Results 151 to 160 of about 2,544 (255)

Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer. [PDF]

J Appl Clin Med Phys
Thongsawad S, Chamchod S, Chawengsaksopak K, Masanga W, Deeharing A, Bawornpatarapakorn S, Prachanukul T, Tannanonta C, Udee N.   +8 more
europepmc   +1 more source

Regional Differences in U.S. Consumer Preferences for Native Woody Shrubs With Varying Aesthetic Characteristics

Agribusiness, EarlyView.
ABSTRACT Native plants offer a variety of aesthetic (e.g., fall colour, fruit, flowers) and functional benefits (e.g., pollinator friendly, wildlife friendly, water management). How these benefits influence consumer choice and perceived value of native versus introduced plants is not well understood.
Alicia Rihn, Ariana P. Torres, Sue Barton, Bridget K. Behe   +3 more
wiley   +1 more source

Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support. [PDF]

Transl Cancer Res
Chow JCL.
europepmc   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source

Guarding against malicious biased threats (GAMBiT) datasets: Revealing cognitive bias in human-subjects red-team cyber range operations. [PDF]

Data Brief
Beltz B, Doty J, Fonken Y, Gurney N, Israelsen B, Lau N, Marsella S, Thomas R, Trent S, Wu P, Yang YT, Zhu Q.   +11 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Accessible and robust machine learning approaches to improve the opsin genotype-phenotype map. [PDF]

Mol Biol Evol
Frazer SA, Oakley TH.
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling. [PDF]

Brief Bioinform
Safari F, Tree JJ, Vafaee F.
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source