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Novel model for predicting the future volume of research articles on applications of Quantum Dots
COLLNET Journal of Scientometrics and Information Management Pub Date : 2022-07-27
Ishan Mishra, Preeti Mulay, Neeru Bhagat

The paper aims to briefly introduce to the topic of quantum heterostructures taking Quantum Dots (QD) as the topic of interest. It reviews different mathematical models explaining the behaviour of QD, and it proposes a novel method to predict the future article volumes on the topic of applications of QD. The paper also reviews the use of QD in quantum computing. The paper opted for a numerical approach to predict future article volumes. Firstly, bibliometric data for the past 40 years is collected and then by assuming a relationship between relevant variables, a governing equation is developed which is then solved using the Finite Difference Method (FDM). The paper provides insights into how a prediction model can be developed without using tons of metrics. It also suggests that a prediction model can be developed using only the past behaviour of the concerned dataset. Due to the chosen research approach, the effectiveness of the model may be less. Therefore, researchers are encouraged to test the proposed method further. The paper includes the practical implications of an easily analysed and executable model. The paper also shows that the model proposed can not only be used to predict future article volumes but also to predict datasets that exhibit a quasi-linear nature. This paper fulfils the need for a new, easier prediction method.



中文翻译:

预测未来量子点应用研究文章数量的新模型

本文旨在简要介绍以量子点(QD)为研究对象的量子异质结构。它回顾了解释量子点行为的不同数学模型,并提出了一种新的方法来预测未来关于量子点应用主题的文章数量。该论文还回顾了量子计算在量子计算中的使用。该论文选择了一种数值方法来预测未来的文章数量。首先,收集过去 40 年的文献计量数据,然后通过假设相关变量之间的关系,开发出一个控制方程,然后使用有限差分法 (FDM) 对其进行求解。该论文提供了有关如何在不使用大量指标的情况下开发预测模型的见解。它还表明可以仅使用相关数据集的过去行为来开发预测模型。由于所选择的研究方法,模型的有效性可能会降低。因此,鼓励研究人员进一步测试所提出的方法。该论文包括一个易于分析和可执行的模型的实际意义。该论文还表明,所提出的模型不仅可以用于预测未来的文章数量,还可以用于预测表现出准线性性质的数据集。本文满足了对一种新的、更简单的预测方法的需求。该论文还表明,所提出的模型不仅可以用于预测未来的文章数量,还可以用于预测表现出准线性性质的数据集。本文满足了对一种新的、更简单的预测方法的需求。该论文还表明,所提出的模型不仅可以用于预测未来的文章数量,还可以用于预测表现出准线性性质的数据集。本文满足了对一种新的、更简单的预测方法的需求。

更新日期:2022-07-27
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