Taxonomic studies on the subtribe Aphrastobraconina Ashmead (Hymenoptera: Braconidae: Braconinae) in China

The species of the subtribe Aphrastobraconina Ashmead from China were studied and five species belonging to three genera (Undabracon Quicke, 1986, Curriea Ashmead, 1900 and Aphrastobracon Ashmead, 1896) recognized. Three new species (Undabracon cariniventris sp. n., Aphrastobracon huanjiangensis sp. n. and A. politus sp. n.) are fully described and illustrated. The genus Undabracon (Quicke, 1986), the species Aphrastobracon flavipennis Ashmead and Curriea tibialis (Ashmead) are reported for the first time from China. A key to the species of the genus Undabracon is provided

Highly-Accurate Electricity Load Estimation via Knowledge Aggregation

Mid-term and long-term electric energy demand prediction is essential for the planning and operations of the smart grid system. Mainly in countries where the power system operates in a deregulated environment. Traditional forecasting models fail to incorporate external knowledge while modern data-driven ignore the interpretation of the model, and the load series can be influenced by many complex factors making it difficult to cope with the highly unstable and nonlinear power load series. To address the forecasting problem, we propose a more accurate district level load prediction model Based on domain knowledge and the idea of decomposition and ensemble. Its main idea is three-fold: a) According to the non-stationary characteristics of load time series with obvious cyclicality and periodicity, decompose into series with actual economic meaning and then carry out load analysis and forecast. 2) Kernel Principal Component Analysis(KPCA) is applied to extract the principal components of the weather and calendar rule feature sets to realize data dimensionality reduction. 3) Give full play to the advantages of various models based on the domain knowledge and propose a hybrid model(XASXG) based on Autoregressive Integrated Moving Average model(ARIMA), support vector regression(SVR) and Extreme gradient boosting model(XGBoost). With such designs, it accurately forecasts the electricity demand in spite of their highly unstable characteristic. We compared our method with nine benchmark methods, including classical statistical models as well as state-of-the-art models based on machine learning, on the real time series of monthly electricity demand in four Chinese cities. The empirical study shows that the proposed hybrid model is superior to all competitors in terms of accuracy and prediction bias

Research Progress of the International Carbon Tariff: A Review

Under the constraints of the target peak carbon dioxide emissions and carbon neutrality, how the international carbon tariff can be levied have become an important question for scholars and research institutions all over the world. This paper aimed to comprehensively sort the relevant literature on the Carbon Border Adjustment Mechanism from an economic perspective. Based on defining the concept connotation and extension of carbon tariff, we summarized and determined the price mechanism, institutional mechanism, and coordination mechanism of the carbon tariff, and analyzed the impact of carbon tariff on the economic environment and other fields. Further, this paper makes an international comparison of the existing reasonably operable carbon tariff, points out the focus and direction of the next research, and strives to provide valuable experience and theoretical reference for the innovative practice of building the international Carbon Border Adjustment Mechanism. Keywords: carbon tariff, border tax adjustment, connotation and extension, mechanism design, economic impac