Adaptation-based control approach via describing function to adjust PWPF modulator parameters

Charge- and current-sensitive preamplifier and pulse-shape discrimination application

Structure-preserving Koopman predictive control for memristive neural dynamics: input-exact and commutator-defect lifting

Long-Time Dynamics of Nonlinear Dirac Equations with a Class of Large Data

Computation of the Co-Content function, using the order six Simpson integration, to extract photovoltaic device parameters

The order six Simpson integration technique has been implemented in an executable program, written in C++, to automatically compute with more precision the Co-Content function, and then, extract with more accuracy the light (Ilig\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{{lig}}$$\end{document}) and saturation current (Isat\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{{sat}}$$\end{document}), the ideality factor (n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}), and the series (Rs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{s}$$\end{document}) and shunt resistance (Rsh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{{sh}}$$\end{document}), of a photovoltaic device, within the one-diode solar cell model. The results are compared relative to the usually used trapezoidal integration technique. The program also calculates the standard deviations of Ilig\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{{lig}}$$\end{document}, Isat\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{{sat}}$$\end{document}, n\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n$$\end{document}, Rs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{s}$$\end{document}, and Rsh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{{sh}}$$\end{document}, using standard statistical procedures. The results are given to the user in three text files, from which the user can easily access or export them to other software. A video is also provided, explaining how to use the executable program. Very accurate parameter extraction, with less than 1% error, is obtained for Rs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{s}$$\end{document}, Rsh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{{sh}}$$\end{document}, n, and Ilig, when the percentage noise pn<\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}_{n} <$$\end{document}0.05%, with just a number of points N≤38\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N\le 38$$\end{document} for Rs\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{s}$$\end{document}, Rsh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{{sh}}$$\end{document}, n, and Ilig, and N≥426\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N\ge 426$$\end{document} for Isat, respectively.

Numerical Aspect of Uniform Ensemble Controllability for Linear Systems

Fractional optimal control problems with trajectory-dependent singular coefficients

We investigate a class of fractional optimal control problems governed by semilinear fractional differential equations with trajectory-dependent singular coefficients. The state equation is formulated using the Caputo fractional derivative and contains a singular coefficient of the form (t−s(t))−β\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$(t-s(t))^{-\beta }$\end{document}, where the prescribed trajectory s(t)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$s(t)$\end{document} satisfies a suitable separation condition ensuring integrability of the singular kernel. This framework combines fractional memory effects with time-dependent singular perturbations, leading to several analytical and numerical difficulties. Existence and uniqueness of mild solutions are established by means of fixed-point arguments and fractional semigroup techniques. A fractional optimality system is then derived using variational methods together with a fractional integration-by-parts formula, leading to a coupled state–adjoint system and an explicit characterization of the optimal control. To illustrate the theoretical results, we develop a numerical scheme based on the Grünwald–Letnikov approximation in time combined with finite-difference discretization in space within a forward–backward sweep iterative framework. Numerical simulations demonstrate that the proposed method effectively captures the interaction between singular perturbations and fractional memory effects while steering the system toward the desired target trajectory. The convergence behavior of the numerical approximation is also discussed.