15 research outputs found
Industry 4.0 and accounting: directions, challenges, opportunities
The purpose of the study was to reveal the directions, challenges and opportunities that arise in the accounting system in the conditions of Industry 4.0, when innovative solutions based on the capabilities of new digital technologies become the management of in-demand enterprises. The role of Industry 4.0 is shown, the features of modern Industry 4 technologies are revealed, the main technologies and production processes are demonstrated, interconnected using industrial protocols to create intelligent data. The rating of digital competitiveness and their readiness to implement Industry 4.0 technologies for 64 countries of the world is given and analyzed. These factors hinder the process of digitalization in countries with a low rating. The influence of Industry 4.0 on the accounting system is determined and the technologies affected by the fourth industrial revolution in terms of accounting (Big Data or Data Analytics, Cloud Computing, AI, Blockchain, Internet of Things (IoT), Robotic Process Automation) and changes waiting for the profession of accountant. The coordination of efforts of business, education and the state in the context of the development of Industry 4.0 technologies was noted
Dynamik der anthropogenen und natΓΌrlichen LandschaftsΓΆkosysteme des Slovetschan-Ovrusch-Grates in der Ukraine
The need for landscape management cannot be satisfied by static data alone. Landscape ecosystems are complex dynamic objects and the successful protection or operation of such areas depends on the quality of monitoring their dynamics and the forecasts built on its basis. Forecasting accuracy also depends on the quality of the modelling and this is why it is necessary to highlight the most universal and key characteristics of ecosystems. The Slovechansko-Ovruchsky ridge, which has a phenomenally high variety of landscapes, is well suited as an object for testing approaches to modelling. The authors singled out 11 territorial parts of the Slovechansko-Ovruchsky ridge at the level of landscapes and tracts. The differentiation of these landscapes was assessed using the synphytoindication method to determine the value of natural and anthropogenic dynamics. This made it possible to build prognostic algorithms for ecosystem changes using geobotanical data and produce prospects to create the best suitable landscape protection strategy or more effective and safe exploitation of landscapes.Upravljanje krajobrazima ne moΕΎe se temeljiti iskljuΔivo na statistiΔkim podacima. Krajobrazni ekosustavi sloΕΎeni su i dinamiΔni objekti. Uspjeh zaΕ‘tite i upravljanja takvim podruΔjima ovisi o kvaliteti praΔenja njihove dinamike i prognozama koje se na temelju toga oblikuju. S druge strane, toΔnost takvih predviΔanja ovisi o kvaliteti modeliranja. Zbog toga je SloveΔansko-OvruΕ‘ki greben, koji posjeduje neka kljuΔna i univerzalna obiljeΕΎja ekosustava koja su i univerzalna, te veliku raznolikost krajobraza, prikladan za testiranje pristupa modeliranju. Autori su izdvojili 11 teritorijalnih dijelova SloveΔansko-OvruΕ‘kog grebena na razini krajobraza i trakta. Diferencijalnost ovih krajolika provjerena je metodom sinfitoindikacije kako bi se utvrdila vrijednost prirodne i antropogene dinamike. To omoguΔuje izgradnju prognostiΔkih algoritama za promjene ekosustava koristeΔi geobotaniΔke podatke. Time se omoguΔuje oblikovanje prikladnije strategije zaΕ‘tite krajobraza te njegova uΔinkovitijeg i sigurnijeg iskoriΕ‘tavanja.Das Landschaftsmanagement kann nicht ausschlieΓlich auf statistischen Angaben beruhen. LandschaftsΓΆkosysteme sind komplexe und dynamische Objekte. Der Erfolg beim Schutz und Verwaltung solcher Gebiete hΓ€ngt von der QualitΓ€t der Γberwachung, deren Dynamik und der darauf beruhenden Prognosen ab. Andererseits hΓ€ngt die Richtigkeit solcher Vorhersagen von der QualitΓ€t der Modellierung ab. Aus diesem Grund ist der Slovetschan-Ovrusch-Grat mit einigen grundlegenden und universellen Eigenschaften und einer Vielfalt der Landschaften dazu geeignet, den Modellieransatz zu testen. Die Autoren haben 11 Gebietsteile des Slovetschan-Ovrusch-Grates auf der Landschafts- und Traktebene herausgehoben. Die Differenziertheit dieser Landschaften wurde mit Hilfe der Synphytoindikationsmethode ΓΌberprΓΌft, damit der Wert der natΓΌrlichen und anthropogenen Dynamik festgestellt werden kann. Dies ermΓΆglicht den Aufbau von prognostischen Algorithmen fΓΌr die Γnderung des Γkosystems unter Anwendung von gebotanischen Daten. Dadurch wird ermΓΆglicht, eine besser geeignete Stategie zum Landschaftsschutz zu entwickeln und die Landschaft auf eine effizientere und sicherere Art und Weise zu nutzen
Peculiarities of the formation of professional skills in teaching of future teachers of geography in the higher education institutions in Ukraine
Assessment of occupational risks: New approaches, improvement, and methodology
Π£ ΡΡΠΎΠΌΡ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΡΠ΄Π½ΡΠΌΠ°ΡΡΡΡΡ ΠΏΠΈΡΠ°Π½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ
ΡΠΈΠ·ΠΈΠΊΡΠ² ΡΠ° Π½ΠΎΠ²Ρ ΠΏΡΠ΄Ρ
ΠΎΠ΄ΠΈ Π΄ΠΎ ΡΡ
ΠΎΡΡΠ½ΠΊΠΈ, ΡΠ΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π² ΡΡΠΎΠΌΡ Π½Π°ΠΏΡΡΠΌΠΊΡ. ΠΠΊΡΡΠ°Π»ΡΠ½ΡΡΡΡ Π²ΠΈΠΊΠ»Π°Π΄Π΅Π½ΠΎΡ ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ ΠΎΠ±ΡΠΌΠΎΠ²Π»Π΅Π½Π° Π²Π΅Π»ΠΈΠΊΠΎΡ ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΠ½ΡΡΡΡ ΡΠΈΡΡΠ°ΡΡΠΉ, ΡΠΎ Π²ΠΈΠ½ΠΈΠΊΠ°ΡΡΡ Π² ΡΡΡΠ°ΡΠ½ΠΈΡ
Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°Ρ
Ρ ΠΌΠ°ΡΡΡ Π²ΠΈΡΠΎΠΊΠΈΠΉ ΡΡΡΠΏΡΠ½Ρ ΡΠΈΠ·ΠΈΠΊΡ Π΄Π»Ρ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ² Π² ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Π°. ΠΠ΅ΡΠΎΡ Π΄Π°Π½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ
ΠΏΡΠ΄Ρ
ΠΎΠ΄ΡΠ² Π΄ΠΎ ΠΎΡΡΠ½ΠΊΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ
ΡΠΈΠ·ΠΈΠΊΡΠ², Π° ΡΠ°ΠΊΠΎΠΆ Π΄Π»Ρ Π²ΠΈΠ²ΡΠ΅Π½Π½Ρ ΡΡΠ·Π½ΠΈΡ
Π°ΡΠΏΠ΅ΠΊΡΡΠ² ΡΠ΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΠΎΡ Π΄ΡΡΠ»ΡΠ½ΠΎΡΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² Π΄Π»Ρ ΠΎΡΡΠ½ΠΊΠΈ ΡΡΡΠΏΠ΅Π½Ρ ΡΠΈΠ·ΠΈΠΊΡΠ². ΠΡΠΎΠ²ΡΠ΄Π½ΠΈΠΌ Π½Π°ΡΠΊΠΎΠ²ΠΈΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Ρ ΠΊΠΎΠΌΠ±ΡΠ½Π°ΡΡΡ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΡ
Ρ Π»ΠΎΠ³ΡΡΠ½ΠΈΡ
ΠΏΡΠ΄Ρ
ΠΎΠ΄ΡΠ² Π΄ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΠΊΡ Π²ΠΈΡΠ²ΡΡΠ»ΡΡΡΡΡΡ Π² ΡΠ΅ΠΌΡ ΡΡΠΎΠ³ΠΎ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ. ΠΡΠ½ΠΎΠ²Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΄Π°Π½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ: Π²ΠΈΡΠ²Π»Π΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ
, Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΠΎΡΡΠ½ΠΊΠΈ ΡΡΡΠΏΠ΅Π½Ρ ΡΠΈΠ·ΠΈΠΊΡΠ² Π²ΠΈΡΠΎΠ±Π½ΠΈΡΠΎΠ³ΠΎ ΡΡΠ°Π²ΠΌΠ°ΡΠΈΠ·ΠΌΡ Ρ ΠΎΡΡΠ½ΠΊΠ° ΡΡΠ²Π½Ρ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΠΎΠ³ΠΎ ΡΡΠ°Π²ΠΌΠ°ΡΠΈΠ·ΠΌΡ Π½Π° ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Π°Ρ
Π£ΠΊΡΠ°ΡΠ½ΠΈ Π² 2020 ΡΠΎΡΡ ΡΠ° ΠΏΠΎΠΏΠ΅ΡΠ΅Π΄Π½ΡΡ
ΡΠΎΠΊΠ°Ρ
. ΠΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²ΠΈ ΠΏΠΎΠ΄Π°Π»ΡΡΠΎΠ³ΠΎ Π²ΠΈΠ²ΡΠ΅Π½Π½Ρ Π² ΡΡΠΎΠΌΡ Π½Π°ΠΏΡΡΠΌΠΊΡ ΠΏΠΎΠ»ΡΠ³Π°ΡΡΡ Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΠΎΡΡΠ½ΠΊΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ²Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ
ΡΠΈΠ·ΠΈΠΊΡΠ² Ρ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠΉ ΡΡΠ΅ΡΡ, Π· ΠΌΠ΅ΡΠΎΡ ΡΠΊΠΎΡΠΎΡΠ΅Π½Π½Ρ Π·Π°Π³Π°Π»ΡΠ½ΠΎΡ ΠΊΡΠ»ΡΠΊΠΎΡΡΡ ΡΠΈΡΡΠ°ΡΡΠΉ, ΠΏΠΎΠ²'ΡΠ·Π°Π½ΠΈΡ
Π· ΡΠΈΠ·ΠΈΠΊΠΎΠΌ Π΄Π»Ρ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ² ΡΡΡΠ°ΡΠ½ΠΈΡ
ΠΏΡΠΎΠΌΠΈΡΠ»ΠΎΠ²ΠΈΡ
ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ², ΡΠΎ Π²ΠΈΠ½ΠΈΠΊΠ°ΡΡΡ Ρ ΠΏΡΠΎΡΠ΅ΡΡ Π²Π΅Π΄Π΅Π½Π½Ρ ΡΡΡΠ΄ΠΎΠ²ΠΎΡ Π΄ΡΡΠ»ΡΠ½ΠΎΡΡΡ. ΠΡΠΈΠΊΠ»Π°Π΄Π½Π΅ Π·Π½Π°ΡΠ΅Π½Π½Ρ Π΄Π°Π½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΠΏΠΎΠ»ΡΠ³Π°Ρ Π² ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ ΠΏΠΎΠ΄Π°Π»ΡΡΠΎΠ³ΠΎ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΠ³ΠΎ Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½Ρ ΠΎΡΡΠΈΠΌΠ°Π½ΠΈΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ², Π· ΠΌΠ΅ΡΠΎΡ ΡΠΎΡΠΌΡΠ²Π°Π½Π½Ρ ΠΎΡΠ½ΠΎΠ²ΠΈ Π΄Π»Ρ Π±Π΅Π·ΠΏΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·Π΄ΡΠΉΡΠ½Π΅Π½Π½Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΎΡ Π΄ΡΡΠ»ΡΠ½ΠΎΡΡΡ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΡΠ² ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ² Ρ ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΡΠΉ, ΡΠΎ Π΄ΡΡΡΡ Ρ ΡΡΠ·Π½ΠΈΡ
ΡΡΠ΅ΡΠ°Ρ
Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ, ΠΏΡΠΎΠΌΠΈΡΠ»ΠΎΠ²ΠΎΡΡΡ, Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²Π°. ΠΠ΄Π΅Π½ΡΠΈΡΡΠΊΠ°ΡΡΡ ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΠ½ΠΈΡ
Π°ΡΠΏΠ΅ΠΊΡΡΠ² ΠΎΡΡΠ½ΠΊΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΡ
ΡΠΈΠ·ΠΈΠΊΡΠ² Ρ ΡΡΠ·Π½ΠΈΡ
ΡΡΠ΅ΡΠ°Ρ
ΡΠΏΡΠΈΡΡΠΈΠΌΠ΅ ΡΡΡΠ½Π΅Π½Π½Ρ ΡΠ°ΠΊΠΈΡ
ΡΠΈΠ·ΠΈΠΊΡΠ² ΡΠ° Π·Π°ΠΏΠΎΠ±ΡΠ³Π°Π½Π½Ρ ΡΡ
Π²ΠΈΠ½ΠΈΠΊΠ½Π΅Π½Π½Ρ Ρ ΠΌΠ°ΠΉΠ±ΡΡΠ½ΡΠΎΠΌΡ.This study raises the issues of occupational risks and the new approaches to their assessment, improving research methods in this direction. The relevance of the stated subject is due to a wide variety of situations that arise in modern industries and have a high degree of risk for employees of enterprises. The purpose of this study is to determine new approaches to occupational risk assessment, as well as to study various aspects of improving production activities in the context of using new methods for assessing the degree of risks. The leading scientific method is a combination of analytical and logical approaches to the set of issues brought into the topic of this study. The main results of this study: identification of new, effective methods for assessing the degree of risks of occupational injuries and estimation of the level of occupational injuries at Ukrainian enterprises in 2020 and several previous years. Prospects for further study in this direction introduce methods for assessing the real level of occupational risks in the production sphere, in order to reduce the total number of situations associated with risk for workers of modern industrial enterprises that arise in the process of conducting work activities. The applied value of this study lies in the possibility of the subsequent practical application of the obtained results, in order to form the basis for the safe implementation of professional activities by employees of enterprises and organizations operating in various spheres of the economy, industry, and production. Identification of various aspects of occupational risks assessment in various areas contributes to the elimination of such risks and prevents their occurrence in the future
Assessment of the Economic Effect from the Introduction of Labour Safety Measures at Enterprises
ΠΡ
ΠΎΡΠΎΠ½Π° ΠΏΡΠ°ΡΡ Π½Π° ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Ρ Π²ΠΈΠ·Π½Π°ΡΠ°ΡΡΡΡΡ Π±ΡΠ»ΡΡΡΡΡΡ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΡΡΠ² ΡΠΊ ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΠΊΠ° Π·Π½Π°ΡΠ½ΠΎΡ ΠΌΡΡΠΎΡ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ ΡΠΎΠ·ΡΠΈΡΠΈΡΠΈ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ² ΡΠ· Π΄ΠΎΡΡΠ³Π½Π΅Π½Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² ΠΎΠΏΠ΅ΡΠ°ΡΡΠΉΠ½ΠΎΡ Π΄ΡΡΠ»ΡΠ½ΠΎΡΡΡ. ΠΠΎΠ΄Π½ΠΎΡΠ°Ρ ΡΠΎΠ»Ρ ΠΎΡ
ΠΎΡΠΎΠ½ΠΈ ΠΏΡΠ°ΡΡ ΡΠΊ ΡΠΈΠ½Π½ΠΈΠΊΠ°, ΡΠΎ ΠΌΠΎΠΆΠ΅ ΠΏΠΎΡΠΈΠ»ΠΈΡΠΈ Π²ΠΈΡΠΎΠ±Π½ΠΈΡΡΠ²ΠΎ, Π²ΠΈΠ·Π½Π°ΡΠ°ΡΡΡΡΡ ΠΏΠΎΠ΄ΡΠ±Π½ΠΎ Π»ΠΈΡΠ΅ Ρ ΠΊΡΠ°ΡΠ½Π°Ρ
ΡΠ· ΡΠΈΠ½ΠΊΠΎΠ²ΠΎΡ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΎΡ.Occupational safety at the enterprise is defined by most economists as a system that greatly expands the ability of enterprises to achieve operational results. At the same time, the role of labor protection as a factor that can increase production is defined similarly only in countries with market economies
Labour Protection as an Effective Management Component of a Modern Enterprise
ΠΠΈΡΠ°Π½Π½Ρ ΠΎΡ
ΠΎΡΠΎΠ½ΠΈ ΠΏΡΠ°ΡΡ, ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎ Π½Π° Π²Π΅Π»ΠΈΠΊΠΎΠΌΡ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Ρ, Ρ ΠΊΠ»ΡΡΠΎΠ²ΠΈΠΌΠΈ ΡΠΊΠ»Π°Π΄ΠΎΠ²ΠΈΠΌΠΈ ΡΠΎΡΡΠ°Π»ΡΠ½ΠΎ-ΡΡΡΠ΄ΠΎΠ²ΠΎΡ ΡΡΠ΅ΡΠΈ, ΡΠΎ Π·Π½Π°ΡΠ½ΠΎ Π²ΠΏΠ»ΠΈΠ²Π°ΡΡΡ Π½Π° ΡΠΎΠ±ΠΎΡΡ Π²ΡΡΠΎΠ³ΠΎ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²Π°. Π£ ΡΡΡΠ°ΡΠ½ΠΈΡ
Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΈΡ
ΡΠΌΠΎΠ²Π°Ρ
, Π΄Π΅ ΠΊΠΎΠΆΠ½Π΅ ΠΏΡΠ΄ΠΏΡΠΈΡΠΌΡΡΠ²ΠΎ Π½Π°ΡΡΠ»Π΅Π½Π΅ Π½Π° Π²ΠΈΠΏΡΡΠΊ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠΎΠ·Π΄Π°ΡΠ½ΠΎΡ, Π²ΠΈΡΠΎΠΊΠΎΡΠΊΡΡΠ½ΠΎΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡΡ ΡΠ° ΠΎΡΡΠΈΠΌΠ°Π½Π½Ρ ΠΏΡΠΈΠ±ΡΡΠΊΡ, Π·Π±Π΅ΡΠ΅ΠΆΠ΅Π½Π½Ρ Π·Π΄ΠΎΡΠΎΠ²'Ρ Ρ ΠΏΡΠ°ΡΠ΅Π·Π΄Π°ΡΠ½ΠΎΡΡΡ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡΠ°ΡΡΠ²Π½ΠΈΠΊΠ° Π·Π½Π°Ρ
ΠΎΠ΄ΡΡΡΡΡ Π½Π° ΠΏΡΡΠΎΡΠΈΡΠ΅ΡΠ½ΠΎΠΌΡ ΠΌΡΡΡΡ.Labour protection issues, especially in a large enterprise, are key components of the social and labour
sphere, which significantly affect the performance of the entire enterprise. In modern economic conditions, where every enterprise is focused on producing competitive, high-quality goods and making a profit, maintaining the health and efficiency of each employee is a priority
Technical and organizational measures and means of ensuring the safety of the production process
The organization of a safe environment at the enterprise is formed up not only due to the perfection of safety regulations and their observance, but also due to the fact that all the proposed safety measures are fully consistent with production needs and can be considered as a function of ensuring the development of production. The relevance of the study is determined by the fact that the use of modern systems for the formation and establishment of the safety of the production process allows you to boost production activities and positions of the enterprise in the foreign market. The novelty of the study is determined by the conformity of the proposed regulatory indicators and the form of their approval at the enterprise. The authors put the mathematical model at the basis of the proposed indicators, which corresponds to the main decisions of modern quality standards. The article shows the possibility of determining diversity and the formation of requirements that are recommended for implementation based on operational indicators of the enterprise. The authors present a mathematical and graphical model for integrating safety requirements into the overall structure of the production process. The practical significance of the study is determined by the possibilities of the formation of individual requirement parameters for ensuring the safety of the production process for local industries
Π€ΡΡΠΎΡΠ½Π΄ΠΈΠΊΠ°ΡΡΠΉΠ½Ρ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌ ΠΏΡΠ°Π²ΠΎΠ±Π΅ΡΠ΅ΠΆΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΡΡΡΡ
The publication discusses the study results of energy dynamics in ecosystems. The goal of the study is to search for objective parameters to determine the indicator of the ecosystem dynamics and basing on the above-ground phytomass quantitative indices, its age and changes of floristic composition we demonstrate the possibility of the accurate phytoindication assessment of ecosystems dynamics. It is determined that the indicators of above-ground phytomass naturally increase during self-development of natural ecosystems (autogenic succession).Materials and methods. The above-ground phytomass is measured in ecosystems that are at different stages of autogenic succession. In order to balance the fluctuation of the indicators, that was caused by the domination of species with different types of photosynthesis, the correction for the age of the above-ground phytomass was used.Results. The dynamics indicator increases during the autogenic succession. External influences deflect it from the main trend. The anthropogenic influence often displaces succession in the opposite direction. The method was tested on the territory of Right-Bank Ukrainian Polissia. Expansion of the database of results of dynamic indicator determination allows to calculate this indicator with accuracy sufficient for practical and theoretical purposes. Such methods allow studying ecosystem thermodynamics (entropy and energy stocks) and ecosystem dynamics in noninvasive and non-contact way without affecting of plant biodiversity. Such approaches could be the most suitable for conducting studies on natural protected areas. Within such objects, it is not allowed to remove the above-ground phytomass, to determine the energy and dynamic indicators.Conclusion. The unit that will correspond the self-development stage, is an index that is directly proportional to the aboveground phytomass quantity and its age. The ecosystem entropy value will be inversely proportional to the self-development stage index. This index can be determined by classical phytoindication methods upon detailed database availability. Measurement errors, associated with phytoindication method range from 3 % to 10 % comparing to 5-10 % for direct method, are commensurate. The phytoindication approach allows application of this methodology on the protected areas. The index of the ecosystems self-development stage can be used to predict the development of specific studied plots and for the needs of ecosystem classificationΠ ΠΏΡΠ±Π»ΠΈΠΊΠ°ΡΠΈΠΈ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌ. Π¦Π΅Π»Ρ ΡΠ°Π±ΠΎΡΡ: Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° Π½Π°Π΄Π·Π΅ΠΌΠ½ΠΎΠΉ ΡΠΈΡΠΎΠΌΠ°ΡΡΡ, Π΅Π΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ»ΠΎΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π°, Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅ΡΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΡΠΈΡΠΎΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌ, ΠΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ, ΠΊΠ°ΠΊ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π½Π°Π΄Π·Π΅ΠΌΠ½ΠΎΠΉ ΡΠΈΡΠΎΠΌΠ°ΡΡΡ ΡΠ°ΡΡΡΡ Π²ΠΎ Π²ΡΠ΅ΠΌΡ ΡΠ°ΠΌΠΎΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ
ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌ (Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΠΉ ΡΡΠΊΡΠ΅ΡΡΠΈΠΈ).ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ°Π΄Π·Π΅ΠΌΠ½Π°Ρ ΡΠΈΡΠΎΠΌΠ°ΡΡΠ° ΠΈΠ·ΠΌΠ΅ΡΡΠ»Π°ΡΡ Π² ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠ°Ρ
, ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π°Ρ
ΠΎΠ΄ΡΡΡΡ Π½Π° ΡΠ°Π·Π½ΡΡ
ΡΡΠ°Π΄ΠΈΡΡ
Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΠΉ ΡΡΠΊΡΠ΅ΡΡΠΈΠΈ. ΠΠ»Ρ ΡΡΠ°Π²Π½ΠΎΠ²Π΅ΡΠΈΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ»Π΅Π±Π°Π½ΠΈΠΉ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, Π²ΡΠ·Π²Π°Π½Π½ΡΡ
Π΄ΠΎΠΌΠΈΠ½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π²ΠΈΠ΄ΠΎΠ² Ρ ΡΠ°Π·Π½ΡΠΌ ΡΠΈΠΏΠΎΠΌ ΡΠΎΡΠΎΡΠΈΠ½ΡΠ΅Π·Π°, Π½Π°ΠΌΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΎ ΠΏΠΎΠΏΡΠ°Π²ΠΊΡ Π½Π° Π²ΠΎΠ·ΡΠ°ΡΡ Π½Π°Π΄Π·Π΅ΠΌΠ½ΠΎΠΉ ΡΠΈΡΠΎΠΌΠ°ΡΡΡ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎ Π²ΠΎΠ·ΡΠ°ΡΡΠ°Π΅Ρ Π²ΠΎ Π²ΡΠ΅ΠΌΡ Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΠΉ ΡΡΠΊΡΠ΅ΡΡΠΈΠΈ. ΠΠ½Π΅ΡΠ½ΠΈΠ΅ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΎΡΠΊΠ»ΠΎΠ½ΡΡΡ Π΅Π³ΠΎ ΠΎΡ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ. ΠΠ½ΡΡΠΎΠΏΠΎΠ³Π΅Π½Π½ΠΎΠ΅ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ ΡΠ°ΡΡΠΎ ΡΠΌΠ΅ΡΠ°Π΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΠΊΡΠ΅ΡΡΠΈΠΈ Π² ΠΏΡΠΎΡΠΈΠ²ΠΎΠΏΠΎΠ»ΠΎΠΆΠ½ΠΎΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΈ. ΠΠ΅ΡΠΎΠ΄ Π°ΠΏΡΠΎΠ±ΠΈΡΠΎΠ²Π°Π½ Π½Π° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ ΠΡΠ°Π²ΠΎΠ±Π΅ΡΠ΅ΠΆΠ½ΠΎΠΉ ΠΠΎΠ»Π΅ΡΡΠ΅ Π² Π£ΠΊΡΠ°ΠΈΠ½Π΅. Π Π°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ Π±Π°Π·Ρ Π΄Π°Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ Π΅Π³ΠΎ Ρ Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ Π΄Π»Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π»Π΅ΠΉ ΡΠΎΡΠ½ΠΎΡΡΡΡ. ΠΡΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ²Π΅ΡΡΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ΅ΡΠΌΠΎΠ΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ (Π·Π°ΠΏΠ°ΡΡ ΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΈ ΡΠ½ΡΡΠΎΠΏΠΈΠΈ) Π° ΡΠ°ΠΊΠΆΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌ Π±Π΅ΡΠΊΠΎΠ½ΡΠ°ΠΊΡΠ½ΡΠΌ ΡΠΏΠΎΡΠΎΠ±ΠΎΠΌ, Π±Π΅Π· Π²Π»ΠΈΡΠ½ΠΈΡ Π½Π° Π±ΠΈΠΎΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅ ΡΠ°ΡΡΠ΅Π½ΠΈΠΉ ΠΈ ΡΡΡΡΠΊΡΡΡΡ ΡΠ΅Π½ΠΎΠ·ΠΎΠ². ΠΠ½ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΡΠΌ Π΄Π»Ρ ΡΠ°ΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π² ΠΏΡΠ΅Π΄Π΅Π»Π°Ρ
ΠΏΡΠΈΡΠΎΠ΄Π½ΠΎ-Π·Π°ΠΏΠΎΠ²Π΅Π΄Π½ΡΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ. ΠΠ° ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΈ ΡΠ°ΠΊΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π½Π΅ ΡΠ°Π·ΡΠ΅ΡΠ°Π΅ΡΡΡ ΠΈΠ·ΡΠΌΠ°ΡΡ Π½Π°Π΄Π·Π΅ΠΌΠ½ΡΡ ΡΠΈΡΠΎΠΌΠ°ΡΡΡ, Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ.ΠΡΠ²ΠΎΠ΄Ρ. ΠΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΠ΅Ρ ΡΡΠ°Π΄ΠΈΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌΡ Π²ΠΎ Π²ΡΠ΅ΠΌΡ Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΠΉ ΡΡΠΊΡΠ΅ΡΡΠΈΠΈ ΠΈ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΠΈΡΠΎΠΌΠ°ΡΡΡ ΠΈ Π΅Π΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ°. ΠΠ½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠ½ΡΡΠΎΠΏΠΈΠΈ ΡΠΊΠΎΡΠΈΡΡΠ΅ΠΌΡ Π±ΡΠ΄Π΅Ρ ΠΎΠ±ΡΠ°ΡΠ½ΠΎ ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ Π΅Π΅ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ. ΠΡΠΎΡ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠΈΡΠΎΠΈΠ½Π΄ΠΈΠΊΠ°ΡΠΈΠΉΠ½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°. ΠΡΠΈ ΡΡΠΎΠΌ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎΡΡΡ ΠΈΠ·ΠΌΠ΅ΡΠ΅Π½ΠΈΡ Π±ΡΠ΄Π΅Ρ ΠΊΠΎΠ»Π΅Π±Π°ΡΡΡΡ Π² Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅ 3 % -10 %. ΠΡ ΠΌΠΎΠΆΠ΅ΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΅Ρ ΠΏΡΠΈ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΡΠ°Π±ΠΎΡ Π² Π·Π°ΠΏΠΎΠ²Π΅Π΄Π½ΠΈΠΊΠ°Ρ
, Π½Π° ΡΠ΅ΠΊΡΠ»ΡΡΠΈΠ²ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΡΡ
, Π² Π»Π΅ΡΠ½ΠΎΠΌ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅ ΠΈ ΡΠ°ΠΊ Π΄Π°Π»Π΅Π΅. ΠΡΠ±ΡΠ°Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ Π²ΡΡΠΎΠΊΠΈΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΡΠ΅ΠΊΡ ΠΈ ΡΠΎΠ·Π΄Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΄Π»Ρ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΈ ΠΎΠΊΡΡΠΆΠ°ΡΡΠ΅ΠΉ ΡΡΠ΅Π΄ΡΠ£ ΠΏΡΠ±Π»ΡΠΊΠ°ΡΡΡ Π°Π½Π°Π»ΡΠ·ΡΡΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π΅Π½Π΅ΡΠ³Π΅ΡΠΈΡΠ½ΠΈΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌ. ΠΠ΅ΡΠΎΡ ΡΠΎΠ±ΠΎΡΠΈ Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡΠ² ΠΊΡΠ»ΡΠΊΠΎΡΡΡ Π½Π°Π΄Π·Π΅ΠΌΠ½ΠΎΡ ΡΡΡΠΎΠΌΠ°ΡΠΈ, ΡΡ Π²ΡΠΊΡ ΡΠ° Π·ΠΌΡΠ½ΠΈ ΡΠ»ΠΎΡΠΈΡΡΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠ»Π°Π΄Ρ, ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΡΠ²Π°ΡΠΈ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΡΡΠΎΡΠ½Π΄ΠΈΠΊΠ°ΡΡΠΉΠ½ΠΎΡ ΠΎΡΡΠ½ΠΊΠΈ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌ, Π²ΠΈΠ·Π½Π°ΡΠΈΡΠΈ ΡΠΊ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠΈ Π½Π°Π΄Π·Π΅ΠΌΠ½ΠΎΡ ΡΡΡΠΎΠΌΠ°ΡΠΈ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎ Π·ΡΠΎΡΡΠ°ΡΡΡ ΠΏΡΠ΄ ΡΠ°Ρ ΡΠ°ΠΌΠΎΡΠΎΠ·Π²ΠΈΡΠΊΡ ΠΏΡΠΈΡΠΎΠ΄Π½ΠΈΡ
Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌ (Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΡ ΡΡΠΊΡΠ΅ΡΡΡ).ΠΠ°ΡΠ΅ΡΡΠ°Π»ΠΈ Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈ. ΠΠ°Π΄Π·Π΅ΠΌΠ½Π° ΡΡΡΠΎΠΌΠ°ΡΠ° Π²ΠΈΠΌΡΡΡΠ»Π°ΡΡ Π² Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠ°Ρ
, ΡΠΎ Π·Π½Π°Ρ
ΠΎΠ΄ΡΡΡΡΡ Π½Π° ΡΡΠ·Π½ΠΈΡ
ΡΡΠ°Π΄ΡΡΡ
Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΡ ΡΡΠΊΡΠ΅ΡΡΡ. ΠΠ»Ρ ΡΡΡΠ²Π½ΠΎΠ²Π°ΠΆΠ΅Π½Π½Ρ ΠΊΠΎΠ»ΠΈΠ²Π°Π½Π½Ρ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡΠ², Π²ΠΈΠΊΠ»ΠΈΠΊΠ°Π½ΠΈΡ
Π΄ΠΎΠΌΡΠ½ΡΠ²Π°Π½Π½ΡΠΌ Π²ΠΈΠ΄ΡΠ² ΡΠ· ΡΡΠ·Π½ΠΈΠΌ ΡΠΈΠΏΠΎΠΌ ΡΠΎΡΠΎΡΠΈΠ½ΡΠ΅Π·Ρ, Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½ΠΎ ΠΏΠΎΠΏΡΠ°Π²ΠΊΡ Π½Π° Π²ΡΠΊ Π½Π°Π·Π΅ΠΌΠ½ΠΎΡ ΡΡΡΠΎΠΌΠ°ΡΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΠΈ. ΠΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎ Π·ΡΠΎΡΡΠ°Ρ ΠΏΡΠ΄ ΡΠ°Ρ Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΡ ΡΡΠΊΡΠ΅ΡΡΡ. ΠΠΎΠ²Π½ΡΡΠ½Ρ Π²ΠΏΠ»ΠΈΠ²ΠΈ Π²ΡΠ΄Ρ
ΠΈΠ»ΡΡΡΡ ΠΉΠΎΠ³ΠΎ Π²ΡΠ΄ ΠΎΡΠ½ΠΎΠ²Π½ΠΎΠ³ΠΎ ΡΡΠ΅Π½Π΄Ρ. ΠΠ½ΡΡΠΎΠΏΠΎΠ³Π΅Π½Π½ΠΈΠΉ Π²ΠΏΠ»ΠΈΠ² ΡΠ°ΡΡΠΎ Π·ΠΌΡΡΡΡ ΡΡΠΊΡΠ΅ΡΡΡ Π² ΠΏΡΠΎΡΠΈΠ»Π΅ΠΆΠ½ΠΎΠΌΡ Π½Π°ΠΏΡΡΠΌΡ. ΠΠ΅ΡΠΎΠ΄ Π°ΠΏΡΠΎΠ±ΠΎΠ²Π°Π½ΠΎ Π½Π° ΡΠ΅ΡΠΈΡΠΎΡΡΡ ΠΡΠ°Π²ΠΎΠ±Π΅ΡΠ΅ΠΆΠ½ΠΎΠ³ΠΎ ΠΠΎΠ»ΡΡΡΡ Π² Π£ΠΊΡΠ°ΡΠ½Ρ. Π ΠΎΠ·ΡΠΈΡΠ΅Π½Π½Ρ Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ² Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΠ° Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ Π²ΠΈΠ·Π½Π°ΡΠ°ΡΠΈ ΡΠ΅ΠΉ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ ΡΠ· Π΄ΠΎΡΡΠ°ΡΠ½ΡΠΎΡ Π΄Π»Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΡ
Ρ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ½ΠΈΡ
ΡΡΠ»Π΅ΠΉ ΡΠΎΡΠ½ΡΡΡΡ. Π¦Π΅ΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΡΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ ΡΠ΅ΡΠΌΠΎΠ΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ (Π·Π°ΠΏΠ°ΡΡΠ² Π΅Π½Π΅ΡΠ³ΡΡ ΠΉ Π΅Π½ΡΡΠΎΠΏΡΡ) ΡΠ° Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌ Π±Π΅Π·ΠΊΠΎΠ½ΡΠ°ΠΊΡΠ½ΠΈΠΌ ΡΠΏΠΎΡΠΎΠ±ΠΎΠΌ Π±Π΅Π· Π²ΠΏΠ»ΠΈΠ²Ρ Π½Π° Π±ΡΠΎΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΡΡ ΡΠΎΡΠ»ΠΈΠ½. ΠΡΠ½ Ρ Π½Π°ΠΉΠ±ΡΠ»ΡΡ ΠΏΡΠΈΠΉΠ½ΡΡΠ½ΠΈΠΌ Π΄Π»Ρ ΡΠ°ΠΊΠΈΡ
Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ Π² ΠΌΠ΅ΠΆΠ°Ρ
ΠΏΡΠΈΡΠΎΠ΄Π½ΠΎ-Π·Π°ΠΏΠΎΠ²ΡΠ΄Π½ΠΈΡ
ΡΠ΅ΡΠΈΡΠΎΡΡΠΉ. Π ΠΌΠ΅ΠΆΠ°Ρ
ΡΠ°ΠΊΠΈΡ
ΠΎΠ±βΡΠΊΡΡΠ² Π½Π΅ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡΡΡΡΡ Π²ΠΈΠ»ΡΡΠ°ΡΠΈ Π½Π°Π΄Π·Π΅ΠΌΠ½Ρ ΡΡΡΠΎΠΌΠ°ΡΡ, Π΄Π»Ρ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ Π΅Π½Π΅ΡΠ³Π΅ΡΠΈΡΠ½ΠΈΡ
Ρ Π΄ΠΈΠ½Π°ΠΌΡΡΠ½ΠΈΡ
ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡΠ².ΠΠΈΡΠ½ΠΎΠ²ΠΊΠΈ. ΠΠΎΠΊΠ°Π·Π½ΠΈΠΊ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ, ΡΠΎ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Ρ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Π½Ρ Π½Π° Π»ΡΠ½ΡΡ Π°Π²ΡΠΎΠ³Π΅Π½Π½ΠΎΡ ΡΡΠΊΡΠ΅ΡΡΡ, Π·Π°Π»Π΅ΠΆΠΈΡΡ Π²ΡΠ΄ ΡΡΡΠΎΠΌΠ°ΡΠΈ ΡΠ° ΡΡ Π²ΡΠΊΡ. ΠΠ½Π°ΡΠ΅Π½Π½Ρ Π΅Π½ΡΡΠΎΠΏΡΡ Π΅ΠΊΠΎΡΠΈΡΡΠ΅ΠΌΠΈ Π±ΡΠ΄Π΅ ΠΎΠ±Π΅ΡΠ½Π΅Π½ΠΎ ΠΏΡΠΎΠΏΠΎΡΡΡΠΉΠ½Π΅ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊΡ ΡΡ Π΄ΠΈΠ½Π°ΠΌΡΠΊΠΈ. Π¦Π΅ΠΉ ΠΏΠΎΠΊΠ°Π·Π½ΠΈΠΊ ΠΌΠΎΠΆΠ΅ Π±ΡΡΠΈ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½ΠΈΠΌ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΡΡΡΠΎΡΠ½Π΄ΠΈΠΊΠ°ΡΡΠΉΠ½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ. ΠΡΠΈ ΡΡΠΎΠΌΡ ΠΏΠΎΡ
ΠΈΠ±ΠΊΠ° Π²ΠΈΠΌΡΡΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ»ΠΈΠ²Π°ΡΠΈΠΌΠ΅ΡΡΡΡ Π² Π΄ΡΠ°ΠΏΠ°Π·ΠΎΠ½Ρ 3 %-10 %. ΠΠΈ ΠΌΠΎΠΆΠ΅ΠΌΠΎ ΡΠ΅ Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈ ΠΏΡΠ΄ ΡΠ°Ρ ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ±ΠΎΡΠΈ Π² Π·Π°ΠΏΠΎΠ²ΡΠ΄Π½ΠΈΠΊΠ°Ρ
, Π½Π° ΡΠ΅ΠΊΡΠ»ΡΡΠΈΠ²ΠΎΠ²Π°Π½ΠΈΡ
Π·Π΅ΠΌΠ»ΡΡ
, Π² Π»ΡΡΠΎΠ²ΠΎΠΌΡ Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΡΠ²Ρ ΡΠΎΡΠΎ. ΠΠ±ΡΠ°Π½ΠΈΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Ρ Π²ΠΈΡΠΎΠΊΠΈΠΉ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΈΠΉ Π΅ΡΠ΅ΠΊΡ ΡΠ° ΡΡΠ²ΠΎΡΡΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ Π²ΡΠ΄Π½ΠΎΡΠΈΠ½ΠΈ Π»ΡΠ΄ΠΈΠ½ΠΈ ΡΠ· Π΄ΠΎΠ²ΠΊΡΠ»Π»Ρ
Lumbricides as a bio-indicators of the influence of electrical transmission line in the conditions of Ukrainian Polissia
The complex interaction of a man and the environment requires the creation of algorithms for predict- ing the effects of anthropogenic impact. This requires the creation of the ecosystem models where all their basic elements and relationships are taken into account. One of the most effective methods for choosing the most informative differential elements of a system is the development and the implemen- tation of bio-indication models. Nowadays the important task is the development of the bio-indication methods for various anthropogenic complex effects.The electrical transmission lines carry out a specific integrated environmental impact. It is created on the changes in the environment under the influence of magnetic and electric fields and the long-term consequences of their interaction with ecosystem components. This effect is negative for the most representatives of biota and for earthworms in particular. We observe how the number of species de- creases, the species diversity reduces and the morphological parameters change towards species with a shorter body length under reaching the electrical transmission lines.Some genera of the earthworms are able to survive under the effects of the modern electrical transmis- sion lines, although this reflects their abundance and morphological parameters. These species include Aporrectodea caliginosa and Aporrectodea trapezoides. They can be used as diagnostic genera of impact of the electrical transmission lines on biota. They can be genera indicators for the determination of the effect of the electrical transmission lines. The presence of Lumbricus terrestris shows the low electrical transmission line effects, and the presence of Aporrectodea roseΠ° or Aporrectodea longΠ° shows low or moderate effects.We can create bioindication models according to the relations between groups of species of dominant and subdominant specimens formed by the body size. Under the condition of creating a wide data-base and establishing the vitality parameters, it is possible to develop more advanced and efficient algorithms for the synbioindicator analysis of the impact of the electrical transmission lines on the environment
Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π½Ρ Π°ΡΡ ΡΡΠ΅ΠΊΡΡΡΠΈ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΏΠΎΡΠΈΡΠ΅Π½Π½Ρ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ Π² ΡΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΏΡΠΎΡΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ SEO-ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΠΉ, Π½Π΅ΠΉΡΠΎΠ½Π½ΠΈΡ ΠΌΠ΅ΡΠ΅ΠΆ ΡΠ° Machine Learning
We have considered a task on designing an intelligent system of commercial distribution of informational products using a personalized approach to visitors based on the categories and tags of content that interests visitors. A general standard architecture of appropriate system has been developed using methods and personalization tools in the Internet environment with a core of automated recommendation of tags (categories) in the form of a neural network with controlled training. A personalized approach to the web site user results in a higher rate of sales. The system that was developed on the basis of modern SEO technologies considering the metrics for assessing the operation of an information and search module in the system makes it possible to select relevant content according to the user's personalized interests. The system has classes and subclasses that include real commercial informational products, interrelated by the built logical links, whose application promotes the intelligent supply of content based on the personalization of needs and interests of the user. In addition, based on modern methods of Machine Learning, the designed system learns to refine the results from searching the content in demand according to the personalized user's preferences. Personalization algorithms make it possible to associate each user with a list of products that are most likely to be of interest, and can predict what customers may want to see even if they are not aware of it yet. The aim of the intelligent system of e-commerce is to represent unique content based on the personalization approach and the use of tags. In addition to a standard text introduction of categories and tags based on images and product descriptions, the designed automation process defines tags and product categories. Recognition of context using deep neural networks now provides a technology for automated addition of tags to the description of goods at e-commerce web sites. The methods can be used to categorize facial expressions and recognize emotionsΠ Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ² Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΊ ΠΏΠΎΡΠ΅ΡΠΈΡΠ΅Π»ΡΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ ΠΈ ΡΠ΅Π³ΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ΅ΡΠΈΡΠ΅Π»ΡΠΌ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ°. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΠΎΠ±ΡΠ°Ρ ΡΠΈΠΏΠΎΠ²Π°Ρ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΠ° ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠ΅ΠΉ ΡΠΈΡΡΠ΅ΠΌΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ ΡΡΠ΅Π΄ΡΡΠ² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² ΠΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΡΠ΅Π΄Π΅ Ρ ΡΠ΄ΡΠΎΠΌ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΡΠ΅Π³ΠΎΠ² (ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ) Π² Π²ΠΈΠ΄Π΅ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠΈ Ρ ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΡΠ΅ΠΌΡΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ. ΠΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠ°ΠΉΡΠ° ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡ ΠΊ Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΠ° ΠΏΡΠΎΠ΄Π°ΠΆ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² SEO-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Ρ ΡΡΠ΅ΡΠΎΠΌ ΠΌΠ΅ΡΡΠΈΠΊ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ°Π±ΠΎΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΠΎ-ΠΏΠΎΠΈΡΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠΎΠ΄ΡΠ»Ρ ΡΠΈΡΡΠ΅ΠΌΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΠ΄Π±ΠΈΡΠ°ΡΡ ΡΠ΅Π»Π΅Π²Π°Π½ΡΠ½ΡΠΉ ΠΊΠΎΠ½ΡΠ΅Π½Ρ ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. Π‘ΠΈΡΡΠ΅ΠΌΠ° ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΠΊΠ»Π°ΡΡΠ°ΠΌΠΈ ΠΈ ΠΏΠΎΠ΄ΠΊΠ»Π°ΡΡΠ°ΠΌΠΈ, ΠΊ ΠΊΠΎΡΠΎΡΡΠΌ ΠΎΡΠ½ΠΎΡΡΡΡΡ ΡΠ΅Π°Π»ΡΠ½ΡΠ΅ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΏΡΠΎΠ΄ΡΠΊΡΡ, ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΡΠΎΡΡΠΌΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½Ρ Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΡΠ·ΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠΎΡΠΎΡΡΡ
ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½Π°Ρ ΠΏΠΎΠ΄Π°ΡΠ° ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ ΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. Π’Π°ΠΊΠΆΠ΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Machine Learning ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° ΡΡΠΈΡΡΡ ΡΡΠΎΡΠ½ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΈΡΠΊΠ° Π²ΠΎΡΡΡΠ΅Π±ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ° ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ΅Π½ΠΈΠΉ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ. ΠΠ»Π³ΠΎΡΠΈΡΠΌΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΡΠ²ΡΠ·Π°ΡΡ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Ρ ΡΠΎ ΡΠΏΠΈΡΠΊΠΎΠΌ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ Π²Π΅ΡΠΎΡΡΠ½Π΅Π΅ Π²ΡΠ΅Π³ΠΎ Π΅Π³ΠΎ Π·Π°ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΡΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠΎΠ³ΡΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠΎ, ΡΡΠΎ ΠΊΠ»ΠΈΠ΅Π½ΡΡ ΠΌΠΎΠ³ΡΡ Ρ
ΠΎΡΠ΅ΡΡ Π²ΠΈΠ΄Π΅ΡΡ, Π΄Π°ΠΆΠ΅ Π΅ΡΠ»ΠΈ Π΅ΡΠ΅ Π½Π΅ Π·Π½Π°ΡΡ ΠΎΠ± ΡΡΠΎΠΌ. Π¦Π΅Π»ΡΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠ΄Π°ΡΠ° ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Π³ΠΎΠ². ΠΡΠΎΠΌΠ΅ ΠΎΠ±ΡΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎ Π²Π²ΠΎΠ΄Π° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ ΠΈ ΡΠ΅Π³ΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠ°, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠΉ ΠΏΡΠΎΡΠ΅ΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅Π³ΠΎΠ² ΠΈ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ ΡΠΎΠ²Π°ΡΠ°. Π Π°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ° Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ ΡΠ΅ΠΏΠ΅ΡΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅Ρ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄ΠΎΠ±Π°Π²Π»Π΅Π½ΠΈΡ ΡΠ΅Π³ΠΎΠ² Π² ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΡΠΎΠ²Π°ΡΠ° ΡΠ°ΠΉΡΠΎΠ² ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΊΠΎΠΌΠΌΠ΅ΡΡΠΈΠΈ. ΠΠ΅ΡΠΎΠ΄Ρ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΌΠΈΠΌΠΈΠΊΠΈ ΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΠΌΠΎΡΠΈΠΉΠ ΠΎΠ·Π³Π»ΡΠ½ΡΡΠ° ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ° ΠΏΡΠΎΠ΅ΠΊΡΡΠ²Π°Π½Π½Ρ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ ΠΏΠΎΡΠΈΡΠ΅Π½Π½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½ΠΈΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² ΡΠ· Π·Π°ΡΡΠΎΡΡΠ²Π°Π½Π½ΡΠΌ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΠΏΡΠ΄Ρ
ΠΎΠ΄Ρ Π΄ΠΎ Π²ΡΠ΄Π²ΡΠ΄ΡΠ²Π°ΡΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΊΠ°ΡΠ΅Π³ΠΎΡΡΠΉ ΡΠ° ΡΠ΅Π³ΡΠ² ΡΡΠΊΠ°Π²ΠΎΠ³ΠΎ Π²ΡΠ΄Π²ΡΠ΄ΡΠ²Π°ΡΠ°ΠΌ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ. Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π° Π·Π°Π³Π°Π»ΡΠ½Π° ΡΠΈΠΏΠΎΠ²Π° Π°ΡΡ
ΡΡΠ΅ΠΊΡΡΡΠ° Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΡΠ° Π·Π°ΡΠΎΠ±ΡΠ² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ Π² ΠΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΡ ΡΠ· ΡΠ΄ΡΠΎΠΌ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΠ²Π°Π½Π½Ρ ΡΠ΅Π³ΡΠ² (ΠΊΠ°ΡΠ΅Π³ΠΎΡΡΠΉ) Ρ Π²ΠΈΠ³Π»ΡΠ΄Ρ Π½Π΅ΠΉΡΠΎΠΌΠ΅ΡΠ΅ΠΆΡ Π· ΠΊΠΎΠ½ΡΡΠΎΠ»ΡΠΎΠ²Π°Π½ΠΈΠΌ Π½Π°Π²ΡΠ°Π½Π½ΡΠΌ. ΠΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠ΄Ρ
ΡΠ΄ Π΄ΠΎ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠ° ΡΠ°ΠΉΡΡ ΠΏΡΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡ Π΄ΠΎ Π±ΡΠ»ΡΡ Π²ΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΠΊΠΎΠ΅ΡΡΡΡΡΠ½ΡΠ° ΠΏΡΠΎΠ΄Π°ΠΆ. Π ΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΡΡΠ°ΡΠ½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² SEO-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΠΉ Π· Π²ΡΠ°Ρ
ΡΠ²Π°Π½Π½Ρ ΠΌΠ΅ΡΡΠΈΠΊ ΠΎΡΡΠ½ΡΠ²Π°Π½Π½Ρ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½ΠΎ-ΠΏΠΎΡΡΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΌΠΎΠ΄ΡΠ»Ρ ΡΠΈΡΡΠ΅ΠΌΠΈ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡ ΠΏΡΠ΄Π±ΠΈΡΠ°ΡΠΈ ΡΠ΅Π»Π΅Π²Π°Π½ΡΠ½ΠΈΠΉ ΠΊΠΎΠ½ΡΠ΅Π½Ρ Π·Π³ΡΠ΄Π½ΠΎ ΡΠ½ΡΠ΅ΡΠ΅ΡΡΠ² ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·ΠΎΠ²Π°Π½ΠΎΠ³ΠΎ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠ°. Π‘ΠΈΡΡΠ΅ΠΌΠ° Π²ΠΎΠ»ΠΎΠ΄ΡΡ ΠΊΠ»Π°ΡΠ°ΠΌΠΈ ΡΠ° ΠΏΡΠ΄ΠΊΠ»Π°ΡΠ°ΠΌΠΈ, Π΄ΠΎ ΡΠΊΠΈΡ
Π½Π°Π»Π΅ΠΆΠ°ΡΡ ΡΠ΅Π°Π»ΡΠ½Ρ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½Ρ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ, ΠΌΡΠΆ ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°Π½Ρ Π»ΠΎΠ³ΡΡΠ½Ρ Π·Π²`ΡΠ·ΠΊΠΈ, Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ ΡΠΊΠΈΡ
Π²ΡΠ΄Π±ΡΠ²Π°ΡΡΡΡΡ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½Π° ΠΏΠΎΠ΄Π°ΡΠ° ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ ΠΏΠΎΡΡΠ΅Π± ΡΠ° Π·Π°ΡΡΠΊΠ°Π²Π»Π΅Π½Ρ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠ°. Π’Π°ΠΊΠΎΠΆ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΡΡΠ°ΡΠ½ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΡΠ² Machine Learning ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ° Π½Π°Π²ΡΠ°ΡΡΡΡΡ ΡΡΠΎΡΠ½ΡΠ²Π°ΡΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΈ ΠΏΠΎΡΡΠΊΡ Π·Π°ΡΡΠ΅Π±ΡΠ²Π°Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ Π·Π³ΡΠ΄Π½ΠΎ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ Π²ΠΏΠΎΠ΄ΠΎΠ±Π°Π½Ρ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠ°. ΠΠ»Π³ΠΎΡΠΈΡΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ Π΄ΠΎΠ·Π²ΠΎΠ»ΡΡΡΡ ΠΏΠΎΠ²βΡΠ·Π°ΡΠΈ ΠΊΠΎΠΆΠ½ΠΎΠ³ΠΎ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΠ° Π· ΡΠΏΠΈΡΠΊΠΎΠΌ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ², ΡΠΊΡ Π½Π°ΠΉΡΠΌΠΎΠ²ΡΡΠ½ΡΡΠ΅ ΠΉΠΎΠ³ΠΎ Π·Π°ΡΡΠΊΠ°Π²Π»ΡΡΡ, Π° ΡΠ°ΠΊΠΎΠΆ ΠΌΠΎΠΆΡΡΡ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°ΡΠΈ ΡΠ΅, ΡΠΎ ΠΊΠ»ΡΡΠ½ΡΠΈ ΠΌΠΎΠΆΡΡΡ Ρ
ΠΎΡΡΡΠΈ Π±Π°ΡΠΈΡΠΈ, Π½Π°Π²ΡΡΡ ΡΠΊΡΠΎ ΡΠ΅ Π½Π΅ Π·Π½Π°ΡΡΡ ΠΏΡΠΎ ΡΠ΅. ΠΠ΅ΡΠΎΡ ΡΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡ ΡΠΈΡΡΠ΅ΠΌΠΈ Π΅-ΠΊΠΎΠΌΠ΅ΡΡΡΡ Ρ ΠΏΠΎΠ΄Π°ΡΠ° ΡΠ½ΡΠΊΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΏΡΠ΄Ρ
ΠΎΠ΄Ρ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ ΡΠ° Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠ΅Π³ΡΠ². ΠΠΊΡΡΠΌ Π·Π²ΠΈΡΠ°ΠΉΠ½ΠΎΠ³ΠΎ ΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΠ³ΠΎ Π²Π²Π΅Π΄Π΅Π½Π½Ρ ΠΊΠ°ΡΠ΅Π³ΠΎΡΡΠΉ ΡΠ° ΡΠ΅Π³ΡΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½Ρ ΡΠ° ΠΎΠΏΠΈΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡ, ΡΠΎΠ·ΡΠΎΠ±Π»Π΅Π½ΠΈΠΉ ΠΏΡΠΎΡΠ΅Ρ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΡΡ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Π½Ρ ΡΠ΅Π³ΡΠ² ΡΠ° ΠΊΠ°ΡΠ΅Π³ΠΎΡΡΠΉ ΡΠΎΠ²Π°ΡΡ. Π ΠΎΠ·ΠΏΡΠ·Π½Π°Π²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ Π·Π° Π΄ΠΎΠΏΠΎΠΌΠΎΠ³ΠΎΡ Π³Π»ΠΈΠ±ΠΎΠΊΠΈΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΠΈΡ
ΠΌΠ΅ΡΠ΅ΠΆ ΡΠ΅ΠΏΠ΅Ρ Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΡΡ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΡΡ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ½ΠΎΠ³ΠΎ Π΄ΠΎΠ΄Π°Π²Π°Π½Π½Ρ ΡΠ΅Π³ΡΠ² Π² ΠΎΠΏΠΈΡΠΈ ΡΠΎΠ²Π°ΡΡ ΡΠ°ΠΉΡΡΠ² Π΅-ΠΊΠΎΠΌΠ΅ΡΡΡΡ. ΠΠ΅ΡΠΎΠ΄ΠΈ ΠΌΠΎΠΆΠ½Π° Π²ΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠ²Π°ΡΠΈ Π΄Π»Ρ ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ ΠΌΡΠΌΡΠΊΠΈ Ρ ΡΠΎΠ·ΠΏΡΠ·Π½Π°Π½Π½Ρ Π΅ΠΌΠΎΡΡ