Search alternatives:
increase decrease » increased release (Expand Search), increased crash (Expand Search)
significant bins » significant bias (Expand Search), significant bit (Expand Search), significant hits (Expand Search)
bins decrease » point decrease (Expand Search), sizes decrease (Expand Search), nn decrease (Expand Search)
increase decrease » increased release (Expand Search), increased crash (Expand Search)
significant bins » significant bias (Expand Search), significant bit (Expand Search), significant hits (Expand Search)
bins decrease » point decrease (Expand Search), sizes decrease (Expand Search), nn decrease (Expand Search)
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Soil disturbance cross-sectional model.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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Test plan table.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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Gas jet subsoiling model.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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Schematic diagram of the airflow passage.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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Variation in soil penetration resistance.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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Supersonic gas jet subsoiler experiment.
Published 2025“…Furthermore, the subsoiling effect was further improved as the working speed decreased and the frequency of air blasts increased, satisfying the subsoiling operation assessment standards. …”
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R project including metadata update.
Published 2025“…<div><p>The increasing number of Barcode of Life Database (BOLD) records per species and genus leads to contradictory species assignments within Barcode Index Numbers (BINs), serving as identifiers for the BOLD ID engine. …”
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Classes of errors and gaps in BOLD metadata.
Published 2025“…<div><p>The increasing number of Barcode of Life Database (BOLD) records per species and genus leads to contradictory species assignments within Barcode Index Numbers (BINs), serving as identifiers for the BOLD ID engine. …”
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Evaluation results.
Published 2024“…Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. …”
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Dataset with steel insert.
Published 2024“…Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. …”
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Reference dataset.
Published 2024“…Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. …”
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Dataset with aluminium insert.
Published 2024“…Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. …”