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Transmission line parameter identification plays a crucial role in power systems. However, existing methods for identifying transmission line parameters face several challenges: (1) when dealing with non-Euclidean data such as power grid data, a considerable amount of noise is introduced, (2) inability to treat the power grid as a global system, limiting calculations to individual transmission lines and overlooking inter-branch correlations, (3) high sensitivity to data contamination. To address these issues, this paper proposes a multi-task noisy graph attention network (MNGAN) for power grid parameter identification, which leverages the spatial structure of the power grid and considers the inter-branch correlations. By computing the homogeneity and average degree of the graph data, we introduce an adaptive attention mechanism to the model. This attention mechanism dynamically adjusts the model's focus on key information, mitigating the impact of data contamination and improving parameter identification accuracy. Additionally, to achieve simultaneous recognition of multiple branch parameters and enhance model training speed and accuracy, we incorporate a multi-task loss function based on homoscedastic uncertainty. Experimental results demonstrate the superiority of our proposed model over other machine learning and deep learning methods, providing more accurate predictions of branch parameters. [ABSTRACT FROM AUTHOR]

Infrastructure impacts economic growth through both capital and labor. A production function that includes physical and human factors is developed that has implications for Solow's (1956) model of economic growth. The proposed production function implies many paths to growth that have been observed in economic history. These include fluctuations or convergence toward balanced growth or fluctuating growth interspersed by regime changes.

A combination of an amino acid-derived chiral phosphine catalyst and methyl acrylate to catalyze the direct Mannich reaction of 3-substituted oxindoles and imines has been reported to afford 3-tetrasubstituted oxindole derivatives which are key structures for biological activities. The products are formed with a quaternary carbon and featured with two adjacent chiral centers. Various N -EDG(electron-donating group) and N -EWG(electron-withdrawing group) protected oxindoles, including 3-aryl and 3-alkyl substituted ones, have been evaluated with aromatic and aliphatic imines under this catalytic system, smoothly giving desired products in good yields as well as excellent diastereo- and enantioselectivities. [ABSTRACT FROM AUTHOR]

Random vibration of mechanical equipment will lead to structural fatigue damage, while strong and sustained vibration will lead to the overall performance decline of the equipment. Vibration sensors can be used to monitor the stability of mechanical operation. However, in some complex unattended environments, it is still a significant challenge to realize real-time equipment monitoring and early warning. Therefore, we propose a self-powered multifunctional vibration sensor based on coupling a dual-mode triboelectric nanogenerator and a non-contact electromagnetic generator (M-HNG) for automatic condition monitoring. The dual-mode triboelectric nanogenerator and non-contact electromagnetic generator can work together to realize real-time self-powered vibration monitoring. We established the prototype model of the vibration sensor, simulated the working principle of each part of the sensor, and fitted the functional relationship between the vibration amplitude and speed of the sensor and the output electrical signal. The results show that the short-circuit current of the sensor is very sensitive to the change in external vibration speed and has good stability in its detection range. Using a non-contact electromagnetic generator can effectively improve the service life of the vibration sensor. The system can continuously monitor the vibration signals generated by machinery during operation and provide an effective way for structural health monitoring, especially in extreme environments or unattended conditions. [Display omitted] • The M-HNG provide an effective way for structural health monitoring. • The M-HNG can continuously monitor mechanical vibration in harsh environments. • The M-HNG has potential applications in self-powered vibration sensing. [ABSTRACT FROM AUTHOR]

In this study, we observed a great reduction in the expression of the endogenous long noncoding RNA ROR (lncRNA-ROR) and the stem cell transcription factor Sox2, in contrast to a marked increase in miR-145 expression, during the course of in vitro induced differentiation of human amniotic epithelial stem cells (HuAECs). Bioinformatics analysis and the luciferase reporter assay revealed binding of miR-145 to specific sites in lncRNA-ROR and Sox2, silencing their expression. Overexpression of a lncRNA-ROR-specific siRNA effectively downregulated the expression levels of Sox2 and other stem cell markers in HuAECs while weakening the efficiency of HuAEC differentiation into β islet-like cells. Moreover, the in vitro response of HuAEC-derived β islet-like cells to extracellular stimuli and C-peptide release by these cells were markedly weakened in the siRNA-ROR transfection group. Furthermore, the in vivo expression of β islet-like cell biomarkers was substantially reduced in HuAECs in the siRNA-ROR transfection group, and their in vivo β islet-like cell differentiation and insulin release capacities were reduced in a streptozocin-induced diabetic rat model. The experimental results indicate that lncRNA-ROR effectively maintains Sox2 gene expression through competitive binding to miR-145, achieving pluripotency maintenance in HuAECs and regulation of their directed β islet-like cell differentiation efficiency. [ABSTRACT FROM AUTHOR]