Results 81 to 90 of about 907,184 (299)

3D In Vitro Models of Breast Cancer: Current Challenges and Future Prospects Toward Recapitulating the Microenvironment and Mimicking Key Processes

Advanced Biology, EarlyView.
In vitro cancer models are advantageous for studying important processes such as tumorigenesis, cancer growth, invasion, and metastasis. The complexity and biological relevance increase depending on the model structure, organization, and composition of materials and cells.
Kyndra S. Higgins, Ah Joung Yu, Cheryl T. Gomillion   +2 more
wiley   +1 more source

Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints

Buildings
This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state ...
Min Li, Rui Xie
doaj   +1 more source

Game Theory-Inspired Evolutionary Algorithm for Global Optimization

Algorithms, 2017
Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields.
Guanci Yang
doaj   +1 more source

Design performance of lead and lag compensator using OPAMP and root locus approach through simulation tool

ITEGAM-JETIA, 2020
The design objective behind lead and lag compensator is to meet the relative stability as well as to meet desired performance. Both in time domain or frequency domain, the compensator design can be carried out.
Badri Narayan Mohapatra, Jijnyasa Joshi
doaj   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Comparisons of different deep learning-based methods on fault diagnosis for geared system

International Journal of Distributed Sensor Networks, 2019
The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system ...
Bing Han, Xiaohui Yang, Yafeng Ren, Wanggui Lan   +3 more
doaj   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

First‐Principles Modeling of Solid Solution Softening and Hardening Effects in Al–Mg–Zr–Si Aluminum Alloys

Advanced Engineering Materials, EarlyView.
The role of various alloying elements in face‐centered cubic aluminum on the barrier of a Shockley partial dislocation during its motion is presented. The study aims to understand how alloying atoms such as Mg, Si, and Zr affect the energy landscape for dislocation motion, thus influencing the solid solution hardening and softening in aluminum, which ...
Inna Plyushchay, Zhengqing Wei, Nebahat Bulut, Sibylle Gemming   +3 more
wiley   +1 more source

Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations

Machine Learning: Science and Technology
Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials.
Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral
doaj   +1 more source

Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer

Sensors
To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning ...
Bing Miao, Shirong Ge, Yunwang Li, Yinan Guo   +3 more
doaj   +1 more source