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significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
significant decrease » significant increase (Expand Search), significantly increased (Expand Search)
a decrease » _ decrease (Expand Search), _ decreased (Expand Search), _ decreases (Expand Search)
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Mean parameter values for the selected crops.
Published 2025“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies done in this field, but very few discuss implementing smart technologies to present a combined sustainable farming system. …”
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Summary statistics of key variables.
Published 2024“…Our results suggest the importance of addressing urban agglomeration costs as a means to facilitate innovative activity.</p></div>…”
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Top view of the experimental setup.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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609
Parameters of energy harvesting.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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610
Graph for Max Amplitude/Length at G<sub>y</sub> = 0.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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611
Graph for maximum Frequency at G<sub>y</sub> = 0.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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Graph for maximum Power at G<sub>y</sub> = 0.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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Summary of experimentation results.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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614
Piezoelectric eel.
Published 2025“…<div><p>Due to dwindling energy reserves and the cost-effectiveness of installation, the global trajectory is shifting towards renewable energy sources as a proficient means of energy acquisition. Among these sources, hydropower stands out as it harnesses the kinetic energy of oceanic water flow to generate power. …”
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Assessment values of machine learning models.
Published 2025“…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”
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620
List of datasets in AqSolDB.
Published 2025“…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”