为探索GS1 Digital Link技术在产品物流中的应用潜力,分析研究了GS1系统和GS1 Digital Link的基本结构、编码特点以及技术优势,充分利用GS1 Digital Link技术可以为产品从源头到零售整个物流过程提供相关对象的Web地址编码的特点,以鲜...为探索GS1 Digital Link技术在产品物流中的应用潜力,分析研究了GS1系统和GS1 Digital Link的基本结构、编码特点以及技术优势,充分利用GS1 Digital Link技术可以为产品从源头到零售整个物流过程提供相关对象的Web地址编码的特点,以鲜活大闸蟹物流过程为例,构建了基于GS1 Digital Link的鲜活大闸蟹Web编码,为实现产品营销与追溯提供了标准化、动态化、多样化的编码数据支撑。展开更多
Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land managem...Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.展开更多
目的评估轻中度拥挤下采用iTero Element 1口内扫描仪获得的数字化模型和传统石膏模型测量数据的准确性。方法选取到昆明医科大学附属口腔医院前兴路门诊部就诊的37名患者,使用iTero Element 1口内扫描仪扫描获得数字化模型和藻酸盐取...目的评估轻中度拥挤下采用iTero Element 1口内扫描仪获得的数字化模型和传统石膏模型测量数据的准确性。方法选取到昆明医科大学附属口腔医院前兴路门诊部就诊的37名患者,使用iTero Element 1口内扫描仪扫描获得数字化模型和藻酸盐取模获得同一患者传统石膏模型,比较数字化模型自动测量和传统石膏模型人工测量牙冠宽度、牙弓宽度、覆盖的差异。结果数字化模型和石膏模型的3次重复测量一致性检验ICC值均在0.9以上,均具有良好的可重复性。轻度拥挤组11、16、21、31、36、41、43、46牙冠宽度、14~24牙弓宽度、22覆盖,中度拥挤组11、23、16、31牙冠宽度、14~24牙弓宽度、11、12覆盖差异有统计学意义(P<0.05)。结论iTero数字化模型在轻中度拥挤时部分数据与手工测量石膏模型存在一定差异,可能是由于牙齿排列不齐,导致测量误差,但测量数据差异较小,不影响iTero数字化模型在临床中的应用。展开更多
文摘为探索GS1 Digital Link技术在产品物流中的应用潜力,分析研究了GS1系统和GS1 Digital Link的基本结构、编码特点以及技术优势,充分利用GS1 Digital Link技术可以为产品从源头到零售整个物流过程提供相关对象的Web地址编码的特点,以鲜活大闸蟹物流过程为例,构建了基于GS1 Digital Link的鲜活大闸蟹Web编码,为实现产品营销与追溯提供了标准化、动态化、多样化的编码数据支撑。
基金This work was financially supported by the National Natural Science Foundation of China(41771470)the China Postdoctoral Science Foundation(2020M672776).
文摘Soil salinization is one of the most important causes of land degradation and desertification,especially in arid and semi-arid areas.The dynamic monitoring of soil salinization is of great significance to land management,agricultural activities,water quality,and sustainable development.The remote sensing images taken by the synthetic aperture radar(SAR)Sentinel-1 and the multispectral satellite Sentinel-2 with high resolution and short revisit period have the potential to monitor the spatial distribution of soil attribute information on a large area;however,there are limited studies on the combination of Sentinel-1 and Sentinel-2 for digital mapping of soil salinization.Therefore,in this study,we used topography indices derived from digital elevation model(DEM),SAR indices generated by Sentinel-1,and vegetation indices generated by Sentinel-2 to map soil salinization in the Ogan-Kuqa River Oasis located in the central and northern Tarim Basin in Xinjiang of China,and evaluated the potential of multi-source sensors to predict soil salinity.Using the soil electrical conductivity(EC)values of 70 ground sampling sites as the target variable and the optimal environmental factors as the predictive variable,we constructed three soil salinity inversion models based on classification and regression tree(CART),random forest(RF),and extreme gradient boosting(XGBoost).Then,we evaluated the prediction ability of different models through the five-fold cross validation.The prediction accuracy of XGBoost model is better than those of CART and RF,and soil salinity predicted by the three models has similar spatial distribution characteristics.Compared with the combination of topography indices and vegetation indices,the addition of SAR indices effectively improves the prediction accuracy of the model.In general,the method of soil salinity prediction based on multi-source sensor combination is better than that based on a single sensor.In addition,SAR indices,vegetation indices,and topography indices are all effective variables for soil salinity prediction.Weighted Difference Vegetation Index(WDVI)is designated as the most important variable in these variables,followed by DEM.The results showed that the high-resolution radar Sentinel-1 and multispectral Sentinel-2 have the potential to develop soil salinity prediction model.
文摘目的评估轻中度拥挤下采用iTero Element 1口内扫描仪获得的数字化模型和传统石膏模型测量数据的准确性。方法选取到昆明医科大学附属口腔医院前兴路门诊部就诊的37名患者,使用iTero Element 1口内扫描仪扫描获得数字化模型和藻酸盐取模获得同一患者传统石膏模型,比较数字化模型自动测量和传统石膏模型人工测量牙冠宽度、牙弓宽度、覆盖的差异。结果数字化模型和石膏模型的3次重复测量一致性检验ICC值均在0.9以上,均具有良好的可重复性。轻度拥挤组11、16、21、31、36、41、43、46牙冠宽度、14~24牙弓宽度、22覆盖,中度拥挤组11、23、16、31牙冠宽度、14~24牙弓宽度、11、12覆盖差异有统计学意义(P<0.05)。结论iTero数字化模型在轻中度拥挤时部分数据与手工测量石膏模型存在一定差异,可能是由于牙齿排列不齐,导致测量误差,但测量数据差异较小,不影响iTero数字化模型在临床中的应用。
