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As the global environmental pollution problem has received more attention, low-carbon development has become mainstream, and replacing fossil-fuel energy with new energy has become a general trend. Green finance, an important measure for promoting low-carbon development, is critical to the development of the non-hydro renewable energy industry. Using China's provincial panel data from 2008 to 2021, we conducted an empirical analysis to explore the impact of green finance on the development of the non-hydro renewable energy industry. We found the following: (1) Nationally, green finance promoted the development of the non-hydro renewable energy industry. (2) The mechanism test results show that green finance promoted the development of the non-hydro renewable energy industry by improving the level of scientific and technological research as well as reducing the corporate financing cost. (3) Regionally, green finance could significantly promote the development of the non-hydro renewable energy industry in energy-rich regions, while the impact was not significant in energy-poor regions. (4) Green finance significantly promoted the development of the non-hydro renewable energy industry in the inland region, while the impact was not significant in the coastal region. (5) Green finance promoted the development of wind power generation more than it did photovoltaic generation. Based on the findings, the non-hydro renewable energy industry in various regions should be developed according to local conditions, giving full play to the promotional role of green finance. [ABSTRACT FROM AUTHOR]

This paper studies the class-agnostic counting problem, which aims to count objects regardless of their class, and relies only on a limited number of exemplar objects. Existing methods usually extract visual features from query and exemplar images, compute similarity between them using convolution operations, and finally use this information to estimate object counts. However, these approaches often overlook the scale information of the exemplar objects, leading to lower counting accuracy for objects with multi-scale characteristics. Additionally, convolution operations are local linear matching processes that may result in a loss of semantic information, which can limit the performance of the counting algorithm. To address these issues, we devise a new scale-aware transformer-based feature fusion module that integrates visual and scale information of exemplar objects and models similarity between samples and queries using cross-attention. Finally, we propose an object counting algorithm based on a feature extraction backbone, a feature fusion module and a density map regression head, called SATCount. Our experiments on the FSC-147 and the CARPK demonstrate that our model outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• Integrate solid oxide fuel cell with supercritical carbon dioxide Brayton cycle and CO 2 capture. • 60.96% of net electrical efficiency and 0.342 kg/kWh reducing CO 2 emission are achievable. • Develop detailed off-design models of components and system. • Investigate three key operating parameters' effects on system performance. • Propose system control strategy to investigate part-load system performance. Solid oxide fuel cell (SOFC) and supercritical carbon dioxide (sCO 2) Brayton cycle are both clean and efficient technologies for energy supply. The present study proposes an innovative SOFC-based system combined with sCO 2 Brayton cycle and CO 2 capture system. The bottoming sCO 2 cycle can improve efficiency of the integrated system by recovering waste heat from the combustion chamber while using oxy-fuel combustor to achieve low-energy CO 2 capture. And the quasi-steady model of the system is developed to evaluate the system design and off-design performances. The simulated results indicate that the net electrical efficiency and reducing CO 2 emission of the system are 60.96% and 0.342 kg/kWh, respectively. The system parametric analysis indicates that the reasonable recirculation ratio, fuel utilization and fuel flow rate are prerequisites for the safe and efficient operation of the integrated system. Based on the analysis, a system control strategy is proposed to satisfy the demand for wide-range load adjustment (30%-100%), while the efficiency of the integrated system is decreased from 60.96% to 50.33% during the load reduction process. [ABSTRACT FROM AUTHOR]

Highlights • The an unexpected antiferromagnetism is investigated by the exchange bias in the defect-induced ferromagnetic SiC. • A single domain model is used to investigate the exchange bias. • Thus, for the first time, this study shows that an antiferromagnetism can also be induced in SiC by defect. Abstract In this paper, the observation of exchange bias in defective ferromagnetic SiC is presented. Two typical features of exchange bias are clearly observed: the shift of magnetic hysteresis upon field cooling and the training effect. A core-shell morphology particle model is employed to quantitatively analyze the exchange bias field dependence on the cooling field. Based on the model, the calculated anisotropy constant is 2.5 × 105 erg/cm3. The training effect can be described by the Kolmogorov-Avrami model, which is used for the classical exchange bias system. The appearance of exchange bias is the signature of the existence of an interface between ferromagnetic and antiferromagnetic layers or cores/shells. [ABSTRACT FROM AUTHOR]