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ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

The constrained disorder principle accounts for quantum effects in biological systems. [PDF]

J Med Life
Ilan Y.
europepmc   +1 more source

Foundation and challenges in modelling dilute active suspensions. [PDF]

Philos Trans A Math Phys Eng Sci
Fung L, Caldag HO, Bees M.
europepmc   +1 more source

Distribution Characteristics and Adaptation Mechanisms of Exotic Spontaneous Plant Diversity in Urban Road Green Spaces of Changchun, China. [PDF]

Plants (Basel)
Liu D, Zhao C, Wang Y, Hu Y.
europepmc   +1 more source

Climate-driven restructuring of sediment microbiomes and ecosystem functions in aquaculture systems. [PDF]

AIMS Microbiol
Mataragka A, Ringø E, Papastathis AK.
europepmc   +1 more source

Multi-Strategy Improved Pelican Optimization Algorithm for Engineering Optimization Problems and 3D UAV Path Planning. [PDF]

Biomimetics (Basel)
Zhang M, Luo M, Kang H.
europepmc   +1 more source

Effect of Caudal Keel Structure on the Head Stability of a Bionic Dolphin Robot. [PDF]

Biomimetics (Basel)
Gong W, Wei Y, Chen H.
europepmc   +1 more source

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Observability of Boolean control networks with stochastic disturbances

Journal of the Franklin Institute, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liyun Tong, Jinling Liang
openaire   +2 more sources

Stochastic Regression Model with Dependent Disturbances

Journal of Time Series Analysis, 2001
In this paper, we consider the estimation of the coefficient of a stochastic regression model whose explanatory variables and disturbances are permitted to exhibit short‐memory or long‐memory dependence. Three estimators of the coefficient are proposed.
Choy, Kokyo, Taniguchi, Masanobu
openaire   +2 more sources