Characteristics Of Complex Systems Research Articles

TBM construction risk analysis based on fuzzy Bayesian network

ABSTRACT Aiming at the characteristics of various TBM construction risk factors, fuzzy and complex systems, and frequent shutdown accidents, a TBM construction risk analysis methodology based on a fuzzy Bayesian network is proposed. This method mainly selects eighteen risk factors to construct an interpretive structural model. These factors affect the TBM construction from the four aspects of the geological risk, equipment risk, management risk, and technical risk encountered during the construction of a full-face mechanical excavation of the tunnel. A five-level hierarchical directed graph is extracted by matrix operation to determine the relationship between factors. In the case of incomplete data, the Bayesian network model can be established by employing the prior probability to describe the state of the root node through expert scoring, ideal for system predictions lacking data. Finally, the main risk factors affecting the TBM construction are introduced and discussed, and the TBM construction of the West Second Tunnel of the Beijing Water Supply Phase II Project is taken as an example to compare the evaluation results with those of the actual construction status. The obtained results are not much different from those of the actual situation, which proves that the proposed method is scientific and can provide a reliable basis for analogous projects.

Pioneering a Framework for Robust Telemedicine Technology Assessment (Telemechron Study)

The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies in Public Health at the Italian National Institute of Health played the role of promoting and utilizing technology assessment tools within partnership projects. This study aims to outline the design, development, and application of assessment methodologies within the telemedicine project proposed by the ISS team, utilizing a specific framework developed within the project. The sub-objectives include evaluating the proposed methodology’s effectiveness and feasibility, gathering feedback for improvement, and assessing its impact on various project components. The study emphasizes the multifaceted nature of action domains and underscores the crucial role of technology assessments in telemedicine, highlighting its impact across diverse realms through iterative interaction cycles with project partners. Both the impact and the acceptance of the methodology have been assessed by means of specific computer-aided web interviewing (CAWI) tools. The proposed methodology received significant acceptance, providing valuable insights for refining future frameworks. The impact assessment revealed a consistent quality improvement trend in the project’s products, evident in methodological consolidations. The overall message encourages similar initiatives in this domain, shedding light on the intricacies of technology assessment implementation. In conclusion, the study serves as a comprehensive outcome of the national telemedicine project, witnessing the success and adaptability of the technology assessment methodology and advocating for further exploration and implementation in analogous contexts.

Structural complexity predicts consensus readability in online discussions

The intricate relationship between structure and function spans various disciplines, from biology to management, offering insights into predicting interesting features of complex systems. This interplay is evident in online forums, where the organization of the threads interacts with the message’s meaning. Assessing readability in these discussions is vital for ensuring information comprehension among diverse audiences. This assessment is challenging due to the complexity of natural language compounded by the social and temporal dynamics within social networks. One practical approach involves aggregating multiple readability metrics as a consensus alignment. In this study, we explore whether the structural complexity of online discussions can predict consensus readability without delving into the semantics of the messages. We propose a consensus readability metric derived from well-known readability tests and a complexity metric applied to the tree structures of Reddit discussions. Our findings indicate that this proposed metric effectively predicts consensus readability based on the complexity of discourse structure.

What about adaptiveness? The case of organisational resilience and cognition

This paper makes the very simple, perhaps straightforward point that adaptiveness cannot be taken for granted when analysing a complex system. The paradigm of Complex Adaptive Systems (CAS) theory makes it clear that a key feature of complex systems is the ability to adapt to changes in their environment. This is, indeed, relevant to many systems (e.g., living and social systems) since change is embedded in the way in which systems evolve over time. At the same time, adaptiveness is a strong assumption to make, since it prioritises change over stability and it can be a straight jacket, especially when it comes to studying complexity in the context of human social organising. By using a Case Study, this paper highlights the limits of a focus on adaptiveness and pushes for a more “neutral” perspective that allows researchers to appreciate a wider set of mechanisms, norms, and behaviours pertaining to complex social systems.

Self-organization as a mechanism of resilience in dryland ecosystems

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

Unbiased Multivariate Multiscale Sample Entropy

The development of multi-channel data acquisition techniques has provided richer prior information for studying the nonlinear dynamic characteristics of complex systems. However, conventional nonlinear feature extraction algorithms prove unsuitable in the context of multi-channel data. Previously, the Multivariate Multiscale Sample Entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although the MMSE algorithm generalized the multiscale sample entropy algorithm, presenting a novel method for multidimensional data analysis, it remains deficient in theoretical underpinning and suffers from shortcomings, such as missing cross-channel correlation information and having biased estimation results. In this paper, unbiased multivariate multiscale sample entropy algorithm (UMMSE) is proposed. UMMSE increases the embedding dimension from <i>M</i> to <i>M</i> + <i>p</i>. This increasing strategy facilitates the reconstruction of a deterministic phase space. By virtue of theoretical scrutiny grounded in probability theory and subsequent experimental validation, this paper illustrates the algorithm's effectiveness in extracting inter-channel correlation information through the integration of cross-channel conditional probabilities. The computation of similarities between sample points across different channels is recognized as a potential source of bias and instability in algorithms.Through simulation experiments, this study delineates the parameter selection range for the UMMSE algorithm. Subsequently, diverse simulation signals are employed to showcase the UMMSE algorithm's efficacy in extracting both within-channel and cross-channel correlation information. Ultimately, this paper demonstrates that the new algorithm has the lowest computational cost compared with traditional MMSE algorithms.

Innovations in Computational Approaches for Nonlinear Problems and Complex System Simulations

Nonlinear problem solving and complex system simulation have become critical issues in many fields of science. The development of novel computational methods is crucial to understanding these complex systems. In this abstract, we explore the dynamic landscape of computational techniques, focusing on their uses in simulating complex systems and tackling nonlinear challenges. Creating complex algorithms that can deal with nonlinearity, chaos, and emergent behaviours is where it's at. Tools for modelling and comprehending such complex systems are few, but machine learning, artificial intelligence, and evolutionary computation are at the forefront. The way problems are solved has been completely rethought because of their nonlinearity-tolerance and ability to operate in high-dimensional domains. In addition, novel opportunities have arisen due to the combination of classical mathematical models with computer methods. The behaviour and emergent features of complex systems are best understood by hybrid approaches that combine differential equations, agent-based modelling, and cellular automata. These techniques provide a fine-grained comprehension of component interactions, illuminating emergent events. Moreover, the advent of high-performance computing has substantially expanded the breadth and resolution of simulations. Scientists are now able to probe increasingly complex systems, shedding light on their dynamics and behaviours. Computational capacities have been vastly improved by parallel computing, distributed systems, and cloud computing infrastructures, allowing for the study of systems that were once thought to be intractable. Nonlinear issues and complex system simulations can benefit greatly from the combination of cutting-edge computational approaches with domain-specific expertise. This abstract is a testament to the expanding significance and potential of these computational approaches in understanding complex systems and opening up new frontiers for research and solving problems.

Using Causal Loop Diagrams (CLDs) to inform the development of Directed Acyclic Graphs (DAGs)

Abstract Complex systems-based approaches, like causal loop diagrams (CLDs), are increasingly being used in population health studies. Traditionally, directed acyclic graphs (DAGs) have been frequently used in causal inference methods in population health studies to define analysis plans and identify potential biases. The use of those two methodologies has been suggested to be incompatible due to DAGs being apparently unsuitable for modelling systems containing feedback loops, a common feature of complex systems. In this presentation we will detail the steps and decisions that a research team could follow to translate a CLD into a series of DAGs.

A Joint Distribution Pricing Model of Express Enterprises Based on Dynamic Game Theory

With the development of sharing economy, a joint distribution mode has been increasingly adopted as the preferred cooperation mode of third-party logistics enterprises to achieve the efficient, resource-saving, and profit-optimal business goals of enterprises. In the joint distribution mode, the distribution price is one of key factors that influences the operation of the joint distribution. Thus, to acquire the optimal pricing for the logistics enterprises, we establish a pricing model based on dynamic game theory for a joint distribution system including one joint distribution company and two express enterprises. In the proposed model, two dimensions of games exist simultaneously, including the game between express competitors and the game between express and distribution enterprises. The multidimensional game leads to more complex system characteristics. Through the stability analysis, we find the Nash equilibrium point and its stability conditions. Numerical simulations are conducted to investigate the complex dynamical behaviors of the game model, such as the system stability region, the bifurcation diagram, the largest Lyapunov exponent, strange attractors, etc. The simulation results indicate that different price adjustment speeds and ranges have a significant impact on the system stability and the profits of all participants in the game. The parameter adjustment control can well dominate the chaotic behaviors of the system. Enterprises should make pricing decisions based on their market positions to promote the continuous and stable development of the operation mode of the multi-agent joint sharing distribution center.

Complexity Study on Multi-Field Coupling Systems for Underground Coal Fires

Underground coal fires are a major disaster that needs to be urgently addressed in the coal mining industry, as they can cause waste of resources, environmental pollution, threaten the life and health of species, and cause serious damage to society and the economy. Currently, the research on the prevention and control of underground coal fires focuses more on the technical level and lacks scientific guidance at the methodological level. Based on this research gap, this paper uses CiteSpace 6.1.R6 to analyze the research hotspots and subjects in the field of underground coal fires and gives a comprehensive research overview of its microscopic chemical reaction substance and macroscopic multi-field coupling characteristics. Then, from the methodological level, it summarizes the complex system characteristics of underground coal fires and puts forward the elements and principles for the prevention and control of underground coal fire disasters under the paradigm of complex system research. This study proposes a new way of thinking for the prevention and control of underground coal fires and helps to build a whole-process prevention and control system.

An ENSO Prediction Model Based on Backtracking Multiple Initial Values: Ordinary Differential Equations–Memory Kernel Function

This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after spatial dimension reduction and has both nonlinear characteristics and the low-cost advantage of a time series model. The ODE–MKF focuses on utilizing more temporal information and includes machine learning to solve complex mathematical inverse problems to establish a predictive model. This study first validates the feasibility of the ODE–MKF via experiments using the Lorenz system. The results demonstrate that the ODE–MKF prediction model could describe the nonlinear characteristics of complex systems and exhibited ideal predictive robustness. The prediction of the El Niño-Southern Oscillation (ENSO) index further demonstrates its effectiveness, as it achieved 24-month lead predictions and effectively improved nonlinear problems. Furthermore, the reliability of the model was also tested, and approximately 18 months of prediction were achieved, which was verified with the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) radiation fluxes. The short-term memory index Southern Oscillation (SO) was further used to examine the applicability of ODE–MKF. A six-month lead prediction of the SO trend was achieved, indicating that the predictability of complex systems is related to their inherent memory scales.

A method for calculating the real contact area of blade-disk structure combined with truncation theory between rough surfaces

The contact characteristics of the blade-disk structure are difficult to predict due to complex working conditions. This paper presented an improved model to predict its real contact area. Firstly, the deformation state and load of the asperities with different radii on the deterministic rough surface are determined based on improved truncation theory. Subsequently, the real contact area of the blade-disk structure is obtained by combined with the finite element method at a lower computational cost. Finally, the application of the proposed method in the wear prediction of dovetail blade-disk structures is demonstrated, which proves that considering roughness is significant for wear prediction. The proposed method has great potential in predicting the wear characteristics of complex systems under real working conditions.

Nonparametric Bayesian Networks as a Tool of Multiscale Time Series Analysis and Remote Sensing Data Integration

Introduction. Nonparametric Bayesian networks are a promising tool for analyzing, visualizing, interpreting and predicting the structural and dynamic characteristics of complex systems. Modern interdisciplinary research involves the complex processing of heterogeneous data obtained using sensors of various physical nature. In the study of the forest fund, both methods of direct dendrological measurements and methods of remote observation using unmanned aerial vehicles are widely used. Information obtained using these methods must be analyzed in conjunction with hydrometeorological monitoring data.Aim. Investigation of the possibility of automating the monitoring of the well-being of the forest fund based on the integration of ground survey data, remote multispectral measurements and hydrometeorological observations using the mathematical apparatus of nonparametric Bayesian networks.Materials and methods. To assess the long-term joint dynamics of natural and climatic indicators and the radial growth of trees, a modified method of multiscale cross-correlation analysis was used with the removal of the background trend described by the moving average model. Relationships between various indicators were estimated based on the unconditional and conditional nonparametric Spearman correlation coefficients, which were used to reconstruct and parameterize the nonparametric Bayesian network.Results. A multiscale nonparametric Bayesian network was constructed to characterize both unconditional and conditional statistical relationships between parameters obtained from remote sensing, hydroclimatic and dendrological measurements. The proposed model showed a good quality of the plant fund state forecasting. The correlation coefficients between the observed and predicted indicators exceed 0.6, with the correlation coefficient comprising 0.77 when predicting the growth trend of annual tree rings.Conclusion. The proposed nonparametric Bayesian network model reflects the relationship between various factors that affect the forest ecosystem. The Bayesian network can be used to assess risks and improve environmental management planning.

The role of learning in complex problem solving using MicroDYN

It is still an open question which cognitive and non-cognitive personality traits are useful for describing and explaining behaviour and performance in complex problems. During complex problem solving (CPS), problem solvers have to interact with the task in a way in which learning ability might be beneficial for successful task completion. By investigating the relationship between learning ability and CPS, while accounting for interactions between complex system characteristics and person characteristics, this paper aims to understand the role of learning processes in CPS more closely. In a sample of N = 241 participants, we performed a preregistered analysis to investigate the relationship between knowledge acquisition performance in a CPS test (MicroDYN) and learning test performance (ADAFI) with a multilevel modeling approach across 10 CPS systems with various characteristics. In line with our expectations, we replicated previous findings on a relationship between learning test and MicroDYN performance and found this relationship to be more pronounced in systems with (vs. without) autonomous changes. Further system and person characteristics also showed effects as expected, with better performance in systems with lower complexity, with more experience with the task, and with more strategic exploration behaviour. Our results provide further evidence for the notion that learning is an important component for the successful completion of CPS tasks.

Complexity in Epidemiology and Public Health Addressing complex health problems through a mix of epidemiologic methods and data.

Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychological, economic, and other processes that may be non-linear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This paper sets out a way to operationalize complex systems thinking in public health, with particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions--patterns, mechanisms, and dynamics--along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems--emergence, interactions, non-linearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.

Pressure dependent analysis in premise plumbing system modeling.

Modeling premise plumbing systems requires accurate treatment of fixture-specific pressure and flow rate relationships. Each fixture in a building may experience different flow rates based on variable service pressure, its unique pressure-flow behavior, and demands throughout the building. Unique experimentally derived pressure-flow parameters for four faucets, a shower/tub fixture, and toilet were developed. The Water Network Tool for Resilience (WNTR) was also used to explore the impact of premise plumbing systems on water distribution systems through two simple skeletonization cases. Minimum pressures for nodes in water distribution system models that represent demand aggregated premise plumbing systems will likely be non-zero and must capture additional pressure drop or elevation differences at the building scale and associated components, such as water meters or backflow preventers. Results showed that flow rates are impacted by pressure in these systems in complex ways, and usage and system characteristics must be considered to be modeled accurately.

Hard c-mean transition network method for analysis of time series.

Transition network is a powerful tool to analyze nonlinear dynamic characteristics of complex systems, which characterizes the temporal transition property. Few, if any, existing approaches map different time series into transition networks with the same size so that temporal information of time series can be captured more effectively by network measures including typical average node degree, average path length, and so on. To construct a fixed size transition network, the proposed approach uses the embedding dimension method to reconstruct phase space from time series and divides state vectors into different nodes based on the hard c-mean clustering algorithm. The links are determined by the temporal succession of nodes. Our novel method is illustrated by three case studies: distinction of different dynamic behaviors, detection of parameter perturbation of dynamical system, and identification of seismic airgun based on sound data recorded in central Atlantic Ocean. The results show that our proposed method shows good performance in capturing the underlying nonlinear and nonstationary dynamics from short and noisy time series.

Emergence in Artificial Life.

Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement "life is complex." Thus understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understanding living systems? Artificial Life (ALife) has been developed in recent decades to study life using a synthetic approach: Build it to understand it. ALife systems are not so complex, be they soft (simulations), hard (robots), or wet(protocells). Thus, we can aim at first understanding emergence in ALife, to then use this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but present at another. This perspective avoids problems of studying emergence from a materialist framework and can also be useful in the study of self-organization and complexity.

Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.

A core feature of complex systems is that the interactions between elements in the present causally constrain their own futures, and the futures of other elements as the system evolves through time. To fully model all of these interactions (between elements, as well as ensembles of elements), it is possible to decompose the total information flowing from past to future into a set of non-overlapping temporal interactions that describe all the different modes by which information can be stored, transferred, or modified. To achieve this, I propose a novel information-theoretic measure of temporal dependency (Iτsx) based on the logic of local probability mass exclusions. This integrated information decomposition can reveal emergent and higher-order interactions within the dynamics of a system, as well as refining existing measures. To demonstrate the utility of this framework, I apply the decomposition to spontaneous spiking activity recorded from dissociated neural cultures of rat cerebral cortex to show how different modes of information processing are distributed over the system. Furthermore, being a localizable analysis, Iτsx can provide insight into the computational structure of single moments. I explore the time-resolved computational structure of neuronal avalanches and find that different types of information atoms have distinct profiles over the course of an avalanche, with the majority of non-trivial information dynamics happening before the first half of the cascade is completed. These analyses allow us to move beyond the historical focus on single measures of dependency such as information transfer or information integration, and explore a panoply of different relationships between elements (and groups of elements) in complex systems.

Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model

To reasonably implement the operational reliability analysis and describe the importance of the influencing parameters for the operation status, a framework for operational reliability evaluation and analysis is proposed. First, an operational reliability evaluation model (OREM) is established based on the data envelopment analysis (DEA) method, which takes quick access recorder (QAR)data as input to comprehensively evaluate the operational characteristics of complex systems to obtain the operational reliability Pr. Then, to enhance the modeling efficiency and simulation performance for the operational reliability analysis of the complex system, we propose an intelligent extremum machine learning model (IEMLM), by integrating extremum response surface method (ERSM), artificial neural network (ANN), improved particle swarm optimization (PSO) algorithm, and Bayesian regularization (BR) algorithm. The operational reliability analysis of a braking system of a civil aircraft is conducted to validate the effectiveness and feasibility of this developed method, by considering the comprehensive influence of system-environment-human. The comparison of IEMLM, RF, and ANN shows that IEMLM improves the analysis accuracy and calculation efficiency. The proposed framework and models can provide useful references for civil aircraft operational reliability analysis, special situation treatment, maintenance, and design.