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Rectangular conductors play an important role in planar transmission line structures, multiconductor transmission lines, in power transmission and distribution systems, LCL filters, transformers, industrial busbars, MEMs devices, among many others. The precise determination of the inductance of such conductors is necessary for their design and optimization, but no explicit solution for the AC resistance and internal inductances per-unit length of a linear conductor with a rectangular cross-section has been found, so numerical methods must be used. The purpose of this paper is to introduce the use of a novel numerical technique, the proper generalized decomposition (PGD), for the calculation of DC and AC internal inductances of rectangular conductors.

The PGD approach is used to obtain numerically the internal inductance of a conductor with circular cross-section and with rectangular cross-section, both under DC and AC conditions, using a separated representation of the magnetic vector potential in a 2D domain. The results are compared with the analytical and approximate expressions available in the technical literature, with an excellent concordance.

The PGD uses simple one-dimensional meshes, one per dimension, so the use of computational resources is very low, and the simulation speed is very high. Besides, the application of the PGD to conductors with rectangular cross-section is particularly advantageous, because rectangular shapes can be represented with a very few number of independent terms, which makes the code very simple and compact. Finally, a key advantage of the PGD is that some parameters of the numerical model can be considered as additional dimensions. In this paper, the frequency has been considered as an additional dimension, and the internal inductance of a rectangular conductor has been computed for the whole range of frequencies desired using a single numerical simulation.

The proposed approach may be applied to the optimization of electrical conductors used in power systems, to solve EMC problems, to the evaluation of partial inductances of wires, etc. Nevertheless, it cannot be applied, as presented in this work, to 3D complex shapes, as, for example, an arrangement of layers of helically stranded wires.

The PGD is a promising new numerical procedure that has been applied successfully in different fields. In this paper, this novel technique is applied to find the DC and AC internal inductance of a conductor with rectangular cross-section, using very dense and large one-dimensional meshes. The proposed method requires very limited memory resources, is very fast, can be programmed using a very simple code, and gives the value of the AC inductance for a complete range of frequencies in a single simulation. The proposed approach can be extended to arbitrary conductor shapes and complex multiconductor lines to further exploit the advantages of the PGD.

Using authentication to secure data and accounts has grown to be a natural part of computing. Even if several authentication methods are in existence, using passwords remain the most common type of authentication. As long and complex passwords are encouraged by research studies and practitioners alike, computer users design passwords using strategies that enable them to remember their passwords. This paper aims to find strategies that allow for the generation of passwords that are both memorable and computationally secure.

The study began with a literature review that was used to identify cognitive password creation strategies that facilitate the creation of passwords that are easy to remember. Using an action-based approach, attack models were created for the resulting creation strategies. The attack models were then used to calculate the entropy for passwords created with different strategies and related to a theoretical cracking time.

The result of this study suggests that using phrases with four or more words as passwords will generate passwords that are easy to remember and hard to attack.

This paper considers passwords from a socio-technical approach and provides insight into how passwords that are easy to remember and hard to crack can be generated. The results can be directly used to create password guidelines and training material that enables users to create usable and secure passwords.