Results 81 to 90 of about 10,654 (236)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications. [PDF]

Mayo Clin Proc Digit Health, 2023
Qureshi HA, Chetty R, Kuklyte J, Ratcliff K, Morrissey M, Lyons C, Rafferty M.   +6 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Electronic legal correspondence in Switzerland – the latest developments [PDF]

Christoph Gasser discusses the latest developments relating to electronic legal correspondence in Swiss law, with references to Austrian law, including the ‘SuisseID’, the first standardized electronic proof of identity in Switzerland.
Gasser, Christoph
core  

Transatlantic Digital Dialogue: Rebuilding Trust through Cooperative Reform [PDF]

, 2015
The Transatlantic Digital Dialogue is a multi-stakeholder working group of experts from Germany and the United States. It was assembled and stewarded by the Stiftung Neue Verantwortung and the German Marshall Fund of the United States to develop a ...

core  

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Managing investment conditions: The case of Macedonia [PDF]

, 2012
Foreign Direct Investments (FDI) are one of the fundamentals that enables and accelerates economic development of a national economy. Therefore, each national economy must layout preconditions that will enable attracting of foreign capital.
Matlievska, Margarita, Petrevska, Biljana   +1 more
core  

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health". [PDF]

BMC Proc, 2023
Tuladhar S, Mwamelo K, Manyama C, Obuobi D, Antunes M, Gashaw M, Vogel M, Shrinivasan H, Mugambwa KA, Korley I, Froeschl G, Hoffaeller L, Scholze S.   +12 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