Results 121 to 130 of about 66,002 (199)

Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators

Advanced Materials, EarlyView.
This study presents a DeepONet‐based machine learning framework for designing stochastic mechanical metamaterials with tailored nonlinear mechanical properties. By leveraging sparse but high‐quality experimental data from in situ micro‐mechanical tests, high predictive accuracy and enable efficient inverse design are achieved.
Hanxun Jin, Boyu Zhang, Qianying Cao, Enrui Zhang, Aniruddha Bora, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. Espinosa   +7 more
wiley   +1 more source

Hybrid ANN-GWO MPPT with MPC-based inverter control for efficient EV charging under partial shading conditions. [PDF]

Sci Prog
Hakam Y, Ahessab H, Tabaa M, ELHadadi B, Gaga A.   +4 more
europepmc   +1 more source

Dual‐Scale Hydration‐Induced Electrical and Mechanical Torsional Energy Harvesting in Heterophilically Designed CNT Yarns

Advanced Materials, EarlyView.
Despite the abundance and vast energy potential of water, its efficient utilization is hindered by single‐mode energy conversion. Herein, heterophilic CNT yarns that allow for simultaneous energy harvesting by means of dual‐scale hydration—electrical energy harvesting through proton gradients and mechanical torsional harvesting through microchannel ...
Jae Myeong Lee, Wonkyeong Son, Myoungeun Oh, Duri Han, Hyunji Seo, Hyeon Jun Sim, Shi Hyeong Kim, Dong‐Myeong Shin, Chang‐Seok Kim, Seon Jeong Kim, Changsoon Choi   +10 more
wiley   +1 more source

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? [PDF]

PLoS One
Ehsanul Karim M, Lei Y.
europepmc   +1 more source

Next‐Generation Image Sensors Based on Low‐Dimensional Semiconductor Materials

Advanced Materials, EarlyView.
Low‐dimensional semiconductor materials are promising candidates for photosensitive components in next‐generation image sensors. This review offers a thorough and timely examination of novel image sensors, covering their working principles, intriguing materials categorized into four main groups, and advanced imaging applications.
Yunxia Hu, Zhaoli Gao, Zhengtang Luo, Liang An   +3 more
wiley   +1 more source

Transfer learning-enhanced CNN-GRU-attention model for knee joint torque prediction. [PDF]

Front Bioeng Biotechnol
Xie H, Wang Y, Liu T, Yan S, Zeng J, Zhang K.   +5 more
europepmc   +1 more source

Harnessing Photo‐Energy Conversion in Nanomaterials for Precision Theranostics

Advanced Materials, EarlyView.
Harnessing photo‐energy conversion in nanomaterials enables precision theranostics through light‐driven mechanisms such as photoluminescence, photothermal, photoelectric, photoacoustic, photo‐triggered surface‐enhanced Raman scattering (SERS), and photodynamic processes. This review explores six fundamental principles of photo‐energy conversion, recent
Jingyu Shi, Yadi Fan, Qin Zhang, Yingying Huang, Mo Yang   +4 more
wiley   +1 more source

Development of a new hybrid model to enhance streamflow estimation using artificial neural network and reptile search algorithm. [PDF]

Sci Rep
Bahmani MJ, Kayhomayoon Z, Milan SG, Hassani F, Malekpoor M, Berndtsson R.   +5 more
europepmc   +1 more source

Toward Zero‐Excess Alkali Metal Batteries: Bridging Experimental and Computational Insights

Advanced Materials, EarlyView.
This review explores zero‐excess alkali metal batteries, highlighting anode–electrolyte interfaces, metal nucleation, dendrite growth, and SEI formation while comparing Li, Na, and K metals. It critically examines electrolyte and separator roles, emphasizing substrate design, electrolyte modifications, interfacial engineering, and solid‐state ...
Pan He, Neubi Francisco Xavier Jr, Matthias Johannes Golomb, Yupei Han, Yuheng Du, Qiong Cai, Yang Xu   +6 more
wiley   +1 more source

Using phenomic selection to predict hybrid values with NIR spectra measured on the parental lines: proof of concept on maize. [PDF]

Theor Appl Genet
Rincent R, Solin J, Lorenzi A, Nunes L, Griveau Y, Pirus L, Kermarrec D, Bauland C, Reymond M, Moreau L.   +9 more
europepmc   +1 more source