Results 101 to 110 of about 366,742 (304)

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine

International Journal of Renewable Energy Development
In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel.
Khac Binh Le, Minh Thai Duong, Dao Nam Cao, Van Vang Le   +3 more
doaj   +1 more source

Wildfire in wet sclerophyll forests: the interplay between disturbances and fuel dynamics

Ecosphere, 2018
There are multiple pathways for vegetation to change following disturbances. Understanding those post‐disturbance pathways is critical for managing wildfire risk since vegetation is fuel in a wildfire context. Across forest systems, there is considerable
Jane G. Cawson, Thomas J. Duff, Matthew H. Swan, Trent D. Penman   +3 more
doaj   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
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, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Classification and Mapping of Fuels in Mediterranean Forest Landscapes Using a UAV-LiDAR System and Integration Possibilities with Handheld Mobile Laser Scanner Systems

Remote Sensing
In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments.
Raúl Hoffrén, María Teresa Lamelas, Juan de la Riva   +2 more
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Preserved boreal zone forest massif mass estimation during fire extinguishing by liquid aerosol

MATEC Web of Conferences, 2017
This study contains results of experimental studies into establishing the principles of decreasing the model of fire source mass when extinguishing the ground cover of deciduous and mixed forests by liquid aerosol.
Shlegel Nikita E., Nyashina Galina S., Zhdanova Alyona O.   +2 more
doaj   +1 more source

Urban Fuel Demand in Ethiopia: An Almost-Ideal Demand System Approach [PDF]

This paper investigates the opportunities for reducing the pressure of urban centers on rural forest areas, using a dataset of 350 urban households in Tigrai in northern Ethiopia. We applied an almost-ideal demand system to fuels.
Bayou, Demeke, Gebreegziabher, Zenebe, Oskam, Arie J.   +2 more
core  

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Federal Forests and the Renewable Fuel Standard [PDF]

, 2008
On December 19, 2007, the President signed into law the Energy Independence and Security Act of 2007 (EISA). This law (PL 110-140) includes an increase in the national Renewable Fuel Standard (RFS) mandating the production of 36 billion gallons of ...

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