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In this paper CNNs are used for solving an optimization problem with two different approaches: CNN is used as a surrogate model of the forward problem, inserted in an optimization loop governed by a genetic algorithm, in the first approach, while a CNN is trained for solving directly the inverse problem in the second approach. The case study is the shape design of a magnetic core used for material testing. [ABSTRACT FROM AUTHOR]

In the paper an original approach to efficiency map optimal synthesis is presented. A permanent magnet motor, working as controlled AC motor of synchronous type (PMSM), is selected as a case study. The first target of this research is to derive a lumped-parameter model of the motor (low-fidelity model), validated by magnetic field analysis (high-fidelity model). In turn, the end target is these two models application in a cost-effective optimisation procedures, where the goal is to identify the motor geometry maximizing the map area which is encompassed by a prescribed value for the motor efficiency. [ABSTRACT FROM AUTHOR]

In computational electromagnetism there are manyfold advantages when using machine learning methods, because no mathematical formulation is required to solve the direct problem for given input geometry. Moreover, thanks to the inherent bidirectionality of a convolutional neural network, it can be trained to identify the geometry giving rise to the prescribed output field. All this puts the ground for the neural meta-modeling of fields, in spite of different levels of cost and accuracy. In the paper it is shown how CNNs can be trained to solve problems of optimal shape synthesis, with training data sets based on finite-element analyses of electric and magnetic fields. In particular, a concept of multi-fidelity model makes it possible to control both prediction accuracy and computational cost. The shape design of a MEMS design and the TEAM workshop problem 35 are considered as the case studies. [ABSTRACT FROM AUTHOR]

The article discusses the integration of deep learning techniques into the field of electromagnetics. Traditionally, electromagnetic problems have been solved using analytical and numerical methods, but the complexity of modern scenarios has led to the exploration of deep learning as a powerful tool. Deep learning, which is inspired by the architecture of the human brain, uses artificial neural networks with multiple layers to learn complex representations from data. The article highlights various applications of deep learning in electromagnetics, including the design of electromagnetic devices, fault detection in electrical machines, wave propagation modeling, and material characterization. Despite the challenges that still exist, the fusion of deep learning and electromagnetics offers efficient and innovative solutions in this field. [Extracted from the article]

Many-objective problems, characterized by a high-dimensional objective space, are a source of challenge for standard methods of multi-objective optimization. An evolutionary algorithm based on the concept of least-conflict solutions is here presented; the shape design of a IPM motor is considered as the case study. In particular, optimization problems dealing with 3 and 4 objective functions have been solved, respectively. [ABSTRACT FROM AUTHOR]

New solutions to a recently proposed benchmark TEAM problem for Pareto optimisation are presented. In the benchmark, an air‐cored solenoid of small size, which can be used, for example, for magnetic fluid hyperthermia, is considered. Two shape optimisations of the solenoid are proposed in the benchmark: synthesising a uniform magnetic field in a control region, considering also a sensitivity function (Problem 1) or synthesising a uniform magnetic field, simultaneously minimising the power losses (Problem 2). The benchmark is solved by means of three different nature‐inspired algorithms and a genetic one, namely micro biogeography‐inspired algorithm, wind‐driven optimisation, and the cuckoo search, taking the genetic algorithm NSGA‐II as a reference, because all these methods have proven to be effective in solving multi‐objective optimisation problems. [ABSTRACT FROM AUTHOR]

The main aim of this work is to determine the optimal shape design of a class of Interior Permanent-Magnet (IPM) motors for electric vehicles (EV), by means of many-objective optimisation which is an emerging area in the automated optimal design. As the case study, a 3-phase, 4-pole single-barrier IPM motor is modelled by means of using finite-element analysis. The goal is to optimise the motor's performance in terms of a number of design criteria such as the power losses, running torque, torque ripple and material costs, by varying size and position of the permanent magnet and flux barriers. [ABSTRACT FROM AUTHOR]

A method of free-form optimisation for shaping an electrode surface is proposed in the context of shape design of high voltage devices. The subsequent comparison of various optimisation approaches in dielectric design is developed. In particular, a mesh-based model, driven by free-form optimisation, is presented and compared with a parameter-based model and a NURBS-based model driven by parametric optimisation algorithms. In each model, the electric field analysis is computed by means of the Boundary Element Method. In order to assess the proposed method in a comparative way, the optimal shape design of a L-shaped tunnel electrode and a ring electrode are considered as the case studies. [ABSTRACT FROM AUTHOR]

This paper proposes a new benchmark for multi-objective optimization. A solution is furnished which has enabled an extensive search and reliable estimation of the shape of the Pareto front. Field uniformity and sensitivity are considered in the context of robust design. It is argued that the benchmark will provide a challenging target for new algorithms, especially those involving numerical modeling using finite-element codes where the number of objective function calls needs to be minimized for practical design processes. [ABSTRACT FROM AUTHOR]

In the paper, the Wind Driven Optimization WDO algorithm is applied to the optimal shape design of a class of switched reluctance motors. The goal of the optimization is to identify a class of geometries which maximize the torque and simultaneously minimize the iron losses of the motor. Because of the twofold design criterion, the multi-objective version of the wind driven algorithm M-WDO is used. Results are eventually compared with those obtained by means of a standard genetic algorithm GA. For construction of optimized model of switched reluctance (SR) motor measurements of resistance and inductances vs. rotor position of commercial motor made of steel and non-optimized model motor made of soft magnetic composite were measured. On the basis of changes in inductance, electromagnetic torques were calculated. Results of B = f(H) and iron loss measurements of soft magnetic composites made of Somaloy 700 and Somaloy 700 HR powder are also shown which will be used for construction of optimized SR model motor. [ABSTRACT FROM AUTHOR]

This study focuses on a class of a 3-phase Switched Reluctance Motor. The aim of the paper is to optimize torque and iron losses as a function of the geometry. To enhance the efficiency of the motor, a procedure of automated optimal design is adopted. Two materials are evaluated and compared: in particular, a Soft Magnetic Composite material might be a good alternative for rotor and stator cores instead of a laminated steel. The analysis model of the motor is based on 2D Finite Element Method (FEM) simulation, while the design optimization is based on evolutionary computing. [ABSTRACT FROM AUTHOR]

This study focuses on a class of a 3-phase Switched Reluctance Motor. The aim of the paper is to optimize torque and iron losses as a function of the geometry. To enhance the efficiency of the motor, a procedure of automated optimal design is adopted. Two materials are evaluated and compared: in particular, a Soft Magnetic Composite material might be a good alternative for rotor and stator cores instead of a laminated steel. The analysis model of the motor is based on 2D Finite Element Method (FEM) simulation, while the design optimization is based on evolutionary computing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the paper, the optimization of an electro-thermo-elastic microactuator is proposed. In particular, the maximum temperature of the actuator is to be minimized, while the total displacement is to be maximized. For solving this problem, the Adaptive Gaussian Process-Assisted Differential Evolution AGDEMO method is applied. [ABSTRACT FROM AUTHOR]

IJAEM Guest Editors Paolo Di Barba SP 1 sp , Maria Evelina Mognaschi SP 1 sp , Slawomir Wiak SP 2 sp SP 1 sp University of Pavia, Pavia, Italy SP 2 sp Lód'z University of Technology, Lód'z, Poland Accordingly, in this special issue (SI) of the IJAEM, a collection of 14 papers, selected after a peer review procedure, covers the main subjects of interest for the community. The worldwide community active in the broad area of computational electromagnetics - which grew up substantially in the last decades - gathers academic and industrial researchers who utilize field based numerical models with the manyfold scope of designing new devices, predicting the behavior of prototypes, or interpreting measurements. [Extracted from the article]

The paper presents simulation and experimental results of an Electric Controlled Permanent Magnet Synchronous (ECPMS) machine that offers an extended magnetic field control capability. This makes it suitable for battery electric vehicle drives. Rotor, stator and additional direct current supplied coil of the machine have been analyzed in detail. Control and power supply systems of the machine have been presented. Influence of the additional excitation on the machine performance has also been discussed. [ABSTRACT FROM AUTHOR]

The paper deals with a multi-objective optimization method inspired by island biogeography. Specifically, a nonlinear model of migration is proposed: a sinusoidal law rules both immigration and emigration curves in order to match the natural behaviour which is observed in a natural habitat. The optimal shape design of an electrostatic micromotor, which is a benchmark in MEMS design, is considered as the case study. [ABSTRACT FROM AUTHOR]

The paper deals with the automated optimal design of a new synchronous motor particularly suitable for electric vehicles. Identifying the shape of rotor pole and excitation magnets, minimizing the mass/volume and simultaneously maximizing the torque, is the main design goal. The formulation of this problem in terms of a bi-objective optimal shape design problem possibly stimulates the identification of unexplored solutions of practical significance. [ABSTRACT FROM AUTHOR]

This paper deals with the shape design of the excitation unit of a magnetic induction tomography (MIT) system by means of a procedure of multiobjective design. As a result of the proposed method, a set of optimal geometries of the basic excitation structure was found. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to demonstrate that the multicriteria formulation of an inverse problem fits well with current industrial electromagnetic design. The optimal shape design of a permanent-magnet (PM) generator is developed as the case study. Two well-known evolutionary algorithms, NSGA-II and MOESTRA, have been implemented and relevant results compared. [ABSTRACT FROM AUTHOR]

We have developed an automated method for optimizing the design of a coil for magnetic stimulation of peripheral nerves, acting on the current distribution of the coil. The method consists of these steps: 1) generating a 3-D finite-element model of a coil laid on a forearm; 2) solving the model for two parameters, f1 and f2 of the activating function, which is responsible for the peripheral nerve stimulation; and 3) starting from an existing prototype, applying an evolutionary algorithm to maximize f1 and minimize f2. The final aim is to obtain a focused and strong stimulation. In order to determine robust solutions, the method considers a sensitivity constraint and obtains two solutions, one without the sensitivity constraint and the other taking it into account. In both cases, the objective functions are improved with respect to the existing prototype. In conclusion, we discuss the structure of the objective space. [ABSTRACT FROM AUTHOR]

The paper proposes the automated optimal design of a class of micro–electro–mechanical (MEMS) devices, based on a procedure of finite element analysis coupled to evolutionary optimization algorithms. A magnetic MEMS, used as an optical switch, is considered as the case study. In particular, the geometry of the device is optimized in order to maximize the actuation torque and minimize the power losses and the device volume. The optimization algorithms belong to the genetic class and, in particular, Migrated Parents - Non-Dominated Sorting Genetic Algorithm MP-NSGA, with three objective functions, is compared to NSGA-III. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The authors explore the possibility of applying a convolutional Naeural Network (CNN) to the solution of coupled electromagnetic and thermal problem, focusing on the classical problem of induction heating systems, traditionally solved by resorting to Finite Element (FE) models. In fact, FE modelling is widely used in the design of induction heating systems due its accuracy, even if the solution of a coupled nonlinear problem is expensive in terms of computational time and hardware resources, notably in 3D analysis. A model based on CNN could be an interesting alternative; in fact, CNN is a learning model selected for its excellent ability to converge, even when trained with a limited dataset. CNNs are able to treat images as input and they are used here as follows: given a temperature map in the workpiece, identify the corresponding vector of current, frequency and process heating time; this mapping is a model of the inverse induction heating problem. Specifically, we consider as an example the induction heating of a cylindrical steel billet, made of C45 steel, placed in a solenoidal inductor coil exhibiting the same axial length of the billet (TEAM 36 problem). A thorough heating process is usually applied before hot working of the billet, as in an extrusion process, but this methodology can be applied also in the design of induction hardening processes. First, a CNN has been trained from scratch by means of a dataset of FE solutions of coupled electromagnetic and thermal problems. For the sake of a comparison, a transfer learning technique is applied using GoogLeNet, i.e. a Deep Convolutional Neural Network able to classify images: starting from the pre-trained GoogLeNet, its training has been subsequently refined with the dataset of solutions from FE analyses. When the training dataset contains a limited number of samples, GoogleNet shows good accuracy in predicting the process parameters; in the case of a high number of samples in the training set, namely beyond a threshold like e.g. 1500, both CNNs show good accuracy of the result. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the paper, a multi-objective optimization of an electrode pair for electrochemotherapy is performed. The aim is to design the electrode pair geometry in order to obtain an effective treatment, which means a homogeneous and suitable electric field distribution. To solve this problem, two recently developed algorithms, M-NSGA and 𝜇-BiMO, are applied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Determining the amount of electromagnetic wave energy absorbed by the human body is an important issue in the analysis of wireless systems. Typically, numerical methods based on Maxwell's equations and numerical models of the body are used for this purpose. This approach is time-consuming, especially in the case of high frequencies, for which a fine discretization of the model should be used. In this paper, the surrogate model of electromagnetic wave absorption in human body, utilizing Deep-Learning, is proposed. In particular, a family of data from finite-difference time-domain analyses makes it possible to train a Convolutional Neural Network (CNN), in view of recovering the average and maximum power density in the cross-section region of the human head at the frequency of 3.5 GHz. The developed method allows for quick determination of the average and maximum power density for the area of the entire head and eyeball areas. The results obtained in this way are similar to those obtained by the method based on Maxwell's equations. [ABSTRACT FROM AUTHOR]

Purpose: The paper presents the Finite Element (FE) evaluation of the magnetic field emitted by a Wireless Power Transfer Systems used to charge the battery of electrical vehicles. An original approach for reducing the mesh size of the 3D FE model is used. Design/methodology/approach: A minicar equipped with a circular coil is considered, while the transmitting coil is coherent with the Society of Automotive Engineers (SAE) standard. The different shape of the coils and a possible misalignment are considered as possible sources of emitted magnetic field, which a person could be exposed to. To this end, a FE model is implemented. Because of the complexity of the mesh, a suitable 3D model is used. This model is previously validated and then used for evaluating the magnetic field around the Wireless Power Transfer Systems (WPTS). Findings: The magnetic flux density around the WPTS is calculated and compared with the International Commission on Non-Ionizing Radiation Protection (ICNIRP) limits. Originality/value: The proposed 3D model, whose validation is shown in the paper, is able to compute the magnetic field with high accuracy despite the presence of a conductive and ferromagnetic thin structure, the steel layer related to the car frame, which would need a very fine mesh with a large number of elements to solve Maxwell equations. [ABSTRACT FROM AUTHOR]

The paper is focused on the optimization of the compensation network of a wireless power transfer system (WPTS) intended to operate in dynamic conditions. A laboratory prototype of a WPTS has been taken as a reference in this work, allowing for the experimental data and all the numerical models here presented to reproduce the configuration of the existing device. The numerical model has been used to perform FEM analysis with variable relative positions of the emitting and receiving coil to simulate the movement in a 'recharge while driving' condition. Inductive lumped parameters, i.e., self and mutual inductances computed from FEM results, have been used for the optimal design of the compensation network necessary for the WPTS operation. The optimal design of the resonance circuits has been developed by defining objective functions, aiming to achieve these goals: transmitted power must be as constant as possible when the vehicle is in movement and the electrical efficiency must be satisfactory high in most of the coupling conditions. The performances of the optimized network are finally compared and discussed. [ABSTRACT FROM AUTHOR]

The paper deals with an evolutionary method for solving many-objective optimization problems exhibiting a high-dimensionality objective space, which is a challenging problem. An application in the optimal synthesis of Compensation Networks (CNs) of wireless power transfer systems for charging the batteries of electric vehicles is developed. This design problem is characterized by a set of multiple objectives in mutual conflict, which should be simultaneously considered. The optimization aims to the maximization of both the efficiency and the transferred power; a further criterion selects the networks with a suitable profile of impedance vs. frequency. Moreover, the minimization of current and voltage values relevant to inductors and capacitors in the networks, respectively, is pursued. These five design criteria are optimized exploiting the concept of the degree of conflict, which is the core of the proposed method, named "EStra-many". The method is applied by considering two approaches: the single-objective one, based on the degree of conflict function only, and the bi-objective approach in which the tradeoff between the degree of conflict function itself and another objective function (in turn, the efficiency, the transferred power, the distance of the resonance frequency from the supply frequency, the maximum value of the inductance current, the maximum value of the capacitor voltage, the distance from the Utopia point, and the number of inductors in the CN), is searched for. This way, all in one, seven different optimization problems are solved. The main element of novelty of the paper is a method to solve an optimization problem characterized by a high number of objective functions. In view of this, instead of considering a weighted sum of the objectives, a preference function inspired by the concept of least-conflict solution is formulated accordingly, the preference function is minimized by a cost-effective evolutionary algorithm of lowest order. [ABSTRACT FROM AUTHOR]

• Hyaluronic acid and IKVAV motif allow melanoma cells to produce ECM. • Hyaluronic acid/IKVAV-based cultures may resemble the tumour microenvironment. • Electroporation efficiency is modulated by ECM and cell organization. Electrochemotherapy (ECT) with bleomycin is an effective antitumor treatment. Still, researchers are investigating new drugs and electroporation conditions to improve its efficacy. To this aim, in vivo assays are accurate but expensive and ethically questionable. Conversely, in vitro assays, although cheaper and straightforward, do not reflect the architecture of the biological tissue because they lack a tridimensional (3D) structure (as in the case of two-dimensional [2D] in vitro assays) or do not include all the extracellular matrix components (as in the case of 3D in vitro scaffolds). To address this issue, 3D in vitro models have been proposed, including spheroids and hydrogel-based cultures, which require a suitable low-conductive medium to allow cell membrane electroporation. In this study, a synthetic scaffold based on hyaluronic acid (HA) and self-assembling peptides (SAPs; EAbuK), condensed with a Laminin-derived adhesive sequence (IKVAV), is proposed as a reliable alternative. We compare SKMEL28 cells cultured in the HA-EAbuK-IKVAV scaffold to the control (HA only scaffold). Three days after seeding, the culture on the HA-EAbuK-IKVAV scaffold showed collagen production. SKMEL28 cells cultured on the HA-EAbuK-IKVAV scaffold started to be electroporated at 400 V/cm, whereas, at the same electric field intensity, those cultured on HA were not. As a reference, 2D experiments showed that electroporation of SKMEL28 cells starts at 600 V/cm using an electroporation buffer and at 800 V/cm in a culture medium, but with very low efficiency (<50 % of cells electroporated). 3D cultures on HA-EAbuK-IKVAV allowed the simulation of a more reliable microenvironment and may represent a valuable tool for studying electroporation conditions. Using Finite Element Analysis (FEA) to compute the transmembrane potential, we detected the influence of inhomogeneity of the extracellular matrix on electroporation effect. Our 3D cell culture electroporation simulations showed that the transmembrane potential increased when collagen surrounded the cells. Of note, in the collagen-enriched HA-EAbuK-IKVAV scaffold, EP was already improved at lower electric field intensities. This study shows the influence of the extracellular matrix on electric conductivity and electric field distribution on cell membrane electroporation and supports the adoption of more reliable 3D scaffolds in experimental electroporation studies. [ABSTRACT FROM AUTHOR]

The design optimization of an induction heating device is considered. The non-linear multi-physics analysis is carried out by means of finite-element method, while the optimal design problem is solved by NSGA-II genetic algorithm. A comparison with the results obtained by a simplified linear analysis is shown. The original contribution of this paper is the Pareto front identification for a design problem in which the field analysis is multi-physics, dynamic, and non-linear. [ABSTRACT FROM AUTHOR]

The optimal shape design of a coreless High-Temperature Superconductor (HTS) winding has been defined in terms of finding the position and length of a set of flux diverters, in such a way to minimize an appropriate objective function. Results for both direct and inverse problems are presented. Additionally, the minimization of the objective function has been pursued by solving a material synthesis problem, in which the geometry of the diverters is given, and their permeability is unknown. [ABSTRACT FROM AUTHOR]

In industrial manufacturing process for tube production the stress relieving is achieved by heating the part to be treated at a very uniform temperature. In the paper, the optimized design of an inductor for stress relieving at temperature close to 600° is considered. The design of the inductor is initially performed by means an automated-design using optimization algorithms and objective functions that are minimized. In the range of obtained solutions, some of them appear more promising in terms of heating uniformity. Starting from these improved solutions, a technologically acceptable solution is proposed. This technological feasible solution is selected between the ones obtained by means of a multi-objective optimization procedure and suitably adapted in order to achieve not only the technological targets but also a cost-effective design. [ABSTRACT FROM AUTHOR]

Most solid tumors are inhomogeneous, because of the different types of cells present, different tissues and fat, etc, along with irregular vascularization, pH variation, and heterogeneous oxygen concentration. It is of interest to study the electric field distribution when electrochemotherapy is administered. The variations in the physical and hence the electrical properties, such as the resistance (or conductance), will vary, and parts of the tumor might not get the same electric field and hence will not have the required or the desired efficacy. In this study, the effect of tissue inhomogeneity is explored using potato and apple tissue samples. For this purpose, agar gel and other tissue mimic materials are used to create the inhomogeneity and pulsed to study the effect of electrical pulses. In addition, the electric field distribution is studied using a numerical model to evaluate the effect of tissue inhomogeneity in terms of electric field distribution when electroporation is applied to permeabilize cells, for enhanced drug uptake. The electric field is evaluated by means of finite element analysis and is compared with the distribution found in a phantom model. Good correlation is obtained between the experimental and simulation results. This research has the potential to study the real tumor inhomogeneity conditions. [ABSTRACT FROM AUTHOR]

The study presented in this article reformulates and generalizes the TEAM benchmark, originally proposed for multi-objective optimization of magnetic devices under dc conditions, now extending it to the ac regime. A solution is furnished which has enabled an extensive search and reliable estimation of the shape of the Pareto front. Field uniformity and losses are considered with reference to a class of power inductors. It is argued that the benchmark provides a challenging target for new algorithms, especially those involving numerical modeling based on finite-element codes, where the number of objective function calls needs to be minimized for practical designs. [ABSTRACT FROM AUTHOR]

The paper tackles the problem of ascertaining the 3kVDC voltage disconnection of traction lines by means of a device capable of detecting the electrostatic field of an overhead cable, as a part of a safety campaign promoted by the Italian railway network RFI SpA within the entire network. The main methods of field detection are reviewed, some of which are commonly used, others are still under study. For each considered device we highlight advantages and limits, obtaining criteria in view of the design and implementation of a new aid for RFI personnel, responsible of the traction line maintenances. [ABSTRACT FROM AUTHOR]

In induction heating the design of the inductor implies the solution of coupled electromagnetic and thermal fields, along with the use of optimal design procedures to identify the best possible device or process. The benchmark model proposed, a graphite disk heated by means of induction, is optimized using different optimization algorithms. The design aim requires to achieve a prescribed and uniform temperature distribution in the workpiece maximizing the system efficiency. [ABSTRACT FROM AUTHOR]

Proper β-adrenergic signaling is indispensable for modulating heart frequency. Studies on extremely-low-frequency pulsed electromagnetic field (ELF-PEMF) effects in the heart beat function are contradictory and no definitive conclusions were obtained so far. To investigate the interplay between ELF-PEMF exposure and β-adrenergic signaling, cultures of primary murine neonatal cardiomyocytes and of sinoatrial node were exposed to ELF-PEMF and short and long-term effects were evaluated. The ELF-PEMF generated a variable magnetic induction field of 0–6 mT at a frequency of 75 Hz. Exposure to 3 mT ELF-PEMF induced a decrease of contraction rate, Ca 2 + transients, contraction force, and energy consumption both under basal conditions and after β-adrenergic stimulation in neonatal cardiomyocytes. ELF-PEMF exposure inhibited β-adrenergic response in sinoatrial node (SAN) region. ELF-PEMF specifically modulated β2 adrenergic receptor response and the exposure did not modify the increase of contraction rate after adenylate cyclase stimulation by forskolin. In HEK293T cells transfected with β1 or β2 adrenergic receptors, ELF-PEMF exposure induced a rapid and selective internalization of β2 adrenergic receptor. The β-adrenergic signaling, was reduced trough Gi protein by ELF-PEMF exposure since the phosphorylation level of phospholamban and the PI3K pathway were impaired after isoproterenol stimulation in neonatal cardiomyocytes. Long term effects of ELF-PEMF exposure were assessed in cultures of isolated cardiomyocytes. ELF-PEMF counteracts cell size increase, the generation of binucleated of cardiomyocytes and prevents the up-regulation of hypertrophic markers after β-adrenergic stimulation, indicating an inhibition of cell growth and maturation. These data show that short and long term exposure to ELF-PEMF induces a reduction of cardiac β-adrenergic response at molecular, functional and adaptative levels. [ABSTRACT FROM AUTHOR]

Purpose of the paper is to study the behaviour of a submerged arc furnace for the production of ferroalloys in order to implement a new voltage-based control system of the furnace. A 3D Finite Element (FE) model has been developed to define an equivalent electric circuit model able to describe the furnace operations. Both field and circuit models are validated by measurements on a furnace installed in an industrial plant. A novel method to control a furnace is proposed. [ABSTRACT FROM AUTHOR]