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学者姓名：成全
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Abstract ：
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 等. "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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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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Abstract ：
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 等. "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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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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[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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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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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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In the era of big data, classifying online tourism resource information can facilitate the matching of user needs with tourism resources and enhance the efficiency of tourism resource integration. However, most research in this field has concentrated on a simple classification problem with a single level of single labelling. In this paper, a Hierarchical Label-Aware Tourism-Informed Dual Graph Attention Network (HLT-DGAT) is proposed for the complex multi-level and multi-label classification presented by online textual information about Chinese tourism resources. This model integrates domain knowledge into a pre-trained language model and employs attention mechanisms to transform the text representation into the label-based representation. Subsequently, the model utilizes dual Graph Attention Network (GAT), with one component capturing vertical information and the other capturing horizontal information within the label hierarchy. The model's performance is validated on two commonly used public datasets as well as on a manually curated Chinese tourism resource dataset, which consists of online textual overviews of Chinese tourism resources above 3A level. Experimental results indicate that HLT-DGAT demonstrates superiority in threshold-based and area-under-curve evaluation metrics. Specifically, the AU(PRC) reaches 64.5 % on the Chinese tourism resource dataset with enforced leaf nodes, which is 3 % higher than the optimal corresponding metric of the baseline model. Furthermore, ablation studies show that (1) integrating domain knowledge, (2) combining local information, (3) considering label dependencies within the same level of label hierarchy, and (4) merging dynamic reconstruction can enhance overall model performance.
Keyword ：
Attention mechanism Attention mechanism Hierarchical multi-label text classification Hierarchical multi-label text classification Natural language processing Natural language processing Tourism resources Tourism resources
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| GB/T 7714 | Cheng, Quan , Shi, Wenwan . Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 62 (1) . |
| MLA | Cheng, Quan et al. "Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network" . | INFORMATION PROCESSING & MANAGEMENT 62 . 1 (2024) . |
| APA | Cheng, Quan , Shi, Wenwan . Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 62 (1) . |
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In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.
Keyword ：
Graph convolutional neural network Graph convolutional neural network Hierarchical multi-label classification Hierarchical multi-label classification Knowledge graph Knowledge graph Science and technology news Science and technology news
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| GB/T 7714 | Cheng, Quan , Cheng, Jingyi , Chen, Jian et al. Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement [J]. | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
| MLA | Cheng, Quan et al. "Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement" . | PEERJ COMPUTER SCIENCE 10 (2024) . |
| APA | Cheng, Quan , Cheng, Jingyi , Chen, Jian , Liu, Shaojun . Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement . | PEERJ COMPUTER SCIENCE , 2024 , 10 . |
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[目的]实现对互联网医疗健康平台用户生成的大量复杂信息的语义发现与关系揭示.[方法]构建基于改进CasRel实体关系抽取模型的在线健康信息语义发现模型,基于CasRel模型在文本编码层引入更适用于医疗健康领域的ERNIE-Health预训练模型,在主体、关系及客体解码层使用多级指针网络标注和神经网络融合主体特征进行关系及客体的解码.[结果]相较于原始CasRel模型,改进后的CasRel实体关系抽取模型在在线健康信息语义发现的实体识别和实体关系抽取任务中,F,值分别提升7.62个百分点和4.87个百分点.[局限]模型的整体效果还需要在数据集的体量扩充、不同疾病类型的健康信息实证环节进行验证.[结论]本研究提出的改进CasRel实体关系抽取模型能有效提升在线健康信息的语义发现能力.
Keyword ：
关系抽取 关系抽取 在线健康信息 在线健康信息 实体抽取 实体抽取 语义发现 语义发现
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| GB/T 7714 | 成全 , 蒋世辉 , 李卓卓 . 基于改进CasRel实体关系抽取模型的在线健康信息语义发现研究 [J]. | 数据分析与知识发现 , 2024 , 8 (10) : 112-124 . |
| MLA | 成全 et al. "基于改进CasRel实体关系抽取模型的在线健康信息语义发现研究" . | 数据分析与知识发现 8 . 10 (2024) : 112-124 . |
| APA | 成全 , 蒋世辉 , 李卓卓 . 基于改进CasRel实体关系抽取模型的在线健康信息语义发现研究 . | 数据分析与知识发现 , 2024 , 8 (10) , 112-124 . |
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