Complex Dynamical Systems Research Articles

The role of agency in language socialization

Abstract Drawing on longitudinal data from the perspective of Complex Dynamic Systems Theory, I portray how L2 language socialization is promoted by an L2 user’s agency borne out of his Ideal L2 self. This paper first showcases a decade-long longitudinal OPI (Oral Proficiency Interview) data set elicited from an L2 Japanese user who speaks Korean as his L1, focusing on the process of affective adverb acquisition observed through transformations in his speech style. Then, employing the Trajectory Equifinality Approach (TEA), I examine holistically how changes in the use of affective adverbs reflects the L2 user’s language socialization by visualizing his language environment and language awareness chronically, including his own reflections through retrospective interviews.

Simulation Modeling of Information Transfer Between Stock Exchanges Based on a Multi-Agent Model

The article substantiates the relevance of solving the problem of stock exchange modeling as an information system accumulating the orders of market participants and executing them. The object of the study is the process of information transfer between stock exchanges. The subject of the study is the use of simulation multi-agent model to study the process of information transfer between stock exchanges. The paper analyzes the possibilities of building a simulation model to analyze rare phenomena in the financial market using a multiagent approach. It is substantiated that this approach allows taking into account those features of complex dynamic systems, for which either no effective analytical approaches have been developed or it is impossible to conduct the necessary numerical experiments. The proposed logic of the market simulator is based on the allocation of certain trader classes, that may differ in the logic of making their decisions on the stock exchange, for this purpose the scheme of agent interaction in the model of the stock exchange is described. On the example of modeling the information transmission and market shocks between markets the possibilities of the proposed simulator are studied. The use of the proposed model in practice allows us to analyze rare phenomena occurring on the stock exchange, which cannot be described analytically, but their recreation by means of the simulation model will allow us to study them. The study of rare phenomena on the stock exchange with the help of simulation modeling allows to obtain different variants of the results of the phenomena at different sets of parameters of the model (agents), i.e. to carry out parameter.

Use of the Green Function in the Study of Morpho-structural Changes

Currently, automation and robotization of research is being introduced into all areas of medicine and veterinary medicine, including histological analysis. At the same time, it is necessary to provide an automated decision-making system for quality control of histological images preparation. The technological process of histologic analysis running in spatial and temporal basis has been studied. The process of histologic analysis is a complex dynamic system including the stages of biomaterial preparation and study of morphostructural changes in tissues. The problem of process flow description is based on the law of mass conservation during biosphere transfer, which takes into account the equation of flow continuity in Euler and Lagrange variables. In controlling the histologic process, the errors associated with process failure were taken into account. The solution was obtained using an impulsive transient Green's function. In the period 2000-2023, according to statistical data, an increase in the number of cancer cases was observed, which makes the development of an automated histological analyzer relevant. The aim of the study was to build a control model for the automated process of histological analysis. Research Methods. The approaches to the device design were based on the theory of histologic analysis, application of continuum mechanics methods, methods of solving differential equations using Green's function. Results. The technique of histologic analysis was studied. The analytical solution describing the control of the automated technological process of histological analysis in conditions of possible disturbances caused by perturbations, such as "marriage" and time delay in the preparation of histological specimens has been obtained. Preparation of high-quality histological images will accelerate the diagnosis in the study of morphostructural changes in tissues, which will help to reduce the risks of developing not only cancer, but also other diseases. Conclusion. Express analyzer of histological analysis will reduce the time of preparation of histological images and the burden on highly qualified medical personnel.

How the dynamics of engagement explain the momentum of achievement and the inertia of disengagement: A complex systems theory approach

Engagement can be understood as a complex dynamic system that unfolds over time and interacts within the student, school, and environment. Most of the research on the dynamics of engagement comes from classroom settings and it is so far inconclusive how and why engagement and disengagement evolve over time. Using person-centered methods, sequence, transition, and covariate analysis, we examined a large dataset of 18 consecutive courses of 245 students over a full program. We identified three engagement states (active, average, and disengaged), as well as three distinct longitudinal engagement trajectories (engaged, fluctuating, and mostly disengaged). Taken together, our results showed that engagement trajectories are rather stable over time conforming to the universal dynamics of complex systems. Engaged students were driven by course materials, their achievement, and their previous engaged states (momentum). Most importantly, our results offer a novel theoretical grounding for the understanding of disengagement which has so far remained unexplained. According to our results, disengagement follows the dynamics of a complex system where stability does not require a hard-wired causal mechanism but rather, it is an attractor state that pulls the system to settle in (inertia). Thus, disengagement becomes a hard to change equilibrium state for those students t (or a stuck state).

Potato yield forecasting based on a fuzzy-possibility model

Potato production is a complex dynamic system that requires ensuring the stability of its functioning in changing, uncertain economic conditions and a scientifically based approach to making effective management decisions. The purpose of the research was to assess and predict potato productivity depending on the level of mineral nutrition and the length of the growing season using expert knowledge and a fuzzy-possibility approach in the conditions of the Republic of Karelia. The experiments were carried out in 2019-2021 on three potato varieties of different early maturity: Udacha, Red Scarlet and Aurora on soddy-weakly podzolic sandy loamy well-cultivated soil. The microfield experiment included two options for applying mineral fertilizers: N80P60K120 и N120P90K180, the variant without fertilizers served as a control. Studies have shown that the application of mineral fertilizers significantly increases the yield of tubers by 10-58%. The yield of potatoes without the use of fertilizers averaged 28.4 t/ha in the experiments. The highest potato yield was obtained on the early variety Udacha (38.4 t/ha) against the background of the use of mineral fertilizers at a dose of N80P60K120. An adequate fuzzy-possibility model (FPM) based on explicit and implicit expert knowledge has been synthesized to predict potato yield. It is shown that the most significant factors in assessing the yield of potatoes of all tested varieties are the level of mineral nutrition (X2) and soil fertility (X5). The synthesized FPM can be used for the planned potato yield under certain external and similar technical and technological conditions.

The distributed order models to characterize the flow and heat transfer of viscoelastic fluid between coaxial cylinders

The distributed order fractional derivatives can describe complex dynamic systems. In this paper, considering the periodic pressure gradient and magnetic field, the time distributed order fractional governing equations are established to simulate the two-dimensional flow and heat transfer of viscoelastic fluid between coaxial cylinders. Numerical solutions are obtained by the L1 approximation for the Caputo derivative (L1-scheme) and the finite difference method, and the effectiveness of numerical method is verified by a numerical example. Results demonstrate that the time distributed fractional Maxwell model can promote the flow while the distributed Cattaneo model can weaken heat transfer than the fractional Maxwell and Cattaneo model, and different weight coefficients have different effects on the fluid. The effect of physical parameters, such as the relaxation time of velocity and temperature λ 1, λ 2, the magnetic parameter M, the amplitude P 0 and frequency w of pressure gradient, and the Prandtl number Pr on velocity and temperature are discussed and analysed in detail.

Raising learner awareness of L2 writing spaces: supporting researchers, teachers, and learners through system mapping

This work examines how system mapping, an established exploratory and analytic technique grounded in complex dynamic systems theory, can operate as a heuristic to increase learners’ language awareness (LA) and develop their L2 writing proficiency. Accordingly, this study addresses repeated calls in the instructed SLA literature for effective research methods that directly serve learners at the point of enquiry. That is, although related research often foregrounds learners and pedagogy rather than theoretical abstraction, questions remain about how participants benefit at the time data are collected. To explore this concern, this work engaged system mapping through a LA pedagogy and followed two intact university classes (one using system mapping, and one not: N = 30) over one semester. Comparative quantitative and qualitative analyses of learners’ narrative reflections and academic essay writing highlighted that system mapping facilitates the raising of ­learners’ LA in service of developing their writing. This work demonstrates how system mapping capitalizes on learning theories that support image-schematic interventions and underscores how system mapping engages with and advances LA pedagogy in service of researchers, instructors, and students alike.

Implementing a Digital Twin-based fault detection and diagnosis approach for optimal operation and maintenance of urban distributed solar photovoltaics

Over the next decades, solar energy power generation is anticipated to gain popularity because of the current energy and climate problems and ultimately become a crucial part of urban infrastructure. Furthermore, in order to facilitate the rapid deployment of solar production in sustainable cities, solar technologies must be digitalized and smart. This will increase their reliability and ensure their optimal performance. This work fits into this perspective and focuses on the effectiveness of processing data using the Digital Twin (DT) framework as a state-of-art information technology for urban distributed solar PV systems. Accordingly, the present paper deals with the DT of five Building-added Photovoltaic (BAPV) systems installed at the solar village in Benguerir city, Morocco. The 3D models were generated in the Rhinoceros platform, the solar AC electrical production of each photovoltaic system is calculated by the ladybug-plugin-integrated statistical model, within the grasshopper environment. Additionally, data assessment and visualization are processed through Python. Then, a hybrid model-based and data-driven fault detection and diagnosis (FDD) approach is proposed to identify and isolate anomalies for decentralized solar PV systems at the urban scale using monitoring and inspection techniques, namely Remote Sensors (RS) and real-time solar production monitoring system. The application of the DT concept for complex dynamic systems has shown its effectiveness in ensuring optimal operating conditions for the energy systems by measuring the spatiotemporal energy performance of distributed solar PV systems at an urban scale, evolving with the changing environment, and identifying degradation based on anomalies occurrence; Only then when the physical models need to be amended. Therefore, the main goal behind this work is to advance knowledge regarding research on condition monitoring of solar systems in cities while considering issues related to anomalies like the degradation phenomenon in PV modules over time. The findings of this study have proved that the state of health and monitoring of PV systems could be successfully detected and efficiently monitored using the combination of real time monitoring and remote aerial sensing.

Expanded Families of Periodic Orbits for the Jupiter–Callisto System

Periodic orbits are widely used in space missions and are fundamental for us to understand the complex dynamic system of circular restricted three-body problem. However, there are still many potential families of periodic orbits that have not been located and computed. This study aims to generate some new and less-known families of periodic orbits for the Jupiter–Callisto system to obtain an extended database. The numerical methods for the computation and continuation of periodic orbit families are provided. Five bifurcation diagrams are explored in depth, and 108 periodic orbit families are located. Of these, 59 families are continued to the entire families. Twenty asymmetrical families also come into view. New discoveries and the characteristics of several particular families are presented and discussed. Synodic resonant periodic orbits appearing under the perturbation of Ganymede are also identified. The expanded families of periodic orbits are expected to provide a reference for future Callisto exploration mission design.

Insights into time fractional dynamics in the Belousov-Zhabotinsky system through singular and non-singular kernels

In the realm of nonlinear dynamics, the Belousov-Zhabotinsky reaction system has long held the fascination of researchers. The Belousov-Zhabotinsky system continues to be an active area of research, offering insights into the fundamental principles of nonlinear dynamics in complex systems. To deepen our understanding of this intricate system, we introduce a pioneering approach to tackle the time fractional Belousov-Zhabotinsky system, employing the Caputo and Atangana-Baleanu Caputo fractional derivatives with the double Laplace method. The solution we obtained is in the form of series which helps in investigating the accuracy of the proposed method. The primary advantage of the proposed technique lies in the low amount of calculations required and produce high degree of precision in the solutions. Furthermore, the existence and uniqueness of the solution are investigated thereby enhancing the overall credibility of our study. To visually represent our results, we present a series of 2D and 3D graphical representations that vividly illustrate the behavior of the model and the impact of changing the fractional order derivative and the time on the obtained solutions.

Self-perceived leadership and entrepreneurship skills: profiling healthcare professionals

Abstract Background Healthcare is a complex system with overarching challenges that arise from the different hierarchically organized structures and the diversity of people interacting and communicating in the same environment. This complexity can be addressed by strengthening healthcare professional leadership and entrepreneurship competencies. This study aims to evaluate the self-perception of healthcare professionals regarding these skills and their association with demographic characteristics and university attributes. Method A cross-sectional survey conducted online from July to December 2021 recruited 245 Lebanese health professionals from different health-related institutions (hospitals, pharmaceutical industry, health professions universities, and others) using snowball sampling. A cluster analysis was performed based on the socio-demographic and work characteristics of the participants to classify their profiles. Bivariate and multivariate analyses were performed after ensuring the adequacy of the models. Significance was set at a P value < 0.05. Results Cluster analysis showed two distinct profiles, reflected by Cluster 1 for older individuals with moderate/high management versus Cluster 2 for younger people with low management profiles. The logistic regression showed that Cluster 1 was significantly associated with higher leadership with administrative, interpersonal, and conceptual skills. The interpersonal skills represented best Cluster 1 (ORa = 7.47), followed by the conceptual skills (ORa = 4.40). Linear regression analysis showed that Cluster 1 was significantly associated with higher decision-making (β = 0.69) and higher tolerance of ambiguity (β = 1.01). No association was found between other subscales, total entrepreneurship scales, and belonging to any cluster (P > 0.05). Conclusion Although most healthcare professionals showed moderate to high perceptions related to their leadership and entrepreneurship, younger ones were aware of the need to develop these skills to meet the challenges of the complex dynamic health system. Educating students and training professionals to acquire these skills would create value in emerging health services while fostering innovation, creativity, and quality improvement in the workplace.

Identification of switched dynamic system for electric multiple unit train modeling

A new iterative algorithm is proposed to identify the high-speed train model which is described as a switched dynamic system including a fast switched nonlinear static subsystem followed by a non-switched linear dynamic subsystem. The non-switched property of the linear part leads to a more complex dynamics of the whole system, compared to the switched Hammerstein system which linear part switches synchronously with its nonlinear part. The proposed iterative algorithm alternates between identifying the switched nonlinear static subsystem and the linear dynamic subsystem. The switched nonlinear subsystem identification incorporates a quadratic error bound constraint with respect to nominal values of model parameters, and is formulated as a second-order cone program. Properties of convergence and asymptotic unbiasedness of the obtained estimates are proved under some necessary assumptions. With real data and simulation examples, the proposed iterative algorithm produces convergent and asymptotically unbiased estimates. In the simulation results, incorporating the quadratic error bounds results in a fast convergence rate in the presence of a small noise variance, and also produces a smaller identification error in the presence of colored noises with a non-zero mean and a large variance.

Insights on the Impacts of Accelerometer Location on the Dynamics and Characteristics of Complex Structures.

There exists a high level of difficulty in understanding the physical responses of complex dynamical systems. To this end, researchers have previously used different measurement techniques, such as displacement sensors or accelerometers, in the laboratory to capture a system's dynamics. A well-known structure in the literature is the Box Assembly with Removable Component (BARC) whose purpose is to gain a deep understanding of testing complex systems. Further breaking down the structure, the Removable Component (RC) portion is often used as a relatively simplified version which retains significant complexity from the original system. However, the placement of accelerometers on the RC have varied greatly throughout the literature which presents a challenge in comparing results and may not accurately represent the system's dynamics. Finite Element Analysis (FEA) is performed for three common accelerometer locations to determine how their placement affects the frequencies and mode shapes for the RC and results are compared against those without accelerometers. Free vibration experiments are carried out to understand the variation of frequencies and damping for each accelerometer location to obtain the overall response for the first mode of vibration. Next, random vibration experiments are run to gain insight on the interaction between linear and nonlinear responses based on excitation level, while showing the influence of an accelerometer's location on system dynamics. The results demonstrate that the location of the accelerometer is highly influential on the linear and nonlinear characteristics of the system. It is proved that for the first mode of vibration, nonlinear softening and nonlinear damping behaviors may take place due to the interaction between the location of accelerometers, direction of excitation, and response axis analyzed.

TELCO GEOBIGDATA FOR THE ANALYSIS OF PRESENCES AND MOVEMENTS: THE 2023 “ADUNATA DEGLI ALPINI” IN UDINE

Abstract. The GEO Big Data phenomenon has become a central topic in the innovation debate. Understanding the presence and movement of individuals within a geographical area is crucial in many fields: urban planning, transport systems, emergencies, tourism, events, … In the last few years a new source of information to reconstruct the dynamics of such complex systems are the data acquired from cellular phone that trace presences and movements with high precision. For privacy issues the data are sold geographically aggregated by TELCO companies. The study of the dynamics of incoming and outgoing, number of trips, number of days and nights spent, number of unique visitors, country of residence and origin, main destination, secondary destination, transit and repeated visits have been analysed for the annual gathering of the ALPINI held in May 2023 in Udine. This work demonstrates how accurate this information is and how it can be used, in addition to analysing the previous edition, to plan locations, logistics, and agendas for future editions.

Exploring the relation between teacher autonomy support and children’s musical creativity

In primary music education a key question is what teachers can do to stimulate students’ musical creativity. For the answer, delving into teacher-student interaction during the creative processes in the naturalistic setting of primary music lessons is required. Twenty-six music lessons from thirteen teachers and their classes of seven Dutch schools were recorded to explore the relation between teachers’ autonomy support and students’ divergent and convergent thought & action. Quantitative sequential analysis and thematic analysis were combined to examine this relation, using a framework offered by Complex Dynamic Systems theory and Enaction theory. In contrast to classical correlational analysis, sequential analysis focuses on the dynamics, and thus on the temporal relation in classroom interaction. The results show that mostly lower-level autonomy support was offered. Especially in creative lessons, higher-level autonomy support is more likely to lead to higher-level student divergent thought and action. For convergent thought and action, the results were less conclusive. An implication of the findings is that (research into) music education could benefit from interventions aimed at enhancing autonomy support in primary school music.

The Covid-19 pandemic as a complex dynamical nonlinear system. Overcoming the Argentinean experience

The Covid-19 pandemic as a complex dynamical nonlinear system. Overcoming the Argentinean experience

Optimizing Automatic Voltage Regulation: A Deep Reinforcement Learning Approach

This paper presents a novel approach to enhance the performance of Automatic Voltage Regulator (AVR) systems in power systems using Deep Reinforcement Learning (DRL). The AVR plays a critical role in maintaining voltage stability and ensuring reliable power delivery. However, conventional control strategies, such as PID controllers, have limitations in handling complex and nonlinear power system dynamics. In this study, the application of DRL techniques is explored, particularly the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm, to AVR control. This algorithm offers the advantage of handling continuous action spaces and enable the controller to learn optimal control policies directly from the system's state information. The results show that the DRL approach outperforms the traditional PID and Neural Network-based control approaches, with the shortest time response and the best voltage regulation performance. The use of DRL in AVR system control shows promising potential for improving the efficiency and accuracy of power system control. This research provides insights into the advantages of DRL for process control and highlights its potential for future applications in power system control.

Systems Biology Modeling of Cancer Nonlinear Dynamics.

Not unlike the climate or what holds the galaxies and planetary motions together, cancer biology has an intrinsic nonlinear dynamic. In this overview we will outline how to connect temporal measurements of a nonlinear dynamical and unstable complex system, such as cancer, with well-established engineering methods, old and new, that are applied in linear dynamical systems.This proof-of-concept is therapeutically relevant in the development of new means to treat or control human cancer by either adding an appropriate external "damping" or a "forcing" term, or by a "control" actuator such that its nonlinear dynamic is steered to a spiral stably into zero forever as a sink attractor.

Student engagement and attendance are central mechanisms interacting with inclusive and equitable quality education: Evidence from Afghanistan and Pakistan

Despite considerable progress in the field of education science, there is currently no consensus on the components that define inclusive and equitable quality education, how they are articulated with one another, and what are the best interventions to foster inclusive and equitable quality education. Current research investigates separately components of what appears to be a complex dynamic system with feedback interactions. To characterize this system and identify structures that encompass inclusive and equitable quality education, we used a community-based system dynamics approach. This approach hypothesizes that community perceptions of the local school system is essential to define it. We therefore conducted 648 participatory Group Model Building workshops with school stakeholders (children, teachers, parents and members of school management committees) resulting in as many models in 99 schools of Afghanistan and Pakistan. To identify common components across models built by participants in two waves of schools’ model building workshops, we applied techniques from multivariate analysis of ecological communities. Even across wide differences in participants’ situations and roles in the educational process, their models expressed a common reinforcing feedback loop which connected child inclusive and equitable quality education to two other components: 1) child engagement in and motivation for education, and 2) child attendance. Increases in any of these three components were perceived to drive increases in the others. We also found that child focus on learning was commonly expressed as interacting with this generic structure. Any educational reform should simultaneously and primarily embrace learners’ diversity, combine policy principles of ensuring easy equitable access to foster attendance, and promote student interest and engagement in learning through child centered pedagogy and non-discriminatory teaching practices while giving school communities power for implementation.

Nonparametric estimation for uncertain fractional differential equations

After uncertainty theory was established, it has become a new branch of mathematics and been applied to describing the indeterministic phenomena as an uncertain dynamic system. As an important part of uncertainty theory, uncertain fractional differential equation is a good tool to model some complex dynamic systems with uncertainties. Due to the lack of corresponding information, models of parametric uncertain fractional differential equation are not always available. Therefore, the requirement for nonparametric estimation of uncertain fractional differential equation is urgent. Utilizing the thought of Legendre polynomial approximation, we propose a method to estimate the nonparametric uncertain fractional differential equations. Then, the error analysis of this method is given. After that, the numerical simulation is applied to illustrating the validity of error analysis and show the errors between the approximating functions and actual functions. The stability of this method has also been verified without highly dense observations. Finally, by means of uncertain hypothesis test and Kolmogorov–Smirnov test, we prove the superiority of uncertain fractional differential equations for some examples.