Results 201 to 210 of about 38,724 (262)

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Cystatin-C as an Early Marker of Cardiac Surgery Associated-Acute Kidney Injury (CSA-AKI) in Children with Congenital Heart Diseases in Southwest Nigeria. [PDF]

Ann Card Anaesth
Makinde RA, Alaje AK, Ajose AO, Adedeji TA, Onakpoya UU.   +4 more
europepmc   +1 more source

Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Advanced Intelligent Discovery, EarlyView.
This study presents an automated system integrating a capillary force gripper and machine learning‐based object detection for sorting and placing submillimeter objects. The system achieved stable and simultaneous manipulation of four object types, with an average task time of 86.0 seconds and a positioning error of 157 ± 84 µm, highlighting its ...
Satoshi Ando, Naoto Watanabe, Chihiro Sekine, Shogen Sekiguchi, Ohmi Fuchiwaki   +4 more
wiley   +1 more source

Students' Perceptions of Staple Elements of the Doctoral Program: Research, Courses, Exams, and Seminars. [PDF]

J Chem Educ
Harshman J, Busby B, Obayuwana E.
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

The effect of school science education on students' climate literacy: a three-level meta-analysis. [PDF]

Front Psychol
Chen J, Ren Y, Yang Y.
europepmc   +1 more source

Variations in Haematological and Biochemical Indices in Aggressive Periodontitis: A Case-Control Study in Ghana. [PDF]

Health Sci Rep
Banyeh M, Ayettey-Adamafio MNB, Owopetu O, Yusuf AA, Zubair LA, Amarquaye P, Tayo B, Anie HNGA, Adanu R, Olugbenga O.   +9 more
europepmc   +1 more source

Assessing Science Practices in Undergraduate Chemistry Laboratories: Why We Need to Do Betterand How We Should. [PDF]

J Chem Educ
Bapu Ramesh V, Seery MK, Cole R.
europepmc   +1 more source

A reflection on 'A hydrazone-based covalent organic framework for photocatalytic hydrogen production': teaching sponges new tricks. [PDF]

Chem Sci
Rodríguez-Camargo A, Lotsch BV.
europepmc   +1 more source

Rethinking the Nature and Extent of Inductive Effects in Organic Compounds. [PDF]

J Chem Educ
Elliott MC, Johnson EC, Gregory KP, Hughes CE.   +3 more
europepmc   +1 more source