The Learning Potential of Boundary Crossing in the Context of Product Introduction
The aim is to explore challenges related to the integration between product development and production in product introduction and, given these challenges, to analyse the learning potential of boundary crossing in the context of product introduction. The
M. Gustavsson, Kristina Säfsten
semanticscholar +1 more source
A Learning Automaton-Based Scheme for Scheduling Domestic Shiftable Loads in Smart Grids
In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game ...
Rajan Thapa +3 more
doaj +1 more source
The coastal zone consists of a diverse range of resources. This area's ecosystems are also quite complex and dynamic. Information about the coastal zone is necessary to provide knowledge to readers, which will be helpful for conservation efforts ...
Muhammad Arif +3 more
doaj +1 more source
Machine learning potential assisted exploration of complex defect potential energy surfaces
Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties.
Chao Jiang +3 more
doaj +1 more source
How discovery learning effect student’s critical thinking in biology based local potential?
This study aims to determine how the effect application of discovery learning on students' critical thinking and how the influence of discovery learning on each indicator of critical thinking.
Ezif Rizqi Imtihana, Riani Ken Utami
doaj +1 more source
The local potential of Peng’Angguran village as a source of biology learning
Local potential-based learning utilizes regional resources to help students recognize and understand their area's potential while simultaneously developing their skills in resource management and participation in relevant local activities.
Arfiyana Destaria Tarmizi +2 more
doaj +1 more source
Efficient ensemble uncertainty estimation in Gaussian processes regression
Reliable uncertainty measures are required when using data-based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian process regression (GPR) type MLIPs a stochastic uncertainty measure ...
Mads-Peter Verner Christiansen +2 more
doaj +1 more source
Author Correction: Machine learning potential for interacting dislocations in the presence of free surfaces. [PDF]
Lanzoni D, Rovaris F, Montalenti F.
europepmc +1 more source
Lattice Thermal Conductivity of Monolayer InSe Calculated by Machine Learning Potential. [PDF]
Han J, Zeng Q, Chen K, Yu X, Dai J.
europepmc +1 more source
Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. [PDF]
Han F +5 more
europepmc +1 more source

