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COVID-19-related school closures worldwide had an enormous impact on literacy instruction and learning. In this article, we share an overview of what research tells us about what happened to elementary learners' reading comprehension during these school closures and what reading comprehension instruction looked like in homes and schools during this time. We also provide recommendations for the path forward on the road to revitalizing reading comprehension achievement.

Under long-term cyclic loading, marine soft clay in coastal areas is more likely to exhibit partially drained behavior than undrained behavior. The partially drained dynamic behavior of marine soft clay tends to exert a significant effect on the post-cyclic characteristics. Thus, this study conducted two series of monotonic shearing tests to investigate the mechanical behavior of marine soft clay after long-term (50,000 cycles) traffic loading in terms of variable confining pressure (VCP). The post-cyclic stress path, pore water pressure, and shear strength were discussed through comparison of the results with those of the standard monotonic shearing test (without cyclic loading history). Partially drained cyclic loading enhanced the post-cyclic shear strength and increased the brittleness of the specimen. The behavior of the pore water pressure and void ratio of post-cyclic soft clay were similar to those of clay during undrained cyclic loading to the reconsolidation process, that is, the specimen exhibited quasi-overconsolidation performance after partially drained cyclic loading. Subsequently, the relationships between the quasi-overconsolidation ratio (QOCR) and the total cyclic stress and strength ratios were developed, and the strength-dependent parameter of the latter relationship was determined through the standard undrained monotonic shearing test of overconsolidation soil. [ABSTRACT FROM AUTHOR]

The measured displacement-driven structural health monitoring (SHM) model is a significant approach to interpreting the influencing mechanism of environmental loads on dam displacement and to predicting the future operating behavior. Considering the large ambient temperature difference in cold area and the insufficient interpretive performance of the hydrostatic-seasonal-time (HST) model on thermal displacement behavior caused by the short-term dynamic fluctuations of ambient temperature, a hydrostatic-thermal-time (HTT) model considering hysteresis effect of boundary temperature is proposed to quantitatively analyze the influencing mechanism of environmental loads on displacement behavior of concrete gravity dams in cold area. Meanwhile, to improve the predictive performance of the proposed HTT model, whale optimization algorithm (WOA)-optimized gated recurrent unit (GRU) network is employed to effectively excavate the complex nonlinear relationship between dam displacement and its explanatory variables. A roller compacted concrete (RCC) gravity dam in cold area is taken as an example, and the cause of the disharmonious horizontal displacement behavior of an overflow dam crest is explained via the proposed HTT model and numerical simulation method. The conclusion is conducive for better understanding the displacement behavior of concrete dams in cold area. Meanwhile, engineering examples show that the proposed HTT model can reasonably interpret the thermal displacement behavior caused by the ambient temperature variations, and the WOA-optimized GRU network-based HTT model exhibits excellent fitting and predicting capabilities. A novel technique is provided for the accurate prediction of dam displacement. [ABSTRACT FROM AUTHOR]

In conventional dam displacement monitoring models, forecast precision is below the standard, the fitting residual sequence contains chaotic components, and information mining of dam prototype observation data is limited. In consideration of the chaotic characteristics of the fitting residual sequence in regression model, the multi-scale wavelet analysis is used to decompose and reconstruct the residual sequence in this study; back propagation neural network and autoregressive integrated moving average model are used to forecast the reconstructed residual sequence by identifying the high-frequency and low-frequency characteristics of signals. By superimposing the residual forecast value with the forecast value of regression model, the combination forecast model for concrete dam displacement considering residual correction is proposed. Examples show that, compared with conventional models, the proposed combination model is better in fitting precision and convergence speed. Forecast capability is significantly improved for dam displacement forecast when effective components contained in residual sequence are considered. A new method of displacement forecast for high slope and other hydraulic structures is presented. [ABSTRACT FROM AUTHOR]