Bioremediation is an environmentally friendly method of reducing heavy metal concentration and toxicity. A chromium-reducing bacterial strain, isolated from the vicinity of an electroplate factory, was identified as Ochrobactrum sp. YC211. The efficiency and capacity per time of Ochrobactrum sp. YC211 for hexavalent chromium (Cr(VI)) removal under anaerobic conditions were superior to those under aerobic conditions. An acceptable removal efficiency (96.5 ± 0.6%) corresponding to 30.2 ± 0.8 mg-Cr (g-dry cell weight-h)(-1) was achieved by Ochrobactrum sp. YC211 at 300 mg L(-1) Cr(VI). A temperature of 30°C and pH 7 were the optimal parameters for Cr(VI) removal. By examining reactivated cells, permeabilized cells, and cell-free extract, we determined that Cr(VI) removal by Ochrobactrum sp. YC211 under anaerobic conditions mainly occurred in the soluble fraction of the cell and can be regarded as an enzymatic reaction. The results also indicated that an Ochrobactrum sp. YC211 microbial fuel cell (MFC) with an anaerobic anode was considerably superior to that with an aerobic anode in bioelectricity generation and Cr(VI) removal. The maximum power density and Cr(VI) removal efficiency of the MFC were 445 ± 3.2 mW m(-2) and 97.2 ± 0.3%, respectively. Additionally, the effects of coexisting ions (Cu(2+), Zn(2+), Ni(2+), SO4(2-), and Cl(-)) in the anolyte on the MFC performance and Cr(VI) removal were nonsignificant (P > 0.05). To our knowledge, this is the first report to compare Cr(VI) removal by different cells and MFC types under aerobic and anaerobic conditions.

In aspect of the natural language processing field, previous studies have generally analyzed sound signals and provided related responses. However, in various conversation scenarios, image information is still vital. Without the image information, misunderstanding may occur, and lead to wrong responses. In order to address this problem, this study proposes a recurrent neural network (RNNs) based multi-sensor context-aware chatbot technology. The proposed chatbot model incorporates image information with sound signals and gives appropriate responses to the user. In order to improve the performance of the proposed model, the long short-term memory (LSTM) structure is replaced by gated recurrent unit (GRU). Moreover, a VGG16 model is also chosen for a feature extractor for the image information. The experimental results demonstrate that the integrative technology of sound and image information, which are obtained by the image sensor and sound sensor in a companion robot, is helpful for the chatbot model proposed in this study. The feasibility of the proposed technology was also confirmed in the experiment.

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The appearance of Hall sign change in perovskite SrRuO3 thin films at Curie temperature was confirmed from our fabricated samples and the result was simulated by our proposed theoretical model. In particular, our simulation results are consistent with experimental results mainly due to the introduction of an impurity band in a two-band model. We found the other important factors in our theory responsible for observed consistency Hall measurements are the itinerant carrier density and its intrinsic carrier type. Eventually the theory possibly interprets the mechanism of Hall sign change.