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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
    Abstract1657)   HTML144)    PDF (1026KB)(1744)      

    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
    Abstract1949)   HTML92)    PDF (1090KB)(1191)      

    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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    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 0-x.  
    Abstract99)   HTML14)    PDF (365KB)(1181)      
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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
    Abstract1019)   HTML424)    PDF (1503KB)(959)      

    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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    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
    Abstract405)   HTML49)    PDF (942KB)(906)      

    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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    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
    Abstract768)   HTML52)    PDF (973KB)(890)      

    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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    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
    Abstract1023)   HTML74)    PDF (842KB)(774)      

    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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    Research progress in Chinese named entity recognition in the financial field
    Qiurong XU, Peng ZHU, Yifeng LUO, Qiwen DONG
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 1-13.   DOI: 10.3969/j.issn.1000-5641.2021.05.001
    Abstract1257)   HTML666)    PDF (821KB)(674)      

    As one of the basic components of natural language processing, named entity recognition (NER) has been an active area of research both domestically in China and abroad. With the rapid development of financial applications, Chinese NER has improved over time and been applied successfully throughout the financial industry. This paper provides a summary of the current state of research and future development trends for Chinese NER methods in the financial field. Firstly, the paper introduces concepts related to NER and the characteristics of Chinese NER in the financial field. Then, based on the development process, the paper provides an overview of detailed characteristics and typical models for dictionary and rule-based methods, statistical machine learning-based methods, and deep learning-based methods. Next, the paper summarizes public data collection tools, evaluation methods, and applications of Chinese NER in the financial industry. Finally, the paper explores current challenges and future development trends.

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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
    Abstract1040)   HTML36)    PDF (1081KB)(662)      

    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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    New records of liverwort species in Zhejiang Province
    Ruiping SHI, Shichen XING, Xia TANG, Shuwen TU, Youfang WANG, Jian WANG
    Journal of East China Normal University(Natural Science)    2022, 2022 (1): 62-69.   DOI: 10.3969/j.issn.1000-5641.2022.01.008
    Abstract433)   HTML45)    PDF (783KB)(632)      

    To better understand the diversity of bryophytes in East China, bryophytes were systematically investigated and collected in the Huangshan - Tianmu Mountain range and Xianxia - Wuyi Mountain range in the region. In the course of field investigation, eighteen new records of liverwort species were found in Zhejiang Province, belonging to 10 families and 13 genera, respectively. Notably, Hattoria is a new genus record for Zhejiang Province. In this paper, the habitats, geographical distributions, and main identifying features of these new records are provided. Moreover, illustrations of rare and endemic liverwort species in China are presented. These new findings further enrich the bryophyte flora of Zhejiang Province and provide new basic information about the flora of the province.

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    YOLO-S: A new lightweight helmet wearing detection model
    Hongcheng ZHAO, Xiuxia TIAN, Zesen YANG, Wanrong BAI
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 134-145.   DOI: 10.3969/j.issn.1000-5641.2021.05.012
    Abstract761)   HTML68)    PDF (1182KB)(623)      

    Traditional worker helmet wearing detection models commonly used at construction sites suffer from long processing times and high hardware requirements; the limited number of available training data sets for complex and changing environments, moreover, contributes to poor model robustness. In this paper, we propose a lightweight helmet wearing detection model—named YOLO-S—to address these challenges. First, for the case of unbalanced data set categories, a hybrid scene data augmentation method is used to balance the categories and improve the robustness of the model for complex construction environments; the original YOLOv5s backbone network is changed to MobileNetV2, which reduces the network computational complexity. Second, the model is compressed, and a scaling factor is introduced in the BN layer for sparse training. The importance of each channel is judged, redundant channels are pruned, and the volume of model inference calculations is further reduced; these changes help increase the overall model detection speed. Finally, YOLO-S is achieved by fine-tuning the auxiliary model for knowledge distillation. The experimental results show that the recall rate of YOLO-S is increased by 1.9% compared with YOLOv5s, the mAP of YOLO-S is increased by 1.4% compared with YOLOv5s, the model parameter is compressed to 1/3 of YOLOv5s, the model volume is compressed to 1/4 of YOLOv5s, FLOPs are compressed to 1/3 of YOLOv5s, the reasoning speed is faster than other models, and the portability is higher.

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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
    Abstract589)   HTML30)    PDF (1754KB)(536)      

    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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    Optimization of LSM-tree storage systems based on non-volatile memory
    Yang YU, Huiqi HU, Xuan ZHOU
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 37-47.   DOI: 10.3969/j.issn.1000-5641.2021.05.004
    Abstract610)   HTML66)    PDF (1368KB)(521)      

    With the advent of the big data era, the financial industry has been generating increasing volumn of data, exerting pressure on database systems. LevelDB is a key-value database, developed by Google, based on the LSM-tree architecture. It offers fast writing and a small footprint, and is widely used in the financial industry. In this paper, we propose a design method for the L0layer, based on non-volatile memory and machine learning, with the aim of addressing the shortcomings of the LSM-tree architecture, including write pause, write amplification, and unfriendly reading. The proposed solution can slow down or even solve the aforementioned problems; the experimental results demonstrate that the design can achieve better read and write performance.

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    Research progress of microplastics and attached organisms in marine environment
    Daoji LI, Xuri DONG
    Journal of East China Normal University(Natural Science)    2022, 2022 (3): 1-7.   DOI: 10.3969/j.issn.1000-5641.2022.03.001
    Abstract822)   HTML909)    PDF (475KB)(514)      

    In recent years, white pollution caused by waste plastics has attracted widespread attention. Microplastics, which are smaller than 5 mm, are widely distributed in the marine environment. The organisms attached to microplastic surfaces include potential pathogenic bacteria that are harmful to marine life and even human health, as well as plastic-degrading bacteria that can reduce their pollution. Microplastics are difficult to degrade, so they can exist in the aquatic environment for a long time, and the microorganisms attached to their surface can also live stably. In addition, microplastics may pass through the food chain to organisms at higher nutritional levels, and may be eaten by fish and affect fish growth. This paper reviews the distribution of microplastics in the ocean and the potential effects of harmful substances contained or attached to the microplastic surface on organisms. The ecological effects of pathogenic microorganisms attached to the surface of microplastics and plastic decomposition microorganisms, as well as the potential of microplastic transmission to high nutritional levels through the food chain were discussed. The ecological risk of microplastic distribution and surface-attached organisms was analyzed. Furtherly, it is still necessary to understand the impact of plastic waste and microplastics on the marine ecosystem, so as to fully understand the ecological effects of marine microplastics and their attachments, and provide a scientific basis for marine plastic pollution control.

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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
    Abstract652)   HTML26)    PDF (5085KB)(494)      

    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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    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
    Abstract394)   HTML14)    PDF (1463KB)(485)      

    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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    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 0-III.  
    Abstract252)   HTML169)    PDF (279KB)(479)      
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    Natural products: A bridge between new targets and novel pesticide discovery
    Zhengqi FANG, Shuanhu GAO, Haibing HE
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 21-30.   DOI: 10.3969/j.issn.1000-5641.2023.01.003
    Abstract829)   HTML34)    PDF (3235KB)(461)      

    Pesticides are important tools to control crop diseases and pest hazards, guaranteeing the crop harvest. Natural products and their derivatives are major sources of novel pesticides and play indispensable roles in various fields, such as insecticide, fungicide, plant growth regulation, immune regulation and so on. In recent years, numerous fields of biotechnology have made great progress, like genomics, proteomics and structural biology. And thus, the identification of pesticide targets based on natural products and the creation of novel pesticide molecules based on target structures developed rapidly. The concept, rational design, received more attention in pesticide creation. In this article, the discovery of active natural products based on existed targets or novel targets verifying by natural products were demonstrated by several cases, and the subsequent progress in the development of new pesticides were also discussed. The cases explained the important role of natural products in bridging new targets and novel pesticides.

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    Electrocatalytic coupling of CO2 with organic compounds to value-added chemicals
    Huan WANG, Jiaxing LU
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 140-148.   DOI: 10.3969/j.issn.1000-5641.2023.01.014
    Abstract388)   HTML13)    PDF (877KB)(446)      

    The efficient fixation and utilization of CO2 under mild conditions is one of the key components of green carbon science. The electrocatalytic coupling of CO2 and organic compounds can produce value-added chemicals, which is beneficial to sustainable development. In this review, we summarize the current methods of synthesizing carboxylic acids, organic carbonates, carbamates, and other chemicals via electrocatalytic CO2 coupling with organic compounds. We also present the latest research progress and opportunities in this field, such as asymmetric electrocarboxylation to construct chiral molecules, electrochemical ring-opening carboxylation, electrochemical N-methylation, electrocarboxylation with non-sacrificial anodes, and paired electrosynthesis.

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    Electrodeposition performance of a copper-based catalyst for the electroreduction of CO2
    Meng’en CHU, Chunjun CHEN, Haihong WU, Mingyuan HE, Buxing HAN
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 129-139.   DOI: 10.3969/j.issn.1000-5641.2023.01.013
    Abstract552)   HTML23)    PDF (4857KB)(442)      

    To improve the catalytic performance of copper-based catalysts in the electroreduction of CO2, nitrotriacetic acid (NTA) was used as an additive to prepare copper-based catalysts having a three-dimensional structure by applying electrodeposition. The prepared catalysts exhibited excellent selectivity and activity for the electroreduction of CO2 to multi-carbon (C2+) products. At –1.26 V vs. RHE, the faradaic efficiency of C2H4 and C2+ products over the Cu-0.5/CP electrode reached 44.0% and 61.6%, respectively, and the total current density reached 12.3 mA·cm–2. In addition, Pd- and Zn-based catalysts were prepared by employing electrodeposition; the results showed that their selectivity for CO was significantly improved, proving that NTA has a certain universality in the preparation of electrocatalysts by using electrodeposition.

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