Top Read Articles

    Published in last 1 year |  In last 2 years |  In last 3 years |  All
    Please wait a minute...
    For Selected: Toggle Thumbnails
    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
    Abstract1409)   HTML77)    PDF (1090KB)(853)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract1195)   HTML111)    PDF (1026KB)(1367)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract1059)   HTML656)    PDF (821KB)(487)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    Braided vector algebra $ V(R',R) $
    Hongmei HU
    Journal of East China Normal University(Natural Science)    2021, 2021 (6): 33-37.   DOI: 10.3969/j.issn.1000-5641.2021.06.004
    Abstract987)   HTML49)    PDF (472KB)(107)      

    Braided vector algebras are an important class of Hopf algebras in braided tensor categories. In this paper, it is shown that braided vector algebras are isomorphic to quantum vector spaces as associative algebras; hence, the algebraic structure of braided vector algebras and three equalities of the pair $ (R',R)$ are recovered from representations of quantized enveloping algebras $ U_q(\mathfrak g)$ .

    Reference | Related Articles | Metrics
    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
    Abstract844)   HTML418)    PDF (1503KB)(766)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract783)   HTML50)    PDF (1003KB)(423)      

    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).

    Table and Figures | Reference | Related Articles | Metrics
    Survey of few-shot instance segmentation methods
    Xueming ZHOU, Dingjiang HUANG
    Journal of East China Normal University(Natural Science)    2022, 2022 (5): 136-146.   DOI: 10.3969/j.issn.1000-5641.2022.05.012
    Abstract739)   HTML21)    PDF (968KB)(286)      

    Instance segmentation is an important task in computer vision. In recent years, the development of meta- and few-shot learning has promoted the combination of computer vision learning tasks, which has overcome the bottleneck of detection and classification with regard to objects that are difficult to manually label and those with high labeling costs. Although great progress has been made with few-shot semantic segmentation and object detection, instance segmentation based on few-shot learning has not become a research hotspot until very recently. Beginning with an overview of few-shot instance segmentation, existing approaches are divided into categories of anchor-based and anchor-free algorithms. The architectures and primary technologies behind those approaches are respectively discussed, and common datasets and evaluation indices are described. Additionally, advantages and disadvantages of algorithm performance are analyzed, and future development directions and challenges are presented.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract718)   HTML28)    PDF (1081KB)(441)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    A fuzzer for query processing functionality of OLAP databases
    Zhaokun XIANG, Ting CHEN, Qian SU, Rong ZHANG
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 74-83.   DOI: 10.3969/j.issn.1000-5641.2021.05.007
    Abstract710)   HTML64)    PDF (831KB)(251)      

    Query processing, including optimization and execution, is one of the most critical functionalities of modern relational database management systems (DBMS). The complexity of query processing functionalities, however, leads to high testing costs. It hinders rapid iterations during the development process and can lead to severe errors when deployed in production environments. In this paper, we propose a tool to better serve the testing and evaluation of DBMS query processing functionalities; the tool uses a fuzzing approach to generate random data that is highly associated with primary keys and generates valid complex analytical queries. The tool constructs constrained optimization problems to efficiently compute the exact cardinalities of operators in queries and furnish the results. We launched small-scale testing of our method on different versions of TiDB and demonstrated that the tool can effectively detect bugs in different versions of TiDB.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract707)   HTML70)    PDF (842KB)(573)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    Data augmentation technology for named entity recognition
    Xiaoqin MA, Xiaohe GUO, Yufeng XUE, Lin YANG, Yuanzhe CHEN
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 14-23.   DOI: 10.3969/j.issn.1000-5641.2021.05.002
    Abstract698)   HTML425)    PDF (689KB)(333)      

    A named entity recognition task is as a task that involves extracting instances of a named entity from continuous natural language text. Named entity recognition plays an important role in information extraction and is closely related to other information extraction tasks. In recent years, deep learning methods have been widely used in named entity recognition tasks; the methods, in fact, have achieved a good performance level. The most common named entity recognition models use sequence tagging, which relies on the availability of a high quality annotation corpus. However, the annotation cost of sequence data is high; this leads to the use of small training sets and, in turn, seriously limits the final performance of named entity recognition models. To enlarge the size of training sets for named entity recognition without increasing the associated labor cost, this paper proposes a data augmentation method for named entity recognition based on EDA, distant supervision, and bootstrap. Using experiments on the FIND-2019 dataset, this paper illustrates that the proposed data augmentation techniques and combinations thereof can significantly improve the overall performance of named entity recognition models.

    Table and Figures | Reference | Related Articles | Metrics
    Electricity theft detection based on t-LeNet and time series classification
    Xiaoqin MA, Xiaohui XUE, Hongjiao LUO, Tongyu LIU, Peisen YUAN
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 104-114.   DOI: 10.3969/j.issn.1000-5641.2021.05.010
    Abstract669)   HTML78)    PDF (855KB)(290)      

    Electricity theft results in significant losses in both electric energy and economic benefits for electric power enterprises. This paper proposes a method to detect electricity theft based on t-LeNet and time series classification. First, a user’s power consumption time series data is obtained, and down-sampling is used to generate a training set. A t-LeNet neural network can then be used to train and predict classification results for determining whether the user exhibits behavior reflective of electricity theft. Lastly, real user power consumption data from the state grid can be used to conduct experiments. The results show that compared with the time series classification method based on Time-CNN (Time Convolutional Neural Network) and MLP (Muti-Layer Perception), the proposed method offers improvements in the comprehensive evaluation index, accuracy rate, and recall rate index. Hence, the proposed method can successfully detect electricity theft.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract659)   HTML28)    PDF (3235KB)(356)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract654)   HTML64)    PDF (1182KB)(512)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract626)   HTML900)    PDF (475KB)(412)      

    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.

    Reference | Related Articles | Metrics
    The Chinese experience at the International Mathematical Olympiad
    Bin XIONG, Peijie JIANG
    Journal of East China Normal University(Natural Science)    2021, 2021 (6): 1-14.   DOI: 10.3969/j.issn.1000-5641.2021.06.001
    Abstract583)   HTML720)    PDF (1371KB)(357)      

    The International Mathematical Olympiad (IMO) is one of the most important and influential global youth intellectual competitions. However, there is little research on how to effectively organize the competition at the national level to help cultivate talent in mathematics, science, and technology. The Mathematical Olympiad originated from a competition to solve mathematical problems. Many outstanding mathematicians and scientists have been prior winners of the IMO and have reaped benefits subsequently to some extent. The Mathematical Olympiad helps to select and train gifted students in mathematics. China’s outstanding historical achievements in the IMO have attracted the attention of the world. Many of China’s students, who exhibited exceptional performance at the IMO, later became outstanding mathematicians, scientists, and technologists. These achievements need to be publicized, and the Chinese experience at the Mathematical Olympiad needs to be summarized and promoted. This article summarizes the history of the IMO and reviews the practices of the IMO in China based on the literature. China uses a number of strategies to ensure outstanding results in the IMO, including: the selection of contestants from existing domestic programs (National High School Mathematics Joint Competition, Chinese Mathematical Olympiad, and National Training Team); a multi-level educational system based on school training; and the accumulation and publication of relevant learning materials. The outbreak of the novel coronavirus has affected the normal proceedings of the IMO, but China has implemented effective countermeasures. There are still some misunderstandings about the Mathematical Olympiad in China. By introducing prior contestants, who have participated in the IMO and made outstanding contributions, China can help the public better appreciate the Mathematical Olympiad. At the same time, the Chinese experience at the IMO is an important reference for other countries in organizing competition training and selecting and nurturing gifted students in mathematics.

    Table and Figures | Reference | Related Articles | Metrics
    The impact of coupling patterns on transport in multilayer networks
    Yaqin HU, Ming TANG
    Journal of East China Normal University(Natural Science)    2021, 2021 (3): 105-113.   DOI: 10.3969/j.issn.1000-5641.2021.03.011
    Abstract571)   HTML70)    PDF (924KB)(122)      

    Multilayer networks can better reflect the structure and characteristics of many systems in the real world. In recent years, multilayer networks have become a focus area for many researchers. Based on the degree-degree correlation of interlayer nodes, we propose an intermediate degree coupling pattern to enhance the traffic capacity of multilayer networks at a low relative cost. In addition, the effectiveness of the intermediate degree coupling pattern is verified using two classic routing strategies, namely shortest path and efficient routing. Compared with the three coupling methods-assortative coupling, disassortative coupling, and random coupling-the intermediate coupling pattern makes the traffic load distribution more uniform on multilayer networks; hence, the traffic capacity of multilayer networks is greatly improved, and the average transport time of packets is effectively reduced. With lower coupling probability, the intermediate coupling pattern can significantly enhance the traffic capacity of a multilayer network when an efficient routing strategy is used. Meanwhile, simulation results show that more uniform network topology results in higher traffic capacity.

    Table and Figures | Reference | Related Articles | Metrics
    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
    Abstract569)   HTML47)    PDF (973KB)(741)      

    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.

    Table and Figures | Reference | Related Articles | Metrics
    Enabling self-attention based multi-feature anomaly detection and classification of network traffic
    Yuting HUANGFU, Liying LI, Haizhou WANG, Fuke SHEN, Tongquan WEI
    Journal of East China Normal University(Natural Science)    2021, 2021 (6): 161-173.   DOI: 10.3969/j.issn.1000-5641.2021.06.016
    Abstract565)   HTML48)    PDF (1174KB)(333)      

    Network traffic anomaly detection based on feature selection has attracted great research interest. Most existing schemes detect anomalies by reducing the dimensionality of traffic data, but ignore the correlation between data features; this results in inefficient detection of anomaly traffic. In order to effectively identify various types of attacks, a model based on a self-attentive mechanism is proposed to learn the correlation between multiple features of network traffic data. Then, a novel multi-feature anomalous traffic detection and classification model is designed, which analyzes the correlation between multiple features of the anomalous traffic data and subsequently identifies anomalous network traffic. Experimental results show that, compared to two benchmark methods, the proposed technique increased the accuracy of anomaly detection and classification by a maximum of 1.65% and reduced the false alarm rate by 1.1%.

    Table and Figures | Reference | Related Articles | Metrics
    Erasure code partition storage based on the CITA blockchain
    Furong YIN, Chengyu ZHU, Bin ZHAO, Zhao ZHANG
    Journal of East China Normal University(Natural Science)    2021, 2021 (5): 48-59.   DOI: 10.3969/j.issn.1000-5641.2021.05.005
    Abstract564)   HTML69)    PDF (1572KB)(346)      

    Blockchain system adopts full replication data storage mechanism, which retains a complete copy of the whole block chain for each node. The scalability of the system is poor. Due to the existence of Byzantine nodes in the blockchain system, the shard scheme used in the traditional distributed system cannot be directly applied in the blockchain system. In this paper, the storage consumption of each block is reduced from O(n) to O(1) by combining erasure code and Byzantine fault-tolerant algorithm, and the scalability of the system is enhanced. This paper proposes a method to partition block data, which can reduce the storage redundancy and affect the query efficiency less. A coding block storage method without network communication is proposed to reduce the system storage and communication overhead. In addition, a dynamic recoding method for entry and exit of blockchain nodes is proposed, which not only ensures the reliability of the system, but also reduces the system recoding overhead. Finally, the system is implemented on the open source blockchain system CITA, and through sufficient experiments, it is proved that the system has improved scalability, availability and storage efficiency.

    Table and Figures | Reference | Related Articles | Metrics