The role of PHF5A in cancer: A review and update

PHF5A is a member of the zinc-finger proteins. To advance knowledge on their role in carcinogenesis, data from experimental studies, animal models and clinical studies in different tumorigenesis have been reviewed. Furthermore, PHF5A as an oncogenic function, is frequently expressed in tumor cells and a potential prognostic marker for different cancers. PHF5A is implicated in the regulation of cancer cell proliferation, invasion, migration and metastasis. Knockdown of PHF5A prevented the invasion and metastasis of tumor cells. Here, the role of PHF5A in different cancers and their possible mechanism in relation to recent literature is reviewed and discussed. However, there is an open promising perspective to their therapeutic management for different cancer types.Comment: 18 pages, 1 figure, 2 table

Roadmap toward Smart Grids in Hydro and Thermal Power System : A Case study of the Ghanaian Power System

The evolution of Smart Grid flings fresh applications and opportunities to enhance the efficiency of power distribution networks. Network operators have the opportunity to make use of different sources of power. Communication between the network operators and the consumersis constantly permitted to allow optimization and balancing of energy usage. This paper seeks to evaluate the state of the Ghanaian Electric Distribution Network with respect to Smart Grid. We evaluate the performance of the traditional distribution network since its partial incorporation with the Smart Grid elements. The operations of the Supervisory Control and Data Acquisition, the Automated Meter Infrastructure and Circuit Breakers are specifically addressed. Road map to optimizing the distribution network in Ghana is presented. It is concluded that optimizing these key elements will transform the role of the distribution system and ensure a safe and reliable power network.©2020 IJAREEIE.fi=vertaisarvioimaton|en=nonPeerReviewed

Proposed algorithm for smart grid DDoS detection based on deep learning

The Smart Grid’s objective is to increase the electric grid’s dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid’s overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.©2023 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

On the performance metrics for cyber-physical attack detection in smart grid

Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.©The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Evaluation of Optimization Algorithms for Customers Load Schedule

This paper introduces a novel concept for customer load scheduling in the Smart Grid (SG). The concept is based on the forthcoming internet of things (IoT). Approximate optimization algorithms are deduced for optimum customer load scheduling, maximization of electric power suppliers performance, and fairness in scheduling customers load. Using these approximate optimization algorithms as constraints, some loads are given priority. Other loads are scheduled in order to control the maximum demand load and electricity bills. To evaluate the effectiveness of the algorithms, we utilize the Mixed Integer Linear Programming (MILP). Simulations are carried out and the impact on reducing the peak-to-average power ratio (PAPR), the electricity bills, and ensuring fairness in customers load schedules are investigated. Simulation results establish that our algorithms significantly cut down on electricity bills, maximizes utility performance, and deliver fairness in customers load schedules.©2021 International Association of Engineers (IAENG).fi=vertaisarvioitu|en=peerReviewed

Radical Cyclization of Trichloroacetamides: Synthesis of Lactams

Trichloroacetamides can act as radical precursors to synthesize nitrogen-containing heterocycles in a variety of processes, mainly involving atom transfer radical cyclizations (ATRC), mediated by Cu(I) or Ru(II) catalysts, and the hydride reductive method, employing either Bu3SnH or (Me3Si)3SiH, or recently NaBH3CN. Additionally, amine-mediated single-electron transfer cyclizations, as well as radical processes promoted by Ni, Fe, Mn, Ti, and Ag, have been developed

Cytotoxic Assessment of 3,3-Dichloro-β-Lactams Prepared through Microwave-Assisted Benzylic C-H Activation from Benzyl-Tethered Trichloroacetamides Catalyzed by RuCl2(PPh3)3

Natural and synthetic ß -lactam derivatives constitute an interesting class of compounds due to their diverse biological activity. Mostly used as antibiotics, they were also found to have antitubercular, anticancer and antidiabetic activities, among others. In this investigation, six new 3,3-dichloro-ß -lactams prepared in a previous work were evaluated for their hemolytic and cytotoxic properties. The results showed that the proposed compounds have non-hemolytic properties and exhibited an interesting cytotoxic activity toward squamous cell carcinoma (A431 cell line), which was highly dependent on the structure and concentration of these -lactams. Among the molecules tested, 2b was the most cytotoxic, with the lowest IC50 values (30-47 µg/mL) and a promising selectivity against the tumor cells compared with non-tumoral cells

Synthesis of Normorphans through an Efficient Intramolecular Carbamoylation of Ketones

An unexpected C-C bond cleavage was observed in trichloroacetamide-tethered ketones under amine treatment and exploited to develop a new synthesis of normophans from 4-amidocyclohexanones. The reaction involves an unprecedented intramolecular haloform-type reaction of trichloroacetamides promoted by enamines (generated in situ from ketones) as counter-reagents. The methodology was applied to the synthesis of compounds embodying the 6-azabicyclo[3.2.1]octane framework