state assessment
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H-INDEX

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(FIVE YEARS 4)

2022 ◽  
Vol 252 ◽  
pp. 113737
Author(s):  
Hoang D. Nguyen ◽  
James M. LaFave ◽  
Young-Joo Lee ◽  
Myoungsu Shin

2022 ◽  
pp. 85-91
Author(s):  
E. V. Krukovich ◽  
G. O. Momot ◽  
E. A. Osipenko

The article highlights one of the current issues of pediatrics - the study of the dynamics of Physical Development (PD) in children and adolescents. The numerous methods of assessment are used. The methods contain one-dimensional, two-dimensional and / or trimeric indicators. They do not fully give an idea of the level of the child's PD and do not reflect the patterns of his growth and development. In some cases, a pediatrician at the outpatient stage requires a comprehensive assessment of PD including age determination and compliance of biological age with the real age, determination of the PD harmony, somatotype determination, assessment of the direction of growth and development along with the calculation of indexes, functional state assessment, assessment of the degree of fat deposition or bioimpedance measurement, which allows determining the risk group. The assessment of PD indicators must be carried out according to regional tables.


2021 ◽  
Vol 12 (2) ◽  
pp. 83-92
Author(s):  
Aigul SERGEYEVA ◽  
Altynbek KHAMIT ◽  
Asima КOSHIM ◽  
Murat MAKHAMBETOV

The rapid pace of urban development triggers complex problems mostly related to urban environment pollution, and shortcomings of city’s improvement. The modern city is characterized by the highest man-made pressure on the natural environment, the main problems being overcrowding, lack of open-access green areas, as well as the decrease of vegetation areas, fact that does not create comfortable living conditions for urban residents. At present, remote sensing methods are some of the priority tools used in vegetation state assessment, particularly, the calculation of vegetation index (NDVI). But often, obtaining the necessary information is limited only to the analysis of satellite data, without geobotanical field surveys, which considerably increase the reliability of the detected results. In addition, the definition of dependencies when using an integrated approach of different man-affected surfaces with a different type of overgrowth within the city remains insufficiently studied. The purpose of this study is to assess the ecological condition of the green area (parks and squares) in Aktobe city. A comprehensive processing of satellite images including the calculation of NDVI index, mapping of green areas and data statistical analysis, was carried out. We learned that the average value of NDVI for green spaces in Aktobe ranges from 0,11 µm to 0,47 µm, which allows for the categorization of planted areas by levels of photosynthetic activity, from “unsatisfactory” to “good”, yet, with 59% of them in an unsatisfactory condition. This means that the city is underdeveloped in terms of modern landscape and infrastructure. The obtained results make it possible to assess the current situation, determine the dynamics of urban green spaces and optimize spatial planning measures for green space management.


2021 ◽  
Vol 233 (1) ◽  
Author(s):  
Tamara V. Bardina ◽  
Marina V. Chugunova ◽  
Lyudmila P. Kapelkina ◽  
Victoria I. Bardina ◽  
Alexander O. Gerasimov

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mengyuan Huang ◽  
Shiwu Li ◽  
Mengzhu Guo ◽  
Lihong Han

The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability.


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