Analysis Of Complex Systems Research Articles

A methodology to improve simulation of multibody systems using estimation techniques

ABSTRACT This paper presents a methodology for state estimation and accuracy improvement of computer simulations of computer aided engineering (CAE) models based on prediction and correction state estimation techniques and sensing. The aim is to simulate the dynamic behaviour of a real system, which can be sensed, and obtain values of states that are not measurable due to economic or technical limitations. This methodology can be applied to both optimization of design processes and on-line control of complex systems. State estimation techniques are currently used only on mathematical models, where the relationships among system variables are expressed by means of mathematical language, making state observer implementation possible but leading to limitations in system modelling and knowledge. Favoured over mathematical models, multibody CAE models (created by means of computer-aided engineering software) have become the essential tool for complex system development, simulation, analysis, optimization and control, such as multibody systems; one of their main advantages is the ease and flexibility in creating and modifying them, allowing the faithful modelling of complex systems.

Standards for the Weighting of Criteria and the Measurement of Interaction

This article discusses the need for standards for the assignment of importance to criteria and the measurement of interaction between them in multiple criteria analyses of complex systems. A strategy for criteria evaluation is considered that is suitable to account for the interaction among a wide variety of imprecisely assessed criteria applied simultaneously. It is based on the results of collecting sample information on preferences according to the specified criteria instead of merely an abstract comparison of the criteria. The comparison of alternatives is based on objectives that determine the formation of preferences. It is facilitated by a rating in terms of preference probabilities. Probabilistic standards grant homogeneity of measurements by different criteria, which is useful for the combination of the criteria. These standards apply to a sampling evaluation conducted via pairwise trichotomic comparison of the alternatives according to each criterion, followed by the combination of these multiple evaluations into a single global score by means of the Choquet Integral with respect to a capacity determined by applying preference concentration to the sets of probabilistic assessments. Examples of practical application are discussed.

Guided assembly of cellular network models from knowledge in literature.

Computational modeling is crucial for understanding and analyzing complex systems. In biology, model creation is a human dependent task that requires reading hundreds of papers and conducting wet lab experiments, which would take days or months. To overcome this hurdle, we propose a novel automated method, that utilizes the knowledge published in literature to suggest model extensions by selecting most relevant and useful information in few seconds. In particular, our novel approach organizes the events extracted from the literature as a collaboration graph with additional metric that relies on the event occurrence frequency in literature. Additionally, we show that common graph centrality metrics vary in the assessment of the extracted events. We have demonstrated the reliability of the proposed method using three different selected models, namely, T cell differentiation, T cell large granular lymphocyte, and pancreatic cancer cell. Our proposed method was able to find high percent of the desired new events with an average recall of 82%.

Risk analysis virtual ENvironment for dynamic event tree-based analyses

Conventional Event-Tree (ET) based methodologies are extensively used as tools to perform reliability and safety assessment of complex and critical engineering systems. One of the disadvantages of these methods is that timing/sequencing of events and system dynamics is not explicitly accounted for in the analysis. In order to overcome these limitations several techniques, also known as Dynamic Probabilistic Risk Assessment (DPRA), have been developed. Monte-Carlo (MC) and Dynamic Event Tree (DET) are two of the most widely used DPRA methodologies to perform safety assessment of Nuclear Power Plants (NPP). Since 2012, the Idaho National Laboratory (INL) is developing its own tool to perform Dynamic PRA: RAVEN (Risk Analysis and Virtual ENvironment). RAVEN has been designed in a high modular and pluggable way to enable easy integration of different programming languages (i.e., Python, C++) and coupling with other application including, among the others, several thermal–hydraulic and severe accident codes (e.g., RELAP5-3D, MELCOR, MAAP5, TRACE, etc.). RAVEN is aimed to provide a framework/container of capabilities for engineers and scientists to analyze the response of systems, physics and multi-physics, employing advanced numerical techniques and algorithms. Moreover, RAVEN models stochastic events, such as components failures, and performs uncertainty quantification (UQ). Such stochastic modeling is employed by using sampling strategies among which both MC and DET algorithms, which are going to be employed in this paper. In addition, RAVEN processes the large amount of data generated by sampling the physical models using data-mining based algorithms and risk assessment techniques. This paper provides an overview of the DET methodologies that have been deployed within the RAVEN framework, showing the potential of such techniques for the analysis of complex systems. A brief background of classical methodologies and their limitation is also reported and represent the motivation for the deployment of such dynamic technique. In addition, results from a pressurized water reactor loss of coolant accident scenario, using RELAP5-3D as physical model, are reported.

Optimizing non-uniform multivariate embedding for multiscale entropy analysis of complex systems

Multivariate signals are ubiquitous in various complex systems. There has been substantial interest in measuring the complexity of them to describe the target system. Traditional entropy-based methods are applicable only to univariate time series and cannot properly assess the structural complexity as a whole. The recently proposed multivariate multiscale entropy is capable of evaluating the complexity of a complex system of multi-channel data, but it is based on uniform time delay embedding and not able to account for multiple time scales structure. To this end, we develop the non-uniform multivariate multiscale entropy based on a non-uniform multivariate embedding which is optimized by solving the proposed algorithm. Experimental results on both synthetic chaotic systems and real-world brain systems of multivariate physiological signals demonstrate that the non-uniform multivariate multiscale entropy outperforms the multivariate multiscale entropy in measuring the complexity of multivariate complex systems, which is potentially applied for critical transition detection and pattern recognition problems.

ОЦЕНКА И АНАЛИЗ АЭРОДИНАМИЧЕСКИХ ПАРАМЕТРОВ ВОЗДУШНЫХ ПОТОКОВ ДЛЯ ЭФФЕКТИВНОГО ВЫБОРА СХЕМ ВОЗДУХООБЕСПЕЧЕНИЯ В УГОЛЬНЫХ ШАХТАХ

The article examines a complex technical system on the example of air supply of coal mines, characterized by: multicomponent, a large number of quantitative and qualitative parameters, nonlinearity of interdependencies between them; incompleteness of information, the complexity of experimental research, the risks of dangerous situations and the catastrophic nature of their consequences; the uniqueness of the modes and conditions of the systems functioning. The aim of the work is to develop a methodology for assessing and analyzing the aerodynamic parameters of air flows for the effective selection of air supply schemes in coal mines using the decomposition method. Research methodology: the solution of the set tasks is based on modern methods of mathematical statistics; graph theory; decision theory; apparatus of mathematical logic; factor analysis; mathematical modeling; set theory; reliability theory and systems analysis. Research results. A method for the analysis and analytical modeling of complex technical systems of variable structure (CTS VS) is developed, focused on the features of these systems and their processes in conditions of incomplete information, different quality data on their state and functioning, which differs from the known ones by a combination of the capabilities of analytical and statistical approaches to the construction of CTS VS models, which allows to adapt to changes in systemic and external factors, improve the accuracy of modeling, and also typify the presentation of fuzzy situational features for effective management of the CTS VS. Conclusion. A method has been developed for assessing the reliability of the parameters of the CTS VS based on the algorithms for analyzing and assessing the reliability of the CTS VS, the algorithm for the formation of the most complete subsets listing in the graph all possible compatible subsets of a given set of vertices differing in that they are based on the analysis of enlarged states and decomposition of the structural diagram, which allows analytical and statistical studies to significantly increase the accuracy of assessing the reliability of CTS VS and reduce the amount of calculations. Result: the proposed methods were used to solve the problems of air distribution control in coal mines.

Football Match Dynamics Explored by Recurrence Analysis.

A widely accepted notion of football matches in performance analysis (PA) is to consider them as dynamic interaction processes with emerging behaviors. The description and analysis of these processes requires specific methods. Recurrence analysis is a technique for analyzing complex systems in many domains like astrophysics, earth sciences, engineering, biology, cardiology, and neuroscience. Its general concept is to examine the recurrence behavior of a system, as in when, how often and how close its trajectory in a phase space returns to a previous state. The aim of the study is to apply recurrence analysis to football matches. Positional data from 21 football matches of a German Bundesliga team were examined. The phase space was made up of the field players' x,y-positions at each second of the match. For each pair of seconds, the average distance of all the players between their positions at these two time points was calculated. Recurrence plots (RPs) were obtained by color-coding these distances. With a recurrence threshold of rt = 9 m and a minimum line length of lmin = 3 s, general recurrence parameters were calculated to characterize the individual recurrence behaviors of each match. Three football-specific recurrence parameters were defined to represent recurrence properties of open play. RPs showed commonalities (typical features indicating set plays and continuous gameplay) as well as unique structures during each match (number, distribution, and sequence of typical features). The recurrence parameters showed several significant correlations with traditional performance indicators like number of goals and passes completed, e.g., the correlation between number of goals and recurrence rate is r = −0.622 (p = 0.003). By extending the sample and design of recurrence studies, there is great potential for recurrence analysis to improve both the practical and theoretical potential of performance analysis.

The phraseological field “Women’s Intellect” in the Russian and English Languages

The real research is devoted to the complex analysis of the set expressions with the meaning of mental abilities of the woman in two languages. The lexico-semantic microsystems and their ingredients are exposed to the detailed research. The work is done on the example of phraseological units, paremias (proverbs and sayings) and aphorisms of two structurally-different languages. The scope of the study is on a joint of the major disciplines of modern linguistic science which include ethnolinguistics, cognitive linguistics, lingvoculturology, and considers the verbalized ideas of the intellectual level of female persons in set expressions in the Russian and English linguistic picture of the world. The study opens new data which can be useful both at an explanation of language identities, and at the solution of some difficulties of the translation. The scientific novelty of the research is caused by the complex system analysis to which these expressions are exposed for the first time. It is necessary to discuss the questions connected with the verbalization of the concepts showing female intelligence, fresh interest of various branches of sciences both in the identity of the person in general and to such a narrow direction as the female intelligence exactly, changing of the woman’s role in the modern society. The scope of the analysis is the lexico-semantic originality of the set expressions designating the female intelligence in the compared languages.

Reliability analysis for complex system with multi-source data integration and multi-level data transmission

The Bayesian theory is appealing for reliability analysis of complex system using the data incorporation. However, there are still gaps among existing methods, such as that the common method is applicable to two-level system, multi-source data for components are not considered, k-out-of-n and standby structures are not studied with series and parallel simultaneously, the reliability is usually estimated as discrete variable, etc. For these problems, a Bayesian-based method is proposed on system under multiple levels. First, multi-source data for each target are integrated in lower level by Bayesian theory to derive the posterior distribution for reliability. Next, they are transmitted to higher level through the deterministic function concerning reliability depending on system structure. Further, the transmitted data from lower level are transformed to induced prior distribution for parameters in distribution of lifetime and integrated with native data in higher level to obtain the posterior for reliability. Finally, the reliability and remaining useful lifetime with respect to times in system-level are presented after the successive information propagation. An illustrative example is given to show the application of proposed method. Together with the sensitivity analysis, it proves that this method is feasible, practical and robust.

A question of justice: Critically researching suicide with Indigenous studies of affect, biosociality, and land-based relations.

This paper considers how Indigenous studies can inform the evolution of critical research on suicide. Aligned with critiques of mainstream suicidology, these methodological approaches provide a roadmap for structural analysis of complex systems and logics in which the phenomenon of suicide emerges. Moving beyond mere naming of social determinants of suicide and consistent with calls for a theory of justice within suicide research, Indigenous studies helps to advance conceptual knowledge of suicide in descriptive means and enhance ethical responses to suicide beyond psychocentric domains. Through centering Indigenous theories of affect, biosociality, and land-based relations, this article examines what new knowledge of suicide can emerge, as well as what ethical responses are possible to suicide and to a world where suicide exists. This new knowledge can inform practices for critical suicide studies which are invested in resisting structural violence, nourish agency, dignity and freedom for those living and dying in often-unlivable presents, and enhancing livability for individuals, communities, and the environment living under shadows of empire. Implications for theory, ethics, and suicide research and prevention practice are considered.

Improved Treatment of the Independent Variables for the Deployment of Model Selection Criteria in the Analysis of Complex Systems.

Model selection criteria are widely used to identify the model that best represents the data among a set of potential candidates. Amidst the different model selection criteria, the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are the most popular and better understood. In the derivation of these indicators, it was assumed that the model’s dependent variables have already been properly identified and that the entries are not affected by significant uncertainties. These are issues that can become quite serious when investigating complex systems, especially when variables are highly correlated and the measurement uncertainties associated with them are not negligible. More sophisticated versions of this criteria, capable of better detecting spurious relations between variables when non-negligible noise is present, are proposed in this paper. Their derivation is obtained starting from a Bayesian statistics framework and adding an a priori Chi-squared probability distribution function of the model, dependent on a specifically defined information theoretic quantity that takes into account the redundancy between the dependent variables. The performances of the proposed versions of these criteria are assessed through a series of systematic simulations, using synthetic data for various classes of functions and noise levels. The results show that the upgraded formulation of the criteria clearly outperforms the traditional ones in most of the cases reported.

Estimating SDG Indicators in Data-Scarce Areas: The Transition to the Use of New Technologies and Multidisciplinary Studies

The Sustainable Development Goals (SDGs) and their indicators provide opportunities to best combine the available knowledge and data to monitor and estimate different metrics and track their progress. The overall picture can be complex as some indicators are often interconnected (e.g., rural and/or urban development with a water body’s status). Two factors can play a crucial role in achieving the SDGs: the use of new technologies for database building and multidisciplinary studies and understanding. This study aims to explore these factors, highlight their importance and provide an example as guidance of their proper and combinative use. Ireland is used as an example of a data-scarce case with poor–slow progress, especially on the environmental SDGs. Two “non-reported” SDG indicators (lack of data) are selected and estimated in this work using freely available data (remote sensing, satellite imagery) and geospatial software for the first time in the country. The results show improvements in rural and urban development; however, this is accompanied by negative environmental consequences. A more holistic approach is needed and a broader conceptual model is presented to avoid any misleading interpretations of the study of SDGs. The transition to the modern technological and multidisciplinary evolution requires respective knowledge and understanding, strongly based on complex systems analysis.

Generalization of the maximum entropy principle for curved statistical manifolds

The maximum entropy principle (MEP) is one of the most prominent methods to investigate and model complex systems. Despite its popularity, the standard form of the MEP can only generate Boltzmann-Gibbs distributions, which are ill-suited for many scenarios of interest. As a principled approach to extend the reach of the MEP, this paper revisits its foundations in information geometry and shows how the geometry of curved statistical manifolds naturally leads to a generalization of the MEP based on the R\'enyi entropy. By establishing a bridge between non-Euclidean geometry and the MEP, our proposal sets a solid foundation for the numerous applications of the R\'enyi entropy, and enables a range of novel methods for complex systems analysis.

Applications of chromatography in giant complex drug-organism system

色谱是一门以分离分析为主,旨在追求复杂事物纯而净的分析化学的重要分支学科。其经过百余年的发展,理论与技术日臻完善,集科学、技术与艺术于一体。近年来,色谱及其与质谱、核磁共振波谱、原子发射光谱等联用技术极大推动了环境、食品、石油化工、生物医药等领域中所涉及复杂体系的研究进展。作为我国传统文化的核心代表,中医药为中国乃至世界人民的健康服务逾千年,从古至今历经上千年临床考验,疗效经久不衰。近年来,中国政府强调继承与创新,加大推进中医药的现代化与国际化。然而中药自身的多成分协同起效复杂性及其与机体时刻新陈代谢变化的复杂性往往相互作用,由此形成了药物-机体复杂巨系统。该复杂巨系统的分析研究是中医药现代化进程的关键瓶颈。色谱的优势在于复杂成分的分离与分析,此恰能为上述复杂巨系统提供技术支撑,色谱及其联用技术已成为推动中医药分子化、数字化、信息化乃至现代化的主流技术。该文综述了色谱及其联用技术在中药复杂体系、复杂生命过程及药物-机体复杂巨系统中的应用进展,介绍了笔者研究团队对中医药现代化的认识、研究思路和研究工作,最后笔者结合对于百年色谱与千年中医药文化之现代化交织的感悟,对色谱技术在此领域的前景做出了展望。

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.

Research on wheel rail system based on Dissipative Structure Theory

At present, the research on wheel-rail system is usually to analyze a problem separately, and draw a conclusion through experiments and simulations. This analysis result has certain accuracy, but it often cannot meet the requirements of optimization analysis of wheel-rail complex system. In this paper, by analyzing the characteristics of openness, away from equilibrium and fluctuation of wheel-rail system, a method of constructing element set based on information entropy is put forward. Entropy is used as a parameter to find out the inflow trend of wheel-rail system, and negative entropy flow and positive entropy flow are input to keep the system stable and orderly. The viewpoint of system engineering is put forward for the optimization of wheel-rail system.

Chronopotentiometric Analysis of Single Histones and Histone Octamer at Charged Surfaces

AbstractHistones play an essential role in the nucleosome structure and organization. The key role of highly positively charged histones is to stabilize negatively charged DNA inside the chromatin. Histones are basic proteins containing a large amount of electroactive residues, namely, arginine and lysine. Additionally, histone H3, on the contrary to other histones, contains cysteine residues. To understand more about the behavior of histones at charged surfaces and the influence of the electroactive amino acid residues content on the catalytic hydrogen evolution reaction, we studied histones using label‐free approaches, namely, chronopotentiometric stripping analysis (CPS) and impedance Cd–t curves. While CPS is sensitive to the structure and stability of proteins and their complexes, Cd–t curves reflect changes of the electrical double layer at the charged surface. Application of both label‐free methods appears useful for analysis of complex systems. Using a combination of these methods, we could discriminate between individual single core histones, their mixture, and their complex (octamer).

Human Resource Petri Net Allocation Model Based on Artificial Intelligence and Neural Network

The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.

Efficient reliability analysis of complex systems in consideration of imprecision

In this work, the reliability of complex systems under consideration of imprecision is addressed. By joining two methods coming from different fields, namely, structural reliability and system reliability, a novel methodology is derived. The concepts of survival signature, fuzzy probability theory and the two versions of non-intrusive stochastic simulation (NISS) methods are adapted and merged, providing an efficient approach to quantify the reliability of complex systems taking into account the whole uncertainty spectrum. The new approach combines both of the advantageous characteristics of its two original components: 1. a significant reduction of the computational effort due to the separation property of the survival signature, i.e., once the system structure has been computed, any possible characterization of the probabilistic part can be tested with no need to recompute the structure and 2. a dramatically reduced sample size due to the adapted NISS methods, for which only a single stochastic simulation is required, avoiding the double loop simulations traditionally employed.Beyond the merging of the theoretical aspects, the approach is employed to analyze a functional model of an axial compressor and an arbitrary complex system, providing accurate results and demonstrating efficiency and broad applicability.

The dynamic response of infinitely long constrained bars and its application for modelling SSI

In dynamic analysis of complex systems usually simplified models are used. In case of floor vibration, an SDOF model is used to calculate the acceleration of the structure, in the seismic design of structures lumped parameter models are recommended to include the effect of the soil-structure interaction (SSI) in the analysis. Nonetheless, for rib stiffened floors, and for finite depth soil layers under the structure these models can be very inaccurate. In these cases (infinitely) long constrained bars (or beams) are recommended as simplified models. In this article an infinitely long, axially constrained bar connected parallelly to a mass-spring system is analysed. An analytical solution is derived, and the impedance functions are determined, which are significantly different from those of simple lumped parameter models. It is shown that this simple model, which depends only on three parameters, is an excellent tool for determining the response of structures excited by horizontal harmonic or earthquake loads, where SSI plays an important role.