Search alternatives:
significantly increased » significant increase (Expand Search)
increased decrease » increased release (Expand Search), increased crash (Expand Search)
shape decrease » shape increases (Expand Search), small decrease (Expand Search), step decrease (Expand Search)
significantly increased » significant increase (Expand Search)
increased decrease » increased release (Expand Search), increased crash (Expand Search)
shape decrease » shape increases (Expand Search), small decrease (Expand Search), step decrease (Expand Search)
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21
Autocorrelation test results.
Published 2024“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”
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22
Pesaran’s CADF test results for Model I.
Published 2024“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”
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23
Descriptive statistics of related variables.
Published 2024“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”
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24
Urbanization and climate jointly shape the composition of plant life forms
Published 2024“…The compositions of plant life forms were significantly controlled by urbanization intensity and annual mean temperature. …”
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25
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26
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27
Generated spline library.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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28
Correlation coefficient matrix.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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29
RMSE versus learning rate.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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30
RMSE versus training parameters.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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31
Assembly process of machine recognition form.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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32
Process of steel truss incremental launching.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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33
CGAN and AutoML stacking device.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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34
U-wave estimates versus R-matrix noise variance.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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35
Sliding window process.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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36
Assembly error angle of a single spline.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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37
Original record form of error matrix.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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38
Form for machine recognition.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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39
RMSE versus architectural parameters.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”
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40
Kalman process.
Published 2025“…Following model updates with measured data, the accumulated prediction error rapidly decreases. The proposed prediction method for shape errors during pushing exhibits high accuracy and versatility in similar projects, significantly reducing time spent on manual error handling and minimizing computational inaccuracies.…”