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  • How to improve human happiness: multi-level mechanisms of individual, interpersonal and social factors

    Subjects: Psychology >> Social Psychology submitted time 2024-06-06

    Abstract: In the 21st century, human society is highly prosperous, but it is still facing a happiness crisis. Since the birth of scientific psychology, psychologists have been committed to understanding and improving human happiness and well-being. With the rapid development of human society and the continuous advancement of science and technology, scientific research on happiness and well-being in scientific psychology has become more comprehensive and diverse, and has gradually transformed from an abstract concept to an interdisciplinary research field that is quantifiable, operational and improvable. Based on the theoretical framework, research paradigm and research conclusions of scientific psychology, this article sorts out and comments on how factors at the individual, interpersonal and social levels affect human happiness and well-being, and explores the great potential and future prospects of scientific psychology in improving human happiness and well-being.

  • 樟子松人工固沙林冠幅—胸径模型

    Subjects: Environmental Sciences, Resource Sciences >> Basic Disciplines of Environmental Science and Technology submitted time 2018-09-03 Cooperative journals: 《干旱区研究》

    Abstract:基于章古台地区22块樟子松(Pinus sylvestris var. mongolica)人工纯林标准地的702棵樟子松立木数据。构建了樟子松固沙林冠幅-胸径关系的基础模型、广义模型及基于混合效应的基础模型和广义模型;比较了随机选择样本木、选择平均胸径树、选胸径较小树和选胸径较大树4种方案,计算混合模型随机参数时的混合模型预测精度;最后分析了不同林木因子和林分变量对冠幅-胸径关系的影响。模型评价指标包括决定系数(R2)、平均绝对误差(MAE)以及均方根误差(RMSE)。结果表明:枝下高(HCB)、相对植距(RS)和林龄(A)对冠幅-胸径关系影响最为显著;混合模型拟合精度(基础混合模型R2、MAE和RMSE分别是:0.7030、0.3866和0.5154;广义混合模型R2、MAE和RMSE为:0.7051、0.3822和0.5136)高于最小二乘法回归(OLS)模型(基础模型R2、MAE和RMSE分别为:0.5875、0.4696、0.6075;广义模型R2、MAE和RMSE分别为:0.6618、0.4155和0.5500)。基础混合模型和广义混合模型差异较小(2模型R2、MAE和RMSE均相差1%左右)。冠幅随HCB和A的增大而减小,随RS的增大而增大。进行冠幅预测时,推荐使用基础混合模型并从每块标准地选择2棵平均木冠幅计算其随机参数,或使用方法较为简单的OLS广义模型预测单木冠幅大小。