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Palatogenesis is a complex developmental process requiring temporospatially coordinated cellular and molecular events. The following review focuses on genetic, epigenetic, and environmental aspects directing palatal formation and their implication in orofacial clefting genesis. Essential for palatal shelf development and elevation (TGF-β, BMP, FGF, and WNT), the subsequent processes of fusion (SHH) and proliferation, migration, differentiation, and apoptosis of neural crest-derived cells are controlled through signaling pathways. Interruptions to these processes may result in the birth defect cleft lip and/or palate (CL/P), which happens in approximately 1 in every 700 live births worldwide. Recent progress has emphasized epigenetic regulations via the class of non-coding RNAs with microRNAs based on critically important biological processes, such as proliferation, apoptosis, and epithelial–mesenchymal transition. These environmental risks (maternal smoking, alcohol, retinoic acid, and folate deficiency) interact with genetic and epigenetic factors during palatogenesis, while teratogens like dexamethasone and TCDD inhibit palatal fusion. In orofacial cleft, genetic, epigenetic, and environmental impact on the complex epidemiology. This is an extensive review, offering current perspectives on gene-environment interactions, as well as non-coding RNAs, in palatogenesis and emphasizing open questions regarding these interactions in palatal development. [ABSTRACT FROM AUTHOR]

Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ( P E ) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict P E . In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models. [ABSTRACT FROM AUTHOR]