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2 2021

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The variability of natural and climatic conditions in investment projects in the field of nature management

Rokochinskiy Anatoliy, Frolenkova Nadia, Turcheniuk Vasyl, Volk Pavlo, Prykhodko Natalіa, Tykhenko Ruslan, Openko Ivan

Journal of Water and Land Development

2021

no. 48

48--54

The article focuses on the actual scientific and practical problem of accounting for the influence of meteorological and climatic factors in the technical and economic calculations in the field of environmental management. It has been proven that the introduction of scientifically sound and effective methods of using meteorological and climatic information in economic calculations significantly reduces the loss caused by weather conditions and improves the implementation of an optimal strategy for agricultural production on reclaimed lands. Such calculations are based on economic and statistical modelling of different variants that accounting for standard hydrometeorological information in the implementation of design, management and economic decisions. This increases the validity and reliability of calculations, as well as their compliance with the actual operating conditions of environmental and economic facilities. Consequently, this attracts increased interest of both public and private investors. Not only under such conditions is a sustainable development of environmental management sectors possible but also the adaptation to global climate change and additional benefits from the efficient economic activity in the new environmental conditions.

A Statistical Evaluation of The Depth of Inheritance Tree Metric for Open-Source Applications Developed in Java

Prykhodko Sergiy, Prykhodko Natalіa, Smykodub Tetyana

Foundations of Computing and Decision Sciences

2021

Vol. 46, No. 2

159--172

The Depth of Inheritance Tree (DIT) metric, along with other ones, is used for estimating some quality indicators of software systems, including open-source applications (apps). In cases involving multiple inheritances, at a class level, the DIT metric is the maximum length from the node to the root of the tree. At an application (app) level, this metric defines the corresponding average length per class. It is known, at a class level, a DIT value between 2 and 5 is good. At an app level, similar recommended values for the DIT metric are not known. To find the recommended values for the DIT mean of an app we have proposed to use the confidence and prediction intervals. A DIT mean value of an app from the confidence interval is good since this interval indicates how reliable the estimate is for the DIT mean values of all apps used for estimating the interval. A DIT mean value higher than an upper bound of prediction interval may indicate that some classes have a large number of the inheritance levels from the object hierarchy top. What constitutes greater app design complexity as more classes are involved. We have estimated the confidence and prediction intervals of the DIT mean using normalizing transformations for the data sample from 101 open-source apps developed in Java hosted on GitHub for the 0.05 significance level.