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https://www.selleckchem.com/products/ha15.html Nearly all (98%) families completed the whole 10-week intervention. Results demonstrated completeness and fidelity of implementation were acceptable in a subsample of 12 families. In sum, 75% of families in the subsample met the criteria (≥75%) for overall implementation of essential program elements (i.e., reach, completeness, and fidelity). Evidence suggests that ANDALE was delivered with high levels of completeness and fidelity in this sample of Latino families with preschool-aged children. These results support implementation of ANDALE in a large, randomized trial. Evidence suggests that ANDALE was delivered with high levels of completeness and fidelity in this sample of Latino families with preschool-aged children. These results support implementation of ANDALE in a large, randomized trial.The present waste-management system in most developing countries are insufficient to combat the challenge of increasing rate of solid waste generation. Accurate prediction of waste generated through modelling approach will help to overcome the challenge of deficient-planning of sustainable waste-management. In modelling the complexity within a system, a paradigm-shift from classical-model to artificial intelligent model has been necessitated. Previous researches which used Adaptive Neuro-Fuzzy Inference System (ANFIS) for waste generation forecast did not investigate the effect of clustering-techniques and parameters on the performance of the model despite its significance in achieving accurate prediction. This study therefore investigates the impact of the parameters of three clustering-technique namely Fuzzy c-means (FCM), Grid-Partitioning (GP) and Subtractive-Clustering (SC) on the performance of the ANFIS model in predicting waste generation using South Africa as a case study. Socio-economic and demographic provincial-data for the period 2008-2016 were used as input-variables and provincial waste quantities as output-variabl

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