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Background and Aim: Periodontal is a group of inflammatory diseases that can cause tooth loss. This study investigated two types of mouthwash, chlorhexidine and green tea of the Irsha and Colgate brands to patients with moderate chronic periodontitis based on gingival factors, including gingival index (GI) and sulcus bleeding index (SBI) . Methods: Ninety patients were divided into three equal groups: chlorhexidine, Colgate green tea, and Irsha green tea . The sampling patients were randomly selected among adults with moderate chronic periodontitis. The SPSS statistical software (version 22) was used for data analysis. An independent t -test was used to investigate the significant difference between treatment groups and paired t -test was used to assess the significance of GI and SBI parameters before and after treatment with mouthwash in each group of patients. P -values less than 0.05 indicated significant differences between the groups . Results: There was no significant difference between the chlorhexidine and Colgate green tea groups regarding the GI index ( P = 0.023). However, Irsha green tea has significantly reduced the GI index compared to chlorhexidine (P = 0.025) and Colgate green tea ( P = 0.021). In addition, the statistical results did not show a significant difference between chlorhexidine and Colgate green tea in terms of the SBI index ( P = 0.116). However, Irsha green tea has significantly reduced the SBI index compared to chlorhexidine ( P = 0.003). The amount of SBI index in the two green tea brands was similar ( P = 0.445) . Conclusion: The effectiveness of Colgate green tea mouthwash in reducing GI and SBI index was similar to chlorhexidine; however, the effect of Irsha green tea mouthwash on these indices was significantly less than that of chlorhexidine mouthwash. Between the two brands of green tea, no noticeable changes in the reduction of the SBI index were observed . [ABSTRACT FROM AUTHOR]

In this paper, a new method of data-driven controller (DDC) design using Simultaneous Perturbation Stochastic Approximation Algorithm (SPSA) and Neural Network (NN) training is presented. This method can be used to control a variety of linear and nonlinear systems. In the simultaneous perturbation stochastic approximation algorithm, the controller is assumed to have a fixed structure and its parameters must be estimated. In this paper, a Proportional, Integral, and Derivative controller (PID) is considered and the parameters that should be estimated by the proposed algorithm are proportional, integral and derivative terms of this controller. In the proposed method, the simultaneous perturbation stochastic approximation algorithm is quantified by using neural network training which increases the convergence speed and also improves the performance of the algorithm against system input changes. Simulations performed on cement grinding particle size distribution process and pitch angle control of aircraft show the high efficiency and potential of the proposed method. [ABSTRACT FROM AUTHOR]