Design Versus Self-Organization

Prokopenko, Mikhail

doi:10.1007/978-1-4471-5113-5_1

Cited by 6 publications

(4 citation statements)

References 67 publications

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“…Thus, it is advisable to use economic laws, concepts, and methods of calculating key performance indicators to knowledge as a resource. Knowledge becomes a resource that is suitable for production, acquisition of value, exchange, sale, depreciation, and destruction [11].…”

Section: Resultsmentioning

confidence: 99%

International Experience of Capitalization of Knowledge in Terms of Innovation Economy

Rudenko,

Berezianko,

Halytsia

et al. 2023

ФКДПТП

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The article examines the problems of knowledge capitalization in the innovation economy paradigm. The modern globalized world is characterized by the commercialization of educational and scientific activities, the formation of educational ecosystems, as well as the capitalization of knowledge as a resource. The paper describes the theoretical basis of knowledge capitalization and defines the stages of the formation of academic capitalism. It is defined that the capitalization of knowledge is an increase, the maximization of the value of own and engaged intellectual assets, as a result of the increase in the influence of higher education institutions and scientific institutions on the scientific-technical and socio-economic development of society, and commercialization is the process of turning knowledge into a commodity.A system of statistical indicators of capitalization of knowledge within the economy has been established (expenditures on education; the number of patents; share of costs for scientific research and development; costs for innovative activities; share of innovative enterprises in the total number of enterprises; share of income from the sale of innovative products to the total volume of products sold ). The methodology for calculating the model of the change in GDP per capita has been developed, taking into account the indicators of knowledge capitalization, and the algorithm of necessary actions regarding its application has been described. The hypothesis about the impact of capitalization of knowledge on the economic development of the state, namely the change in macroeconomic indicators, such as GDP (gross domestic product) per capita, has been confirmed.The author’s formula for determining the coefficient of the economic effect from the capitalization of knowledge is proposed, which reflects the ratio of investments in knowledge-creating (innovative activity) to the income received from the implementation of an innovative product. Investments in knowledge-creating activities are the number of expenses for scientific research and development, so the innovative product is a patent, as a formalized unit of knowledge. The calculation of the coefficient of the economic effect of knowledge capitalization for Ukraine and the Republic of Poland was carried out, which clearly showed the expediency of implementing the knowledge capitalization experience of Poland and the need for a balanced internal policy to stimulate educational, scientific and innovative activities with the attraction of appropriate investments in Ukraine.

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Section: Resultsmentioning

confidence: 99%

International Experience of Capitalization of Knowledge in Terms of Innovation Economy

Rudenko,

Berezianko,

Halytsia

et al. 2023

ФКДПТП

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“…Some earlier works in this area focus on self‐organization paradigms 9,10 . These research studies that are mainly inspired by swarm colonies focus on communications between agents and the emergence of global patterns from local interactions 11–13 . However, other researchers have tried to combine the bottom‐up paradigm of self‐organization with top‐down self‐adaptive methods.…”

Section: Related Workmentioning

confidence: 99%

An approach for semantic interoperability in autonomic distributed intelligent systems

Yaghoobirafi¹,

Farahani

2022

J Software Evolu Process

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The complexity and dynamism of distributed intelligent systems have motivated the utilization of autonomic computing in such systems. However, the interoperability of agents in order to attain self‐managing properties is itself a great challenge that requires more attention. Although a number of semantic approaches exist for central autonomic systems, there are not many attempts to develop mechanisms for facilitating semantic interoperability to attain a distributed feedback loop. In this paper, a semantic approach is presented to overcome this challenge. The main component of this approach is AutonoML language that provides the shared semantics for autonomic computing in distributed environments. Besides this language, the method of its application for enhancing the interoperability of the distributed MAPE‐K loop is presented. In order to evaluate this mechanism, three well‐known metrics from the ontological assessment area are utilized, and the results are compared with an existing autonomic ontology that shows greater relational and attributes richness of the proposed semantic structures. Moreover, in order to evaluate the applicability of the mechanism, a case from NASA‐ANTS project is studied, and the results show that the proposed mechanism is capable of facilitating the achievement of more stable self‐adaptive solutions in less time and by exchanging fewer messages.

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“…This can be induced by internal and external factors, but critically order is lost upon removal of the source of energy (see also in the context of biological systems). Classical examples of self‐organizing systems are spontaneous magnetisation , lasers , the Belousov‐Zhabotinski reaction (classic Turing patterns) and in biology, bird flocking . The emergence of organoids does not belong to either of these classes as in the initial phases of the process it is not clear that all the components of the system (i.e.…”

Section: Genetically Encoded Self‐assemblymentioning

confidence: 99%

Organoids and the genetically encoded self‐assembly of embryonic stem cells

2015

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Understanding the mechanisms of early embryonic patterning and the timely allocation of specific cells to embryonic regions and fates as well as their development into tissues and organs, is a fundamental problem in Developmental Biology. The classical explanation for this process had been built around the notion of positional information. Accordingly the programmed appearance of sources of Morphogens at localized positions within a field of cells directs their differentiation. Recently, the development of organs and tissues from unpatterned and initially identical stem cells (adult and embryonic) has challenged the need for positional information and even the integrity of the embryo, for pattern formation. Here we review the emerging area of organoid biology from the perspective of Developmental Biology. We argue that the events underlying the development of these systems are not purely linked to “self‐organization,” as often suggested, but rather to a process of genetically encoded self‐assembly where genetic programs encode and control the emergence of biological structures.

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Design Versus Self-Organization

Cited by 6 publications

References 67 publications

International Experience of Capitalization of Knowledge in Terms of Innovation Economy

International Experience of Capitalization of Knowledge in Terms of Innovation Economy

An approach for semantic interoperability in autonomic distributed intelligent systems

Organoids and the genetically encoded self‐assembly of embryonic stem cells

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