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All Journal Emerging Science Journal
Kostyrin, Evgeniy V.
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Environmental Science

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Journal : Emerging Science Journal

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New Assessment Model of Financing Treatment of Patients with Complete Tooth Loss Grachev, Dmitry I.; Martynenko, Aleksandr V.; Perekhodov, Sergey N.; Kostyrin, Evgeniy V.; Mustafaev, Magomet Sh.; Akhmedov, Kamalutdin G.; Deshev, Aslan V.; Rozanov, Daniil G.; Korotkova, Nadezhda L.; Kerasov, Stefan N.; Arutyunov, Sergey A.
Emerging Science Journal Vol 8, No 5 (2024): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-05-014

Abstract

According to the World Health Organization, the global prevalence of complete tooth loss is estimated to be 7% among individuals aged 20 years and older, while for those aged 60 and over, this rate significantly increases to 23%. This study is relevant due to the psychological trauma, social challenges, and functional limitations caused by tooth loss, as well as the uneven availability of dental care worldwide. The goal of this research is to develop and implement a new model to assess the socioeconomic feasibility of investing in digital technologies for diagnosing and treating patients with complete tooth loss using removable polymer prostheses produced through additive 3D printing. The study employs scenario analysis, the clustered rankings coordination method, statistical methods, expert opinion assessment using Kendall’s coefficient of rank concordance, system analysis and design, questionnaires, sociometry, and functional modeling. The practical significance of this research lies in providing a quantitative assessment of economic opportunities for effectively using RPDs in three groups: RPDs without additional fixation means; those with special adhesive agents for improved fixation; and implant-supported prosthetics with conditionally removable dentures similar to RPDs. The scientific novelty of this study is the development of a new evaluation model that justifies the choice of prosthetic treatment technology for patients with complete tooth loss, enabling the most rational use of resources. Doi: 10.28991/ESJ-2024-08-05-014 Full Text: PDF
A Model for Fostering Labor Productivity and Wage Management: A Long-Term Outlook to 2034 Kostyrin, Evgeniy V.; Moussa Pascal, Loua; Kostyrin, Dmitriy
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-017

Abstract

This research aims to develop a complex system of managerial decision-making support, which includes an economic-mathematical model of maximizing the salary share in the company’s revenue, consistent with the financial interests of business owners and the state, analyzing the sensitivity of the maximum wage share to key parameters of the model, and developing software based on MS Excel and PTC Mathcad Prime 3.1. The research methodology included system analysis, non-linear programming, sensitivity analysis, and dynamic system modeling linking time-related changes in wages, labor productivity, enterprises’ reinvestment, and financial stability. Modeling results showed that a balanced policy allows employees to increase wages by 59.83% and net profit by 2.97. The return on sales increased by 2.28 times, and government revenues from taxes and social contributions increased by 58.78%. The maximum sustainable share of wages in revenue reaches 54.73%, which is 9.83% higher than the base indicator. The novelty of the research lies in the development and practical implementation of an economic and mathematical model that maximizes the share of wages in the company’s revenue while balancing the financial results of employees, owners, and the state within the framework of the Russian fiscal system. JEL Classification: C44, I11, J31, M21.
Co-Authors Akhmedov, Kamalutdin G. Arutyunov, Sergey A. Deshev, Aslan V. Grachev, Dmitry I. Kerasov, Stefan N. Korotkova, Nadezhda L. Kostyrin, Dmitriy Martynenko, Aleksandr V. Moussa Pascal, Loua Mustafaev, Magomet Sh. Perekhodov, Sergey N. Rozanov, Daniil G.