Parametric identification of the mathematical model of the micro-arc oxidation process.

The article is aimed at solving the problem of parametric identification of non-linear object models using the example of a mathematical model of the micro-arc oxidation process. An algorithm for parametric identification, based on an experiment in the micro-arc oxidation process, the results of which form a training and control sample is proposed; sequential training of neural networks and calculation of the parameters estimates of the nonlinear model according to experimental data are performed. Experimental testing of the proposed method of neural network parametric identification on the example of the micro-arc oxidation process confirmed that the standard deviation of current and voltage from the nominal values does not exceed ±4%. The obtained results were used in the development of an intelligent hardware-software complex for the production of protective coatings by the micro-arc oxidation method.

Mathematical modelling of drug delivery from pH-responsive nanocontainers.

Targeted drug delivery systems represent a promising strategy to treat localised disease with minimum impact on the surrounding tissue. In particular, polymeric nanocontainers have attracted major interest because of their structural and morphological advantages and the variety of polymers that can be used, allowing the synthesis of materials capable of responding to the biochemical alterations of the environment. While experimental methodologies can provide much insight, the generation of experimental data across a wide parameter space is usually prohibitively time consuming and/or expensive. To better understand the influence of varying design parameters on the release profile and drug kinetics involved, appropriately-designed mathematical models are of great benefit. Here, we developed a continuum-scale mathematical model to describe drug transport within, and release from, a hollow nanocontainer consisting of a core and a pH-responsive polymeric shell. Our two-layer mathematical model accounts for drug dissolution and diffusion and includes a mechanism to account for trapping of drug molecules within the shell. We conduct a sensitivity analysis to assess the effect of varying the model parameters on the overall behaviour of the system. To demonstrate the usefulness of our model, we focus on the particular case of cancer treatment and calibrate the model against release profile data for two anti-cancer therapeutical agents. We show that the model is capable of capturing the experimentally observed pH-dependent release.

The Ockham's razor applied to COVID-19 model fitting French data.

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland France have been considered for fitting and validating the model. An extremely simple, two-dimensional model with only two parameters demonstrated to be able to reproduce the time series concerning the number of daily demises caused by Covid-19, the hospitalizations, intensive care and emergency accesses, the daily number of positive tests and other indicators, for the different French regions. These results might contribute to stimulate a debate on the suitability of much more complex models for reproducing and forecasting the pandemic evolution since, although relevant from a mechanistic point of view, they could lead to nonidentifiability issues.

Experimental identification of high order Lamb waves and estimation of the mechanical properties of a dry human skull.

We experimentally investigate and characterize high order Lamb wave modes in a dry human skull. Specifically, we show that the diploë supports distinct wave modes in the sub-1.0 MHz frequency regime, and we employ these modes for the estimation of equivalent mechanical properties of cortical and trabecular bones. These modes are efficiently generated in a parietal region by direct contact excitation with a wedge beam transducer, and are recorded via infrared laser vibrometry. Frequency/wavenumber data are estimated using a matrix pencil method applied to wavefield measurements recorded on the outer cortical surface. The semi-analytical finite element model of an equivalent three-layered plate provides the platform for the identification of wave modes based on their through-the-thickness profiles, and supports the estimation of equivalent mechanical properties in conjunction with an optimization algorithm developed for this purpose. The results presented herein illustrate how high order Lamb waves can be used to gain understanding of the wave properties of a human skull and to estimate the orthotropic and equivalent isotropic mechanical properties of cortical and trabecular bones.

Predicting Composite Component Behavior Using Element Level Crashworthiness Tests, Finite Element Analysis and Automated Parametric Identification.

Fibre reinforced plastics have tailorable and superior mechanical characteristics compared to metals and can be used to construct relevant components such as primary crash structures for automobiles. However, the absence of standardized methodologies to predict component level damage has led to their underutilization as compared to their metallic counterparts, which are used extensively to manufacture primary crash structures. This paper presents a methodology that uses crashworthiness results from in-plane impact tests, conducted on carbon-fibre reinforced epoxy flat plates, to tune the related material card in Radioss using two different parametric identification techniques: global and adaptive response search methods. The resulting virtual material model was then successfully validated by comparing the crushing behavior with results obtained from experiments that were conducted by impacting a Formula SAE (Society of Automotive Engineers) crash box. Use of automated identification techniques significantly reduces the development time of composite crash structures, whilst the predictive capability reduces the need for component level tests, thereby making the development process more efficient, automated and economical, thereby reducing the cost of development using composite materials. This in turn promotes the development of vehicles that meet safety standards with lower mass and noxious gas emissions.

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

Fractional order PID for tracking control of a parallel robotic manipulator type delta.

This paper presents the tracking control for a robotic manipulator type delta employing fractional order PID controllers with computed torque control strategy. It is contrasted with an integer order PID controller with computed torque control strategy. The mechanical structure, kinematics and dynamic models of the delta robot are descripted. A SOLIDWORKS/MSC-ADAMS/MATLAB cosimulation model of the delta robot is built and employed for the stages of identification, design, and validation of control strategies. Identification of the dynamic model of the robot is performed using the least squares algorithm. A linearized model of the robotic system is obtained employing the computed torque control strategy resulting in a decoupled double integrating system. From the linearized model of the delta robot, fractional order PID and integer order PID controllers are designed, analyzing the dynamical behavior for many evaluation trajectories. Controllers robustness is evaluated against external disturbances employing performance indexes for the joint and spatial error, applied torque in the joints and trajectory tracking. Results show that fractional order PID with the computed torque control strategy has a robust performance and active disturbance rejection when it is applied to parallel robotic manipulators on tracking tasks.

Adaptive Control in Passive rehabilitation routines using ELLTIO / Control Adaptable en rutinas de rehabilitación pasiva utilizando ELLTIO

ABSTRACT: The Exoskeleton for Lower Limb Training with Instrumented Orthosis (ELLTIO) is a mechatronic device that can be used to assist in passive kinesitherapy to increase human muscles strength and resistance [1]. This paper presents an alternative for passive rehabilitation process using an exoskeleton for knee and ankle. The main idea is assist a pro fessional physiotherapist in the design and performance of exercises routines for his patients using the prototype. The knee and ankle joint's movements are recorded and storage during the exercises to propose a similar computer generated trajectories which the exoskeleton on should follow. An adaptive controller is implemented to track the trajectories and adapt the user parameters.

RESUMEN: El exoesqueleto para el entrenamiento de miembros inferiores con órtesis instrumentada (ELLTIO) por sus siglas en ingles "Exoskeleton for Lower Limb Training with Instrumented Orthosis" es un dispositivo mecatrónico que se puede utilizar para ayudar en la fisioterapia pasiva para aumentar la fuerza y resistencia de los músculos humanos. En este trabajo se presenta una alternativa para el proceso de rehabilitación pasiva utilizando un exoesqueleto de rodilla y tobillo. La idea principal es ayudar a un fisioterapeuta profesional en el diseño y ejecución de rutinas de ejercicios para sus pacientes utilizando el prototipo. Los movimientos de la articulación de la rodilla y el tobillo se registran y se almacenan durante los ejercicios para proponer trayectorias similares generadas por computadora que el exoesqueleto debe seguir. Se implementa un controlador adaptativo para rastrear las trayectorias y adaptar los parámetros del usuario.

Characterization of nitrogen substrate limitation on Escherichia coli's growth by parameter identification tools.

Carbon-to-nitrogen ratio (CNR) has shown to be a relevant factor in microorganisms growth and metabolites production. It is usual that this factor compromises the productivity yield of different microorganisms. However, CNR has been rarely modeled and therefore the nature of its specific influence on metabolites production has not been understood clearly. This paper describes a parametric characterization of the CNR effect on the Escherichia coli metabolism. A set of parameters was proposed to introduce a mathematical model that considers the biomass, substrate and several byproducts dynamical behavior under batch regimen and CNR influence. Identification algorithm used to calculate the parameters considers a novel least mean square strategy that formalizes the CNR influence in E. coli metabolism. This scheme produced a step-by-step method that was suitable for obtaining the set of parameters that describes the model. This method was evaluated under two scenarios: (a) using the data from a set of numerical simulations where the model was tested under the presence of artificial noises and (b) the information obtained from a set of experiments under different CNR. In both cases, a leave-one-experiment-out cross-validation study was considered to evaluate the model prediction capabilities. Feasibility of the parametric identification method was proven in both considered scenarios.

An efficient identification approach for stable and unstable nonlinear systems using Colliding Bodies Optimization algorithm.

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme.

Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens.

The study of mediation has a long tradition in the social sciences and a relatively more recent one in epidemiology. The first school is linked to path analysis and structural equation models (SEMs), while the second is related mostly to methods developed within the potential outcomes approach to causal inference. By giving model-free definitions of direct and indirect effects and clear assumptions for their identification, the latter school has formalized notions intuitively developed in the former and has greatly increased the flexibility of the models involved. However, through its predominant focus on nonparametric identification, the causal inference approach to effect decomposition via natural effects is limited to settings that exclude intermediate confounders. Such confounders are naturally dealt with (albeit with the caveats of informality and modeling inflexibility) in the SEM framework. Therefore, it seems pertinent to revisit SEMs with intermediate confounders, armed with the formal definitions and (parametric) identification assumptions from causal inference. Here we investigate: 1) how identification assumptions affect the specification of SEMs, 2) whether the more restrictive SEM assumptions can be relaxed, and 3) whether existing sensitivity analyses can be extended to this setting. Data from the Avon Longitudinal Study of Parents and Children (1990-2005) are used for illustration.

Assessing uncertainty in model parameters based on sparse and noisy experimental data.

To perform parametric identification of mathematical models of biological events, experimental data are rare to be sufficient to estimate target behaviors produced by complex non-linear systems. We performed parameter fitting to a cell cycle model with experimental data as an in silico experiment. We calibrated model parameters with the generalized least squares method with randomized initial values and checked local and global sensitivity of the model. Sensitivity analyses showed that parameter optimization induced less sensitivity except for those related to the metabolism of the transcription factors c-Myc and E2F, which are required to overcome a restriction point (R-point). We performed bifurcation analyses with the optimized parameters and found the bimodality was lost. This result suggests that accumulation of c-Myc and E2F induced dysfunction of R-point. We performed a second parameter optimization based on the results of sensitivity analyses and incorporating additional derived from recent in vivo data. This optimization returned the bimodal characteristics of the model with a narrower range of hysteresis than the original. This result suggests that the optimized model can more easily go through R-point and come back to the gap phase after once having overcome it. Two parameter space analyses showed metabolism of c-Myc is transformed as it can allow cell bimodal behavior with weak stimuli of growth factors. This result is compatible with the character of the cell line used in our experiments. At the same time, Rb, an inhibitor of E2F, can allow cell bimodal behavior with only a limited range of stimuli when it is activated, but with a wider range of stimuli when it is inactive. These results provide two insights; biologically, the two transcription factors play an essential role in malignant cells to overcome R-point with weaker growth factor stimuli, and theoretically, sparse time-course data can be used to change a model to a biologically expected state.