Results 151 to 160 of about 7,834 (264)

Unified FPGA framework for secure satellite image transmission: adaptive orthogonal moments with integrated confusion-diffusion cryptography. [PDF]

Sci Rep
Mchichou I, Tahiri MA, Amakdouf H, Sayyouri M, Aldakheel EA, Khafaga DS.   +5 more
europepmc   +1 more source

A Dual‐Branch Flux‐Based Extended Memristor Model With Machine‐Learning‐Assisted Calibration

Advanced Electronic Materials, EarlyView.
Multilayer oxide memristors integrated in crossbar arrays are described through a dual‐branch, flux‐controlled compact model. A three‐stage calibration workflow combining Latin hypercube sampling, Bayesian optimization, and gradient‐based refinement extracts device parameters from experimental data.
Davide Rossetti, K. Nikiruy, A.S. Demirkol, Alon Ascoli, M. Ziegler, R. Tetzlaff, Fernando Corinto   +6 more
wiley   +1 more source

A numerical approach to fractional Volterra-Fredholm integro-differential problems using shifted Chebyshev spectral collocation. [PDF]

Sci Rep
Hamood MM, Sharif AA, Ghadle KP.
europepmc   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Approximation of Anisotropic Pair Potentials Using Multivariate Interpolation. [PDF]

J Phys Chem B
Fakhraei M, Kieslich CA, Howard MP.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

An Optimized Pedestrian Inertial Navigation Method Based on the Birkhoff Pseudospectral Method. [PDF]

Sensors (Basel)
Zhang Z, Zhao D, Tian D.
europepmc   +1 more source

SOC Mismatch–Driven Inter‐Particle Synchronization via Internal Li‐Ion Transfer: An Unrecognized Electrode‐Emergent Degradation Pathway in Ni‐Rich Composite Cathodes

Advanced Energy Materials, EarlyView.
Composite cathode degradation is not merely the sum of its particles. Kinetically mismatched populations develop state‐of‐charge differences that drive spontaneous internal Li‐ion transfer; the accompanying transient currents accelerate surface degradation.
Seheon Oh, Suyoung Kim, Inho Kim, Kangwoo Ahn, Min Wook Pin, Minkyu Kim   +5 more
wiley   +1 more source

Matrix-Product State Skeletons in Onsager-Integrable Quantum Chains. [PDF]

J Stat Phys
Camp I, Jones NG.
europepmc   +1 more source

Farmers' Preferences for Gene Editing Crops and Influencing Factors

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Gene editing (GE) is gaining momentum worldwide, but limited data on UK farmers' preferences hinders our understanding of its potential impact amid deregulation debates. Based on a survey of 200 English arable farmers, we employ a Latent Class Analysis and Multinomial Logit regressions to investigate current preferences for GE crops.
Bertolozzi‐Caredio Daniele, Vigani Mauro   +1 more
wiley   +1 more source