This paper aims at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset -FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos.

Pyrolysis of lignocellulose biomass to produce various fuels and chemicals has gained increasing interest in recent decades. An in-depth understanding of the biomass pyrolysis reaction mechanisms is essential for the advancement of pyrolysis techniques. Quantum chemistry (QC) modeling is a powerful approach for the pyrolysis mechanism investigation at the atomic/molecular level. Despite a short history of only about 2 decades, its application to the biomass pyrolysis mechanism exploration has been well-developed, along with the fast advances of supercomputer and computational codes in the new century. This review addresses the recent progress on the pyrolysis mechanism of the three basic biomass components (cellulose, hemicellulose, and lignin) by QC modeling. On the basis of the QC modeling results reported in the literature, the current review critically summarizes the key developments about the pyrolysis chemistry of biomass by focusing on their microscopic elementary reactions, the formation routes of typical products, bimolecular interactions within or between biomass components, and catalytic effects of various catalysts. Notably, there are great gaps between the theoretical models employed in QC modeling and the natural biomass substance in the pyrolysis process. Therefore, a brief analysis of the challenges and future research perspectives is provided for the biomass pyrolysis mechanism research.

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Over seventy congeners of polychlorinated naphthalenes (PCNs) in sewage sludge of 8 urban wastewater treatment plants (WWTPs) in Beijing were analyzed by isotope dilution, and high resolution gas chromatography/high resolution mass spectrometry (HRGC/HRMS) method. The total PCN concentrations determined in the samples range from 1.48 to 28.21 ng/g dw (dry weight) with TEQ concentrations of 0.11-2.45 pg/g dw. These levels were lower than those found in other regions. DiCNs and TrCNs were the most dominant homologues of PCNs. The similar congener profiles in all the samples suggest the similarity in certain sources. Contamination from industrial input might be the most significant source of PCNs in the sludges in this study, and thermal processes such as waste incineration and coal combustion may be another source of the PCNs contamination.sewage sludge, polychlorinated naphthalenes (PCNs), high resolution gas chromatography/high resolution mass spectrometry (HRGC/