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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]

Maintaining energy balance and economical operation is significant for energy hub (EH) which serves as the central component. Implementing real‐time regulation for heating and cooling equipment within the EH is challenging due to their slow response time in response to the stochastic fluctuation in renewable energy sources and demands while the opposite is true for electric energy storage equipment (EST), a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment. With this motivation, this study proposes a deep reinforcement learning based two‐timescale energy management strategy for EH, which controls heating & cooling equipment on a long timescale of 1 h, and EST on a short timescale of 15 min. The actions of the EST are modelled as discrete to reduce the action spaces, and the discrete‐continuous hybrid action sequential TD3 model is proposed to address the problem of handling both discrete and continuous actions in long timescale policy. A joint training approach based on the centralized training framework is proposed to learn multiple levels of policies in parallel. The case studies demonstrate that the proposed strategy reduces the economic cost and carbon emissions by 1%, and 0.5% compared to the single time‐scale strategy respectively. [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]

• The novel MIPs were synthesized by using six different template molecules. • MIPs could adsorb PBDEs selectively in Triton X-100 simulated washing waste. • MIPs showed superior selective adsorption capacity of BDE-15 than activated carbon. • Van Der Waals interaction and hydrogen bond played major role in adsorption. • Molecular volume, planarity, and polarity of PBDEs mainly affected the adsorption. Surfactant enhanced elution is an effective method for removing hydrophobic organic pollutants from soils. The key to the development of leaching technology is selective removal of targeted pollutants in soil washing effluent and recycling of surfactant solutions. In this study, a molecular imprinting technique was applied to selectively sorb polybrominated diphenyl ethers (PBDEs) in soil washing effluent. The novel molecular imprinted polymers (MIPs) using different template molecules were synthesized by precipitation polymerization. Adsorption behaviors and mechanisms of MIPs were studied through experiments and theoretical calculations. The results show that 4-bromo-4′-hydroxybiphenyl and toluene can be effective imprinting molecule for MIPs synthesis. The maximal adsorption capacity of selected dummy molecular imprinted polymer (D 1 -MIP) was 1032.36 µmol/g, and that of part molecular imprinted polymer (P-MIP) was 981.13 µmol/g. Their imprinting factors in 5 PBDEs adsorption ranged from 2.13 to 5.88, the recovery percentage of Triton X-100 can reach 99.09%, confirming the feasibility of reusing surfactant. Various PBDEs could be removed by MIPs, and Quantitative Structure Property Relationship analysis revealed that PBDEs' molecular volume, planarity, polarity, and hydrophobicity have major influences on their adsorption performance. DFT calculation revealed that Van der Waals force and hydrogen bonding played important roles during selective adsorption. These results can provide effective theoretical guidance for surfactant enhanced soil elution in practical engineering applications. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper investigates the design of waveforms for multiple-input multiple-output (MIMO) radar systems that can exploit multipath returns to enhance target detection performance. By making reasonable use of multipath information in the waveform design, MIMO radar can effectively improve the signal-to-interference and noise ratio (SINR) of the receiver under a constant modulus (CM) constraint. However, optimizing the waveform design under these constraints is a challenging non-linear and non-convex problem that cannot be easily solved using traditional methods. To overcome this challenge, we proposed a novel waveform design method for MIMO radar in multipath scenarios based on deep learning. Specifically, we leveraged the powerful nonlinear fitting ability of neural networks to solve the non-convex optimization problem. First, we constructed a deep residual network and transform the CM constraint into a phase sequence optimization problem. Next, we used the constructed waveform optimization design problem as the loss function of the network. Finally, we used the adaptive moment estimation (Adam) optimizer to train the network. Simulation results demonstrated that our proposed method outperformed existing methods by achieving better SINR values for the receiver. [ABSTRACT FROM AUTHOR]

The electrochemical carbon dioxide reduction reaction (CO2RR), which can produce value‐added chemical feedstocks, is a proton‐coupled‐electron process with sluggish kinetics. Thus, highly efficient, cheap catalysts are urgently required. Transition metal oxides such as CoOx, FeOx, and NiOx are low‐cost, low toxicity, and abundant materials for a wide range of electrochemical reactions, but are almost inert for CO2RR. Here, we report for the first time that nitrogen doped carbon nanotubes (N‐CNT) have a surprising activation effect on the activity and selectivity of transition metal‐oxide (MOx where M = Fe, Ni, and Co) nanoclusters for CO2RR. MOx supported on N‐CNT, MOx/N‐CNT, achieves a CO yield of 2.6–2.8 mmol cm−2 min−1 at an overpotential of −0.55 V, which is two orders of magnitude higher than MOx supported on acid treated CNTs (MOx/O‐CNT) and four times higher than pristine N‐CNT. The faraday efficiency for electrochemical CO2‐to‐CO conversion is as high as 90.3% at overpotential of 0.44 V. Both in‐situ XAS measurements and DFT calculations disclose that MOx nanoclusters can be hydrated in CO2 saturated KHCO3, and the N defects of N‐CNT effectively stabilize these metal hydroxyl species under carbon dioxide reduction reaction conditions, which can split the water molecules and provide local protons to inhibit the poisoning of active sites under carbon dioxide reduction reaction conditions. [ABSTRACT FROM AUTHOR]

The fugitive dust generated by agricultural harvesting machinery not only causes harm to production safety, but also affects the living environment of people in agricultural areas. This is also one of the hot issues that have emerged in the green development of rural areas in recent years, which is related to agricultural safety and hygiene culture. Due to the lack of relevant research, many researchers still have considerable controversy on the issue of agricultural dust. Therefore, in combination with the actual production of agricultural mechanization, according to the cause of dust generation and particle characteristics, the selection of appropriate dust reduction technology and detection methods is of great significance for the research on the control of dust from agricultural harvesting machinery. Aiming at the dust problem in agricultural mechanization production, this research first introduces the relationship between fugitive dust and atmospheric particulate matter and the main components of fugitive dust, and then focuses on the causes of dust generated by wheat harvesters and peanut harvesters in field operations, and explains the main hazards of dust to human health, ecological environment, and climate. This study introduces four fugitive dust emission reduction technologies and five particle measurement methods, and compares and analyzes their feasibility in the application of agricultural harvesting machinery dust control. Finally, we put forward conclusions and suggestions on the dust control technology of agricultural harvesting machinery in order to provide reference for the control of agricultural harvesting machinery dust, improve the field operation environment, and promote the green development of modern agriculture. [ABSTRACT FROM AUTHOR]

Earth current generated by the operation of urban rail transit can enter into the neutral point of grounding transformer, which will result in the deterioration of transformer operation performance and cause electrical safety problems. In this article, the neutral current for 220 kV transformers has been measured in the real power system, the characteristics of dc and harmonic current are analyzed, and a novel suppression method for the dc and harmonic current simultaneously combining an active converter with a dc blocking capacitor is proposed, which solves not only the traditional dc bias problem but also the intrusion of the harmonic current from urban rail transit. The active converter is designed to compensate harmonic voltage to suppress harmonic current, and the capacitor provides blocking for the dc current. The protection scheme in case of the high fault current of the neutral point is presented. The improved control method is designed to control the neutral current and adjust the fundamental impedance for the requirements of relay protection. The effectiveness of the proposed method is verified by the digital simulations and the laboratory experiments based on the field working conditions, and the results demonstrate the satisfying performances. [ABSTRACT FROM AUTHOR]

The mysterious properties of individual U atoms on transition metal surfaces play indispensable parts in supplementing our understanding of uranium-transition metal systems, which are important subjects for both nuclear energy applications and fundamental scientific studies. By using scanning tunneling microscopy and density functional theory calculations, the adsorptions, reactions and electronic properties of individual U atoms on Cu(111), Ag(111), Au(111) and Ru(0001) surfaces were comparatively studied for the first time in this work. Upon the deposition of a small amount of U onto Cu(111) or Ag(111) at 8 K, individual U atoms show relatively high activity and can either be adsorbed on intact substrate surfaces or induce various surface vacancies surrounded by clusters of substrate atoms. By contrast, the majority of U atoms tend to dispersedly adsorb on intact surfaces of Au(111) and Ru(0001) rather than producing surface vacancies at the same temperature. In all cases, Kondo resonance manifested as asymmetric dip feature around Fermi energy is only observed in the differential tunneling conductance spectra of single U adatoms on Ag(111). [ABSTRACT FROM AUTHOR]

The effect of atomic hydrogen adsorption on AnO2 (An = Th, U, and Pu) surfaces is studied in the framework of density functional theory and Hubbard-corrected density functional theory. Several adsorption coverages (1/3, 1/2, 2/3, and 1 monolayer) are considered. For the band insulator ThO2, surface metallicity induced by hydrogen adsorption is observed due to the electron donation of the hydrogen to the surface. But this effect is found to be strongly suppressed by electronic correlation for the Mott insulators UO2 and PuO2 because the electrons from the adsorbed hydrogen atoms occupy the localized 5f orbitals of the surface U/Pu atoms. [ABSTRACT FROM AUTHOR]

The p-aminothiophenol (PATP) coupling reaction on plasmon substrates such as Ag and Au nanoparticles has received extensive attention since the catalytic effect of the surface plasmon was found. Currently, in situ kinetic studies of this reaction are rare, especially those focusing on the specific role of the hot electron–hole carriers. Here, in situ electrochemical surface-enhanced Raman spectroscopy (SERS) is developed to study the plasmon catalytic reaction of PATP in a controlled aqueous environment involving the factors of O2, electron and hole carriers, and solution pH. Ag nanoparticles supported on graphite serve as a SERS substrate, which could separate hot electron–hole pairs effectively and is beneficial to study the effects of hot carriers on plasmon-driven reactions. In situ electrochemical SERS measurements reveal two reaction paths for the PATP coupling reaction. One is that plasmon-induced hot holes activate the dehydrogenation of PATP and then the radical coupling reaction to form p,p′-dimercaptoazobenzene (DMAB) under O2-free conditions. Another is likely to be that the surface Ag2O/AgOH, which is generated from Ag and 1O2/O2−, catalyzes the oxidation of PATP and then the coupling process under O2-rich conditions. Benefitting from the potential/atmosphere controlled measurements in situ, the intermediate species of PATP(NH)/PATP(N) are observed with vibrational bands at around 1056, 1202, 1253, 1395, 1514 and 1540 cm−1. [ABSTRACT FROM AUTHOR]

The phase relation between supercooled liquid silicon (l-Si) and amorphous silicon (a-Si) is discussed based on experimental results. Electrostatically levitated l-Si samples were supercooled down to low temperatures, 300 K below the melting temperature (Tcl: 1683 K), and solidified accompanied by the release of latent heat. It was found that solidified Si samples melted again at 1480 K caused by the latent heat. Also, it was found that the Si samples that rapidly quenched near the solidification temperature contained a large amount of a-Si with tetrahedral coordination. These two findings show that the supercooled l-Si samples solidified into a-Si and a-Si melted, confirming the idea of a first-order phase transition between two metastable phases proposed by Turnbull et al. [Metall. Mater. Trans. A 29, 1825 (1998)]. [ABSTRACT FROM AUTHOR]