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The present work is about the development of automatic techniques for the optimisation of active shields for stationary magnetic fields. An active magnetic shield is basically made by a number of coils, fed with suitably chosen currents. In this way a magnetic field as equal as possible to the disturbing one is generated. The resulting effect is the reduction of the disturbing field in the area of interest. To achieve reasonable results a stochastic optimisation procedure has been used to optimise the non linear part of the problem (i.e. the geometry of shielding coils); the linear part can be optimised “on the fly” in a much more straightforward way through the solution of a least squares problem. The stochastic optimiser used is based on the very fast simulated reannaling (VFSR), allowing to get a good optimum with a much reduced sampling of the objective function. It is used combining it with a deterministic optimiser (Nelder‐Mead simplex method), to get a faster optimisation process as soon as the valley of the global optimum is located. Basically the VFSR has a different point generating function and a different cooling schedule with respect to the standard Boltzmann annealing, but the concept is clearly the same.

In this paper a code for the solution of nonlinear thermo‐magnetic problems, arising in the transient analysis of thermal and electromagnetic fields in resistive toroidal field coils of tokamak devices, is presented. A formulation based on the electric vector potential T and the magnetic total scalar potential Ψ for the analysis of the thermo‐magnetic skin effect phenomenon in the toroidal field coils of proposed tokamaks has been used, and the results obtained are reported, reviewed and discussed.

An adaptive FEM for 3D magnetostatic problems involving non‐linear materials and permanent magnets is presented. The problems are formulated in terms of scalar potentials and discretized on a tetrahedral mesh using linear shape functions. Local error is estimated by approximately solving an independent differential problem in each tetrahedral element.

This study investigated disparities in dual diagnosis (comorbid substance use and depressive/anxiety disorders) among US adults by nativity and racial–ethnic origin and socioeconomic, cultural, and psychosocial factors that may account for the observed disparities.

The study drew on data from two waves of the National Epidemiological Survey on Alcohol and Related Conditions. Racial–ethnic categories included African, Asian/Pacific Islander, European, Mexican, Puerto Rican, and other Hispanic/Latino. Substance use and depressive/anxiety disorders were assessed per DSM-IV. A four-category measure of comorbidity was constructed: no substance use or psychiatric disorder; substance use disorder only; depressive/anxiety disorder only; and dual diagnosis. The data were analyzed using multinomial logistic regression.

The prevalence of dual diagnosis was low but varied by nativity, with the highest rates among Europeans and Puerto Ricans born in US states, and the lowest among Mexicans and Asians/Pacific Islanders. The nativity and racial–ethnic effects on likelihood of having dual diagnosis remained significant after all adjustments.

The limitations included measures of immigrant status, race–ethnicity, and stress and potential misdiagnosis of mental disorder among ethnic minorities.

This new knowledge will help to guide public health and health care interventions addressing immigrant mental and behavioral health gaps.

This study addressed the research gap in regard to the prevalence and correlates of dual diagnosis among immigrants and racial–ethnic minorities. The study used the most current and comprehensive data addressing psychiatric conditions among US adults and examined factors rarely captured in epidemiologic surveys (e.g., acculturation).

To provide a detailed investigation about methods commonly used for the computation of high‐frequency electromagnetic fields in the vicinity of obstacles that can reflect or diffract them. This is useful to create an elementary block that can be used to evaluate with a high accuracy the propagation of high‐frequency electromagnetic waves in real urban environment.

The approach is based on a realistic application of the asymptotic theory of the uniform theory of diffraction. Therefore, the effect of material roughness and its electromagnetic properties on the reflection are taken into account.

Provides information about the mechanisms involved in electromagnetic field propagation in urban environment, and the relative importance of each one.

In urban environment the buildings obviously have finite dimensions. The diffraction equations examined in the paper are strictly valid only in the case of infinite wedges; therefore, the behaviour of real building edges has not been taken rigorously into account.

A source of information for researchers interested in the development of a simulator for the electromagnetic propagation in urban environment.

This paper is aimed at providing to researchers, in a more comprehensive way, all information needed for the study of electromagnetic propagation in an environment containing many close scatterers.

Discusses the combined use of a modified version of the global optimization technique simulated annealing and a deterministic optimizer based on Shor’s method. Describes the features of the proposed technique and reports on some results regarding a standard benchmark problem.

Global optimization in electrical engineering using stochastic methods requires usually a large amount of CPU time to locate the optimum, if the objective function is calculated either with the finite element method (FEM) or the boundary element method (BEM). One approach to reduce the number of FEM or BEM calls using neural networks and another one using multiquadric functions have been introduced recently. This paper compares the efficiency of both methods, which are applied to a couple of test problems and the results are discussed.

To present a neural network‐based approach to the design of electromagnetic devices.

A neural model is created which reproduces the relationship between the design parameters of the device and the performance parameters, typically field values.

The neural model is a single hidden layer MLP network, trained by using a set of cases calculated, for example, by means of a finite element analysis. The design problem can be solved by fixing the performance values at the output of the network and by calculating the corresponding input values. The relationship between the input and the output of the neural network is represented by three equations systems. By means of these three systems, we can forward the domain of the input, and we can back propagate the desired output throughout the network layers. In such a way, both the domain of the design parameters and the domain of the desired performances values can be projected in the same space. Whatever point inside the intersection between the two projected domains corresponds to a solution of the design problem.

Presents a procedure which is able to find a point belonging to such an intersection.

The aim of the paper is to compare two different approaches to multi‐objective optimisation in magnetostatics; in this way, the case study is investigated as a possible benchmark.

A Tabu search method modified with ε‐constraint algorithm is compared with a multi‐objective multi‐individual evolution strategy. The case study is the automated shape design of a magnetic pole. In order to reduce the computational cost of solving the direct problem, which requires repeated analyses of the magnetic field, a neural network has been used to approximate the objective functions that depend on the design variables.

An approximation of the Pareto front for each method is obtained. A twofold comparison between the two methods is made, based on both the result accuracy and the computational cost.

Two different methods were already tested on a case study proposed as a benchmark for multi‐objective optimization in magnetostatics. The paper represents a contribution to bridge the gap between analytical and numerical benchmarks in electromagnetism.

The use of niching genetic algorithms can provide a method of a more widespread search of the design space for a device than more conventional methods. It provides, in effect, a breadth first rather than a depth first search. Thus several alternative designs may be evaluated in parallel.