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Protection of cultural relics and sites is of great significance. In this study, the new gray-green thin-layer biological crust on the rammed soil surface at the Shanhaiguan Great Wall in China was found. The emergence of this material has substantially improved the resistance of the rammed Earth Great Wall to rain erosion. 16S rRNA gene sequencing on the surface crusts of rammed Earth was performed. Results show the biological crusts were mainly algae-based composite crusts containing fungi. At the genus level, microalgae and Sphingomycetes were predominant. Under scanning electron microscopy (SEM), algae filaments dominated by filamentous algae overlapped and intertwined with each other. Furthermore, polysaccharide organic matter secreted by algae formed a covering film. The two formed a complex spatial network structure to envelop soil particles, which enhances erosion resistance. The conformable biological crusting is expected to be used as a new civil engineering material for the protection of rammed Earth sites in the future.

As the main insulation material of the power electronic transformer (PET), polyimide (PI) is easy to accumulate space charge under the continuous high-frequency pulse voltage, which will threaten the operation of PET and cause electric field distortion. At present, the microscopic mechanism of charge transport under high-frequency voltage is unclear. Numerical simulation is an effective method to explore the transport characteristics of space charge. The simulation of charge transport under DC voltage is mainly based on the bipolar charge transport (BCT) model. However, the charge transport behaviors under high-frequency pulse voltage cannot be simulated by the traditional models accurately. Given this, based on the BCT model, the High-Frequency Voltage Charge Transport (HFCT) model is proposed, considering nonlinear charge mobility of the polymer and the effect of the frequency. Finally, the space charge and electric field distribution under square pulse wave voltages with different rising times are simulated based on the HVCT model, concluding that the reduction of rising time will promote the accumulation of space charge. In view of this, the HVCT model is expected to optimize the insulation design of PET and provide a reference for the dynamic simulation of space charge.

The rapid growth of photovoltaic installed capacity exacerbates the power management challenges faced by photovoltaic power stations, emphasizing the importance of accurate and stable photovoltaic generation forecasting. As a result, researchers conducted research and developed several photovoltaic power prediction models. However, many prediction models focus exclusively on the algorithm structure in order to improve model accuracy, oblivious to how the dataset is constructed and divided for the prediction model. This paper proposes a comprehensive model to address this gap. To be more precise, the differential evolution algorithm is constantly looking for optimal values between different populations and determining the best way to construct datasets for prediction tasks. Multi-task learning enables the transfer of knowledge between related tasks via parameter sharing layers, referring to the accuracy and stability of prediction models. Overall, the proposed model achieves high prediction accuracy and stability. The prediction error of the proposed model is less than 450W in RMSE, NRMSE is less than 2.5%, and R-Square is greater than 99% in multiple prediction tasks. Additionally, when compared to other single-task prediction models with an R-Square greater than 96%, the proposed model further reduces the root mean squared error by an average of 28% and the standard deviation of root mean squared error by 54%.

The polarization/depolarization curve (PDC), which contains abundant insulation information, is widely used to evaluate the insulation condition of the cross-linked polyethylene (XLPE) cables. However, the insulation condition assessing the result of the XLPE cables is usually inaccurate using the PDC technology because the parameters of the current Debye model are determined in a uniform aging hypothesis, instead of a real non-uniform situation. To solve this problem, the modified Debye model considering the real non-uniform situation is proposed, and the effectiveness of the modified model is proved by the comparison of the PDC test results with non-uniform and uniform aging materials. It is found that the parameters of the extended Debye model tend to overestimate the cable insulation performance, while the characteristic parameters obtained by the modified Debye model better reflect the real situation of cable non-uniform aging. Result analysis shows that the proposed method has better accuracy in the insulation condition evaluation of cables.

Objective: This research probed into the molecular mechanisms of long non-coding RNA (lncRNA) VPS9D1 Antisense RNA 1 (VPS9D1-AS1) in lung adenocarcinoma (LUAD).Methods: lncRNA expression level was evaluated bioinformatically, and its downstream miRNA/mRNA regulatory axis was predicted by bioinformatics methods as well. qRT-PCR was used to measure VPS9D1-AS1, miRNA-30a-5p, and kinesin family member 11 (KIF11) expression. Western blot was performed to measure KIF11 protein expression. Proliferation, migration, and invasion of LUAD cells were all observed by cell biological function experiments. Dual-luciferase assay detected binding between miRNA-30a-5p and VPS9D1-AS1 or KIF11, respectively. RIP experiment detected interaction between VPS9D1-AS1 and miRNA-30a-5p.Results: VPS9D1-AS1 and KIF11 were increased in LUAD, whereas miRNA-30a-5p was decreased. VPS9D1-AS1 promoted the malignant progression of LUAD cells and could sponge miRNA-30a-5p. MiRNA-30a-5p could restore the impact of VPS9D1-AS1 on LUAD cells. KIF11 was a target downstream of miRNA-30a-5p. VPS9D1-AS1 could upregulate KIF11 expression through competitively sponging miRNA-30a-5p, and KIF11 could restore the impact of miRNA-30a-5p on LUAD cells.Conclusion: VPS9D1-AS1 could foster malignant progression of LUAD via regulating miRNA-30a-5p/KIF11 axis, suggesting that VPS9D1-AS1 is key to regulating the malignant progression of LUAD.