The aim of this study is to develop a novel framework for managing risks in smart supply chains by enhancing business continuity and resilience against potential disruptions. This research addresses the growing uncertainty in supply chain environments, driven by both natural phenomena-such as pandemics and earthquakes—and human-induced events, including wars, political upheavals, and societal transformations. Recognizing that traditional risk management approaches are insufficient in such dynamic contexts, the study proposes an adaptive framework that integrates proactive and remedial measures for effective risk mitigation. A fuzzy risk matrix is employed to assess and analyze uncertainties, facilitating the identification of disr

Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

The emergence of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has resulted in a global health crisis leading to widespread illness, death, and daily life disruptions. Having a vaccine for COVID-19 is crucial to controlling the spread of the virus which will help to end the pandemic and restore normalcy to society. Messenger RNA (mRNA) molecules vaccine has led the way as the swift vaccine candidate for COVID-19, but it faces key probable restrictions including spontaneous deterioration. To address mRNA degradation issues, Stanford University academics and the Eterna community sponsored a Kaggle competition.This study aims to build a deep learning (DL) model which will predict deterioration rates at each base of the mRNA

The new tridentate Schiff base ligand (HL)namely 2-{[1-(3-amino-phenyl)-ethylidene]-hydrazono methyl}- phenol containing (N N O)as donors atoms was prepared in two steps:Step (1): By the reaction of 3- aminoacetophenone with hydrazine monohydrate under reflux in methanol and drops of glacial acetic acid gave the intermediate compound 3-(1- hydrazono ethyl)-phenol amine.Step (2): By the reaction of 3-(1-hydrazono ethyl)-phenol amine with salicyaldehyde under reflux in methanol, gave the ligand (HL).The prepared ligand was characterized by I.R, U.V-Vis,1H- 13C NMR spectra and melting point and reacted with some metal ions under reflux in methanol with (1:1) ratio gave complexes of the general formula: [MClL]. Where: M= Mn(II), Fe(II), Co(II),

A novel metal complexes Cu (II), Co (II), Cd (II), Ru (III) from azo ligand 5-((2-(1H-indol-2-yl)
ethyl) diazinyl)-2-aminophenol were synthesized by simple substitution of tryptamine with 2-aminophenol.
Structures of all the newly synthesized compounds were characterized by FT IR, UV-Vis, Mass spectroscopy
and elemental analysis. In addition measurements of magnetic moments, molar conductance and atomic
absorption. Then study their thermal stability by using TGA and DSC curves. The DCS curve was used to
calculate the thermodynamic parameters ΔH, ΔS and Δ G. Analytical information showed that all complexes
achieve a metal:ligand ratio of [1:1]. In all complex examinations, the Ligand performs as a tri