This study presents a detailed morphology and taxonomic study of Polysiphonia subtilissima collected from Abdul Rehman Goth, Karachi coast, Pakistan. Polysiphonia is a filamentous heterotrichous red algae, characterized by its branching structures and attachment mechanisms. P. subtilissima is notable for its broad salinity tolerance and wide distribution across marine and freshwater ecosystems. This research provides an in-depth examination of the internal and external structures of P. subtilissima, contributing to its systematic study and documenting its first recorded occurrence in Pakistani coastal areas, bordering the northern Arabian Sea. The findings enhance the understanding of the species taxonomy and its ecological role in

This work involves synthesis and characterization of some new 1, 3, 4-thiadiazole or pyrazoline derivatives heterocyclic containing indole ring. The new 2-amino-1, 3, 4-thiadiazole derivatives [IV] and [V] a, b were synthesized by cyclization reaction of 2-methyl-1H-indole-carbothiosemicarbazide [III] in H2SO4 acid or by reaction of indole-3-acetic acid or indole-3-butanoic acid with thiosemicarbazide in the presence of phosphorous oxychloride, respectively. Amide derivatives [VI]-[VIII] were synthesized by the reaction equimolar of 2-amino-1, 3, 4-thiadiazoles and (acetyl chloride, benzoyl chloride, anisoyl chloride and heptanoyl chloride) in DMF and pyridine as accepter. The new pyrazolone derivatives [XI] a, b were synthesized from heati

In this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme  value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS  & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients

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