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One Terminal Digital Algorithm for Adaptive Single Pole Auto-Reclosing Based on Zero Sequence Voltage
S. Jamali , A. Parham,
Volume 4, Issue 3 (July 2008)
Abstract

This paper presents an algorithm for adaptive determination of the dead time

during transient arcing faults and blocking automatic reclosing during permanent faults on

overhead transmission lines. The discrimination between transient and permanent faults is

made by the zero sequence voltage measured at the relay point. If the fault is recognised as

an arcing one, then the third harmonic of the zero sequence voltage is used to evaluate the

extinction time of the secondary arc and to initiate reclosing signal. The significant

advantage of this algorithm is that it uses an adaptive threshold level and therefore its

performance is independent of fault location, line parameters and the system operating

conditions. The proposed algorithm has been successfully tested under a variety of fault

locations and load angles on a 400KV overhead line using Electro-Magnetic Transient

Program (EMTP). The test results validate the algorithm ability in determining the

secondary arc extinction time during transient faults as well as blocking unsuccessful

automatic reclosing during permanent faults.


Structural Connectivity of Brain Regions May Predict Human Intelligence
Elahe Parham, Mohamad Feshki, Alireza Fallahi, Hamid Soltanian-Zadeh,
Volume 22, Issue 3 (September 2026)
Abstract
The discovery of relationships between brain connectivity and human intelligence is of great interest. In this study, we identify structural connections correlated with human intelligence and investigate the predictability of intelligence from brain structural connectivity. The study uses data from 137 healthy subjects from the Human Connectome Project (HCP, 1200 Subjects Release). Structural connectivity was estimated using tractography derived from diffusion tensor imaging (DTI) data. A connectivity matrix was constructed using the mean fractional anisotropy (FA) of white-matter pathways between 116 regions defined by the AAL atlas. Global graph measures and correlation analysis were applied to identify connections relevant to predicting fluid intelligence (Gf) and crystallized intelligence (Gc). For prediction, three regression models of linear regression, support vector regression (SVR), and multi-layer perceptron (MLP) were employed. Most connections associated with Gf or Gc were located in the right hemisphere. Connections originating from prefrontal, right temporal, limbic, and right occipital regions were related to Gf, whereas connections originating from prefrontal, temporal, and left parietal regions were related to Gc. Among the models, SVR showed superior performance, achieving R² values of 0.45 and 0.52 for Gf and Gc, respectively. No significant relationships were observed between global graph measures and Gf or Gc scores. These findings demonstrate that DTI-based structural connectivity can be used to predict both fluid and crystallized intelligence, with fine-grained regional definitions enabling more specific connectivity patterns than in previous studies.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.