Results 131 to 140 of about 1,142,277 (286)

Nurturing student creativity through assessment for learning in music classrooms. [PDF]

Res Stud Music Educ, 2022
Bolden B, DeLuca C.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Automated Neuromuscular Assessment: Machine-Learning-Based Facial Palsy Classification Using Surface Electromyography. [PDF]

Sensors (Basel)
Manzoor I, Popescu A, Ricchizzi S, Spolaore A, Gorbachuk M, Tatagiba M, Naros G, Machetanz K.   +7 more
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Integrating data-based decision making, Assessment for Learning and diagnostic testing in formative assessment

, 2015
F. M. Van der Kleij, Jorine A. Vermeulen, K. Schildkamp, T. Eggen   +3 more
semanticscholar   +1 more source

Comparison of PRISM and numeric scale for self-assessment of learning progress during a clinical course in undergraduate dental students. [PDF]

BMC Med Educ, 2022
Schmalz G, Kreher D, Sensky T, Büchi S, Ziebolz D.   +4 more
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Predicting suicidal and self-harm ideation using ecological momentary assessment: deep learning analysis in a general population sample. [PDF]

BMC Psychiatry
Kim H, Heo SJ, Park S, Lee J, Do G, Park JY.   +5 more
europepmc   +1 more source

The Quality of Assessment for Learning score for evaluating written feedback in anesthesiology postgraduate medical education: a generalizability and decision study. [PDF]

Can Med Educ J, 2023
Choo EK, Woods R, Walker ME, O'Brien JM, Chan TM.   +4 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source