Anti-inflammatory drugs inhibit inflammation, particularly those classified as nonsteroidal anti-inflammatory drugs (NSAIDs). Several studies have reported that propolis has both anti-ulcerogenic and anti-inflammatory effects. In this study, we investigated the bioactive compound and in vivo anti-inflammatory properties of both smooth and rough propolis from Tetragronula sp. To further identify anti-inflammatory markers in propolis, LC-MS/MS was used, and results were analyzed by Mass Lynx 4.1. Rough and smooth propolis of Tetragonula sp. were microcapsulated with maltodextrin and arabic gum. Propolis microcapsules at dose 25–200 mg/kg were applied for carrageenan-induced rat’s paw-inflammation model. Data were analyzed by one-way ANOVA and Kruskal–Wallis statistical tests. LC-MS/MS experiments identified seven anti-inflammatory compounds, including [6]-dehydrogingerdione, alpha-tocopherol succinate, adhyperforin, 6-epiangustifolin, deoxypodophyllotoxin, kurarinone, and xanthoxyletin. Our results indicated that smooth propolis at 50 mg/kg inhibited inflammation to the greatest extent, followed by rough propolis at a dose of 25 mg/kg. SPM and RPM with the dose of 25 mg/kg had inflammatory inhibition value of 62.24% and 58.12%, respectively, which is comparable with the value 70.26% of sodium diclofenac with the dose of 135 mg/kg. This study suggests that propolis has the potential candidate to develop as a non-steroid anti-inflammatory drug.

This article proposes a comparative method to assess the performance of artificial neural network's direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method.

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