Results 241 to 250 of about 74,543,651 (311)

<i>Primula pingwuensis</i> (Primulaceae), a new species in <i>Primula</i> sect. <i>Auganthus</i> from Sichuan, China. [PDF]

PhytoKeys
Zhang SY, Fang M, Hu YF, Hu Y, Gu JL, Wu ZK, Shao JW.   +6 more
europepmc   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Traditional and modern educational resource use in Emergency Medicine registrars (trainees) and specialists in South Africa. [PDF]

Afr J Emerg Med
Smit A, Lalloo V.
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

A Roadmap for Using Hybridisation Capture-Based Target Enrichment of Ancient Environmental DNA in Palaeoecology. [PDF]

Mol Ecol Resour
Foster NR, Holman LE, Armbrecht L, Courtin J, Jensen T, Pedersen MW, Schreiber L, Schroeder H, Seersholm FV, Zampirolo G, Bohmann K, Zimmermann HH.   +11 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Requiring code sharing to strengthen transparency and trust in research. [PDF]

PLoS Med
Lumbard H, Cadwallader L, Soper D, PLOS Medicine Staff Editors.   +3 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

The discovery of pesticide lead compounds from microbial secondary metabolites. [PDF]

J Pestic Sci
Kimishima A.
europepmc   +1 more source