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Green power trading has become a concerted global action to address climate change and promote green and low-carbon transformation, and its effect on carbon emission reduction in China's power sector is becoming increasingly pronounced. Exploring the development of China's green power trading market and assessing the impact of green power trading on the electricity market can promote the construction of a new type of power system and help realize the goals of reaching peak carbon emissions and carbon neutrality. The Beijing-Tianjin-Hebei region, one of the important economic hubs and urban agglomerations in China, fully launched green power trading in 2021. The study of green power participation in the electricity market in the Beijing-Tianjin-Hebei region can provide experience and policy references for the development of China's green power market. Therefore, this study analyzed the coupling mechanism between the green power trading market and the electricity market, proposed a non-cooperative game model considering the coupling between the two markets as well as a system dynamics model, and analyzed the impact of green power trading on the behavioral decisionmaking of market players and on the electricity market in the Beijing-Tianjin-Hebei region. The results showed that: 1 The proportion of green power generation affected the behavioral decisions of market players, and increasing the proportion of green power generation increased the profits of green power manufacturers and power grid enterprises and reduced the profits of thermal power plant operators and the cost of power purchases by electricity users. 2 Without considering the scenario of purchasing electricity from outside, feed-in tariffs for green and thermal power were on a downward and then upward trend; the green power installed capacity under construction and generation capacity were increasing, and the green power feed-in was estimated to account for about 63% of the total power in 2030; and the thermal power installed capacity under construction showed a rising, then falling, then rising trend. However, its rate of increase was smaller than that of green power. 3 Considering the scenario of purchasing electricity from outside, the green and thermal feed-in tariffs would decrease; thermal power generation would increase from January 2021 to April 2024, while thermal power generation would decrease from May 2024 to December 2030; green power generation would increase from January 2021 to April 2029 and decrease slightly from May 2029 to December 2030. The sensitivity analysis of purchased power revealed that increasing purchased power had a weakening inhibitory effect on thermal power in the region and promoted green power development. Based on the above conclusions, this study puts forward the following policy recommendations: accelerating the transformation of the power supply structure and promoting the changing roles of green power and thermal power in the power market; adjusting the pricing model of thermal power and promoting the multi-dimensionalization of electricity value; and encouraging inter-provincial and inter-regional green power trading to accelerate the construction of a unified national electricity market. [ABSTRACT FROM AUTHOR]

Cascade reactions have always had an irreplaceable position in biocatalysis. In this work, we are inspired by the cascade reactions catalyzed by natural enzymes and combining the peroxidase mimetic activity of metal organic frameworks (MOF) nanosheets and the glucose oxidation mimetic enzyme activity of gold nanoparticles (AuNPs) constitutes a dual nanozyme mimetic cascade reaction. AuNPs/Zn-TCPP nanosheets with dual mimetic enzyme activities were prepared by in situ reduction to generate AuNPs on the surface of 2D MOF. And used to further detect the content of hydrogen peroxide or glucose.

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery. [ABSTRACT FROM AUTHOR]

In order to accelerate the construction of a clean, low-carbon, safe and efficient energy system, China set the provincial weight of responsibility for renewable energy power consumption and established a renewable energy power absorption guarantee mechanism in 2019. As a market incentive policy, it has enduring effect on the low-carbon transformation of the power industry. Firstly, the operation mechanism of the renewable energy consumption guarantee mechanism is analyzed. The general framework, core elements and supporting measures are clarified. Secondly, a stock-flow diagram is constructed based on the system dynamics method. It contains the green electricity market sub-module, the green-certificate market sub-module and the excess power absorption market sub-module. Finally, multiple scenarios are set up to simulate the impact of the green-certificate market and excess power absorption market improvements on the installed capacity and tariff of China's green power market. The renewable energy guarantee mechanism is an effective means to promote the consumption of green electricity in China. In addition, in the short term the cost of electricity for users has increased, but in the long term the cost of electricity shows a fluctuating downward trend. This study provides theoretical references for the formulation of clean and low-carbon policy objectives in the power industry and the optimization of market mechanisms. [ABSTRACT FROM AUTHOR]

Speech-centric machine learning systems have revolutionized a number of leading industries ranging from transportation and healthcare to education and defense, fundamentally reshaping how people live, work, and interact with each other. However, recent studies have demonstrated that many speech-centric ML systems may need to be considered more trustworthy for broader deployment. Specifically, concerns over privacy breaches, discriminating performance, and vulnerability to adversarial attacks have all been discovered in ML research fields. In order to address the above challenges and risks, a significant number of efforts have been made to ensure these ML systems are trustworthy, especially private, safe, and fair. In this paper, we conduct the first comprehensive survey on speech-centric trustworthy ML topics related to privacy, safety, and fairness. In addition to serving as a summary report for the research community, we highlight several promising future research directions to inspire researchers who wish to explore further in this area. [ABSTRACT FROM AUTHOR]