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Xiong, Shaofeng,Yin, Zhoulan,Zhou, Yuanjin,Peng, Xianzhong,Yan, Wenbin,Liu, Zhixiong,Zhang, Xiangyu Korean Chemical Society 2013 Bulletin of the Korean Chemical Society Vol.34 No.10
Dual-frequency ultrasound assisted photocatalysis (DUAP) method was proposed to degrade a stable organic model effluent, cresol red (CR), using the prepared $Fe^{3+}$-doped $TiO_2$ with active carbon fiber loading ($Fe^{3+}-TiO_2/ACF$) as photocatalyst. The influence of key factors, including Fe doping amount and power density of dual-frequency ultrasounds (20/40 kHz), on the degradation efficiency was investigated. The degradation efficiency rises to 98.7% in 60 min accompanied by the color removal of CR liquid samples from yellow to colorless transparent at optimal conditions. A synergy index of 1.40 was yielded by comparison with single ultrasound assisted photocatalysis (SUAP) and the photocatalysis without ultrasound assisted (UV/$TiO_2$), indicating that a clear synergistic effect exists for the DUAP process. Obvious enhancement of degradation efficiency for the DUAP process should be attributed to production of large amount of free radicals by strong cavitational effects of dual ultrasounds.
Shaofeng Xiong,Zhoulan Yin,Yuanjin Zhou,Xianzhong Peng,Wenbin Yan,Zhixiong Liu,Xiangyu Zhang 대한화학회 2013 Bulletin of the Korean Chemical Society Vol.34 No.10
Dual-frequency ultrasound assisted photocatalysis (DUAP) method was proposed to degrade a stable organic model effluent, cresol red (CR), using the prepared Fe3+-doped TiO2 with active carbon fiber loading (Fe3+- TiO2/ACF) as photocatalyst. The influence of key factors, including Fe doping amount and power density of dual-frequency ultrasounds (20/40 kHz), on the degradation efficiency was investigated. The degradation efficiency rises to 98.7% in 60 min accompanied by the color removal of CR liquid samples from yellow to colorless transparent at optimal conditions. A synergy index of 1.40 was yielded by comparison with single ultrasound assisted photocatalysis (SUAP) and the photocatalysis without ultrasound assisted (UV/TiO2), indicating that a clear synergistic effect exists for the DUAP process. Obvious enhancement of degradation efficiency for the DUAP process should be attributed to production of large amount of free radicals by strong cavitational effects of dual ultrasounds.
Identification and Functional Characterization of R3 MYB Transcription Factor Genes in Soybean
Shucai Wang,Jinsong Pang,Hongwei Xun,Zhibing Zhang,Yunxiao Zhou,Xueyan Qian,Yingshan Dong,Xianzhong Feng,Bao Liu 한국식물학회 2018 Journal of Plant Biology Vol.61 No.2
In Arabidopsis, trichome formation is regulatedby a MYB-bHLH-WD40 (MBW) transcriptional activatorcomplex, which can activate the expression of GLABRA2(GL2) and R3 MYB genes. GL2 is required for trichomeformation, whereas R3 MYBs inhibit trichome formation byblocking the formation of the MBW complex, thus inhibitingthe expression of GL2. By using the amino acid sequence of theArabidopsis R3 MYB transcription factor TRICHOMELESS1(TCL1) to BLAST the soybean (Glycine max) protein database,we found that there are a total of six R3 MYB genes insoybean, namely Glycine max TRICHOMELESS1 through 6(GmTCL1-GmTCL6). By generating transgenic plants, wefound that trichome formation in soybean plants overexpressingeach of the GmTCLs remained largely unchanged, and theexpression of putative GL1 and GL2 genes in the transgenicplants was unaffected. However, all the GmTCLs interactedwith GLABRA3 (GL3) in transfected Arabidopsis protoplasts,expression each of the GmTCLs in Arabidopsis inhibitedtrichome formation, and the expression levels of GL1 andGL2 were greatly reduced in the Arabidopsis transgenicplants. Moreover, phenotypic complementary analysis showedthat GmTCL1 is functionally equivalent to TCL1. Takentogether, these results suggest that GmTCLs are functionalR3 MYBs, however, they do not regulate trichome formationin soybean.
Research on Action Strategies and Simulations of DRL and MCTS-based Intelligent Round Game
Yuxiang Sun,Bo Yuan,Yongliang Zhang,Wanwen Zheng,Qingfeng Xia,Bojian Tang,Xianzhong Zhou 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.9
The reinforcement learning problem of complex action control in multiplayer online battlefield games has brought considerable interest in the deep learning field. This problem involves more complex states and action spaces than traditional confrontation games, making it difficult to search for any strategy with human-level performance. This paper presents a deep reinforcement learning model to solve this problem from the perspective of game simulations and algorithm implementation. A reverse reinforcement-learning model based on high-level player training data is established to support downstream algorithms. With less training data, the proposed model is converged quicker, and more consistent with the action strategies of high-level players’ decision-making. Then an intelligent deduction algorithm based on DDQN is developed to achieve a better generalization ability under the guidance of a given reward function. At the game simulation level, this paper constructs Monte Carlo Tree SearchIntelligent Decision Model for turn-based antagonistic deduction games to generate next-step actions. Furthermore, a prototype game simulator that combines offline with online functions is implemented to verify the performance of proposed model and algorithm. The experiments show that our proposed approach not only has a better reference value to the antagonistic environment using incomplete information, but also accurate and effective in predicting the return value. Moreover, our work provides a theoretical validation platform and testbed for related research on game AI for deductive games.