Results 131 to 140 of about 63,859 (268)

Mentes ativas (active minds): effects of a remote cognitive stimulation program. [PDF]

Dement Neuropsychol
da Silva SA, Dos Santos G, Ordonez TN, de Souza MAA, Bacelar DDS, Rodrigues YDS, Fioramonte BM, Gutierrez BAO, da Silva TBL.   +8 more
europepmc   +1 more source

Science Dropouts [PDF]

Science, 1964
openaire   +2 more sources

Experimental Demonstration of Temporally Aware Fault‐Tolerant Sensor Fusion Using Memristive Associative Learning

Advanced Electronic Materials, EarlyView.
In dynamic driving scenarios, the proposed approach ensures only temporally aligned sensor inputs to make driving decisions, preventing false activations. By enabling selective hardware‐level learning, it achieves fast, reliable responses under noisy conditions.
Kapil Bhardwaj, Ashfak Nehal, Dmitrii Semenov, Roman Sotner, Sayani Majumdar   +4 more
wiley   +1 more source

Defining and reporting treatment dropout in blended therapy for mental health: scoping review and analysis. [PDF]

NPJ Digit Med
Eicher SC, Fenski F, Behr S, Hammelrath L, Boettcher J, Schaeuffele C, Knaevelsrud C.   +6 more
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Experiences From an Internet-Delivered Treatment Program for Individuals With Obesity: Pilot Study. [PDF]

JMIR Form Res
Anderzen-Carlsson A, Imhagen A, Jansson S, Galavazi M, Karlsson J.   +4 more
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

Long-term lifestyle monitoring adherence in patients after cardiac intervention: a prospective observational trial. [PDF]

Eur Heart J Digit Health
Goevaerts WF, Tenbült-Van Limpt NCCW, Goossen SA, Birk MV, Liu Y, Regis M, Brouwers RWM, Lu Y, Kop WJ, Kemps HMC.   +9 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

Participants' experiences with the Delphi method: an online evaluation of a three-round Delphi across German-speaking countries. [PDF]

BMJ Open
Niederberger M, Dieudonné J, Schifano J, Vogt S.   +3 more
europepmc   +1 more source