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Electrical resistive tomography (ERT) is a non‐destructive testing technique based upon the reconstruction of the electrical conductivity profile inside a body from measurement made on its boundary. In the literature about the inverse problems the ERT is considered still challenging being both non‐linear, ill‐posed and very limited in resolution. Purpose of the paper is to assess the performances of an approach exploiting the circuital behaviour of a particular class of problems, highlighting its advantages in terms of simplicity and reduction of the computer burden.

In this paper, an electrical property of a particular class of problems is pointed out; the same property is used to formulate in terms of a circuital model the ERT problem. The proposed methodology consists basically of combining properly simplified data previously evaluated and collected. The overall procedure is presented with reference to an underground structure diagnostics problem.

The effectiveness of the proposed approach has been evaluated quantitatively by comparing the simplified procedure results with the ones obtained by performing fully 3D FEM analysis.

The consistently low errors obtained state the convenience of the method also taking into account that the reconstruction process consists merely in post‐processing previously collected data.

The purpose of this paper is to evaluate the performances of a resolution scheme able to follow the dynamics of brain tissue properties in combined ElectroEncefaloGraphic (EEG) – MagnetoEncefaloGraphic (MEG) techniques for the brain analysis, minimizing the computation burden.

The estimation process in combined EEG‐MEG is performed by a Moore‐Penrose pseudo‐inverse computation. This is affected by the uncertain knowledge of the living tissues' electric properties. In principle, it is possible to estimate those properties from the EEG‐MEG signals. The estimation process becomes in this case non‐linear. A resolution scheme is proposed, based on the exploitation of the different dynamics characterizing sources and tissues properties.

The proposed resolution scheme provides a reasonable estimate of the sources for a computationally affordable frequency of non‐liner estimations.

The proposed approach has not been tested yet on experimental data, and as such, its sensitivity to environmental uncertainty is not known yet.

The proposed strategy can be easily implemented to perform realistic measurement processing.

The paper presents a novel strategy to estimate tissues properties and EEG‐MEG signal sources based on the exploitation of their different dynamics, possibly taking advantages from an impedance tomography preliminary analysis for the tissue properties dynamics.

The paper aims to illustrate a numerical technique to calculate fields and inductances of rotating electrical machines.

The technique is based on an integral formulation of the nonlinear magnetostatic model in terms of the unknown magnetization. The solution is obtained by means of a Picard-Banach iteration whose convergence can be theoretically proved.

The proposed method has been used to build a model of a large turbine generator. In particular, the influence of end effects on flux linkages has been computed. It has been demonstrated that the 2D solution underestimates the flux linkages as well as the no load voltage of 2 per cent, while the leakage fluxes are computed by the 2D solution with errors as high as 20 per cent.

The method is advantageous in comparison to standard methods.

The purpose of this paper is to propose a planning strategy for the radio frequency ablation (RFA) treatment of hepatic tumors. The goal is to give to the surgeon the opportunity of controlling the shape and the size of the treated volume and preserving the healthy tissues.

A FEM model of the human torso is built from radiographic and MRI scans of the patients, and then the RFA treatment “dynamically optimized” by controlling currents in multiple external electrodes, in such a way to drive currents in the desired regions, burning the tumor while trying to preserve healthy regions. A suitable cellular death model is considered in order to achieve an effective description of the biological modifications in the tumor volume.

A numerical method to plan the RFA treatment of hepatic tumors has been defined, aiming to preserve as much as possible healthy tissues.

The method depends on the knowledge of inner structure and properties of the patient's torso; while the structure of tissues can be determined by TAC or MRI scans, the physiological properties are much more uncertain.

The proposed approach allows optimized RFA treatments to be designed, allowing reduction of damage to healthy tissues deriving from application of the treatment.

The problem of optimal design of RFA treatments has been previously tackled in literature, but in this paper, dynamical optimization techniques and a cell death rate model have been included.

The purpose of this paper is to present a new approach to drive the excitation field sources in the eddy current testing (ECT) of tubular conductive structures.

The magnetic field used for ECT is generated by pairs of counter‐series connected coils, driven by AC currents. The phase and amplitude of the currents is electronically controlled in order to shape the primary field map, allowing circumferential sweeps until the presence of defects is detected, and then “focusing” the field on the defective section of the tube, increasing in this way the sensibility of the ECT probes in the targeted area, in order to determine with higher precision, the position, and the shape of the defect.

If suitably designed, the field measurement system allows to enable/disable a number of probes to enhance the resolution in the defect area while keeping low the number of required data channels.

The analyzed geometry is limited to circular‐shaped tubes, of infinite extent. Future work should be on the extension of the methodology to general shapes, and to finite length cylinders.

The proposed method allows to enhance resolution in ECT of tubes at the end of production lines, guaranteeing a first, simple yet effective quality assessment of tubes in industrial environments.

The paper presents a new technique to test conductive tubes using fixed excitation system, but allowing to focus magnetic field in defective regions. The method could be helpful for industrial diagnostics.

The purpose of this paper is to develop an effective, yet simple analytical framework for optimization of permanent-magnet synchronous machines. Also, single/multi-objective optimizations are performed for a case-study machine with surface-mounted permanent magnets.

First, an accurate magnetic equivalent circuit is developed which takes all the material such as iron saturation and PM parameters into account. Then, through a Fourier analysis, it is combined with the d-q model of PM synchronous machines to achieve an optimization framework including the developed torque, back-EMF and a number of design considerations. Finally, a genetic algorithm (GA) is employed in the single/multi-objective design optimizations, which offers several design characteristics upon different desired outcomes.

An analytical design framework for the optimization of permanent-magnet synchronous machines is developed in this paper that can effectively account for all material properties such as iron saturation and PM characteristics, and take into account the design considerations, all of which are shown as superiorities of the proposed approach over the existing method. In addition, the proposed framework is relatively simpler in terms of implementing. The model is verified by employing finite element method. Moreover, sensitivity analysis is carried out to investigate the influence of the design parameters on the machine performance, which provides valuable information for the designer of such devices. Finally, a GA is utilized to perform single/multi-objective optimization schemes whose objectives are minimizing the torque ripples, back-EMF total harmonic distortion and PM volume.

The proposed framework is new approach that could be employed in the design optimization of PM synchronous machines. Contrary to existing method, it is simpler and more effective in taking the material properties such as iron saturation and PM characteristics into account.

This paper aims to develop a methodology for progressive finite element (FE) modeling of transformers, from simple to complex models of both magnetic cores and windings.

The progressive modeling of transformers is performed via a subproblem (SP) FE method. A complete problem is split into SPs with different adapted overlapping meshes. Model refinements are performed from ideal to real flux tubes, one-dimensional to two-dimensional to three-dimensional models, linear to nonlinear materials, perfect to real materials, single wire to volume conductor windings and homogenized to fine models of cores and coils, with any coupling of these changes.

The proposed unified procedure efficiently feeds each SP via interface conditions (ICs), which lightens mesh-to-mesh sources transfers and quantifies the gain given by each refinement on both local fields and global quantities, with a clear view on its significance to justify its usefulness, if any. It can also help in education with a progressive understanding of the various aspects of transformer designs.

Models of different accuracy levels are sequenced with successive additive corrections supported by different adapted meshes. The way the sources act at each correction step, up to the full models with their actual geometries, is given a particular care and generalized, allowing the proposed unified procedure. For all the considered corrections, the sources are always of IC type, thus only needed in layers of FE along boundaries, which lightens the required mesh-to-mesh projections between subproblems.

Students with Autism Spectrum Disorders (ASD) are one of the least included in the general education environment, only falling behind children with intellectual disabilities, multiple disabilities, and deaf/blindness (U.S. Department of Education, 2015). Teacher attitudes, knowledge and training of ASD, and administrative support are essential components of successful inclusive environments (Ferraioli & Harris, 2011; Harding, 2009). Researchers have also identified evidence-based practices to support students with ASD (National Autism Center, 2015; Wong et al., 2014). This chapter provides research related to inclusion of students with ASD, factors that may influence inclusion rates, and provides educators a few practices to try if they are given the opportunity to work with a student with ASD in their inclusive classroom.

Physical activity is an eminent practice for the maintenance of physical and mental health. Physical fitness always enables students to perform their various tasks efficiently specifically in academic performance. Student health is a principal factor for the performance of all kinds of activities particularly in academic performance. This study aimed to evaluate physical activity among university students and the factors for being inactive.

A questionnaire was designed and distributed among the students via WhatsApp, Skype and email, however, some responses were collected physically to maintain the quality of data. The questionnaire was categorized into demographic factors, current physical activity status and reasons for physical inactivity. Each section was further divided into questions and total 24 questions were asked from each individual for evaluation of inactiveness.

The finding of this research explored that enormous number of students are doing part time jobs and unable to find enough time for relaxation. The research was limited and evaluated limited factors and explored that 60.6% of students had less time for entertainment and this is only 2 h for this 60.6% of students. Among all factors of physical activity walking was preferable for 48.34% of the students. Students want to participate in healthy activities. Moreover, students are unable to perform physical exercise due to busy schedules of jobs (47.02%), part-time job burden (15.89%), study burden (35.10%) and poor health conditions (1.32%).

This study concluded that higher percentage of students have stress of limited resources and under this stress condition they are unable to take a balanced diet which they considered cost-effective. They did not perform maximum in their academic and daily activities and did not participate in sports activities. It was also observed that institutions did not provide a sufficient platform for physical activity for students. The research shows the factors which affect the student’s academic performance as being physically inactive. Evaluation of results explored that numerous students have limited resources during their career development and their attention remain diverted to overcome their limited resources which keep them physically unfit. The findings also explored that physically inactive students have heavy study and job burden which is not overcome and analyzed by their institutions.