Results 101 to 110 of about 171,040 (210)

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

A Data-Driven Prediction Method for Proton Exchange Membrane Fuel Cell Degradation

Energies
This paper proposes a long short-term memory (LSTM) network to predict the power degradation of proton exchange membrane fuel cells (PEMFCs), and in order to promote the performance of the LSTM network, the ant colony algorithm (ACO) is introduced to ...
Dan Wang, Haitao Min, Honghui Zhao, Weiyi Sun, Bin Zeng, Qun Ma   +5 more
doaj   +1 more source

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High‐Fidelity Communication in the BNST‐NAc Circuit

Advanced Science, EarlyView.
This cross‐species study reveals that pathological hyperactivity of BNST neurons in depressive states disrupts inhibitory period and isolated spikes in the BNST‐NAc circuit. DBS achieves its antidepressant effects by precisely restoring network inhibitory periods and high‐fidelity signal transmission.
Xin Lv, Yu Cao, Yuhan Wang, Yunhao Wu, Yingying Zhang, Kuanghao Ye, Xian Qiu, Qingfang Sun, Liuguan Bian, Halimureti Paerhati, Valerie Voon, Shikun Zhan, Bomin Sun   +12 more
wiley   +1 more source

Performance Curve Prediction Method for Centrifugal Compressor Based on BOA-BPNN

Shiyou jixie
To enhance the status monitoring accuracy and operation and maintenance intelligence level of compressors， taking PCL803 centrifugal compressor in a natural gas pipeline as an example， a centrifugal compressor performance curve prediction method based on
Zhu Wangyou, Liu Jiahao, Yang Junming, Liu Zhen, Hou Lei   +4 more
doaj  

Neural Network‐Based Permittivity Engineering of Magnetic Absorbers for Customizable Microwave Absorption

Advanced Science, EarlyView.
A neural network‐enabled permittivity engineering paradigm is introduced, transcending traditional trial‐and‐error design. By decoupling electromagnetic parameters and screening a high‐throughput feature space, an ultrathin (1.0 mm) magnetic absorber is inversely designed, experimentally achieving a superior and customizable 5.1 GHz bandwidth and ...
Chenxi Liu, Jinzhe Li, Sen Li, Zhongqiu Guo, Yao Chen, Tian Li, Renchi Qin, Jiaxu Sun, Yongxi Lu, Fanbin Meng   +9 more
wiley   +1 more source

Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model

Advanced Science, EarlyView.
Understanding protein sequence–function relationships remains challenging due to poorly defined motifs and limited residue‐level annotations. An annotation‐agnostic framework is introduced that segments protein sequences into “protein words” using attention patterns from protein language models.
Hedi Chen, Jingrui Zhong, Xiaochun Zhang, Jingke Chen, Lin Guo, Xiaoliang Xiong, Xiaonan Zhang, Xiangyu Liu, Bailong Xiao, Boxue Tian   +9 more
wiley   +1 more source

Lattice Genome Framework for Regionally Tailored Component‐Level Multi‐Objective Design in Additive Manufacturing

Advanced Science, EarlyView.
A Lattice Genome framework links geometric and process “genes” to lattice “phenotypes” via correction‐calibrated high‐throughput simulations and a growing performance database. Genome‐driven retrieval and recombination of unit cells enables component‐level, regionally tailored multi‐objective design: stress fields are programmed under constant relative
Haoyuan Deng, Yufan Zhao, Mingyang Cao, Haiou Yang, Pan Wang, Hongze Wang, Hao Wang, Akihiko Chiba, Xin Lin   +8 more
wiley   +1 more source

Mechanistic Analysis of Large Atomic Models of Molten Salt

Advanced Science, EarlyView.
This work uncovers the physical mechanism of large atomic models for molten salts by linking atomic contribution to electronic structure features. We demonstrate that energy predictions are physically determined by the local occupancy of frontier orbitals.
Yuliang Guo, Xiaobo Sun, Xiaoli Xi, Zuoren Nie   +3 more
wiley   +1 more source

Understanding Fabrication Variability in Core‐Shell Soft Biomaterials Using Stochastic Artificial Intelligence

Advanced Science, EarlyView.
Fabrication‐induced variability remains a fundamental limitation in the scalable design of soft biomaterials. In this work, a stochastic machine learning approach based on Gaussian processes modeling is employed to establish quantitative links between biofabrication parameters, material properties, and their intrinsic variability.
Maria Alexaki, Lília M. S. Dias, Raquel C. Gonçalves, Dinis O. Abranches, Albano N. Carneiro Neto, Rute A. S. Ferreira, Paulo S. B. André, João F. Mano, Mariana B. Oliveira   +8 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source