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Regional land-use change is the leading cause of ecosystem carbon stock change; it is essential to investigate the response of LUCC to carbon stock to achieve the strategic goal of "double carbon" in a region. This paper proposes a residual network algorithm, the Residual Multi-module Fusion Network (R-MFNet), to address the problems of blurred feature boundary information, low classification accuracy, and high noise, which are often encountered in traditional classification methods. The network algorithm uses an R-ASPP module to expand the receptive field of the feature map to extract sufficient and multi-scale target features; it uses the attention mechanism to assign weights to the multi-scale information of each channel and space. It can fully preserve the remote sensing image features extracted by the convolutional layer through the residual connection. Using this classification network method, the classification of three Landsat-TM/OLI images of Zhengzhou City (the capital of Henan Province) from 2001 to 2020 was realized (the years that the three images were taken are 2001, 2009, and 2020). Compared with SVM, 2D-CNN, and deep residual networks (ResNet), the overall accuracy of the test dataset is increased by 10.07%, 3.96%, and 1.33%, respectively. The classification achieved using this method is closer to the real land surface, and its accuracy is higher than that of the finished product data obtained using the traditional classification method, providing high-precision land-use classification data for the subsequent carbon storage estimation research. Based on the land-use classification data and the carbon density data corrected by meteorological data (temperature and precipitation data), the InVEST model is used to analyze the land-use change and its impact on carbon storage in the region. The results showed that, from 2001 to 2020, the carbon stock in the study area showed a downward trend, with a total decrease of 1.48 × 10 7 t. Over the course of this 19-year period, the farmland area in Zhengzhou decreased by 1101.72 km 2 , and the built land area increased sharply by 936.16 km 2 . The area of land transfer accounted for 29.26% of the total area of Zhengzhou City from 2001 to 2009, and 31.20% from 2009 to 2020. The conversion of farmland to built land is the primary type of land transfer and the most important reason for decreasing carbon stock. The research results can provide support, in the form of scientific data, for land-use management decisions and carbon storage function protections in Zhengzhou and other cities around the world undergoing rapid urbanization. [ABSTRACT FROM AUTHOR]

Near-zero carbon emission power (NZCEP) plants, consisting of gas-fired units; wind turbines; power-to-gas (P2G); and carbon capture, utilization and storage (CCUS) systems, have recently received a lot of attention due to their enormous benefits in reducing carbon emissions and increasing the consumption of renewable energy. However, a complex environment of interest and a combination of risks makes their development very slow. This paper establishes a risk analysis framework for NZCEP considering multi-stakeholder participation. Firstly, a synthetic risk factor system was constructed based on stakeholders' interests. Subsequently, interval type II trapezoidal fuzzy numbers were used and final weights were calculated from both subjective and objective aspects. Finally, we applied an acronym in Portuguese of the interactive and multi-criteria decision-making (TODIM) method to site selection to achieve a balance of interests of all stakeholders. In addition, a case study was conducted. The case result demonstrates that Zhengzhou in Henan Province is the best choice for a NZCEP power plant. A further finding is that government plays an important role in the development of NZCEP plants, with site selection results being the most sensitive to changes in the government's risk appetite. Moreover, human resources are an important factor in the siting of an NZCEP plant. [ABSTRACT FROM AUTHOR]

In order to carry out a quantitative analysis of the teaching effect of college English courses based on multivariate matrix evaluation model, this paper takes as research objects 87-year 2020-enrolled undergraduate students from a university in Zhengzhou City, Henan Province, central China. On the basis of summarizing and analyzing previous research works, this study expounded the research status and significance of quantitative analysis of the teaching effect of college English courses, elaborated the development background, current status, and future challenges of multivariate matrix evaluation model, introduced the data source and the subjects, contents, and means of teaching effect quantification in this research, analyzed the feedback and guiding function of the teaching effect quantification result of college English courses, discussed the motivation and diagnosis function of the teaching effect quantification result of college English courses, conducted analyses of factors affecting the teaching effect quantification of college English courses, constructed the quantitative dimension of teaching effect of college English courses based on the multivariate matrix evaluation model, and finally designed the quantification system of teaching effect of college English courses based on the multivariate matrix evaluation model. The research results show that the quantitative analysis process of the teaching effect of college English courses has the characteristics of complex structure and many hierarchies, and there are also mutual influences and functions between its subsystems. In order to ensure the rationality and effectiveness of comprehensive quantification, the index system construction of the multivariate matrix evaluation model follows scientific, systematic, and focused principles. The teaching effect quantification process combines four influencing factors of perspective, content, mode, and method with the quantification standard, and constructs a hierarchical quantification structure with target layer, restriction factor layer, and restriction sub-factor layer. The tools it uses usually include observations, interviews, self-evaluation, mutual evaluation, student files, reading notes, project presentations, etc. Teachers adjust teaching goals and plans in time by observing the quantification results of students at all levels, which will help improve students' enthusiasm for learning and give full play to students' autonomy and initiative in English courses. [ABSTRACT FROM AUTHOR]

By using the observation data of four DSG5-type disdrometers, four automatic weather stations and two dual-polarization Doppler weather radars, this paper analyzes the characteristics of raindrop size distributions (DSDs) and their integral parameters during the severe precipitation in Henan Province, China, specially focusing on the extremely severe rainstorm on the 20 July 2021 in Zhengzhou City. The results show that: (1) In line with most of the severe precipitation in northern China, the July 2021 extreme precipitation were dominated by small raindrops, but medium-sized raindrops with diameter D in 2–4 mm contributed 61.28% to the rain rate R during the time period of extremely severe rainfall at Zhengzhou (ZZ) station, and the raindrops with D > 4 mm contributed 53.74% to the radar reflectivity factor Z. (2) Quantitative evaluation of the gamma model of DSD for different values of R is carried out, and the fitting effect of the gamma model is better for the raindrop spectrum with a single peak structure. However, this method cannot reflect the double-peak structure when R is large. (3) Some differences have been found in the Z - R relationship between Z and R among the four stations (GY, XM, XY, and ZZ). The maximum value of fitting coefficient a appears at the ZZ station, which is close to the a values in the Meiyu season in eastern China. For the other three stations, the a values are comparable to those of typhoons in the western Pacific, but the fitting coefficient b values are less different. (4) The average vertical profiles of dual-polarization radar parameters reveal the microphysical characteristics of the extremely severe rainstorm, that is, there were active ice-phase and warm-rain microphysical processes in the convective system of the extremely severe rainfall, of which the warm-rain process was more dominant. • The DSDs and microphysical characteristics at four weather stations of Zhengzhou City during the 20 July 2021 extremely heavy rainfall period were discussed. • There were three stations with diameter-controlled raindrop size distribution characteristic, only one station was controlled by both number concentration and diameter, the scatter pairs of parameters lg N w - D m of DSDs recorded of ZZ station indicate maritime-like convection. • Quantitative evaluation of the gamma model of DSDs for different rain rate was conducted. The fitting effect of the gamma model is better for the DSD with a single peak structure, while the method cannot reflect the double-peak structure when rain rate is large during the heavy rainfall period. • During the extreme rainfall period, there are active ice-phase and warm-rain microphysical processes in the convection system, and the warm-rain process is dominant. [ABSTRACT FROM AUTHOR]