Influencing factors and growth state classification of a natural Metasequoia population

2018 ◽  
Vol 30 (1) ◽  
pp. 337-345 ◽  
Author(s):  
Mu Liu ◽  
Zhongke Feng ◽  
Chenghui Ma ◽  
Liyan Yang
2018 ◽  
pp. 67-72 ◽  
Author(s):  
D. V. Borisenko ◽  
◽  
I. V. Prisukhina ◽  
S. A. Lunev ◽  
◽  
...  

2021 ◽  
Vol 27 (4) ◽  
pp. 875-893
Author(s):  
Ol'ga S. BELOMYTTSEVA ◽  
Anna S. BALANDINA

Subject. The article discusses the taxation of interest income from deposits and bonds in the Russian Federation from perspectives of individual investors and the State, classification of people’s income into active and passive. Objectives. We outline actions to adjust the fiscal policy on personal income tax to unify the taxation and stimulate the innovating activity of individuals. Methods. The study is based on methods of logic and comparative analysis. Results. Tactically, payers of interest income are now bound to inform taxpayers on accrued interest income, and the need to qualify coupons of government, municipal and corporate bonds for relief. The strategic result is determined as the need to qualify active and passive income. Conclusions and Relevance. The findings can be an agenda of the State Duma of the Russian Federation and promulgated in the Tax Code of the Russian Federation.


2020 ◽  
Vol 497 (4) ◽  
pp. 4843-4856 ◽  
Author(s):  
James S Kuszlewicz ◽  
Saskia Hekker ◽  
Keaton J Bell

ABSTRACT Long, high-quality time-series data provided by previous space missions such as CoRoT and Kepler have made it possible to derive the evolutionary state of red giant stars, i.e. whether the stars are hydrogen-shell burning around an inert helium core or helium-core burning, from their individual oscillation modes. We utilize data from the Kepler mission to develop a tool to classify the evolutionary state for the large number of stars being observed in the current era of K2, TESS, and for the future PLATO mission. These missions provide new challenges for evolutionary state classification given the large number of stars being observed and the shorter observing duration of the data. We propose a new method, Clumpiness, based upon a supervised classification scheme that uses ‘summary statistics’ of the time series, combined with distance information from the Gaia mission to predict the evolutionary state. Applying this to red giants in the APOKASC catalogue, we obtain a classification accuracy of $\sim 91{{\ \rm per\ cent}}$ for the full 4 yr of Kepler data, for those stars that are either only hydrogen-shell burning or also helium-core burning. We also applied the method to shorter Kepler data sets, mimicking CoRoT, K2, and TESS achieving an accuracy $\gt 91{{\ \rm per\ cent}}$ even for the 27 d time series. This work paves the way towards fast, reliable classification of vast amounts of relatively short-time-span data with a few, well-engineered features.


2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.


2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuhang Zhang ◽  
Yan Huang ◽  
Tingting Xu ◽  
Chang Liu ◽  
Liangyan Tao

Purpose The classification of aircraft failures has been a significant part of functional hazard analysis (FHA). Aiming at the shortcomings of the traditional FHA method in the evaluation of aircraft risk, the purpose of this paper is to put forward a new approach by combining the gray comprehensive relation calculation method in the gray system theory with the traditional FHA in order to deal with the problem of “little data, poor information.” Design/methodology/approach This paper combines FHA, 1–9-scale method and gray relation analysis. At first, aircraft failure scenarios are chosen and data from experts are collected; then gray system theory is applied to find the relevance of such scenarios. Finally, the classification according to relevance is determined. Findings In the past, “little data, poor information” made it difficult for researchers to implement FHA. In this paper, the authors manage to deal with the problem of “poor information” and provide an approach to find the seriousness of aircraft failure. Research limitations/implications Due to the use of expert-evaluating methods, the classification of failures is still a little subjective and can be improved in this area. In the future, the method can be improved from the perspective of combining FMEA to analyze more complex indicators or using multisource heterogeneous solutions to solve fuzzy numbers, probabilities, gray numbers and indicators that cannot be assigned. Practical implications The paper uses FHA to divide the failure state and establishes a gray evaluation model of the aircraft failure state classification to verify the relevant method. Some aircraft safety design requirements are used to check the safety hazards of the aircraft during the design process, and to provide rational recommendations for the functional design of the aircraft. Social implications Improving the safety of aircraft is undoubtedly of great practical significance and has become a top priority in the development of the civil aviation industry. In this paper, the FHA method and the failure state of the aircraft are studied. The original FHA method is innovated by using the gray system theory applicable to the poor information state. Therefore, to some extent, this study has significance for improving the safety of civil aircraft flight, ensuring people’s travel safety and enhancing the society’s trust in civil aviation. Originality/value The main innovation of this paper is integrating the FHA method and the gray system theory. This study calculates the comprehensive relation degree of each failure under different flight stages, and uses FHA to divide the failure state, and finally establishes a gray evaluation model of the aircraft failure state classification to analyze the different conditions of the landing gear brake system, so that it improves the present situation, and the problem with the character of “little data, poor information” can be addressed better.


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