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27221 - 27230 von 32585

	27221	Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques Enthalten in Scientific reports Bd. 14, 9.8.2024, Nr. 1, date:12.2024: 1-14
	27222	Modeling and Prediction of Temporal Biogeomechanical Properties Using Novel Machine Learning Approach Enthalten in Rock mechanics and rock engineering Bd. 56, 9.5.2023, Nr. 8, date:8.2023: 5635-5655
	27223	Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms Enthalten in Friction Bd. 12, 2.4.2024, Nr. 6, date:6.2024: 1322-1340
	27224	Modeling and prediction of tribological properties of graphite-filled PTFE self-lubricating composites based on machine learning algorithms Enthalten in Journal of materials science Bd. 60, 12.6.2025, Nr. 26, date:7.2025: 10788-10813
	27225	Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media Enthalten in Scientific reports Bd. 10, 7.8.2020, Nr. 1, date:12.2020: 1-13
	27226	Modeling and validation of bending force for 6-high tandem cold rolling mill based on machine learning models Enthalten in The international journal of advanced manufacturing technology Bd. 123, 28.9.2022, Nr. 1-2, date:11.2022: 389-405
	27227	Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor Enthalten in Scientific reports Bd. 14, 10.1.2024, Nr. 1, date:12.2024: 1-20
	27228	Modeling climate change impacts and predicting future vulnerability in the Mount Kenya forest ecosystem using remote sensing and machine learning Enthalten in Environmental monitoring and assessment Bd. 197, 6.5.2025, Nr. 6, date:6.2025: 1-21
	27229	Modeling clinopyroxene-liquid trace element partition coefficients in the upper mantle: pioneering a machine learning approach Enthalten in Contributions to mineralogy and petrology Bd. 180, 3.6.2025, Nr. 6, date:6.2025: 1-19
	27230	Modeling CO2 Loading Capacity of Diethanolamine (DEA) Aqueous Solutions Using Advanced Deep Learning and Machine Learning Algorithms: Application to Carbon Capture Enthalten in The Korean journal of chemical engineering Bd. 41, 12.3.2024, Nr. 5, date:5.2024: 1427-1448

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