Results 111 to 120 of about 1,324,645 (291)

Molecular Dynamics Studies of Shape Memory Polymers: From Bead–Spring Models to Atomistic Simulations

Advanced Engineering Materials, EarlyView.
Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley   +1 more source

A Graph-Space Optimal Transport Approach Based on Kaniadakis κ-Gaussian Distribution for Inverse Problems Related to Wave Propagation. [PDF]

Entropy (Basel), 2023
da Silva SLEF, de Araújo JM, de la Barra E, Corso G.   +3 more
europepmc   +1 more source

Enhancing Low‐Temperature Performance of Sodium‐Ion Batteries via Anion‐Solvent Interactions

Advanced Functional Materials, EarlyView.
DOL is introduced into electrolytes as a co‐solvent, increasing slat solubility, ion conductivity, and the de‐solvent process, and forming an anion‐rich solvent shell due to its high interaction with anion. With the above virtues, the batteries using this electrolyte exhibit excellent cycling stability at low temperatures. Abstract Sodium‐ion batteries
Cheng Zheng, Qian Yao, Yanan Sun, Hongyu Lv, Zhongchao Bai, Guoliang Zhang, Nana Wang, Jian Yang   +7 more
wiley   +1 more source

Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights

Entropy, 2016
There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR) is proposed here.
Zhi-Gen Shang, Hong-Sen Yan
doaj   +1 more source

Generalised exponential-Gaussian distribution: a method for neural reaction time analysis. [PDF]

Cogn Neurodyn, 2023
Marmolejo-Ramos F, Barrera-Causil C, Kuang S, Fazlali Z, Wegener D, Kneib T, De Bastiani F, Martinez-Flórez G.   +7 more
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

MATHEMATICAL ANALYSIS OF NON-GAUSSIAN REGIME ORIGINATION IN NUMERICAL INTEGRATION OF ISOELECTROFOCUSING PROBLEM

Вестник Донского государственного технического университета, 2018
The mathematical analysis of the problem of so-called “non-Gaussian” regimes in the computational solution of the integro-differential isoelectric focusing (IEF) problem is done.
Lyudmila V. Sakharova
doaj  

An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations. [PDF]

J Appl Stat, 2022
Park S, Lim J.
europepmc   +1 more source

Flexibility and Dynamicity Enhances and Controls Supramolecular Self‐Assembly of Zinc(II) Metallogels

Advanced Functional Materials, EarlyView.
Zinc(II) coordination complexes with tunable aryloxy‐imine ligands exhibit controllable supramolecular self‐assembly into hierarchical fibrous structures. Coordination‐driven stacking, not π–π interactions, enables gelation, dynamic assembly/disassembly, and enhanced nanomechanical properties.
Merlin R. Stühler, Hesam Makki, Dorothee Silbernagl, Mathias Dimde, Kai Ludwig, Bengt E. Tegner, Christopher Greve, Konstantin Rausch, Eva M. Herzig, Anna Köhler, Alex J. Plajer   +10 more
wiley   +1 more source

Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy. [PDF]

Entropy (Basel), 2022
Chen Y, Hua Z, Tang Y, Li B.
europepmc   +1 more source