胃口
发表于 2025-3-21 17:18:36
书目名称Understanding Risk-Taking影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0941640<br><br> <br><br>书目名称Understanding Risk-Taking读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0941640<br><br> <br><br>
audiologist
发表于 2025-3-21 21:02:09
Understanding Risk-Taking978-3-030-28650-7Series ISSN 2523-7268 Series E-ISSN 2523-7276
拥护
发表于 2025-3-22 03:48:55
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厚颜无耻
发表于 2025-3-22 06:04:23
https://doi.org/10.1007/978-3-030-28650-7Interpretative research; Refexivity; Identity; Instrumental rationality; Voluntary risk taking; health st
Aura231
发表于 2025-3-22 12:42:57
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Flavouring
发表于 2025-3-22 13:48:26
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HUMID
发表于 2025-3-22 20:03:07
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APRON
发表于 2025-3-22 22:00:26
safety margins of the current design practice by comparing the aforementioned methods (i) and (ii) with nonlinear numerical simulations fully accounting for soil-structure interaction based on three-dimensional elements for both concrete foundation and soil, contact surfaces for their interface, and beam elements for reinforcing bars.
孵卵器
发表于 2025-3-23 02:07:13
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Toxoid-Vaccines
发表于 2025-3-23 09:35:48
Jens O. Zinnper and Landsat 8 Operational Land Imager are used. ArcGIS is used to extract the features from the images. Finally, ensemble classification, i.e., Random forest algorithm is used to make the prediction. The accuracy measured about 92%. Also, in this study, precision, recall, and F1 scores calculated to justify the prediction accuracy.