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One of the health hazards of PM2.5 exposure is to induce pulmonary inflammatory responses. In our previous study, we demonstrated that exposing both the immortalized and primary human bronchial epithelial cells to PM2.5 results in a significant upregulation of VEGF production, a typical signaling event to trigger chronic airway inflammation. Further investigations showed that PM2.5 exposure strongly induces ATR/CHK1/p53 cascade activation, leading to the induction of DRAM1-dependent autophagy to mediate VEGF expression by activating Src/STAT3 pathway. In the current study, we further revealed that TIGAR was another transcriptional target of p53 to trigger autophagy and VEGF upregulation in Beas-2B cells after PM2.5 exposure. Furthermore, LKB1, but not ATR and CHK1, played a critical role in mediating p53/TIGAR/autophagy/VEGF pathway activation also by linking to Src/STAT3 signaling cascade. Therefore, on combination of the previous report, we have identified both ATR/CHK1/p53/DRAM1- and LKB1/p53/TIGAR- dependent autophagy in mediating VEGF production in the bronchial epithelial cells under PM2.5 exposure. Moreover, the in vivo study further confirmed VEGF induction in the airway potentially contributed to the inflammatory responses in the pulmonary vascular endothelium of PM2.5-treated rats. Therefore, blocking VEGF expression or autophagy induction might be the valuable strategies to alleviating PM2.5-induced respiratory injuries.

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The surge of carbon dioxide emission plays a dominant role in global warming and climate change, posing an enormous threat to the development of human being and a profound impact on the global ecosystem. Thus, it is essential to analyze the carbon dioxide emission change trend through an accurate prediction to inform reasonable energy-saving emission reduction measures and effectively control the carbon dioxide emission from the source. This paper proposed a hybrid model by combining the random forest and extreme learning machine together for the carbon dioxide emission forecasting in this paper; the random forest is applied for influential factors analysis and the extreme learning machine for the prediction. To improve the performance of the prediction model, moth-flame optimization is adopted to optimize initial weight and bias in extreme learning machine. A case study whose data is derived from Hebei Province, China, during the period 1995-2015 is conducted to verify the effectiveness of the proposed model. Results show that the novel model outperforms the compared parallel models in carbon dioxide emission prediction and has the potential to improve the accuracy of CO emission forecasting.

Carbon price forecasting is significant to both policy makers and market participants. However, since the complex characteristics of carbon prices are affected by many factors, it may be hard for a single prediction model to obtain high-precision results. As a consequence, a new hybrid model based on multi-resolution singular value decomposition (MRSVD) and the extreme learning machine (ELM) optimized by moth–flame optimization (MFO) is proposed for carbon price prediction. First, through the augmented Dickey–Fuller test (ADF), cointegration test and Granger causality test, the external factors of the carbon price, which includes energy and economic factors, are selected in turn. To select the internal factors of the carbon price, the carbon price series are decomposed by MRSVD, and the lags are determined by partial autocorrelation function (PACF). MFO is then used for the optimization of ELM parameters, and external and internal factors are input to the MFO-ELM. Finally, to test the capability and effectiveness of the proposed model, MRSVD-MFO-ELM and its comparison models are used for carbon price forecast in the European Union (EU) and China, respectively. The results show that the performance of the model is significantly better than other models.

Accurate power-load forecasting for the safe and stable operation of a power system is of great significance. However, the random non-stationary electric-load time series which is affected by many factors hinders the improvement of prediction accuracy. In light of this, this paper innovatively combines factor analysis and similar-day thinking into a prediction model for short-term load forecasting. After factor analysis, the latent factors that affect load essentially are extracted from an original 22 influence factors. Then, considering the contribution rate of history load data, partial auto correlation function (PACF) is employed to further analyse the impact effect. In addition, ant colony clustering (ACC) is adopted to excavate the similar days that have common factors with the forecast day. Finally, an extreme learning machine (ELM), whose input weights and bias threshold are optimized by a bat algorithm (BA), hereafter referred as BA-ELM, is established to predict the electric load. A simulation experience using data deriving from Yangquan City shows its effectiveness and applicability, and the result demonstrates that the hybrid model can meet the needs of short-term electric load prediction.

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