Environmental pollution and greenhouse gas emissions are becoming significant environmental issues in China, thus the sustainable development and revival of the country is impossible using the conventional path of encouraging economic growth at the expense of the environment. In response to the global warming, the prices of the traditional energy rise considerably, and a series of environmental problems, China must improve its own mode of economic development. Hundreds of Chinese cities have billions of square meters of buildings and most industry and the annual energy demand is an astronomical figure. China’s government is facing increasing pressure in the low carbon international backdrop, and the low carbon city becomes the inevitable developmental direction of Chinese city in the foreseeable future. The description is first centered on energy structure/energy consumption per unit/urbanized status, and urban energy consumption status, and then concerned with the efforts and measures of Chinese government, to realize the energy saving. Finally, we present the developmental prospect and barriers and the promotion measures related to the low carbon city under the government policy, financial incentives and funding supports, etc.

Smart contracts are at the core of blockchain technology, but the cost of fixing their security vulnerabilities is high, making pre-deployment vulnerability detection crucial. Existing methods rely on fixed rules, which have limitations in accuracy and scalability, and their efficiency decreases with the complexity of the rules. Neural-network-based methods can identify some vulnerabilities but are inefficient in multi-vulnerability scenarios and depend on source code. To address these issues, we propose a multi-vulnerability-based smart contract detection method called RTMS. RTMS takes bytecode as input, disassembles it into opcodes, uses the gas consumed by the contract for data slicing, and extends the length of input opcodes through a layered structure. It employs a weighted binary cross-entropy (BCE) function to handle data imbalance and combines channel-sequence attention mechanisms to extract vulnerability correlation features. By using transfer learning, it reduces training parameters and computational costs. Our RTMS model can detect multiple vulnerabilities simultaneously, enhancing detection accuracy and efficiency. In experiments with 100,000 real contract samples, the model achieved a Jaccard coefficient of 0.9312, a Hamming loss of 0.0211, and an F1 score that improved by about 11 percentage points compared to existing models, demonstrating its superiority and stability.

To optimize the trajectories of the sun tracking system, the dual-axis sun tracking control method for a photovoltaic power system is discussed in this paper. The optimization goal is maximization of an electric energy production in the photovoltaic system considering the tracking system’s consumption. Firstly, the elevation angle and azimuth angle trajectories are described as a nonlinear and bounded optimization problem. Then the predictable trajectories of elevation angle and azimuth angle are derived by using the known formulas and knowledge. However, many factors affect the tracking trajectories, such as machine accuracy, formula error, the partial shading due to the shadow of clouds, trees, birds, dirt, and buildings, etc. Firstly, a novel system structure is introduced to improve the electrical energy consumption. Then, the perturbation and observation (P & O) control method is proposed to improve the tracking trajectories by decreasing the errors of machine and formula by combining with the conventional predicted method. The tracking efficiency of the dual-axis sun tracking system using the predicted method is compared with that using the predicted method combined with the P & O method. The simulation results presented in the paper show that the proposed method has excellent tracking characteristics compared with the fixed title, single-axis tracking, and dual-axis tracking using the single predicted method.