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Borate materials continue to command considerable attention due to their remarkable capacity for applications in deep ultraviolet (UV) wavelengths. Herein, two new metal borates KSrM3B2O9(M = Al and Ga) were extracted via the application of flux techniques. These two crystals adopt a centrosymmetric space group P21/c(no. 14), showcasing a layered structural configuration composed of isolated [BO3] plane triangles and [AlO4]/[GaO4] tetrahedra. Thermal analysis revealed that KSrM3B2O9(M = Al and Ga) exhibits an incongruent nature and possesses good thermal stability up to 1083 and 983 °C, respectively. Notably, these compounds display a short UV-transmission cutoff edge, approximately around 194 and 200 nm, accompanied by band gaps of 5.47 and 4.83 eV, respectively. Furthermore, KSrM3B2O9(M = Al and Ga) demonstrates a moderate optical birefringence of 0.026 and 0.025, respectively. Additionally, first-principles calculations were employed to shed light on the intricate interplay between the structure and properties of these compounds.

Unsupervised domain adaptation aims to transfer knowledge from labeled source domain to unlabeled target domain. The semi-supervised method based on mean-teacher framework is one of the main stream approaches. By enforcing consistency constraints, it is hopeful that the teacher network will distill useful source domain knowledge to the student network. However, in practice negative transfer often emerges because the performance of the teacher network is not guaranteed to be always better than the student network. To address this limitation, a novel Adaptive Mutual Learning (AML) strategy is proposed in this paper. Specifically, given a target sample, the network with worse prediction will be optimized by pushing its prediction close to the better prediction. This is in the spirit of traditional knowledge distillation. On the other hand, the network with better prediction is further refined by requiring its prediction to stay away from the worse prediction. This can be regarded conceptually as reverse knowledge distillation. In this way, two networks learn from each other according to their respective performance. At inference phase, the averaged output of these two networks can be taken as the final prediction. Experimental results demonstrate that our AML achieves competitive results.

Origami structures often serve as the building block of mechanical systems due to their rich static and dynamic behaviors. Experimental observation and theoretical modeling of origami dynamics have been reported extensively, whereas the data-driven modeling of origami dynamics is still challenging due to the intrinsic nonlinearity of the system. In this study, we show how the dynamic mode decomposition (DMD) method can be enhanced by integrating geometry information of the origami structure to model Kresling origami dynamics in an efficient and accurate manner. In particular, an improved version of DMD with control, that we term geometry-informed dynamic mode decomposition (giDMD), is developed and evaluated on the origami chain and dual Kresling origami structure to reveal the efficacy and interpretability. We show that giDMD can accurately predict the dynamics of an origami chain across frequencies, where the topological boundary state can be identified by the characteristics of giDMD. Moreover, the periodic intrawell motion can be accurately predicted in the dual origami structure. The type of dynamics in the dual origami structure can also be identified. The model learned by the giDMD also reveals the influential geometrical parameters in the origami dynamics, indicating the interpretability of this method. The accurate prediction of chaotic dynamics remains a challenge for the method. Nevertheless, we expect that the proposed giDMD approach will be helpful towards the prediction and identification of dynamics in complex origami structures, while paving the way to the application to a wider variety of lightweight and deployable structures.

Autophagy and apoptosis play significant roles in PRRSV infection and replication. However, the interaction between these 2 processes in PRRSV replication is still far from been completely understood. In our studies, the exposure of MARC-145 cells to PRRSV confirmed the activation of autophagy and subsequent induction of apoptosis. The inhibition of autophagy by 3-methyladenine (3-MA) caused a significant increase in PRRSV-induced apoptosis, showing a potential connection between both mechanisms. Moreover, we observed an increase in Bad expression (a pro-apoptotic protein) and Beclin1 (an autophagy regulator) in virus-infected cells up to 36h. Co-immunoprecipitation assays showed the formation of Bad and Beclin1 complex in PRRSV infected cells. Accordingly, Bad co-localized with Beclin1 in MARC-145 infected cells. Knockdown of Beclin1 significantly decreased PRRSV replication and PRRSV-induced autophagy, while Bad silencing resulted in increased autophagy and enhanced viral replication. Furthermore, PRRSV infection phosphorylated Bad (Ser112) to promote cellular survival. These results demonstrate that autophagy can favor PRRSV replication by postponing apoptosis through the formation of a Bad-Beclin1 complex.