Results 91 to 100 of about 75,968 (304)
The finite-time optimal output regulation problem is investigated in this paper. In order to simplify the design process of linear quadratic finite-time optimal output regulation control, a new information fusion estimation-based optimal output ...
Huihui Yang
doaj +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen +2 more
wiley +1 more source
Optimal Control Problems for the Bilinear System of Special Structure
We consider three optimal control problems (linear terminal, bilinear and quadratic functionals) with respect to the special bilinear system with a matrix of rank 1.
V.A. Srochko, E. Aksenyushkina
doaj
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Advanced Experiment Design Strategies for Drug Development
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang +3 more
wiley +1 more source
Controlling Chaos—Forced van der Pol Equation
Nonlinear systems are typically linearized to permit linear feedback control design, but, in some systems, the nonlinearities are so strong that their performance is called chaotic, and linear control designs can be rendered ineffective.
Matthew Cooper +2 more
doaj +1 more source
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas +4 more
wiley +1 more source
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo +3 more
wiley +1 more source

