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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
    Abstract2054)   HTML93)    PDF (1090KB)(1277)      

    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.  
    Abstract108)   HTML14)    PDF (365KB)(1204)      
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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
    Abstract1132)   HTML39)    PDF (1081KB)(705)      

    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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    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
    Abstract659)   HTML31)    PDF (1754KB)(605)      

    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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    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
    Abstract671)   HTML26)    PDF (5085KB)(521)      

    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
    Abstract420)   HTML14)    PDF (1463KB)(506)      

    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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    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
    Abstract881)   HTML40)    PDF (3235KB)(495)      

    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
    Abstract401)   HTML14)    PDF (877KB)(492)      

    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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    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 0-III.  
    Abstract265)   HTML169)    PDF (279KB)(490)      
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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
    Abstract599)   HTML23)    PDF (4857KB)(472)      

    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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    Research progress on pollution and degradation of plastic waste
    Kaizhen MIAO, Jiaolong MENG, Xuefeng JIANG
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 170-176.   DOI: 10.3969/j.issn.1000-5641.2023.01.017
    Abstract854)   HTML44)    PDF (1177KB)(456)      

    Plastics are widely used in daily life owing to their light weight, portability, and affordability. However, post-consumer-waste plastics do not degrade easily in the natural environment, making plastic pollution a new global environmental issue. Thus, exploration in the field of plastic degradation has increased in recent years. To promote the treatment of plastic waste and provide a scientific reference for environmental protection and sustainable development, this study describes the current state of plastic pollution. It also systematically summarizes various research fields of plastic degradation and presents the development prospect of photocatalysis and bio-based plastics in the future.

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    3D obstacle-avoidance for a unmanned aerial vehicle based on the improved artificial potential field method
    Lanfeng ZHOU, Mingyue KONG
    Journal of East China Normal University(Natural Science)    2022, 2022 (6): 54-67.   DOI: 10.3969/j.issn.1000-5641.2022.06.007
    Abstract667)   HTML21)    PDF (2858KB)(452)      

    This paper aims to address the challenge of seeking an optimal safe path for a UAV (unmanned aerial vehicle) from an initial position to a target position, while avoiding all obstacles in a three-dimensional environment. An improved APF (artificial potential field) method combined with the regular hexagon guidance method is proposed to solve unreachable and local minimum problems near obstacles as observed with traditional artificial potential field methods. First, we add a distance correction factor to the repulsive potential field function to solve problems associated with unreachable targets. Then, a regular hexagon-guided method is proposed to improve the local minimum problem. This method can judge the environment when the UAV is trapped in a local minimum point or trap area and select the appropriate planning method to guide the UAV to escape from the local minimum area. Then, 3D modeling and simulation were carried out via Matlab, taking into account a variety of scenes involving complex obstacles. The results show that this method has good feasibility and effectiveness in real-time path planning of UAVs. Lastly, we demonstrate the performance of the proposed method in a real environment, and the experimental results show that the proposed method can effectively avoid obstacles and find the optimal path.

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    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
    Abstract914)   HTML25)    PDF (968KB)(428)      

    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.

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    Progress in synthesis of methyl glyoxylate by selective oxidation of methyl glycolate with molecular oxygen
    Hao WANG, Guofeng ZHAO, Yong LU
    Journal of East China Normal University(Natural Science)    2023, 2023 (1): 104-113.   DOI: 10.3969/j.issn.1000-5641.2023.01.011
    Abstract607)   HTML8)    PDF (834KB)(404)      

    Methyl glyoxylate is widely used in organic synthesis and chemical production. The application of traditional preparation methods is limited by high cost, low efficiency, and significant environmental pollution. During the coal to ethylene glycol process, methyl glycolate is produced as an intermediate product of the hydrogenation of dimethyl oxalate (DMO) to ethylene glycol. Methyl glycolate can be selectively obtained from DMO via hydrogenation, and therefore, has the potential to serve as raw material for methyl glyoxylate. However, only few studies have considered this process. Herein , the applications, traditional preparation methods, and state-of-the-art research progress of methyl glycolate oxidation are reviewed. Recent research on selective oxidation of related alcohols (such as ethanol) to aldehydes and ketones is also summarized.

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    Disinfection effect of seven frequently used disinfectants on bacteriophage MS2
    Yu LI, Zhenming GE
    Journal of East China Normal University(Natural Science)    2023, 2023 (2): 161-167.   DOI: 10.3969/j.issn.1000-5641.2023.02.017
    Abstract386)   HTML15)    PDF (1485KB)(314)      

    In this study, the MS2 phages were used as an indicator microorganism to test the function of seven disinfectants with different components: hydrogen peroxide, ethanol, fermented lactic acid disinfectant, iodine disinfectant, quaternary ammonium disinfectant, chlorine-containing disinfectant, and peracetic acid disinfectant. Our results showed that the virus disinfection rate varied notably between the selected disinfectants. The iodine disinfectant exhibited the strongest disinfection effect, followed by the quaternary ammonium salt disinfectant and the peracetic acid disinfectant, while the disinfection effects of hydrogen peroxide, ethanol, and fermentation lactic acid disinfectant were inadequate. The test results provide a reference for the efficient utilization of various disinfectants to eliminate harmful microorganisms in the environment.

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    Research on travel time prediction based on neural network
    Zhaoyang WU, Jiali MAO
    Journal of East China Normal University(Natural Science)    2023, 2023 (2): 106-118.   DOI: 10.3969/j.issn.1000-5641.2023.02.012
    Abstract289)   HTML10)    PDF (1993KB)(313)      

    The popularity of positioning devices has generated a large volume of vehicle driving data, making it possible to use historical data to predict the driving time of vehicles. Vehicle driving data consists of two parts: the sequence of road segments that the vehicle travels through, the departure time, the total length of the path, and other external information. The questions of how to extract sequence features in road segments and how to effectively fuse sequence features with external features become the key issues in predicting the travel time. To solve the aforementioned problems, a transformer-based travel time prediction model is proposed, which consists of two parts: a road segment sequence processing module and a feature fusion module. First, the road segment sequence processing module uses the self-attention mechanism to process the road segment sequence and extract the road segment sequence features. The model can not only fully consider the spatiotemporal correlation of road speeds between each road segment and other road segments, but also ensures the parallel input of data into the model, avoiding the low efficiency problem caused by sequential input of data when using recurrent neural networks. The feature fusion module fuses the road segment sequence features with external information, such as departure time, and obtains the predicted travel time. On this basis, the number of road segments connected by the intersection is determined by the upstream and downstream intersection features of the road segment, and the input model is combined with the road segment characteristics to further improve the prediction accuracy of the driving time. Comparative experiments with mainstream prediction methods on real data sets show that the model improves prediction accuracy and training speed, reflecting the effectiveness of the proposed method.

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    A scheme of vegetation classification system at city scale and its application in Shanghai
    Liangjun DA, Zhiwen GAO, Yongchuan YANG, Kun SONG, Xueyan GUO, Hong LIANG, Tiyuan XIA, Huafeng WANG, Ting ZHOU, Daigui ZHANG, Zhihui TIAN, Yuandong HU, Bo JIA
    Journal of East China Normal University(Natural Science)    2023, 2023 (3): 1-8.   DOI: 10.3969/j.issn.1000-5641.2023.03.001
    Abstract327)   HTML241)    PDF (574KB)(301)      

    The development of city-scale vegetation maps is helpful for vegetation management and conservation. Vegetation classification systems in China mainly consider natural vegetation and most classification systems operate at the national or provincial scale, making them unsuitable for city-scale classification. Until now, the lack of a classification system designed specifically for urban vegetation has limited the studies on urban vegetation. Based on the origin, disturbance, and function, our classification system divides urban vegetation into natural, secondary, and cultivated vegetations. Based on the function, cultivated vegetation is further divided into artificial forest land, landscape green land , and urban agricultural vegetations. Based on the Classification System of China’s Vegetation in 1980 and the three newly proposed preliminary guideline documents for classification of natural vegetation, we establish a new urban vegetation classification system. We applied the principles of this new urban system in Shanghai and other areas in China, to further refine the system and ensure it has both academic and practical values. This work provides the theoretical basis for compiling information about urban vegetation and provides technical support for the recognition, protection, construction, and management of urban vegetation.

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    Multimodal-based prediction model for acute kidney injury
    Wei DENG, Fang ZHOU
    Journal of East China Normal University(Natural Science)    2023, 2023 (4): 52-64.   DOI: 10.3969/j.issn.1000-5641.2023.04.006
    Abstract252)   HTML13)    PDF (1179KB)(283)      

    Acute kidney injury is a clinical disease with a high morbidity rate, and early identification of potential patients can facilitate medical interventions to reduce morbidity and mortality. In recent years, electronic health records have been widely used to predict an individual’s potential risk. Most of the existing acute kidney injury prediction models tackle the issue of sparsity and irregularity in the physiological variables data by aggregating data or imputing the missing value, but ignore the patient’s health status implied by the missing information. Moreover, they do not consider the characteristics of and correlation between the various modalities. To solve the above issues, we present a multi-modal disease prediction model for acute kidney injury. The proposed model considers a variety of modal data, including physiological variables, disease, and demographic data. A new mask and time span based long short term memory (LSTM) network is designed to learn the time span and missing information of individual Physiological variables, and furthermore, to capture their numerical changes and frequency changes. The multi-head self-attention mechanism is introduced to promote interaction learning of each modality representation. Experiments on the real-world application of acute kidney injury risk prediction and mortality risk prediction demonstrate the effectiveness and rationality of the proposed model.

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    Benchmarking join order selection of query optimizers
    Ting CHEN, Zhaokun XIANG, Jinkai XU, Rong ZHANG
    Journal of East China Normal University(Natural Science)    2022, 2022 (5): 48-60.   DOI: 10.3969/j.issn.1000-5641.2022.05.005
    Abstract344)   HTML9)    PDF (1151KB)(278)      

    Join order selection, i.e., the determination of the cheapest join order from available alternatives, is one of the most critical tasks in query optimization. The enormous search space of a join order makes it difficult to find an optimal join order in an efficient manner. Although there are many optimization algorithms for join order selection, existing benchmarks are unsuitable for evaluating these join order selection strategies because they cannot configure the depths of the joins or cover all join styles. To effectively evaluate the quality of join order selection algorithms used in an optimizer, a generic evaluation tool for join order selection is implemented in this study. The tool takes the primary key-based deterministic data generation method for portable application scenario migration, a join order sampling algorithm to reduce the investigated join spaces, and a result-guided parameter instantiation algorithm to support a valid query generation. We applied the tool on OceanBase and PostgreSQL, and the experiment results show its effectiveness in evaluating the performance of join order selection in query optimizers in a generic and efficient manner.

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    Text matching based on multi-dimensional feature representation
    Ming WANG, Te LI, Dingjiang HUANG
    Journal of East China Normal University(Natural Science)    2022, 2022 (5): 126-135.   DOI: 10.3969/j.issn.1000-5641.2022.05.011
    Abstract297)   HTML15)    PDF (750KB)(269)      

    Text semantic matching is the basis of many natural language processing tasks. Text semantic matching techniques are required in many scenarios, such as search, question, and answer systems. In practical application scenarios, the efficiency of text semantic matching is crucial. Although the representational learning semantic-matching model is less accurate than the interactive model, it is more efficient. The key to improve the performance of learning-based semantic-matching models is to extract sentence vectors with high-level semantic features. On this basis, this paper presents the design of a feature-fusion module and feature-extraction module based on the ERINE model to obtain sentence vectors with multidimensional semantic features. Further, the performance of the model is improved to obtain semantic information by designing a loss function of semantic prediction. Finally, the accuracy on the Baidu Qianyan dataset reaches 0.851, which indicates good performance.

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