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Dihydrochalcones (DHCs) constitute a specific class of flavonoids widely known for their various health-related advantages. Melatonin (MLT) has received attention worldwide as a master regulator in plants, but its roles in DHC accumulation remain unclear. Herein, the elicitation impacts of MLT on DHC biosynthesis were examined in Lithocarpus litseifolius, a valuable medicinal plant famous for its sweet flavor and anti-diabetes effect. Compared to the control, the foliar application of MLT significantly increased total flavonoid and DHC (phlorizin, trilobatin, and phloretin) levels in L. litseifolius leaves, especially when 100 μM MLT was utilized for 14 days. Moreover, antioxidant enzyme activities were boosted after MLT treatments, resulting in a decrease in the levels of intracellular reactive oxygen species. Remarkably, MLT triggered the biosynthesis of numerous phytohormones linked to secondary metabolism (salicylic acid, methyl jasmonic acid (MeJA), and ethylene), while reducing free JA contents in L. litseifolius. Additionally, the flavonoid biosynthetic enzyme activities were enhanced by the MLT in leaves. Multiple differentially expressed genes (DEGs) in RNA-seq might play a crucial role in MLT-elicited pathways, particularly those associated with the antioxidant system (SOD, CAT, and POD), transcription factor regulation (MYBs and bHLHs), and DHC metabolism (4CL, C4H, UGT71K1, and UGT88A1). As a result, MLT enhanced DHC accumulation in L. litseifolius leaves, primarily by modulating the antioxidant activity and co-regulating the physiological, hormonal, and transcriptional pathways of DHC metabolism. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the low integration of the current power information system security assessment method with classified protection 2.0, strong subjectivity, and the vague quantification of objective indexes, this article proposes to establish a safety index system suitable for the electric power information system with the existing safety inspection standards for power systems on the basis of the classified protection 2.0 evaluation system. Additionally, it quantifies each index through a quantitative method combining subjective and objective methods and uses the analytic hierarchy process entropy method to obtain the combined weight of the evaluation index system. Finally, this paper summarizes the expert scores and the graded protection evaluation reports of the six systems into the original data for comprehensive weight calculation and analyzes the rationality of the weight change. The comparison and analysis of the closeness of the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and the evaluation score of the equal guarantee have confirmed the scientificity and rationality of the evaluation model. It provides a reasonable logical idea for the implementation of the classified protection 2.0 system in the power information system. [ABSTRACT FROM AUTHOR]

Under the global goal of "carbon peaking and carbon neutrality", new power system is facing a new energy development trend of increasing the capacity of new energy and reducing the capacity and quantity of coal power. Furthermore, new power systems are also moving towards new models such as the Energy Internet and energy storage diversification. This paper combines the needs of new power systems' "source-load interaction" and a differentiated analysis of the specific carbon emission characteristics of each subject of the Energy Internet "source-network-load-storage". This paper analyzes the energy efficiency coefficient and carbon emission factors of each link of "source-grid-load-storage". Therefore, the paper proposes a carbon control model for the Energy Internet. This paper focuses on the characteristics of coal power with the largest carbon reduction. Therefore, this article constructs a multi-dimensional low-carbonization evaluation index system for coal-fired power plants. In addition, this article designs a fusion weight calculation method for subjective and objective indicators based on CRITIC-G1. Additionally, this paper uses the ideal solution–gray relational method to evaluate the specified samples. Finally, this article verifies the effectiveness and feasibility of the implementation of the low-carbonization evaluation index system for coal-fired power plants. [ABSTRACT FROM AUTHOR]

Monolithic integrated mode converters with high integration are essential to photonic integrated circuits (PICs), and they are widely used in next-generation optical communications and complex quantum systems. It is expected that PICs will become more miniaturized, multifunctional, and intelligent with the development of micro/nano-technology. The increase in design space makes it difficult to realize high-performance device design based on traditional parameter sweeping or heuristic design, especially in the optimal design of reconfigurable PIC devices. Combining the mode coupling theory and adjoint calculation method, we proposed a design method for a switchable mode converter. The device could realize the transmission of TE0 mode and the conversion from TE0 to TE1 mode with a footprint of 0.9 × 7.5 μm2 based on the phase change materials (PCMs). We also found that the mode purity could reach 78.2% in both states at the working wavelength of 1.55 μm. The designed method will provide a new impetus for programmable photonic integrated devices and find broad application prospects in communication, optical neural networks, and sensing. [ABSTRACT FROM AUTHOR]

Current development of high-performance fiber-reinforced cementitious composites (HPFRCC) mainly relies on intensive experiments. The main purpose of this study is to develop a machine learning method for effective and efficient discovery and development of HPFRCC. Specifically, this research develops machine learning models to predict the mechanical properties of HPFRCC through innovative incorporation of micromechanics, aiming to increase the prediction accuracy and generalization performance by enriching and improving the datasets through data cleaning, principal component analysis (PCA), and K-fold cross-validation. This study considers a total of 14 different mix design variables and predicts the ductility of HPFRCC for the first time, in addition to the compressive and tensile strengths. Different types of machine learning methods are investigated and compared, including artificial neural network (ANN), support vector regression (SVR), classification and regression tree (CART), and extreme gradient boosting tree (XGBoost). The results show that the developed machine learning models can reasonably predict the concerned mechanical properties and can be applied to perform parametric studies for the effects of different mix design variables on the mechanical properties. This study is expected to greatly promote efficient discovery and development of HPFRCC. [ABSTRACT FROM AUTHOR]

Two new abietane diterpenoids (1,2), along with five known diterpenoids (3–7), were first isolated and purified from the stems of Clerodendrum bracteatum. The structures of the new compounds were established by extensive analysis of mass spectrometric and 1-D, 2-D NMR spectroscopic data. Their antioxidant activities were determined on DPPH radical scavenging and ABTS. The in vitro cytotoxic activities of the compounds were evaluated against the HL-60 and A549 cell lines by the MTT method. [ABSTRACT FROM AUTHOR]