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Black soldier fly larva (BSFL), Hermetia illucens (L.) (Diptera: Stratiomyidae), presents an attractive waste management solution that not only addresses the issue of waste but also generates alternative proteins and fats. Substrate quality and pre‐treatment by fermentation may play a crucial role in waste reduction efficiency and larval quality. The aim of this study was to investigate the growth performance, waste reduction efficiency and nutritional composition of BSFL rearing using different ratios of coconut endosperm (CE) and soybean curd residue (SC) with pre‐treatment by self‐fermentation (F), fermentation with baker's yeast (Y) and without pre‐treatment (C). Seven‐day‐old larvae were randomly separated into 15 experimental groups with three replicates. The experiment ended when the prepupae appeared. Based on the results, the larvae fed a diet with a high percentage of fresh SC exhibited the highest weight and growth rate, whereas the shortest development time was observed in larvae reared on Y. Pre‐treatment of the substrate with fermentation proved effective in waste reduction. The nutritional composition of the larvae showed that the highest crude protein was produced in larva reared on fresh 100% SC, and the highest ether extract was found when fed CE at 100% with pre‐treatment with F or Y. In conclusion, it is recommended to employ a fresh diet with a high SC content to optimise protein production, whereas a high level of CE with F or Y may be performed to obtain a high ether extract content. However, incorporating yeast fermentation with an SC content exceeding 25% provided high efficiency in waste reduction. Therefore, the selection of the ratio between CE and SC and of pre‐treatment techniques depends on the producer's objective. [ABSTRACT FROM AUTHOR]

Although single exponential smoothing is a popular forecasting method for a wide range of applications involving stationary time series data, consistent rules about choosing the initial value and determining the value for the smoothing constant (α) are still required, because they directly impact the forecast accuracy. The purpose of this study is to mitigate these shortcomings. First, a new method for setting the initial value by weighting is derived, and its performance is compared with two other traditional methods. Second, the optimal (α) was automatically solved using Solver in Microsoft Excel, after which was determined by minimizing the mean squared error (MSE). This was accomplished by comparing the from Solver with step search by setting the smoothing constant by varying its value from 0.001 to 1 in increments of 0.001 and then choosing the optimal value from this range that has the lowest MSE. The experimental results show that from Solver and the optimal with step search are not different, and the initial value set by the proposed method outperformed the existing ones regarding the MSE. [ABSTRACT FROM AUTHOR]