Harbin Normal University

Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground meteorological observation data from the hydrological years 1961 to 2015 at two meteorological stations in Mohe and Mishan, Heilongjiang Province, China, this study integrates physical parameters of snow density and snow albedo from the ERA5-Land reanalysis data to systematically compare the performance of three machine learning and three deep learning models in retrieving daily snow depth. Four feature combination schemes were designed to evaluate the contributions of meteorological factors, lagged snow depth terms, and snow physical parameters. The results indicate that, for both machine learning and deep learning models, the first-order lagged value of snow depth is the most important variable determining prediction accuracy. In terms of model performance, machine learning methods excelled, with XGBoost performing particularly outstandingly, achieving optimal prediction accuracy and stability under the best feature combination (coefficient of determination, R2, reaching 0.989; root mean square error, RMSE, of 1.19 cm). Among deep learning methods, 1D CNN demonstrated strong local feature extraction capabilities, achieving prediction accuracy comparable to the best-performing machine learning model (R2 of 0.9878, RMSE of 1.26 cm). Notably, models specifically designed for time-series data, such as LSTM (R2 of 0.9848, RMSE of 1.41 cm), and the more complex 1D CNN-LSTM hybrid model (R2 of 0.9803, RMSE of 1.60 cm) did not show significant advantages in this study. This indicates that model complexity and predictive performance are not simply positively correlated. Through comprehensive analysis of data from both stations, this study demonstrates that a prediction framework centered on historical snow depth as the core driving factor, combined with key meteorological elements, is highly robust. Although the inclusion of ERA5-Land snow physical parameters did not significantly improve model accuracy, it provides important insights for the future development of hybrid models that integrate physical mechanisms with data-driven approaches. The findings offer an effective solution for reconstructing long-term snow depth time series and hold significant application value for simulating cryospheric hydrological processes and studying climate change.

ABSTRACT This study explores the experiences and perspectives of Chinese children with intellectual disabilities (ID) participating in sports competitions from multiple viewpoints, utilizing the Capabilities Approach as a theoretical framework. Through online semi-structured interviews with 15 participants (7 children with ID, 5 parents, and 3 coaches) from diverse regions of China, the research examines how sports participation co bbntributes to capability development and life value realization. Thematic analysis developed four key themes: (1) the progressive recognition of sports’ value beyond physical activity; (2) multidimensional capability development across physical, social, emotional, and agentic domains; (3) the complex interplay of sociocultural, institutional, and personal factors that facilitate or hinder participation; and (4) the processes and critical junctures through which resources are converted into expanded capabilities. The findings demonstrate that sports participation represents a powerful pathway for developing capabilities, particularly agency and autonomy, where children with ID often experience the most restriction. The study contributes to extending the Capabilities Approach to a previously understudied population within a non-Western cultural context, while providing evidence-based insights for policy development and program implementation in China. Realizing the potential of sports for capability development requires coordinated efforts across multiple levels – from families and coaches to policy makers – to create more inclusive pathways for children with ID to realize their full potential as valued members of society.

Bi2O2Se is an emerging n-type semiconductor, but conventional growth methods often rely on high temperatures or complex multisource systems that introduce defects and limit device integration. Herein, we report a simplified and modified physical vapor deposition (PVD) strategy enabling the growth of single-crystal Bi2O2Se nanosheets at a lower temperature of 500 °C. The self-powered photoelectric detector with an asymmetric structure was fabricated using a Bi2O2Se nanosheet as channel material, exhibiting an ultralow dark current of ∼10 fA, weak-light detection capability (50 nW/cm2), and detectivity up to 1.06 × 1013 Jones. The devices also show fast response time and excellent long-term stability with <10% degradation after 12 months in the atmospheric environment. Furthermore, single-pixel imaging demonstrates high contrast and fidelity. This work establishes a practical route for low-temperature growth of high-quality Bi2O2Se nanosheets and highlights its strong potential for weak-light detection, broadband sensing, and chip-scale photonic systems.