Results 81 to 90 of about 5,008 (215)

Bioengineered Interfaces for Peripheral Nerve Sensory Restoration

Advanced Materials, EarlyView.
Half of amputees abandon their prosthetics for lack of feeling. This review charts the full path from peripheral nerve injury to restored sensation, through surgical, regenerative, noninvasive, and implanted approaches, and shows how injury type and interface material properties determine which strategy can deliver naturalistic feedback, and why ...
Sydney Swedick, Salim El Hadwe, Kesi Liang, George G. Malliaras, Damiano G. Barone   +4 more
wiley   +1 more source

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Prediction of concrete fatigue durability using Bayesian neural networks

Computer Assisted Methods in Engineering and Science, 2022
The utility of Bayesian neural networks to predict concrete fatigue durability as a function of concrete mechanical parameters of a specimen and characteristics of the loading cycle is investigated.
Marek Słoński
doaj  

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

Advanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng, Min Hong, Zhiquan Zeng, Chuanyu Liu, Qian Wang, Melissa Jane Meadowcroft, Vlad Badilita, Carlos Miguel Costa, Senentxu Lanceros‐Méndez, Ying Li, Chenyao Ma, Weibo Gong, Surendra Kumar Martha, Aloysius Soon, Piao Ma, Di Zhang, Teguh Ariyanto, Sudaryanto, Hiroshi Kakinuma, Jie Zhao, Jiayu Peng, Pengfei Ou, Jun Lu, Shin‐ichi Orimo, Hao Li   +24 more
wiley   +1 more source

Spatial Component Analysis of MRI data for Alzheimer's Disease Diagnosis: a Bayesian network approach

Frontiers in Computational Neuroscience, 2014
This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is
Ignacio eA. Illán, Juan Manuel Gorriz, Javier eRamírez, Anke eMeyer-Baese   +3 more
doaj   +1 more source

Ecological Risk of Water Resource Use to the Wellbeing of Macroinvertebrate Communities in the Rivers of KwaZulu-Natal, South Africa

Frontiers in Water, 2020
The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of ...
Olalekan A. Agboola, Colleen T. Downs, Gordon O'Brien, Gordon O'Brien   +3 more
doaj   +1 more source

Advances in Bio‐Integrated Edge Computing Systems for Smart and Connected Health

Advanced Materials, EarlyView.
This review explores advances in bio‐integrated edge computing for healthcare, focusing on co‐designing computing, sensing, and energy strategies to overcome integration and power challenges for optimized, decentralized operation. ABSTRACT Recent advances in bio‐integrated healthcare technologies have enabled continuous, high‐resolution monitoring and ...
Gyuwon Kim, Seungwon Yoo, Tae Woog Kang, Yunuo Huang, Woon‐Hong Yeo   +4 more
wiley   +1 more source

Rock typing and causality analysis in unconventional formation using Bayes nets

Petroleum Research
Unconventional formations are characterized by high heterogeneity and anisotropy, making it difficult to interpret logging data, perform core analysis, and create accurate petrophysical models.
Evgeny Chekhonin, Alexander Nikitin, Dimitri Pissarenko, Yuri Popov, Raissa Romushkevich, Dzhulia Zagranovskaya   +5 more
doaj   +1 more source

Hybrid time Bayesian networks

International Journal of Approximate Reasoning, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Manxia Liu, Arjen Hommersom, Maarten van der Heijden, Peter J. F. Lucas   +3 more
openaire   +9 more sources

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source