Development of the Modularity Measure for Assembly Process Structures

This study is aimed at exploring the problem of quantification of process modularity degree. Modularity as a system design principle is apprehended here as the extent to which processes can be decomposed into modules to be executed in parallel and/or in series. For this purpose, a new method is proposed to measure relative modularity of different assembly process structures. This method is compared with other relative modularity measures, namely singular value modularity index, degree of process module, and process module independence, in order to verify its effectiveness. For this purpose, selected representative types of assembly process structures are used. This testing proved that the proposed relative modularity indicator for manufacturing and/or assembly process structures reflects the expected system property in adequate way.

Toolset for Supporting the Research of Lattice Based Number Expansions

The world of generalized number systems contains many challenging areas. Computer experiments often support the theoretical research. In this paper we introduce a toolset that helps to analyze some properties of lattice based number expansions. The toolset is able to (1) analyze the expansions, (2) decide the number system property, (3) classify and visualize the periodic points. The toolset is implemented in Python, published alongside with a database that stores plenty of special expansions, and is able to store the custom properties like signature, operator eigenvalues, etc. Researchers can connect to the server and request/upload data, or perform experiments on them. We present an introductory usage of the toolset and detail some results that has been observed by the toolset. The toolset can be downloaded from http://numsys.info domain.

Sensitivity Matrices as Keys to Local Structural System Properties of Large-Scale Nonlinear Systems.

Sensitivities are shown to play a key role in a very efficient algorithm, presented in this paper, to establish three fundamental structural system properties: local structural identifiability, local observability and local strong accessibility. Sensitivities have the advantageous property to be governed by linear dynamics, also if the system itself is nonlinear . By integrating their linear dynamics over a short time period, and by sampling the result, a sensitivity matrix is obtained. If this sensitivity matrix satisfies a rank condition, then the local structural system property under investigation holds. This rank condition will be referred to in this paper as the sensitivity rank condition (SERC). Applying a singular value decomposition (SVD) to the sensitivity matrix not only determines its rank but also pinpoints exactly the system components causing a possible failure to satisfy the local structural system property. The algorithm is very efficient because integration of linear systems over short time-periods and computation of an SVD are computationally cheap. Therefore, it allows for the handling of large-scale systems in the order of seconds, as opposed to conventional algorithms that mostly rely on Lie series expansions and a corresponding Lie algebraic rank condition (LARC). We extensively discuss the (dis)advantages of both algorithms and to which extent their results coincide. A series of examples is presented to illustrate these results.

The Formation of Entrepreneurial Activity in the Master (Graduate) Programs

This article is devoted to understanding entrepreneurial activity among students. The aim of the study is to examine the issue of entrepreneurial activity formation, examining entrepreneurship as a central over-system property of the personality, which can be formed and developed. The results of a study of entrepreneurship among university youth are presented. The methodology of Catella, the study of entrepreneurship, the technique of diagnostics of readiness to risk by G. Schubert, ”Motivation to success” and ”Motivation to avoiding failures” by Ehlers, the technique of diagnostics of level of subjective control, methods of mathematical statistics, which were used in this study, allowed us to see the correlation, providing a formed entrepreneurship: high levels of risk tolerance, combined with high motivation for success and low motivation to avoid failures. dents the study has shown that the first bachelor’s courses students’ competences are not formed in the educational process. The study has revealed the tendencies of the educational process modernization at the department of “Organization of work with youth”, which lead to the students’ entrepreneurial activity formation. Keywords: entrepreneurial activity, entrepreneurship, student environment, entrepreneurial thinking

A conceptual framework for resilience: fundamental definitions, strategies and metrics

The resilience system property has become more and more relevant, mainly because of the increasing dependance on a rapidly growing number of software-intensive, complex, socio-technical systems, which are facing uncertainty about changes they are expected to experience during their life-cycle and ways to deal with them. Methodologies for the systematic design and validation of resilience for such systems are thus highly necessary, and require contributions from several different fields. This paper contributes to current resilience research by providing a conceptual framework intended to serve as a common ground for the development of such methodologies. Its main points are: the identification of the main categories of changes a system should face; a clear definition of the different facets of resilience one could want to achieve, expressed in terms of the system dynamics; a mapping of each of these facets to design strategies that are better suited to achieve it; and the corresponding identification of possible metrics that can be used to assess its achievement.

Artificial Intelligence in Causality Healthcare Sector

The ideology of explainability in Artificial Intelligence (AI) is a prevailing issue which requires attention in the healthcare sector. The issue of explain ability is as ancient as AI and the sophisticated AI signified an understandable retraceable technique. Nonetheless, their demerits were in handling the uncertainties of the actual world. As a result of the advent of probabilistic education, applications have now been considered successful and considerably invisible. Comprehensive AI handles the implementation of traceability and transparency of statistical black box techniques of Machine Learning (ML), certainly Deep Learning (DL). Based on the approach of this paper, it can be argued that there is need for researchers to go beyond the comprehensive AI. To accomplish the dimension of explainability in the healthcare sector, causability aspects have to be incorporated. In the same manner that usability incorporates measurements for the quality of usage, causability incorporates the evaluation of explainable quality. In this research, we provide a number of fundamental definitions to effectively discriminate between causability and explainability, including the application case of DL and human comprehensibility in the field of histopathology. The fundamental contribution of this paper is the ideology of causability that has been differentiated from the notion of explainability whereby causability is based on personal property whereas explainability is the system property.

Another Look into the Central Nervous System Decussation Reveals A New Model for Evolution

Most large fibers in central nervous system of vertebrates are decussated. The cause or the evolutionary force of this phenomenon is not convincingly described. Here we show a pattern for this nervous system property that simultaneously is found to a more basic rule in evolution.The idea here is the "function to anatomy" theorem. We claim that functions of physical, biochemical, enzymatic, etc., in lower scale of living organisms "emerge" to anatomy in larger scales. And reversely, the anatomy we see in multicelular organisms are/were functions in microscopic scales.We propose descriptions, predictions, and a probable road map to what this can implicate in biological and medical issues.

Review article: Towards a context-driven research: a state-of-the-art review of resilience research on climate change

Since the 1970s, Holling's socio-ecological systems (SES) approach has been a most predominant theoretical force in resilience research in the context of the climate crisis. From Holling's approach, however, two contrasting scientific approaches to resilience have developed, namely, naturalism and constructivism. While naturalist resilience research takes SES as complex systems marked by non-linearity and evolutionary changes, constructivist resilience research focuses on the embeddedness of SES in heterogenous contexts. In naturalist resilience research resilience is defined as a system property, while in constructivist resilience research resilience is politically loaded and historically contingent. The aim of this paper is to review and structure current developments in resilience research in the field of climate change studies, in terms of the approaches, definitions, models and commitments that are typical for naturalism and constructivism; identify the key tension between naturalist and constructivist resilience research in terms of the widely discussed issue of adaptation and transformation, and discuss its implications for sustainable development; and propose a research agenda of topics distilled from the adaptation-transformation tension between naturalist and constructivist resilience research.