Results 171 to 180 of about 179,819 (285)
Der FH Bielefeld zum Fünfzigsten / Fiftieth Anniversary of the Bielefeld UAS
Klaus Hurrelmann
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Abstract Deep learning neural networks (DLNNs) hold great potential for modeling groundwater flow, but their performance depends on data availability. Physics‐informed neural networks (PINNs) help to reduce the reliance of DLNNs on data by integrating physical laws into the training process. This approach is increasingly used in applications related to
Adhish Virupaksha +5 more
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Stocheometric Crop Weather Model to predict the Finger Millet Grain Yield
K.S. MURALIDHARA, M.B. RAJEGOWDA
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Octopamine signaling from clock neurons plays dual roles in Drosophila long-term memory. [PDF]
Kurata Y, Yoshii T, Sakai T.
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Integrating Satellite Data Into Meso‐Scale Habitat Modeling for Non‐Perennial Rivers and Streams
Abstract Non‐perennial rivers and streams (NPRs) are widespread and projected to become more common, yet effective tools for their characterization and management remain limited. This study introduces a novel approach to assess aquatic habitat availability in NPRs, applied to a regulated reach of the Sangone River (Northern Italy).
G. Negro +8 more
wiley +1 more source
Weekly, monthly and seasonal rainfall at Bengaluru in Karnataka
B.T. RAVINDRABABU +3 more
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ShineGAL4 drivers for tissue and cell-type specific optogenetics in Drosophila. [PDF]
Girard V +4 more
europepmc +1 more source
Automated Crop Residue Estimation via Unsupervised Techniques Using High-Resolution UAS RGB Imagery [PDF]
Fatemeh Azimi, Jinha Jung
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