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Electrochemical conversion of CO2 and water to valuable chemicals and fuels is one of the promising alternatives to replace fossil fuel-based processes in realizing a carbon-neutral cycle. For practical application of such technologies, suppressing hydrogen evolution reaction and facilitating the activation of stable CO2 molecules still remain major challenges. Furthermore, high production selectivity toward high-value chemicals such as ethylene, ethanol, and even n-propanol is also not easy task to achieve. To settle these challenges, deeper understanding on underlying basis of reactions such as how intermediate binding affinities can be engineered at catalyst surfaces need to be discussed. In this review, we briefly outline recent strategies to modulate the binding energies of key intermediates for CO2 reduction reactions, based on theoretical insights from density functional theory calculation studies. In addition, important design principles of catalysts and electrolytes are also provided, which would contribute to the development of highly active catalysts for CO2 electroreduction.

Emotion recognition has become a popular topic of interest, especially in the field of human computer interaction. Previous works involve unimodal analysis of emotion, while recent efforts focus on multi-modal emotion recognition from vision and speech. In this paper, we propose a new method of learning about the hidden representations between just speech and text data using convolutional attention networks. Compared to the shallow model which employs simple concatenation of feature vectors, the proposed attention model performs much better in classifying emotion from speech and text data contained in the CMU-MOSEI dataset., Comment: Inaccurate scientific facts listed in document

Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requires laboratory tests that require resources typically lacking in a breakout emergency. In this study, we propose new quantitative methods utilizing machine learning and deep learning to correctly classify host species given raw DNA sequence data of the influenza virus, and provide probabilities for each classification. The best deep learning models achieve top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%, on a dataset of 11 host species classes.

A new drilling and blasting method is discussed which involves drilling of charge holes radially in advance on the wall of a pilot tunnel. Three-dimensional dynamic analysis for the representative single blast hole of the new and existing methods was carried out to compare the relative decreasing effects of the vibration on the ground surface. Results show that ground vibration directly above the tunnel is less with the bidirectional drilling and blasting method than it is with the existing blasting method. Ground response was also found to be influenced by the direction of the blast holes. It is concluded that ground vibration can be reduced by controlling the direction of blast holes according to the location of the ground structures which may be influenced., A new drilling and blasting method is discussed which involves drilling of charge holes radially in advance on the wall of a pilot tunnel. Three-dimensional dynamic analysis for the representative single blast hole of the new and existing methods was carried out to compare the relative decreasing effects of the vibration on the ground surface. Results show that ground vibration directly above the tunnel is less with the bidirectional drilling and blasting method than it is with the existing blasting method. Ground response was also found to be influenced by the direction of the blast holes. It is concluded that ground vibration can be reduced by controlling the direction of blast holes according to the location of the ground structures which may be influenced.