Results 181 to 190 of about 108,936 (256)

The development trajectory of self-advocacy in patients with adjuvant chemotherapy after breast cancer surgery and its predictors. [PDF]

Medicine (Baltimore)
Lei S, Zhao S, Chen X.
europepmc   +1 more source

A Dual‐Branch Flux‐Based Extended Memristor Model With Machine‐Learning‐Assisted Calibration

Advanced Electronic Materials, EarlyView.
Multilayer oxide memristors integrated in crossbar arrays are described through a dual‐branch, flux‐controlled compact model. A three‐stage calibration workflow combining Latin hypercube sampling, Bayesian optimization, and gradient‐based refinement extracts device parameters from experimental data.
Davide Rossetti, K. Nikiruy, A.S. Demirkol, Alon Ascoli, M. Ziegler, R. Tetzlaff, Fernando Corinto   +6 more
wiley   +1 more source

Bayesian Networks and Causal Discovery. [PDF]

Entropy (Basel)
Gao X, Wang Z.
europepmc   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Comparative Evidence-Based Model Choice: A Sketch of a Theory. [PDF]

Entropy (Basel)
Bandyopadhyay PS, Shetty S, Brittan G.
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Peripheral frataxin levels govern long-term clinical progression in Friedreich ataxia. [PDF]

BMJ Neurol Open
Rummey C, Blair IA, Mesaros C, Rojsajjakul T, Dong Y, Wilmot G, Zesiewicz T, Mathews K, Hoyle JC, Seeberger L, Corben LA, Delatycki MB, Finkel RS, Roxburgh RH, Duquette A, Yoon G, Gomez CM, Subramony SH, Perlman S, Mccormack S, Lynch DR.   +20 more
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

The Italian Organizational Culture and Readiness Scale for System-Wide Integration of EBP (OCRSIEP) Scale: Psychometric Adaptation of an Existing Scale. [PDF]

Worldviews Evid Based Nurs
Napolitano D, Lo Cascio A, Bozzetti M, Curcio F, Amato S, Guardione R, Stievano A, Iozzo P, D'Angelo D, Notarnicola I, Tartaglini D, Ivziku D.   +11 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source