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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 Abstract （462）   HTML （12）    PDF（pc） （1324KB）（513）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （337）   HTML （10）    PDF（pc） （936KB）（220）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Knowledge graph empowered object-oriented programming C++ teaching reform and practice Zhuang PEI, Xiuxia TIAN, Bingxue LI Journal of East China Normal University(Natural Science)    2024, 2024 (5): 104-113.   DOI: 10.3969/j.issn.1000-5641.2024.05.010 Abstract （337）   HTML （4）    PDF（pc） （3157KB）（365）       Knowledge map    Save Against the backdrop of the national new engineering education initiative, early C++ teaching has failed to meet the requirements of high-level sophistication, innovation, and challenges. Furthermore, issues such as fragmented knowledge points, difficulty in integrating theory with practice, and single-perspective bias are prevalent in this field. To address these problems, we propose an innovative teaching model that effectively integrates QT(Qt Toolkit) and C++ by merging the two courses. This model facilitates the teaching process via a course knowledge graph deployed on the Zhihuishu platform. The breadth of teaching is expanded by effectively linking course knowledge points, integrating and sharing multimodal teaching resources, enhancing multiperspective learning, showcasing the course’s innovative nature, and avoiding single-perspective bias. Simultaneously, the depth of teaching is increased through the construction of a knowledge graph that integrates QT and object-oriented programming (C++), organically combining the knowledge points of both courses. This approach bridges the gap between theory and practice by enhancing the course’s sophistication and level of challenge. Consequently, this study pioneers the reform of C++ teaching by providing valuable references and insights for programming courses under the new engineering education framework. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （324）   HTML （14）    PDF（pc） （1448KB）（250）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （310）   HTML （15）    PDF（pc） （17080KB）（116）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on calculation of two-dimensional transition metal chalcogenides compounds MX2-MX-MX2 (M = V, Cr, Mn, and Fe; X = S, Se, and Te) Wenjie DING, Wenhui XIE Journal of East China Normal University(Natural Science)    2024, 2024 (3): 45-53.   DOI: 10.3969/j.issn.1000-5641.2024.03.005 Abstract （308）   HTML （8）    PDF（pc） （1969KB）（661）       Knowledge map    Save The crystal structure, stability, electronic structure, and magnetism of two-dimensional transition metal chalcogenides compounds, MX2-MX-MX2 (M = V, Cr, Mn, and Fe; X = S, Se, and Te), were systematically investigated using first-principles calculations based on the density functional theory (DFT). Furthermore, the magnetic coupling mechanisms of these materials were analyzed. The results show that the formation energies of these compounds are negative, indicating that the compounds can be fabricated experimentally. MnS2-MnS-MnS2 and MnSe2-MnSe-MnSe2 exhibit ferromagnetic half-metal properties, whereas CrS2-CrS-CrS2 transforms into a ferromagnetic half-metal under applied stress. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Research on the impact of a typhoon on the accretion-erosion of mudflats: Based on UAV photogrammetry and in situ hydrodynamic measurements Xinmiao ZHANG, Liming XUE, Benwei SHI, Wenxiang ZHANG, Tianyou LI, Biaobiao PENG, Xiuzhen LI, Yaping WANG Journal of East China Normal University(Natural Science)    2024, 2024 (4): 150-160.   DOI: 10.3969/j.issn.1000-5641.2024.04.014 Abstract （300）   HTML （10）    PDF（pc） （2685KB）（247）       Knowledge map    Save Extreme events such as typhoons can change mudflats by tens of centimeters. It is important for coastal management and ecosystem maintenance to recognize changes in accretion-erosion during typhoons and to understand the mechanisms driving it. In this study, Unmanned Aerial Vehicle (UAV) photogrammetry based on the Structure-from-Motion (SfM) algorithm was used to generate Digital Elevation Models (DEM) of a mudflat in Eastern Chongming, Yangtze Estuary, before and after the passage of Typhoon “In-Fa” (July 2021). Hydrodynamic measurements were conducted from bare flats to marshes to explore the mechanisms of DEM changes. Changes in accretion-erosion observed by UAV photogrammetry presented an obvious zonation of eroded bare flats and accreted marshes. The accuracy of the DEMs is 4.1 cm. Under the impact of the typhoon, the erosion of the bare flat and the accretion of the marsh have a amplitude of ±32 cm. During typhoons, the wave height and water depth in the bare flat increases to the condition of wave breaking, and the surface sediment is eroded and carried by rising tides. But in marshes, the sediment carrying capacity of water columns decreases, and the sediments are deposited. Consequently, the mudflat presents an obvious zonation of accretion-erosion. This study provides a new perspective for deeply understanding the impact of typhoons on the accretion-erosion of mudflats by combining UAV photogrammetry and hydrodynamic measurements. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Locally lightweight course teaching-assistant system based on IPEX-LLM Jiarui ZHANG, Qiming ZHANG, Fenglin BI, Yanbin ZHANG, Wei WANG, Erjin REN, Haili ZHANG Journal of East China Normal University(Natural Science)    2024, 2024 (5): 162-172.   DOI: 10.3969/j.issn.1000-5641.2024.05.015 Abstract （296）   HTML （7）    PDF（pc） （15203KB）（54）       Knowledge map    Save This study introduces and implements a local, lightweight, intelligent teaching-assistant system. Using the IPEX-LLM (Intel PyTorch extention for large language model) acceleration library, the system can efficiently deploy and execute large language models that are fine-tuned using the QLoRA (quantum-logic optimized resource allocation) framework on devices with limited computational resources. Combining this with enhanced retrieval techniques, the system provides flexible course customization through four major functional modules: intelligent Q&A, automated question generation, syllabus creation, and course PPT generation. This system is intended to assist educators in improving the quality and efficiency of lesson preparation and delivery, safeguarding data privacy, supporting personalized student learning, and offering real-time feedback. Performance tests exemplified by the optimized Chatglm3-6B model show the rapid inference capability of the system via the processing of a 64-token output task within 4.08 s in a resource-constrained environment. A practical case study comparing the functionality of the system with native Chatglm-6B and ChatGPT 4.0 further validates its superior accuracy and practicality. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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An efficient algorithm for solving time-dependent Gross-Pitaevskii equation Lisha SHU, Guangjiong DONG Journal of East China Normal University(Natural Science)    2024, 2024 (3): 84-90.   DOI: 10.3969/j.issn.1000-5641.2024.03.009 Abstract （286）   HTML （8）    PDF（pc） （747KB）（460）       Knowledge map    Save The Gross-Pitaevskii equation is widely applied in Bose-Einstein condensate research, yet is rarely analytically determined; thus, it is important to develop a numerical method with high precision to resolve this. Accordingly, a numerical method was developed in this work, considering the splitting step method, Crank-Nicolson algorithm, and Numerov algorithm with four-order accuracy. The corresponding test shows that compared with the finite difference method using five points, the proposed algorithm is more efficient and costs less memory. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （281）   HTML （10）    PDF（pc） （2268KB）（205）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （279）   HTML （5）    PDF（pc） （1051KB）（215）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （278）   HTML （10）    PDF（pc） （1453KB）（164）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Heterogeneous data generation tools for online education scenarios Wei ZHOU, Ke WANG, Huiqi HU Journal of East China Normal University(Natural Science)    2024, 2024 (5): 114-127.   DOI: 10.3969/j.issn.1000-5641.2024.05.011 Abstract （271）   HTML （14）    PDF（pc） （795KB）（82）       Knowledge map    Save In the digital education application domain, developers of platforms such as online classrooms face the challenges of privacy issues and existing datasets’ insufficient size in their pursuit of data-driven optimization. To address this, a set of heterogeneous data models adapted to the characteristics of education were constructed, and corresponding data generation tools (E-Tools) that can be used to simulate data interactions in complex educational scenarios were implemented. Experimental results have shown that the tool can maintain an efficient data generation speed (64–74 $ {\rm{MB}}\cdot {{\rm{s}}^{-1}} $) under a variety of data sizes, demonstrating good linear scaling ability, which validates the model’s effectiveness and the tool’s ability to generate larger data volumes. A heterogeneous data query load reflecting students’ learning behaviors was also designed to provide strong support for performance evaluation and the education platform’s optimization. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Prompting open-source code large language models for student program repair Zhirui CHEN, Xuesong LU Journal of East China Normal University(Natural Science)    2024, 2024 (5): 93-103.   DOI: 10.3969/j.issn.1000-5641.2024.05.009 Abstract （270）   HTML （9）    PDF（pc） （906KB）（239）       Knowledge map    Save Advancements in machine-learning technology has enabled automated program-repair techniques that learn human patterns of erroneous-code fixing, thereby assisting students in debugging and enhancing their self-directed learning efficiency. Automatic program-repair models are typically based on either manually designed symbolic rules or data-driven methods. Owing the availability of large language models that possess excellent natural-language understanding and code-generation capabilities, researchers have attempted to use prompt engineering for automatic program repair. However, existing studies primarily evaluate commercial models such as Codex and GPT-4, which may incur high costs for large-scale adoption and cause data-privacy issues in educational scenarios. Furthermore, these studies typically employ simple prompt forms to assess the program-repair capabilities of large language models, whereas the results are not analyzed comprehensively. Hence, we evaluate two representative open-source code large language models with excellent code-generation capability using prompt engineering. We evaluate different prompting methods, such as chain-of-thought and few-shot learning, and analyze the results comprehensively. Finally, we provide suggestions for integrating large language models into programming educational scenarios. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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A formal verification method for embedded operating systems Yang WANG, Jingcheng FANG, Xiong CAI, Zhipeng ZHANG, Yong CAI, Weikai MIAO Journal of East China Normal University(Natural Science)    2024, 2024 (4): 1-17.   DOI: 10.3969/j.issn.1000-5641.2024.04.001 Abstract （268）   HTML （13）    PDF（pc） （1364KB）（359）       Knowledge map    Save The operating system is the core and foundation of the entire computer system. Its reliability and safety are vital because faults or vulnerabilities in the operating system can lead to system crashes, data loss, privacy breaches, and security attacks. In safety-critical systems, any errors in the operating system can result in significant loss of life and property. Ensuring the safety and reliability of the operating system has always been a major challenge in industry and academia. Currently, methods for verifying the operating system’s safety include software testing, static analysis, and formal methods. Formal methods are the most promising in ensuring the operating system’s safety and trustworthiness. Mathematical models can be established using formal methods, and the system can be formally analyzed and verified to discover potential errors and vulnerabilities. In the operating system, formal methods can be used to verify the correctness and completeness of the operating system’s functions and system safety. A formal scheme for embedded operating systems is proposed herein on the basis of existing formal verification achievements for operating systems. This scheme uses VCC (verified C compiler), CBMC (C bounded model checker), and PAT (process analysis toolkit) tools to verify the operating system at the unit, module, and system levels, respectively. The schema, upon being successfully applied to a task scheduling architecture case of a certain operating system, exhibits a certain universality for analyzing and verifying embedded operating systems. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Group contrastive learning for weakly-supervised 3D point cloud semantic segmentation Zhihong ZHENG, Haichuan SONG Journal of East China Normal University(Natural Science)    2024, 2024 (2): 108-118.   DOI: 10.3969/j.issn.1000-5641.2024.02.012 Abstract （268）   HTML （9）    PDF（pc） （1305KB）（279）       Knowledge map    Save Three-dimensional point cloud semantic segmentation is an essential task for 3D visual perception and has been widely used in autonomous driving, augmented reality, and robotics. However, most methods work under a fully-supervised setting, which heavily relies on fully annotated datasets. Many weakly-supervised methods have utilized the pseudo-labeling method to retrain the model and reduce the labeling time consumption. However, the previous methods have failed to address the conformation bias induced by false pseudo labels. In this study, we proposed a novel weakly-supervised 3D point cloud semantic segmentation method based on group contrastive learning, constructing contrast between positive and negative sample groups selected from pseudo labels. The pseudo labels will compete with each other within the group contrastive learning, reducing the gradient contribution of falsely predicted pseudo labels. Results on three large-scale datasets show that our method outperforms state-of-the-art weakly-supervised methods with minimal labeling annotations and even surpasses the performance of some classic fully-supervised methods. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Arrangement and analysis of type specimens of the Shanghai Natural History Museum Herbarium Ruiping SHI, Bicheng LI, Chunqing Wen, Yunfei ZHANG, Qianqian WU, Xiangkun QIN Journal of East China Normal University(Natural Science)    2024, 2024 (4): 82-99.   DOI: 10.3969/j.issn.1000-5641.2024.04.009 Abstract （266）   HTML （5）    PDF（pc） （13433KB）（79）       Knowledge map    Save To ascertain the status and promote the utilization and sharing of type specimens in Herbarium of Shanghai Natural History Museum (SHM), the collecting information of normal specimens in SHM with type specimens in specimens of plant resource sharing platform and journal of plant taxonomy were compared, 418 type specimens were confirmed. There are 239 species belonging to 147 genera in 69 families, including 390 type specimens newly discovered. The quantity, type, species, dominant groups, collectiion location, collection time, and the name type specimen collector were collected and analysed in the herbarium. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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Bioinformatics-based construction of immune prognostic gene model for hepatocellular carcinoma and preliminary model validation Linding XIE, Yuan ZHANG, Yihong CAI Journal of East China Normal University(Natural Science)    2024, 2024 (4): 100-110.   DOI: 10.3969/j.issn.1000-5641.2024.04.010 Abstract （251）   HTML （5）    PDF（pc） （4520KB）（366）       Knowledge map    Save The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases were used to collect RNA sequence information from patients with hepatocellular carcinoma (HCC). The key genes involved in the immune response mechanism to HCC were screened using the non-negative matrix factorization (NMF) clustering method and weighted gene co-expression network analysis (WGCNA). Prognostic gene models were constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis, and biological functions were analyzed using gene set enrichment analysis (GSEA). Subsequently, to assess the immune infiltration and the related functional differences between the patients in two different risk groups , we used single-sample gene set enrichment analysis (ssGSEA). We constructed column line graphs in combination with independent risk factors to predict overall patient survival time using the “RMS” package in R. Finally, preliminary clinical validation was performed using the Human Protein Atlas (HPA) database with real-time quantitative fluorescent PCR (RT-qPCR). In conclusion, we integrated the clinical characteristics of patients based on risk scores to construct a verifiable and reproducible column line chart, providing a reliable reference for the precise treatment of patients in clinical oncology. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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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 Abstract （249）   HTML （40）    PDF（pc） （196KB）（177）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics | Comments（0）

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OpenRank contribution evaluation method and empirical study in open-source course Jie WANG, Wenrui HUANG, Shengyu ZHAO, Xiaoya XIA, Fanyu HAN, Wei WANG, Yanbin ZHANG Journal of East China Normal University(Natural Science)    2024, 2024 (5): 11-19.   DOI: 10.3969/j.issn.1000-5641.2024.05.002 Abstract （249）   HTML （22）    PDF（pc） （2282KB）（355）       Knowledge map    Save This study presents an OpenRank-based method for evaluating open-source contributions, designed to address the challenge of quantifying student contributions in open-source projects. Taking the “Open-Source Software Design and Development” course as a case study, we developed a method to assess student contributions in open-source practice. The OpenRank algorithm, which is based on developer collaboration networks, evaluates student contributions in discussions, problem-solving, and coding. Experimental results indicate that OpenRank not only aligns with traditional grading methods but also provides a more comprehensive view of student contributions. Combining OpenRank with traditional grading offers a more scientific and thorough evaluation of student contributions and skills in open-source projects. Table and Figures | Reference | Related Articles | Metrics | Comments（0）