Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment

The selection of a given method for the seismic vulnerability assessment of buildings is mostly dependent on the scale of the analysis. Results obtained in large-scale studies are usually less accurate than the ones obtained in small-scale studies. In this paper a study about the feasibility of using Artificial Neural Networks (ANNs) to carry out fast and accurate large-scale seismic vulnerability studies has been presented. In the proposed approach, an ANN was used to obtain a simplified capacity curve of a building typology, in order to use the N2 method to assess the structural seismic behaviour, as presented in the Annex B of the Eurocode 8. Aiming to study the accuracy of the proposed approach, two ANNs with equal architectures were trained with a different number of vectors, trying to evaluate the ANN capacity to achieve good results in domains of the problem which are not well represented by the training vectors. The case study presented in this work allowed the conclusion that the ANN precision is very dependent on the amount of data used to train the ANN and demonstrated that it is possible to use ANN to obtain simplified capacity curves for seismic assessment purposes with high precision.info:eu-repo/semantics/publishedVersio

Adaptive optical networks using photorefractive crystals

The capabilities of photorefractive crystals as media for holographic interconnections in neural networks are examined. Limitations on the density of interconnections and the number of holographic associations which can be stored in photorefractive crystals are derived. Optical architectures for implementing various neural schemes are described. Experimental results are presented for one of these architectures

The effect of cyber-attacks on stock returns

A widely debated issue in recent years is cybercrime. Breaches in the security of accessibility, integrity and confidentiality of information involve potentially high explicit and implicit costs for firms. This paper investigates the impact of information security breaches on stock returns. Using event-study methodology, the study provides empirical evidence on the effect of announcements of cyber-attacks on the market value of firms from 1995 to 2015. Results show that substantial negative market returns occur following announcements of cyber-attacks. Financial entities often suffer greater negative effects than other companies and non-confidential cyber-attacks are the most dangerous, especially for the financial sector. Overall findings seem to show a link between cybercrime and insider trading