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In contrast to the marine environment, coastal regions encompass substantial saline soils characterized by complex corrosive chemical compositions. This poses notable challenges to the durability of concrete structures erected in coastal dredger fill silty soil environments. This research undertook concrete chloride corrosion assessments in both a dredger fill silty soil environment and a simulated solution environment. The findings demonstrated a progressive escalation in the free chloride concentration within concrete specimens, as the exposure duration was extended from 60 to 120 d, and discernible convection zones were observed with depths ranging from 6 to 8 mm. The investigation revealed a diminishing trend in the apparent chloride diffusion coefficient, corresponding to the elongation of exposure time and the augmentation of burial depth. Paradoxically, the burial depth and exposure duration exhibited converse effects on the apparent surface chloride concentration. Empirical formulations were derived to express the apparent surface chloride concentrations and apparent chloride diffusion coefficients as dependent on the exposure time and burial depth variables. These models exhibited an excellent goodness of fit, reaching up to 0.96. Notably, concrete specimens interred at a depth of 0.0 m displayed a favorable likeness to the simulated solution environment throughout the 60 d exposure period. [ABSTRACT FROM AUTHOR]

Several aryl hydrocarbon receptor (AhR) agonists have been reported to promote the generation of regulatory T cells (Treg cells), and the action mechanisms need to be identified. In this study, we addressed the underlying mechanism of AhR activation to induce the generation of Treg cells in the view of cellular metabolism. Naïve CD4+ T cells were purified with mouse CD4+ CD62L+ T Cells Isolation Kits. The proportions of Treg cells were detected by flow cytometry. The value of oxygen consumption rate (OCR) of CD4+ T cells was detected by the Seahorse XFe 96 analyzer. The activation of fatty acid oxidation (FAO)‐related metabolic pathways was detected by Western blotting. Intracellular localization of Lkb1 was detected by immunofluorescence. The Strad‐Mo25‐Lkb1 complex formation and K63 chain ubiquitination modification of Lkb1 were detected by co‐immunoprecipitation. The binding of AhR to the Skp2 promoter was detected by constructing luciferase reporter gene. AhR or carnitine palmitoyltransferases 1 was knockdown in dextran sulphate sodium (DSS)‐induced colitis or collagen‐induced arthritis (CIA) mice by infecting mice with adeno‐associated virus via the tail vein injection. Compared to the control group, exogenous and endogenous AhR agonists 3,3′‐diindolylmethane (DIM) and 2‐(1′H‐indole‐3′‐carbonyl)‐thiazole‐4‐carboxylic acid methyl ester (ITE) were shown to preferentially upregulate the mRNA expression of FAO‐related enzymes and the value of OCR. Consistently, pharmacological or genetic inhibition of FAO markedly diminished the induction of DIM and ITE on the differentiation of Treg cells. DIM and ITE functioned mainly through activating the liver kinase B1 (Lkb1)‐AMPK pathway via promotion of Lkb1‐Strad‐Mo25 complex formation and Lkb1 K63 ubiquitination. DIM and ITE were also shown to upregulate the mRNA expression of Skp2, a ubiquitination‐related enzyme, and facilitate the binding of AhR to the xenobiotic responsive element of Skp2 promoter region by luciferase reporter gene assay. Furthermore, the contribution of Skp2/K63 ubiquitination/Lkb1/FAO axis was verified in (DSS)‐induced colitis or CIA mice. In summary, these findings indicate that AhR activation promotes Treg cell generation by enhancing Lkb1‐mediated FAO via the Skp2/K63‐ubiquitination pathway, and AhR agonists may be used as inducers of Treg cells to prevent and treat autoimmune diseases. [ABSTRACT FROM AUTHOR]

The flow behaviors of ultrahigh strength steel BR1500HS at elevated temperature were studied by performing hot tension tests at the temperatures of 773 K, 873 K, 1023 K and 1173 K, and strain rates of 0.01 s−1, 0.1 s−1 and 1 s−1 on a Gleeble 3500 thermo-mechanical simulator. The true stress-strain curves were obtained and their characteristics were analyzed. Relationships among the maximum stress, temperature and strain rate were described by means of the conventional hyperbolic sine equation. The average deformation activation energy in the whole deformation temperatures was determined as Q = 235.257 KJ/mol by regression analysis. Based on σ(dσ/dε) verse σ2 curves, the values of dynamic recovery (DRV) rate coefficient, r, saturated stress, σrec, and yield strength, σ0, under different deformation conditions were calculated. In order to estimate the DRV volume fractions, the modified Avrami type equation including r (r = 106.911351Z−0.059) as a function of the temperature compensating parameter, Z, was established, and then the effects of deformation conditions on the DRV kinetics were described in details.

In recent years, generative adversarial networks (GANs) have made great progress in the field of hyperspectral image classification (HIC), which alleviates the problem of insufficient training samples to a large extent. At present, GANs in the field of HIC are all based on convolutional neural networks (CNNs). But CNNs cannot extract sequence information well, and it is difficult to model remote dependencies. However, the hyperspectrum is rich in spectral sequence information, and Transformer has been proven to be good at processing sequence information. Therefore, in order to process spectral information and alleviate the problem of insufficient training samples of hyperspectral images (HSIs), we put forward a new frame Transformer with residual upscale GAN (TRUG). TRUG includes a generator G and a discriminator D. In the G, we propose residual upscale (RU) to improve the resolution of generated features, while also extracting texture features and capturing context relationships. In addition, we visualized the generated fake images for more intuitive analysis. In the D, we adopt the Transformer block with progressively decreasing scale and use grid self-attention mechanism in the first layer to better extract features for classification. In addition, GANs are prone to the problem of unstable training. In order to solve this problem, we improve the normalization algorithm and add relative position coding. We applied a pure Transformer-based GAN to HIC datasets. Experimental results show that the proposed TRUG model has a better performance than other models on the three datasets. [ABSTRACT FROM AUTHOR]