Shape resonances of electron-molecule system formed in the low-energy electron attachment to four low-lying conformers of serine （serine 1, serine 2, serine 3, and serine 4） in gas phase are investigated using the q...Shape resonances of electron-molecule system formed in the low-energy electron attachment to four low-lying conformers of serine （serine 1, serine 2, serine 3, and serine 4） in gas phase are investigated using the quantum scattering method with the non-empirical model potentials in single-center expansion. In the attachment energy range of 0-10 eV, three shape resonances for serine 1, serine 2, and serine 4 and four shape resonances for serine 3 are predicted. The one-dimensional potential energy curves of the temporary negative ions of electron-serine are calculated to explore the correlations between the shape resonance and the bond cleavage. The bond-cleavage selectivity of the different resonant states for a certain conformer is demonstrated, and the recent experimental results about the dissociative electron attachment to serine are interpreted on the basis of present calculations.展开更多
The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the cro...The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.展开更多
Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In ord...Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.展开更多
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a cla...A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.展开更多
Silicene, a new allotrope of silicon in a twodimensional honeycomb structure, has attracted intensive research interest due to its novel physical and chemical properties. Unlike carbon atoms in graphene, silicon atoms...Silicene, a new allotrope of silicon in a twodimensional honeycomb structure, has attracted intensive research interest due to its novel physical and chemical properties. Unlike carbon atoms in graphene, silicon atoms prefer to adopt sp2/sp3-hybridized state in silicene,enhancing chemical activity on the surface and allowing tunable electronic states by chemical functionalization. The silicene monolayers epitaxially grown on Ag(111) surfaces demonstrate various reconstructions with different electronic structures. In this article, the structure, phonon modes, electronic properties, and chemical properties of silicene are reviewed based on theoretical and experimental works in recent years.展开更多
It is generally accepted that taxa exhibit genetic variation in phenotypic plasticity, but many questions remain unan- swered about how divergent plastic responses evolve under dissimilar ecological conditions. Hormon...It is generally accepted that taxa exhibit genetic variation in phenotypic plasticity, but many questions remain unan- swered about how divergent plastic responses evolve under dissimilar ecological conditions. Hormones are signaling molecules that act as proximate mediators of phenotype expression by regulating a variety of cellular, physiological, and behavioral re- sponses. Hormones not only change cellular and physiological states but also influence gene expression directly or indirectly, thereby linking environmental conditions to phenotypic development. Studying how hormonal pathways respond to environ- mental variation and how those responses differ between individuals, populations, and species can expand our understanding of the evolution of phenotypic plasticity. Here, we explore the ways that the study of hormone signaling is providing new insights into the underlying proximate bases for individual, population or species variation in plasticity. Using several studies as exem- plars, we examine how a ＇norm of reaction＇ approach can be used in investigations of hormone-mediated plasticity to inform the following： 1） how environmental cues affect the component hormones, receptors and enzymes that comprise any endocrine sig- naling pathway, 2） how genetic and epigenetic variation in endocrine-associated genes can generate variation in plasticity among these diverse components, and 3） how phenotypes mediated by the same hormone can be coupled and decoupled via independent plastic responses of signaling components across target tissues. Future studies that apply approaches such as reaction norms and network modeling to questions concerning how hormones link environmental stimuli to ecologically-relevant phenotypic re- sponses should help unravel how phenotypic plasticity evolves展开更多
基金This work is supported by the National Natural Science Foundation of China （No.21303212 and No.21573209）, the Ministry of Science and Technology of China （No.2013CB834602）.
文摘Shape resonances of electron-molecule system formed in the low-energy electron attachment to four low-lying conformers of serine （serine 1, serine 2, serine 3, and serine 4） in gas phase are investigated using the quantum scattering method with the non-empirical model potentials in single-center expansion. In the attachment energy range of 0-10 eV, three shape resonances for serine 1, serine 2, and serine 4 and four shape resonances for serine 3 are predicted. The one-dimensional potential energy curves of the temporary negative ions of electron-serine are calculated to explore the correlations between the shape resonance and the bond cleavage. The bond-cleavage selectivity of the different resonant states for a certain conformer is demonstrated, and the recent experimental results about the dissociative electron attachment to serine are interpreted on the basis of present calculations.
基金supported by Guangxi key Laboratory Fund of Embedded Technology and Intelligent System under Grant No. 2018B-1the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034+1 种基金the National Natural Science Foundation of China under Grant No. 61771488, No. 61671473 and No. 61631020in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory
文摘The high-frequency(HF) communication is one of essential communication methods for military and emergency application. However, the selection of communication frequency channel is always a difficult problem as the crowded spectrum, the time-varying channels, and the malicious intelligent jamming. The existing frequency hopping, automatic link establishment and some new anti-jamming technologies can not completely solve the above problems. In this article, we adopt deep reinforcement learning to solve this intractable challenge. First, the combination of the spectrum state and the channel gain state is defined as the complex environmental state, and the Markov characteristic of defined state is analyzed and proved. Then, considering that the spectrum state and channel gain state are heterogeneous information, a new deep Q network(DQN) framework is designed, which contains multiple sub-networks to process different kinds of information. Finally, aiming to improve the learning speed and efficiency, the optimization targets of corresponding sub-networks are reasonably designed, and a heterogeneous information fusion deep reinforcement learning(HIF-DRL) algorithm is designed for the specific frequency selection. Simulation results show that the proposed algorithm performs well in channel prediction, jamming avoidance and frequency channel selection.
基金Supported by the National Basic Research Program of China （2013CB733600）, the National Natural Science Foundation of China （21176073）, the Doctoral Fund of Ministry of Education of China （20090074110005）, the Program for New Century Excellent Talents in University （NCET-09-0346）, Shu Guang Project （09SG29） and the Fundamental Research Funds for the Central Universities.
文摘Fault diagnosis and monitoring are very important for complex chemical process. There are numerous methods that have been studied in this field, in which the effective visualization method is still challenging. In order to get a better visualization effect, a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed. FDA can reduce the dimension of the data in terms of maximizing the separability of the classes. After feature extraction by FDA, SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states. Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.
基金Project(2013CB733605)supported by the National Basic Research Program of ChinaProject(21176073)supported by the National Natural Science Foundation of ChinaProject supported by the Fundamental Research Funds for the Central Universities,China
文摘A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
基金supported by the Australian Research Council(ARC)through Discovery Project(DP 140102581)LIEF Grants(LE100100081 and LE110100099)
文摘Silicene, a new allotrope of silicon in a twodimensional honeycomb structure, has attracted intensive research interest due to its novel physical and chemical properties. Unlike carbon atoms in graphene, silicon atoms prefer to adopt sp2/sp3-hybridized state in silicene,enhancing chemical activity on the surface and allowing tunable electronic states by chemical functionalization. The silicene monolayers epitaxially grown on Ag(111) surfaces demonstrate various reconstructions with different electronic structures. In this article, the structure, phonon modes, electronic properties, and chemical properties of silicene are reviewed based on theoretical and experimental works in recent years.
文摘It is generally accepted that taxa exhibit genetic variation in phenotypic plasticity, but many questions remain unan- swered about how divergent plastic responses evolve under dissimilar ecological conditions. Hormones are signaling molecules that act as proximate mediators of phenotype expression by regulating a variety of cellular, physiological, and behavioral re- sponses. Hormones not only change cellular and physiological states but also influence gene expression directly or indirectly, thereby linking environmental conditions to phenotypic development. Studying how hormonal pathways respond to environ- mental variation and how those responses differ between individuals, populations, and species can expand our understanding of the evolution of phenotypic plasticity. Here, we explore the ways that the study of hormone signaling is providing new insights into the underlying proximate bases for individual, population or species variation in plasticity. Using several studies as exem- plars, we examine how a ＇norm of reaction＇ approach can be used in investigations of hormone-mediated plasticity to inform the following： 1） how environmental cues affect the component hormones, receptors and enzymes that comprise any endocrine sig- naling pathway, 2） how genetic and epigenetic variation in endocrine-associated genes can generate variation in plasticity among these diverse components, and 3） how phenotypes mediated by the same hormone can be coupled and decoupled via independent plastic responses of signaling components across target tissues. Future studies that apply approaches such as reaction norms and network modeling to questions concerning how hormones link environmental stimuli to ecologically-relevant phenotypic re- sponses should help unravel how phenotypic plasticity evolves
