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Parthenogenesis, the development of unfertilized egg cells into embryos, is a key component of apomixis. AtBBM (BABY BOOM), a crucial regulator of embryogenesis in Arabidopsis, possesses the capacity to shift nutritional growth toward reproductive growth. However, the mechanisms underlying AtBBM‐induced parthenogenesis remain largely unexplored in dicot plants. Our findings revealed that in order to uphold the order of sexual reproduction, the embryo‐specific promoter activity of AtBBM as well as repressors that inhibit its expression in egg cells combine to limiting its ability to induce parthenogenesis. Notably, AtRKD5, a RWP‐RK domain‐containing (RKD) transcription factor, binds to the 3′ end of AtBBM and is identified as one of the inhibitory factors for AtBBM expression in the egg cell. In the atrkd5 mutant, we successfully achieved enhanced ectopic expression of AtBBM in egg cells, resulting in the generation of haploid offspring via parthenogenesis at a rate of 0.28%. Furthermore, by introducing chimeric Arabidopsis and rice BBM genes into the egg cell, we achieved a significant 4.6‐fold enhancement in haploid induction through the atdmp8/9 mutant. These findings lay a strong foundation for further exploration of the BBM‐mediated parthenogenesis mechanism and the improvement of haploid breeding efficiency mediated by the dmp8/9 mutant. [ABSTRACT FROM AUTHOR]

Plutonium oxycarbide plays a crucial role in the fabrication of a carbide fuel and the corrosion of plutonium. In this work, a machine-learning (ML) scheme is used to predict the thermodynamic stability of plutonium oxycarbide PuOxC1−x. The training data are generated within the framework of density-functional theory (DFT) and its Hubbard correction. Four ML schemes combined with three structural descriptors are considered and their performance is compared. The optimal ML model for the DFT data set yields remarkably small average errors of approximately 3 meV per atom for mixing energy and 0.003 Å for the lattice parameter, indicating its high prediction accuracy. Utilizing the ML model, we predict the convex hull of PuOxC1−x as well as several ordered atomic structures for a specific value of x. The enhanced energetic stability observed in these ordered structures could probably be attributed to the strong hybridization between Pu 5f/6d and C/O 2p, namely robust Pu–C and Pu–O bonds. [ABSTRACT FROM AUTHOR]

Sulfate radicals are increasingly recognized for their potent oxidative capabilities, making them highly effective in degrading persistent organic pollutants (POPs) in aqueous environments. These radicals excel in breaking down complex organic molecules that are resistant to traditional treatment methods, addressing the challenges posed by POPs known for their persistence, bioaccumulation, and potential health impacts. The complexity of predicting interactions between sulfate radicals and diverse organic contaminants is a notable challenge in advancing water treatment technologies. This study bridges this gap by employing a range of machine learning (ML) models, including random forest (DF), decision tree (DT), support vector machine (SVM), XGBoost (XGB), gradient boosting (GB), and Bayesian ridge regression (BR) models. Predicting performances were evaluated using R2, RMSE, and MAE, with the residual plots presented. Performances varied in their ability to manage complex relationships and large datasets. The SVM model demonstrated the best predictive performance when utilizing the Morgan fingerprint as descriptors, achieving the highest R2 and the lowest MAE value in the test set. The GB model displayed optimal performance when chemical descriptors were utilized as features. Boosting models generally exhibited superior performances when compared to single models. The most important ten features were presented via SHAP analysis. By analyzing the performance of these models, this research not only enhances our understanding of chemical reactions involving sulfate radicals, but also showcases the potential of machine learning in environmental chemistry, combining the strengths of ML with chemical kinetics in order to address the challenges of water treatment and contaminant analysis. [ABSTRACT FROM AUTHOR]

The main goal of integrated energy service providers (IESP) in the future is to pursue profitability and reduce carbon emissions while ensuring sufficient energy supply. However, the multiple uncertainties associated with renewable energy resources and the multi‐energy consumers pose significant challenges in the optimal planning of the integrated energy system (IES) based on energy hubs (EH). This includes the global optimization of device types, EH capacity, and the optional interconnections between adjacent EHs. This study aims to develop a comprehensive mathematical model of EH that considers the temperature limitations on device operation. The thermal bus and associated devices are divided into high and low temperature parts, and a multi‐scenario stochastic programming model is proposed for IES planning. Scenarios are generated using non‐parametric kernel density estimation and the Copula function, a scenario reduction technique is implemented to alleviate computational burden. Additionally, case studies illustrate effectiveness of the proposed approach, the operation and forms of EHs, effect of the interconnections and the trade‐off between carbon emissions and economic costs are further studied by extensive simulations. This study can be a good guide for IESPs to invest in the construction of the EH‐based IES. [ABSTRACT FROM AUTHOR]