Role of microRNA miR171 in plant development.

MicroRNAs (miRNAs) are endogenous non-coding small RNA with 19-24 nucleotides (nts) in length, which play an essential role in regulating gene expression at the post-transcriptional level. As one of the first miRNAs found in plants, miR171 is a typical class of conserved miRNAs. The miR171 sequences among different species are highly similar, and the vast majority of them have both "GAGCCG" and "CAAUAU" fragments. In addition to being involved in plant growth and development, hormone signaling and stress response, miR171 also plays multiple and important roles in plants through interactions with microbe and other small-RNAs. The miRNA functions by regulating the expression of target genes. Most of miR171's target genes are in the GRAS gene family, but also include some NSP, miRNAs, lncRNAs, and other genes. This review is intended to summarize recent updates on miR171 regarding its function in plant life and hopefully provide new ideas for understanding miR171 function and regulatory mechanisms.

Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach.

The aim of this research is to forecast seasonal fluctuations in electricity consumption, and electricity usage efficiency of industrial sectors and identify the impacts of the novel coronavirus disease 2019 (COVID-19). For this purpose, a new seasonal grey prediction model (AWBO-DGGM(1,1)) is proposed: it combines buffer operators and the DGGM(1,1) model. Based on the quarterly data of the industrial enterprises in Zhejiang Province of China from the first quarter of 2013 to the first quarter of 2020, the GM(1,1), DGGM(1,1), SVM, and AWBO-DGGM(1,1) models are employed, respectively, to simulate and forecast seasonal variations in electricity consumption, the added value, and electricity usage efficiency. The results indicate that the AWBO-DGGM(1,1) models can identify seasonal fluctuations and variations in time series data, and predict the impact of COVID-19 on industrial systems. The minimum mean absolute percentage errors (MAPEs) of the electricity consumption, added value, and electricity usage efficiency of industrial enterprises separately are 0.12%, 0.10%, and 3.01% in the training stage, while those in the test stage are 6.79%, 4.09%, and 2.25%, respectively. The electricity consumption, added value, and electricity usage efficiency of industrial enterprises in Zhejiang Province will still present a tendency to grow with seasonal fluctuations from 2020 to 2022. Of them, the added value is predicted to increase the fastest, followed by electricity consumption.

An optimised grey buffer operator for forecasting the production and sales of new energy vehicles in China.

In recent years, under a series of policy shocks, the production and sales of new energy vehicles in China show the characteristics of trend mutation and non-smoothness. In order to forecast the production and sales of new energy vehicles in China, an optimised grey buffer operator is proposed by introducing accumulation and translation transformations. Meanwhile, a genetic algorithm is employed to ascertain its optimum parameters. Forecasting results indicate that the optimised buffer operator can significantly improve the adaptability of the grey model to the production and sales data of new energy vehicles in China, and exhibits much higher prediction accuracy than those of the classical buffer operator and grey model. Besides, the prediction results show that the production and sales of China's new energy vehicles will continue to grow from 2018 to 2020, with an average annual growth rate of 27.53% and 30.49%, respectively.

Investigation of a Brownfield Conflict Considering the Strength of Preferences.

By employing the Graph Model for Conflict Resolution methodology, this paper models and analyzes a brownfield conflict that occurred at the Changzhou Foreign Language School in Jiangsu, China, in 2016. This conflict made national headlines when news reports revealed that a large number of students and staff suffered from health issues after the school moved to a new site that is built on recently restored land adjacent to the original "Chang Long Chemical" block. Since stakeholders in the conflict hold different strengths of preference, a new option prioritization technique is employed to elicit both crisp preferences and the strength of preferences for the decision-makers (DMs) in the conflict. The conflict analysis result is consistent with the actual trajectory of the conflict and provides strategic insights into the conflict. More specifically, equilibrium results suggest that the firm should have been required to thoroughly clean the site, the local government should not have relocated the school, and the environmental agency and other stakeholders should have closely monitored the firm's activities. In short, strategic insights garnered from this case study indicate that positive interactions should be fostered among the local government, the enterprise, and the public to ensure sustainable brownfield land redevelopment in the future.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly.