Results 111 to 120 of about 70,734 (340)
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging [PDF]
Bokun Tian +6 more
openalex +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
Parametric design method of building structured cabling system oriented to data mining
This paper puts forward the method of secondary development and design software through the research of building structured cabling system (SCS), and uses data mining technology to carry out parametric design of building SCS. Then, the feasibility of the design is tested by comparing the data of the traditional design and the parametric design ...
Bing Wang, Wei Yue
wiley +1 more source
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 +2 more
wiley +1 more source
A new algorithm for power metering based on Bayesian compressive sensing and FFT
The presence of harmonic and inter-harmonic components in grid voltage and current signals produces energy that variably impacts different electrical devices, making it imperative to meter these components separately.
LIU Jian +6 more
doaj +1 more source
An Efficient Adaptive Response Surface Method for Reliability Analysis of Geotechnical Engineering Systems Using Adaptive Bayesian Compressive Sensing and Monte Carlo Simulation (ABCS-MCS) [PDF]
Peiping Li, Yu Wang
openalex +1 more source
A skin‐conformal wearable device based on laser‐induced graphene is developed for continuous strain measurement across the circumference of the forearm for gesture recognition and hand‐tracking applications. Post material optimization, the strain sensor array is integrated with a wearable wireless readout circuit for real‐time control of a robotic arm,
Vinay Kammarchedu +2 more
wiley +1 more source
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai +10 more
wiley +1 more source

