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    Trade between Poland and China within the Belt and Road Initiative and 17 + 1 Framework: Opportunity and challenge
    Natalia BORUCZKOWSKA, Yuna DI
    Journal of East China Normal University(Natural Science)    2020, 2020 (S1): 188-192.   DOI: 10.3969/j.issn.1000-5641.202092324
    Online available: 22 January 2021

    Abstract539)   HTML43)    PDF (433KB)(2212)      
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    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 0-x.  
    Abstract114)   HTML14)    PDF (365KB)(1213)      
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    Recognition of classroom learning behaviors based on the fusion of human pose estimation and object detection
    Zejie WANG, Chaomin SHEN, Chun ZHAO, Xinmei LIU, Jie CHEN
    Journal of East China Normal University(Natural Science)    2022, 2022 (2): 55-66.   DOI: 10.3969/j.issn.1000-5641.2022.02.007
    Abstract1814)   HTML153)    PDF (1026KB)(1881)      

    As a result of ongoing advances in artificial intelligence technology, the potential for learning analysis in teaching evaluation and educational data mining is gradually being recognized. In classrooms, artificial intelligence technology can help to enable automated student behavior analysis, so that teachers can effectively and intuitively grasp students’ learning behavior engagement; the technology, moreover, can provide data to support subsequent improvements in learning design and implementation of teaching interventions. The main scope of the research is as follows: Construct a classroom student behavior dataset that provides a basis for subsequent research; Propose a behavior detection method and a set of feasible, high-precision behavior recognition models. Based on the global features of the human posture extracted from the Openpose algorithm and the local features of the interactive objects extracted by the YOLO v3 algorithm, student behavior can be identified and analyzed to help improve recognition accuracy; Improve the model structure, compress and optimize the model, and reduce the consumption of computing power and time. Four behaviors closely related to the state of learning engagement: listening, turning sideways, bowing, and raising hands are recognized. The accuracy of the detection and recognition method on the verification set achieves 95.45%. The recognition speed and accuracy of common behaviors, such as playing with mobile phones and writing, are greatly improved compared to the original model.

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    Comprehensive review on green synthesis of bio-based 2,5-furandicarboxylic acid
    Lei ZHAO, Zelin LI, Bolong LI, Shuchang BIAN, Jianhua WANG, Hailan ZHANG, Chen ZHAO
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 160-169.   DOI: 10.3969/j.issn.1000-5641.2023.01.016
    Abstract2098)   HTML96)    PDF (1090KB)(1306)      

    Bio-based 2,5-furandicarboxylic acid (FDCA) is expected to partially replace petroleum-based terephthalic acid (PTA) for the synthesis of high-performance polymer materials. This review article summarizes the latest achievements on the various synthesis routes of FDCA from 5-hydroxymethylfurfural (HMF), furoic acid, furan, diglycolic acid, hexaric acid, 2,5-dimethylfuran, and 2-methylfuran. In particular, the direct oxidation, heterogeneous thermal catalytic oxidation, photoelectric catalytic oxidation of HMF and furoic acid carboxylation, disproportionation, carbonylation, and other routes to synthesize FDCA are reviewed in detail. Based on the comparative analysis of the advantages and disadvantages of each route, the HMF route and the furoic acid route are considered the most promising candidates for the large-scale production of FDCA. Further exploration and future research should be carried out to improve the catalytic production and separation efficiency of FDCA, simplify the reaction process, and reduce production wastes.

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    Study on the mechanism of methylene blue degradation by TiO2 photocatalyst
    ZHANG Dan, XU Bin, ZHU Pei-juan, LIAN Zheng-hao, ZHAO Ya-ping
    Journal of East China Normal University(Natural Sc    2013, 2013 (5): 35-42.  
    Abstract3888)      PDF (2193KB)(4445)      
    In order to carry out the study, the experiments of photodegradation of methylene blue under different conditions were conducted, using different kinds of free radical scavengers, such as (CH3)3OH, H2O2, KI, NaN3 and C6H4O2. The effects of these free radical scavengers were observed. The concentration of H2O2 generated during the photodegradation of methylene blue by TiO2 was also reported. And the results of these experiments indicated that the main active oxygen substances in the reaction are ·OH, O·-2 and 1O2.
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    Study of electronic structures and the micro-solvation effect of SO3 and HSO3 in atmospheric aerosols
    Jianan CHEN, Zhipeng LI, Yanrong JIANG, Zhubin HU, Haitao SUN, Zhenrong SUN
    Journal of East China Normal University(Natural Science)    2022, 2022 (1): 31-42.   DOI: 10.3969/j.issn.1000-5641.2022.01.005
    Abstract433)   HTML49)    PDF (942KB)(1036)      

    In this study, we used negative ion photoelectron spectroscopy (NIPES) combined with quantum chemical calculation to explore the electronic structures, micro-solvation effect, and stabilization mechanism of two compounds, SO3 and HSO3, that are readily abundant in the atmosphere. Vertical detachment energies of (3.31 ± 0.02) and (3.91 ± 0.02) eV and adiabatic detachment energies of (3.02 ± 0.05) and (3.56 ± 0.05) eV were measured for SO3 and HSO3, respectively. These results are reproduceable when using a nuclear ensemble approach and Dyson orbitals in the calculation. The typical density of states method, however, cannot demonstrate the nuclear vibration effect, ionization probability, and orbital relaxation effect during the ionization process. We studied the micro-solvation effect of HSO3·(H2O)n (n = 0 ~ 5) and found that system stability was enhanced by an increase in the surrounding water molecules, whereby electrostatic interaction played a dominant role and the induction effect made an increasingly important contribution. We believe this work will help improve the modeling of atmospheric sulfate aerosols and provide a scientific basis for the effective control of haze formation.

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    Structure-conductive property relationship of polypyrrole
    YU Bo, XU Xue-cheng
    Journal of East China Normal University(Natural Sc    2014, 2014 (4): 77-87.  
    Abstract2376)      PDF (1700KB)(4483)      
    Polypyrrole was synthesized by oxidative chemical polymerization. Samples presenting different conductivities were obtained by adjusting the preparing temperature. The polypyrrole samples have been investigated by means of four probe method, Solid state 13C NMR spectrum, XRD, FTIR spectroscopy, Raman spectrum and XPS, respectively. The experimental result suggested the fact that the conductivity of polypyrrole samples gradually decreases as the preparation temperature increases. The analysis indicates it is because the changing of internal structure follows the varying of external preparation conditions. The commonly structure of polypyrrole is a linear chain of monomers bonded by α-α carbons at a low preparation temperature. Through such a structure polypyrrole can form both a relatively planar configuration and a relatively planar conformation. This sort of structure provides the sample with a preferable regularity, a longer conjugated chain length and a higher conductivity. However, the ratio of α-α linkage drops as the preparation temperature increases, by which the structure of molecular is damaged, the degree of order declines. The carrier mobility of polypyrrole, as well as the conductive property, became lower.
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    Survey of early time series classification methods
    Mengchen YANG, Xudong CHEN, Peng CAI, Lyu NI
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 115-133.   DOI: 10.3969/j.issn.1000-5641.2021.05.011
    Abstract1078)   HTML425)    PDF (1503KB)(1057)      

    With the increasing popularity of sensors, time-series data have attracted significant attention. Early time series classification (ETSC) aims to classify time-series data with the highest level of accuracy and smallest possible size. ETSC, in particular, plays a critical role in fintech. First, this paper summarizes the common classifiers for time-series data and reviews the current research progress on minimum prediction length-based, shapelet-based, and model-based ETSC frameworks. There are pivotal technologies, advantages, and disadvantages of the representative ETSC methods in separate frameworks. Next, we review public time-series datasets in fintech and commonly used performance evaluation criteria. Lastly, we explore future research directions pertinent to ETSC.

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    Preparation and characterization of Ag@Au bimetallic nanoparticles
    Tianchen ZHAO, Xiaolei ZHANG, Shitao LOU
    Journal of East China Normal University(Natural Science)    2022, 2022 (1): 43-51.   DOI: 10.3969/j.issn.1000-5641.2022.01.006
    Abstract814)   HTML54)    PDF (973KB)(920)      

    Ag nanoparticles were first prepared using a seed-based thermal synthetic procedure. The monometallic particles were then transformed into bimetallic particles via a galvanic replacement reaction. A transmission electron microscope (TEM), scanning transmission electron microscope (STEM), and absorption spectrum were subsequently used for characterization. By controlling the amount of seed added, the ultrasonic exposure, and the centrifugal time, we can effectively tune the size of the particles and the localized surface plasmon resonance peak positions. The TDBC film can be wrapped on the surface of the metallic nanostructures by a ligand exchange reaction to achieve strong coupling between surface plasmon and molecular excitons.

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    Application of Cu-based catalysts in the electroreduction of carbon dioxide
    Jing TANG, Zining ZHANG, Xiang ZHENG
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 149-159.   DOI: 10.3969/j.issn.1000-5641.2023.01.015
    Abstract1167)   HTML39)    PDF (1081KB)(731)      

    To achieve the national strategy of carbon neutralization, the electroreduction of carbon dioxide into usable reagents via renewable energy has caused widespread concern in the scientific community. Cu-based electrocatalysts can reduce carbon dioxide to high value-added multi carbon products, but the catalytic mechanism still needs to be studied to improve its selectivity and efficiency. Depending on the state of the Cu, Cu-based catalysts can be divided into Cu alloy/composite catalysts, single-atom, oriented crystalline, and oxidized Cu-based catalysts. This paper introduced the common preparation methods, structural characteristics, effect of electro catalytic reduction of carbon dioxide, and possible catalytic mechanism of the four types of Cu-based catalysts mentioned above.

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    The development and application of a physical-biogeochemical coupling model based on FVCOM
    SHI Shenyang, GE Jianzhong, CHEN Jianzhong, ZHENG Xiaoqin, DING Pingxing
    Journal of East China Normal University(Natural Science)    2020, 2020 (3): 55-67.   DOI: 10.3969/j.issn.1000-5641.201941008
    Abstract704)   HTML162)    PDF (1395KB)(729)      
    By combining the hydrodynamic model FVCOM with the biological model ERSEM, based on FABM, this paper develops a new physical-biogeochemical model: FVCOM-FABM-ERSEM. The combined model is suitable for application to coastal areas and is one of the most comprehensive ecosystem models for the lower trophic levels of the marine food-web. Using the combined model, a one-dimensional vertical (1DV) model and a three-dimensional Changjiang Estuary model were established. The results of the 1DV model were consistent with observation data from the European L4 Station. This paper also simulates the physical and biogeochemical processes of Changjiang Estuary from 2013 to 2016 with the 3D Changjiang Estuary model. The distribution of temperature, salinity, nitrate, phosphate, and chlorophyll-a levels were all found to be consistent with observation data from cruises and MODIS data in the spring when algal blooms occur. The characteristics of the front dynamics of Changjiang Estuary were well represented. The relationship between salinity, turbidity, nutrients, and chlorophyll around the plume front was determined through modeling, indicating a significant co-occurrence effect along the front of physical and biological processes.
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    Joint extraction of entities and relations for domain knowledge graph
    Rui FU, Jianyu LI, Jiahui WANG, Kun YUE, Kuang HU
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 24-36.   DOI: 10.3969/j.issn.1000-5641.2021.05.003
    Abstract1080)   HTML75)    PDF (842KB)(816)      

    Extraction of entities and relationships from text data is used to construct and update domain knowledge graphs. In this paper, we propose a method to jointly extract entities and relations by incorporating the concept of active learning; the proposed method addresses problems related to the overlap of vertical domain data and the lack of labeled samples in financial technology domain text data using the traditional approach. First, we select informative samples incrementally as training data sets. Next, we transform the exercise of joint extraction of entities and relations into a sequence labeling problem by labelling the main entities. Finally, we fulfill the joint extraction using the improved BERT-BiGRU-CRF model for construction of a knowledge graph, and thus facilitate financial analysis, investment, and transaction operations based on domain knowledge, thereby reducing investment risks. Experimental results with finance text data shows the effectiveness of our proposed method and verifies that the method can be successfully used to construct financial knowledge graphs.

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    A preliminary study on phytoplankton composition at Bashayer II marine oil terminal, Red Sea, Sudan
    Osama SAAD S., Amjed AHMED G.
    Journal of East China Normal University(Natural Science)    2020, 2020 (S1): 89-93.   DOI: 10.3969/j.issn.1000-5641.202092209
    Abstract386)   HTML15)    PDF (375KB)(580)      
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    JIANG Dong-Xing, FU Xiao-Long, YUAN Fang, WU Hai-Yan, LIU Qi-Xin
    Journal of East China Normal University(Natural Sc    2015, 2015 (S1): 119-125.   DOI: 10.3969/j.issn.1000-5641.2015.z1.020
    Abstract1755)      PDF (984KB)(4686)      
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    Catalytic asymmetric synthesis of chiral heterocyclic compounds with CO2 as the C1 synthon
    Zhipeng ZHAO, Ying SUN, Xiaotong GAO, Feng ZHOU
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 31-40.   DOI: 10.3969/j.issn.1000-5641.2023.01.004
    Abstract432)   HTML14)    PDF (1463KB)(518)      

    As the main component of greenhouse gases, CO2 represents an inexpensive and readily available renewable C1 synthon. In the past few decades, great efforts have been made toward the development of chemical processes that use CO2 as a promising fossil fuel alternative for C1 feedstocks for the production of industrially attractive chemicals. This could provide access to materials of commercial interest from an abundant, nontoxic, renewable, and low-cost carbon source, thus offering interesting opportunities for the chemical industry, organic synthesis, and so on. Considering the importance of chiral heterocycles in organic synthesis and drug development, the development of highly stereoselective and efficient catalytic asymmetric reactions using CO2 as a C1 synthon for these chiral heterocycles has received considerable attention. Successful examples for chiral lactones, carbonates, and carbamates have already been demonstrated. In this paper, we summarize the recent advances in this field.

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    Prediction of remaining useful life of aeroengines based on the Transformer with multi-feature fusion
    Yilin MA, Huiling TAO, Qiwen DONG, Ye WANG
    Journal of East China Normal University(Natural Science)    2022, 2022 (5): 219-232.   DOI: 10.3969/j.issn.1000-5641.2022.05.018
    Abstract681)   HTML31)    PDF (1754KB)(627)      

    As the core components of aircraft, engines play a vital role during flight. Accurate prediction of the remaining useful life of the aeroengine can help prognostics and health management, thus preventing major accidents and saving maintenance costs. In view of the lack of consideration of different time steps and the relationship between different sensors and operating conditions in existing methods, a remaining useful life prediction method based on the Transformer was proposed, which fuses multi-feature outputs from different encoder layers. This method selects two input data with different time steps, analyzes the relationship between the sensors using permutation entropy, and extracts features independently from the operating condition data. The experimental results on the public aeroengine dataset CMAPSS (Commercial Modular Aero-Propulsion System Simulation) show that the proposed method is superior to other advanced remaining useful life prediction methods.

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    Methodology for building a business-oriented data asset system: Feature hierarchies
    REN Yinzi
    Journal of East China Normal University(Natural Science)    2020, 2020 (5): 137-145.   DOI: 10.3969/j.issn.1000-5641.202091009
    Abstract1142)   HTML86)    PDF (1226KB)(930)      
    This paper proposes a new method for building a business-oriented data asset system. Data assets are one of the core components of the data middle office concept and require business-oriented asset mapping to realize the transformation from the asset to the broader business value. The proposed methodology uses feature hierarchies and describes how to organize data assets based on a tree structure, with the root as an object, the branch as a category, and a leaf/flower as a tag. There are energetic connections between the various object trees and the growth of these trees is supplied by the business. The instantiation of feature hierarchies can be realized in two modes: overall planning and local interception. The asset results are divided into two major parts, namely asset inventories and asset entities; these can be quickly configured as data service results for business use through service management tools in order to realize the value of the underlying data assets.
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    Review of zeolite-confined subnanometric cluster catalysts
    Yue MA, Hao XU, Yueming LIU, Kun ZHANG, Peng WU, Mingyuan HE
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 82-94.   DOI: 10.3969/j.issn.1000-5641.2022.00.009
    Abstract682)   HTML26)    PDF (5085KB)(532)      

    The design of efficient and stable supported metal catalysts to prevent metal species from sintering into large nanoparticles under harsh preparation and reaction conditions is key for various important processes, including the conversion of C1 resources and dehydrogenation of low carbon alkanes to C2 and C3 olefins. Zeolites with uniform subnano micropores and various three-dimensional crystalline structures have been proven as ideal supports for preparing highly efficient and stable metal catalysts via encapsulating subnanometric metal clusters within their pores, cages, and channels. Interactions between metal clusters and the zeolite skeleton can regulate their geometric and electronic structure. The development of zeolite-confined subnanometric cluster catalysts aims to take advantage of this joint confinement effect and induce synergy between guest metal species and active sites in host zeolite frameworks. This can further improve the catalytic activity of resultant composite catalysts, for applications in multiple catalytic reaction processes . In this review, typical preparation methods of zeolite-confined subnanometric clusters and their catalytic applications in selective hydrogenation of CO2 and alkynes, hydrogen generation by formic acid decomposition and ammonia borane hydrolysis, and propane dehydrogenation to propene are discussed.

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    Review of deep learning in cognitive radio
    Bo LIU, Xiaodong BAI, Gengxin ZHANG, Jun SHEN, Jidong XIE, Laiding ZHAO, Tao HONG
    Journal of East China Normal University(Natural Science)    2021, 2021 (1): 36-52.   DOI: 10.3969/j.issn.1000-5641.201922017
    Abstract930)   HTML77)    PDF (1163KB)(647)      

    The development of wireless communication has made spectrum resources increasingly scarce. Existing spectrum resources, however, are not currently used in an efficient way. This contradiction can usually be attributed to the problem created by static spectrum allocation strategies. Cognitive radio (CR) is widely regarded as a feasible solution to solve the problem of static spectrum allocation. In recent years, deep learning, an emerging field of machine learning, has contributed to a number of notable research and application achievements. It has become one of the driving technologies behind artificial intelligence. In this paper, we investigated the application of deep learning to CR; this includes the development of cognitive radio and deep learning as well as the usage of deep learning models in key technologies for CR (such as spectrum prediction, spectrum environment sensing, signal analysis, etc.). Lastly, we summarize and discuss conclusions from this review.

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    Preparation and stability study of lyophilized lentiviral vector
    Hongwei SHEN, Minghao LI, Nan XU, Jiaqi SHAO, Jing WANG, Lei YU
    Journal of East China Normal University(Natural Science)    2021, 2021 (3): 114-127.   DOI: 10.3969/j.issn.1000-5641.2021.03.012
    Abstract965)   HTML57)    PDF (1003KB)(612)      

    In this paper, we studied a new preparation technique for lyophilized lentiviral vectors. We determined the optimal formulation for a freeze-drying protective agent by screening and optimizing potential candidates. The candidates were evaluated on the basis of physical and chemical properties of the freeze-drying process, including appearance, excipient, color, and solubility. The optimal formulation was determined to be trehalose 0.30 g/mL, L-histidine 0.31 mg/mL, L- alanine 0.178 mg/mL, CaCl2 0.020 mg/mL, and MgSO4 0.015 mg/mL. With this technique, the prepared lyophilized lentiviral vector had good appearance, low residual water content, intact structure, and good re-dispersibility. The biological titer of the lentiviral vector reached 9.37 × 107 IU/mL, and the recovery rate of the titer was 50.15%. We also conducted research on potential influencing factors, including a high temperature accelerated experiment and repeated freeze-thaw stability experiments. These experiments showed that the lyophilizing technology can be used for the preparation of lentiviral vector solids and can be effectively used to improve the storage of lentiviral vectors under different temperature conditions, exposure to repeated freeze-thaw cycles, and tolerance to adverse environments (e.g., high temperatures).

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