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1,285 results on '"Di Barba"'

1. Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates

Author

Versaci Mario, Angiulli Giovanni, di Barba Paolo, and Morabito Francesco Carlo

Subjects
non-destructive testing and evaluation, eddy currents, soft computing, fuzzy similarities, classification problems, Physics, QC1-999
Abstract

Steel plates bi-axially loaded are characterized by mechanical deformations whose 2D image representations are very difficult to achieve. In this work, the authors propose an innovative approach based on eddy current techniques for obtaining 2D electrical maps to assess the mechanical integrity of a steel plate. The procedure, also exploiting fuzzy similarity computations, translates the problem of the assessment of the mechanical integrity of a steel plate into a suitable classification problem. The results obtained by this proposed procedure show performances comparable to those provided by well-established soft computing approaches with a higher computational complexity.

Published
2020
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2. A method of magnetic field measurement in a scanning electron microscope using a microcantilever magnetometer

Author

Orłowska Karolina, Mognaschi Maria E., Kwoka Krzysztof, Piasecki Tomasz, Kunicki Piotr, Sierakowski Andrzej, Majstrzyk Wojciech, Podgórni Arkadiusz, Pruchnik Bartosz, di Barba Paolo, and Gotszalk Teodor

Subjects
scanning electron microscope, magnetometry, microcantilever, Technology
Abstract

Scanning electron microscopy (SEM) is a perfect technique for micro-/nano-object imaging [1] and movement measurement [2, 3] both in high and environmental vacuum conditions and at various temperatures ranging from elevated to low temperatures. In our view, the magnetic field expanding from the pole-piece makes it possible to characterize the behaviour of electromagnetic micro- and nano electromechanical systems (MEMS/NEMS) in which the deflection of the movable part is controlled by the electromagnetic force. What must be determined, however, is the magnetic field expanding from the e-beam column, which is a function of many factors, like working distance (WD), magnification and position of the device in relation to the e-beam column. There are only a few experimental methods for determination of the magnetic field in a scanning electron microscope. In this paper we present a method of the magnetic field determination under the scanning electron column by application of a silicon cantilever magnetometer. The micro-cantilever magnetometer is a silicon micro-fabricated MEMS electromagnetic device integrating a current loop of lithographically defined dimensions. Its stiffness can be calibrated with a precision of 5% by the method described by Majstrzyk et al. [4]. The deflection of the magnetometer cantilever is measured with a scanning electron microscope and thus, through knowing the bias current, it is possible to determine the magnetic field generated by the e-beam column in a defined position and at a defined magnification.

Published
2020
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3. Energy-based PINNs for solving coupled field problems: concepts and application to the multi-objective optimal design of an induction heater

Author

Baldan, Marco and Di Barba, Paolo

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Physics-informed neural networks (PINNs) are neural networks (NNs) that directly encode model equations, like Partial Differential Equations (PDEs), in the network itself. While most of the PINN algorithms in the literature minimize the local residual of the governing equations, there are energy-based approaches that take a different path by minimizing the variational energy of the model. We show that in the case of the steady thermal equation weakly coupled to magnetic equation, the energy-based approach displays multiple advantages compared to the standard residual-based PINN: it is more computationally efficient, it requires a lower order of derivatives to compute, and it involves less hyperparameters. The analyzed benchmark problems are the single- and multi-objective optimal design of an inductor for the controlled heating of a graphite plate. The optimized device is designed involving a multi-physics problem: a time-harmonic magnetic problem and a steady thermal problem. For the former, a deep neural network solving the direct problem is supervisedly trained on Finite Element Analysis (FEA) data. In turn, the solution of the latter relies on a hypernetwork that takes as input the inductor geometry parameters and outputs the model weights of an energy-based PINN (or ePINN). Eventually, the ePINN predicts the temperature field within the graphite plate.

Published
2024

5. Electric field computation and measurements in the electroporation of inhomogeneous samples

Author

Bernardis Alessia, Bullo Marco, Campana Luca Giovanni, Di Barba Paolo, Dughiero Fabrizio, Forzan Michele, Mognaschi Maria Evelina, Sgarbossa Paolo, and Sieni Elisabetta

Subjects
clinical electroporation, electric field model, equivalent circuit, resistivity identification, potato samples, 87.50.c, 07.05.tp, Physics, QC1-999
Abstract

In clinical treatments of a class of tumors, e.g. skin tumors, the drug uptake of tumor tissue is helped by means of a pulsed electric field, which permeabilizes the cell membranes. This technique, which is called electroporation, exploits the conductivity of the tissues: however, the tumor tissue could be characterized by inhomogeneous areas, eventually causing a non-uniform distribution of current. In this paper, the authors propose a field model to predict the effect of tissue inhomogeneity, which can affect the current density distribution. In particular, finite-element simulations, considering non-linear conductivity against field relationship, are developed. Measurements on a set of samples subject to controlled inhomogeneity make it possible to assess the numerical model in view of identifying the equivalent resistance between pairs of electrodes.

Published
2017
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6. Biogeography-inspired multiobjective optimization for helping MEMS synthesis

Author

Di Barba Paolo, Mognaschi Maria Evelina, Venini Paolo, and Wiak Slavomir

Subjects
biogeography-inspired optimisation, finite-element method, MEMS, multiphysics analysis, Pareto optimality, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The aim of the paper is to assess the applicability of a multi-objective biogeography-based optimisation algorithm in MEMS synthesis. In order to test the performances of the proposed method in this research field, the optimal shape design of an electrostatic micromotor, and two different electro-thermo-elastic microactuators are considered as the case studies.

Published
2017
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12. Influence of aging and chronic heart failure on temporal dispersion of myocardial repolarization

Author

Piccirillo G, Moscucci F, Pascucci M, Pappadà MA, D’Alessandro G, Rossi P, Quaglione R, Di Barba D, Barillà F, and Magrì D

Subjects
Cardiac repolarization, QT variability index, QT dynamic, sudden cardiac death, ventricular arrhythmias, chronic heart failure, aging, Geriatrics, RC952-954.6
Abstract

Gianfranco Piccirillo,1 Federica Moscucci,1 Matteo Pascucci,1 Maria Antonella Pappadà,1 Gaetana D’Alessandro,1 Pietro Rossi,2 Raffaele Quaglione,1 Daniele Di Barba,1 Francesco Barillà,1 Damiano Magrì3 1Department of Cardiovascular, Respiratory, Nephrological and Geriatric Sciences, Policlinico Umberto I, “Sapienza” University of Rome, Rome, Italy; 2Division of Cardiology, S. Giovanni Calabita Fatebenefratelli Hospital, Rome, Italy; 3Department of Clinical and Molecular Medicine, Sant’Andrea Hospital, “Sapienza” University of Rome, Rome, Italy Background and purpose: QT and Tpeak-Tend (Te) intervals are associated with sudden cardiac death in patients with chronic heart failure (CHF). We studied age-dependent influence on short-term temporal dispersion of these two variables in patients with postischemic CHF. Method: We grouped 75 CHF and 53 healthy control subjects into three age subsets: ≤50 years, >50 years and ≤65 years, and >65 years. We then calculated the following indices: QT and Te variability index (QTVI and TeVI), the ratio between the short-term variability (STV) of QT or Te, and the STV of resting rate (RR) (QT/RR STV and Te/RR STV). Results: In all different age subgroups, patients with CHF showed a higher level of QTVI than age-matched control subjects (≤50 years: P < 0.0001; >50 years and ≤65 years: P < 0.05; >65 years: P

Published
2013

20. A Source Identification Problem in Magnetics Solved by Means of Deep Learning Methods

Author

Sami Barmada, Paolo Di Barba, Nunzia Fontana, Maria Evelina Mognaschi, and Mauro Tucci

Subjects
deep learning, source identification problem, magnetic field, conditional variational autoencoder, automatic differentiation, image reconstruction, Mathematics, QA1-939
Abstract

In this study, a deep learning-based approach is used to address inverse problems involving the inversion of a magnetic field and the identification of the relevant source, given the field data within a specific subdomain. Three different techniques are proposed: the first one is characterized by the use of a conditional variational autoencoder (CVAE) and a convolutional neural network (CNN); the second one employs the CVAE (its decoder, more specifically) and a fully connected deep artificial neural network; while the third one (mainly used as a comparison) uses a CNN directly operating on the available data without the use of the CVAE. These methods are applied to the magnetostatic problem outlined in the TEAM 35 benchmark problem, and a comparative analysis between them is conducted.

Published
2024
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22. Field models and numerical dosimetry inside an extremely-low-frequency electromagnetic bioreactor: the theoretical link between the electromagnetically induced mechanical forces and the biological mechanisms of the cell tensegrity

Author

Mognaschi, Maria Evelina, Di Barba, Paolo, Magenes, Giovanni, Lenzi, Andrea, Naro, Fabio, and Fassina, Lorenzo

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

We have implemented field models and performed a detailed numerical dosimetry inside our extremely-low-frequency electromagnetic bioreactor which has been successfully used in $\textit{in vitro}$ Biotechnology and Tissue Engineering researches. The numerical dosimetry permitted to map the magnetic induction field (maximum module equal to about 3.3 mT) and to discuss its biological effects in terms of induced electric currents and induced mechanical forces (compression and traction). So, in the frame of the tensegrity-mechanotransduction theory of Ingber, the study of these electromagnetically induced mechanical forces could be, in our opinion, a powerful tool to understand some effects of the electromagnetic stimulation whose mechanisms remain still elusive.

Published
2019
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26. A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

Author

Manuele Bertoluzzo, Paolo Di Barba, Michele Forzan, Maria Evelina Mognaschi, and Elisabetta Sieni

Subjects
deep learning, dynamic wireless power transfer system, fast surrogate model, optimization, magnetic field, finite element analysis, Technology
Abstract

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions.

Published
2023
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27. From Fields to Circuits

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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28. Numerical Methods for Field Analysis of MEMS

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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29. Coupled Fields: Multi-physics Analysis of MEMS

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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30. Steady Conduction Field and Boundary-Value Problems

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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31. Engineering Electrostatics and Boundary-Value Problems

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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32. Engineering Magnetostatics and Boundary-Value Problems

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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33. Numerical Case Studies: Forward Problems

Author

Di Barba, Paolo, Mognaschi, Maria Evelina, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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34. Numerical Methods for MEMS Design: Automated Optimization

Author

Di Barba, Paolo, Mognaschi, Maria Evelina, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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35. Numerical Methods for MEMS Design: Inverse Problems

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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38. Finite Element Evaluation of the Electric Field Distribution in a Non-Homogeneous Environment

Author

Elisabetta Sieni, Monica Dettin, Annj Zamuner, Maria Teresa Conconi, Bianca Bazzolo, Cristian Balducci, Paolo Di Barba, Michele Forzan, Patrizia Lamberti, and Maria Evelina Mognaschi

Subjects
finite element analysis, conductivity inhomogeneity, electroporation, 3D scaffold, Technology, Biology (General), QH301-705.5
Abstract

Finite element analysis is used in this study to investigate the effect of media inhomogeneity on the electric field distribution in a sample composed of cells and their extracellular matrix. The sample is supposed to be subjected to very high pulsed electric field. Numerically computed electric field distribution and transmembrane potential at the cell membrane in electroporation conditions are considered in order to study cell behavior at different degrees of inhomogeneity. The different inhomogeneity grade is locally obtained using a representative model of fixed volume with cell–cell distance varying in the range of 1–283 um. The conductivity of the extracellular medium was varied between plain collagen and a gel-like myxoid matrix through combinations of the two, i.e., collagen and myxoid. An increase in the transmembrane potential was shown in the case of higher aggregate. The results obtained in this study show the effect of the presence of the cell aggregates and collagen on the transmembrane potential. In particular, by increasing the cell aggregation in the two cases, the transmembrane potential increased. Finally, the simulation results were compared to experimental data obtained by culturing HCC1954 cells in a hyaluronic acid-based scaffold. The experimental validation confirmed the behavior of the transmembrane potential in presence of the collagen: an increase in electroporation at a lower electric field intensity was found for the cells cultured in the scaffolds where there is the formation of collagen areas.

Published
2023
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39. Introduction

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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40. Device Miniaturization Principles

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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41. MEMS Modelling: Distributed versus Lumped Parameter Models

Author

Di Barba, Paolo, Wiak, Slawomir, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Di Barba, Paolo, and Wiak, Slawomir

Published
2020
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44. Learning-Based Approaches to Current Identification from Magnetic Sensors

Author

Sami Barmada, Paolo Di Barba, Alessandro Formisano, Maria Evelina Mognaschi, and Mauro Tucci

Subjects
electromagnetic inverse problems, regularization, machine learning, neural networks, measurement uncertainty, Chemical technology, TP1-1185
Abstract

Direct measurement of electric currents can be prevented by poor accessibility or prohibitive technical conditions. In such cases, magnetic sensors can be used to measure the field in regions adjacent to the sources, and the measured data then can be used to estimate source currents. Unfortunately, this is classified as an Electromagnetic Inverse Problem (EIP), and data from sensors must be cautiously treated to obtain meaningful current measurements. The usual approach requires using suited regularization schemes. On the other hand, behavioral approaches are recently spreading for this class of problems. The reconstructed model is not obliged to follow the physics equations, and this implies approximations which must be accurately controlled, especially if aiming to reconstruct an inverse model from examples. In this paper, a systematic study of the role of different learning parameters (or rules) on the (re-)construction of an EIP model is proposed, in comparison with more assessed regularization techniques. Attention is particularly devoted to linear EIPs, and in this class, a benchmark problem is used to illustrate in practice the results. It is shown that, by applying classical regularization methods and analogous correcting actions in behavioral models, similar results can be obtained. Both classical methodologies and neural approaches are described and compared in the paper.

Published
2023
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