Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial dist...Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.展开更多
Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are n...Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.展开更多
目的:提出一种相位式呼吸门控放疗中门控窗口的选择方法。方法:采用数字表法随机选取某院已完成基于实时位置管理(real position management,RPM)系统的相位式呼吸门控放疗的38例患者。针对每个患者的参考呼吸波形,分别计算所有可选的10...目的:提出一种相位式呼吸门控放疗中门控窗口的选择方法。方法:采用数字表法随机选取某院已完成基于实时位置管理(real position management,RPM)系统的相位式呼吸门控放疗的38例患者。针对每个患者的参考呼吸波形,分别计算所有可选的100(10×10)个窗口内的呼吸信号标准差,作为衡量该窗口内呼吸运动稳定性的指标。在100个可选窗口中,将与临床所选窗口占空比相同但呼吸信号标准差最小的窗口作为建议窗口。比较并分析38例患者的临床窗口和基于该方法选择的建议窗口之间的呼吸信号标准差差异,并进行配对样本t检验。结果:38例患者中,临床窗口的呼吸信号标准差为(0.114±0.050)cm,建议窗口的呼吸信号标准差为(0.108±0.049)cm,二者相比有统计学差异(P=0.009)。27例患者的建议窗口与临床窗口一致,其余11例患者的建议窗口与临床窗口相比呼吸信号标准差有所减小(窗口内呼吸运动稳定性提高),其中1例下降幅度超过30%,8例下降幅度在5%~30%,2例下降幅度在5%以内。结论:在相位式呼吸门控放疗中,基于窗口内的呼吸信号标准差选择门控窗口的方法可以保证和提高临床所选门控窗口的质量。展开更多
Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers ha...Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.展开更多
基金funded by the Science and Disruptive Technology Program, AIRCAS(Grant No. 2024-AIRCAS-SDPT- 15)the National Natural Science Foundation of China (Grant No. 42471372).
文摘Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.
基金supported by the National Natural Science Foundation of China (Grant No. 42061004)the Joint Special Project of Agricultural Basic Research of Yunnan Province (Grant No. 202101BD070001093)the Youth Special Project of Xingdian Talent Support Program of Yunnan Province
文摘Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.
文摘目的:提出一种相位式呼吸门控放疗中门控窗口的选择方法。方法:采用数字表法随机选取某院已完成基于实时位置管理(real position management,RPM)系统的相位式呼吸门控放疗的38例患者。针对每个患者的参考呼吸波形,分别计算所有可选的100(10×10)个窗口内的呼吸信号标准差,作为衡量该窗口内呼吸运动稳定性的指标。在100个可选窗口中,将与临床所选窗口占空比相同但呼吸信号标准差最小的窗口作为建议窗口。比较并分析38例患者的临床窗口和基于该方法选择的建议窗口之间的呼吸信号标准差差异,并进行配对样本t检验。结果:38例患者中,临床窗口的呼吸信号标准差为(0.114±0.050)cm,建议窗口的呼吸信号标准差为(0.108±0.049)cm,二者相比有统计学差异(P=0.009)。27例患者的建议窗口与临床窗口一致,其余11例患者的建议窗口与临床窗口相比呼吸信号标准差有所减小(窗口内呼吸运动稳定性提高),其中1例下降幅度超过30%,8例下降幅度在5%~30%,2例下降幅度在5%以内。结论:在相位式呼吸门控放疗中,基于窗口内的呼吸信号标准差选择门控窗口的方法可以保证和提高临床所选门控窗口的质量。
文摘Omics data provides an essential means for molecular biology and systems biology to capture the systematic properties of inner activities of cells. And one of the strongest challenge problems biological researchers have faced is to find the methods for discovering biomarkers for tracking the process of disease such as cancer. So some feature selection methods have been widely used to cope with discovering biomarkers problem. However omics data usually contains a large number of features, but a small number of samples and some omics data have a large range distribution, which make feature selection methods remains difficult to deal with omics data. In order to overcome the problems, wepresent a computing method called localized statistic of abundance distribution based on Gaussian window(LSADBGW) to test the significance of the feature. The experiments on three datasets including gene and protein datasets showed the accuracy and efficiency of LSADBGW for feature selection.
