Results 131 to 140 of about 13,707,469 (301)

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Forest essays, level 6

, 2016
Man find hoary bat, becomes bat man -- Wood duckling leaps for life -- Day in the life of a girl flunkey -- Learning about worms from the inside out -- Water journeys from forest to faucet -- Young ant queen begins her reign -- Not just a pretty face ...

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Minnesota Forest Scene, Issue 24 (Spring 2020)

, 2020
1 electronic resource (PDF, 14 pages; includes color photographs)University of Minnesota. Department of Forest Resources; Department of Forest Resources, University of Minnesota. (2020). Minnesota Forest Scene, Issue 24 (Spring 2020).
Department of Forest Resources, University of Minnesota, University of Minnesota. Department of Forest Resources   +1 more
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Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Minnesota Forest Scene, Issue 19 (Fall 2017)

, 2017
1 electronic resource (PDF, 6 pages; includes color photographs)University of Minnesota. Department of Forest Resources; University of Minnesota, Department of Forest Resources. (2017). Minnesota Forest Scene, Issue 19 (Fall 2017).
University of Minnesota, Department of Forest Resources, University of Minnesota. Department of Forest Resources   +1 more
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Meta random forest : random forest with simple random forest as base model

, 2020
The abundance of data along with the significant rise of computational power boost the popularity and usage of machine learning in recent decision making and forecasting.
Kurniawan, Billy
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Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Modeling Site Specific Heterogeneity in an On-Site Stratified Random Sample of Recreational Demand [PDF]

Using estimation of demand for the George Washington/Jefferson National Forest as a case study, it is shown that in a stratified/clustered on-site sample, latent heterogeneity needs to be accounted for twice: first to account for dispersion in the data ...
Bergstrom, John C., Sardana, Kavita
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Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns, Lateef Abdul, Stephan Barth, Martin Glauer, Manuel Günther, Fabian Neuhaus, Jan‐Ole Perschewski, Thomas Bjarsch, Henrik Abel, Johannes Bosch, Niklas Meyer, Peter Hofmann, Marc Drechsler, Alexander Dyck, Dhanunjaya Kumar Nerella, Marvin Tegeler, Oleg Shchyglo, Ingo Steinbach, Timo Klecker, Henk Birkholz, Ehsan Borzabadi Farahani, Bernard Fedelich, Cetin Haftaoglu, Tarakeshwar Lakshmipathy, Elena Garcia Trelles, Christoph Schweizer, Reza Darvishi Kamachali, Lukas Koschmieder, Janin Eiken, Markus Apel, Sung‐Min Wi, Jiangdong Zhao, Andreas Fezer, Nicolas Christ, Patrick Schneider, Eldor Urinov, Veit Königer, Volker Knoblauch, Janik Harter, Thomas Seifert, Thitichai Janpheng, Florian Fuchs, Fabian Teichert, Jörg Schuster, Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann, Felix Wentzien, Heike Wittek, Benjamin Klie, Alexander Röhrs, Ludger Overmeyer, Achraf Atila, Marcel Sadowski, Leopold Talirz, Jan Janssen, Jesper R. Pedersen, Peter Beck, Tejs Vegge, Ivano E. Castelli, Wolfgang Wenzel, Tilmann Hickel, Jörg Schaarschmidt   +67 more
wiley   +1 more source