A Convergent Approach to the Viability of the Dynamical Systems: The Cognitive Value of Complexity

The integration of information and industrial technologies, digitalization and differentiation of sciences are accompanied by an increase in various types of complexity. This limits the capabilities of computer modelling, data mining, and predictive analytics. The increasing cognitive complexity of information flows and their diversity creates problems of safety, reliability and stability of the functioning of a complex dynamic system in extreme conditions. Here we show the possibility of cognitive visualization of signals of different nature through their geometrization in the form of a topological 3D model of functioning. Its projections are spatio-temporal signatures, the configurations of which reflect the dynamic, energetic and structural features of the model. An increase in the number of components of the signature configuration and its area under external influence indicates an increase in structural and functional complexity. Therefore, the signal structure can be analyzed in real time using complementary probabilistic and deterministic methods. A set of tools for the synthesis and analysis of 3D models has innovative potential for monitoring the functioning of elements of complex dynamic systems, risk management and predictive analytics.

INFORMATION AND TELECOMMUNICATION NETWORK SECURITY MANAGEMENT METHODOLOGY

Исследуются вопросы ситуативного управления сложными динамическими системами. Анализируются релевантные работы в области ситуативного управления системами защиты. Приводятся результаты разработки алгоритма эффектив -ного управления, позволяющего уменьшать пространство состояний управляемого объекта. Показано, что представленная методика позволяет спрогнозировать количество итераций управления в зависимости от сегмента пространства состояний и выбранного количества переходов. Данный подход позволяет воздействовать на сложные динамические системы в реальном времени, причем затраты на вычислительные мощности системы управления и ее подсистем сокращаются. The issues of situational management of complex dynamic systems are investigated. Relevant works in the field of situational management of protection systems are analyzed. The article presents the results of the development of an efficient control algorithm that allows reducing the state space of the controlled object. The presented technique makes it possible to predict the number of control iterations depending on the segment of the state space and the selected number of transitions. This approach allows you to act on complex dynamic systems in real time, while the cost of the computing power of the control system and its subsystems is reduced. Keywords: INFORMATION AND TELECOMMUNICATION NETWORK, SCRIPT FORECAST, MANAGEMENT SYSTEM, SITUATIONAL MANAGEMENT, ITCN SECURITY SYSTEM

Interdependent Dynamic Nitroaldol and Boronic Ester Reactions for Complex Dynamers of Different Topologies

Complex dynamic systems displaying interdependency between nitroaldol and boronic ester reactions have been demonstrated. Nitroalkane-1,3-diols, generated by the nitroaldol reaction, were susceptible to ester formation with different boronic acids in aprotic solvents, whereas hydrolysis of the esters occurred in the presence of water. The boronic ester formation led to significant stabilization of the nitroaldol adducts under basic conditions. The use of bifunctional building blocks was furthermore established, allowing for main chain nitroaldol-boronate dynamers as well as complex network dynamers with distinct topologies. The shape and rigidity of the resulting dynamers showed an apparent dependency on the configuration of the boronic acids.

Trajectories of Idea Emergence in Dialogic Collaborative Problem Solving: Toward a Complex Dynamic Systems Perspective

According to the complex dynamic systems (CDS) perspective, learning emerges at various system levels. This study built a coherent theoretical framework based on CDS and Bakhtinian dialogic theory and further employed the concept of attractor (i.e., certain stable states that recur over time) in CDS theory to investigate the trajectories of idea emergence and how they diversified group outcomes in dialogic collaborative problem solving (D-CPS). Two contrasting groups were compared using visual and qualitative analysis approaches. The analysis based on idea tree diagrams showed that new ideas emergent in group discussion tended to attract local utterances and performed features of attractors in CDS in both high-performing and low-performing groups. The analysis based on idea hierarchy diagrams revealed how ideas emerged at various system levels. It was also found that status problems were likely to affect the functioning of regulative feedback loops, which might give rise to different structures of idea evolution. This study proposed CDS theory as an alternative perspective, augmented by the ethical considerations of Bakhtinian dialogism, for examining the dynamics of D-CPS.

Analysis of the deployment strategies of reinforcement learning controllers for complex dynamic systems

This paper benchmarks several strategies for deploying reinforcement learning (RL)-based controllers on heterogeneous hybrid systems. Sample inefficiency is often a significant cost for RL controllers because we need sufficient data to train them, and the controllers may take time to converge to an acceptable control policy. This can be doubly costly if system health is degrading, or if the network of such systems in turn cannot afford a gradually improving controller in its constituents. Learning speed improvement can be achieved via transfer learning across controllers trained on different tasks: simulations, data-driven models, or separate instances of similar systems. This paper discusses near- and far- transfers across tasks of varying similarities. These approaches are applied on a test-bed of models of cooling towers operating on office and residential buildings on a university campus.

From replication to substantiation: A complexity theory perspective

In contemporary methodological thinking, replication holds a central place. However, relatively little attention has been paid to replication in the context of complex dynamic systems theory (CDST), perhaps due to uncertainty regarding the epistemology–methodology match between these domains. In this paper, we explore the place of replication in relation to open systems and argue that three conditions must be in place for replication research to be effective: results interpretability, theoretical maturity, and terminological precision. We consider whether these conditions are part of the applied linguistics body of work, and then propose a more comprehensive framework centering on what we call substantiation research, only one aspect of which is replication. Using this framework, we discuss three approaches to dealing with replication from a CDST perspective theory. These approaches are moving from a representing to an intervening mindset, from a comprehensive theory to a mini-theory mindset, and from individual findings to a cumulative mindset.

A discourse dynamics exploration of attitudinal responses towards COVID-19 in academia and media

This study analyzes attitudinal positioning in academic and media discourse pertaining to COVID-19 from the COVID-19 Corpus and Coronavirus Corpus, using a discourse dynamics approach. Underpinning this approach is the Complex Dynamic Systems Theory (CDST), which we employ to examine the discursive practices of a discourse event across time periods (timescales). The analysis identified significant differences in attitudinal markers and noteworthy developmental patterns in attitude positioning; the developmental trajectories of attitude construction were characterized by a nonlinear developmental pattern subject to fluctuations and variability. We also discerned the existence of dynamic interaction between the uses of attitudinal markers and the reported cases of COVID-19. Methodologically, we demonstrate how the integration of the discourse dynamics approach with corpus linguistics strengthens the social contextualization of data by enabling the identification of developmental patterns of targeted language features over time, and the interconnections of these language features with contextually important social factors.