Introduction: Gastroretentive Drug Delivery System like floating tablets out performs traditional dosing forms. When compared to conventional tablets, floating tablets have higher bioavailability, higher drug concentration in the systemic circulation, and lower frequency of dose. Metronidazole floating tablets were formulated using a variety of polymer combinations by the direct compression process. Polyvinyl pyrrolidone (PVP K132), Guar gum, and Xanthum gum are the polymers employed in the composition. Materials and Methods: The drug-excipient compatibility was determined by FTIR Spectroscopy and a total of nine formulations of floating tablets have been prepared. Among these, an appropriate formulation was chosen. The angle of repose, Bulk, and tapped density of metronidazole tablet mixes were previously evaluated. Carr's index, Hauser's ratio, physical appearance, hardness, weight fluctuation, friability, floating qualities, and in-vitro dissolving testing were used to characterize the tablets. Results: The flow properties were found optimum in all the pre-compression parameters and hence suitable for the direct compression method. The evaluation of tablets showed a good floating effect of about 4-7 hr in almost all the formulations. The floating tablets showed drug release of more than 92% after 6 hr and hence gastric retention was achieved. The study findings revealed that formulation 13 was the best, with a floating lag time of less than a minute and more than 7 hr of retention in the gastric pH with optimum floating behavior and drug release in the stomach. Conclusion: Hence, the prepared floating system involving a combination of polymers was found to be a reliable tool for gastric retention.
Cloud technology has raised significant prominence providing a unique market economic approach for resolving large-scale challenges in heterogeneous distributed systems. Through the use of the network, it delivers secure, quick, and profitable information storage with computational capability. Cloud applications are available on-demand to meet a variety of user QoS standards. Due to a large number of users and tasks, it is important to achieve efficient scheduling of tasks submitted by users. One of the most important and difficult non-deterministic polynomial-hard challenges in cloud technology is task scheduling. Therefore, in this paper, an efficient task scheduling approach is developed. To achieve this objective, a hybrid genetic algorithm with particle swarm optimization (HGPSO) algorithm is presented. The scheduling is performed based on the multi-objective function; the function is designed based on three parameters such as makespan, cost, and resource utilization. The proper scheduling system should minimize the makespan and cost while maximizing resource utilization. The proposed algorithm is implemented using WorkflowSim and tested with arbitrary task graphs in a simulated setting. The results obtained reveal that the proposed HGPSO algorithm outperformed all available scheduling algorithms that are compared across a range of experimental setups.
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