Understanding population distribution and ethnic composition is essential for formulating effective regional development plans, providing insights into the evolutionary characteristics of populations, unraveling historical human heritage, and fostering cultural integration. The Dehong Autonomous Prefecture in Yunnan Province, China, stands out not only for its concentration of various ethnic minorities but also for its proximity to Myanmar. Utilizing toponymic data, night-time light data, demographic data, and cen…

Abstract This study aims to examine the impact of background colors on pro-environmental behavior surveys in both Chinese and English. A total of 146 unbalanced bilingual undergraduate students (individuals with higher proficiency in Chinese, L1, than in English, L2) in China were randomly assigned to either the green or black background versions of the survey. Prior to the survey, an Implicit Association Test (IAT) was administered to another group of 46 unbalanced bilingual undergraduates to assess implicit asso…

Model merging has emerged as a compelling data-free paradigm for multi-task learning, enabling the fusion of multiple fine-tuned models into a single, powerful entity. A key technique in merging methods is sparsification, which prunes redundant parameters from task vectors to mitigate interference. However, prevailing approaches employ a ``one-size-fits-all'' strategy, applying a uniform sparsity ratio that overlooks the inherent structural and statistical heterogeneity of model parameters. This often leads to a s…

Forests play a crucial role in maintaining the ecological balance of the Earth. While existing publicly available datasets typically offer high accuracy in identifying large-scale forest concentrations, discrepancies arise in forest extraction within smaller regions. This variation complicates the selection of appropriate forest cover datasets for specific areas. This study focuses on the southern and northern regions of China, represented by Hunan Province and Heilongjiang Province, respectively. It systematicall…

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