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1 – 6 of 6Yishou Wang, Zhibin Han, Tian Gao and Xinlin Qing
The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on…
Abstract
Purpose
The purpose of this study is to develop a cylindrical capacitive sensor that has the advantages of high resolution, small size and designability and can be easily installed on lubricant pipeline to monitor lubricant oil debris.
Design/methodology/approach
A theoretical model of the cylindrical capacitive sensor is presented to analyze several parameters’ effectiveness on the performance of sensor. Numerical simulations are then conducted to determine the optimal parameters for preliminary experiments. Experiments are finally carried out to demonstrate the detectability of developed capacitive sensors.
Findings
It is clear from experimental results that the developed capacitive sensor can monitor the debris in lubricant oil well, and the capacitance values increase almost linearly when the number and size of debris increase.
Research limitations/implications
There is lot of further work to do to apply the presented method into the application. Especially, it is necessary to consider several factors’ influence on monitoring results. These factors include the flow rate of the lubricant oil, the temperature, the debris distribution and the vibration. Moreover, future work should consider the influence of the oil degradation to the capacitance change and other contaminations (e.g. water and dust).
Practical implications
This work conducts a feasibility study on application of capacitive sensing principle for detecting debris in aero engine lubricant oil.
Originality/value
The novelty of the presented capacitance sensor can be summarized into two aspects. One is that the sensor structure is simple and characterized by two coaxial cylinders as electrodes, while conventional capacitive sensors are composed of two parallel plates as electrodes. The other is that sensing mechanism and physical model of the presented sensor is verified and validated by the simulation and experiment.
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Shuifa Ke, Dan Qiao and Zhangchun Chen
The purpose of this paper is to analyze the influence of different factors on forestry production, with an aim to explore the degree of connection between forestry economic growth…
Abstract
Purpose
The purpose of this paper is to analyze the influence of different factors on forestry production, with an aim to explore the degree of connection between forestry economic growth and influencing factors such as forestry investment, labor input, afforestation area, scientific and technologies progress, and the reform of property-rights regimes.
Design/methodology/approach
According to the data of China Forestry Statistical Yearbook from 1978 to 2017, this paper uses the grey correlation analysis to observe and analyze the factors influencing China’s forestry economics growth.
Findings
The results show that capital investment demonstrates the largest impact on the forestry output value, followed by property system, afforestation area, labor input and technologies progress. The correlation coefficients of the above factors are 0.874451654,0.85827468,0.835138412,0.832985604 and 0.825747493. This means that forestry capital investment plays a major role in contributing to forest economic growth; forest property system also plays a positive role in the growth of forestry economy.
Originality/value
This paper uses continuous data collected during 1978‒2017, which are quite extensive as compared to data used in the existing research, considering the influencing factors are comprehensive, especially the impact of property right system reform on forestry economic growth.
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Yin Kedong, Zhe Liu, Caixia Zhang, Shan Huang, Junchao Li, Lingyun Lv, Xiaqing Su and Runchuan Zhang
In recent years, China's marine industry has maintained rapid growth in general, and marine-related economic activities have continued to improve. The purpose of this research is…
Abstract
Purpose
In recent years, China's marine industry has maintained rapid growth in general, and marine-related economic activities have continued to improve. The purpose of this research is to analyze the basic situation of China's marine economy development, identify the problems therein, forecast development trends and propose policy recommendations accordingly.
Design/methodology/approach
This research conducts a comprehensive and detailed analysis of the development of China's marine economy with rich data in diversified aspects. The current situation of China's marine economy development is analyzed from the perspective of scale and structure, and the external and internal development environment of China's marine economy is discussed. With the application of measurement and prediction method such as trend extrapolation, exponential smoothing, grey forecasting and neural network method, the future situation of China's marine economy development is forecasted.
Findings
In a complex environment where uncertainties at home and abroad have increased significantly, China's marine economy development suffers tremendous downward pressure in recent years. As China has achieved major achievements in the prevention and control of the COVID-19 epidemic, the marine economy development will gradually return to normal. It is estimated that the gross marine production value in 2022 will exceed 10 trillion yuan. China's marine economy will continue to maintain a steady growth trend in the future, and its development prospects will remain promising.
Originality/value
This research explores the current situation and trends of China's marine economy development and puts forward policy recommendations to promote the steady and health development of China's marine economy accordingly.
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Jun Gao, Niall O’Sullivan and Meadhbh Sherman
The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed…
Abstract
Purpose
The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.
Design/methodology/approach
This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.
Findings
The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.
Practical implications
The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].
Originality/value
The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.
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En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…
Abstract
Purpose
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.
Design/methodology/approach
A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.
Findings
Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.
Originality/value
In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.
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