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    Species and life cycles report on Tettigoniidea and Gryllidea in Minhang District, Shanghai
    Zhuqing HE, Xinyi LIAO
    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 119-124.   DOI: 10.3969/j.issn.1000-5641.2023.06.011
    Abstract5031)   HTML14)    PDF(pc) (1055KB)(807)       Save

    This research study focuses on Tettigoniidea and Gryllidea insects distributed across Shanghai Pujiang Country Park, with data collected twice a month from April to December of Year 2020 and 2021. The results show that 8 species of Tettigonioidea, 16 species of Grylloidea, and 1 species of Gryllotalpidae live in Shanghai Pujiang Country Park. The adult phase and voltinism in their life cycles, moreover, were found to be stable. Most Tettigoniidea and Gryllidea tend to overwinter in soil as diapause eggs, and a proportion of them overwinter as nymphs. The research suggests, furthermore, that using the calling songs of Tettigoniidea and Gryllidea can be a simple and effective way to carry out studies about phenology and ecology of singing insect.

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    Machine learning-based remote sensing retrievals of dissolved organic carbon in the Yangtze River Estuary
    Hao CHEN, Xianqiang HE, Run LI, Fang CAO
    Journal of East China Normal University(Natural Science)    2024, 2024 (4): 123-136.   DOI: 10.3969/j.issn.1000-5641.2024.04.012
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    Dissolved organic carbon (DOC) is the largest reservoir of active organic matter in the ocean. Accurate characterization of the spatial and temporal patterns of DOC in large-river estuaries and neighboring coastal margins will help improve our understanding of biogeochemical processes and the fate of fluvial DOC across the estuary−coastal ocean continuum. By retrieving the absorption properties of colored dissolved organic matter (CDOM) in the dissolved organic matter (DOM) pool using machine learning models, and based on the correlation between CDOM absorption and DOC concentrations, we developed an ocean DOC algorithm for the GOCI satellite. The results indicated that the Nu-Supporting Vector Regression model performed best in retrieving CDOM absorption properties, with mean absolute percent differences (MAPD) of 32% and 8.6% for the CDOM absorption coefficient at 300 nm (aCDOM(300)) and CDOM spectral slope over the wavelength range of 275 ~ 295 nm (S275–295). Estimates of DOC concentrations based on the seasonal linear relationship between aCDOM(300) and DOC were achieved with high retrieval accuracy, with MAPD of 11% and 14% for the training dataset using field measurements and validation datasets on satellite platforms, respectively. Application of the DOC algorithm to GOCI satellite imagery revealed that DOC levels varied dramatically at both seasonal and hourly scales. Elevated surface DOC concentrations were largely associated with summer and lower DOC concentrations in winter as a result of seasonal cycles of Yangtze River discharges. The DOC also changed rapidly on an hourly scale due to the influence of the tide and local wind regimes. This study provides a useful method to improve our understanding of DOC dynamics and their environmental controls across the estuarine −coastal ocean continuum.

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    Optimization and application of detection methods for perfluorinated compounds in complex environmental matrices
    Yushan LI, Jing YANG, Ye LI, Dingye YANG, Fangfang DING, Yuyi WANG, Min LIU
    Journal of East China Normal University(Natural Science)    2024, 2024 (6): 39-51.   DOI: 10.3969/j.issn.1000-5641.2024.06.004
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    The detection method for perfluorinated compounds (PFASs) and the sample pretreatment process for complex environments were optimized using solid-phase extraction in conjunction with ultra-high performance liquid chromatography–tandem mass spectrometry. This optimized method allowed concurrent extraction and identification of 41 PFASs in diverse environmental matrices. Optimal ion pairs and mass spectrometry parameters for the targets were determined through manual tuning of single standards for instrument optimization. The optimal chromatographic mobile phase was identified as a combination of 2 mmol/L ammonium acetate solution and methanol. Higher recovery rates and shorter extraction times compared to accelerated solvent extraction in the context of sample extraction and purification were found for ultrasonic extraction of solid samples at 30°C for 10 minutes. The use of a parallel quantitative concentrator for nitrogen evaporation resulted in an average recovery rate of 104.3%, with a process time half as long as the time required for the traditional water bath method. The average recovery rates were 108.2% and 105.5% when using 2 mmol/L ammonium acetate solution (pH = 3) and 1 mL of 0.5% ammonia methanol solution for solid-phase extraction column elution and washing, respectively. The optimized method was applied to actual samples (soil, sediment, and water), achieving detection limits of 0.01 ~ 0.34 ng, matrix spiking recovery rates of 67.9% ~ 174.9%, and relative standard deviations for parallel samples of 0.03% ~ 28.10%. Overall, the optimized sample preparation method is more time- and solvent-efficient than previous methods, offers better sensitivity and recovery rates, and thus provides a solid technical foundation for large-scale environmental sample detection.

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    Diversity of plants in Chinese Taoist temples and the distribution pattern of Taoist tree species
    Wei CHANG, Yongchuan YANG, Cheng JIN, Xinyang WANG, Li HUANG, Lihua ZHOU, Siwei HU
    Journal of East China Normal University(Natural Science)    2023, 2023 (3): 9-19.   DOI: 10.3969/j.issn.1000-5641.2023.03.002
    Abstract2221)   HTML253)    PDF(pc) (1768KB)(1103)       Save

    In this study, we obtained tree species from 72 Taoist temples across China. We subsequently documented the tree species composition, distribution pattern, and impact factors in different regions to determine the role of Taoist temples in biodiversity protection. The results showed that: ① Among 72 Taoist temples sampled across China, we observed a total of 354 species of trees, belonging to 85 families and 208 genera; ② The tree species in the Taoist temples were mainly native species, and the mean value for the proportion of native species in each Taoist temple was 62.5%±19.8% (mean ± standard deviation). Most of the Taoist temples (77.8%), moreover, housed threatened tree species; ③ Taoist tree species originated largely from subtropical regions, with the Yangtze River Basin being the most represented, especially in the southwest and south-central regions where a relatively large proportion of ethnic minorities reside; ④ The main factors affecting the distribution of Taoist tree species were geography and climate, but their composition was indistinguishable within each climate zone. As the climatic zone moves northward, there is no religious tree species replacement phenomenon (i.e., replacing the original religious tree species by native tree species with similar morphology or cultural meaning). The above results indicate that Taoist temples are rich in plant resources, which are potential biodiversity treasures. Thus, they play an essential role in protecting and maintaining biodiversity, with the potential to serve as a reference for regional ecological restoration and urban green space construction.

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    A case study on the application of the automatic labelling of the subject knowledge graph of Chinese large language models: Take morality and law and mathematics as examples
    Sijia KOU, Fengyun YAN, Jing MA
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 81-92.   DOI: 10.3969/j.issn.1000-5641.2024.05.008
    Abstract2179)   HTML22)    PDF(pc) (1324KB)(2711)       Save

    With the rapid development of artificial intelligence technology, large language models (LLMs) have demonstrated strong abilities in natural language processing and various knowledge applications. This study examined the application of Chinese large language models in the automatic labelling of knowledge graphs for primary and secondary school subjects in particular compulsory education stage morality and law and high school mathematics. In education, the construction of knowledge graphs is crucial for organizing systemic knowledge . However, traditional knowledge graph methods have problems such as low efficiency and labor-cost consumption in data labelling. This study aimed to solve these problems using LLMs, thereby improving the level of automation and intelligence in the construction of knowledge graphs. Based on the status quo of domestic LLMs, this paper discusses their application in the automatic labelling of subject knowledge graphs. Taking morality and rule of law and mathematics as examples, the relevant methods and experimental results are explained. First, the research background and significance are discussed. Second, the development status of the domestic large language model and automatic labelling technology of the subject knowledge graph are then presented. In the methods and model section, an automatic labelling method based on LLMs is explored to improve its application in a subject knowledge graph. This study also explored the subject knowledge graph model to compare and evaluate the actual effect of the automatic labelling method. In the experiment and analysis section, through the automatic labelling experiments and results analysis of the subjects of morality and law and mathematics, the knowledge graphs of the two disciplines are automatically labeled to achieve high accuracy and efficiency. A series of valuable conclusions are obtained, and the effectiveness and accuracy of the proposed methods are verified. Finally, future research directions are discussed. In general, this study provides a new concept and method for the automatic labelling of subject knowledge graphs, which is expected to promote further developments in related fields.

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    Automatic generation of Web front-end code based on UI images
    Jin GE, Xuesong LU
    Journal of East China Normal University(Natural Science)    2023, 2023 (5): 100-109.   DOI: 10.3969/j.issn.1000-5641.2023.05.009
    Abstract1930)   HTML260)    PDF(pc) (1748KB)(521)       Save

    User interfaces (UIs) play a vital role in the interactions between an application and its users. The current popularity of mobile Internet has led to the large-scale migration of web-based applications from desktop to mobile. Web front-end development has become more extensive and in-depth in application development. Traditional web front-end development relies on designers to give initial design drafts and then programmers to write the corresponding UI code. This method has high industry barriers and slow development, which are not conducive to rapid product iteration. The development of deep learning makes it possible to automatically generate web front-end code based on UI images. Existing methods poorly capture the features of UI images, and the accuracy of the generated code is low. To mitigate these problems, we propose an encoder–decoder model, called image2code, based on the Swin Transformer, which is used to generate web front-end code from UI images. Image2code regards the process of generating web front-end code from UI images as an image captioning task and uses Swin Transformer with a sliding window design as the backbone network of the encoder and decoder. The sliding window operation limits the attention calculation to one window, which reduces the amount of calculation by the attention mechanism while simultaneously ensuring that feature connections remain across windows. In addition, image2code generates Emmet code, which is much simpler and can be directly converted to HTML code, improving the efficiency of model training. Experimental results show that image2code performs better than existing representative models, such as pix2code and image2emmet, in the task of web front-end code generation on existing and newly constructed datasets.

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    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 0-x.  
    Abstract1878)   HTML53)    PDF(pc) (365KB)(1750)       Save
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    Study on the influence of a knowledge graph-based learning system design on online learning results
    Kechen QU, Jinchang LI, Deming HUANG, Jia SONG
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 70-80.   DOI: 10.3969/j.issn.1000-5641.2024.05.007
    Abstract1801)   HTML25)    PDF(pc) (2268KB)(1409)       Save

    Drawing on constructivism and competency-based theory, this paper proposes an online learning system design method based on a knowledge graph, which breaks the traditional knowledge structure and builds a multi-dimensional competence framework of knowledge and skills with the goal of improving competence. A learning system with a knowledge graph as the underlying logic and linked digital learning resources was built. Teaching practice and empirical research were then carried out. First, the learning system was verified with a questionnaire. Second, taking the ability to “read English academic papers” as the learning task, experimental and control groups were created to evaluate the understanding of knowledge and skills, memory level, and comprehensive application ability of the participants. The results showed that the effectiveness and usability of the learning system were higher in the experimental group than in the control group in terms of total, knowledge, skill, and ability scores. Among these, total and ability scores showed significant differences, indicating that the system played a role in promoting the effect of online learning.

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    Method for improving the quality of trajectory data for riding-map inference
    Jie CHEN, Wenyi SHEN, Wenyu WU, Jiali MAO
    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 14-27.   DOI: 10.3969/j.issn.1000-5641.2023.06.002
    Abstract1795)   HTML18)    PDF(pc) (5823KB)(1006)       Save

    The trajectory optimization of cycling is hindered by the errors of positioning equipment, riding habits of non-motor vehicles, and other factors. It leads to quality problems, such as abnormal data and missing positioning information in the riding trajectory, impacting the application of trajectory-based riding-map inference and riding-path planning. To solve these problems, this paper creates a framework for improving the quality of cycling-trajectory data, based on the construction of a grid index, screening of abnormal trajectory points, elimination of wandering trajectory segments, elimination of illegal trajectory segments, calibration of drift trajectory segments, and recovery of missing trajectory. Comparative and ablation experiments are conducted by using a real non-motor-vehicle cycling-trajectory dataset. The experimental results verify that the proposed method improves the accuracy of cycling-map inference.

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    An online learning behavior evaluation framework: Based on the fuzzy analytic hierarchy process and the fuzzy synthetic evaluation method
    Yi ZHANG, Wenxu PI, Zexian WU, Yanbin ZHANG, Cheqing JIN, Wei WANG, Bin SU
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 1-10.   DOI: 10.3969/j.issn.1000-5641.2024.05.001
    Abstract1795)   HTML52)    PDF(pc) (196KB)(285)       Save

    To address the limitations currently experienced regarding the comprehensiveness and effectiveness of online learning evaluation in the smart education context, this paper proposes a novel framework for assessing online learning behavior based on the fuzzy analytic hierarchy process(FAHP) and the fuzzy synthetic evaluation method(FSEM). Drawing upon the CIPP(context, input, process, product) educational evaluation model and integrating the educational evaluation tag taxonomy system, the framework identifies five key dimensions: learning exploration, programming practice, knowledge acquisition, collaborative innovation, and communication interaction. These dimensions are further delineated into secondary and tertiary indicators to ensure comprehensive evaluation coverage. The framework utilizes FAHP-FSEM to determine the weights of each indicator level and employs consistency testing to validate the scientific and rational nature of the evaluation process. Implemented on the Shuishan Online platform, the framework leverages extensive multi-source process learning data to facilitate comprehensive evaluation from multiple perspectives and across various dimensions. Student profiles and learning behavior patterns are presented via a visual dashboard. This framework provides robust data support for enhancing personalized learning outcomes and advancing educational reform, demonstrating its broad applicability and potential.

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    Algorithm for security management and privacy protection of education big data based on smart contracts
    Shaojie QIAO, Yuhe JIANG, Chenxu LIU, Cheqing JIN, Nan HAN, Shuaiwei HE
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 128-140.   DOI: 10.3969/j.issn.1000-5641.2024.05.012
    Abstract1774)   HTML19)    PDF(pc) (1051KB)(334)       Save

    Conventional education big data management is faced with security risks such as privacy data leakage, questionable data credibility, and unauthorized access. To avoid the above risks, a novel type of education big data security management and privacy protection method, Algorithm for security management and privacy protection of education big data based on smart contracts (ASPES), is proposed. It integrates an improved key splitting and sharing algorithm based on the secret sharing of Shamir, a hybrid encryption algorithm based on SM2-SHA256-AES, and a smart contract management algorithm based on hierarchical data access control. Experiments are conduced on the real dataset of MOOCCube and the results indicate that the execution efficiency and security of ASPES are significantly improved when compared with the state-of-the-art methods, which can effectively store and manage education big data and realize the reasonable distribution of educational resources. By embedding smart contracts into the blockchain and inputting operations like data reading and writing into the blockchain, ASPES can optimize the management path, improve management efficiency, ensure the fairness of education, and considerably improve the quality of education.

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    Personalized knowledge concept recommendation for massive open online courses
    Chao KONG, Jiahui CHEN, Dan MENG, Huabin DIAO, Wei WANG, Liping ZHANG, Tao LIU
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 32-44.   DOI: 10.3969/j.issn.1000-5641.2024.05.004
    Abstract1760)   HTML18)    PDF(pc) (1453KB)(292)       Save

    In recent years, massive open online courses (MOOCs) have become a significant pathway for acquiring knowledge and skills. However, the increasing number of courses has led to severe information overload. Knowledge concept recommendation aims to identify and recommend specific knowledge points that students need to master. Existing research addresses the challenge of data sparsity by constructing heterogeneous information networks; however, there are limitations in fully leveraging these networks and considering the diverse interactions between learners and knowledge concepts. To address these issues, this study proposes a novel method, heterogeneous learning behavior-aware knowledge concept recommendation (HLB-KCR). First, it uses metapath-based random walks and skip-gram algorithms to generate semantically rich metapath embeddings and optimizes these embeddings through a two-stage enhancement module. Second, a multi-type interaction graph incorporating temporal contextual information is constructed, and a graph neural network (GNN) is employed for message passing to update the nodes, obtaining deep embedded representations that include time and interaction type information. Third, a semantic attention module is introduced to integrate meta-path embeddings with multi-type interaction embeddings. Finally, an extended matrix factorization rating prediction module is used to optimize the recommendation algorithm. Extensive experiments on the large-scale public MOOCCubeX dataset demonstrate the effectiveness and rationality of the HLB-KCR method.

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    Effects of cascade reservoirs in the Yangtze River Basin on estuarine saltwater intrusion and freshwater resources during late summer and early autumn
    Zhi JIN, Jianrong ZHU, Wei QIU
    Journal of East China Normal University(Natural Science)    2024, 2024 (1): 90-103.   DOI: 10.3969/j.issn.1000-5641.2014.01.010
    Abstract1739)   HTML21)    PDF(pc) (6592KB)(971)       Save

    Large cascade reservoirs in basins impound water in late summer and early autumn and release water in the dry season of the following year. These activities affect seasonal river discharge into the sea which, in turn, affects saltwater intrusion in estuaries and the utilization of freshwater resources. This study evaluated the effective storage capacity of large cascade reservoirs and the value of cross-basin water transfers by the South-to-North Water Transfer Project in the Yangtze River Basin. The estuarine and coastal three-dimensional numerical model ECOM-si was used to simulate and analyze the impact of major projects on estuarine saltwater intrusion and freshwater resources. In 2020, the effective storage capacity of large reservoirs built in the middle and upper reaches of the Yangtze River Basin was 70.611 billion cubic meters with a mean reduction in monthly river discharge of 13,398 m3/s during the storage period of September to October. By 2035, the completion of additional reservoirs in the basin will raise the total effective storage capacity of these reservoirs to 94.388 billion cubic meters and reduce the average monthly runoff by 17909 m3/s during the storage period. Using data on average monthly river discharge measured at the Datong Hydrological Station from 1950 to 2020, and by taking into account variations in river discharge by major projects in the basin, the average monthly river discharge from August to October from 2020 to 2035 in regular- and extra dry hydrological years was calculated. Numerical simulation results show that saltwater intrusion from September to October will increase due to impoundment in cascade reservoirs and decreased river discharge. During regular hydrological years, freshwater can be obtained from the four water reservoirs in the South Branch of the Yangtze River Estuary from September to October. However, water from the Dongfengxisha, Taicang, Chenhang, and Qingcaosha reservoirs is unsuitable for water intake during these months, particularly in extremely dry years. In 2020, the total number of consecutive days with unsuitable water intake from the four reservoirs was 28.75, 24.99, 29.63, and 37.47 days, respectively, and is predicted to rise to 46.53, 44.18, 47.56, and 50.75 days, respectively, in 2035. The impoundment of basin reservoirs in late summer and early autumn during average- and extremely dry hydrological years exposes them to strong northerly winds which can significantly decrease water intake. Basin reservoirs should reduce storage capacity and release water during extremely dry years to ensure the safety of freshwater resources in the Yangtze River Estuary.

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    Educational resource content review method based on knowledge graph and large language model collaboration
    Jia LIU, Xin SUN, Yuqing ZHANG
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 57-69.   DOI: 10.3969/j.issn.1000-5641.2024.05.006
    Abstract1728)   HTML32)    PDF(pc) (1448KB)(442)       Save

    Automated content reviews on digital educational resources are urgently in demand in the educational informatization era. Especially in the applicability review of whether educational resources exceed the standard, there are problems with knowledge which are easy to exceed national curriculum standards and difficult to locate. In response to this demand, this study proposed a review method for educational resources based on the collaboration of an educational knowledge graph and a large language model . Specifically, this study initially utilized the ontology concept to design and construct a knowledge graph for curriculum education in primary and secondary schools. A knowledge localization method was subsequently designed based on teaching content generation, sorting, and pruning, by utilizing the advantages of large language models for text generation and sorting tasks. Finally, by detecting conflicts between the core knowledge sub-graph of teaching content and the knowledge graph teaching path, the goal of recognizing teaching content that exceeded the national standard was achieved. Experimental results demonstrate that the proposed method effectively addresses the task of reviewing exceptional standard knowledge in educational resource content. This opens up a new technological direction for educational application based on the knowledge graph and large language model collaboration.

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    Surface-height- and uncertainty-based depth estimation for Mono3D
    Yinshuai JI, Jinhua XU
    J* E* C* N* U* N* S*    2025, 2025 (1): 72-81.   DOI: 10.3969/j.issn.1000-5641.2025.01.006
    Abstract1701)   HTML19)    PDF(pc) (1215KB)(580)       Save

    Monocular three-dimensional (3D) object detection is a fundamental but challenging task in autonomous driving and robotic navigation. Directly predicting object depth from a single image is essentially an ill-posed problem. Geometry projection is a powerful depth estimation method that infers an object’s depth from its physical and projected heights in the image plane. However, height estimation errors are amplified by the depth error. In this study, the physical and projected heights of object surface points (rather than the height of the object itself) were estimated to obtain several depth candidates. In addition, the uncertainties in the heights were estimated and the final object depth was obtained by assembling the depth predictions according to the uncertainties. Experiments demonstrated the effectiveness of the depth estimation method, which achieved state-of-the-art (SOTA) results on a monocular 3D object detection task of the KITTI dataset.

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    SA-MGKT: Multi-graph knowledge tracing method based on self-attention
    Chang WANG, Dan MA, Huarong XU, Panfeng CHEN, Mei CHEN, Hui LI
    Journal of East China Normal University(Natural Science)    2024, 2024 (5): 20-31.   DOI: 10.3969/j.issn.1000-5641.2024.05.003
    Abstract1695)   HTML26)    PDF(pc) (936KB)(423)       Save

    This study proposes a multi-graph knowledge tracing method integrated with a self-attention mechanism (SA-MGKT), The aim is to model students’ knowledge mastery based on their historical performance on problem-solving exercises and evaluate their future learning performance. Firstly, a heterogeneous graph of student-exercise is constructed to represent the high-order relationships between these two factors. Graph contrastive learning techniques are employed to capture students’ answer preferences, and a three-layer LightGCN is utilized for graph representation learning. Secondly, we introduce information from concept association hypergraphs and directed transition graphs, and obtain node embeddings through hypergraph convolutional networks and directed graph convolutional networks. Finally, by incorporating the self-attention mechanism, we successfully fuse the internal information within the exercise sequence and the latent knowledge embedded in the representations learned from multiple graphs, leading to a substantial enhancement in the accuracy of the knowledge tracing model. Experimental outcomes on three benchmark datasets demonstrate promising results, showcasing remarkable improvements of 3.51%, 17.91%, and 1.47% respectively in the evaluation metrics, compared to the baseline models. These findings robustly validate the effectiveness of integrating multi-graph information and the self-attention mechanism in enhancing the performance of knowledge tracing models.

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    Species diversity of bryophytes on Dajinshan Island, Shanghai
    Ruiping SHI, Peng ZHENG, Qi WU, Yiran WANG, Xiaolu DING, Xuedi GAO, Youfang WANG, Jian WANG
    Journal of East China Normal University(Natural Science)    2024, 2024 (4): 71-81.   DOI: 10.3969/j.issn.1000-5641.2024.04.008
    Abstract1685)   HTML9)    PDF(pc) (734KB)(1637)       Save

    The aim of this study was to update the bryophyte list of Dajinshan Island and provide the scientific basic data for in situ conservation. Based on five field investigations on the island, 67 species belonging to 38 genera in 20 families are reported herein. Compared with historical data for Dajinshan Island, 23 species are newly recorded in the island. Of these, 13 species are newly recorded in Shanghai. One epiphyllous liverwort species, Cololejeunea raduliloba Steph., is newly reported on Dajinshan Island. Taking into account climate change and the physiological and ecological characteristics of bryophytes, the changes in bryophyte species composition on Dajinshan Island are discussed. Our results highlight the importance of timely updating of a regional checklist, when conserving bryophyte biodiversity.

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    Spatiotemporal differential distribution characteristics and ecological risk assessment of antibiotics in the Yangtze River Estuary and offshore areas
    Yuyi WANG, Ye HUANG, Jing YANG, Fangfang DING, Tianhao HE, Yushan LI, Lin HUANG, Ye LI, Min LIU
    Journal of East China Normal University(Natural Science)    2024, 2024 (6): 136-150.   DOI: 10.3969/j.issn.1000-5641.2024.06.012
    Abstract1680)   HTML27)    PDF(pc) (1301KB)(417)       Save

    As the ultimate destination of antibiotics in rivers, the mechanism and change law of migration from rivers to oceans have not been fully and systematically studied. In this study, surface water samples were collected in the Yangtze River Estuary and its offshore areas in the wet season and dry season of 2023. Solid-phase extraction and high performance liquid chromatography-mass spectrometry were used to identify 20 antibiotics in five categories. This analysis was conducted to assess the concentrations, temporal and spatial distribution, and potential ecological risks. The results showed that the concentrations of 20 antibiotics ranged from not detected ~ 63.32 ng·L–1, and showed a decreasing trend from inland to marine areas from the mouth-adjacent section (average 108 ng·L–1) to the estuary section (average 50 ng·L–1), to the coastal section outside the mouth (average 40 ng·L–1), and the coastal area (average 29 ng·L–1). The concentrations of the five types of antibiotics from high to low were chloramphenicols > macrolides > sulfonamides > quinolones > tetracyclines. The categories and amount of detected antibiotics showed significant seasonal differences. The seasonal-change pattern showed that the ML concentration season was significantly higher in the dry than the wet season, while the concentrations of TCs were significantly higher in the wet season than the dry season. Antibiotics, such as Sulfamethoxazole, Ciprofloxacin, Chlortetracycline, Erythromycin, Chloramphenicol, Ofloxacin, posed a significant threat to the water in the study area, and the ecological risks should not be overlooked.

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    Impact of coastal ecological restoration project on bird diversity and community dynamics
    Kun HE, Ziyu ZHANG, Annan SONG, Qifan SHEN, Jiayi WANG, Xuechu CHEN
    Journal of East China Normal University(Natural Science)    2023, 2023 (3): 158-166.   DOI: 10.3969/j.issn.1000-5641.2023.03.015
    Abstract1629)   HTML32)    PDF(pc) (831KB)(447)       Save

    Yingwuzhou Wetland is an artificially restored coastal salt marsh wetland aimed at improving ecosystem services. Development of the wetland has restored the original damaged coastal ecosystem through comprehensive coastline ecological engineering measures. The birds in the study site have been investigated and researched using the route survey method since 2018, and changes in the bird population and species diversity have been analyzed to evaluate the effectiveness of coastal zone ecological restoration projects and the impact of different wetland habitat types on bird diversity. The results showed that 67 bird species were recorded in the wetland, belonging to 13 orders and 32 families, with the largest number of birds belonging to Passeriformes, including 42 species belonging to 18 families. There were 35 species of resident birds, 24 species of winter migratory birds, 10 species of summer migratory birds, and 8 species of migratory birds. Among these, one species of national class I and seven species of class II are in the List of Key Protected Wild Animals in China, respectively. Remiz consobrinus, Gallinula chloropus, Acridotheres cristatellus, Tachybaptus ruficollis, Spodiopsar cineraceus, Hirundo rustica, and Passer montanus were the dominant species. The number of wetland bird species increased annually. There were significant differences in the bird species, quantity, and Shannon–Wiener indexes among different seasons. The declining trends of bird species, quantity, and Shannon–Wiener index were in the orders of fall > winter > spring > summer, fall > winter > summer > spring, and fall > spring > winter > summer, respectively. The bird numbers and species were the highest in the natural wetland complex area. Declining trends of the Shannon–Wiener index in different habitat areas were observed for the natural wetland complex area, salt marsh wetland restoration area, clear water conservation area, lawn activity area, and wetland purification exhibition area. The ecological restoration of the coastline has enriched the bird diversity of the wetland. Habitats with rich patch types and high patch mosaic have a markedly positive impact on bird diversity. The results of this study can provide a scientific basis for the coastal ecological restoration and sustainable development of coastline wetlands.

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    Sentence classification algorithm based on multi-kernel support vector machine
    Kaiyan XIAO, Jie LIAN
    Journal of East China Normal University(Natural Science)    2023, 2023 (6): 85-94.   DOI: 10.3969/j.issn.1000-5641.2023.00.008
    Abstract1612)   HTML9)    PDF(pc) (1621KB)(379)       Save

    Mainstream sentence classification algorithms rely on a single word vector model to obtain the feature vector representation of text, which leads to insufficient text mapping ability. Therefore, a multi-kernel learning method is used to fuse multiple text representations based on different word vectors to improve the accuracy of sentence classification. In the process of fusing different kernel functions, traditional kernel function coefficient optimization methods often lead to long training time and difficulty in finding a local optimum. To address this problem, a new kernel function coefficient optimization method that continuously approximates the optimal kernel function coefficient value based on parameter space segmentation and breadth first search was developed. In this study, a support vector machine (SVM) was used as a classifier to perform classification experiments on seven text datasets, and the experimental results showed that the multi-kernel learning classification results were significantly better than those of single-kernel learning. Moreover, the proposed optimization method performed better than traditional methods with less training cost.

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