In the present paper, chitosan Schiff base has been synthesized from chitosan’s reaction with the salicyldehyde. The AuNPs was manufacture by extract of onion peels as a reducing agent. The Au NPs that have been prepared were characterized through the UV-vis spectroscopy, XRD analyses and SEM microscopy. The polymer blends of the chitosan Schiff base / PVP has been prepared through using the approach of solution casting. Chitosan Schiff base / PVP Au nano-composites was prepared. Nano composites and polymer blends have been characterized by FTIR which confirm the formation of Schiff base by revealing a new band of absorption at 1651cm-1 as a result of the (C=N) imine group. SEM, DSC and TGA confirms the thermal stability of the pr

The drive of this exploration is to investigate the mucoadhesive assets of A. indica (Azadirachta indica) fruit mucilage by incorporating it into mucoadhesive microspheres with Acyclovir (AVR) as a model drug. The study was performed to check the impact of the mucilage proportion on particle size and swelling index. Nine batches of AVR mucoadhesive microspheres were made with varying proportions of Polyacrylic acid 934P and A. indica fruit mucilage (AIFM). A central composite design with design expert software to check the impact of dependent variables (A. indica mucilage and Polyacrylic acid 934 P levels) on particle size and swelling index as a response. As part of congeniality studies, the batches w

The incidence of disease and damage will increase, if environmental control and acceptable management practices are not provided during the rearing period. Ascites affect young broilers with rapid growth, and the most critical factor in causing ascites syndrome is the lack of oxygen in body tissues (hypoxia). This research aimed to investigate the effect of olive leaves hydroalcoholic extract and probiotics (LactoFeed) on experimental ascites caused by levothyroxine in male broiler chickens. The present study was an interventional type, and for its implementation, a single-factor design was used in eight groups with 3 replicates. Data were analyzed based on a one-way analysis of variance. Blood parameters of male chick

Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

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