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学者姓名：阳成虎
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目的：分析老年慢性病全周期健康管理的现实梗阻，为完善政策和服务体系提供参考。方法：对福建省F市慢性病管理的政策制定者、服务供给者和老年慢性病患者进行半结构化访谈，运用扎根理论构建老年人慢性病健康管理现实梗阻框架。结果：共提炼出12个主范畴和4个核心范畴，数据应用失耦、主体协同失调、资源配置失衡、服务交互失联是老年慢性病健康管理碎片化的主要原因。结论：通过技术智慧化、主体协同化、资源均等化、服务同频化，优化老年慢性病管理路径，为老年人提供公平可及、系统连续、优质高效的全周期健康管理服务，更好地落实健康老龄化国家战略。
Keyword ：
全周期健康管理 全周期健康管理 扎根理论 扎根理论 老年慢性病 老年慢性病
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| GB/T 7714 | 林彬龙 , 吴晓园 , 吴心怡 et al. 老年慢性病全周期健康管理的现实梗阻与优化路径研究 [J]. | 卫生经济研究 , 2024 , 41 (05) : 5-8,12 . |
| MLA | 林彬龙 et al. "老年慢性病全周期健康管理的现实梗阻与优化路径研究" . | 卫生经济研究 41 . 05 (2024) : 5-8,12 . |
| APA | 林彬龙 , 吴晓园 , 吴心怡 , 阳成虎 . 老年慢性病全周期健康管理的现实梗阻与优化路径研究 . | 卫生经济研究 , 2024 , 41 (05) , 5-8,12 . |
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Reusing electric vehicles (EV) batteries that reach the end of their useful first life is an environmental and cost -competitive option; however, the process of recycling EV batteries is not yet mature. Due to complex electrochemical reactions and physical conditions, the quality of used EV batteries (cores) is highly uncertain. The remanufacturer needs to make the acquisition decision under quality distributional ambiguity. Perfect quality distribution of cores cannot be known to the remanufacturer in practice. We develop distributionally robust optimization models based on phi -divergence measures and the imprecise Dirichlet model (DRO-IDM) to derive robust decisions. First, we find that the bounds of quality probability intervals are identified solely based on the collected data by introducing the imprecise Dirichlet model. The derived finite -sample boundary can reduce the scope of the uncertainty set and avoid the no -direction search issue. Second, our models can hedge against distributional uncertainty, reduce the probability of a robust solution that deviates from the optimal solution, and correct bias in decision making. Third, we extend the DRO-IDM to develop data -driven models, that can reassess the value of multisource quality information to improve the estimation accuracy of core quality and maximize the remanufacturer's profit. Our study provides new insights for remanufacturers: the new remanufacturing process proposed in our work can assist remanufacturers in utilizing the values of cores without disassembly; the information -aware algorithm can offer the remanufacturing sector a valuable tool for efficiently filtering out invalid information in optimizing acquisition decisions; this capability empowers decision -makers to leverage multiple sources of information and expedite the process of digital transformation in remanufacturing; our approach can also provide a manner of integrating information fusion and distribution learning into remanufacturing.
Keyword ：
Data-driven model Data-driven model Decision analysis Decision analysis Distributionally robust optimization Distributionally robust optimization Multisource information Multisource information Quality uncertainty Quality uncertainty
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| GB/T 7714 | Yang, Cheng-Hu , Su, Xiao-Li , Ma, Xin et al. A data-driven distributionally robust optimization approach for the core acquisition problem [J]. | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2024 , 318 (1) : 253-268 . |
| MLA | Yang, Cheng-Hu et al. "A data-driven distributionally robust optimization approach for the core acquisition problem" . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 318 . 1 (2024) : 253-268 . |
| APA | Yang, Cheng-Hu , Su, Xiao-Li , Ma, Xin , Talluri, Srinivas . A data-driven distributionally robust optimization approach for the core acquisition problem . | EUROPEAN JOURNAL OF OPERATIONAL RESEARCH , 2024 , 318 (1) , 253-268 . |
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背景 随着人口老龄化进程加快，居民疾病谱变化，以糖尿病为代表的慢性病患病率逐年攀升，亟需建立广覆盖、高效率的基层医防融合模式。已有研究多聚焦于健康管理服务需求及服务采纳的影响因素，鲜有对数字技术下慢病医防融合服务需求进行识别与分析。目的 探索数字健康背 景下居民对糖尿病医防融合服务需求，以及不同服务内容对服务对象接受度与满意度的影响，以期为公众完善全过程、全方位的医防融合服务提供理论依据。方法 结合相关研究与实际工作，确立了 20 项糖尿病医防融合服务需求调查项目，并于 2023 年 1—6 月，采用便利抽样法调查福建省、广东省和云南省的糖尿病患病及风险人群，获取 410 名受访者数据，根据性别、年龄、文化程度、居住地类型和医保类型五类人口学特征，依据 Kano 模型分析法进行属性分类分析，考察不同属性的服务需求与居民满意度的关系，进而提出糖尿病医防融合服务供给策略。结果 不同人口学特征的居民对糖尿病医防融合服务需求显示出共性和个性差异，其中，不同年龄段和文化程度的人群服务需求差异较大。糖尿病防治群体的医防融合服务需求聚焦在筛防和诊疗环节，但互联网与社交媒体提供的相关便捷服务与用户的满意度无关。结论 应当提升糖尿病基层医防融合服务个性化水平，充分满足服务人群的“糖尿病与并发症初步筛查”等必备属性需求，完善“建立全周期个人电子健康档案”等期望属性服务，以及提升“风险预测”“远程健康监测”等魅力属性需求的服务。
Keyword ：
Kano 模型 Kano 模型 医防融合 医防融合 数字健康 数字健康 服务需求 服务需求 糖尿病 糖尿病
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| GB/T 7714 | 吴心怡 , 吴晓园 , 阳成虎 et al. 数字健康背景下糖尿病基层医防融合服务的需求研究 [J]. | 中国全科医学 , 2024 . |
| MLA | 吴心怡 et al. "数字健康背景下糖尿病基层医防融合服务的需求研究" . | 中国全科医学 (2024) . |
| APA | 吴心怡 , 吴晓园 , 阳成虎 , 张永泽 . 数字健康背景下糖尿病基层医防融合服务的需求研究 . | 中国全科医学 , 2024 . |
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目的：探究医疗保障经办精细化治理的实践路径。方法：基于扎根理论，以国家医疗保障局评选的44个典型服务案例作为分析样本进行文本分析。结果：分步提取出47个副范畴、16个次范畴、4个主范畴。结论：医疗保障经办精细化治理体系以机制治理、对象治理、结构治理、技术治理为关键要素，以满足人民群众医保服务需求为导向，四大关键要素互动共生，最终实现精细化治理目标。
Keyword ：
体系构建 体系构建 医保经办 医保经办 扎根理论 扎根理论 精细化治理 精细化治理
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| GB/T 7714 | 吴心怡 , 吴晓园 , 林彬龙 et al. 医疗保障经办精细化治理体系构建研究——基于扎根理论 [J]. | 卫生经济研究 , 2023 , 40 (09) : 40-42,48 . |
| MLA | 吴心怡 et al. "医疗保障经办精细化治理体系构建研究——基于扎根理论" . | 卫生经济研究 40 . 09 (2023) : 40-42,48 . |
| APA | 吴心怡 , 吴晓园 , 林彬龙 , 阳成虎 . 医疗保障经办精细化治理体系构建研究——基于扎根理论 . | 卫生经济研究 , 2023 , 40 (09) , 40-42,48 . |
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在人口老龄化纵深发展过程中,老年人慢性疾病高发频发,康复医疗作为保持或恢复老年人身体机能的有效手段,存在庞大的市场需求.该文采用扎根分析方法研究老年人康复医疗体系的影响因素,通过对政策制定者、行业专家、老年人的半结构化访谈,从产业层、个体层、社会层三个方面阐述老年人康复医疗体系需求影响理论模型的作用机制,以期为福建省主动应对老龄化挑战,提升老年健康服务水平,加快推进老年人康复医疗工作发展提供参考.
Keyword ：
康复医疗 康复医疗 影响因素 影响因素 扎根理论 扎根理论 理论框架 理论框架
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| GB/T 7714 | 林彬龙 , 王发圣 , 阳成虎 . 福建省老年人康复医疗体系构建的影响因素研究 [J]. | 海峡科学 , 2023 , (3) : 99-103 . |
| MLA | 林彬龙 et al. "福建省老年人康复医疗体系构建的影响因素研究" . | 海峡科学 3 (2023) : 99-103 . |
| APA | 林彬龙 , 王发圣 , 阳成虎 . 福建省老年人康复医疗体系构建的影响因素研究 . | 海峡科学 , 2023 , (3) , 99-103 . |
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In intelligent manufacturing systems, the industrial informatics has features of multi-source, multi-noise, and time series. It is difficult for small and medium enterprises (SMEs) to directly exploit the enormous amounts of data due to the limited budgets and computing capabilities. Edge intelligence is a key technique to power intelligent manufacturing systems and provide knowledge transferred from the cloud to SMEs at the edges. To address edge-cloud collaboration issue, we propose a refined data-driven distributionally robust newsvendor model based on & phi;-divergence measures and imprecise Dirichlet models (DRN-IDM). We construct new distributional uncertainty sets by effectively integrating local censored demand data and cloud knowledge, which helps SMEs to make intelligent production decisions and reduce significant decision deviations, even under a small censored data set. In particular, the novel demand uncertainty sets can depict the distance between distributions and probability intervals. Then, we transform the DRN-IDM model into a convex optimization model that is amenable to algorithmic implementation. Additionally, based on the coefficient of variation of limited historical data, we propose an adaptive demand information fusion procedure to achieve excellent synergy effect from cloud knowledge. We also validate the effectiveness of the DRN-IDM model and the practicability of adaptive procedure using extensive numerical studies with both simulated and real-life data. Furthermore, we measure the relative expected value of cloud knowledge and investigate the effect of censored demand samples. Our results verify the effectiveness condition of the DRN-IDM model and indicate that cloud knowledge can improve the precision and robustness of SMEs' production decisions with small-scale censored data. Interestingly, the verified adaptive procedure can be applied in the learning criteria design of metaheuristics in intelligent manufacturing systems, and the reconstructed uncertainty set can narrow the search space to improve the convergence performance of algorithms.
Keyword ：
Data-driven newsvendor problem Data-driven newsvendor problem Distributionally robust optimization Distributionally robust optimization Edge-cloud collaboration Edge-cloud collaboration Edge production Edge production Intelligent manufacturing systems Intelligent manufacturing systems
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| GB/T 7714 | Yang, Cheng-hu , Su, Xiao-li , Wu, Peng . A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
| MLA | Yang, Cheng-hu et al. "A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126 (2023) . |
| APA | Yang, Cheng-hu , Su, Xiao-li , Wu, Peng . A data-driven distributionally newsvendor problem for edge-cloud collaboration in intelligent manufacturing systems . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
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In this paper, a data-driven newsvendor problem is studied by mapping high-dimensional and mixed-frequency features of historical data to replenishment decisions. Instead of relying on a known demand distribution, we propose using machine learning algorithms that incorporate demand features into the replenishment decisions to solve single-and multi-product newsvendor problems. In particular, our algorithms simultaneously optimize the demand estimation and replenishment decisions. To extract key features from the historical data, we propose a frequency alignment method to transform high-dimensional mixed-frequency data and historical data into the same frequency. We then propose two variable selection policies based on the empirical risk minimization principle, and employ the regularization method to tackle the parameter proliferation issue. In addition, a feature-based machine learning algorithm is designed to solve the multi-product newsvendor problem. Finally, we numerically justify the performances of proposed machine learning algorithms. We find that (1) data-informed replenishment decisions can effectively leverage the identified key demand features to avoid losing demand information, and (2) the optimal replenishment quantity behaves robustly and shows minimal variation across different cost structures. Our work provides meaningful insights for newsvendors making replenishment decisions under stochastic demand.
Keyword ：
Data-driven Data-driven Machine learning Machine learning Mixed-frequency data Mixed-frequency data Newsvendor Newsvendor Variables selection Variables selection
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| GB/T 7714 | Yang, Cheng-Hu , Wang, Hai-Tang , Ma, Xin et al. A data-driven newsvendor problem: A high-dimensional and mixed-frequency method [J]. | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2023 , 266 . |
| MLA | Yang, Cheng-Hu et al. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method" . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 266 (2023) . |
| APA | Yang, Cheng-Hu , Wang, Hai-Tang , Ma, Xin , Talluri, Srinivas . A data-driven newsvendor problem: A high-dimensional and mixed-frequency method . | INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS , 2023 , 266 . |
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该文从先天性信任、历史性信任、角色能力信任、社会规则信任、社会范畴信任5 个维度出发,运用AHP法和熵权法组合确定新能源汽车动力电池闭环供应链成员间信任程度影响因素的综合权重,测算样本信任度,并通过RFECV法识别出18 个关键影响因素,丰富了供应链信任理论,为提升供应链整体信任水平提供参考,对于促进新能源动力电池闭环供应链成员间合作具有指导意义.
Keyword ：
信任合作 信任合作 影响因素 影响因素 新能源动力电池 新能源动力电池 闭环供应链 闭环供应链
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| GB/T 7714 | 温红林 , 林彬龙 , 阳成虎 . 新能源动力电池闭环供应链成员间信任影响因素研究 [J]. | 海峡科学 , 2023 , (11) : 52-57 . |
| MLA | 温红林 et al. "新能源动力电池闭环供应链成员间信任影响因素研究" . | 海峡科学 11 (2023) : 52-57 . |
| APA | 温红林 , 林彬龙 , 阳成虎 . 新能源动力电池闭环供应链成员间信任影响因素研究 . | 海峡科学 , 2023 , (11) , 52-57 . |
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To improve the accuracy of demand forecasting for new electronic products, especially in scenarios with limited historical data, a novel forecasting model was proposed in this study which integrated K-means based on Euclidian distance, Multi-layer perceptron algorithm, and Quantile Regression with Gradient Boosted Trees (KEM-QRGBT). The model also incorporated grid search with K-fold cross-validation to enable the adaptive selection of the optimal parameters for product data. Additionally, the KEM-QRGBT model, which can balance the intricacies of learning parameter patterns with its ability to quantify demand uncertainty, exhibited proficiency in quantifying the uncertainty inherent in demand forecasting. Using a case study from a manufacturing enterprise in Turkey, the effectiveness of the model was validated. Results demonstrate that, for new electronic products with limited historical data, the KEM-QRGBT model with adaptive parameter selection improves demand forecasting accuracy, outperforming benchmark methods, and other machine learning models. The proposed algorithm provides a strong evidence for the demand forecasting of new electronic products, particularly in cases where historical data is limited. © 2023 School of Science, IHU. All Rights Reserved.
Keyword ：
Deep learning Deep learning E-learning E-learning Forecasting Forecasting K-means clustering K-means clustering Learning systems Learning systems Life cycle Life cycle Trees (mathematics) Trees (mathematics)
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| GB/T 7714 | Lin, Binlong , Wu, Yi , Wu, Juanjuan et al. Demand Forecasting for the Full Life Cycle of New Electronic Products Based on KEM-QRGBT Model [J]. | Journal of Engineering Science and Technology Review , 2023 , 16 (6) : 90-97 . |
| MLA | Lin, Binlong et al. "Demand Forecasting for the Full Life Cycle of New Electronic Products Based on KEM-QRGBT Model" . | Journal of Engineering Science and Technology Review 16 . 6 (2023) : 90-97 . |
| APA | Lin, Binlong , Wu, Yi , Wu, Juanjuan , Yang, Chenghu . Demand Forecasting for the Full Life Cycle of New Electronic Products Based on KEM-QRGBT Model . | Journal of Engineering Science and Technology Review , 2023 , 16 (6) , 90-97 . |
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我国物流行业市场主体信用缺失现象频发，制约了行业的健康高效发展。区块链具有去中心化、不可篡改、可信任性、可追溯、全网记账等优势，为物流行业监管机制的创新提供了路径。该文在梳理物流行业监管现状的基础上，构建了基于区块链技术的物流行业信用监管系统，从技术路线选择、业务内容和逻辑结构三个维度解构了信用监管系统，进而提出加快区块链技术与物流行业信用监管的深度融合，提升信用监管效率的对策。
Keyword ：
信用监管 信用监管 区块链 区块链 物流行业 物流行业
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| GB/T 7714 | 黄锥良 , 刘兰英 , 邵博 et al. 基于区块链技术的物流行业信用监管问题及对策研究 [J]. | 海峡科学 , 2022 , 5 (06) : 76-80 . |
| MLA | 黄锥良 et al. "基于区块链技术的物流行业信用监管问题及对策研究" . | 海峡科学 5 . 06 (2022) : 76-80 . |
| APA | 黄锥良 , 刘兰英 , 邵博 , 阳成虎 . 基于区块链技术的物流行业信用监管问题及对策研究 . | 海峡科学 , 2022 , 5 (06) , 76-80 . |
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