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Electrochemical Impedance Spectroscopy (EIS) is a widely used tool for selecting a pertinent Equivalent Circuit Model (ECM) of Li-ion cells which is characterized by non integer order operators. The main drawback of EIS is the long time required to scan a whole spectrum down to low frequencies. Thus, chronopotentiometry (CP) is an alternative method which consists in identifying impedance parameters using an excitation current sequence lasting a few seconds and the corresponding induced voltage variations. An impedance fractional model needs to be synthesized in order to allow simulation also in the time domain. The pertinence of CP is demonstrated using experimental results obtained with a Samsung 3.4 Ah Li-ion cell. Due to its inability to take into account the inductive behavior, fractional model used in the time domain exhibit limitations. Nevertherless, parameters identified in the time domain present similar results to the ones identified in the frequency domain under the condition of a restricted frequency range.

In wastewater treatment, understanding and modeling the nitrification process is crucial to implement control. However, the complexity of this process makes it challenging to create simplified models. This study introduces an innovative method for estimating linear parameter-varying (LPV) models in the context of biological nitrification processes. The research focuses on the development of a continuous-time LPV model for a submerged aerated biofiltration system, considering the conversion of ammonium to nitrate in wastewater treatment. The methodology adopts the reinitialized partial moment approach within a global identification framework. The resultant LPV model is structured to capture the dynamics of the biological nitrification process, considering various factors like flow rates, feed concentrations and environmental regulations. Application of this approach to measured data from a wastewater treatment plant, demonstrates its effectiveness in accurately estimating the LPV model parameters. The results not only offer valuable insights into the dynamics and the nonlinear behaviour of the nitrification process but also contribute to the design and optimization of wastewater treatment plants, particularly those employing submerged aerated nitrifying biofilters.

Electrochemical Impedance Spectroscopy (EIS) is a useful tool for selecting a pertinent Equivalent Circuit Model (ECM) of a Li-ion battery. Impedance model is designed to describe low, middle and high frequency electrochemical processes involved. When considering low frequency restricted in the Warburg zone, diffusion impedance is modeled thanks to a Constant Phase Element (CPE) which behaves as a fractional integrator of order n close to 0.5. Phenomena observed in middle frequency are described using specific circuits called Zarc which consist in connecting a CPE in parallel with a resistor. Therefore, the global impedance model is characterized by non integer order operators and parameters can be estimated by a Complex Non linear Least Squares (CNLS) algorithm which requires a proper initialization in order to guarantee the convergence to a global optimum. The paper presents a method to analyze EIS data measurements in order to select automatically the number of middle frequency Zarc circuits required (one or two) and to initialize properly the CNLS algorithm. The method is validated using experimental open source EIS data.

In order to study the global decline of biodiversity, accurate models of wildlife population dynamics are required. In this paper, we propose new population dynamics models to evaluate the climate impacts on wildlife populations. The considered population is modelled by a parameter-varying partial differential equation. The parameter-varying formulation allows us to introduce in models prior information such as temperatures and precipitations to model the climate impacts. The parameter estimation is performed with specific tools: an iterative identification procedure based on a Levenberg-Marquardt algorithm, a Galerkin method and a proper orthogonal decomposition. The goal of the present paper is to test these tools on simulated dataset. The perspectives are to model specific community of birds in order to evaluate the effect of climate and agricultural changes in population dynamics and distributions.

This paper deals with the identification of battery impedance parameters in the frequency domain using Electrochemical Impedance Spectroscopy (EIS) measurements and fractional modeling. Unlike other classical models used for frequency identification algorithms, fractional modeling allows to perform simulations also in the time domain offering interesting perspectives for identification in the time domain in future works. The objective of the paper is to propose an automatic initialization of a Complex Nonlinear Least Squares algorithm based on fractional modeling and EIS measurements in order to accurately estimate impedance parameters whatever the kind of diffusion involved: Finite Length Warburg (FLW) or Finite Space Warburg (FSW) diffusion. Fractional models are used to approximate non-integer orders and are based on a simplified Randles equivalent circuit. When no sufficient low frequency measurements are available, diffusion can be modeled thanks to a single fractional integrator whose order is close to 0.5 (Warburg zone). Otherwise, dedicated fractional models are proposed to take into account FLW or FSW diffusion. The method is validated using not only simulation data but also EIS measurements performed on a commercial 3.5 Ah Li-ion cell and open source experimental data.

In this paper, the problem of fouling detection in a parallel flow heat exchanger is tackled. Because the behavior of a heat exchanger is governed by coupled partial differential equations, we suggest describing its dynamics by using a 2D linear time-invariant gray-box Roesser model. A non-linear optimization of a standard non-convex cost function is introduced to estimate the state-space matrices of the Roesser model, more specifically to estimate its physical parameters. After the parameter estimation, the output temperatures of the obtained model are compared to the output temperatures of the process and residuals are generated in order to detect a potential fouling. Numerical simulations illustrate the efficiency of this new fouling detection approach.

This paper deals with the identification of High Voltage Direct Current (HVDC) cables in order to obtain an accurate model. The context of large penetration of renewable energy sources in electric production requires to consider new power grids. Nowadays the HVDC multi-terminal networks are going to develop, and with them, new identification and control problems appear. We propose an output-error method to the parametric estimation of HVDC cable models. A π-model, which takes into account the coupling between core and screen of the cable, is estimated. The approach is tested with simulated noise-free data given by the Wideband cable model developed on EMTP-RV software. The goal is to evaluate if the studied model represents the high-frequency behavior of the cable and the feasibility to estimate the parameters of such a model.

In hydrogeology, understanding the behavior of aquifers is an important issue. May it be for forecasting the availability of water on a long term or for estimating the impact of pollutants when injected at difierent regions, modeling of an aquifer is very useful for hydrogeologists. This paper gives an overlook of the difierent types of tests on a particularsite, namely the Hydrogeological Experimental Site (HES) of Poitiers (France). The aim is to show that the HES is a huge instrumented test bed allowing difierent types of experiments. Thus, system identification users can expect to gather large series of input-output datasets so as to experiment their own tools. Note that a large database exists concerning already-run tests, but what is interesting is that new experiments can be programmed so as to account for customized datasets. As a counterpart, the use of HES data should promote proper modeling of the underlying aquifer, thus contributing to the hydrological research field.

This paper deals with a grey-box model identification of a catalytic ozonation pilot process used in wastewater treatment. The grey-box model is presented. For the parametric estimation, a non convex optimization problem must be solved. Then, we propose to solve this problem, in practice, by using a Levenberg-Marquardt algorithm applied to a composite criterion based on a priori knowledge which imposes a physical meaning to the solution. The method is applied to experimental data.

CuO thin films were prepared by radio frequency magnetron sputtering using a copper target in a (Ar + O2) reactive mixture. Different sputtering parameters were varied including oxygen flow rate, total pressure, target-substrate distance, substrate temperature and target orientation. As expected, the thin film chemical composition is strongly dependent on the oxygen flow rate. CuO thin films having a good electronic conductivity (9.3 × 10–1S·cm–1) were obtained with an oxygen concentration of 12%. The texture and the columnar growth are amplified when the target is tilted. Preliminary electrochemical results highlight that CuO thin film performances in lithium systems are tightly related to their morphology and structure.

The course of the conversion reaction during the electrochemical insertion/deinsertion of lithium in CuO thin film electrodes was surveyed by cyclic voltammetry (CV) and X-ray photoelectron spectroscopy (XPS). The electrochemical processes were studied through the comprehensive acquisition and interpretation of XPS and Auger spectra (Cu 2p, O 1s, Cu LMM) recorded at different stages of the first cycle of a Li/CuO cell. The reduction process consisting of three main steps leads successively to Cu+and Cu0and involves a Li2O2peroxide type phase as an intermediate, whereas Li2O is the main lithiated oxide at the end of the discharge. Only the two last steps appear reversible in charge. No evidence of Cu2+was found at the end of the charge at 3.5 V/Li+/Li, showing the irreversibility of the first step in our experimental conditions. Complementary XPS depth profile analyses were performed to check the evolution of the active material composition over the thickness of the electrode.

In this paper, a recent subspace-based identification approach is used to obtain an experimental model of a smart rotor, which is a wind turbine rotor equipped with a number of additional actuators and sensors to react to turbulence in a more detailed way. To make sure that the eventual controller is robust, it is essential to quantify the model uncertainty. In this paper a novel bounded error approach is tailored towards the characteristics of the smart rotor. It is shown that the suggested approach can provide realistic uncertainty bounds on the estimated model of a prototyped smart rotor.

In this paper, a practical solution for the identification of Continuous-Time (CT) Input-Output (IO) Linear Parameter Varying (LPV) systems is proposed. For this particular class, we formulate an output error identification problem and present a parameter estimation scheme in which a prediction error based cost function is minimized using nonlinear programming. Because the cost function possesses local minima, the success of any iterative parameter estimation algorithm depends on appropriate initial seeds. One approach would be to generate an initial estimate using CT Reinitialized Partial Moments (RPM) models extended to CT IO LPV systems. We assume that the inputs, outputs and scheduling parameters are directly measurable, and that the functional dependence of the system coefficients on the parameters is of a polynomial form. The performance of the proposed method is evaluated by Monte Carlo simulation analysis.

This paper deals with the development of a new finger articulation prototype used as a joint for anthropomorphic robotic hand. We focus on modeling strategies applied to the finger prototype and its experimental identification. Self-sensing characteristic is achieved by an optimal design of highly back-driveable and efficient electromechanical actuators based on a DC motor, which requires an accurate modeling due to the absence of external sensors. The non-linear model of a finger joint can be reformulated as a Linear Parameter-Varying (LPV) model. On the contrary of classic method used for modeling, a quasi-LPV model is derived from Linear Time Invariant (LTI) interpolation, using a simulated annealing algorithm. Then, our proposed quasi-LPV model is validated from the Inverse Dynamic Model (IDM), and shows a good efficiency in predicting the non-linear behavior of the end effector.

In this paper, we present a simulator of front face thermal characterization experiment, consisting in measuring the temperature at the surface of a thin slab where a random heat flux is applied, in order to validate and compare different fractional identification algorithms. Indeed, heat transfer in homogeneous medium obeys to diffusion phenomenon which can be modeled with the help of fractional systems. We use models based on an original continuous fractional integrator whose order 1/2 acts only over a limited spectral band. These models are obtained thanks to series expansion of the slab thermal impedance which lead to multiple 0.5 derivative order systems. Simulations taking into account the influence of heat losses across each face are performed and show the interest to take them into account so as to obtain accurate heat conductivity and diffusivity estimates.

In this paper, we present an identification algorithm based on a porous impedance model which allows to characterize an ultracapacitor. We simulate experiments which consist in applying a current sequence charging an ultracapacitor and measuring the induced voltage drop change across its terminals. The analysis of the porous part of the impedance shows that the charging process can be modeled with the help of a fractional model made of a classical integrator in parallel with a fractional integrator of order 1/2, whose action is blocked outside a limited spectral band. Numerical simulations show the ability of the fractional model, thanks to an output error identification technique, to obtain accurate estimation of the voltage drop change evolution as well as the impedance frequency response using time data series. Furthermore, the fractional parameter estimates allow to provide accurate values of the capacity and the high frequency internal equivalent series resistance.

The benchmark problem, “Design and optimisation of restricted complexity controllers”, for an active suspension system is studied. An initial fractional controller based on the first and third generation Crone methodologies is designed first. The high-level parameters of the rational controller are then fine-tuned using a tuned in closed-loop approach. The optimisation technique uses the power spectral density of the closedloop simulation of the residual force to be minimised. This final controller is finally assessed with real-time implementation.

Black box modeling of diffusion processes can be performed by fractional systems. The simulation of these particular systems is based on a new fractional integrator, with limited spectral range for non integer order. Parameter estimation of this class of systems is performed by an OE identification technique. This paper presents the application of this new methodology to the modeling of the dynamics of a lead-acid battery.

We present in this article a method of identification of the non-linear systems whose the input output functional is regular and homogeneous. The model is a Volterra series truncated in its first tenns. The Volterra kernels are expanded on orthonormal bases and each term of the series is balanced by a function of activation. The amplitude of these functions depends on the distance of the system of its point of initial operation. Two examples in simulation, a polynomial monovariable system and a fluidic system, illustrate this method.

A new class of linear system invariant is presented. An estimation of frequential moments is performed by a least squares algorithm. The linears relationships between moments and transfer function parameters are also used to estimate local frequency models.

This paper describes a new method for quantifying errors in continuous time system identification. The key idea is to estimate a global model constituted of a nominal model and a model of bias error. The latter is composed of particular orthogonal basis functions and gives an information about the true bias error. An application to a chemical reaction column is used to illustrate this new approach.

Listeria monocytogenesis a pathogenic intracellular microorganism whose infection induces pleiotropic biological changes associated with host cell gene expression regulation. Here we define the gene expression profiles of the human promyelocytic THP1 cell line before and after L. monocytogenesinfection. Gene expression was measured on a large scale via oligonucleotide microarrays with probe sets corresponding to 6,800 human genes. We assessed and discussed the reproducibility of the hybridization signatures. In addition to oligonucleotide arrays, we also performed the large scale gene expression measurement with two high-density membranes, assaying for 588 and 18,376 human genes, respectively. This work allowed the reproducible identification of 74 up-regulated RNAs and 23 down-regulated RNAs as a consequence ofL. monocytogenesinfection of THP1. The reliability of these data was reinforced by performing independent infections. Some of these detected RNAs were consistent with previous results, while some newly identified RNAs encode gene products that may play key roles inL. monocytogenesinfection. These findings will undoubtedly enhance the understanding of L. monocytogenesmolecular physiology and may help identify new therapeutic targets.

Listeria monocytogenes is a pathogenic intracellular microorganism whose infection induces pleiotropic biological changes associated with host cell gene expression regulation. Here we define the gene expression profiles of the human promyelocytic THP1 cell line before and after L. monocytogenes infection. Gene expression was measured on a large scale via oligonucleotide microarrays with probe sets corresponding to 6,800 human genes. We assessed and discussed the reproducibility of the hybridization signatures. In addition to oligonucleotide arrays, we also performed the large scale gene expression measurement with two high-density membranes, assaying for 588 and 18,376 human genes, respectively. This work allowed the reproducible identification of 74 up-regulated RNAs and 23 down-regulated RNAs as a consequence of L. monocytogenes infection of THP1. The reliability of these data was reinforced by performing independent infections. Some of these detected RNAs were consistent with previous results, while some newly identified RNAs encode gene products that may play key roles in L. monocytogenes infection. These findings will undoubtedly enhance the understanding of L. monocytogenes molecular physiology and may help identify new therapeutic targets.

In the present report, we investigated in detail the effects of SR 144528, a selective antagonist of the peripheral cannabinoid receptor (CB2), on two well-characterized functions mediated by CB2: the induction of the early response gene krox24 and the inhibition of adenylyl cyclase. We generated Chinese hamster ovary cells doubly transfected with human CB2 and a luciferase reporter gene linked to either the murine krox24 regulatory sequence or multiple cAMP responsive elements. Our results show that (1) SR 144528 antagonizes the effect of receptor agonists—it inhibits the krox24 reporter activity and prevents the inhibition of forskolin-induced cAMP reporter activity mediated by CP 55,940; (2) CB2 is autoactivated—CB2 mediates signaling in the absence of ligand, and this basal activity is reduced by pretreating the cells with pertussis toxin; (3) SR 144528 is an inverse agonist—it reproduces the effects of pertussis toxin; and (4) inhibition of precoupled CB2 by a long-term pretreatment of cells with SR 144528 potentiates krox24 response to cannabinoid receptor agonists and restores activation of adenylyl cyclase. Taken together, these data provide evidences for the inverse agonist property of SR 144528 and the constitutive activation of CB2 in Chinese hamster ovary-expressing cells.

Interleukin‐13 (IL‐13) is a recently described human lymphokine which is produced by activated T‐cells. Its effect on the production of IL‐6 by normal keratinocytes and keratinocyte cell lines of human origin was studied and compared to that of IL‐4. IL‐13, similarly to IL‐4, stimulated IL‐6 expression by these cells in a dose‐ and time‐dependent manner. Contamination with endotoxin was excluded by the use of polymyxin B and heat‐inactivated cytokines. Further, we showed that IL‐13, like IL‐4, not only stimulated IL‐6 production but also was able to induce overexpression of this cytokine in response to an inflammatory signal such as lipopolysaccharide (LPS). In a previous study, we demonstrated that IL‐13, by inhibiting IL‐6 and other cytokines produced by monocytes, exhibited an ‘anti‐inflammatory profile’ comparable to that displayed by IL‐4. In contrast, we show here that IL‐13, by stimulating IL‐6 production by keratinocytes, may favour the installation of an inflammatory process at a local level and, here again, it acted like IL‐4. Therefore, according to the type of target cell, these two ‘TH2 type’ cytokines induce similar opposing effects on IL‐6 production and are likely to be important cytokines in the regulation of inflammation at both systemic and local levels.

AbstractA kinetic model for the oxidation of atrazine by H2O2/UV in dilute aqueous solutions ([Atrazine]0< 2 μM) has been tested in a batch reactor. In this model, direct photolysis and oxidation by hydroxyl radicals are assumed to be the main reactions in the decomposition of atrazine by H2O2/UV. The data showed that the model can be used to predict the effects of some parameters (hydrogen peroxide dose, pH, bicarbonate alkalinity, …) and to estimate values of quantum yield of photolysis, rate constants for the reaction of hydroxyl radicals with atrazine and of the scavenging term (SkiSi) of natural waters.

The G-protein-coupled central cannabinoid receptor (CB1) has been shown to be functionally associated with several biological responses including inhibition of adenylate cyclase, modulation of ion channels and induction of the immediate-early gene Krox-24. Using stably transfected Chinese Hamster Ovary cells expressing human CB1 we show here that cannabinoid treatment induces both phosphorylation and activation of mitogen-activated protein (MAP) kinases, and that these effects are inhibited by SR 141716A, a selective CB1 antagonist. The two p42 and p44 kDa MAP kinases are activated in a time- and dose-dependent manner. The rank order of potency for the activation of MAP kinases with various cannabinoid agonists is CP-55940 > delta 9-tetrahydrocannabinol > WIN 55212.2, in agreement with the pharmacological profile of CB1. The activation of MAP kinases is blocked by pertussis toxin but not by treatment with hydrolysis-resistant cyclic AMP analogues. This suggests that the signal transduction pathway between CB1 and MAP kinases involves a pertussis-toxin-sensitive GTP-binding protein and is independent of cyclic AMP metabolism. This coupling of CB1 subtype and mitogenic signal pathway, also observed in the human astrocytoma cell line U373 MG, may explain the mechanism of action underlying cannabinoid-induced Krox-24 induction.

In the present report, we investigated in detail the effects of SR 144528, a selective antagonist of the peripheral cannabinoid receptor (CB2), on two well-characterized functions mediated by CB2: the induction of the early response gene krox24 and the inhibition of adenylyl cyclase. We generated Chinese hamster ovary cells doubly transfected with human CB2 and a luciferase reporter gene linked to either the murine krox24 regulatory sequence or multiple cAMP responsive elements. Our results show that (1) SR 144528 antagonizes the effect of receptor agonists-it inhibits the krox24 reporter activity and prevents the inhibition of forskolin-induced cAMP reporter activity mediated by CP 55,940; (2) CB2 is autoactivated-CB2 mediates signaling in the absence of ligand, and this basal activity is reduced by pretreating the cells with pertussis toxin; (3) SR 144528 is an inverse agonist-it reproduces the effects of pertussis toxin; and (4) inhibition of precoupled CB2 by a long-term pretreatment of cells with SR 144528 potentiates krox24 response to cannabinoid receptor agonists and restores activation of adenylyl cyclase. Taken together, these data provide evidences for the inverse agonist property of SR 144528 and the constitutive activation of CB2 in Chinese hamster ovary-expressing cells.