Identification and molecular analysis of mercury resistant bacteria in Kor River, Iran

Mercury (Hg) is one of the most important toxic pollutants widespread in the environment. It is being extensively used in industrial applications (chlor-alkali electrolysis, fungicides, disinfectants, dental products, etc), resulting in local hot spots of pollution and serious effects on biota and humans. The aim of this study was to identify mercury resistant bacteria and extract their plasmids and DNA. In this study, mercury-resistant bacteria were isolated and characterized from mercury-polluted sediments in Kor River in Iran. The samples were cultured in different media cultures, identified using biochemical tests, and due to the relationship between antibiotic and mercury resistance, they were isolated based on these two factors. The plasmids and DNA were extracted from the most resistant bacteria to both antibiotic and mercury and the sizes were determined using agarose gel electrophoresis. A 12.3 Kb plasmid from Serattia sp. and Escherichia coli and using Sau3A1 enzyme, some DNA fragments (4, 6, 8 and 10 Kb) from Pseudomonas sp., Serattia sp. and Escherichia coli were also extracted.Key words: Mercury, resistant, bacteria, DNA, plasmid extraction, restriction endonuclease

Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

The growing use of information and communication technologies (ICT) in power grid operational environments has been essential for operators to improve the monitoring, maintenance and control of power generation, transmission and distribution, however, at the expense of an increased grid exposure to cyber threats. This paper considers cyberattack scenarios targeting substation protective relays that can form the most critical ingredient for the protection of power systems against abnormal conditions. Disrupting the relays operations may yield major consequences on the overall power grid performance possibly leading to widespread blackouts. We investigate methods for the enhancement of substation cybersecurity by leveraging the potential of machine learning for the detection of transformer differential protective relays anomalous behavior. The proposed method analyses operational technology (OT) data obtained from the substation current transformers (CTs) in order to detect cyberattacks. Power systems simulation using OPAL-RT HYPERSIM is used to generate training data sets, to simulate the cyberattacks and to assess the cybersecurity enhancement capability of the proposed machine learning algorithms