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In this study, the corrosion electrochemistry and corrosion behavior of two steels were studied under the simulated alumina production conditions. The corrosion rate of 16Mn steel is greater than that of Q235 steel. The effect of S2− concentration on corrosion rate was significantly higher than that of S2O32−. The synergistic corrosion rates of Q235 and 16Mn steels increase at first and then decrease with the sulfur content, and the peak value appears when the concentration of S2− and S2O32− is 4 g/L and 3 g/L respectively. There are two main types of corrosion products: one is surface octahedral grain, which is composed of Fe2O3, Fe3O4 and Al2O3.The other is the interlayer corrosion between the surface layer and the matrix, which is composed of FeS, FeS2 and NaFeO2.The formation mechanism of the corrosion and corrosion mechanism were obtained by analyzing the phenomenon of ion competitive adsorption. Further validation and analysis of ion competition adsorption phenomenon were conducted using first-principles calculations based on density functional theory (DFT). The formation of corrosion products on the steel surface was investigated at an ion level, and the adsorption energies of OH− and S2− at the top site of Fe(110) surface were calculated. It was found that S2− is more likely to be adsorbed on the Fe(110) surface compared to OH−. The corrosion mechanism of steel is discussed preliminarily. [ABSTRACT FROM AUTHOR]

Methods based on deep learning have achieved great success in the field of video action recognition. When these methods are applied to real-world scenarios that require fine-grained analysis of actions, such as being tested on a tea ceremony, limitations may arise. To promote the development of fine-grained action recognition, a fine-grained video action dataset is constructed by collecting videos of tea ceremony actions. This dataset includes 2745 video clips. By using a hierarchical fine-grained action classification approach, these clips are divided into 9 basic action classes and 31 fine-grained action subclasses. To better establish a fine-grained temporal model for tea ceremony actions, a method named TSM-ConvNeXt is proposed that integrates a TSM into the high-performance convolutional neural network ConvNeXt. Compared to a baseline method using ResNet50, the experimental performance of TSM-ConvNeXt is improved by 7.31%. Furthermore, compared with the state-of-the-art methods for action recognition on the FineTea and Diving48 datasets, the proposed approach achieves the best experimental results. The FineTea dataset is publicly available. [ABSTRACT FROM AUTHOR]

Organic montmorillonite (OMMT) was prepared from Na-montmorillonite (MMT) by Hexadecylamine (HDA) modification. The composite material has good smoothness, acidity, and salt resistance. OMMT was characterized using small-angle X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and a video optical contact angle measuring instrument. The results showed that the layer spacing was enlarged from 1.44 nm to 2.87 nm after the modification, and the hydrophobicity performance was greatly improved. The organic modification of MMT was successful. The surface morphology, roughness, and anticorrosion properties of the organic montmorillonite/epoxy (OMMT/EP) composite coating were investigated and compared with those of the epoxy (EP) coating. The OMMT/EP composite coating had a flatter surface than the EP coating. The roughness was reduced from 65.5 nm to 10.3 nm. The electrochemical impedance spectroscopy showed that the composite coating's thickness positively affected its anticorrosion performance, the corrosion current density (Icorr) decreased with the increase in thickness, and its maximum impedance was much larger than that of EP coating. The protection efficiency of the OMMT/EP composite coating was 77.90%, which is a significant improvement over the EP's 31.27%. In addition, the corrosion resistance of the composite coating gradually decreased with increasing immersion time, but the change was insignificant. [ABSTRACT FROM AUTHOR]

The residence time of particles in the flow field is highly sensitive to the fluid's flow characteristics and the particles' motion. We proposed a dynamic entry mode with the constant-pulse (CP) condition in response to the conditions of symmetric impinging streams and asymmetric impinging streams. Single-particle motion behaviors and particle residence time in a novel cross-type structure reactor we designed were studied and discussed in this article. This was done by looking at how these variables changed depending on the impulse inlet velocity conditions, the reactor geometry, and the particle properties. The results demonstrate that when particle relaxation time is short, those particles without oscillatory motions were directly accelerated to leave the active area. When its relaxation time is reasonably set, even a single particle can hugely elevate the oscillatory motions. With the structure of the novel cross type, the particle merely exhibits three oscillatory under the condition of the symmetric pulse inlet flow and asymmetric pulse inlet flow, notwithstanding, CP contains at least three oscillatory motions. The ttot is the duration that an individual particle spends in the working region. The ttot and active area were affected by inlet velocity, parameters of particles and the shape of the reactor parameters of particles and the shape of the reactor. The ttot raised with τ* increased in a certain range. T is the inlet velocity variation period. The particle resides time at T = Tb reaches a maximum in the CP. The maximum residence times Tb was affected by particle relaxation time, under CP with a diameter ratio of 5 and inlet velocity difference of 0.8. The particle retention time in the novel cross-type structure reactor was heightened by 7 times. The CP and the novel cross-type structure reactor can improve the retention time of particles in the flow field. [ABSTRACT FROM AUTHOR]

Ultrasound image super-resolution (SR) aims to transform low-resolution images into high-resolution ones, thereby restoring intricate details crucial for improved diagnostic accuracy. However, prevailing methods relying solely on image modality guidance and pixel-wise loss functions struggle to capture the distinct characteristics of medical images, such as unique texture patterns and specific colors harboring critical diagnostic information. To overcome these challenges, this paper introduces the Multi-Modal Regularized Coarse-to-fine Transformer (M2Trans) for Ultrasound Image SR. By integrating the text modality, we establish joint image-text guidance during training, leveraging the medical CLIP model to incorporate richer priors from text descriptions into the SR optimization process, enhancing detail, structure, and semantic recovery. Furthermore, we propose a novel coarse-to-fine transformer comprising multiple branches infused with self-attention and frequency transforms to efficiently capture signal dependencies across different scales. Extensive experimental results demonstrate significant improvements over state-of-the-art methods on benchmark datasets, including CCA-US, US-CASE, and our newly created dataset MMUS1K, with a minimum improvement of 0.17dB, 0.30dB, and 0.28dB in terms of PSNR. Our code and dataset will be available at: https://github.com/eezkni/M2Trans.