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This study examines the relationship between higher education improvement and tacit knowledge importance. In this context, the scarcity of empirical and theoretical studies on acquiring tacit knowledge to enhance academic performance in higher education suggests that this research area holds significant importance for experts and policymakers. Consequently, this study aims to explore the factors that influence academic research performance at Chinese universities by acquiring tacit knowledge.

To achieve the study aims, the current approach utilizes the research technique based on the socialization, externalization, internalization and combination (SECI) model and knowledge management (KM) theory. To analyze the study objective, the authors collected data from post-graduate students at Chinese universities and analyzed it using structural equation modeling (SEM) to test the model and hypotheses.

The results indicated that social interaction, internalization and self-motivation have a positive impact on academic research performance through the acquisition of tacit knowledge. Furthermore, the findings suggest that academic researchers can acquire more knowledge through social interaction than self-motivation, thereby advancing research progress.

This study addresses the critical issues surrounding the acquisition of tacit knowledge and presents a comprehensive framework and achievements that can contribute to achieving exceptional academic performance.

The widespread use of washing machines in textile dyeing and finishing boosts product quality while leading to significant wastewater production. This wastewater poses environmental risks due to the textile industry's high pollution levels and water consumption. Sustainability hinges on minimizing water usage and treating wastewater for reuse. This study employs Matlab R2020a and Python 2023 to model experimental designs for treating textile production wastewater using the Fenton oxidation method, aiming to address sustainability concerns in the industry.

The Fenton oxidation process's efficacy and optimal operating conditions were determined through experimental sets employing the Box–Behnken design. Assessing machine learning algorithms on the data, Matlab R2020a utilized an artificial neural network (ANN), while Python 2023 employed support vector regression (SVR), decision trees (DT), and random forest (RF) models. Evaluation of model performance relied on regression coefficient (R2) and mean square error (MSE) outcomes. This methodology aimed to refine the Fenton oxidation process and identify the most efficient parameters, leveraging a combination of experimental design and advanced computational techniques across different programming platforms.

The study identified optimal conditions: pH 3, Fe+2 concentration of 0.75 g/L, and H2O2 concentration of 5 mM, yielding 87% COD removal. The Box–Behnken design achieved a high R2 of 0.9372, indicating precise predictions. Artificial neural networks (ANN) and support vector regression (SVR) exhibited successful applications, notably achieving an R2 of 0.99936 and low MSE of 0.00416 in the ANN (LOGSIG) model. However, decision trees (DT) and random forests (RF) proved less effective with limited datasets. The findings underscore technology integration in treatment modeling and the environmental imperative of wastewater purification and reuse.

This study, in which water use and wastewater treatment are evaluated with technological integration such as machine learning and data management, reveals how to contribute to targets 6, 9, 12, and 14 within the scope of UNEP 2030 sustainable development goals.