Results 181 to 190 of about 1,581,551 (302)

Experimental investigation of dynamic shear stiffness and damping ratio characteristics of marine soils in Lingdingyang Bay, China. [PDF]

Sci Rep
Wu Y, Qin B, Fu Y, Wu Q.
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Metropolitan coastal night lighting aligns with ecological and plastic divergence in closely related <i>Ligia</i> isopods. [PDF]

PNAS Nexus
Sato DX.
europepmc   +1 more source

East Bay Energy Consortium Joint Committee Workshop at Bristol Town Hall, Burnside Building, Meeting Notes, March 15, 2010 [PDF]

, 2010
East Bay Energy Consortium
core   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Spatial and temporal variation of benthic ecological quality evaluation in the Bohai Bay (China) using benthic indices. [PDF]

Sci Rep
Zeng R, Lu W, Xu Y, Ai Y, Zhang Y, Liu J, Liu Z.   +6 more
europepmc   +1 more source

East Bay Energy Consortium Meeting Minutes, December 7, 2009 [PDF]

, 2009
East Bay Energy Consortium
core   +1 more source

Disentangling Aleatoric and Epistemic Uncertainty in Physics‐Informed Neural Networks: Application to Insulation Material Degradation Prognostics

Advanced Intelligent Systems, EarlyView.
Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, Jose I. Aizpurua   +4 more
wiley   +1 more source

Using colored dissolved organic matter fluorescence to trace Pacific-derived water in the Eastern Canadian Arctic. [PDF]

Sci Rep
Dmitrenko IA, Stedmon CA, Babb DG, Rudels B, Kirillov SA, Ehn J, Burgers T, Dahl-Jensen D.   +7 more
europepmc   +1 more source

East Bay Energy Consortium Meeting Minutes, November 2, 2009 [PDF]

, 2009
East Bay Energy Consortium
core   +1 more source