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学者姓名：成全
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[目的/意义]探讨区块链技术融入现代社会信用体系的机理,旨在解决政府在公共信用数据共享中存在数据质量低、协同不足等问题,以推动政府公共信用数据供应链的有序发展.[方法/过程]通过构建数据供给方、政府监管方与数据使用方之间的三方演化博弈模型,分析各方在策略选择、系统均衡点的稳定性以及相关参数对演化博弈过程和结果的影响.[结果/结论]博弈系统至少存在一个确定的稳定策略点.数据供给方数据共享、政府监管方严格监管和数据使用方使用区块链平台受到边际成本、区块链平台补贴、共享数据增值收益、数据利用收益等多个变量的影响,变量间的数值关系也影响着系统的演化稳定策略.根据仿真结果提出了优化激励机制、提供数据安全保护、降低区块链使用门槛及成本等管理建议.
Keyword ：
供应链治理 供应链治理 区块链 区块链 政府公共信用 政府公共信用 数据共享 数据共享 演化博弈 演化博弈
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| GB/T 7714 | 成全 , 董嘉鑫 . 区块链技术采纳的政府公共信用数据供应链治理演化博弈研究 [J]. | 情报探索 , 2024 , (1) : 1-11 . |
| MLA | 成全 等. "区块链技术采纳的政府公共信用数据供应链治理演化博弈研究" . | 情报探索 1 (2024) : 1-11 . |
| APA | 成全 , 董嘉鑫 . 区块链技术采纳的政府公共信用数据供应链治理演化博弈研究 . | 情报探索 , 2024 , (1) , 1-11 . |
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近年来，基于深度学习模型的药物推荐在智慧医疗领域得到了广泛的研究和应用。提出了一种基于图嵌入的双层图卷积网络药物推荐模型。构建患者属性知识图谱和患者用药知识图谱，利用图嵌入生成嵌入表示，将患者属性知识图谱的嵌入表示放入加载了注意力机制和双向传播机制的多层图注意力网络层进行信息传播与融合，将得到患者的特征表示和患者用药知识图谱嵌入表示进行聚合，再次放入多层图注意力网络层训练，从而挖掘患者属性和患者用药之间的高阶关联，最终完成药物推荐。以重症监护医学信息数据库数据集中的患者基本信息、生理特征和患者用药数据作为对象开展实证研究。实验结果证明，其在推荐准确率、召回率、F1分数和NDCG四个评价指标上均优于基线方法。
Keyword ：
图卷积网络 图卷积网络 智慧医疗 智慧医疗 知识图谱 知识图谱 药物推荐 药物推荐
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| GB/T 7714 | 江钰哲 , 成全 . 图嵌入式双层图卷积网络药物推荐模型 [J]. | 计算机工程与应用 , 2024 , 60 (07) : 315-324 . |
| MLA | 江钰哲 等. "图嵌入式双层图卷积网络药物推荐模型" . | 计算机工程与应用 60 . 07 (2024) : 315-324 . |
| APA | 江钰哲 , 成全 . 图嵌入式双层图卷积网络药物推荐模型 . | 计算机工程与应用 , 2024 , 60 (07) , 315-324 . |
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Acute complication prediction model is of great importance for the overall reduction of premature death in chronic diseases. The CLSTM-BPR proposed in this paper aims to improve the accuracy, interpretability, and generalizability of the existing disease prediction models. Firstly, through its complex neural network structure, CLSTM-BPR considers both disease commonality and patient characteristics in the prediction process. Secondly, by splicing the time series prediction algorithm and classifier, the judgment basis is given along with the prediction results. Finally, this model introduces the pairwise algorithm Bayesian Personalized Ranking (BPR) into the medical field for the first time, and achieves a good result in the diagnosis of six acute complications. Experiments on the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset show that the average Mean Absolute Error (MAE) of biomarker value prediction of the CLSTM-BPR model is 0.26, and the average accuracy (ACC) of the CLSTM-BPR model for acute complication diagnosis is 92.5%. Comparison experiments and ablation experiments further demonstrate the reliability of CLSTM-BPR in the prediction of acute complication, which is an advancement of current disease prediction tools.
Keyword ：
Bayesian Personalized Ranking (BPR) Bayesian Personalized Ranking (BPR) Biological system modeling Biological system modeling Business process re-engineering Business process re-engineering Classification algorithms Classification algorithms disease predictions disease predictions Long Short-Term Memory (LSTM) Long Short-Term Memory (LSTM) MIMICs MIMICs Prediction algorithms Prediction algorithms Predictive models Predictive models sudden illnesses sudden illnesses Time series analysis Time series analysis
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| GB/T 7714 | Chen, Xi , Cheng, Quan . Acute Complication Prediction and Diagnosis Model CLSTM-BPR: A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking [J]. | TSINGHUA SCIENCE AND TECHNOLOGY , 2024 , 29 (5) : 1509-1523 . |
| MLA | Chen, Xi 等. "Acute Complication Prediction and Diagnosis Model CLSTM-BPR: A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking" . | TSINGHUA SCIENCE AND TECHNOLOGY 29 . 5 (2024) : 1509-1523 . |
| APA | Chen, Xi , Cheng, Quan . Acute Complication Prediction and Diagnosis Model CLSTM-BPR: A Fusion Method of Time Series Deep Learning and Bayesian Personalized Ranking . | TSINGHUA SCIENCE AND TECHNOLOGY , 2024 , 29 (5) , 1509-1523 . |
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Energy supply policy quality is an important factor that would impact national energy security. While existing research conducted policy evaluation from the lens of performance assessment, less study has been devoted to evaluating energy supply policy from the perspective of policy making. This study takes the energy supply policy documents issued by China's central government during the '13th Five-Year Plan' period (2016-2020) as the research sample, and pioneers the use of the extended policy modelling consistency (PMC) index model combined with the text mining methodology to construct a policy evaluation index system with the characteristics of the energy supply policy which conducts a more in-depth quantitative evaluation of the energy supply policy documents. The results show that the average PMC index value of China's energy supply policies is 7.26, and the overall quality is high. The concerns of existing policies are mainly focused on reform and development planning, safety production management and project engineering construction, but there is still room for improvement in policy predictability, coordination of policy areas, clarity of policy basis and goals and comprehensiveness of policy tool combinations. Based on this, China should improve the comprehensiveness of its energy supply policy tool combinations in terms of policy design norm; strengthen policy predictability and coordination of policy areas in terms of policy implementation guarantee; and clarify the basis and goals of policies in terms of policy orientation enhancement, to provide a reference basis for the formulation and improvement of future energy supply policies and to realise the sustainable development of energy supply. This study takes the energy supply policies issued by China's central government during the '13th Five-Year Plan' period as the object of research and utilises the extended policy modelling consistency index model to conduct a multidimensional evaluation of energy supply policies. The results show that the overall quality of China's energy supply policies is high, but there is still room for improvement in terms of policy predictability, coordination of policy areas, clarity of policy basis and goals and comprehensiveness of policy tool combinations. Based on this, this paper gives the corresponding suggestions to provide a reference basis for the formulation and improvement of subsequent energy supply policies.image
Keyword ：
energy supply policy energy supply policy PMC index model PMC index model policy evaluation policy evaluation text mining text mining
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| GB/T 7714 | Cheng, Quan , Chen, Zhongzhen , Lin, Minwang et al. Quantitative evaluation of China's energy supply policies in the '13th Five-Year Plan' period (2016-2020): A PMC index modelling approach incorporating text mining [J]. | ENERGY SCIENCE & ENGINEERING , 2024 , 12 (3) : 596-616 . |
| MLA | Cheng, Quan et al. "Quantitative evaluation of China's energy supply policies in the '13th Five-Year Plan' period (2016-2020): A PMC index modelling approach incorporating text mining" . | ENERGY SCIENCE & ENGINEERING 12 . 3 (2024) : 596-616 . |
| APA | Cheng, Quan , Chen, Zhongzhen , Lin, Minwang , Wang, Haiyan . Quantitative evaluation of China's energy supply policies in the '13th Five-Year Plan' period (2016-2020): A PMC index modelling approach incorporating text mining . | ENERGY SCIENCE & ENGINEERING , 2024 , 12 (3) , 596-616 . |
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研究政策工具对企业原始性创新能力的影响是提升企业原始性创新能力的基本前提，也是政府制定原始性创新政策的重要依据。本研究基于2011—2019年的规模以上工业企业科技活动数据，选取六种相关的原始性创新政策工具，并将其划分为供给型、需求型及环境型，然后通过改进柯布-道格拉斯生产函数，从政策工具协同作用及政策工具类型分布两类情境进行回归分析。研究扩展了企业原始性创新的政策工具箱，揭示了政策工具在实践应用中对企业原始性创新能力的影响。实证分析结果表明，相关部门应进一步开发与运用需求型、环境型政策工具，优化创新政策工具体系，加强对企业原始性创新政策工具的协同管理，强化监管力度，确保企业原始性创新的有效性与持续性。
Keyword ：
企业原始性创新 企业原始性创新 原始性创新能力 原始性创新能力 政策工具 政策工具
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| GB/T 7714 | 成全 , 蒋世辉 , 王海燕 . 政策工具对企业原始性创新能力的影响——基于省域工业企业科技活动面板数据的实证 [J]. | 管理评论 , 2024 , 36 (05) : 75-88 . |
| MLA | 成全 et al. "政策工具对企业原始性创新能力的影响——基于省域工业企业科技活动面板数据的实证" . | 管理评论 36 . 05 (2024) : 75-88 . |
| APA | 成全 , 蒋世辉 , 王海燕 . 政策工具对企业原始性创新能力的影响——基于省域工业企业科技活动面板数据的实证 . | 管理评论 , 2024 , 36 (05) , 75-88 . |
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研究政策工具对企业原始性创新能力的影响是提升企业原始性创新能力的基本前提,也是政府制定原始性创新政策的重要依据.本研究基于2011-2019 年的规模以上工业企业科技活动数据,选取六种相关的原始性创新政策工具,并将其划分为供给型、需求型及环境型,然后通过改进柯布-道格拉斯生产函数,从政策工具协同作用及政策工具类型分布两类情境进行回归分析.研究扩展了企业原始性创新的政策工具箱,揭示了政策工具在实践应用中对企业原始性创新能力的影响.实证分析结果表明,相关部门应进一步开发与运用需求型、环境型政策工具,优化创新政策工具体系,加强对企业原始性创新政策工具的协同管理,强化监管力度,确保企业原始性创新的有效性与持续性.
Keyword ：
企业原始性创新 企业原始性创新 原始性创新能力 原始性创新能力 政策工具 政策工具
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| GB/T 7714 | 成全 , 蒋世辉 , 王海燕 . 政策工具对企业原始性创新能力的影响 [J]. | 管理评论 , 2024 , 36 (5) : 75-88 . |
| MLA | 成全 et al. "政策工具对企业原始性创新能力的影响" . | 管理评论 36 . 5 (2024) : 75-88 . |
| APA | 成全 , 蒋世辉 , 王海燕 . 政策工具对企业原始性创新能力的影响 . | 管理评论 , 2024 , 36 (5) , 75-88 . |
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为了研究安全事故案例报告中上下文语义指代和复杂领域内容对机器自动识别与抽取信息的性能影响，通过考虑局部特征增强构建了BERT+Multi-CNN+BiGRU+CRF(BMulCBC)模型。BERT负责将非结构化文本转化输入，Multi-CNN和BiGRU负责向量局部特征与序列特征编码，CRF则负责完成准确的实体标签解码。研究结果表明：模型实体识别的精确率、召回率和F1值分别为65.94%,74.02%,69.75%,在精确率和F1值上皆优于同类对比模型。研究结果可为安全事故事理图谱推理提供理论支持。
Keyword ：
命名实体识别 命名实体识别 安全事故 安全事故 局部特征增强 局部特征增强 案例报告 案例报告 深度学习 深度学习
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| GB/T 7714 | 成全 , 张双宝 . 基于深度学习的特征增强式安全事故文本实体识别模型研究 [J]. | 中国安全生产科学技术 , 2024 , 20 (06) : 58-66 . |
| MLA | 成全 et al. "基于深度学习的特征增强式安全事故文本实体识别模型研究" . | 中国安全生产科学技术 20 . 06 (2024) : 58-66 . |
| APA | 成全 , 张双宝 . 基于深度学习的特征增强式安全事故文本实体识别模型研究 . | 中国安全生产科学技术 , 2024 , 20 (06) , 58-66 . |
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[目的/意义]用户健康信息需求研究能够发现用户潜在需求,解决用户健康信息盲区,帮助用户实现更好的自我健康管理.研究目标为挖掘识别用户信息需求主题,提取用户特征,促进完善网络社区交互性与多元性发展,为更好地改善健康信息服务提出建议与意见.[方法/过程]针对在线健康社区的母婴群体,提出在线健康社区用户信息需求层级多标签分类模型.通过扎根理论提出在线健康社区用户信息需求主题体系,利用ALBERT对母婴健康需求类数据进行预训练,使用双向GRU与注意力机制构建基础分类器,以此来构建层级多标签分类模型Multi-BiGRU-Attention,实现在线健康社区提问数据的层级多标签分类.[结果/结论]实验对比发现,随着层级的增加,研究提出的模型相比于单层的基础分类器BiGRU-Attention在micro-Precision,micro-Recall,micro-F1等各项指标上均有所提升,说明该模型的层级结构信息能够一定程度上改善模型效果;相比于层级多标签相关模型,在各项指标上均有所提升,说明该模型存在一定的适用性与扩展性.
Keyword ：
信息需求 信息需求 在线健康社区 在线健康社区 层级多标签分类 层级多标签分类 用户需求 用户需求
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| GB/T 7714 | 成全 , 郑抒琳 . 在线健康社区用户信息需求的层级多标签分类研究 [J]. | 情报理论与实践 , 2023 , 46 (2) : 100-108 . |
| MLA | 成全 et al. "在线健康社区用户信息需求的层级多标签分类研究" . | 情报理论与实践 46 . 2 (2023) : 100-108 . |
| APA | 成全 , 郑抒琳 . 在线健康社区用户信息需求的层级多标签分类研究 . | 情报理论与实践 , 2023 , 46 (2) , 100-108 . |
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【目的】构建儿童读物层级多标签分类模型，实现对儿童读物的自动化分类，以引导儿童读者选择适合自身发展情况的读物。【方法】将分级阅读的理念具化成儿童读物层级分类标签体系，采用深度学习技术构建ERNIE-HAM模型，并将其应用于儿童读物的层级多标签文本分类。【结果】通过对比4种预训练模型，ERNIE-HAM模型在儿童读物层级分类的第二层级、第三层级分类中具有较好的表现；对比单层级算法，层级算法在第二层级和第三层级的AU (-P-R-C-)值都提升了约11个百分点；对比HFT-CNN和HMCN两个层级多标签分类模型，ERNIE-HAM模型在第三层级的分类结果中AU (-P-R-C-)值分别提升12.79和6.48个百分点。【局限】ERNIE-HAM模型的整体分类效果有待进一步提升，未来在数据集的体量扩充和算法设计上需要进一步完善和探索。【结论】ERNIE-HAM模型在儿童读物层级多标签分类任务上具有有效性。
Keyword ：
儿童读物分类 儿童读物分类 分类体系 分类体系 分级阅读 分级阅读 层级多标签文本分类 层级多标签文本分类
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| GB/T 7714 | 成全 , 董佳 . 面向分级阅读的儿童读物层级多标签分类研究 [J]. | 数据分析与知识发现 , 2023 , 7 (07) : 156-169 . |
| MLA | 成全 et al. "面向分级阅读的儿童读物层级多标签分类研究" . | 数据分析与知识发现 7 . 07 (2023) : 156-169 . |
| APA | 成全 , 董佳 . 面向分级阅读的儿童读物层级多标签分类研究 . | 数据分析与知识发现 , 2023 , 7 (07) , 156-169 . |
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Background: Online medical and health communities provide a platform for internet users to share experiences and ask questions about medical and health issues. However, there are problems in these communities, such as the low accuracy of the classification of users' questions and the uneven health literacy of users, which affect the accuracy of user retrieval and the professionalism of the medical personnel answering the question. In this context, it is essential to study more effective classification methods of users' information needs.Objective: Most online medical and health communities tend to provide only disease-type labels, which do not give a comprehensive summary of users' needs. The study aims to construct a multilevel classification framework based on the graph convolutional network (GCN) model for users' needs in online medical and health communities so that users can perform more targeted information retrieval.Methods: Using the Chinese online medical and health community "Qiuyi" as an example, we crawled questions posted by users in the "Cardiovascular Disease" section as the data source. First, the disease types involved in the problem data were segmented by manual coding to generate the first-level label. Second, the needs were identified by K-means clustering to generate the users' information needs label as the second-level label. Finally, by constructing a GCN model, users' questions were automatically classified, thus realizing the multilevel classification of users' needs.Results: Based on the empirical research of questions posted by users in the "Cardiovascular Disease" section of Qiuyi, the hierarchical classification of users' questions (data) was realized. The classification models designed in the study achieved accuracy, precision, recall, and F1-score of 0.6265, 0.6328, 0.5788, and 0.5912, respectively. Compared with the traditional machine learning method naive Bayes and the deep learning method hierarchical text classification convolutional neural network, our classification model showed better performance. At the same time, we also performed a single-level classification experiment on users' needs, which in comparison with the multilevel classification model exhibited a great improvement.Conclusions: A multilevel classification framework has been designed based on the GCN model. The results demonstrated that the method is effective in classifying users' information needs in online medical and health communities. At the same time, users with different diseases have different directions for information needs, which plays an important role in providing diversified and targeted services to the online medical and health community. Our method is also applicable to other similar disease classifications.(JMIR Form Res 2023;7:e42297) doi: 10.2196/42297
Keyword ：
behavior behavior cardiovascular cardiovascular cardiovascular disease cardiovascular disease China China community community graph convolutional network graph convolutional network medical medical multilevel classification multilevel classification online online online medical health community online medical health community
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| GB/T 7714 | Cheng, Quan , Lin, Yingru . Multilevel Classification of Users' Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network [J]. | JMIR FORMATIVE RESEARCH , 2023 , 7 . |
| MLA | Cheng, Quan et al. "Multilevel Classification of Users' Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network" . | JMIR FORMATIVE RESEARCH 7 (2023) . |
| APA | Cheng, Quan , Lin, Yingru . Multilevel Classification of Users' Needs in Chinese Online Medical and Health Communities: Model Development and Evaluation Based on Graph Convolutional Network . | JMIR FORMATIVE RESEARCH , 2023 , 7 . |
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