Newcastle Disease is one of the most important disease world wide distributions which invade the flock in different age resulting in large economic losses. This study aimed to evaluate the effect of treatment with 4 different concentrations (0.25, 0.5, 1.0 and 2.0 %) of Sodium deoxycholate (SDC) on the vaccinal virus (La Sota) using inoculation in the fragments of Chorioallantoic membrane. The treatment with each of the above 4 concentrations of SDC resulted in an increase in the Hemagglutination titer (HA) of the virus (28, 29.6, 211.6, 214.6) respectively as compared to the HA titer value for the untreated virus (26.6). No significant differences were noticed among all concentrations with regard to their effect on the HA titer, except the concentrations of 1.0 and 2.0 % where significant differences were recorded (P > 0.05). The results of this study suggest that SDC has an important activity in enhancement of the replication of NDV through increasing the Hemagglutination titer, which has a great importance in vaccine production.
In this study, several ionanofluids (INFs) were prepared in order to study their efficiency as a cooling medium at 25 °C. The two-step technique is used to prepare ionanofluid (INF) by dispersing multi-walled carbon nanotubes (MWCNTs) in two concentrations 0.5 and 1 wt% in ionic liquid (IL). Two types of ionic liquids (ILs) were used: hydrophilic represented by 1-ethyl-3-methylimidazolium tetrafluoroborate [EMIM][BF4] and hydrophobic represented by 1-hexyl-3-methylimidazolium hexafluorophosphate [HMIM][PF6]. The thermophysical properties of the prepared INFs including thermal conductivity (TC), density and viscosity were measured experimental
We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measure
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
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