Potassium-sodium niobate K1-xNaxNbO3 (KNN) is one of the most promising lead-free piezoelectric materials. While there have been many studies on the microstructures and properties of KNN ceramics, the phase transitions and ferroelectric domain structures of KNN thin films are not well understood. In this work, we employ three-dimensional (3D) phase-field simulations to obtain the ferroelectric domain structures of KNN (0 ≤ x ≤ 0.5) thin films under a range of temperatures (0 K to 1300 K) and equiaxial misfit strains (–1.5% to 1.5%), based on which we establish the misfit strain-temperature phase diagrams of KNbO3 and K0.5Na0.5NbO3 thin films. We identify a wide variety of complex domain structures with coexisting ferroelectric phases, implying enhanced dielectric and piezoelectric properties. We expect this work to provide guidance for the strain engineering of domain structures and properties of KNN thin films.

From the perspective of data science, we propose a cancer diagnosis method combining miRNA-lncRNA interaction pairs and class weight competition. First, miRNA-lncRNA interaction data is introduced into joint expression profiles, and the complex mechanism of cancer development is demonstrated in depth through the reappearance of key association information. This is an information ensemble of three carcinogenic mechanisms at dataset construction level: classical genetics, epigenetics, and the complex interaction effect between miRNAs and lncRNAs. Then, we put forward a hybrid feature selection algorithm. By preserving the interaction relationship between miRNAs and lncRNAs, it quickly and steadily removes irrelevant and redundant features and solves the high-dimensional disaster problem of cancer expression profiles. This is an information ensemble of multiple feature selection algorithms and the significant association relationship found between multi-dimensional features at feature selection level. A diversity sampling and multi-algorithm learners are used to construct a multiple heterogeneous classification models, which overcomes the small size of normal samples and the local optimum of single algorithm and single mode. This is an information ensemble of multiple classification model structures and multiple classification model state parameters at classification modeling level. At decision level, the proposed class weight which does not depend on the sample size is constructed to address the issue of unbalanced sample class of cancers. The ensemble of multi-category multi-state information at four levels (dataset construction, feature selection, classification modeling, and decision) constitutes the framework of the proposed method. We classify BRCA, LUAD and LUSC in TCGA. Compared with the state-of-the-art classification methods, the proposed method has improved classification accuracy by 9.25%∼21.25%, sensitivity by 6.45%∼66.45%, and specificity by 10.11%. In addition, we find that lincRNA instead of miRNA always appears in each group of feature genes, which provides a new clue for the locus target selection in cancer treatment. INDEX TERMS Cancer diagnosis, joint expression profiles, miRNA-lncRNA, feature selection embedded interaction pairs, class weight competition, locus target discovery.

In the past 20 years, the development of aluminum matrix composites (AMCs) has made a qualitative leap. This article comprehensively introduces the performance characteristics and the preparation methods of aluminum matrix composites. The powder metallurgy method (P/M) is elaborated in detail. And the applications of aluminum matrix composites in aerospace, automobile and other fields are described. Finally, the future development of aluminum matrix composites is prospected.