Modeling Of Complex Systems Research Articles

Study on the Impact of a Combination of Synthetic Wollastonite and 2-Mercaptobenzothiazole-Based Fillers on UHMWPE Polymeric Matrix

This study investigates the impact of a binary filler on the physicomechanical and tribological properties, as well as structure, of polymeric composite materials based on ultra-high-molecular-weight polyethylene. The organic modifier—2-mercaptobenzothiazole and wollastonite particles synthesized from two different systems (modeled and derived from waste) were used as the binary filler. The synthesis of wollastonite was carried out in the complex model system (CaSO4·2H2O–SiO2·nH2O–KOH–H2O) and from technogenic waste (borogypsum). It was demonstrated that the introduction of the binary filler made it possible to obtain an optimal combination of mechanical and tribological properties. It was found that during the wear of polymeric composite materials loaded with organic fillers, the fillers migrate to the friction surface, providing a shield against abrasive wear of the steel counterface. Due to the modification of ultra-high-molecular-weight polyethylene by 2-mercaptobenzothiazole, the interdiffusion of polymeric matrix macromolecules and interphase coupling with wollastonite particles improve. The 2-mercaptobenzothiazole organic compound used as the filler facilitates the relaxation processes within the composite under external loads.

A transformation methodology for Capella to Event-B models with DSL verification

Capella/Arcadia helps engineers design complex system models, but as models grew in complexity, simulation and verification became necessary. An automatic model-to-model transformation approach was proposed to interpret the dynamic behavior of the semi-formal Capella models. Custom domain-specific languages were introduced to assess the syntax of these models. The approach was applied to the Adaptive Exterior Light system, transforming Capella models into Event-B models for safety verification. The paper provides traceability between Capella and Event-B meta-models to aid interpretation of verification results.

Nonlinear Dynamics of Complex Systems: Modeling and Analysis

Nonlinear dynamics plays a pivotal role in understanding and modeling complex systems. This article provides an overview of key concepts, methodologies, and recent advances in the modeling and analysis of nonlinear dynamics in complex systems.

On the Robustness of Belief-Rule-Based Expert Systems

Belief rule base (BRB) expert system has been widely used in complex system modeling. Robustness is crucial to the modeling performance and safety of BRB. For a better understanding and utility of BRB, there is thereby an urgent need to know what kind of influence each part of BRB may have when the disturbance occurs. Aiming at this, a more comprehensive analysis of BRB robustness is conducted in this article. First, the Lipschitz condition for BRB is defined. With the definitions, a new robustness analysis method of BRB is proposed, which is conducted from four aspects: 1) the input transformation; 2) the matching degree calculation; 3) the matching degree normalization; and 4) the rule aggregation. Moreover, five guidelines for BRB construction are proposed by analyzing its robustness, which can offer a practical guide for users to establish, adjust, and improve the BRB model for specific applications. The robustness analysis of the BRB expert system for the relay health-state evaluation is conducted to verify the effectiveness of the proposed method.

Semiparallel Service Systems in CPSS: Theory and Application

In this article, we study the design and innovation of smarter service systems considering the deep integration of real service systems and virtual service elements. Specifically, we consider the perspective of life-cycle service system design and innovation that must consider the “social space” factors, such as stakeholders’ behaviors, which increases the complexity of service cyber–physical–social systems (CPSSs). We propose a new methodology framework, the semiparallel service system (SPSS) framework, following the CPSS architecture and artificial societies, computational experiments, and parallel execution (ACP) methodologies emerging in complex system modeling. We design two decomposition-then-aggregation loops to reduce the complexity of service systems innovation. In the first loop, we expand the Vee model of systems engineering to the N model to show how to develop an innovative service system step by step. In the second loop, the SPSS, in which the hybrid service system is parallel with the real service system, maintains the continuous innovations of the system. Following our framework, we can design prototypes, perform experiments, and run the hybrid service system. We use two cases, an Internet hospital platform design and a new service design in a customs bonded warehouse, to demonstrate the advantages and potential of our proposed methodology. Finally, we discuss the future research challenges and directions.

Analysis of operation models of complex technical systems

Analysis of operation models of complex technical systems

Identification of fractional Hammerstein model for electrical stimulated muscle: An application of fuzzy-weighted differential evolution

Fractional order representations model complex systems with less parameters, improved accuracy and enhanced robustness in control systems but the system identification of fractional order systems is highly complicated and challenging task because of substantial nonlinearity of the input, unknown linear/nonlinear parameters of the blocks, and unknown fractional order. In this paper, parameter estimation technique is proposed based on fuzzy-weighted differential evolution algorithm for fractional electrically stimulated muscle models, which is generalization of fractional Hammerstein controlled autoregressive model vital for rehabilitation of spinal cord injury (SCI) patients. The system identification problem of fractional electrically stimulated muscle models (FESMMs) is formulated via mean square error approximation between the true and estimated response of FESMMs. The parameters of FESMMs are identified by employing fuzzy weighted differential evolution algorithm optimization knacks of with polynomial, cubic spline and sigmoidal input nonlinearities on various noise variances in the system dynamics. Comparison of results from actual to calculated responses indicates closeness up to 4 decimal places of accuracy for FESMM with polynomial type nonlinearity, up to 6 decimal places for FESMM with sigmoidal type kernel, and up to 5 decimal for FESMM with cubic spline type nonlinearity for different low and high signal to noise scenarios i.e., σ2=0.0022,0.022,0.22, which verify the precision, efficacy, stability and reliability of the fuzzy weighted differential evolution algorithm for system identification of FESMMs in rehabilitation scenarios of SCI.

Efficient parameter generation for constrained models using MCMC

Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in complexity, it becomes correspondingly difficult to find parameter values that satisfy system constraints. We propose a Markov Chain Monte Carlo (MCMC) approach for the problem of constrained model parameter generation by designing a Markov chain that efficiently explores a model’s parameter space. We demonstrate the use of our proposed methodology to analyze responses of a newly constructed bistability-constrained model of protein phosphorylation to perturbations in the underlying protein network. Our results suggest that parameter generation for constrained models using MCMC provides powerful tools for modeling-aided analysis of complex natural processes.

ENSURING THE FORMATION OF CONTROL ACTIONS FOR CONTROLLING COMPUTER NODE BASED ON PROJECTION ALGORITHMS OF STOCHASTIC APPROXIMATION

In modern conditions, it is important that the implementation of the planned goals leads to the formation of the planned financial and administrative conditions, even in the case of the risk of unauthorized changes in computer systems and environments. There is a need to develop and implement intrusion detection systems that can collect data from various sources within computer systems and networks they protect. Operating systems have system logs, entries in system logs serve as a basis for analyzing the various causes of system failures, identifying and debugging them. Computer attacks are becoming more sophisticated, decreasing efficiency makes it difficult to use traditional anomaly detection methods, so new ones are required. These new approaches and methods are usually based, among other things, on the analysis of data obtained from the system log. After attacks, as more and more components fail, the state of the computer system gradually deteriorates, while number of resources available to perform tasks decrease. Therefore, in this study we consider control of computer nodes based on linear semi-Markov model with changes in the input stream of requests. The approach is based on the use of semi-Markov models of complex systems and analytical nodal modeling. A linear semi-Markov model is offered to describe behavior of a computer node with changes in the input stream of requests. Due to this model, estimates of the temporal functional characteristics for various operation modes of a computer node can be obtained. On its basis, it is possible to provide a response to unauthorized intrusions and, to some extent, compensate for them.

Understanding recent deep‐learning techniques for identifying collective variables of molecular dynamics

AbstractHigh‐dimensional metastable molecular dynamics (MD) can often be characterised by a few features of the system, that is, collective variables (CVs). Thanks to the rapid advance in the area of machine learning and deep learning, various deep learning‐based CV identification techniques have been developed in recent years, allowing accurate modelling and efficient simulation of complex molecular systems. In this paper, we look at two different categories of deep learning‐based approaches for finding CVs, either by computing leading eigenfunctions of transfer operator associated to the underlying dynamics, or by learning an autoencoder via minimisation of reconstruction error. We present a concise overview of the mathematics behind these two approaches and conduct a comparative numerical study of these two approaches on illustrative examples.

Link prediction by adversarial Nonnegative Matrix Factorization

Networks are now more crucial than ever for modeling complex systems with interconnected components. Many methods have been developed to infer unobserved links or predict latent links based on observed network topology. However, the effectiveness of current methods is often limited due to the nature of sparseness in large-scale networks. Nonnegative Matrix Factorization (NMF) is one of the most popular low-rank approximation methods which has been successfully applied to large-scale data. However, maintaining generalization from limited observations is still an open challenge. To overcome these challenges, we propose a novel link prediction method based on adversarial NMF, which reconstructs a sparse network by an efficient adversarial training algorithm. Unlike the conventional NMF methods, our model considers potential test adversaries beyond the pre-defined bounds and provides a robust reconstruction with good generalization power. Besides, to preserve the local structure of a network, we use the common neighbor algorithm to extract the node similarity and apply it to low-dimensional latent representation. Simultaneously, we use Frobenius-norm regularization to prevent the factorization from overfitting. We provide an effective Majorization-Minimization method to learn the model parameters. Our method outperforms the state-of-the-art methods, as shown by extensive experiment results on twelve real-world datasets.

Enhancing multiplex global efficiency

Modeling complex systems that consist of different types of objects leads to multilayer networks, in which vertices are connected by both inter-layer and intra-layer edges. In this paper, we investigate multiplex networks, in which vertices in different layers are identified with each other, and the only inter-layer edges are those that connect a vertex with its copy in other layers. Let the third-order adjacency tensor A∈RN×N×L\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {A}\\in \\mathbb {R}^{N\ imes N\ imes L}$$\\end{document} and the parameter γ≥0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma \\ge 0$$\\end{document}, which is associated with the ease of communication between layers, represent a multiplex network with N vertices and L layers. To measure the ease of communication in a multiplex network, we focus on the average inverse geodesic length, which we refer to as the multiplex global efficiency eA(γ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$e_\\mathcal {A}(\\gamma )$$\\end{document} by means of the multiplex path length matrix P∈RN×N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P\\in \\mathbb {R}^{N\ imes N}$$\\end{document}. This paper generalizes the approach proposed in [15] for single-layer networks. We describe an algorithm based on min-plus matrix multiplication to construct P, as well as variants PK\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P^K$$\\end{document} that only take into account multiplex paths made up of at most K intra-layer edges. These matrices are applied to detect redundant edges and to determine non-decreasing lower bounds eAK(γ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$e_\\mathcal {A}^K(\\gamma )$$\\end{document} for eA(γ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$e_\\mathcal {A}(\\gamma )$$\\end{document}, for K=1,2,⋯,N-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K=1,2,\\dots ,N-2$$\\end{document}. Finally, the sensitivity of eAK(γ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$e_\\mathcal {A}^K(\\gamma )$$\\end{document} to changes of the entries of the adjacency tensor A\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {A}$$\\end{document} is investigated to determine edges that should be strengthened to enhance the multiplex global efficiency the most.

Potential impacts of rapidly changing european use of fossil fuels on global warming

The balance of primary energy sources for Europe has been changing rapidly over recent decades, shifting towards more renewables and using fossil fuels with lower carbon emissions. However, the latter is being impacted by the Russia-Ukraine conflict. Here, we determine the potential bounds of how this may affect global warming, based on whether the European use of Russian gas and oil is replaced with either less efficient burning of coal (with and without the gas and oil then used in new markets elsewhere) or with renewables. We perform calculations as perturbations from a baseline carbon dioxide (CO2) trajectory associated with ‘middle range’ and ‘low’ Shared Socioeconomic Pathways (SSP), SSP2-45 and SSP1-26. We calculate the CO2 perturbations as a simulated step change in emissions for the year 2023, which then decays linearly to zero by 2043. The emission profiles drive the FaIR simple climate model. FaIR links greenhouse gas emissions to global warming levels and includes a representation of warming uncertainty based on projections made using more complex Earth system models. We find that the direct impact of the conflict on the global mean temperature is likely to be relatively small, amounting to the worst case of nearly one-hundredth of a degree. This warming is equivalent to approximately an extra half year of current global CO2 emissions. However, we suggest that it is important to consider the implications of the precedents set by the European response to the reduced availability of Russian gas and oil. Such action may reveal the potential for faster uptake of low-carbon energy sources or the converse of backtracking on current Nationally Determined Contributions (NDCs).

Thermal design and heat transfer optimisation of a liquid organic hydrogen carrier batch reactor for hydrogen storage

Liquid organic hydrogen carriers (LOHCs) are considered a promising hydrogen storage technology. Heat must be exchanged with an external medium, such as a heat transfer fluid, for the required chemical reactions to occur. Batch reactors are simple but useful solutions for small-scale storage applications, which can be modelled with a lumped-parameter approach, adequately reproducing their dynamic performance. For such reactors, power is consumed to circulate the external heat transfer fluid and stir the organic liquid inside the reactor, and heat transfer performance and power consumption are two key parameters in reactor optimisation. Therefore, with reference to the hydrogen release phase, this paper describes a procedure to optimise the reactor thermal design, based on a lumped-parameter model, in terms of heat transfer performance and minimum power consumption. Two batch reactors are analysed: a conventional jacketed reactor with agitation nozzles and a half-pipe coil reactor. Heat transfer performance is evaluated by introducing a newly defined dimensionless parameter, the Heat Transfer Ratio (HTR), whose value directly correlates to the heat rate required by the carrier's dehydrogenation reaction. The resulting model is a valid tool for adequately reproducing the hydrogen storage behaviour within dynamic models of complex and detailed energy systems.

Analysis of operation models of complex technical systems

A retrospective analysis of existing models of operation of complex technical systems with metrological support has been carried out. The models of failures and degradation of complex technical systems based on diffusion monotone and diffusion non-monotone distributions, the model of failures and degradation of complex technical systems based on three-parameter diffusion distribution are described. Failure and degradation models are used together with the classical model of E. I. Sychev and more complex models developed on its basis. For complex technical systems that allow full resource recovery during repair, a model of operation of complex technical systems has been developed, taking into account 4 degrees (groups) of degradation of complex technical systems. The model is described by a system of linear algebraic equations of the 21st order. The functional dependence of the stationary readiness coeffi cient on the calibration intervals of measuring instruments included in complex technical systems of various degradation groups is constructed. For complex technical systems that do not allow full restoration of the resource during repair, a model of operation of complex technical systems has been developed, taking into account 3 groups of degradation of complex technical systems. The model is described by a system of linear algebraic equations of the 24th order. The model allows you to simulate the main stages of the life cycle of a fl eet of complex technical systems, including, among other things, the processes of updating a fl eet of complex technical systems through the purchase of new samples and upgrades, existing samples of complex technical systems. The models presented in the article allow calculating optimal values of intervals between verifi cations and optimal values of tolerances for controlled parameters for different groups of degradation of complex technical systems with metrological support, ensuring the maximum level of stationary availability coeffi cient. The developed set of models can be used to classify complex technical systems in order to set requirements for their metrological support. The models can also be used to calculate the technical and economic indicators of the development of a fl eet of complex technical systems.

Hybrid mechanistic and neural network modeling of nuclear reactors

An accurate and efficient model is the foundation for dynamic characteristics analysis and control design of nuclear reactors. Nevertheless, the widely-used mechanistic models of nuclear reactors inevitably have some deviations from the actual reactors due to inaccurate model parameters and model assumptions. This paper proposes a hybrid mechanistic and neural network modeling method for nuclear reactors to eliminate or minimizing these deviations. The high-efficiency point reactor kinetics model was adopted as the mechanistic reactor core model, and the simulation results obtained using the commercial program RELAP5 were taken as the reference operational data, based on which the reactivity feedback in the mechanistic model that is difficult to be accurately determined during reactor operations was calibrated using a genetic algorithm optimized BP neural network. Dynamic simulation results of a Generation III large pressured water reactor under three typical reactivity change transients show that the core power and outlet coolant temperature obtained with the hybrid model match much better with the reference results than those of the mechanistic model, demonstrating that the hybrid modeling method can improve the accuracy of the mechanistic reactor model effectively. This study can provide a valuable reference for accurate and efficient modeling of complex systems.

Supervised Video Cloth Simulation: Exploring Softness and Stiffness Variations on Fabric Types Using Deep Learning

Physically based cloth simulation requires a model that represents cloth as a collection of nodes connected by different types of constraints. In this paper, we present a coefficient prediction framework using a Deep Learning (DL) technique to enhance video summarization for such simulations. Our proposed model represents virtual cloth as interconnected nodes that are subject to various constraints. To ensure temporal consistency, we train the video coefficient prediction using Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM), and Transformer models. Our lightweight video coefficient network combines Convolutional Neural Networks (CNN) and a Transformer to capture both local and global contexts, thus enabling highly efficient prediction of keyframe importance scores for short-length videos. We evaluated our proposed model and found that it achieved an average accuracy of 99.01%. Specifically, the accuracy for the coefficient prediction of GRU was 20%, while LSTM achieved an accuracy of 59%. Our methodology leverages various cloth simulations that utilize a mass-spring model to generate datasets representing cloth movement, thus allowing for the accurate prediction of the coefficients for virtual cloth within physically based simulations. By taking specific material parameters as input, our model successfully outputs a comprehensive set of geometric and physical properties for each cloth instance. This innovative approach seamlessly integrates DL techniques with physically based simulations, and it therefore has a high potential for use in modeling complex systems.

Research on the Impact of New Parts Price Increase on the Stability of Closed-Loop Supply Chain

In the closed-loop supply chain system of component remanufacturing, new parts suppliers are likely to be affected by certain factors that lead to sudden increases in supply prices, and this sudden increase in new parts prices may trigger the chain collapse of the closed-loop supply chain system and impact the stability of the closed-loop supply chain. Therefore, this paper combines closed-loop supply chain theory, evolutionary game theory, and system dynamics theory, which are internalized to construct a complex system model. Furthermore, the impact of different price increases of new parts on the stability of the closed-loop supply chain is analyzed through scenario simulation. The results show that a 25% increase in the price of new parts will delay the time for remanufacturers and retailers to reach a stable state of product flow, reduce the profitability of remanufacturers and retailers, and inhibit the willingness of remanufacturers to carry out high-green production in the early stage. A 50% increase in the price of new parts can break the closed-loop supply chain from the remanufacturer, severely undermining the stability of the closed-loop supply. According to the simulation results, this paper presents a timely government dynamic reward and punishment optimization scheme for remanufacturers to improve the tolerance level of the closed-loop supply chain for the price increase of new parts and to provide a reference for enhancing the stability of the closed-loop supply chain and optimizing the government supervision strategy.

Deep learning for full-field ultrasonic characterization

This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in layered components from full waveform data. In this vein, two logics, namely the direct inversion and physics-informed neural networks (PINNs), are explored. The direct inversion entails three steps: (i) spectral denoising and differentiation of the full-field data, (ii) building appropriate neural maps to approximate the profile of unknown physical and regularization parameters on their respective domains, and (iii) simultaneous training of the neural networks by minimizing the Tikhonov-regularized PDE loss using data from (i). PINNs furnish efficient surrogate models of complex systems with predictive capabilities via multitask learning where the field variables are modeled by neural maps endowed with (scaler or distributed) auxiliary parameters such as physical unknowns and loss function weights. PINNs are then trained by minimizing a measure of data misfit subject to the underlying physical laws as constraints. In this study, to facilitate learning from ultrasonic data, the PINNs loss adopts (a) wavenumber-dependent Sobolev norms to compute the data misfit, and (b) non-adaptive weights in a specific scaling framework to naturally balance the loss objectives by leveraging the form of PDEs germane to elastic-wave propagation. Both paradigms are examined via synthetic and laboratory test data. In the latter case, the reconstructions are performed at multiple frequencies and the results are verified by a set of complementary experiments highlighting the importance of verification and validation in data-driven modeling.

Substrate microtopographies induce cellular alignment and affect nuclear force transduction

Cellular physiology has been mainly studied by using two-dimensional cell culture substrates which lack in vivo-mimicking extracellular environment and interactions. Thus, there is a growing need for more complex model systems in life sciences. Micro-engineered scaffolds have been proven to be a promising tool in understanding the role of physical cues in the co-regulation of cellular functions. These tools allow, for example, probing cell morphology and migration in response to changes in chemo-physical properties of their microenvironment. In order to understand how microtopographical features, what cells encounter in vivo, affect cytoskeletal organization and nuclear mechanics, we used direct laser writing via two-photon polymerization (TPP) to fabricate substrates which contain different surface microtopographies. By combining with advanced high-resolution spectral imaging, we describe how the constructed grid and vertical line microtopographies influence cellular alignment, nuclear morphology and mechanics. Specifically, we found that growing cells on grids larger than 10 × 20 μm2 and on vertical lines increased 3D actin cytoskeleton orientation along the walls of microtopographies and abolished basal actin stress fibers. In concert, the nuclei of these cells were also more aligned, elongated, deformed and less flattened, indicating changes in nuclear force transduction. Importantly, by using fluorescence lifetime imaging microscopy for measuring Förster resonance energy transfer for a genetically encoded nesprin-2 molecular tension sensor, we show that growing cells on these microtopographic substrates induce lower mechanical tension at the nuclear envelope. To conclude, here used substrate microtopographies modulated the cellular mechanics, and affected actin organization and nuclear force transduction.