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• Organics generated a minimum viscosity of the sodium aluminate solution. • Minimum viscosity of the solution nearly occurred at CMC of anionic organics. • Micelles and variation in solution structures changed viscosity of the sodium aluminate solution. • Organics increased the viscous flow activation energy of sodium aluminate solutions. Complicated organic behavior in the sodium aluminate solution limits the economical alumina production and improvement in alumina quality by Bayer process. Sodium gluconate (SG) or sodium stearate (SS) in sodium aluminate solution containing sodium oxalate (SO) changed the interfacial occurrence of anionic organics. The single anionic organic compounds or mixed organics initially decreased and then raised the viscosity of the sodium aluminate solution, and further increasing total organic carbon (TOC) remarkably increased the viscosity of the industrial sodium aluminate solution. Correspondingly, increasing concentration of anionic organic compound raised initially and then reduced diffusion coefficient. The minimum viscosity of the solution nearly occurred at the critical micelle concentration (CMC) of the anionic organic compound. Anionic organics also increased the viscous flow activation energy of sodium aluminate solutions. The disordered structure of sodium aluminate solution reduced the viscosity and increased the diffusion coefficient at low concentrations of organics less than CMC. Further increasing the concentration of organic compound more than CMC, micelles then contributed to the ordered structure of the sodium aluminate solution, increasing viscosity and reducing the diffusion coefficient of the sodium aluminate solution. Therefore, a large amount of micelles and the different solution structures synergistically changed the viscosity and diffusion coefficient of the sodium aluminate solution. [ABSTRACT FROM AUTHOR]

Feature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to obtain better classification performance. Therefore, this paper proposes a YOLOX detection framework based on an improved ConvNeXt which incorporates many advantages of Swin-Transformer with efficient classification performance. To address the problems of unstable IOU convergence and inaccurate regression coordinates of the algorithm, this paper adopts Manhattan distance as the new penalty term, and the new loss function combines the advantages of Euclidean distance and Manhattan distance, which has no gradient explosion in the early training period and can drop smoothly to a stable point in the late training period. The proposed algorithm is robust to negative effects such as dust, light, shadows, and occlusions, and is well-suited for masked-face detection in industrial and public scenarios. The proposed model is tested on the VOC dataset and the AIZOO open-source masked-face dataset, obtaining 5.58% AP improvement on the VOC dataset and reaching 96.665% AP on the masked-face dataset, which is better than other algorithms. • Designing a deep neural network to use for masked-face detection. • Anchor-free model is used to avoid time-consuming post-processing. • The improved ConvNeXt network improves classification performance. • Adopting Manhattan distance-based localization loss makes training stable. • The Mosaic data augmentation strategy alleviates the overfitting problem. [ABSTRACT FROM AUTHOR]