The Standard Fireworks Rajaratnam College for Women, is a women's general degree college located in Sivakasi, Tamil Nadu. It was established in the year 1968. The college is affiliated with Madurai Kamaraj University. This college offers different courses in arts, commerce and science..
A novel high-performance biopolymer blend membranes composed of naturally derived chia seed mucilage and polyvinyl alcohol (PVA) were successfully fabricated using the solution-casting method. The incorporation of ammonium formate (AF) as a dopant markedly improved the ionic conductivity of the membranes. Fourier transform infrared (FTIR) spectroscopy reveals the formation of interactions among PVA, chia seed mucilage, and AF, while X-ray diffraction (XRD) analysis suggested a reduction in crystallinity of the resulting biopolymer electrolytes. The optimized membrane composition of 1 g of PVA–chia seed blend with 0.3 wt
The core objective of the current context is the designing and synthesizing of novel thiazole derivatives of Schiff base metal complexes [Cu(II), Co(II), Ni(II) and Zn(II)] has been synthesized and characterized by various physicochemical and spectroscopic techniques. DNA binding with CT DNA and antimicrobial screening emphasize the higher activity exhibited by these complexes which has a highly conjugative planar ligand, 2-amino-6-methylbenzothiazole in its natural environment that binds through groove mode of binding. The synthesized complexes showed significant antibacterial activity against a few gram + ve and gram − ve organisms when compared with the standard antibiotic Ciprofloxacin. All the complexes showed good free radical scavenging activity which is comparable to that of Vitamin C and BHT (Butylated hydroxytoluene) used as Standard. The results were indicated that Cu(II) complex could be responsible for the potential contender eliciting antioxidant activity. It can be attributed to the combined effect of the substituents and thiazole structural core present in the ligands. The Cu(II) complex was the most effective, displaying the lowest IC50 values against Caco-2 cancer cell lines, though it was still less potent than the reference drug, Doxorubicin. Computational studies using Gaussian 09 W software provided insights into the optimized molecular structures and biological accessibility of these compounds. Additionally, drug-likeness and pharmacokinetic properties were screened using the SWISS ADME online platform, evaluating the compounds for their potential as drug candidates. The results showed Cu(II) and Co(II) compounds had absolute specificity for these organisms, which implied a good application prospect in pharmaceutical probes.
Let G=(V, E) be a simple graph. A set D⊂V(G) is called a dominating set if every vertex in is adjacent to at least one vertex in V\D. This study introduces the concepts of enclave dominating vertices and enclave dominating sets in graphs and defines a new domination parameter termed the enclave domination number. The investigation determines the exact number of minimum enclave dominating sets for several standard graphs, as well as for graphs constructed through combinatorial operations involving path and wheel-related structures. In addition, new characterizations are presented, and several fundamental properties of the enclave domination number are established, thereby contributing to the broader understanding of domination theory in graph structures. The enclave dominating vertex and the enclave dominating sets are formally introduced, and new characterizations and key properties of the enclave domination number are provided, highlighting its significance and potential applications within graph theory and related fields.
The rapid growth in the number of people using the internet has rendered it ever more essential to sort network traffic. Because contemporary network data contains encryption and changes all the time, old approaches like checking the contents of payload and traffic detection based on port numbers are becoming less useful. Several researchers have employed Artificial Intelligence (AI) models to execute traffic classification grounded on Software Defined Networking (SDN). The objective of this research is to provide an efficient machine learning (ML) methodology for the classification of traffic inside an SDN framework. Traffic classification in SDN improves quality of service (QoS) by allowing flow management that takes into account the demands of applications. The study included the SDN dataset, model selection, model implementation, and implementation methodologies, involving pre-processing, feature extraction, ML algorithms, and model evaluation metrics. They proposed techniques Deep Cognitive Reinforcement Network (DCRN), to design the network and produce network data using the specified SDN model. We utilized the Anaconda Python framework to apply several ML methods to sort traffic. The DCRN model was the best of the supervised and unsupervised learning algorithms tested, with an accuracy rate of 99.9 %. These results demonstrate that combining ML with SDN constitutes an efficient classification method for identifying and classifying both offline and real-time network traffic.
