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A series of advanced equipment exposed to sub-/supercritical water environments at high temperatures, high pressures, and extreme water chemistry with high salt and dissolved oxygen content faces serious corrosion problems. Obtaining on-site corrosion data for typical materials in harsh environments is crucial for operating and maintaining related equipment and optimizing various corrosion prediction models. First, this article introduces the advantages and disadvantages, usage scenarios, and future development potential of several in situ monitoring technologies, including ultrasonic thickness measurement, the infrared thermography method, microwave imaging, eddy current detection, and acoustic emission. Considering the importance of electrochemical corrosion data in revealing microscale and nanoscale corrosion mechanisms, in situ testing techniques such as electrical resistance probes, electrochemical corrosion potential, electrochemical impedance spectroscopy, and electrochemical noise that can be applied to sub-/supercritical water systems were systematically discussed. The testing platform and typical data obtained were discussed with thick and heavy colors to establish a mechanical prediction model for corrosion behavior. It is of great significance to promote the development of corrosion monitoring techniques, such as breaking through testing temperature limitations and broadening the industrial application scenarios and maturity. [ABSTRACT FROM AUTHOR]

Simple Summary: This study addresses the critical need for accurate prediction of key environmental factors—temperature, humidity, ammonia, and hydrogen sulfide—in pig houses, essential for pigs' growth and health. Traditional methods face challenges in predictive accuracy and stability. We introduce an innovative OTDBO–TCN–GRU model, a hybrid framework combining the dung beetle algorithm, temporal convolutional network, and gated recurrent unit, enhanced by the Osprey optimization algorithm (OOA). This model synergistically merges DBO's optimization power, TCN's long-term dependency handling, and GRU's proficiency in nonlinear sequence management, offering improved global detection capabilities. The OTDBO–TCN–GRU model showcases superior accuracy in environmental prediction, evident from its mean absolute error (MAE) of 0.0474, mean squared error (MSE) of 0.0039, and correlation coefficient of 0.9871. It significantly surpasses the traditional DBO–TCN–GRU and OOA models, reducing MAE and MSE by 37.2% and 66.7%, and 48.7% and 74.2%, respectively. Moreover, it outperforms mainstream models like GRU, LSTM, and XGBoost in terms of accuracy. This model significantly improves the forecasting of environmental conditions within pig houses, which is vital for maintaining optimal living conditions and ensuring the well-being of pigs. Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses. [ABSTRACT FROM AUTHOR]

Y2O3 and SiO2 co-doped ZrO2 powders with various additions of SiO2 substitution Y2O3 have been prepared by co-precipitation method. The microstructure, phase and chemical composition, phase transformation process, and phase stability after repeated calcination at 1300 °C of these powders have been investigated. The main phase of all powders is t′-ZrO2. There is no phase transformation when the powders are heated from room temperature to 1400 °C. Increasing the ratio (1:4 to 4:1) of SiO2 substitution Y2O3 will reduce the tetragonality of the powder at 1300 °C. The amorphous SiO2 will inhibit the growth of grains to a certain extent during the calcination process. The increase of the substitution ratio of SiO2for Y2O3will lead to the m-ZrO2more stable and the t′-ZrO2more unstable. The relationship among the SiO2 substitution Y2O3 addition, phase composition, and high-temperature phase stability of the powders has been discussed. Highlight: The main phase of the Y2O3 and SiO2 co-doped ZrO2 powder is t′-ZrO2. Increasing the ratio of SiO2 substitution Y2O3 will reduce the tetragonality of the powder at 1300 °C. The amorphous SiO2 will inhibit the growth of grains to a certain extent during the calcination process. The increase of the substitution ratio of SiO2 for Y2O3 will lead to the m-ZrO2 more stable and the t′-ZrO2 more unstable. [ABSTRACT FROM AUTHOR]