Yue Ma^1,2, Douwen Qin^1,2, Weiqiang Liu^1,2, Xiuting Ju^1,2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.11, pp. 3527-3538, 2025, DOI:10.32604/phyton.2025.069643 - 01 December 2025

Abstract Comprehensive evaluation based on mineral element content is one of the effective methods for the exploration and utilization of wild tulip germplasm resources. In this study, Tulipa iliensis, Tulipa tianschanica and Tulipa heterophylla distributed in China were used as the research objects. The contents of 10 mineral elements (N, K, P, S, Ca, Mg, Cu, Zn, Fe, Mn) in roots, bulbs and leaves were determined, and the three wild tulips were comprehensively evaluated by correlation analysis, principal component analysis and cluster analysis. The results showed distinct variations in mineral element content among different organs of T. iliensis, T. tianschanica and T.… More >

Guanhui Li^1,2,#, Jiati Tang^1,#, Qifang Zhang³, Guilin Ou^1,3, Yingchao Lin¹, Liping Chen⁴, Xiang Li⁴, Shengjiang Wu¹, Zhu Ren¹, Zeyu Zhao^1,2, Xuekun Zhang², Benbo Xu^2,*, Xun Liu³, Kesu Wei^1,*

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2385-2398, 2025, DOI:10.32604/phyton.2025.068213 - 29 August 2025

Abstract To clarify the relationships between the main chemical components in flue-cured tobacco in Guizhou and field meteorological factors during the tobacco growing period, the contributions of meteorological factors to the chemical composition of flue-cured tobacco and related components were explored in this study. The flue-cured tobacco variety Y87 was used as the experimental material, and tobacco samples and meteorological data were collected from seven typical tobacco-growing areas in Guizhou Province. Using a random forest model and canonical correlation analysis, the impact and contribution of the monthly mean temperature, precipitation, and sunshine duration during the field… More >

Kun Liu¹, Chen Bao^1,*, Sidong Liu²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025

Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >

Jian Gao¹, Hongliang Hao², Caifeng Wen^1,*, Yongsheng Wang³, Zhanhua Han⁴, Edwin E. Nykilla², Yuwen Zhang²

Energy Engineering, Vol.122, No.3, pp. 1175-1198, 2025, DOI:10.32604/ee.2025.061083 - 07 March 2025

Abstract Power quality is a crucial area of research in contemporary power systems, particularly given the rapid proliferation of intermittent renewable energy sources such as wind power. This study investigated the relationships between power quality indices of system output and PSD by utilizing theories related to spectra, PSD, and random signal power spectra. The relationship was derived, validated through experiments and simulations, and subsequently applied to multi-objective optimization. Various optimization algorithms were compared to achieve optimal system power quality. The findings revealed that the relationships between power quality indices and PSD were influenced by variations in More >

Xiuyun Liu¹, Jixia Sun², Feng Ming³, Minxiao Liu^2,*, Xinyu Wang², Yingjie Zhang^2,*

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 379-391, 2025, DOI:10.32604/phyton.2025.060726 - 06 March 2025

Abstract To study the main aroma components of Phalaenopsis orchid and their relationship with colors, 10 fragrant cultivars with different colors, like pink, rose, yellow, and purple, were used as samples in this experiment. Headspace-gas chromatography-mass spectrometry was used to determine the main components of floral fragrance and analyze the correlation between floral color and fragrance. The results showed that the main aroma components of the 10 fragrant cultivars of Phalaenopsis were alcohols, alkenes, esters, and benzene ring compounds, and the main aroma components of different cultivars were diverse. The main aroma components of yellow fragrant flowers… More >

Jian Luo^1,*, Bo Xu¹, Tardi Tjahjadi², Jian Yi¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018 - 18 July 2024

Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >

Chen Xing¹, Leihua Yao^1,*, Yingdong Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2825-2848, 2024, DOI:10.32604/cmes.2024.049330 - 08 July 2024

Abstract In pumped storage projects, the permeability of rock masses is a crucial parameter in engineering design and construction. The rock mass permeability coefficient (K) is influenced by various geological parameters, and previous studies aimed to establish an accurate relationship between K and geological parameters. This study uses the improved sparrow search algorithm (ISSA) to optimize the parameter settings of the deep extreme learning machine (DELM), constructing a prediction model with flexible parameter selection and high accuracy. First, the Spearman method is applied to analyze the correlation between geological parameters. A sample database is built by comprehensively… More >

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187 - 27 February 2024

Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on… More >

Tian-Jiao Wei¹, Guang Li¹, Yan-Ru Cui¹, Jiao Xie¹, Xing-Ai Gao¹, Xing Teng¹, Xin-Ying Zhao¹, Fa-Chun Guan^1,*, Zheng-Wei Liang^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 29-43, 2024, DOI:10.32604/phyton.2023.046078 - 26 January 2024

Abstract Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield. It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments. In this study, the root biomass, root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline… More >

Xiaoyan Su^1,*, Shuwen Shang¹, Zhihui Xu², Hong Qian¹, Xiaolei Pan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1813-1826, 2024, DOI:10.32604/cmes.2023.030957 - 17 November 2023

Abstract With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs More > Graphic Abstract