Analysis Of Complex Systems Research Articles

Team Behavior and Performance: An Exploration in the Context of Professional Rugby Union.

To explore complex system behavior and subsequent team performance in professional rugby union. Here, we present 2 studies. In the first, we used global positioning system technology to measure player clustering during stoppages in play in nearly 100 games of professional rugby union to explore team (complex system) behavior and performance. In the second, we measured stress hormones (cortisol and testosterone) prior to team meetings and analyzed these relative to amount of time and the frequency with which players looked at peer presenters, as well as subsequent training performance, to explain how stress may lead to behaviors observed in the first study and subsequent match performance. No link between player clustering during stoppages of play and performance was observed. When players (complex system agents) demonstrated greater levels of stress (as indicated by greater cortisol-awakening response and a greater decline in testosterone-to-cortisol ratio across the morning), they tended to look at peer presenters more; however, training quality declined (P = .02). Correlational analysis also showed that training quality was related to testosterone-to-cortisol ratio (P = .04). Team behavior is complex and can be unpredictable. It is possible that under stress, complex system agents (ie, rugby union players) look at (and cluster toward) their teammates more; however, meaningful interaction may not necessarily occur. Furthermore, while complex system (team) analysis may be valuable strategically in rugby union in the context of describing behavior, without understanding "how" or "why" intrateam/interagent behaviors emerge it may have little meaning.

Analytical comparison of cross impact steady state, DEMATEL, and page rank for analyzing complex systems

Analytical comparison of cross impact steady state, DEMATEL, and page rank for analyzing complex systems

Methodology for Building a Prototype System for Complex Data Analysis of Thematic Sites

One of the modern directions of obtaining information for making informed decisions is the analysis of data from open Internet sources, the analysis of media containing hundreds of thousands of publications. It is critically important not only to obtain reliable information, but also the time needed to obtain and analyze it. The purpose of the research in this work is the development and testing of a complex methodology for quickly building a prototype of a system for complex analysis of thematic sites. A technology of interconnected methods, methodologies, and tools for building a graph database, a knowledge graph, data analysis using methods and mo dels of machine learning with the provision of analytical results to users has been created. The main task of this work is to use these technologies to analyze data from well-known world sites in order to build a prototype of a systems for complex analysis of data from Internet sources.

An analysis of human error and reliability in the operation of fixed CO2 systems on cargo ships using HEART Dempster-Shafer evidence theory approach

CO2 fixed fire extinguishing system activation accidents may occur as a result of unintentional system activation or human error. Crew members present in the area could suffer serious consequences, including death, if CO2 gas is released into a protected compartment such as the engine room, CO2 room, or cargo area. It is therefore crucial to investigate and address the causes of such accidents. The use of Dempster-Shafer (D-S) theory and the Human Error Assessment and Reduction Technique (HEART) to the Human Reliability Analysis (HRA) of CO2 fixed fire extinguishing system activation accidents is discussed in this study. On the one hand, D-S theory allows for uncertainty-based reasoning and can be extremely advantageous, especially when analysing complex systems where there are many potential challenges. On the other hand, HEART is an organised method to examine and reduce human error in high-risk systems. Together, these techniques provide a powerful tool for identifying and addressing the underlying causes of CO2 fixed fire extinguishing system activation accidents. The study revealed that the total human error probability (HEP) for the CO2 fixed fire extinguishing system was 3.10E-01 and the reliability of human performance for the given process was 6.90E-01. The results of this paper highlight many factors such as equipment failure, inadequate training, and poor maintenance practices, as well as factors contributing to the risk of accidental system activation. By addressing these factors, the probability of CO2-fixed fire suppression system activation accidents can be significantly reduced.

A review on deep learning in fault analysis of complex systems

A review on deep learning in fault analysis of complex systems

Artificial Intelligence‐based Module Type Package‐compatible Smart Sensors in the Process Industry

AbstractImage analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants.

Gastric cancer: Neural networks, complex system analysis, statistics and computer modeling for best management.

37 Background: We examined factors of alive supersystems – gastric cancer (GC) patients (GCP) and cell ratio factors (CRF) significantly affecting GCP survival. CRF - ratio between cancer cells (CC) and blood cells subpopulations. Methods: We analyzed data of 799 consecutive GCP (T1-4N0-2M0) (age=57.1±9.4 years; tumor size=5.4±3.1 cm) radically operated (R0) and monitored in 1975-2023 (m=558, f=241; total gastrectomies (G)=173, distal G=461; proximal G=165; combined G=247 with resection of esophagus, pancreas, liver, duodenum, diaphragm, colon transversum, splenectomy, etc; only surgery-S=624, adjuvant chemoimmunotherapy-AT=175 (5-FU + thymalin/taktivin); T1=238, T2=220, T3=184, T4=157; N0=437, N1=109, N2=253, M0=799. Variables selected for prognosis study were input levels of 45 blood parameters, sex, age, TNMG, cell type, tumor size. Survival curves were estimated by the Kaplan-Meier method. Differences in curves between groups of GCP were evaluated using a log-rank test. Multivariate Cox modeling, discriminant analysis, clustering, SEPATH, Monte Carlo, bootstrap and neural networks computing were used to determine any significant dependence. Results: Overall life span (LS) was 2128.9±2300.3 days and cumulative 5-year survival (5YS) reached 58.4%, 10 years – 51.9%, 20 years – 39%, 30 years – 27.2%. 318 GCP lived more than 5 years (LS=4304.5±2290.6 days), 169 GCP – more than 10 years (LS=5919.5±2020 days). 290 GCP died because of GC (LS=651±347.2 days). Cox modeling displayed that GCP survival significantly depended on CRF: healthy cells/CC, erythrocytes/CC, monocytes/CC, phase transition (PT) in terms of synergetics early—invasive cancer; PT N0--N12, age, G1-3, hemorrhage time, ESS, sex, AT, prothrombin index, residual nitrogen. Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early—invasive cancer (rank=1); PT N0--N12 (2); healthy cells/CC (3), erythrocytes/CC (4), thrombocytes/CC (5), monocytes/CC (6), segmented neutrophils/CC (7), leucocytes/CC (8), lymphocytes/CC (9), stick neutrophils/CC (10), eosinophils/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0). Conclusions: 5-year survival of GCP after radical procedures significantly depended on: PT “early-invasive cancer”; PT N0--N12; CRF; blood cell circuit; biochemical factors; hemostasis system; AT; GC characteristics; GC cell dynamics; tumor localization; anthropometric data; surgery type. Best diagnosis and treatment strategies for GC are: 1) screening and early detection of GC; 2) availability of experienced abdominal surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunotherapy for GCP with unfavorable prognosis.

Multiscale Thermal-Hydraulic Analysis of the ATHENA Core Simulator

In the framework of the ALFRED research and development program, the ATHENA facility will be constructed for thermal-hydraulic analysis of full-scale ALFRED components and systems. The source system of the facility is the core simulator, which aims to be representative of an ALFRED average fuel assembly. Computational fluid dynamics (CFD) codes are gaining attention for the analysis of complex systems in pool-type reactors since they are able to reproduce three-dimensional phenomena. In this paper, a multiscale approach based on porous media is proposed to reduce the computational cost of the core simulator CFD model. The multiscale approach starts with the detailed simulation of the infinite lattice domain of the fuel assembly to characterize the porous media hydraulic behavior. Then the porous media are applied in the system model. Three different approaches are investigated: (1) adopting a single porous media for the entire fuel assembly, (2) representing the bundle with two porous domains, and (3) adopting the so-called hybrid medium. The results have been compared with the reference detailed CFD simulation for performance evaluation. The first step of the analysis is the application of the multiscale approach on the CIRCE fuel pin simulator to carry out a turbulence model validation against experimental data and a comparison of the three approaches with a proven CFD model. Then the approach is applied on the ATHENA core simulator exploiting the CIRCE results. The results obtained with the porous media models are compared with a detailed CFD simulation of the core simulator to evaluate the performance of the three approaches. Eventually, the best solution is applied on a model of the entire ATHENA core simulator integrated with the feeding region. The model is tested also in transient conditions. The numerical experiment demonstrates the effectiveness of the multiscale approach in reducing the computational cost while maintaining high accuracy in representing the quantities of interest.

Complex physical-model based dynamic system safety analysis of Aviation Piston Engine considering hybrid uncertainty of fault

Aviation piston engines constitute an essential segment of general aviation, whilst safety issues are becoming increasingly prominent by high system complexity. However, the conventional methods are gradually becoming insufficient to support the safety analysis in complex systems with multi-physical during the lifetime. Therefore, an innovative analysis that can depict the dynamic safety and potential coupled faults of the entire engine with multi-physical systems considering the effects of hybrid uncertainty faults is proposed in this study. Consequently, safety feedback is provided to the design in a more timely, accurate, and efficient manner based on the safety distribution throughout the lifecycle during the design stage. The analysis involves an integration of the nominal physical model and stochastic fault model via functionalized process variables. Firstly, a deterministic nominal model of the multi-physical domain of the engine is developed to characterize the coupled relationship between different domains by differential equations. Secondly, a stochastic fault model with hybrid uncertainty is quantitatively integrated into the nominal model for safety analysis by affecting process variables. Specifically, hybrid uncertainty encompasses the uncertainty of occurrence and severity. Finally, a simplified dynamic safety variation of the entire-engine is investigated by the approach. Result of Monte Carlo simulations of safety highlighted the coupled failures of different components in the respective safety ranges could be discriminated from the entire-engine perspective. The identification of potential coupled faults demonstrates the effectiveness and advantages of the proposed method in the design stage.

The Thermophysical and Physicochemical Properties of the Aqueous Dispersion of Graphene Oxide Dual-Beam Thermal Lens Spectrometry.

Modern heat-conducting materials require special attention to analyze their thermophysical properties. Compared to classical methods, thermal lens spectrometry (TLS) has advantages due to its high sensitivity to physical and chemical composition. To avoid a systematic error in the analysis of complex systems, it is necessary to realize the limits of the applicability of the method. This study considers the features of thermal-diffusivity measurements by TLS in the stationary state for dispersed systems with absorbances up to 0.05. The limits of applicability of the method in analyzing heterogeneous systems are shown, and a mathematical apparatus is proposed for indicating a systematic error in finding thermal diffusivity that does not exceed 1%. Graphene oxide (GO), which has attractive physicochemical properties, was used as the object of analysis. GO belongs to 2D objects, the study of which requires highly sensitive methods and special attention when discussing the results. The thermophysical properties of aqueous dispersions of graphene oxide in a wide range of concentrations (up to 2 g/L) and lateral sizes (up to 4 µm) were studied by TLS. It has been found that with increasing nanophase concentration, the thermal diffusivity of graphene oxide dispersions passes through a minimum, which can be used in solving thermal insulation problems. It has been established that prolonged laser irradiation of the dispersion leads to a change in thermal diffusivity, which indicates the photochemical reduction of graphene oxide.

Low-probability states, data statistics, and entropy estimation.

A fundamental problem in the analysis of complex systems is getting a reliable estimate of the entropy of their probability distributions over the state space. This is difficult because unsampled states can contribute substantially to the entropy, while they do not contribute to the maximum likelihood estimator of entropy, which replaces probabilities by the observed frequencies. Bayesian estimators overcome this obstacle by introducing a model of the low-probability tail of the probability distribution. Which statistical features of the observed data determine the model of the tail, and hence the output of such estimators, remains unclear. Here we show that well-known entropy estimators for probability distributions on discrete state spaces model the structure of the low-probability tail based largely on a few statistics of the data: the sample size, the maximum likelihood estimate, the number of coincidences among the samples, and the dispersion of the coincidences. We derive approximate analytical entropy estimators for undersampled distributions based on these statistics, and we use the results to propose an intuitive understanding of how the Bayesian entropy estimators work.

Digital tools for production planning as part of decision-making processes in the manufacturing systems

The concept of digitalisation is making inroads into all areas of production systems as part of Industry 4.0. In our case, we are dealing specifically with the digital planning of production resources and production processes. Digitized solutions are included in this paper as case studies. Our goal was to develop digital models of manufacturing systems and simulation models for manufacturing processes in the Witness Horizon digital simulation environment. Simulation can be considered as one of the technologies used in the digital factory concept. It is a powerful tool that is often used for the design and analysis of complex systems. Decisions about a system are made through the construction of its computer models and experimentation on the models.

Statistical performance of local attractor dimension estimators in non-Axiom A dynamical systems.

We investigate various estimators based on extreme value theory (EVT) for determining the local fractal dimension of chaotic dynamical systems. In the limit of an infinitely long time series of an ergodic system, the average of the local fractal dimension is the system's global attractor dimension. The latter is an important quantity that relates to the number of effective degrees of freedom of the underlying dynamical system, and its estimation has been a central topic in the dynamical systems literature since the 1980s. In this work, we propose a framework that combines phase space recurrence analysis with EVT to estimate the local fractal dimension around a particular state of interest. While the EVT framework allows for the analysis of high-dimensional complex systems, such as the Earth's climate, its effectiveness depends on robust statistical parameter estimation for the assumed extreme value distribution. In this study, we conduct a critical review of several EVT-based local fractal dimension estimators, analyzing and comparing their performance across a range of systems. Our results offer valuable insights for researchers employing the EVT-based estimates of the local fractal dimension, aiding in the selection of an appropriate estimator for their specific applications.

A Petri Nets-Based Simulation Methodology for Modular Modeling and Performance Evaluation of Car-Sharing Networks

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.

Analysis of Effect of Propeller Rotational Speed Variations on Torque and Power Using Computational Fluid Dynamics (CFD) Method

The speed on the ship is inseparable from a good propeller design in order to get the optimal thrust generated by the propeller motion. The propeller is a very important part in determining the ship's motion. The propeller itself is a tool used to generate thrust that comes from engine power transmitted through the shaft. Computational Fluid Dynamics (CFD) is a very suitable method for analyzing complex systems that are difficult to solve using manual calculations. With these advantages, CFD is often used to analyze a pattern of a system, one of which is the propeller, which in this study uses variations in flow velocity and propeller speed which can affect the value of the torque and power produced by the propeller.

SONAL DATA OF LEGAL ENTITIES AS A POTENTIAL SPECIAL OBJECT OF LEGAL PROTECTION IN CYBERSPACE

The article examines the main approaches to the personal data of legal entities as a potential special object of legal protection in cyberspace. The arguments for and against providing legal protection to legal entities' data of this type are presented. The article proposes its arguments for the impossibility of protecting the personal information of legal entities through the existing system of legal norms regulating the protection of personal data of citizens, based on the recognized theory of the essence of a legal entity, the goals of its identification, and taking into account the ultimate beneficiaries of legal entities - individuals. The research focused on four ways of analyzing complex systems: system analysis and a comprehensive description of personal data as an independent phenomenon of legal reality and an object of legal protection; functional analysis, which enables the consideration of personal data in the legal regulation system; specific scientific research methods, primarily sociological and psychological, which determined the main parameters of the regulation and implementation of individual and legal entity rights; and an axiological approach that revealed the true significance of technology for protecting the right to personal data.

Analysing the Real Estate Market in Almaty City: A System Dynamics Approach

The real estate market is a complex system that is influenced by a variety of factors. Understanding the dynamics of this system is crucial for investors, developers, and policymakers who want to make informed decisions. The Massachusetts Institute of Technology (MIT) has developed a powerful tool for analyzing complex systems called System Dynamics. This paper will provide an overview of System Dynamics and MIT analysis, followed by an overview of the real estate market. Finally, we will explore how System Dynamics can be used to analyze the real estate market and make predictions about its future trends. Combining these three supporting points will give us a comprehensive understanding of the real estate market and how it can be analysed using System Dynamic. This article employs the system dynamics modelling approach to shed light on the specific real estate market dynamics in Almaty сity. By utilising this methodology, we aim to provide a comprehensive analysis of the real estate market in Almaty, understanding its unique characteristics and uncovering the drivers of price volatility. This article seeks to contribute to the body of knowledge on real estate market analysis by utilising system dynamics modelling to examine the dynamics of the real estate market in Almaty. By leveraging the advantages of this progressive method, we seek to uncover the specific drivers of price volatility and provide valuable insights for policymakers, investors, and other stakeholders. Through this research, we aim to enhance the understanding of the real estate market in Almaty and offer a foundation for evidence-based decision-making in the region.

Networked scatter plot bidirectional coupling detection and its application in the diagnosis of depression

Identifying the bidirectional coupling of bivariate time series is a basic problem in the analysis of complex systems in contemporary science. As an intuitive statistical graphical analysis tool, scatter plot allows direct analysis of the correlation between two bivariate time series. However, the scatter plot itself have the problems of not being able to conduct bidirectional coupling analysis and data points overlap. In this work, by introducing inner composition alignment (IOTA) and ordinal pattern transition network (OPTN) into scatter plot, the limitations of scatter plot are alleviated and a new approach for measuring bivariate time series bidirectional coupling is proposed. To facilitate the integration of the three methods, the IOTA scatter plot proposed and the new scatter plot been partitioned and coded. The coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. For the constructed OPTN, the mean output degree that commonly used in OPTN is used as a quantitative indicator of coupling strength. To test the characteristics and performance of the new method, it is used to analyse the unidirectional coupled Lorentz model and to compare it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, and suitable for short time series. Finally, the real EEG dataset from depressed patients at rest are analysed and some useful and interesting results are obtained by treating the relevant brain areas as routers.

On a class of McKean-Vlasov stochastic functional differential equations with applications

This paper studies a class of McKean-Vlasov stochastic functional differential equations in which the dynamics of the underlying process may depend on its history as well as the distribution of the history. These stochastic processes provide a general and versatile framework for modeling and analyzing complex systems with inherent distribution- and memory-dependent dynamical behaviors. Under mild conditions, this paper establishes the well-posedness for the associated McKean-Vlasov stochastic functional differential equations and derives a propagation of chaos as well as a continuity with respect to the initial distribution result. Several examples are studied as an illustration.

Fluid flow and mass transport in fractured media with curved fractures and varying apertures: A 3D modeling approach

AbstractFractured media have been extensively studied due to their significant impact on hydraulic properties of subsurface systems. However, accurately modeling and simulating flow and transport in complex 3D fractured media still remains a challenge. For this purpose, this work aims to develop a new 3D modeling approach and investigate flow and mass transport in fractured media. First, a modeling approach is presented for generating 3D complex structures and high quality finite element grids. This approach allows for the presence of a range of complex geometries, including curved fracture, multiple fractures and the coexistence of fracture‐inclusions. The resulting models and grids are of high quality, enabling accurate simulation and analysis of complex systems. Furthermore, stress‐dependent aperture and roughness are integrated in the computational scheme using Barton–Bandis law. The Galerkin finite element method is used for numerical discretization, then pressure and concentration are calculated in a unified formulation. Later, numerical tests are performed to analyze the role of 3D fractures in flow and mass transport. Simulation results show that fractures have a larger impact on pressure distribution and flow rate compared to inclusions. Fracture roughness increases the aperture then the effective conductivity is improved as well. Curved fractures may reduce flow rate more than planar fractures due to the tortuosity effect. The variation of equivalent permeability tensor is analyzed with varying orientation, roughness, and number of fractures. The impact of stress and fracture roughness causes a variation of fracture permeability and a high pressure gradient on the fracture‐matrix interface, resulting in different patterns of concentration evolution. Overall, this investigation provides insights into the role of 3D fractures in subsurface flow and transport, with implications for various applications.