Jiaqi Gao¹, Kangfeng Zheng^1,*, Xiujuan Wang², Chunhua Wu¹, Bin Wu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 251-270, 2024, DOI:10.32604/cmc.2024.055560 - 15 October 2024

Abstract User identity linkage (UIL) refers to identifying user accounts belonging to the same identity across different social media platforms. Most of the current research is based on text analysis, which fails to fully explore the rich image resources generated by users, and the existing attempts touch on the multimodal domain, but still face the challenge of semantic differences between text and images. Given this, we investigate the UIL task across different social media platforms based on multimodal user-generated contents (UGCs). We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation… More >

Ting Cai¹, Chun Ye¹, Zhiwei Ye^1,*, Ziyuan Chen¹, Mengqing Mei¹, Haichao Zhang¹, Wanfang Bai², Peng Zhang³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1157-1175, 2024, DOI:10.32604/cmc.2024.055080 - 15 October 2024

Abstract The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging. Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features. The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection, because of its simplicity, efficiency, and similarity to reinforcement learning. Nevertheless, existing methods do not consider crucial correlation information, such as dynamic redundancy and label correlation. To tackle these concerns, the paper proposes a More >

Abbas Ali Hassan, Fardin Abdali-Mohammadi^*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 971-983, 2024, DOI:10.32604/cmc.2024.053817 - 15 October 2024

Abstract From a medical perspective, the 12 leads of the heart in an electrocardiogram (ECG) signal have functional dependencies with each other. Therefore, all these leads report different aspects of an arrhythmia. Their differences lie in the level of highlighting and displaying information about that arrhythmia. For example, although all leads show traces of atrial excitation, this function is more evident in lead II than in any other lead. In this article, a new model was proposed using ECG functional and structural dependencies between heart leads. In the prescreening stage, the ECG signals are segmented from… More >

Sihui Tong¹, Boyuan Cao¹, Dongqing Tian², Qinwei Ma¹, Guangyan Liu^1,*, Li Shi², Libin Sun²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010870

Abstract Graphite materials serve critical roles as moderators, reflectors and core structural components in high-temperature gas-cooled nuclear reactors. These materials may experience a variety of loads during the reactor operation, including thermal, radiation, fatigue and dynamic loads, potentially leading to crack initiation and propagation. Consequently, it is imperative to investigate the fracture properties of graphite materials. Currently, there exists a dearth of comprehensive studies on the fracture toughness of graphite materials with varying grain sizes, especially regarding dynamic fracture toughness. This study introduces a novel approach utilizing a digital-image-correlation-based virtual extensometer to analyze crack propagation in… More >

Caifeng Wen^1,2, Boxin Zhang^1,*, Yuanjun Dai³, Wenxin Wang⁴, Wanbing Xie¹, Qian Du¹

Energy Engineering, Vol.121, No.10, pp. 2961-2979, 2024, DOI:10.32604/ee.2024.041677 - 11 September 2024

Abstract Power quality improvements help guide and solve the problems of inefficient energy production and unstable power output in wind power systems. The purpose of this paper is mainly to explore the influence of different energy storage batteries on various power quality indicators by adding different energy storage devices to the simulated wind power system, and to explore the correlation between system entropy generation and various indicators, so as to provide a theoretical basis for directly improving power quality by reducing loss. A steady-state experiment was performed by replacing the wind wheel with an electric motor,… More >

Adel A. Rezk^1,2,*, Mohamed M. El-Malky³, Heba I. Mohamed^4,*, Hossam S. El-Beltagi^1,5

Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1733-1748, 2024, DOI:10.32604/phyton.2024.052739 - 30 July 2024

Abstract Twenty-four rice genotypes were examined to assess genetic variability, heritability, and correlations for seven-grain quality traits, eight nutritional elements, and protein. ANOVA revealed significant differences for the quality traits studied. For every trait under study, the phenotypic coefficient of variation was higher than the correspondence genotypic coefficient of variation. Heritability in a broad sense varied from 29.75% for grain length to 98.31% for the elongation trait. Hulling percentage recovery had a highly significant positive correlation with milling and head rice percentage. Consequently, milling percentage had a highly positive correlation with head rice percentage. In amylose… 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 >

Muhammad Saleem¹, Jianhua Zhang¹, Muhammad Qasim², Rashid Iqbal³, Li Song^1,*

Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1325-1344, 2024, DOI:10.32604/phyton.2024.048493 - 27 June 2024

Abstract Variations in the nutrients and water that plants require for metabolism, development, and the maintenance of cellular homeostasis are the main causes of abiotic stress in plants. It has, however, hardly ever been studied how these transporter proteins, such as aquaporin which is responsible for food and water intake in cell plasma membranes, interact with one another. This review aims to explore the interactions between nutrient transporters and aquaporins during water and nutrient uptake. It also investigates how symbiotic relationships influence the plant genome’s responses to regulatory processes such as photoperiodism, senescence, and nitrogen fixation. More >

Jiahui Liu^1,2, Lang Li^1,2,*, Di Li^1,2, Yu Ou^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4641-4657, 2024, DOI:10.32604/cmc.2024.051613 - 20 June 2024

Abstract Correlation power analysis (CPA) combined with genetic algorithms (GA) now achieves greater attack efficiency and can recover all subkeys simultaneously. However, two issues in GA-based CPA still need to be addressed: key degeneration and slow evolution within populations. These challenges significantly hinder key recovery efforts. This paper proposes a screening correlation power analysis framework combined with a genetic algorithm, named SFGA-CPA, to address these issues. SFGA-CPA introduces three operations designed to exploit CPA characteristics: propagative operation, constrained crossover, and constrained mutation. Firstly, the propagative operation accelerates population evolution by maximizing the number of correct bytes… More >