On BIBO stability of infinite-dimensional linear state-space systems

In this paper we consider BIBO stability of systems described by infinite-dimensional linear state-space representations, filling the so far unattended gap of a formal definition and characterization of BIBO stability in this general case. Furthermore, we provide several sufficient conditions guaranteeing BIBO stability of a particular system and discuss to which extent this property is preserved under additive and multiplicative perturbations of the system

Two-dimensional Navier--Stokes simulation of deformation and break up of liquid patches

The large deformations and break up of circular 2D liquid patches in a high Reynolds number (Re=1000) gas flow are investigated numerically. The 2D, plane flow Navier--Stokes equations are directly solved with explicit tracking of the interface between the two phases and a new algorithm for surface tension. The numerical method is able to pursue the simulation beyond the breaking or coalescence of droplets. The simulations are able to unveil the intriguing details of the non-linear interplay between the deforming droplets and the vortical structures in the droplet's wake.Comment: 13 pages including 4 postscript figures; Revised version as resubmitted to PRL. Title has change

Novel Strategy for Finding the Optimal Parameters of Ion Selective Electrodes

The detection limit (DL) of an analytical method determines the range of its applicability. For ion selective electrodes (ISE) used in potentiometric measurements, this parameter can vary by several orders of magnitude depending on the inner solution concentrations or the time of measurement. The detection limit of ISE can be predicted using the Nernst-Planck-Poisson model (NPP), as a general approach to the description of the time-dependent electro-diffusion processes. To find the optimal parameters, we need to formulate the inverse electro-diffusion problem. In this work, we combine the Nernst-Planck-Poisson model with the Hierarchical Genetic Strategy with real number encoding (HGS-FP). We use the HGS-FP method to approximate inner solution concentrations as well as the measuring time that provide a linear dependence of the membrane potential over the widest concentration range. We show that the HGS-FP method allows us to find the solution of the inverse problem. The presented calculations show a great future potential of the NPP method combined with the HGS-FP strategy

Electrochemical Impedance Spectroscopy (EIS) of ion sensors Direct modeling and inverse problem solving using the Nernst-Planck-Poisson (NPP) model and the HGS(FP) optimization strategy

The Nernst-Planck-Poisson (NPP) model is used to numerically simulate electrochemical impedance spectra (EIS) of ion-selective electrodes (ISEs). By using the Hierarchical Genetic Strategy with real number encoding (HGS(FP)) the reverse problem is solved. The NPP-HGS(FP) method allows estimation of physicochemical parameters of ISEs with plastic membranes, which is illustrated here by using NPP-HGS(FP) for obtaining the values of the diffusion coefficients of ions in the ISE membrane phase.The NPP-HGS(FP) method allows calculation of the most accurate solution of the inverse problem and can be effectively used to facilitate the process of finding the parameters for optimal ISE performance.The method presented here not only allows for interpretation of the EIS spectra but also for accounting for the mechanism of the processes occurring at the interface in terms of physicoelectrochemically valid concepts. (C) 2011 Elsevier B.V. All rights reserved

Remodeling of T Cell Dynamics During Long COVID Is Dependent on Severity of SARS-CoV-2 Infection

Several COVID-19 convalescents suffer from the post-acute COVID-syndrome (PACS)/long COVID, with symptoms that include fatigue, dyspnea, pulmonary fibrosis, cognitive dysfunctions or even stroke. Given the scale of the worldwide infections, the long-term recovery and the integrative health-care in the nearest future, it is critical to understand the cellular and molecular mechanisms as well as possible predictors of the longitudinal post-COVID-19 responses in convalescent individuals. The immune system and T cell alterations are proposed as drivers of post-acute COVID syndrome. However, despite the number of studies on COVID-19, many of them addressed only the severe convalescents or the short-term responses. Here, we performed longitudinal studies of mild, moderate and severe COVID-19-convalescent patients, at two time points (3 and 6 months from the infection), to assess the dynamics of T cells immune landscape, integrated with patients-reported symptoms. We show that alterations among T cell subsets exhibit different, severity- and time-dependent dynamics, that in severe convalescents result in a polarization towards an exhausted/senescent state of CD4+ and CD8+ T cells and perturbances in CD4+ Tregs. In particular, CD8+ T cells exhibit a high proportion of CD57+ terminal effector cells, together with significant decrease of naïve cell population, augmented granzyme B and IFN-γ production and unresolved inflammation 6 months after infection. Mild convalescents showed increased naïve, and decreased central memory and effector memory CD4+ Treg subsets. Patients from all severity groups can be predisposed to the long COVID symptoms, and fatigue and cognitive dysfunctions are not necessarily related to exhausted/senescent state and T cell dysfunctions, as well as unresolved inflammation that was found only in severe convalescents. In conclusion, the post-COVID-19 functional remodeling of T cells could be seen as a two-step process, leading to distinct convalescent immune states at 6 months after infection. Our data imply that attenuation of the functional polarization together with blocking granzyme B and IFN-γ in CD8+ cells might influence post-COVID alterations in severe convalescents. However, either the search for long COVID predictors or any treatment to prevent PACS and further complications is mandatory in all patients with SARS-CoV-2 infection, and not only in those suffering from severe COVID-19

Modeling Non Equilibrium Potentiometry to Understand and Control Selectivity and Detection Limit

The majority of present theoretical interpretations of ion-sensor response focus on phase boundary potentials. They assume electroneutrality and equilibrium or steady-state, thus ignoring electrochemical migration and time-dependent effects, respectively. These theoretical approaches, owing to their idealizations, make theorizing on ion distributions and electrical potentials in space and time domains impossible. Moreover, they are in conflict with recent experimental reports on ion-sensors, in which both kinetic (time-dependent) discrimination of ions to improve selectivity, and non-equilibrium transmembrane ion-transport for lowering detection limits, are deliberately used.For the above reasons, the Nernst-Planck-Poisson (NPP) equations are employed here to model the non-equilibrium response in a mathematically congruent manner. In the NPP model, electroneutrality and steady-state/equilibrium assumptions are abandoned. Consequently, directly predicting and visualizing the selectivity and the low detection limit variability over time, as well as the influence of other parameters, i.e. ion diffusibility, membrane thickness and permittivity, and primary to interfering ion concentration ratios on ion-sensor responses, are possible. Additionally, the NPP allows for solving the inverse problem i.e. searching for optimal sensor properties and measurement conditions via target functions and hierarchical modeling. The conditions under which experimentally measured selectivity coefficients are true (unbiased) and detection limits are optimized are demonstrated, and practical conclusions relevant to clinical measurements and bioassays are derived