科研成果

Particulate nitrate (pNO3–) has often been found to be the major component of fine particles in urban air-sheds in China, the United States, and Europe during winter haze episodes in recent years. However, there is a lack of knowledge regarding the experimentally determined contribution of different chemical pathways to the formation of pNO3–. Here, for the first time, we combine ground and tall-tower observations to quantify the chemical formation of pNO3– using observationally constrained model approach based on direct observations of OH and N2O5 for the urban air-shed. We find that the gas-phase oxidation pathway (OH+NO2) during the daytime is the dominant channel over the nocturnal uptake of N2O5 during pollution episodes, with percentages of 74% in urban areas and 76% in suburban areas. This is quite different from previous studies in some regions of the US, in which the uptake of N2O5 was concluded to account for a larger contribution in winter. These results indicate that the driving factor of nitrate pollution in Beijing and different regions of the US is different, as are the mitigation strategies for particulate nitrate.

A low-pressure reactor (LPR) was developed for the measurement of ambient organic peroxy (RO2) radicals with the use of the laser-induced fluorescence (LIF) instrument. The reactor converts all the ROx (= RO2 + HO2 + RO + OH) radicals into HO2 radicals. It can conduct different measurement modes through altering the reagent gases, achieving the speciated measurement of RO2 and RO2# (RO2 radicals derived from the long-chain alkane, alkene and aromatic hydrocarbon). An example of field measurement results was given, with a maximum concentration of 1.88 x 10(8) molecule/cm(3) for RO2 and 1.18 x 10(8) molecule/cm(3) for RO2#. Also, this instrument quantifies the local ozone production rates directly, which can help to deduce the regional ozone control strategy from an experimental perspective. The new device can serve as a potent tool for both the exploration of frontier chemistry and the diagnosis of the control strategies. (C) 2020 Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences. Published by Elsevier B.V. All rights reserved.

[pls Right Click on picture and Save As...to download the picture]Based on  research of basic academic procedures and activities on ancient China Studies, we design a DH cyberinfrastructure conceptual model for ancient China studies(DHC4ACS). The framework is designed to be a digital environment based on open network protocols includes three modules : ·Digitization Module. This module is designed to convert physical objects into digital objects, optical characters recognize, proofread, and finally form an authoritative full-text database with error rate not more than 0.03%. In this module, individuals and different institutes like libraries, museums, archives, galleries could participate in. The authoritative full-text database provide open APIs and rules for any academic use.·Datafication & applications Module. This module is designed to create a large amount of authoritative humanistic datasets, annotate unstructured full texts to transform them into structured ones, and develop various computational applications or digital tools based on datasets and structured full texts. In this module, humanists, IT experts, librarians and field experts work cooperatively or separately, all datasets and structured full texts databases provide open APIs and rules for any academic use.·Digital Ecosystem Module. This module is designed to provide an open digital environment where brings people, information, and computational tools together. The ecosystem runs not only through knowledge lifetime from digitization to datafication, but also through scholars’ academic lifetime from information retrieve, computational tools development to academic achievements production. Humanists could conduct research easily in such digital environment: if they want to retrieve information, they just type the keywords and get it smoothly; if they want to do some further analysis, they can reuse existing datasets and computational tools; while if there is no available data and suitable tools, they could create dataset by themselves according to their requirements or develop new digital tools along with digital technology experts.

To turn the DH Cyberinfrastructure Conceptual Model for Ancient China Studies into reality, we picked ancient Chinese literatures like rare books, rubbings, paintings, calligraphic works, maps as experimental objects. We planned to establish a completed practical procedure for above DHC4ACS.

Figure 1  shows humanists’ academic activities and procedures in traditional Chinese studies steps as follows: (a) Forefathers recorded faithfully what had happened around them, what they thought, talked about and wrote down originally. Some of their records nowadays we call “historical materials”, and others we call philosophy, fiction, prose, drama, poetry and so on. (b) With passage of time, some records have been passing down, while others went missing or were buried with nobles as burial objects. (b.1) For the burial objects underground, archeologists dug them out, classified those objects , recognized the ancient characters on them, sorted them chronologically，compiled and published them as primary literatures. (b.2) For the records passing down: (b.2.1) Some deliberate or unintentional mistakes were made on them for unknown reasons. Different versions were produced and remained to be distinguished and corrected by younger generation. (b.2.2) Authors died, their thoughts and analects became hard to understand. Many activities conducted by younger generations: ·they tried to translate, interpret, comment, make notes in their own way based on their own knowledge, and accordingly some of them formed their academic circles or factions; ·some of them didn’t know which one was right, so they attempted to argue, debate, correct, or just collected their forefathers’ interpretations, comments, notes, and compiled them into a book; ·others of them proofread, annotated, compiled, rewrote, or excerpted according to their own requirements and purposes. (b.2.3) During such procedures, some people recorded faithfully what had happened around them, what they thought, talked about and wrote down originally, and also some of the literatures went missing or were buried underground. (c) With time going on, above academic activities carries out repetitively while outputs are different.

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset.

Lead-based organic-inorganic hybrid perovskite materials are widely used in optoelectronic devices due to their excellent photophysical properties. However, the main issues which hinder its commercialization are the toxicity caused by lead and the intrinsic instability of the material. Recently, many lead-free halide materials with good intrinsic stability have been reported, among which bismuth-based halide materials have attracted extensive research due to their structure and promising optoelectronic properties. In this review, bismuth-based materials are divided into binary BiX3 (X = I, Br, Cl), ternary A(a)Bi(b)X(a)(+3)(b) (A = Cs, Rb, MA, Ag, etc.), and quaternary A(2)AgBiX(6) (A = Cs, Rb, MA, etc.) according to its elemental composition. The structure and optoelectronic properties of bismuth-based halide materials, which may be helpful for the development of bismuth-based halide materials and lead-free perovskites in the future, are summarized and highlighted.

Automatic transistor sizing is a challenging problem in circuit design due to the large design space, complex performance tradeoffs, and fast technology advancements. Although there have been plenty of work on transistor sizing targeting on one circuit, limited research has been done on transferring the knowledge from one circuit to another to reduce the re-design overhead. In this paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning (RL) to transfer the knowledge between different technology nodes and topologies. Moreover, inspired by the simple fact that circuit is a graph, we learn on the circuit topology representation with graph convolutional neural networks (GCN). The GCN-RL agent extracts features of the topology graph whose vertices are transistors, edges are wires. Our learning-based optimization consistently achieves the highest Figures of Merit (FoM) on four different circuits compared with conventional black box optimization methods (Bayesian Optimization, Evolutionary Algorithms), random search and human expert designs. Experiments on transfer learning between five technology nodes and two circuit topologies demonstrate that RL with transfer learning can achieve much higher FoMs than methods without knowledge transfer. Our transferable optimization method makes transistor sizing and design porting more effective and efficient.