The ability to predict enzyme−metal−organic framework (MOF) properties such as enzyme loading, immobilization yield, activity retention, and reusability can maximize product yield and extend the operational life of enzyme−MOF biocatalysts. However, this is challenging due to the vast combinations of available metal and ligand building blocks for MOF and enzymes. Therefore, several machine learning (ML) algorithms are applied in this study using data collected from 127 journal articles to estimate these biocatalyst parameters. Twelve input variables, including the metal and ligand properties of MOF, as well as the enzyme properties, were integrated and fed into two ML algorithmsrandom forest and Gaussian process regression (GPR)to predict model outputs. A 10-fold cross-validation approach with grid search was applied to obtain the optimal hyperparameter values. The random forest model (RFM) provided more accurate estimates of the enzyme loading, immobilization yield, and reusability of the biocatalyst than the GPR model, with relatively high R 2 values of 0.85, 0.77, and 0.91, respectively. Both models are less effective in predicting the enzyme activity retention, however, with R 2 values of 0.63 or lower. Sensitivity analysis of the input variables revealed the most significant variables for each corresponding output parameter, allowing further optimization of the RFM. The final RFM was then tested with a second unseen dataset collected from experiments. Findings confirmed the validity of the predictive model, including a relative error of less than 25%. Our model can aid in the synthesis of enzyme−MOF biocatalysts by providing valuable estimates of these output parameters for different MOF precursors and enzymes, saving experimental time and cost.

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Research on online social networks (OSNs) has focused overwhelmingly on their benefits and potential, with their negative effects overlooked. This study builds on the limited existing work on the so-called ‘dark side’ of using OSNs. The authors conducted a systematic review of selected databases and identified 46 negative effects of using OSNs from the users’ perspective, which is a rich spectrum of users’ negative experiences. This article then proposed nomenclature and taxonomy for the dark side of using OSNs by grouping these negative effects into six themes: cost of social exchange, cyberbullying, low performance, annoying content, privacy concerns and security threats. This study then conducted structured interviews with experts to confirm the sense-making and validity of the proposed taxonomy. This study discusses the confirmed taxonomy and outlines directions for future research.