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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing
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of these digital twins requires reliable and predictive models for microstructure formation during steel processing. These models should be based on accurate predictions of phase stability and defect kinetics
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for translation and testing model predictions; bioinformaticians, investigating evolutionary conservation of sequence, (co)expression and regulatory modules; and modelers, developing crop-specific integrated plant
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, different time scales of predictability. Famous examples include various states in the transition to turbulent fluid flow or metastable chemical configurations. However, such transient stochastic phenomena
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continuously and accurately after surgery to measure and evaluate patients’ recovery progress, and timely detect and even predict clinical adverse events like delirium, cardiac arrhythmias and pneumonia. In
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective
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well as reservoir containment. Objectives: Design ensembles of reservoir models for UHS that capture the different scales of heterogeneity and their associated geological and conceptual uncertainties. Designing
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predict the impacts of regenerative agriculture spanning - biophysical, social, economic. At the farm level, these insights are crucial for developing and implementing effective business models. At
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, pre-operative determination via endoscopies and imaging remains unreliable for patient selection. The objective of this study is to develop an image-based machine learning prediction model to assess