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Accurate, timely, and fine-resolution crop phenology is essential for determining the optimal timing of agronomic management practices supporting precision agriculture and food security. Synthetic Aperture Radar (SAR) methods, unaffected by cloud occlusion, have been widely applied in monitoring maize phenology. Nonetheless, their reliance on manual threshold settings, which depend on the user's expertise, limits their applicability. Furthermore, the neglect of SAR's potential for monitoring other phenological periods (e.g., seven-leaves date (V7), jointing date (JD), tassel date (TD), and milky date (MID)) hinders their robustness, particularly for regional-scale applications. To address these issues, this study used an adaptive dynamic threshold to evaluate the ability of the Sentinel-1 cross-polarization ratio (CR) in detecting the three-leaves date (V3), V7, JD, TD, MID, and maturity date (MD) of maize. We analyzed the effect of incidence angle, precipitation, and wind speed on Sentinel-1 features to identify the optimal feature for time series fitting. Then, we employed linear regression to determine the optimal threshold and developed an adaptive dynamic threshold for phenology detection. This approach effectively mitigated the speckle noise of Sentinel-1 and minimized artificial interference caused by customary conventional thresholds. Finally, we mapped phenology across 8.3 million ha in Heilongjiang Province. The results indicated that the approach has a higher ability to detect JD (RMSE = 11.10 d), MID (RMSE = 10.31 d), and MD (RMSE = 9.41 d) than that of V3 (RMSE = 32.07 d), V7 (RMSE = 56.37 d), and TD (RMSE = 43.33 d) in Sentinel-1. Compared with Sentinel-2, the average RMSE of JD, MID, and MD decreased by 4.14%, 35.28%, and 26.48%. Moreover, when compared to different thresholds, the adaptive dynamic threshold can quickly determine the optimal threshold for detecting each phenological stage. CR is least affected by incident angle, precipitation, and wind speed, effectively suppressing noise to reflect phenological development better. This approach supports the rapid and feasible mapping of maize phenology across broad spatial regions with a few samples. [ABSTRACT FROM AUTHOR]

Aesthetic literacy originates from rich aesthetic experiences, one of the principal ways people gain such experiences is through arts appreciation. Sixty-seven students participated in 119 art appreciation activities at out-of-school public art venues in China. A digital education platform was used to record students’ appreciation process. A recommender system was used to recommend relevant art knowledge to students. The data obtained were used to analyse the learning characteristics of students in out-of-school art appreciation and the effectiveness of the recommender system in forming aesthetic experiences. The analysis showed that students were satisfied with this learning model, with NPS = 37.313, but their self-regulation was low (k = 0.606) and universal (std = 2.755). The efficiency of the recommender system was significantly associated with several aspects of students’ motivational learning strategies, including intrinsic goal orientation (0.503), task value (0.638), and critical thinking (0.643). Focus group interviews of students with different characteristics revealed that the autonomous and interactive nature of out-of-school art appreciation was the main attraction for students and the recommender system was able to complement and improve students’ art knowledge and broaden their artistic horizons. Overall, in an informal learning environment, the use of an AI recommender system can effectively enable students to form aesthetic experiences. [ABSTRACT FROM AUTHOR]