聚羟基脂肪酸酯(PHA)具有良好的生物降解性、生物相容性和可持续性,在包装、食品和医疗等领域具有广泛的应用前景。因此,亟待对国内外近二十余年来的有关PHA的研究进行总结和梳理,为PHA领域的协同发展提供理论依据。本研究以Web of Scie...
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聚羟基脂肪酸酯(PHA)具有良好的生物降解性、生物相容性和可持续性,在包装、食品和医疗等领域具有广泛的应用前景。因此,亟待对国内外近二十余年来的有关PHA的研究进行总结和梳理,为PHA领域的协同发展提供理论依据。本研究以Web of Science(WOS)核心合集数据库和中国知网(CNKI)全文数据库为数据检索源,借助CiteSpace和VOSviewer文献计量工具,对2000-2023年PHA领域的相关文献进行知识图谱的可视化分析。结果表明,在2000-2023年间,国内外对PHA领域的研究目前处于快速增长阶段,且英文文献的发文量和年增长率均显著高于中文文献。国际对该领域的关注度和研究要早于国内,并且我国在PHA领域的研究对全球贡献较大,但有影响力的研究较为缺乏,亟待进一步提高。关键词聚类和时空突现分析表明,未来的研究热点主要集中在以下3个方面:1)降低PHA生产成本以实现工业化生产;2)关注PHA降解酶的筛选与鉴定,以及PHA降解过程中的微生物群落结构和功能研究;3)PHA的共聚改性、复合改性以及与其他生物材料的功能化改性。
本文利用MATLAB的界面设计功能对抽样分布理论构建了一个模拟实验界面,以可视化动态演示的形式呈现抽象的理论,将几种常见抽样分布的频数直方图与概率密度函数进行比较。在该界面中,学生可以自主进行实验操作,修改参数,观察多种情况下的实验结果。本项目实现了用户与界面的互动,省略了复杂的证明过程,从而帮助学生更好地理解抽样分布理论。This paper uses the interface design function of MATLAB to build a simulation experiment interface for the theory of sampling distribution, presenting the abstract theory in a visual and dynamic demonstration form. It compares the frequency histograms and probability density functions of several common sampling distributions. In this interface, students can conduct experimental operations autonomously, modify parameters, and observe the experimental results under various conditions. This project achieves interaction between users and interfaces, omits complex proof processes, and helps students better understand the theory of sampling distribution.
利率期限结构及相关问题一直是金融学的研究热点。本文基于动态Svensson模型拟合四因子Lt、St、Ct1、Ct2,与筛选过的宏观经济变量进行小波相干性分析,得到国债利率期限结构对M2和MLAI有预测能力。根据中国经济周期波动的特点,对数据进行小波分解,将第5、6、7层分量作为短周期、中周期、长周期的波动分量,分析国债利率期限结构对M2、MLAI在短、中、长期的影响。The term structure of interest rates and related issues have been a hot research topic in finance. In this paper, based on the dynamic Svensson model fitting the four factors Lt, St, Ct1, Ct2, and wavelet coherence analysis with screened macroeconomic variables, we get that the term structure of treasury bond interest rate has the ability to predict M2 and MLAI. According to the characteristics of China’s business cycle fluctuations, the data is decomposed by wavelet, and the 5th, 6th, and 7th components are taken as short cycle, medium cycle, and long cycle fluctuation components to analyze the impact of the term structure of treasury bond interest rates on M2 and MLAI in the short, medium, and long term.
利率期限结构及相关问题一直是金融学的研究热点。文章借助于动态Svensson模型、深度学习,构建了国债利率期限结构的深度学习预测模型。选取2012年1月~2024年5月银行间零息国债收益率数据,对其进行了实证分析,并与DS模型、加入宏观经济变量的DS模型、不加入宏观经济变量的深度学习模型的预测结果进行了对比,结果显示文章提出的深度学习预测模型效果显著提升,鲁棒性更好。The term structure of interest rates and related issues has always been a research hotspot in finance. With the help of the dynamic Svensson model and deep learning, this paper constructs a deep learning prediction model for the term structure of treasury bond interest rates. This paper selects the yield data of inter-bank zero-interest treasury bond bonds from January 2012 to April 2024 to conduct an empirical analysis. The results show that compared with the prediction results of the DS model, the DS model with macroeconomic variables, and the deep learning model without macroeconomic variables, the deep learning prediction model proposed in this paper has significantly improved its effectiveness and has better robustness.
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