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A functioning roadway system is key to any society and economy. Traffic events such as maintenance and accidents reduce roadway capacity and can have immediate, large and lasting impact on traffic conditions. This paper investigates the role of information dissemination as a control tool for reducing the negative effects of a traffic event. It examines how timely information can prevent unnecessary route changes and counteract misinformation, restoring traffic equilibrium faster. Systems that recover equilibrium more rapidly are considered more resilient. A traffic flow evolution model is applied to analyze network behavior post-disruption, with a model parameter interpreted as an information dissemination penetration rate adjustable by operators. Numerical experiments investigate the effects of the penetration rate on travel times, revealing a critical rate for quickly re-establishing a new equilibrium. Beyond this rate, greater traffic oscillations can be expected. The study determines the best information penetration rate to maximize network resilience while ensuring stability. [ABSTRACT FROM AUTHOR]

Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various applications, including identifying emerging trends, monitoring network security, understanding social dynamics, tracking disease outbreaks, and understanding financial dynamics. In this paper, we present a comprehensive benchmarking study that compares 12 data-driven methods for anomaly detection in temporal graphs. We conduct experiments on two temporal graphs extracted from Twitter and Facebook, aiming to identify anomalies in group interactions. Surprisingly, our study reveals an unclear pattern regarding the best method for such tasks, highlighting the complexity and challenges involved in anomaly emergence detection in large and dynamic systems. The results underscore the need for further research and innovative approaches to effectively detect emerging anomalies in dynamic systems represented as temporal graphs. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative study of the effects of using arbitrary-order operators in Neural Networks. It is based on a Recurrent Wavelet First-Order Neural Network (RWFONN), which can accurately identify several chaotic systems (measured by the mean square error and the coefficient of determination, also known as R-Squared, r 2 ) under a fixed parameter scheme in the neural algorithm. Using fractional operators, we analyze whether the identification capabilities of the RWFONN are improved, and whether it can identify signals from fractional-order chaotic systems. The results presented in this paper show that using a fractional-order Neural Network does not bring significant advantages in the identification process, compared to an integer-order RWFONN. Nevertheless, the neural algorithm (modeled with an integer-order derivative) proved capable of identifying fractional-order dynamical systems, whose behavior ranges from periodic and multi-stable to chaotic oscillations. That is, the performances of the Neural Network model with an integer-order derivative and the fractional-order network are practically identical, making the use of fractional-order RWFONN-type networks meaningless. The results deepen the work previously published by the authors, and contribute to developing structures based on robust and generic neural algorithms to identify more than one chaotic oscillator without retraining the Neural Network. [ABSTRACT FROM AUTHOR]

This work presents the development of an embedded platform using Field Programmable Gate Arrays (FPGAs) for real-time simulation of dynamic systems in industrial plants. The platform, Real-Time Simulator for Dynamic Systems in FPGA (RTSDS-FPGA), is designed for industrial and academic applications. In industrial contexts, the RTSDS-FPGA facilitates the optimization and tuning of embedded control algorithms, while in academia, it supports research on new embedded solutions in automation and control. It is also an educational tool for undergraduate and postgraduate students developing embedded control system projects. Additionally, the simulator accelerates the simulation of slow dynamic systems, significantly reducing overall simulation time. Experimental results demonstrate the platform's capability to perform real-time simulations effectively, validating its accuracy and performance through comparative analyses with established software tools such as Matlab/Simulink. [ABSTRACT FROM AUTHOR]

The simultaneous clustering of observations and features of datasets (known as co-clustering) has recently emerged as a central machine learning application to summarize massive datasets. However, most existing models focus on continuous and dense data in stationary scenarios, where cluster assignments do not evolve over time. This work introduces a novel latent block model for the dynamic co-clustering of data matrices with high sparsity. To properly model this type of data, we assume that the observations follow a time and block dependent mixture of zero-inflated distributions, thus, combining stochastic processes with the time-varying sparsity modeling. To detect abrupt changes in the dynamics of both cluster memberships and data sparsity, the mixing and sparsity proportions are modeled through systems of ordinary differential equations. The inference relies on an original variational procedure whose maximization step trains fully connected neural networks in order to solve the dynamical systems. Numerical experiments on simulated and real world datasets demonstrate the effectiveness of the proposed methodology in the context of count data. [ABSTRACT FROM AUTHOR]

In this article, we argue that social systems with fission–fusion (FF) dynamics are best characterized within a complex adaptive systems (CAS) framework. We discuss how different endogenous and exogenous factors drive scale-dependent network properties across temporal, spatial and social domains. Importantly, this view treats the dynamics themselves as objects of study, rather than variously defined notions of static 'social groups' that have hitherto dominated thinking in behavioural ecology. CAS approaches allow us to interrogate FF dynamics in taxa that do not conform to more traditional conceptualizations of sociality and encourage us to pose new types of questions regarding the sources of stability and change in social systems, distinguishing regular variations from those that would lead to system-level reorganization. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

We conducted mathematical simulations to identify effective treatment options for depression. These simulations involved treatment component nodes that targeted specific symptoms within the depressive symptom network, examining how interventions for certain symptoms could improve the overall symptomatology. The results indicate that targeting a symptom with higher centrality can lead to significant improvement. Furthermore, while interventions targeting symptoms with the lowest centrality did not show any improvement, targeting multiple lower‐centrality symptoms simultaneously may lead to an improvement in overall symptomatology. Insights from these simulations are expected to advance the understanding of effective intervention strategies for depression and the application of psychological networks in clinical settings. [ABSTRACT FROM AUTHOR]

The dynamics of mastering the degrees of freedom in motor learning are still far from being understood. The present work explored coordination dynamics in a redundant task, relating it to performance and adaptation in a serial stimulus tracking task. One hundred and sixty-three children (10–14 years of age) continuously responded to sequential stimuli (containing five stimuli) by pressing the respective sensors before the next stimulus presentation. Participants performed 120 trials with a fixed sequence (4–2–5–3–1) and a fixed interstimuli interval (800 ms) to learn the first pattern (practice phase). Then, a changed sequence (4–2–5–1–3) with a shorter interval (700 ms) was presented for 40 trials (adaptation phase). To measure coordination and its change, we calculated the correlation matrix of the stimulus–touch interval between the five sensors in blocks of 20 trials of the practice phase and classified individuals in terms of clusters. We found associations between coordination dynamics, performance curves, and adaptation in both coordination and performance. Furthermore, using network analyses, we found a tendency for all groups to increase the clustering coefficient. We discuss the possibility of this result representing a process of progressive segregation. [ABSTRACT FROM AUTHOR]

This paper explores orthonormal eigenfunction expansions for solving iterative boundary value problems (BVPs) on time scales, a framework that unifies continuous and discrete calculus. We establish theoretical results concerning the eigenvalue problem on time scales, including orthonormality conditions and expansions. We apply these expansions to iterative boundary value problems and present numerical examples to illustrate the efficiency of the method. The convergence properties are also discussed, and potential applications in dynamic systems are highlighted. [ABSTRACT FROM AUTHOR]

We introduce a new inductive bias for learning in dynamic event-based human systems. This is intended to partially address the issue of deep learning in chaotic systems. Instead of fitting the data to polynomial expansions that are expressive enough to approximate the generative functions or of inducing a universal approximator to learn the patterns and inductive bias, we only assume that the relationship between the input features and output classes changes over time, and embed this assumption through a form of dynamic contrastive learning in pre-training, where pre-training labels contain information about the class labels and time periods. We do this by extending and integrating two separate forms of contrastive learning. We note that this approach is not equivalent to inserting an extra feature into the input data that contains time period, because the input data cannot contain the label. We illustrate the approach on a recently designed learning algorithm for event-based graph time-series classification, and demonstrate its value on real-world data. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Ciencia y Poder Aéreo is the property of Escuela de Postgrados de la Fuerza Aerea Colombiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on developmental systems and dynamic systems theories, we propose the lifeworld approach--a conceptual framework for research and a hypothesis concerning early social-cognitive development. As a framework, the lifeworld approach recognizes the social embeddedness of development and shifts the focus away from individual developmental outcomes toward the reciprocal interplay of processes within and between individuals that co-constitutes early social-cognitive development. As a hypothesis, the lifeworld approach proposes that the changing developmental system--spanning the different individuals as their subsystems--strives toward attractor states through regulation at the behavioral level, which results in both the emergence and further differentiation of developmental attainments. The lifeworld approach--as a framework and a hypothesis, including key methodological approaches to test it--is exemplified by research on infants' self-awareness, prosocial behavior and social learning. Equipped with, first, a conceptual framework grounded in a modern view on development and, second, a growing suite of methodological approaches, developmental science can advance by analyzing the mutually influential relations between intra-individual and interactional processes in order to identify key mechanisms underlying early social-cognitive development. [ABSTRACT FROM AUTHOR]

This paper presents a First-Order Recurrent Neural Network activated by a wavelet function, in particular a Morlet wavelet, with a fixed set of parameters and capable of identifying multiple chaotic systems. By maintaining a fixed structure for the neural network and using the same activation function, the network can successfully identify the three state variables of several different chaotic systems, including the Chua, PWL-Rössler, Anishchenko–Astakhov, Álvarez-Curiel, Aizawa, and Rucklidge models. The performance of this approach was validated by numerical simulations in which the accuracy of the state estimation was evaluated using the Mean Square Error (MSE) and the coefficient of determination ( r 2 ), which indicates how well the neural network identifies the behavior of the individual oscillators. In contrast to the methods found in the literature, where a neural network is optimized to identify a single system and its application to another model requires recalibration of the neural algorithm parameters, the proposed model uses a fixed set of parameters to efficiently identify seven chaotic systems. These results build on previously published work by the authors and advance the development of robust and generic neural network structures for the identification of multiple chaotic oscillators. [ABSTRACT FROM AUTHOR]

In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task's subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms. [ABSTRACT FROM AUTHOR]

Student-generated questions are an important mechanism for learning and self-regulation, yet their scarcity in classroom discourse points to a need to understand how students decide to ask or withhold their questions. This study examined how 12 graduate students in an introductory statistics course made decisions about asking questions during whole group instruction. Help seeking decisions were framed as arising from a dynamic system of a student's situated role identity as a class participant. Qualitative analysis of post-class interviews illustrated the highly contextual, dynamic nature of students' decisions to ask or withhold questions. Analyses revealed an array of microdecisions that involved determining whether help was needed, anticipating social impressions, and considering impacts on others. Results revealed a prominent configuration of ontological beliefs, self-perceptions, goals, and perceived action possibilities that led students to withhold questions. The findings highlight tensions and dynamics among role identity elements, including distinct appraisals of one's own vs. classmates' questions. The study illuminates how context, questions, and interacting identity elements frame students' willingness to ask questions during class. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In psychotherapy research, the first applications of dynamical systems research (DSR) date back to the 1990s. Over time, DSR has developed three main lines of research: the study of oscillations in synchronization; the study of oscillations between stability and flexibility of process variables (S–F oscillations); the mathematical modeling to analyze the evolution of psychotherapy process. However, the connections among the empirical results and their implications for psychotherapy practice are unclear. For this reason, for the first time in the literature, this work carries out a comprehensive review of all three lines of research, including the main scientific contributions from the 1990s to the present day. For each line of research, the work critically analyzes the results, proposes future developments, and underlines the connections between empirical results and implications for psychotherapy practice. Furthermore, the work highlights the model of change that emerges from the empirical results, and its clinical correlates. In the conclusions, the author summarizes the results and the evolution of psychotherapy process in accordance with the DSR. [ABSTRACT FROM AUTHOR]

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution. [ABSTRACT FROM AUTHOR]

Motivational development can no longer be considered as the stable phenomenon as it was considered in the past. It is now an element of individual difference which is constantly influenced by contextual factors. This study applied a dynamic system perspective and drew upon Dörnyei's (2005) L2 Motivational Self-System to examine both male and female Iranian EFL learners' motivational and behavioral variability over time with different levels of proficiency. To this end, 590 beginner to upper intermediate male and female students completed the Motivational Factors Questionnaire at one-month intervals during the semester. The results indicated that the students' motivation could possibly be explored under dynamic systems' principles. The findings demonstrated how students' motivation changed over time and how it could be predicted in a moderately stable manner. The outcomes also showed that students' motivational and behavioral developments were mediated by their proficiency levels and gender. Implications and directions for further research were also stated. [ABSTRACT FROM AUTHOR]

This work proposes a low-cost Arduino-based methodology for simulation and control of dynamic systems. The proposed methodology allows the real-time Hardware-In-the-Loop simulation of multivariable systems and the evaluation of multivariable control techniques. In order to obtain a HIL simulation, two Arduinos are interconnected. One Arduino is used to simulate the plant, and the other is used to implement the controller. In order to evaluate the performance of the proposed methodology, three multivariable dynamic systems are simulated in real-time: an RLC system, a twin rotor MIMO system, and a microgrid. A state feedback controller by using a state observer is used to implement the controller over each dynamic system. An additional I+PD control structure is also used to compare the state feedback control method. An additional software is designed to display the data in a graphical interface in real-time and to analyze and evaluate the performance of each proposed plant and controller. [ABSTRACT FROM AUTHOR]

Little's Law understanding (LLU) is a newly developed concept. It examines individuals' ability to comprehend Little's Law (LL) and its application to dynamic systems. LL provides a mathematical formula for the relationship between the main parameters of queueing systems. These systems consist of items that arrive, wait to be processed, and then leave after the process is complete. Queues are an indispensable part of dynamic systems, including economic and managerial ones. This study examines the relationship between rational-intuitive thinking style and LLU both directly and indirectly through Cognitive Reflection Test (CRT). An online survey was designed and conducted. The number of participants was 477 people. According to the results, rational thinking positively predicts LLU. In addition, CRT fully mediates the relationship between rationality and its sub-dimension of rational favorability on LLU. Current study highlights the role of rationality and specially, its favorability subscale on LLU. For the first time, this study examines the cognitive mechanisms that predict understanding LL, as an underlying law of dynamic systems. [ABSTRACT FROM AUTHOR]

One of the most important branches of nonlinear control theory of dynamical systems is the so-called sliding mode. Its aim is the design of a (nonlinear) feedback law that brings and maintains the state trajectory of a dynamic system on a given sliding surface. Here, dynamics become completely independent of the model parameters and can be tuned accordingly to the desired target. In this paper we study this problem when the feedback law is subject to strong structural constraints. In particular, we assume that the control input may take values only over two bounded and disjoint sets. Such sets could be also not perfectly known a priori. An example is a control input allowed to switch only between two values. Under these peculiarities, we derive the necessary and sufficient conditions that guarantee sliding-mode control effectiveness for a class of time-varying continuoustime linear systems that includes all the stationary state-space linear models. Our analysis covers several scientific fields. It is only apparently confined to the linear setting and also allows one study an important set of nonlinear models. We describe fundamental examples related to epidemiology where the control input is the level of contact rate among people and the sliding surface permits to control the number of infected. We prove the global convergence of epidemic sliding-mode control schemes applied to two popular dynamical systems used in epidemiology, i.e., SEIR and SAIR, and based on the introduction of severe restrictions like lockdowns. Results obtained in the literature regarding control of many other epidemiological models are also generalized by casting them within a general sliding-mode theory. [ABSTRACT FROM AUTHOR]

In the course of digitization, there is an increased interest in sensor data, including data from old systems with a service life of several decades. Since the installation of sensor technology can be quite expensive, soft sensors are often used to enhance the monitoring capabilities. Soft sensors use easy-to-measure variables to predict hard-to-measure variables, employing arbitrary models. This is particularly challenging if the observed system is complex and exhibits dynamic behavior, e.g., transient responses after changes in the system. Data-driven models are, therefore, often used. As recent studies suggest using Transformer-based models for regression tasks, this paper investigates the use of Transformer-based soft sensors for modelling the dynamic behavior of systems. To this extent, the performance of Multilayer Perceptron (MLP) and Long Short-term Memory (LSTM) models are compared to Transformers, based on two data sets featuring dynamic behavior in terms of time-delayed variables. The outcomes of this paper demonstrate that while the Transformer can map time delays, it is outperformed by MLP and LSTM. This deviation from previous Transformer evaluations is noteworthy as it may be influenced by the dynamic characteristics of the input data set, and its attention-based mechanism may not be optimized for sequential data. It is important to mention that the previous studies in this area did not focus on time-delayed dynamic variables. [ABSTRACT FROM AUTHOR]

Temporal networks are known as the most important tools for representing and storing dynamic systems. This type of network accurately demonstrates all the dynamic changes that occur in a dynamic system. In different applications of dynamic systems, different representation of network models has been used to represent temporal networks. In the last decade, controllability in dynamic systems has become one of the most important challenges in this field. Controllability means the transfer of the network from an initial state to a desired final state in a certain period of time. The most common representation of network model used in control processes is the layered model. But this model has a high overhead, and on the other hand, it slows down the network control processes. In this article, we have proposed a new model for storing and representing temporal networks, which uses a tree structure to save all dynamics of network. Considering that in the proposed model only essential network control information is stored, this model has a very low data overhead compared to the layered model, and this makes the control processes run at a higher speed. [ABSTRACT FROM AUTHOR]

In the inverse problem, it is common that either the corresponding component parameters or the corresponding input signals are obtained for a given output or response. Most model-based solutions to the inverse problem involve optimization using the so-called forward model. The forward model typically comprises the mechanistic model in some form. Most commonly, inverse problems are formulated in a static setting where a wealth of theoretical results and numerical methods are available. However, there are many important dynamic applications wherein time-dependent information needs to be discerned from time-dependent data. Recently, data-based approaches, or model-free methods, have been invoked whereby feature extraction methods such as Support vector machines (SVM) and artificial neural networks (ANN) are used. Herein we develop an inverse solution for dynamic systems through easy-to-understand least-squares meta-model mathematics. The input and output training data are interchanged, so that a mixed input comprising both component parameters and discrete-time excitations can be found for a given discrete-time output. Single-value decomposition (SVD) makes any matrix inversion tractable. The inverse meta-model is compared to the optimization method and ANN using mechanistic models for fidelity, and is shown to have better accuracy and much increased speed. [ABSTRACT FROM AUTHOR]

This study aims to highlight the theory-method relationship, as well as some problems strongly intertwined with the methodological. It is also intended to show how conceptualizations inspired by Piagetian and dynamic systems, for example, go hand in hand with methodologies that are close to them and from which they can be approached. Methodologically, a microgenetic approach is chosen in a singular version, which recovers the spirit conceived in the Geneva School from a cultural and idiographic perspective. Assuming development as change implies taking into account the temporality of the processes, for which we will rely on treatments, some already known (MIN-MAX) and others relatively new (Phase Spaces), which allow us to represent and understand in a more detailed way how change occurs in time, in this case, in the process of understanding the visual metaphor in children from 3 to 4 years of age. [ABSTRACT FROM AUTHOR]

Epilepsy is a common neurological disorder, with one third of patients not responding to currently available antiepileptic drugs. The proportion of pharmacoresistant epilepsies has remained unchanged for many decades. To cure epilepsy and control seizures requires a paradigm shift in the development of new approaches to epilepsy diagnosis and treatment. Contemporary medicine has benefited from the exponential growth of computational modeling, and the application of network dynamics theory to understanding and treating human brain disorders. In epilepsy, the introduction of these approaches has led to personalized epileptic network modeling that can explore the patient's seizure genesis and predict the functional impact of resection on its individual network's propensity to seize. The application of the dynamic systems approach to neurostimulation therapy of epilepsy allows designing stimulation strategies that consider the patient's seizure dynamics and long‐term fluctuations in the stability of their epileptic networks. In this article, we review, in a nontechnical fashion suitable for a broad neuroscientific audience, recent progress in personalized dynamic brain network modeling that is shaping the future approach to the diagnosis and treatment of epilepsy. [ABSTRACT FROM AUTHOR]

In this work, a dynamic analysis describing the charge and discharge process of a supercapacitor for the DC-link between a photovoltaic source and a constant power load is presented. The analysis results in a complete nonlinear and dynamic model that can be used for simulation and control for DC–DC converters, achieving fast recharge times and accurate steady-state voltages in the DC link to avoid overcharging the supercapacitor during low power absorption scenarios. The proposed approach includes parasitic elements for the supercapacitor and efficiency effects on the conversion stage, proposing equations useful for design and control. Stability is also discussed for the charge process of the supercapacitor. Validation of the analytical model is performed by comparison with LTSpice simulation, confirming a good agreement between theory and simulation. [ABSTRACT FROM AUTHOR]

Linear dynamic systems with impulsive effects are considered. For such a system we define a new impulsive exponential matrix. Necessary and sufficient conditions for exponential stability and boundedness have been established. The fundamental tool is an impulsive exponential matrix for exponential stability. [ABSTRACT FROM AUTHOR]

The suggested methods for solving fault diagnosis and estimation problems are based on the use of the Jordan canonical form. The diagnostic observer, virtual sensor, interval, and sliding mode observer design problems are considered. Algorithms have been developed to solve these problems for both linear and nonlinear systems, considering the presence of external disturbances and measurement noise. It has been shown that the Jordan canonical form allows reducing the dimensions of interval observers and virtual sensors, thus simplifying the design process in comparison to the identification canonical form. The theoretical results are illustrated through examples. [ABSTRACT FROM AUTHOR]

The appearance and disappearance of the optimal solution for the change of system parameters in optimization theory is a fundamental problem. This paper aims to address this issue by transforming the solutions of a constrained optimization problem into equilibrium points (EPs) of a dynamical system. The bifurcation of EPs is then used to describe the appearance and disappearance of the optimal solution and saddle point through two classes of bifurcation, namely the pseudo bifurcation and saddle-node bifurcation. Moreover, a new class of pseudo-bifurcation phenomena is introduced to describe the transformation of regular and degenerate EPs, which sheds light on the relationship between the optimal solution and a class of infeasible points. This development also promotes the proposal of a tool for predicting optimal solutions based on this phenomenon. The study finds that the bifurcation of the optimal solution is closely related to the bifurcation of the feasible region, as demonstrated by the 5-bus and 9-bus optimal power flow problems. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurodegenerative disorder and is characterized by the degeneration of the dopamine (DA) neurons in the substantia nigra pars compacta, leading to a loss of DA in the basal ganglia. The presence of aggregates of alpha-synuclein (α-synuclein) is seen as the main contributor to the pathogenesis and progression of PD. Evidence suggests that the secretome of mesenchymal stromal cells (MSC) could be a potential cell-free therapy for PD. However, to accelerate the integration of this therapy in the clinical setting, there is still the need to develop a protocol for the large-scale production of secretome under good manufacturing practices (GMP) guidelines. Bioreactors have the capacity to produce large quantities of secretomes in a scalable manner, surpassing the limitations of planar static culture systems. However, few studies focused on the influence of the culture system used to expand MSC, on the secretome composition. In this work, we studied the capacity of the secretome produced by bone marrow-derived mesenchymal stromal cells (BMSC) expanded in a spinner flask (SP) and in a Vertical-Wheel™ bioreactor (VWBR) system, to induce neurodifferentiation of human neural progenitor cells (hNPCs) and to prevent dopaminergic neuron degeneration caused by the overexpression of α-synuclein in one Caenorhabditis elegans model of PD. Results showed that secretomes from both systems were able to induce neurodifferentiation, though the secretome produced in the SP system had a greater effect. Additionally, in the conditions of our study, only the secretome produced in SP had a neuroprotective potential. Lastly, the secretomes had different profiles regarding the presence and/or specific intensity of different molecules, namely, interleukin (IL)-6, IL-4, matrix metalloproteinase-2 (MMP2), and 3 (MMP3), tumor necrosis factor-beta (TNF-β), osteopontin, nerve growth factor beta (NGFβ), granulocyte colony-stimulating factor (GCSF), heparin-binding (HB) epithelial growth factor (EGF)-like growth factor (HB-EGF), and IL-13. Overall, our results suggest that the culture conditions might have influenced the secretory profiles of cultured cells and, consequently, the observed effects. Additional studies should further explore the effects that different culture systems have on the secretome potential of PD. [ABSTRACT FROM AUTHOR]

Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models. In the present paper, we propose a realization of HODMD that is based on the low-rank tensor decomposition of potentially high-dimensional datasets. It is used to compute the HODMD modes and eigenvalues to effectively reduce the computational complexity of the problem. The proposed extension also provides a more efficient realization of the ordinary dynamic mode decomposition with the use of the tensor-train decomposition. The high efficiency of the tensor-train-based HODMD (TT-HODMD) is illustrated by a few examples, including forecasting the load of a power system, which provides comparisons between TT-HODMD and HODMD with respect to the computing time and accuracy. The developed algorithm can be effectively used for the prediction of high-dimensional dynamical systems. [ABSTRACT FROM AUTHOR]

Considering that chaotic systems are immersed in multiple areas of science and nature and that their dynamics are governed by a great sensitivity to the initial conditions and variations in their parameters, it is of great interest for the scientific community to have tools to characterize and reproduce these trajectories. Two dynamic chaotic systems whose equations are based on the jerky system are used as benchmarks, i.e., the Memristive Shaking Chaotic System (MSCS) and the Unstable Dissipative System of type I (UDSI). One characteristic common to them is their simple mathematical structure and the complexity of their solutions. Therefore, this paper presents a strategy for identifying chaotic trajectories using a recurrent wavelet first-order neural network (RWFONN) that is trained online with an error filtering algorithm and considering the Morlet-wavelet as an activation function. The parameters of the network are adjusted considering the Euclidean distance between the solutions. Finally, the results depict proper identification of the chaotic systems studied through analysis and numerical simulation to validate the behavior and functionality of the proposed network. [ABSTRACT FROM AUTHOR]

A characterization of the migratory phenomenon is presented, from the macro level with implications for public policies, from its causes, effects, using dynamic systems, non-linear and complex differential equation systems at the macro, institutional, meso-organizational level and as a complete system through level of interaction of individual networks. A mathematical model of differential equations is presented to explain the relationship between migration and organizations, to explain the complex phenomenon of migration, and the complexity of the dynamic system. [ABSTRACT FROM AUTHOR]

In this article, we present a dynamic systems theoretical and methodological approach to investigating joint attention in early childhood (EC) centres. Joint attention, defined as a social interaction in which child and caregiver share a common focus of attention, is an important interactive context for collaborative learning. We overview the principles of dynamics systems theory and explain how these principles conceptualise joint attention as emerging from synchronised, 'in-the-moment' interactions between child and educator. Drawing on data from a project investigating the dynamic nature of joint attention in infant EC rooms, state space grids (SSGs) are then presented as a research tool that graphically captures and represents the mutually influencing contributions of educator, infant and the environment. We conclude by considering other ways to use SSG analysis to provide a more nuanced understanding of the dynamics of educator child interactions in EC settings. [ABSTRACT FROM AUTHOR]

Smooth social interaction requires interindividual coordination. This Theory Section addresses the nature of the processes involved and the potential dangers of malfunctioning coordination. In her invited article, Butler provides a general overview of the processes involved, including interpersonal synchronisation, and advocates a dynamic systems framework for further research. In their commentary, Carré and Cornejo concur in principle but highlight the importance of the meaning attributed to the spontaneous expressive movements in naturally occurring interactions and the nature of the respective social situations. Suhay, in her commentary from a political science perspective, highlights the need for synergy based on social coordination for successful democratic governing. In commenting on the problem of political polarisation because of malfunctioning social coordination, introduced by Butler, Suhay adds the important role played by social identity and emotion. Of particular importance are affective reactions based on "evaluative biases" in favour of information that bolsters one's views and rejection of information that challenges them. I conclude this editorial by referring to recent data on affective polarisation, pleading for more multidisciplinary research on the phenomenon, and arguing for a more substantial contribution from the cognitive and emotion sciences. In particular, I outline the "predictive coding" framework and the potential contributions from emotion science. [ABSTRACT FROM AUTHOR]

Coordinated group behaviour can result in conflict or social cohesion. Thus having a better understanding of coordination in social groups could help us tackle some of our most challenging social problems. Historically, the most common way to study group behaviour is to break it down into sub-processes, such as cognition and emotion, then ideally manipulate them in a social context in order to predict some behaviour such as liking versus distrusting a target person. This approach has gotten us partway to understanding many important collective behaviours, but I argue that making major changes in the world will require a more integrated approach. In this review, I introduce dynamic systems theory, with a focus on interpersonal systems, where all the processes we typically study in individuals, such as cognition and emotion, become intertwined between social partners over time. I focus on the concept of coordination, defined as a temporal correlation between interacting components of a system (or systems) arising due to coupling between them. Finally, I show how this perspective could be used to guide investigations of social problems such as polarisation. [ABSTRACT FROM AUTHOR]

The propensity to focus attention inwards is fundamental to human mental life and internally-directed cognition (IDC) [e.g., mindwandering, (mal)adaptive self-reflection]. Yet, understanding of the mechanisms through which internal attention shapes IDC is limited. We argue that understanding the systemic complexity and dynamics of how internal attention interacts with other cognitive processes can significantly facilitate our capacity to predict and model (mal)adaptive IDC. We, therefore, introduce the Attention-to-Thoughts model—a dynamic systems theory and computational model of internal attention in IDC. Through the model we aim to, first, conceptually and computationally define momentary states of this dynamic system; second, simulate and predict differential temporal trajectories of this dynamic system through which IDC emerges. Through experimental simulations, we explore how Attention-to-Thoughts may be used to better understand how internal attention selection is expressed from moment-to-moment; how internal attention unfolds by documenting how, as a function of contextual demands for focused attention, internal attentional selection iteratively transacts with working-memory and emotion; and, in turn, how maladaptive IDC (e.g., repetitive negative thinking, cognitive dyscontrol) emerges from temporal trajectories of the dynamic system of internal attention. Finally, we highlight key conceptual, computational, and methodological directions for the study of internal attention, IDC, and related phenomena (e.g., mindfulness). [ABSTRACT FROM AUTHOR]

Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far the systems that LFIT handled were mainly restricted to synchronous deterministic dynamics. However, other dynamics exist in the field of logical modeling, in particular the asynchronous semantics which is widely used to model biological systems. In this paper, we propose a modeling of discrete memory-less multi-valued dynamic systems as logic programs in which a rule represents what can occur rather than what will occur. This modeling allows us to represent non-determinism and to propose an extension of LFIT to learn regardless of the update schemes, allowing to capture a large range of semantics. We also propose a second algorithm which is able to learn a whole system dynamics, including its semantics, in the form of a single propositional logic program with constraints. We show through theoretical results the correctness of our approaches. Practical evaluation is performed on benchmarks from biological literature. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to model plural nominal concordance errors for a Spanish corpus of four learners of Spanish as a foreign language: SONIA (level A1/A2), NATI (level B1), JAKO (level B2), MIRKA (level C1). In the framework of dynamical systems, it is postulated that "correct" and "incorrect" concordances constitute stable discrete attractors to which the continuous flow of the system converges in three simple simulation models: Lokta-Volterra, coordination game (evolutionary games theory), gradient ascent. As a result, the models better approximate the global error pattern than the session pattern. In the latter, the model based on Lokta-Volterra offers better performance for SONIA, NATI and JAKO. MIRKA obtains the worst performance in all the models, but its error decreases with the gradient ascent model. It is concluded that learning patterns are self-organized and emerge from the dynamics of concordance microsystems. [ABSTRACT FROM AUTHOR]

Roll-to-roll (R2R) dry transfer is an important process for manufacturing of large-scale two-dimensional (2D) materials and printed flexible electronics. Existing research has demonstrated the feasibility of dry transfer of 2D materials in a roll-to-roll setting with mechanical peeling. However, the process presents a significant challenge to system control due to the lack of understanding of the mechanical peeling behavior and the complexity of the nonlinear system dynamics. In this study, an R2R peeling process model is developed to understand the dynamic interaction among the peeling process parameters, including adhesion energy, peeling force, angle, and speed. Both simulation and experimental studies are conducted to validate the model. It is shown that the dynamic system model can capture the transient behavior of the R2R mechanical peeling process and be used for the process analysis and control design. [ABSTRACT FROM AUTHOR]

Recent work has shown that people use temporal information including order, delay, and variability to infer causality between events. In this study, we build on this work by investigating the role of time in dynamic systems, where causes take continuous values and also continually influence their effects. Recent studies of learning in these systems explored short interactions in a setting with rapidly evolving dynamics and modeled people as relying on simpler, resource-limited strategies to grapple with the stream of information. A natural question that arises from such an account is whether interacting with systems that unfold more slowly might reduce the systematic errors that result from these strategies. Paradoxically, we find that slowing the task indeed reduced the frequency of one type of error, albeit at the cost of increasing the overall error rate. To explain these results we posit that human learners analyze continuous dynamics into discrete events and use the observed relationships between events to draw conclusions about causal structure. We formalize this intuition in terms of a novel Causal Event Abstraction model and show that this model indeed captures the observed pattern of errors. We comment on the implications these results have for causal cognition. [ABSTRACT FROM AUTHOR]

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The article proposes a new nonlinear mathematical model that describes the process of possibility (instability of the state) or impossibility (stability of the state) of separating the region from the state. The model is described by the Cauchy problem for a nonlinear two-dimensional dynamic system. The model assumes that only two categories of citizens live in a particular region of a state: the first category, which is a supporter of the center (unionists) and opposes the secession of the region; the second - supporters of the separation of the region (separatists), i.e. its separation from the center, in order to form a new independent state. The general mathematical model implies the presence of both federal and external sides, which influence the separatists and unionists, respectively, by various factors, in order to change their opinion (will). Natural conditions are proposed under which secession of the region is considered possible (for example, the presence of a majority or qualified majority of citizens of the region who support separatism). In the case of variable coefficients of the model, under some restrictions, in quadratures, an exact solution to the Cauchy problem was found for a two-dimensional dynamic system, and in the case of constant coefficients, conditions were found under which secession of the region is impossible. In the particular case, the absence of external stakeholders for the region, in the case of constant coefficients and opposite values of demographic factors of the sides, the problem is actually reduced to a predator-victim model and is described by a nonlinear two-dimensional dynamic system of the Lotka-Volterra type. At the same time, conditions were found for the coefficients of attracting opponents to allies, demographic factors and baseline conditions under which it is impossible to separate the region. The article also proposes a new nonlinear mathematical model that describes in a certain politically conflicting region of the state the presence of three groups of the population with different political priorities. One part of the population (unionists) is politically oriented towards preserving the region as part of the state, the second part of the region supports the idea of separatism, separation of the region from the state in order to form a new independent state (separatists), and the third part of the region supports the idea of irredentism of the region, that is, separation in order to join another, possibly bordering state (irredentists). Weak (a simple majority of the region's population) and strong (a qualified majority of the region's population) conditions are offered, which in a legal sense may not have direct consequences, but may determine the aspirations of the majority of the region's population. The model is described by a nonlinear three-dimensional dynamic system with variable coefficients. With some constraints on model parameters, accurate analytical solutions are found. Additional conditions were also found under which: the region remains within the state; possible secession of the region; the irredentism of the region is possible, that is, its accession to another state. [ABSTRACT FROM AUTHOR]

Copyright of Epistemus. Journal of Music, Cognition & Culture Research is the property of Facultad de Psicologia Universidad Autonoma de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article provides a conceptual commentary on the special series on potential burnout of teachers of students with emotional and behavioral difficulties (EBD). The issue of teacher burnout is considered through a dynamic, ecological systems theory framework to summarize the complexities of working with students with EBD and to highlight the importance of addressing their needs from a whole child in context perspective. Current limits of eividence-based practices, service delivery structures, and research approaches are also considered and possible steps forward are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we used Lyapunov theory and Linear Matrix Inequalities (LMI) to design a generalized observer by adding more complexity in the output of the dynamic systems. Our designed observer is based on the optimization problem, minimizing error between trajectories of master and slave systems subject to the incremental quadratic constraint. Moreover, an algorithm is given in our paper used to demonstrate a method for obtaining desired observer and gain matrixes, whereas these gain matrixes are obtained with the aid of LMI and incremental multiplier matrix (I M M) . Finally, discussion of two examples are an integral part of our study for the explanation of achieved analytical results using MATLAB and SCILAB. [ABSTRACT FROM AUTHOR]