1,087 research outputs found
Deviation and Rectification of Education for College Students’ Ideal and Belief From the Perspective of New Media: A Case Study of “the Hongyan Spirit”
Get PDFThe Hongyan Spirit of Chongqing is a significant part of education for college students’ ideal and belief in China. Against the background of new media, this very spirit is thrown into the realistic crisis of being dispelled, entertained and forgotten. In order to strengthen the efficiency of education of college students’ ideal and belief in real earnest, it is of paramount importance, based on the deep understanding of the times’ implication for the Hongyan Spirit from the perspective of new media, to switch the system of communication utterance, innovate the way how dissemination vehicles integrate and explore an educational path tallied with features of new media
sj-tif-7-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-7-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-5-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-5-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-6-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-6-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-docx-8-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-docx-8-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-4-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-4-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-1-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-1-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-3-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-3-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
sj-tif-2-taj-10.1177_20406223231173891 – Supplemental material for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype
No full textSupplemental material, sj-tif-2-taj-10.1177_20406223231173891 for Trends in mortality from infection among patients with hematologic malignancies: differences according to hematologic malignancy subtype by Xuejiao Yin, Xuelian Hu, Hongyan Tong and Liangshun You in Therapeutic Advances in Chronic Disease</p
Landsat-based dataset for mapping annual center-pivot irrigated cropland in Brazil
No full text<p>Center-pivot irrigated cropland (CPIC) is a critical component of irrigation and plays an essential role in improving water use efficiency and increasing food production. To automatically extract the spatial distribution of CPIC in Brazil based on the remote sensing technology, we constructed a training dataset that supports the semantic segmentation models.</p><p>The dataset were built with the <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-5">Landsat 5</a> , 7 and 8<a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/landsat-7"> </a> images as well as the CPIC maps from <a href="https://metadados.snirh.gov.br/geonetwork/srv/por/catalog.search#/metadata/e2d38e3f-5e62-41ad-87ab-990490841073">ANA reference</a> data. We used the Landsat images in 2005, 2010 and 2015 to build the dataset.</p><p>The samples in train_images and train_masks were used to train and valid the Convolutional Neural Network models; </p><p>The samples in valid_data were used to test the model's prediction accuracy.</p><p>Pixels with values 255 and 0 in the mask samples represent the CPIC and background categories.</p><p><strong>For technical details that used to create the dataset, please refer to </strong><i><strong>https://doi.org/</strong></i><strong>10.1016/j.isprsjprs.2023.10.007.</strong></p>
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