Results 121 to 130 of about 11,242,189 (224)
Dois romances distópicos e satíricos - doi: 10.4025/actascilangcult.v33i1.10059
EVARISTO, Bernardine. Blonde roots. London: Penguin, 2009, 261 p. ISBN 9780141031521. LEVY, Andrea. The long song. New York: Farrar, Straus and Giroux, 2010, 313 p. ISBN 9780374192174.
openaire +2 more sources
This study presents a laser‐assisted glass‐frit encapsulation method for perovskite solar cells, comparing air‐, nitrogen‐, and CO2‐filled cavities. Results show that excluding oxygen and moisture significantly improves stability under illumination. The findings reveal the critical role of trapped gases in degradation, providing a pathway to durable ...
Marta Pereira +5 more
wiley +1 more source
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng +4 more
wiley +1 more source
Shaping Tomorrow's Scientists: A Call to Action. [PDF]
Wu MM +3 more
europepmc +1 more source
Two‐photon polymerization enables high‐resolution microfabrication, but performing alignment when printing multiple structures is difficult. Here, we present a fast, robust, and open‐source protocol for automated alignment on Nanoscribe systems. Achieving ≈0.4 μm accuracy in under 5 s, our protocol reduces time and error in multimaterial printing. This
Daniel Maher +4 more
wiley +1 more source
Correction: Association of body mass index changes with short-term mortality risks in ICU patients with sepsis across different admission BMI states: analysis of the MIMIC-IV database. [PDF]
Liu W, Zeng W, Huang Z, Yuan Q.
europepmc +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
gnSPADE integrates gene‐network structures into a probabilistic topic modeling framework to achieve reference‐free cell‐type deconvolution in spatial transcriptomics. By embedding gene connectivity within the generative process, gnSPADE enhances biological interpretability and accuracy across simulated and real datasets, revealing spatial organization ...
Aoqi Xie, Yuehua Cui
wiley +1 more source
FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng +7 more
wiley +1 more source

