Using constrained-disorder principle-based systems to improve the performance of digital twins in biological systems
T Sigawi, Y Ilan - Biomimetics, 2023 - mdpi.com
Digital twins are computer programs that use real-world data to create simulations that
predict the performance of processes, products, and systems. Digital twins may integrate …
predict the performance of processes, products, and systems. Digital twins may integrate …
rAAV manufacturing: the challenges of soft sensing during upstream processing
Recombinant adeno-associated virus (rAAV) is the most effective viral vector technology for
directly translating the genomic revolution into medicinal therapies. However, the …
directly translating the genomic revolution into medicinal therapies. However, the …
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
B5GEMINI: AI-driven network digital twin
Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins
(DT) to create a virtual representation of the physical objects of a telecommunications …
(DT) to create a virtual representation of the physical objects of a telecommunications …
[HTML][HTML] Global, high-resolution mapping of tropospheric ozone–explainable machine learning and impact of uncertainties
C Betancourt, TT Stomberg, AK Edrich… - Geoscientific Model …, 2022 - gmd.copernicus.org
Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution
which is challenging to map on a global scale. Here, we present a data-driven ozone …
which is challenging to map on a global scale. Here, we present a data-driven ozone …
[HTML][HTML] Generating evidential bev maps in continuous driving space
Safety is critical for autonomous driving, and one aspect of improving safety is to accurately
capture the uncertainties of the perception system, especially knowing the unknown …
capture the uncertainties of the perception system, especially knowing the unknown …
Statistical downscaling of SEVIRI land surface temperature to WRF near-surface air temperature using a deep learning model
The analysis of the near-surface air temperature is vital for many applications such as urban
heat islands and climate change studies. In particular, extreme weather events are typically …
heat islands and climate change studies. In particular, extreme weather events are typically …
Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction
Deep learning models have proven to be a powerful tool for the prediction of molecular
properties for applications including drug design and the development of energy storage …
properties for applications including drug design and the development of energy storage …
Biodiesel production from jatropha: a computational approach by means of artificial intelligence and genetic algorithm
In the past couple of years, the world has come to realize the importance of renewable
sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies …
sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies …
Cheminformatics and artificial intelligence for accelerating agrochemical discovery
Y Djoumbou-Feunang, J Wilmot, J Kinney… - Frontiers in …, 2023 - frontiersin.org
The global cost-benefit analysis of pesticide use during the last 30 years has been
characterized by a significant increase during the period from 1990 to 2007 followed by a …
characterized by a significant increase during the period from 1990 to 2007 followed by a …