Results 141 to 150 of about 9,029 (261)

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Effectiveness of game-based learning in mental health nursing education on self-directed learning and academic self-efficacy among nursing students. [PDF]

BMC Med Educ
Rafi A, Heidari M, Behbahani SMS, Bostan H, Tamimi A, Ahmadi M, Naeimi S.   +6 more
europepmc   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

Psychosocial Factors Explaining Academic Dropout among Scholarship Students at a Public University in Peru. [PDF]

F1000Res
Llonto-Caicedo Y, Alarcón-Villanueva G, Jiménez-Garay OD, De Jesus Paredes-Olano DM, Acuña-Romero YM, Ruiz-Díaz IG, Sánchez-Alamo DM, Vargas-Espinoza JL, Zambrano-Infante OJ, Morán-Santamaría RO.   +9 more
europepmc   +1 more source

At-risk students and the dropout rate [PDF]

, 2021
openaire   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Group metacognitive therapy for students with anxiety: an uncontrolled pre-post study of feasibility and associated outcomes. [PDF]

Front Psychiatry
Jørgensen HF, Helvig EK, Schei H, Stormyren S, Steffensen E, Johnson SU.   +5 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Perceived Suitability for Being a Dental Hygienist and Self-Compassion: A 6-Month Longitudinal Cohort Study. [PDF]

J Dent Educ
Izumi M, Liu HY, Akifusa S.
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source