Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor tensors and plays an important role in the dimension reduction of data representation. TriD, on the other hand, is incapable of precisely encoding similarity relationships for tensor data with a complex manifold structure. To address this shortcoming, we take advantage of hypergraph learning and propose a novel hypergraph regularized nonnegative triple decomposition for multiway data analysis that employs the hypergraph to model the complex relationships among the raw data. Furthermore, we develop a multiplicative update algorithm to solve our optimization problem and theoretically prove its convergence. Finally, we perform extensive numerical tests on six real-world datasets, and the results show that our proposed algorithm outperforms some state-of-the-art methods.
The objective of this study is to evaluate users' perceptions and preferences on the design features of the COVID-19 prevention promotion icon from the perspective of users' aesthetic and perceptual needs. In this study, 120 officially published icons from 24 countries and regions were collected from online platforms for ranking tests, and then the top-ranked icons were subjectively rated by the semantic differential method. By evaluating the quality of users' perceptions of multiple semantic dimensions of icons, we extracted the perceptual semantic words that users valued as the main icon design features. Spearmen correlations were applied to derive possible correlations between user rankings and semantic scales, and a Friedman test was also conducted to determine the true differences in user perceptions and preferences for different styles of icons. Factor analysis was conducted to extract six perceptual words that influence the design features of the COVID-19 prevention promotion icon. The methodology adopted in this study facilitated the screening of design features related to icon effectiveness, and the findings show that "Interesting," "Simple," "Familiar, "Recognizable," "Concrete," and "Close(semantic distance)" are the key features that influence users' perception and preference of COVID-19 icon design. The results of this study can be used as the basis for designing and improving publicity icons for preventive measures in COVID-19, and the methods adopted in this study can be applied to evaluate other types of icon design.
MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to predict microRNAs' association with diseases at the microbial level. To this end, we propose a novel model, termed as GCNA-MDA, where dual-autoencoder and graph convolutional network (GCN) are integrated to predict miRNA-disease association. The proposed method leverages autoencoders to extract robust representations of miRNAs and diseases and meantime exploits GCN to capture the topological information of miRNA-disease networks. To alleviate the impact of insufficient information for the original data, the association similarity and feature similarity data are combined to calculate a more complete initial basic vector of nodes. The experimental results on the benchmark datasets demonstrate that compared with the existing representative methods, the proposed method has achieved the superior performance and its precision reaches up to 0.8982. These results demonstrate that the proposed method can serve as a tool for exploring miRNA-disease associations in microbial environments.
Covalent organic frameworks (COFs) have emerged as promising electrode materials for sodium-ion batteries, combining molecular-level tunability with crystalline porous architectures and structural robustness. However, high-voltage all-COF batteries remain rare, as constructing complementary low-potential anodes and high-potential cathodes remains challenging. Herein, an isoreticular redox-core tailoring strategy is employed to construct complementary n-type and p-type COF electrodes for sodium dual-ion batteries. Two highly crystalline two-dimensional COFs with closely related architectures were synthesized using electron-deficient triazine and electron-rich triphenylamine redox cores, respectively. Triazine-based Trz-COF functions as a low-potential n-type anode that predominantly operates within 0.01-1.4 V vs. Na+/Na, whereas triphenylamine-based TPA-COF serves as a high-potential p-type cathode that predominantly operates within 3.4-4.2 V vs. Na+/Na for reversible PF6- storage. In situ spectroscopic analyses and theoretical calculations reveal that variation of the redox-active core dictates the charge-storage behavior and electrode potential, while the preserved framework architecture maintains efficient ion transport. Pairing these COFs yields a high-voltage all-COF sodium dual-ion battery operating over 2.0-4.2 V with an average discharge voltage of 3.40 V, delivering 116 mAh g-1 and retaining 76 mAh g-1 after 1000 cycles at 0.5 A g-1. This study establishes redox-core tailoring within an isoreticular COF family as a molecular design strategy for regulating electrochemical function in crystalline organic frameworks and demonstrates its application to high-voltage all-COF sodium dual-ion batteries.