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The major challenge in the task of entity alignment (EA) lies in the heterogeneity of the knowledge graph. The traditional solution to EA is to first map entities to the same space via knowledge embedding and then calculate the similarity between entities from different knowledge graphs. However, these methods mainly rely on manually labeled seeds of EA, which limits their applicability. Some researchers have begun using pseudo-labels rather than seeds for unsupervised EA. However, directly using pseudo-labels causes new problems, such as noise in the pseudo-labels. In this article, we propose a model called the Semantically Driven Hybrid Network (SDHN) to reduce the impact of noise in the pseudo-labels on the performance of EA models. The SDHN consists of two modules: a Teacher–Student Network (TSN) and a Rotation and Penalty (RAP) module. The TSN module reduces the impact of noise in two ways: (1) The TSN’s teacher network guides its student network to construct pseudo-labels based on semantic information instead of directly creating pseudo-labels. (2) It adaptively fuses semantic information into student networks to improve the final representation of entity embedding. Finally, the TSN enhances the performance of models of entity alignment via the RAP module. The results of experiments on multiple benchmark datasets showed that the SDHN outperforms state-of-the-art models.

Doping PEA into perovskites endows the perovskites with better morphology and crystallinity, inhibits the transition from I− to I2 and decreases the induced defects; thus, evidently the efficiency and stability of PSCs could be improved.

Plant diseases caused by phytopathogenic fungi are a serious threat in the process of crop production and cause large economic losses to global agriculture. To obtain high-antifungal-activity compounds with novel action mechanisms, a series of 4-substituted mandelic acid derivatives containing a 1,3,4-oxadiazole moiety were designed and synthesized. In vitro bioassay results revealed that some compounds exhibited excellent activity against the tested fungi. Among them, the EC50 values of E13 against Gibberella saubinetii (G. saubinetii), E6 against Verticillium dahlia (V. dahlia), and E18 against Sclerotinia sclerotiorum (S. sclerotiorum) were 20.4, 12.7, and 8.0 mg/L, respectively, which were highly superior to that of the commercialized fungicide mandipropamid. The morphological studies of G. saubinetii with a fluorescence microscope (FM) and scanning electron microscope (SEM) indicated that E13 broke the surface of the hyphae and destroyed cell membrane integrity with increased concentration, thereby inhibiting fungal reproduction. Further cytoplasmic content leakage determination results showed a dramatic increase of the nucleic acid and protein concentrations in mycelia with E13 treatment, which also indicated that the title compound E13 could destroy cell membrane integrity and affect the growth of fungi. These results provide important information for further study of the mechanism of action of mandelic acid derivatives and their structural derivatization.

The stable cross-shaped CsPbI

Enhancing the stability of quasi-2D perovskite light-emitting diodes by using a copolymer to regulate crystallization and protect perovskite films.

CdSe QDs are incorporated into PTB7-Th:PC71BM organic solar cells, which convert the ultraviolet light to visible light to improve the photon harvesting efficiency and gather on the top of active layers and enhance the electron transport capacity.

Based on previous experimental results, machine learning is employed to intelligently screen interface materials for use in PSCs at the atomic level. The results of the high-throughput predictions were validated via DFT calculations and experiments.