Results 161 to 170 of about 4,492 (243)

Incorporating Cellular Stochasticity in Solid-Fluid Mixture Biofilm Models. [PDF]

Entropy (Basel), 2020
Carpio A, Cebrián E.
europepmc   +1 more source

Comparison of two numerical weather prediction models in simulating south foehn in the Alpine Rhine Valley

Quarterly Journal of the Royal Meteorological Society, EarlyView.
ICON outperforms COSMO in simulating a south foehn event in the Alpine Rhine Valley, with improved accuracy in temperature, wind speed, and foehn timing. This enhanced performance likely results from a more accurate representation of surface energy balance and gravity wave pattern on the lee side of the mountain.
Yue Tian, Julian Quimbayo Duarte, Shweta Singh, Juerg Schmidli   +3 more
wiley   +1 more source

A computational model of coronary arteries with in-stent restenosis coupling hemodynamics and pharmacokinetics with growth mechanics. [PDF]

Sci Rep
Ranno A, Manjunatha K, Koritzius T, Steinbrecher I, Hosters N, Nachtsheim M, Nilcham P, Schaaps N, Turoni-Glitz A, Datz J, Popp A, Linka K, Vogt F, Behr M.   +13 more
europepmc   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park

Remote Sensing in Ecology and Conservation, EarlyView.
A comparison of Convolutional Neural Network (CNN) and Random Forest (RF) model predictions of benthic habitats within Apollo Marine Park. The CNN (left) and RF (right) classification maps show the spatial distribution of three habitat types: high energy circalittoral rock with seabed‐covering sponges, low complexity circalittoral rock with non‐crowded
Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young   +6 more
wiley   +1 more source

PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning. [PDF]

PLoS One
Bano S, Abbas G, Bilal M, Abbas ZH, Ali Z, Waqas M.   +5 more
europepmc   +1 more source

Classification of tree species and standing dead trees in Boreal forests using UAV‐based RGB, multispectral, and LiDAR point clouds

Remote Sensing in Ecology and Conservation, EarlyView.
We evaluated single‐ and multi‐sensor UAV approaches for classifying tree species and standing dead trees in boreal forests, focusing on key biodiversity indicators such as European aspen. Using spectral and structural features extracted from RGB, multispectral (MSP), and LiDAR point clouds for 1,205 field‐measured trees, we compared classification ...
Anton Kuzmin, Lauri Korhonen, Topi Tanhuanpää, Mikko Kukkonen, Matti Maltamo, Timo Kumpula   +5 more
wiley   +1 more source

Predicting the <i>S. cerevisiae</i> Gene Expression Score by a Machine Learning Classifier. [PDF]

Life (Basel)
Pawłowski PH, Zielenkiewicz P.
europepmc   +1 more source

From Prediction to Prevention: An Explainable GeoAI Framework for Flood Susceptibility and Urban Exposure Assessment Using Machine and Deep Learning Models

Sustainable Development, EarlyView.
ABSTRACT Rapid urbanisation and intensifying rainfall have increased cities' vulnerability to flooding, posing major challenges to sustainable development. Although machine learning models have improved flood prediction accuracy, most remain limited by their black‐box nature and lack of actionable insights.
Abdulwaheed Tella, Izni Zahidi, Chow Ming Fai, Chukwuma Okolie, Quoc Bao Pham   +4 more
wiley   +1 more source