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Inverted perovskite solar cells (PSCs) have attracted increasing attention in recent years owing to their low-temperature fabrication proces s. However, they suffer from a limited number of electron transport materials available with [6,6]-phenyl C-61 buty ...
Hydropower plants play a crucial role in the power system facing ambitious renewable energy targets. Due to their inherent controllability, they are well suited to provide flexibility to the grid. However, an increased flexibility provision leads to a prol ...
The prediction of departure from nucleate boiling (DNB) has always been a crucial aspect of thermal-hydraulic codes for the analysis of Light Water Reactors. In this paper, GeN-Foam, a multi-physics code developed based on OpenFOAM, has been enhanced to in ...
We study the limit behaviour of sequences of non-convex, vectorial, random integral functionals, defined on W1,1, whose integrands are ergodic and satisfy degenerate linear growth conditions. The latter involve suitable random, scale-dependent weight-funct ...
To bring educational robots to classrooms, we need to consider teachers' self-efficacy and challenges in managing a robot-mediated classroom, and how to support them in overcoming these challenges. Orchestration tools are designed to support teachers by pr ...
Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the statistical guarantees on the excess risk achieved by early-stopped unconstrain ...
The log law of the wall, joining the inner, near-wall mean velocity profile (MVP) in wall-bounded turbulent flows to the outer region, has been a permanent fixture of turbulence research for over hundred years, but there is still no general agreement on th ...
Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans.The success in practice stems from the ability to successfully train massive neural ...
Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...
