Development of control processes of research and production subsystems at scientific enterprises of rocket and space engineering

2021 ◽  
pp. 3-9
Author(s):  
В.Е. Барковская ◽  
М.С. Абрашкин
Keyword(s):  
Production Management ◽  
Synergetic Effect ◽  
Spatial Models ◽  
Economic Structure ◽  
Management Process ◽  
Development Effectiveness ◽  
Logical Modeling ◽  
Knowledge Intensive ◽  
Technological Platforms ◽  
Science Intensity

В статье уточнено влияние процессов управления научно-исследовательской и производственной подсистемами наукоёмких предприятий РКМ на эффективность их развития. Доказано с использованием методов логико-абстрактного моделирования, что сложившаяся организационно-экономическая структура наукоёмких предприятий РКМ не совершенна и требует изменения управления научно-исследовательской и производственной подсистемами, совокупность которых за счёт синергетического эффекта, проявляемого во внутриорганизационной и межорганизационной интеграции, позволит дать оценку общей эффективности их развития. Проведен анализ показателей наукоемкости предприятий РКМ, который установил, что целесообразна их научно-производственная интеграция в пространственных моделях, внедрение и использование технологических платформ. The article specifies R&D and production management process upon knowledge-intensive rocket and space enterprises in respect or their development effectiveness. It is approved with the method of abstract logical modeling that contemporary organization and economic structure of the knowledge-intencive enterprises has to be modified in terms of R&D and production subsystem management. Both of them will lead to increase development by synergetic effect caused by inter- and cross- organization integration. The analysis of the indicators of the science intensity of enterprises of the knowledge-intensive rocket and space enterprises was carried out, it was established that their scientific and industrial integration in spatial models, the introduction and use of technological platforms is expedient.

scholarly journals Managed attenuation of the mining enterprise as a component of the development strategy of the territory

2020 ◽  
Vol 177 ◽  
pp. 05005
Author(s):  
Dmitriy Yadransky ◽  
Elena Chumak ◽  
Rinat Latypov
Keyword(s):  
Development Strategy ◽  
Labor Productivity ◽  
Productivity Growth ◽  
Middle Urals ◽  
Negative Consequences ◽  
Industrial Region ◽  
Labor Productivity Growth ◽  
Balanced Development ◽  
Mining Enterprise ◽  
Logical Modeling

The article critically examines the positive and negative consequences of labor productivity growth at mining enterprises in the conditions of the old industrial region. It is suggested that for enterprises of the middle Urals it is necessary to form a mining production development strategy based on the directions of the regional strategy, which is not always connected with labor productivity growth by increasing mining volumes. The article is aimed at studying the factors affecting the prospects of mining enterprises activity from the standpoint of choosing strategic alternatives to their development. The methods of analysis: logical analysis, structural analysis, logical modeling, literature analysis. Using the logical modeling method, the following hypothesis was verified: that mining enterprises strategic development features in the conditions of an old industrial region should consider the strategy of municipalities in which these enterprises are located. For such mining enterprises, the increase in productivity through increased production is not unequivocally positive. It is concluded that in order to ensure the activities coherence of regions and enterprises, it is necessary to ensure balanced development, which can be achieved through the application of a managed strategy attenuation of the mining enterprise.

scholarly journals Distinction between the concepts of mathematical and logical modeling

2019 ◽  
Vol 19 (3) ◽  
pp. 262-267 ◽  
Author(s):  
E. N. Kolybenko
Keyword(s):  
Mathematical Modeling ◽  
Problem Solving ◽  
Practical Significance ◽  
Material Objects ◽  
Knowledge Domain ◽  
Organic Unity ◽  
Control Processes ◽  
Management Processes ◽  
Logical Modeling ◽  
High Level

Introduction. Technologies of mathematical and logical modeling of problem solving according to the existing practice of their distribution are divided into two areas: widespread mathematical modeling and infological modeling which is currently underdeveloped, especially for sophisticated systems. Fundamental differences between these technologies, in particular for the machining preproduction, are that logical modeling is informationally and logically related to organization systems, and mathematical modeling is associated with control processes in the organization systems. Logical modeling is used to operate with geometric objects in the technological schemes of their interaction through basing methods, geometric shaping in a static (ideal) setting of the corresponding schemes. Mathematical simulation is used to operate material objects in the control processes of their transformations through cutting methods, i.e. imperfectly, considering heterogeneous errors. Between the organization systems under study and management processes in them, there are information and logical links of their organic unity, which deny their separate consideration. In the information deterministic technology for solving problems of a high-level automation, the distinction between the concepts of “mathematical” and “logical” modeling is relevant; it has scientific novelty and practical significance.Materials and Methods. To characterize the properties of the concepts of “mathematical modeling”, “logical modeling” and the knowledge functions resulting from the formulation of these concepts, fundamentally different methods and appropriate tools are used. The differentiation of the concepts under consideration is based on the differentiation of technologies (methods, appropriate tools, algorithms, operations) for solving applied problems of any knowledge domain.Research Results. The ideas of “logical modeling” and “mathematical modeling” are conceptual general-theoretical notions with invariant properties required for solving practical problems of any application domain. In accordance with the distinction between these concepts, the problem solving technologies are divided into two types: system engineering technology – in the organization of information object systems, and system science – in the management processes of transformation of the corresponding material objects. These areas should exist in the information and logical link of their organic unity.Discussion and Conclusions. The author distinguishes between the concepts of “logical modeling” and “mathematical modeling”, which is a key condition for a successful transition to the deterministic information technology of a high-level automation in solving practical problems of any knowledge domain, for example, of the production design machining

scholarly journals Lower complexity bounds for lifted inference

2014 ◽  
Vol 15 (2) ◽  
pp. 246-263 ◽  
Author(s):  
MANFRED JAEGER
Keyword(s):  
Knowledge Bases ◽  
Modeling Languages ◽  
Complexity Bounds ◽  
Lifted Inference ◽  
Probabilistic Relational Models ◽  
Lower Complexity ◽  
Logical Modeling ◽  
Inference Techniques ◽  
And Function ◽  
Lower Complexity Bounds

AbstractOne of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that operate on the level of the abstract model representation language, rather than on the level of ground, propositional instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about probabilistic relational models. Artificial Intelligence 117, 297–308). However, it is not immediate that these results also apply to the type of modeling languages that currently receive the most attention, i.e., weighted, quantifier-free formulas. In this paper we extend these earlier results, and show that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing the equality predicate.

Reasoning on the Semantic Web

2011 ◽  
pp. 110-133
Author(s):  
R. Brussee
Keyword(s):  
Real World ◽  
Description Logic ◽  
World Knowledge ◽  
Closed World Assumption ◽  
Sharp Distinction ◽  
Ontology Language ◽  
Closed World ◽  
Present Information ◽  
Logical Modeling ◽  
Case Reasoning

We describe reasoning as the process needed for using logic. Efficiently performing this process is a prerequisite for using logic to present information in a declarative way and to construct models of reality. In particular we describe description logic and the owl ontology language and explain that in this case reasoning amounts to graph completion operations that can be performed by a computer program. We give an extended example, modeling a building with wireless routers and explain how such a model can help in determining the location of resources. We emphasize how different assumptions on the way routers and buildings work are formalized and made explicit in our logical modeling, and explain the sharp distinction between knowing some facts and knowing all facts (open vs. closed world assumption). This should be helpful when using ontologies in applications needing incomplete real world knowledge.

scholarly journals Contributions from the 2019 Literature on Bioinformatics and Translational Informatics

2020 ◽  
Vol 29 (01) ◽  
pp. 188-192
Author(s):  
Malika Smaïl-Tabbone ◽  
Bastien Rance ◽  
Keyword(s):  
Open Data ◽  
Leading Edge ◽  
Open Science ◽  
Editorial Committee ◽  
Cancer Pathways ◽  
Sequence Of Events ◽  
Independent Review ◽  
Logical Modeling ◽  
Translational Informatics ◽  
Selection Of

Objectives: Summarize recent research and select the best papers published in 2019 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the section editors to select a list of 15 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 931 retrieved papers covering the various subareas of BTI, the review process selected four best papers. The first paper presents a logical modeling of cancer pathways. Using their tools, the authors are able to identify two known behaviours of tumors. The second paper describes a deep-learning approach to predicting resistance to antibiotics in Mycobacterium tuberculosis. The authors of the third paper introduce a Genomic Global Positioning System (GPS) enabling comparison of genomic data with other individuals or genomics databases while preserving privacy. The fourth paper presents a multi-omics and temporal sequence-based approach to provide a better understanding of the sequence of events leading to Alzheimer’s Disease. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.

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