Effect of Microwave Power and Time on Total Phenolic Contents and Antioxidant Characteristics of Microwave Assisted Extracts of Watermelon Rind Powder Microwave Assisted Extracts of Watermelon Rind Powder

Watermelon is gaining importance as a functional food due to its therapeutic effect. The therapeutic effect of watermelon has been reported and has been attributed to antioxidant constitutes. The major component in watermelon rind is citrulline that has a strong antioxidant effect which protect body from free-radical damage. Objective: This study was conducted to investigate the effect of microwave powers (150 W, 300 W & 450 W) and time intervals (1, 3 & 5 minutes) on total phenolic content (TPC) and total flavonoid content (TFC) and antioxidant characteristics i.e. DPPH and ferric reducing antioxidant potential (FRAP) of microwave assisted extracts of watermelon rind powder. Methods: The extracts collected after Microwave assisted extraction (MAE) of watermelon rind wereanalyzed for their antioxidant potential through different tests including total phenolic contents (TPC), total flavonoid content (TFC), DPPH assayand FRAP. Results: Microwave assisted extraction by using ethanol as a solvent at different microwave powers and various time intervals showed that total antioxidant potential was significantly higher at low microwave power such as TPC ranges obtained at 150W for 1, 3 & 5 minutes of time intervals show ranges (159.84, 160.04 & 169.71 mg GAE/100 g). While TFC ranges at 150W for time 1, 3 & 5 minutes were (21.31, 24.15 & 42.20 mg CEQ/100g) whereas DPPH ranges at 150W for time 1, 3 & 5 minutes were (53.14, 54.87 & 68.17 % ascorbic acid inhibition) and FRAP values at 150W for time 1, 3 & 5 minutes were (201.71, 221.50 & 326.43 mg FE/100g). While high microwave power 450W can result in disruption of some antioxidants at various time intervals. Conclusions: Watermelon rind is a rich source of many antioxidants andmicrowave assisted extraction technique should be implemented in the food and nutraceutical industries and microwave assisted extracts of watermelon rind should be utilize for the development of new functional food to combat many health related problems

Team Learning from Demonstration Tlfd : A Framework to Build Collaboration in a Team of Agents Via Imitation

This dissertation addresses the problem of building collaboration in a team of autonomous agents and presents imitation learning as an effective mechanism to build this collaboration. Imitation learning involves learning from an expert by observing her demonstrating a task and then mimicking her. This mechanism requires less time and technical expertise on behalf of domain experts/ knowledge engineers and makes it convenient for them to transfer knowledge to a software agent. The research extends the idea of a demonstration to multi-human demonstrations and presents a framework of Team Learning from Demonstration (TLfD) that allows a group of human experts to train a team of agents via demonstrations. A major challenge faced by the research is to cope with the overhead of demonstrations and inconsistencies in human demonstrations. To reduce the demonstration overhead, the dissertation emphasizes on a modular approach and enables the framework to train a team of a large number of agents via smaller numbers of demonstrators. The framework learns the collaborative strategy in the form of weighted naïve Bayes model where the parameters of the model are learned from the demonstration data and its weights are optimized using Artificial Immune Systems. The framework is thoroughly evaluated in the domain of RoboCup Soccer Simulation 3D which is a promising platform for a multi-agent domain and addresses many complex real-world problems. A series of experiments were conducted using RoboCup Soccer in which the agents were trained to perform different types of tasks through TLfD framework. The experiments were started with training a single agent how to score a goal in an empty soccer field. The later experiments increased the complexity of the task and the number of agents involved. The final experiment eventually trained a full-fledged team of nine soccer players and enabled them to play soccer against other competition quality teams. A number of test matches were played against different opponent teams, and the results of the matches were evaluated on the basis of different performance and behavioral metrics. The performance metrics described how well the imitating team played in the field whereas the behavioral metrics assessed how closely they had imitated the human demonstrations. Our soccer simulation 3D team KarachiKoalas served as a benchmark to evaluate the quality of the imitating team, and the dissertation closely compared the two teams and found that the team that was trained via imitation gave comparable performance to KarachiKoalas. The results showed the effectiveness of TLfD framework and supported the idea of using imitation to build collaboration among multiple agents. However, the framework, in its current form, does not support strategy building in an incremental manner in which a naïve strategy is learned via imitation and is refined in stages. The ability to build strategies incrementally can be a crucial requirement in complex systems. In future, the framework can be extended to incorporate the ability to refine an already learned strategy via human expert’s feedback.