemical fingerprints to distinguish varieties. Results showed that a total of 21 anthocyanins were detected in the skins, including cyanidin, delphinidin, petunidin, peonidin and malvidin 3-monoglucosides (or 3,5-diglucosides) along with the corresponding acetyl and p-coumaroyl derivatives. The highest and lowest total amount of anthocyanins were detected in ‘Ruby Cabernet’ and ‘Muscat Rouge’, respectively. In the 21 Vitis vinifera grapes, there were 3~11 monoglucoside anthocyanins detected, however, there… More >

Sami Ullah¹, Hanita Daud¹, Nooraini Zainuddin¹, Sarat C. Dass², Alamgir Khalil³, Hadi Fanaee-T⁴, Ilyas Khan^5,*

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3065-3074, 2021, DOI:10.32604/cmc.2021.012624 - 28 December 2020

Abstract Oil spills cause environmental pollution with a serious threat to local communities and sustainable development. Accidental oil spills can be modelled as a stochastic process where each oil spill event is described by its spatial locations and incidence-time and hence allow for space-time cluster analysis. Space-time cluster analysis can detect space-time pattern distribution of oil spills which can be useful for implementing preventive measures and evidence-based decision making. This study aims to detect the space-time clusters of accidental oil spills in Rivers state, Nigeria through the Space-time Scan Statistic. The Space-time Scan Statistic was applied… More >