Results 161 to 170 of about 1,608,822 (283)

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source

Impact of Learning Style on Medical Students' Satisfaction With Hands-On Procedural Training. [PDF]

Cureus
Cohn H, Sternberg M, Evans M, Reddy S, Vu E, Barkho K, Hedges W, Khan S, DeWeert D.   +8 more
europepmc   +1 more source

Providing Instruction Based on Students' Learning Style Preferences Does Not Improve Learning. [PDF]

Front Psychol, 2020
Rogowsky BA, Calhoun BM, Tallal P.
europepmc   +1 more source

PRELIVE: A Framework for Predicting Lipid Nanoparticles In Vivo Efficacy and Reducing Reliance on Animal Testing

Advanced Functional Materials, EarlyView.
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy, Chuan‐En Lu, Kai Liu, Audrey Gallud   +3 more
wiley   +1 more source

Assessing individual learning styles of undergraduate medical students utilizing Kolb's learning style inventory. [PDF]

Pak J Med Sci
Zareen N, Rashid F, Alvi T, Sajid RF.
europepmc   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

Learning Style Preference and Its Correlation with Gender. [PDF]

Niger Med J
Buowari DY, Joe AI, Erekosima I.
europepmc   +1 more source

Conductance‐Dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Advanced Functional Materials, EarlyView.
A nanoscale SrTiO3 memristor is shown to exhibit dynamic synaptic behavior through the interaction of local electrical and global optical signals. Its photoresponse depends quantitatively on the conductance state, which evolves and decays over tunable timescales, enabling ultralow‐power, biorealistic learning mechanisms for advanced in‐memory and ...
Christoph Weilenmann, Hanglin He, Marko Mladenović, Till Zellweger, Kevin Portner, Klemens Bauer, Guillaume Bellec, Mathieu Luisier, Alexandros Emboras   +8 more
wiley   +1 more source

Does learning style influence performance in objective structured clinical examinations? [PDF]

BMC Med Educ
Ezanno AC, Pirot F, Lavolé J, Gall GL, Mestiri R, Delattre C, Le Burel S, Le Mener S, Gominet M, Ficko C, Nielly H.   +10 more
europepmc   +1 more source

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines. [PDF]

Entropy (Basel), 2020
Troussas C, Krouska A, Sgouropoulou C, Voyiatzis I.   +3 more
europepmc   +1 more source