Coupling control and human factors in mathematical models of complex systems

Precise dynamic mathematical models of complex systems are important in control and diagnostic systems design and allow testing a complex system in virtual environment at a low cost. They can be also utilized in rapid prototyping using a concept of hardware in the loop. Ever improving methods of experimental identification and using approaches in non-linear approximation can considerably increase the precision of dynamic models of complex systems. The article deals with non-linear approximation of transfer gains of a complex system and evaluates the influence of operational point selection on precision of the resulting model using methods of experimental data driven identification. The object of control is represented by two similar small turbojet engines at the Departments of the authors, the iSTC-21v and TKT-1, both based on the same power section having two degrees of freedom: fuel mass flow rate and variable convergent nozzle position.

In the field of urban and green mobility, electric car-sharing networks have recently emerged as one of the promising alternatives, dealing with both environmental and transportation issues. Despite the increasing success of the shared mobility concept over the world, the design and management of such urban mobility systems implies strategic, tactical and operational challenges. To support designers and operators, all these issues constitute an emerging and challenging research topic, aiming to develop techniques and tools for modeling, analysis and optimization of such complex systems. In this contribution, based on the modeling and analysis power of stochastic timed Petri nets, a discrete event simulation approach is developed, taking into account their dynamic behavior complexities due to their self-service mode and characteristics including capacities of the stations, energy availability, rebalancing and maintenance activities. A real-life application is presented, to demonstrate the applicability and the potential of the proposed modeling and simulation analysis approach. <i>Note to Practitioners</i>&#x2014;To support designers, providers and managers of shared mobility programs, performance and optimization models and decision-making tools are needed. In this contribution, we deals with a powerful and modular stochastic timed Petri net methodology suitable for performance modeling and simulation analysis of such complex dynamical systems. The authors believe that this discrete event dynamic approach has significant promise for the future and to influence economic viability and operational efficiency of such emerging urban transportation systems.

Recent advances in theory and applications of complex dynamical systems have contributed much to the successful handling of certain problems in biology, physics, economics, engineering, and so forth that until recently were thought too difficult to be analyzed. These complex systems may be characterized by systems with uncertainty, impulse, time delay, stochastic perturbation, hybrid dynamics, distributed dynamics, and chaotic dynamics. The overall aim of this special issue is to bring together the latest or innovative knowledge and advances in mathematics for handling complex systems, whichmay depend largely onmethods frommathematical analysis, artificial intelligence, statistics, and engineering, including nonlinear dynamics, time series analysis, neural networks, and evolutionary computation. The solicited papers in this special issue should provide solutions tomodeling, analysis, control, and applications of real-world complex systems. The topics in this special issue included, but are not limited to, modelling, analysis, and applications of complex systems such as impulsive systems, pulse-modulated systems, switching systems, delayed systems, discontinuous control system theory, bifurcation theory and chaotic dynamics. The response to this special issue on modelling, analysis, and applications of complex systemswas beyond our expectation. We received 39 papers in this research field.This special issue includes thirty-one high-quality peer-reviewed articles. These articles contain some new, novel, and innovative techniques and ideas that may stimulate further research in every branch of theory and applications of complex systems. Acknowledgments

The railway system is a complex system because of many constraints, randomness and high security requirements, so it is difficult to establish an accurate mathematical model for it, which brings great challenges to railway scheduling. Parallel intelligence theory can effectively solve the modeling, analysis and control of complex systems, so it has a wide range of applications in the field of intelligent transportation. In this paper, we build an artificial system according to the actual system, and use the surrogate modelling to carry out the calculation experiments, and proposed a parallel scheduling system based on the parallel intelligence method. Select a case to design scenario and perform computational experiment, the scheduling strategy is given and the scheduling strategy is analyzed.

Usually scientists build physical models depending on how they perceive the world. But the current state of affairs in science has shown that where the scale is very small compared to our usual world, it is not justified to use models that could be used in the macro world. One of the options that can take place in the micro world, but has no analogues in our ordinary world, which we observe every day, is that space can change or have a fractional dimension. It is possible that the dimension of space will have certain values, depending on the conditions in which our complex system is observed in space, or depending on the frame of reference of the observer. And thus the calculations in the mathematical modeling of complex systems must be adjusted in accordance with the dimension of space.

AFARS—an algorithm for analyzing the reliability of systems

Intelligent Transport Systems (ITS) work with information and control technologies providing the core of ITS functions. Some of these technologies like loop detectors are well known to transportation professionals. However, there are a number of less familiar technologies and system concepts that are keys to ITS functions. Although information and control technologies act as a technical core of ITS, human factors also remain vitally important and potentially very complex issues. The process of operating ITS is influenced by a number of random factors. Along with an assessment of dependence upon separate random factors, the classification of those in the whole hierarchical structure of operating ITS is presented. Statistical information on operating ITS is renewed and replenished in the course of time. With the growth of information amounts, the costs of storing them also increase. Therefore, the article presents relevant algorithms for obtaining required statistical assessments with the least statistical information. It is deduced that while modelling the process of operating ITS, an analytical description of random factors applying non‐parametric assessment is suitable.

The high selectivities of liquid chromatography and mass spectrometry make liquid chromatography-mass spectrometry one of the most popular tools for quantitative analysis in complex chemical, biological, and environmental systems, while the potential mathematical selectivity of liquid chromatography-mass spectrometry is rarely investigated. This work discussed the mathematical selectivity of liquid chromatography-mass spectrometry by three-way calibration based on the trilinear model, with an application to quantitative analysis of coeluting aromatic amino acids in human plasma. By the trilinear decomposition of the constructed liquid chromatography-mass spectrometry-sample trilinear model and individual regression of the decomposed relative intensity versus concentration, the proposed three-way calibration method successfully achieved quantitative analysis of coeluting aromatic amino acids in human plasma, even in the presence of uncalibrated interferent(s) and a varying background. This analytical method can ease the requirements for sample preparation and complete chromatographic separation of components, reduce the use of organic solvents, decrease the time of chromatographic separation, and increase the peak capacity of liquid chromatography-mass spectrometry. As a "green analytical method", the liquid chromatography-mass spectrometry three-way calibration method can provide a promising tool for direct and fast quantitative analysis in complex systems containing uncalibrated spectral interferents, especially for the situation where the coelution problem is difficult to overcome.

Modeling a flexible manufacturing cell using stochastic Petri nets with fuzzy parameters

“Complexity and Disaster Forensics: New Paradigms, Models and Approaches for Natural Hazards Management in the Pacific Islands Region” assesses and applies complex systems theory, modeling and analysis to disaster forensics policy and research. To better understand the root cause and complex causality of disasters, complex systems theory, with roots in the fields of statistical physics, information theory, and non-linear dynamics, and systems analysis, is applied to help communities and nations achieve important social development goals, reduce institutional brittleness and increase disaster resilience by promoting positive transformations in the co-evolving and mutually dependent human-environmental condition, and by capitalizing on opportunities provided by human creativity, diplomatic openings, technologic capacities and environmental change. The case studies, investigations and models outlined in this chapter collectively demonstrate the quality, breadth and depth of complex systems and disaster forensics methodologies. Game-theoretic (“Small World”) decision analyses, “Large world” (complex systems) models of mutually interactive game design are put forth to capture the complexity of climate related disasters and to reduce the threat of climate refugees in the Pacific Island region. The resulting risk management lessons learned were applied to communities in the Pacific Island of Vanuatu, the most natural disaster prone country in the world.

Human factors in modern traffic systems

We, the Publisher, have retracted the following article: Ting Zhao (2024). Artificial Intelligence in Mathematical Modeling of Complex Systems. EAI Endorsed Trans e-Learn. https://doi.org/10.4108/eetel.5256 The article has been retracted due to misconduct during the peer review process. This does not implicate any wrongdoing from the author’s side. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.

Предметом вивчення в статті є методи, що дозволяють вирішити проблему невизначеності в процесі побудови систем підтримки прийняття рішень авіадиспетчера при виникненні особливих випадків в польоті. Метою є аналіз і обґрунтування вибору математичного апарату для побудови СППР авіадиспетчера. Завдання: аналіз ряду відомих методів інтелектуального аналізу даних, а саме: еволюційних алгоритмів, нейронних мереж, нечіткої логіки та байєсівських мереж з точки зору доцільності їх застосування при побудови систем підтримки прийняття рішень авіадиспетчера управління повітряним рухом при виникненні особливих випадків в польоті. Використовуваними методами є: методи аналізу і синтезу складних інформаційних систем, методи імітаційно-статистичного моделювання. Отримано такі результати. В результаті проведеного аналізу було встановлено, для побудови моделі СППР авіадиспетчера найбільш ефективним є використання апарату байєсівських мереж, який представляє собою перспективний ймовірнісний інструментарій, що дозволяє моделювати складні ієрархічні статичні та динамічні системи. Це обумовлено тим, що на відміну від популярних на даний час моделей "чорних скриньок" байєсівська мережа дозволяє отримати зрозуміле пояснення одержаних висновків, має їх логічну інтерпретацію, надає можливість врахування невизначеностей параметричного, статичного і структурного характеру і, що є особливо важливим, ґрунтується на фундаментальних положеннях теорії ймовірностей, яка розроблялась не одне сторіччя. Висновки. Напрямком подальших досліджень є побудова системи підтримки прийняття рішень авіадиспетчера з використанням байєсівських мереж і технології ймовірнісного програмування

This special issue collects contributions from the participants of the “Information in Dynamical Systems and Complex Systems” workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported here in reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems.

In this paper we focused our attention on a particular hybrid PN model called FOHPN, that is based on the fluidification of discrete PNs, and whose main feature is that the instantaneous firing speed of continuous transitions keeps constant during each macroperiod. In the first part of the paper we discuss in detail the advantages of fluidification, and provide a brief survey of the most important formalisms within the hybrid PN framework. Finally, we showed how FOHPNs can be efficiently used to model SC, and how interesting optimization problems can be solved via numerical simulation, by simply solving on-line a certain number of LPPs.