Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

复合煤岩循环加卸荷红外辐射及能量演化特征

为了深入分析循环加卸荷过程中复合煤岩能量演化特征,以平庄煤业集团风水沟矿为研究对象,利用三轴试验机和长波热像仪对复合煤岩在不同加卸荷速率下进行循环加卸荷试验并实现红外辐射监测,得到复合煤岩在加卸了过程中各部分表面平均红外温度变化,研究了不同加卸荷速率下弹性能密度、耗散能密度随温度及轴向载荷变化的规律,确定了总能量、弹性能和耗散能与轴向载荷和红外辐射温度之间的拟合关系。结果表明,煤岩红外辐射温度变化分为起始升温、温度平稳、温度骤降和快速升温4个阶段,其中快速升温阶段各能量增长速率最大。总能量、弹性能、耗散能与轴向载荷、煤体红外辐射温度高度相关,可以采用非线性曲线较好地拟合。研究成果为采用红外辐射温度变化法来分析循环加卸载过程中煤岩能量演化特征提供了依据。

纽曼系统护理模式结合早期肠内营养疗法对胃癌术后患者胃肠功能的影响

目的探讨纽曼系统护理模式结合早期肠内营养疗法对胃癌患者术后胃肠功能的影响。方法选取2017年7月至2018年7月间府谷县人民医院收治的92例胃癌根治术患者,采用随机数表法分为观察组和对照组,每组46例。对照组患者采用常规护理,观察组患者采用纽曼系统护理模式结合早期肠内营养疗法干预,比较两组患者术后排便、排气和肠鸣音恢复时间、术后腹胀情况及胃肠功能恢复情况。结果观察组患者术后排便、排气及肠鸣音恢复时间均少于对照组,差异均有统计学意义(均P <0. 05)。观察组患者术后1d和2d的腹胀发生率均低于对照组,差异均有统计学意义(均P <0. 05);术后3d两组腹胀率比较,差异无统计学意义(P> 0. 05)。观察组患者胃肠功能恢复优良率为56. 2%,高于对照组的30. 4%,差异有统计学意义(P <0. 05)。结论纽曼系统护理模式结合早期肠内营养疗法可较好改善胃癌患者术后的肠胃功能,有效降低腹胀发生率。