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  1. 4921

    Regulatory culture and architectural project by Mario Losasso

    Published 2024-06-01
    “…They are attentive to a quality that does not depend on construction solutions or on techno-typological, morphological or physical-chemical characteristics, but which is linked to a system of needs to be satisfied, the outcome of the interaction between construction and cultural, socio-economic and environmental context. Today’s regulatory landscape has expanded, increasing sensitivities to environmental challenges, health and the qualities of living space. …”
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  2. 4922

    PETRO- AND PALEOMAGNETIC STUDIES OF BASALTS OF THE UPPER DEVONIAN APPAINSKAYA SUITE (WESTERN YAKUTIA) by К. M. Konstantinov, М. D. Tomshin, Sh. Z. Ibragimov, М. Z. Khuzin, I. K. Konstantinov, A. A. Yakovlev, E. V. Artemova

    Published 2016-12-01
    “…In the coastal outcrops at the Ygyatta river, two nappes are observed, a (stratigraphically) lower outcrop 17÷23/10 containing plagiophyre palagonite basalts (upper five meters are outcropped), and an upper outcrop 16/10 containing olivinophyric palagonite basalts (upper three meters are outcropped). …”
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  3. 4923

    Training Manual for Prevention of Covid-19 Disease among Hospital Personnel by Ali Poormohammadi, Ghasem Azarian

    Published 2020-06-01
    “…Most respiratory droplets containing viruses are in this size range. Medium droplets (10 to 50μm) remain in the air for several minutes. …”
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    Article
  4. 4924

    “Locality – Adaptation” Research of Hydropower Resettlement Communities in the Jinsha River Basin: A Case Study of Ludila Hydropower Station by Fang WANG, Zhuoqi LI, Haoyi XU, Jiaqi YAN

    Published 2025-04-01
    “…ObjectiveWithin human – environment systems, river basins constitute the earliest geographical units of human activity and currently represent the most critical zones of strained human – environment interactions. …”
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  5. 4925

    On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

    Published 2025-08-01
    “…However, together with the non-temporal FCN model, they exhibited a larger ensemble spread than XGBoost, and they yielded lower global ET estimates than what is currently understood. XGBoost showed a smaller ensemble spread compared to neural networks, in particular when conditions were poorly represented in the training data, but it was more sensitive to the covariate setup. …”
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  6. 4926