Results 91 to 100 of about 4,584,642 (232)

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Advanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim, Yousung Jung, Silvana Botti, Miguel A. L. Marques, Tigany Zarrouk, Miguel A. Caro, Sanggyu Chong, Michele Ceriotti, Marivi Fernandez‐Serra, Sara Navarro‐Rodriguez, Aakash Ashok Naik, Janine George, Ömer Hasan Omar, Victor Trinquet, Gian‐Marco Rignanese, Andrey Ustyuzhanin, Sonia Conesa‐Boj, Juan Rojo, Minh‐Tuan Dau, Simon M.‐M. Dubois, Jean‐Christophe Charlier, Jose H. Garcia, Romain Gautier, Rachid Laref, Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Camilla Coletti, Antonio Rossi, Jan‐Lucas Uslu, Chiara Zanardi, Vincenzo Palermo, Wide Hogenhout, Natalia Konchakova, Peter Klein, Tejs Vegge, Stephan Roche   +38 more
wiley   +1 more source

Solution of polynomial Lyapunov and Sylvester equations

, 2006
A two-variable polynomial approach to solve the one-variable polynomial Lyapunov and Sylvester equations is proposed. Lifting the problem from the one-variable to the two-variable context gives rise to associated lifted equations which live on finite ...
Peeters, Ralf, Rapisarda, Paolo
core   +1 more source

Circular Bernstein polynomial distributions [PDF]

This paper introduces a new non-parametric approach to the modeling of circular data, based on the use of Bernstein polynomial densities which generalizes the standard Bernstein polynomial model to account for the specific characteristics of circular ...
José Antonio Carnicero, Michael P. Wiper, Concepción Ausín   +2 more
core  

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

AI‐Powered Smart Displays and AR Glasses: Perspectives and Challenges

Advanced Intelligent Systems, EarlyView.
This perspective examines how AI is reshaping smart displays and AR glasses. The latest in image‐quality enhancement, sensor‐integrated display optimization, and physiological sensing in display systems, followed by AI‐assisted AR technologies including waveguide optimization, image compensation, foveated rendering, and multimodal sensing, is discussed.
Luke Benoit, Yizhou Qian, Yi‐Chien Chen, Ziyang Shen, Po‐Sheng Chiu, Youngeon Lee, Cheng‐Han Ke, Chih‐Lung Lin, Shin‐Tson Wu   +8 more
wiley   +1 more source

Autonomous Charging and Battery Swapping for Unmanned Aerial Vehicles: A Review

Advanced Intelligent Systems, EarlyView.
This review evaluates major autonomous charging and battery swapping methods for unmanned aerial vehicles (UAVs) against a single question: how close each is to sustained unattended operation. Rather than ranking them by charging performance, it identifies the limitation that most restricts each method, and briefly outlines the supporting technologies ...
Zelin Wu, Yuanwen Zhang, Xiangdong Meng, Guangyu Zhang, Huiwen Zhang, Yuquan Leng   +5 more
wiley   +1 more source

Physical Reservoir Computing using a Cellulose Nanofiber Hydrogel for Intelligent Green Electronics

Advanced Intelligent Systems, EarlyView.
Cellulose nanofiber (CNF) hydrogel serves as a biodegradable physical reservoir computer capable of electronic input and output. The CNF–water network enables sustainable green electronics that activate upon contact with liquid water, providing a degradable, robust, and field‐deployable computing element suitable for environmentally compatible device ...
Takeshi Kato, Yusaku Abe, Nozomi Akashi, Itsuo Hanasaki   +3 more
wiley   +1 more source

Multivariate Degradation Reliability Modeling of EMU Wheels Considering Time-Varying Dependence

工程科学与技术
Objective Flange height, tread wear, and flange thickness of electric multiple unit (EMU) wheels exhibit distinct degradation patterns during long-term operation, while the dependence among these indicators varies with operating mileage.
QI Jinping, WANG Kailong
doaj  

AI‐Based Mitochondrial Network Characterization as a Biomarker for DNM1L‐Related Disease Diagnosis

Advanced Intelligent Systems, EarlyView.
This work provides proof‐of‐concept that AI‐based analysis of mitochondrial network morphology in patient‐derived fibroblasts can identify a phenotypic signature associated with DNM1L deficiency. This automated approach may ultimately provide a complementary functional readout for the interpretation of novel DNM1L variants following genetic testing ...
Uliana Musokhranova, Ioannis Georvasilis, Sofía Illescas, Matina Saritzoglou, Marcos Frías, Mònica Roldán, Anna Ardissone, Isabella Moroni, Àngels García‐Cazorla, Alfonso Oyarzábal   +9 more
wiley   +1 more source

A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection [PDF]

We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion.
Sotirov, R., Takano, Y.
core