No abstract

Condensable particulate matter is the predominant contributor to the total particulate matter emissions of coal-fired power plants. In the studied ultralow-emission coal-fired power plant, the emission concentrations of condensable and filterable particulate matter in the stack were 1.6 mg/Nm3 and 7.9 mg/Nm3. The organic fraction in condensable particulate matter was mainly composed of alkanes, esters, and other complex organic compounds. The organic fraction comprised 54% of the total concentrations of condensable particulate matter tested at the stack. The organic fraction in condensable particulate matter might contribute significantly to the organic carbon in atmospheric PM2.5. SO4 2– accounted for the highest concentrations in the inorganic fraction of condensable particulate matter. Na and Ca were predominant metal elements in the inorganic fraction. The inorganic fraction of condensable particulate matter mainly contributed to the water-soluble ions in atmospheric PM2.5. The total particulate matter elimination effect of the air pollution control devices used in the studied plant was good. The removal efficiency of the electrostatic precipitator for condensable particulate matter was much higher than those of the wet flue gas desulfurization system and the wet electrostatic precipitator. The wet flue gas desulfurization system performed well in eliminating the inorganic fraction of condensable particulate matter. Further studies should be conducted on the pollutant control effects of the wet electrostatic precipitator. It is important to study the emission characteristics, chemical compositions, and control methods for condensable particulate matter from coal-fired power plants.

Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments.

Developing drugs that can effectively block STAT3 activation may serve as one of the most promising strategy for cancer treatment. Currently, there is no putative STAT3 inhibitor that can be safely and effectively used in clinic. In the present study, we investigated the potential of dihydroartemisinin (DHA) as a putative STAT3 inhibitor and its antitumor activities in head and neck squamous cell carcinoma (HNSCC). The inhibitory effects of DHA on STAT3 activation along with its underlying mechanisms were studied in HNSCC cells. The antitumor effects of DHA against HNSCC cells were explored both in vitro and in vivo. An investigation on cooperative effects of DHA with cisplatin in killing HNSCC cells was also implemented. DHA exhibited remarkable and specific inhibitory effects on STAT3 activation via selectively blocking Jak2/STAT3 signaling. Besides, DHA significantly inhibited HNSCC growth both in vitro and in vivo possibly through induction of apoptosis and attenuation of cell migration. DHA also synergized with cisplatin in tumor inhibition in HNSCC cells. Our findings demonstrate that DHA is a putative STAT3 inhibitor that may represent a new and effective drug for cancer treatment and therapeutic sensitization in HNSCC patients.