Results 61 to 70 of about 953 (204)

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

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide‐Semiconductor Characterization

Advanced Intelligent Systems, EarlyView.
A tandem neural network directly solves the multivalued inverse problem of extracting semiconductor parameters from transistor measurements. Trained on only 1000 simulations, the network infers six material parameters (e.g., defect states, carrier concentration, mobility) in under 1 ms, demonstrating a broadly applicable framework for semiconductor ...
Masatoshi Kimura, Keisuke Ide, Kuan‐Ju Zhou, Atsushi Shimizu, Takayoshi Katase, Hidenori Hiramatsu, Kei Terayama, Hideo Hosono, Toshio Kamiya   +8 more
wiley   +1 more source

Random Finite Set Tracking for Anomaly Detection in the Presence of Clutter

, 2020
In this paper, a sequential Bayesian framework is proposed to address the task of joint anomaly detection and tracking for surveillance applications in the presence of clutter. This is achieved by modeling the anomaly as a switching unknown control input
Forti N., Millefiori L. M., Willett P., Braca P.   +3 more
core   +1 more source

Surface Charges Guided Quasi‐TEM‐mode Microwave Propagation along Dielectric Nanowire

Advanced Physics Research, EarlyView.
According to Maxwell's equations, a single‐conductor transmission line cannot allow TEM‐mode electromagnetic wave propagation, however, this is based upon the assumption that dielectric material is isolated from the environment. In practice, surface electrostatic charges widely existed in nano‐materials with large surface‐to‐volume ratio.
BoYan Xu, Kaibo Zhang, Haojie Gao, Zhiyuan Gao, Kai Wang, Lihuan Zhao, Qi Yang, Bifeng Cui   +7 more
wiley   +1 more source

Machine Learning Paradigm for Advanced Battery Electrolyte Development

Carbon Energy, EarlyView.
Electrolyte materials determine ion transport kinetics within the bulk and interphases, ultimately influencing the performance of battery systems. As data‐driven paradigms increasingly reshape materials discovery, this review provides an application‐oriented exploration of the intersection between machine learning and electrolyte science. By evaluating
Chang Su, Mengfan Pei, Keyu Chen, Xigao Jian, Fangyuan Hu   +4 more
wiley   +1 more source

Random Set Based Road Mapping using Radar Measurements [Elektronisk resurs]

, 2010
This work is concerned with the problem of multi-sensor multi-target tracking of stationary road side objects, i.e. guard rails and parked vehicles, in the context of automotive active safety systems.
Gustafsson, Fredrik,, Danielsson, Lars,, Lundquist, Christian,   +2 more
core   +1 more source

Extending Bayesian RFS SLAM to multi-vehicle SLAM

, 2012
In this paper we present a novel solution to the Multi-Vehicle SLAM (MVSLAM) problem by extending the random finite set (RFS) based SLAM filter framework using two recently developed multi-sensor information fusion approaches.
Ba-Ngu Vo, Vo, Ba-Ngu, Wang, D., Wang, H., Moratuwage, Diluka, Wang, Danwei, Diluka Moratuwage, Danwei Wang, Han Wang, Moratuwage, D., Wang, Han   +10 more
core   +1 more source