Results 281 to 290 of about 986,697 (337)

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

Toward a Unified Nomenclature System for Highly Pathogenic Avian Influenza Virus (H5N1)

Emerging Infectious Diseases, 2008
doaj   +1 more source

Proposed Nomenclature for Landmarks in Anterior-Segment OCT: The APOSTEL-AS Panel Consensus.

JAMA Ophthalmol
Fraser AS, Ang M, Bellchambers A, Chu CJ, Denniston AK, Downie LE, Evans T, Hau S, Huang AS, Keane PA, Liu X, Mehta JS, Ometto G, Petzold A, Tsui E, Fraser TS, Xu B, Thaung C, Solebo AL.   +18 more
europepmc   +1 more source

Are the SUN Criteria Sufficient for Tubercular Uveitis? Findings From a Multinational Registry Study. [PDF]

Invest Ophthalmol Vis Sci
See A, Rojas-Carabali W, Cifuentes-González C, Arora A, Sitaula RK, Joshi SN, Karki P, Babu K, Agarwal M, Biswas J, Palani J, Guex-Crosier Y, Schlaen A, Utami A, Ayachit A, Sen A, La Distia Nora R, Putera I, Androudi S, Mishra C, Mahendradas P, Khatri A, Emebet GT, Kempen JH, de-la-Torre A, Agrawal R, Programme for Ocular Inflammation & Infection Translational Research (PROTON) study group.   +26 more
europepmc   +1 more source

The Nomenclature of Diseases from the Standpoint of Public Medicine.

, 1896

openalex   +1 more source

From Origami to Bistable and Laminate Structures: A Review for Multifunctional Applications from Structural Perspective of Shape‐Changing Structures

Advanced Intelligent Systems, EarlyView.
This review explores how shape‐changing structures—origami, bistable, and laminate structures—enable multifunctionality in soft robotics and metamaterials. Starting from structural design, it examines core principles, real‐world applications, and ongoing challenges.
Lingchen Kong, Yaoyao Fiona Zhao
wiley   +1 more source

Morris B. Bender (1905-1983). [PDF]

J Neurol
Larner AJ.
europepmc   +1 more source

Design Challenges to Expand the Functionality of Drones: Deformable Rotorcraft and Nature‐Inspired Flapping Drones

Advanced Intelligent Systems, EarlyView.
This review systematically analyzes deformable drones, including extendable, foldable, and tilting configurations, along with nature‐inspired flapping rotorcraft. By classifying deformation principles and structural mechanisms, design trade‐offs, functional capabilities, and future development potential are highlighted.
Ju‐Hee Lee, Sumin Jung, Jinwoo Jang, Yeji Han, Minchae Kang, Jihun Seong, Suyeon Seo, Eunsol Park, Haesol Kwak, Min‐Woo Han   +9 more
wiley   +1 more source

Mycobacterium tuberculosis complex Lineage 1: A neglected cause of tuberculosis. [PDF]

PLoS Negl Trop Dis
Rojas VMR, Zwyer M, Bouaouina S, Brites D, Borrell S, Gagneux S.   +5 more
europepmc   +1 more source

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source