Results 31 to 40 of about 116,653,606 (265)

Seismic Fragility Analysis of Structures Based on Adaptive Gaussian Process Regression Metamodel

Shock and Vibration, 2021
Metamodel-based seismic fragility analysis methods can overcome the challenge of high computational costs of problems considering the uncertainties of earthquakes and structural parameters; however, the accuracy of metamodels is difficult to control.
Yanjie Xiao, Feng Yue, Xun'an Zhang
doaj   +1 more source

Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry

FEBS Letters, EarlyView.
EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri, Ayaka Nagasato‐Ichikawa, Keita Iida, Shuhei Kimura, Akira Imamoto, Mariko Okada   +5 more
wiley   +1 more source

Reliability Analysis of Tuned Mass Damper-Equipped Structures Under Stochastic Excitation

Vibration
Tuned mass dampers (TMDs) are commonly used to reduce excessive vibrations in engineering structures. Although their vibration control performance has been widely studied, the reliability of TMD-equipped structures under stochastic excitations has not ...
Lun Shao, Alexandre Saidi, Abdel-Malek Zine, Mohamed Ichchou   +3 more
doaj   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Multi-element stochastic reduced basis methods [PDF]

, 2007
This paper presents mutli-element Stochastic Reduced Basis Methods (ME-SRBMs) for solving linear stochastic partial differential equations. In ME-SRBMs, the domain of definition of the random inputs is decomposed into smaller subdomains or random ...
Surya Mohan, P., Andy J. Keane, Keane, Andy J., Prasanth B. Nair, P. Surya Mohan, Nair, Prasanth B.   +5 more
core   +1 more source

Computational analysis of stochastic delay dynamics in maize streak virus.

PLoS ONE
ObjectivesThe primary goal of this research is to analyze the transmission dynamics of Maize Streak Virus (MSV) by means of a computational and stochastic modeling technique where the time delay and uncertainty factors in the epidemic process are vital ...
Sana Iqbal, Naveed Shahid, Ali Raza, Marek Lampart, Nauman Ahmed, Dumitru Baleanu, Hala H Taha   +6 more
doaj   +1 more source

Observer‐Based Adaptive Event‐Triggered Tracking Control for Fuzzy TS Systems With Premise Mismatch

International Journal of Adaptive Control and Signal Processing, EarlyView.
This paper presents an adaptive logistic event‐triggered observer‐based tracking controller for Takagi‐Sugeno fuzzy systems under constrained inputs and network delays. Leveraging a hybrid LMI and Secretary Bird Optimization approach, this strategy significantly minimizes communication overhead and computational burden while ensuring optimal reference ...
Oussama Djadane, Salama Makni, Mourad Kchaou, Ahmed El Hajjaji   +3 more
wiley   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Uncertainty Quantification in Computational Electromagnetics: The stochastic approach [PDF]

, 2013
Models in electromagnetism are more and more accurate. In some applications, the gap between the experience and the model comes from the deviation on input data of the model which are not perfectly known.
CLENET, Stephane
core  

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source