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Inverse problems in electromagnetism, namely, the recovery of sources (currents or charges) or system data from measured effects, are usually ill-posed or, in the numerical formulation, ill-conditioned and require suitable regularization to provide meaningful results. To test new regularization methods, there is the need of benchmark problems, which numerical properties and solutions should be well known. Hence, this study aims to define a benchmark problem, suitable to test new regularization approaches and solves with different methods.

To assess reliability and performance of different solving strategies for inverse source problems, a benchmark problem of current synthesis is defined and solved by means of several regularization methods in a comparative way; subsequently, an approach in terms of an artificial neural network (ANN) is considered as a viable alternative to classical regularization schemes. The solution of the underlying forward problem is based on a finite element analysis.

The paper provides a very detailed analysis of the proposed inverse problem in terms of numerical properties of the lead field matrix. The solutions found by different regularization approaches and an ANN method are provided, showing the performance of the applied methods and the numerical issues of the benchmark problem.

The value of the paper is to provide the numerical characteristics and issues of the proposed benchmark problem in a comprehensive way, by means of a wide variety of regularization methods and an ANN approach.

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.

Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification.

Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness.

This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.

This paper examines the ability of those governmental reforms adopted in response to the COVID-19 outbreak to affect earnings management (EM).

The paper focuses on the Italian decision to suspend the recapitalization obligation to guarantee the respect of the going concern’s assumption. By analysing a sample of unlisted entities, this analysis uses different techniques to detect EM before and after the suspension of that obligation.

The results suggest that EM decreased after the decision to suspend recapitalization obligations.

Accounting quality depends on not only accounting standards but also management practices in response to those government measures instituted during the COVID-19 outbreak.

The results are a novelty in the literature. In terms of the institutional theory, they provide evidence of EM decrease, thereby validating the assumption that regulation can enable and empower social actors – particularly their actions – despite the visions of repression and constraint conjured by that concept. Isomorphism theory supports the thesis and results that indicate that EM decreases not only in emerging markets, where corporate governance mechanisms are less able to obstruct EM, but also in the developed countries. Thus, insightful and novel conceptualizations can still be achieved by using institutional theory. Yet the findings also extend agency theory assumptions and demonstrate that also the issuance of less severe regulation can reduce agency costs and, in turn, also EM.