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Artificial intelligence based prediction and multi-objective RSM optimization of tectona grandis biodiesel with Elaeocarpus Ganitrus.
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Department of Engineering, FH Campus Wien - University of Applied Sciences, Favoritenstraße 226, Vienna, 1100, Austria.
Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi's biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy.
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Department of Gastroenterology & Hepatology, Tytgat Institute for Liver and Intestinal Research, Amsterdam University Medical Center, Amsterdam, The Netherlands.
[The many ways flowers send signals to pollinators].
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Institut d'Écologie et des Sciences de l'Environnement de Paris (iEES Paris), Paris, France - Sorbonne Université, 4 place Jussieu, 75005 Paris, France.
The evolutionary success of angiosperms, which make up more than 95 percent of the world's terrestrial flora, is largely based on their interactions with animal pollinators. Indeed, it is estimated that, on average, 87.5 percent of flowering plants are pollinated by animals.
View Article and Find Full Text PDFDevelopment of an Analytical Model for Predicting the Shear Viscosity of Polypropylene Compounds.
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Institute for Plastics Processing (IKV) in Industry and Craft, RWTH Aachen University, Seffenter Weg 201, 52074 Aachen, Germany.
The need for an efficient adaptation of existing polypropylene (PP) formulations or the creation of new formulations has become increasingly important in various industries. Variations in viscosity resulting from changes in raw materials, fillers, and additives can have a significant impact on the processing and quality of PP products. This study presents the development of an analytical model designed to predict the shear viscosity of complex PP blends.
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