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Vigorously developing new energy sources is an important approach to achieving the carbon peaking and carbon neutrality goals. However, the randomness, volatility, and intermittency of new energy pose severe challenges to the supply-demand balance and stability of the electric power system. Therefore, there is an urgent need to develop diversified flexible resources to guarantee the system’s safe and stable operation. This study summarizes the electricity demand and the power structure development pathways in China under the carbon neutrality goal. Based on this, it analyzes the different flexibility demands of electric power systems with different proportions of wind and solar power generation capacities and elaborates on the characteristics of various types of flexible resources needed for power generation, transmission, load, and storage from four aspects: peak capacity (adequacy), ramping flexibility, stability, and inertia. Drawing from the international experience regarding flexible resource development, we propose the principles for flexible resource development: supply security guarantee, low-carbon development, and economic optimization. Considering the existing problems in the flexible resource development in China, we propose a flexible resource development pathway that aligns with the emission reduction goal of the power sector and the medium- to long-term development trend of the electric power structure of China. Furthermore, key initiatives to ensure the development of flexible resources are proposed from five aspects: power source, power grid, load, energy storage, and market mechanism.

The carbon peaking and carbon neutralization goals as well as the carbon trading system reforms of the European Union (EU) necessitate the synergy of energy conservation, pollution control, and carbon reduction in China to achieve low- and zero-carbon transformation of its manufacturing industry. This study focuses on the impact of the EU Carbon Border Adjustment Mechanism on China’s manufacturing industry, clarifies the concept of a near-zero-carbon manufacturing system, and elaborates on its core content from the major dimensions of key technologies, measurement basis, and market driving force. It also proposes the technical development directions of the near-zero-carbon manufacturing system from the aspects of product manufacturing and power supply and suggests the establishment of a source-grid-load carbon measurement system to clarify carbon emission responsibilities. Moreover, the future development path for China’s carbon market is explored after reviewing the carbon markets both in China and abroad. The practical solutions proposed by the study is expected to provide a basic reference for promoting the high-quality development and low-carbon transformation of China’s manufacturing industry.

RNA terminal phosphorylase B (RTCB) has been shown to play a significant role in multiple physiological processes. However, the specific role of RTCB in the mouse colon remains unclear. In this study, we employ a conditional knockout mouse model to investigate the effects of RTCB depletion on the colon and the potential molecular mechanisms. We assess the efficiency and phenotype of Rtcb knockout using PCR, western blot analysis, histological staining, and immunohistochemistry. Compared with the control mice, the Rtcb-knockout mice exhibit compromised colonic barrier integrity and prominent inflammatory cell infiltration. In the colonic tissues of Rtcb-knockout mice, the protein levels of TNF-α, IL-8, and p-p65 are increased, whereas the levels of IKKβ and IκBα are decreased. Moreover, the level of GSK3β is increased, whereas the levels of Wnt3a, β-catenin, and LGR5 are decreased. Collectively, our findings unveil a close association between RTCB and colonic tissue homeostasis and demonstrate that RTCB deficiency can lead to dysregulation of both the NF-κB and Wnt/β-catenin signaling pathways in colonic cells.

A complete electricity market plays a decisive role in the allocation of electricity resources and is key to ensuring active supply-demand interactions between market players and promoting the consumption of renewable energies. Currently, a targeted,comprehensive analysis on factors that affect renewable energy consumption is urgently required. Focusing on trading mechanisms that can promote renewable energy consumption at a full time scale, this study summarizes the structures and mechanisms of typical electricity markets abroad and sorts out the key factors that can promote renewable energy consumption. Based on this, the study explores the current structures and mechanisms of China’s electricity market and examines the challenges faced during the electricitymarket construction. Moreover, the research progress of China’s electricity market for high-proportion renewable energy consumption is reviewed from the aspects of market mechanism, trading mode, and new consumption pattern. Furthermore, we propose that key measures should be taken in terms of multiple-market collaborative cooperation, linking of medium- and long-term spot trading,ancillary services market construction, and establishment of a demand-side response mechanism.

Building a new power system that adapts to large-scale high-proportion new energy sources and supports the achievement of carbon peaking and carbon neutrality goals is an overall reform measure for the energy and power sectors. Transparent power grid is an advanced form of Internet Plus Smart Energy and is crucial for supporting energy transformation and developing new power systems. This paper puts forward a concept of transparent power grid energy ecosystem, sorts out the main business requirements, goals, vision, and basic characteristics of a transparent power grid, and devises a development blueprint for the transparent power grid. A key scientific and technical system of the transparent power grid is elaborated from two perspectives: incremental scientific and technical elements as well as theories and scientific methods involved, and the perception layer, network layer, platform layer, and application layer technologies required by the key technologies and basic equipment of the transparent power grid are discussed. The research suggests that, with the “transparent power grid new infrastructure” as the core, the low-carbon transformation of energies should be promoted to build a safe and efficient modern energy system; it is necessary to develop small and micro smart sensors, software systems connected to the national industrial Internet, smart equipment, and data-driven value-added power services. Special scientific and technical research projects should be conducted to form the key technology and equipment systems of the transparent power grid. This paper highlights the key role of the transparent power grid in realizing the optimal allocation of resources, and can provide a basic reference for the research on market-driven power energy ecosystems.

The accurate positioning of target is an important link in robot technology. Based on machine learning algorithm, this study firstly analyzed the location positioning principle of binocular vision of robot, then extracted features of the target using speeded-up robust features (SURF) method, positioned the location using Back Propagation Neural Networks (BPNN) method, and tested the method through experiments. The experimental results showed that the feature extraction of SURF method was fast, about 0.2 s, and was less affected by noise. It was found from the positioning results that the output position of the BPNN method was basically consistent with the actual position, and errors in X, Y and Z directions were very small, which could meet the positioning needs of the robot. The experimental results verify the effectiveness of machine learning method and provide some theoretical support for its further promotion and application in practice.

Epoxy resin, characterized by prominent mechanical and electric‐insulation properties, is the preferred material for packaging power electronic devices. Unfortunately, the efficient recycling and reuse of epoxy materials with thermally cross‐linked molecular structures has become a daunting challenge. Here, we propose an economical and operable recycling strategy to regenerate waste epoxy resin into a high‐performance material. Different particle size of waste epoxy micro‐spheres (100–600 μm) with core‐shell structure is obtained through simple mechanical crushing and boron nitride surface treatment. By using smattering epoxy monomer as an adhesive, an eco‐friendly composite material with a "brick‐wall structure" can be formed. The continuous boron nitride pathway with efficient thermal conductivity endows eco‐friendly composite materials with a preeminent thermal conductivity of 3.71 W m−1 K−1 at a low content of 8.5 vol% h‐BN, superior to pure epoxy resin (0.21 W m−1 K−1). The composite, after secondary recycling and reuse, still maintains a thermal conductivity of 2.12 W m−1 K−1 and has mechanical and insulation properties comparable to the new epoxy resin (energy storage modulus of 2326.3 MPa and breakdown strength of 40.18 kV mm−1). This strategy expands the sustainable application prospects of thermosetting polymers, offering extremely high economic and environmental value. [ABSTRACT FROM AUTHOR]

Icing on transmission lines may cause damage to tower components and even lead to structural failure. Aiming at the lack of research on predicting mechanical characteristic parameters of weak components of transmission towers, and the cumbersome steps of building a finite element model (FEM), the study of prediction for mechanical characteristic parameters of weak components of towers based on a finite element simulation and machine learning is proposed. Firstly, a 110 kV transmission tower in a heavily iced area is taken as an example to establish its FEM. The locations of the weak components are analyzed, and the accuracy of FEM is verified. Secondly, meteorological and terrain parameters are considered as input parameters of the prediction model. The axial stresses and nodal displacements of four weak components are selected as output parameters. The FEM of the 110 kV transmission tower is used to obtain input and output datasets. Thirdly, five machine learning algorithms are considered to establish the prediction models for mechanical characteristic parameters of weak components, and the optimal prediction model is obtained. Finally, the accuracy of the prediction method is verified through an actual tower collapse case. The results show that ACO-BPNN is the optimal model that can accurately and quickly predict the mechanical characteristic parameters of the weak components of the transmission tower. This study can provide an early warning for the failure prediction of transmission towers in heavily iced areas, thus providing an important reference for their safe operation and maintenance. [ABSTRACT FROM AUTHOR]