Search alternatives:
after optimization » based optimization (Expand Search), model optimization (Expand Search), path optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
library based » laboratory based (Expand Search)
binary basic » binary mask (Expand Search)
based after » based water (Expand Search), named after (Expand Search), cases after (Expand Search)
basic codon » basic column (Expand Search)
after optimization » based optimization (Expand Search), model optimization (Expand Search), path optimization (Expand Search)
codon optimization » wolf optimization (Expand Search)
library based » laboratory based (Expand Search)
binary basic » binary mask (Expand Search)
based after » based water (Expand Search), named after (Expand Search), cases after (Expand Search)
basic codon » basic column (Expand Search)
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Supporting data for "clinical-oriented surgical planning based on finite element method and automate-generated surgical templates assisting the spinal surgery"
Published 2024“…Remaining algorithm needed was reimplemented from open-source libraries.…”
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COSMO-Bench
Published 2025“…<p dir="ltr"><b><i>Abstract</i></b>: Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). …”
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Aluminum alloy industrial materials defect
Published 2024“…</p><h2>Description of the data and file structure</h2><p dir="ltr">This is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.…”
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Code
Published 2025“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”
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Core data
Published 2025“…</p><p><br></p><p dir="ltr">This architecture was implemented using the PyTorch library and trained using cross-entropy loss. The model was optimized to classify RNA sequences, achieving robust performance across multiple test sets.…”
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<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)
Published 2025“…All samples were filtered before HPLC analysis with hydrophobic filters of 0.22 μm. After several tests to optimize the distinctive cape fig betalains while reducing chromatographic time, the initial solvent composition was set to 10 % B and increasing linearly to 29 % over 15 min, then to 33 % over 5 min. …”