Pd-SnO2 composite nanoceramics have been prepared from SnO2 and Pd nanoparticles through traditional pressing and sintering. Their responses to CO at room temperature are found to depend greatly on the content of Pd. For those samples with 1.0 and 5.0 mol% Pd, their resistance increases dramatically upon being exposed to CO in air; while for samples of 0.2 mol% Pd, their resistance decreases greatly upon being exposed to CO in air, and extraordinary room-temperature CO sensing capabilities, including high sensitivities around 15, short response time of 20 s and recovery time of 60 s for 100 ppm CO in air, a high selectivity against H2, have been observed for them. X-ray photoelectron spectroscopy analyses showed that Pd2+ was formed in samples of 1 mol% Pd, while both Pd2+ and Pd4+ were formed in samples of 0.2 mol% Pd. It is proposed that for Pd-SnO2 composite nanoceramics, Pd2+ is responsible for CO-induced increase while Pd4+ is responsible for CO-induced decrease in resistance.

Machine Learning algorithms and Neural Networks are widely applied to many different areas such as stock market prediction, face recognition and population analysis. This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market. It is a type of deep neural network which is optimized by Q Learning. To make the DQN adapt to financial market, we first discretize the action space which is defined as the weight of portfolio in different assets so that portfolio management becomes a problem that Deep Q-Network can solve. Next, we combine the Convolutional Neural Network and dueling Q-net to enhance the recognition ability of the algorithm. Experimentally, we chose five lowrelevant American stocks to test the model. The result demonstrates that the DQN based strategy outperforms the ten other traditional strategies. The profit of DQN algorithm is 30% more than the profit of other strategies. Moreover, the Sharpe ratio associated with Max Drawdown demonstrates that the risk of policy made with DQN is the lowest.

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