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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]

This study examined how temporal associations between parents' physiological and behavioral responses may reflect underlying regulatory difficulties in at-risk parenting. Time-series data of cardiac indices (second-by-second estimates of inter-beat intervals – IBI, and respiratory sinus arrhythmia – RSA) and parenting behaviors were obtained from 204 child welfare-involved parents (88% mothers, M age = 32.32 years) during child-led play with their 3- to 7-year-old children (45.1% female; M age = 4.76 years). Known risk factors for maltreatment, including parents' negative social cognitions, mental health symptoms, and inhibitory control problems, were examined as moderators of intra-individual physiology-behavior associations. Results of ordinary differential equations suggested increases in parents' cardiac arousal at moments when they showed positive parenting behaviors. In turn, higher arousal was associated with momentary decreases in both positive and negative parenting behaviors. Individual differences in these dynamic processes were identified in association with parental risk factors. In contrast, no sample-wide RSA-behavior associations were evident, but a pattern of increased positive parenting at moments of parasympathetic withdrawal emerged among parents showing more total positive parenting behaviors. This study illustrated an innovative and ecologically-valid approach to examining regulatory patterns that may shape parenting in real-time and identified mechanisms that should be addressed in interventions. [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]

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns. [ABSTRACT FROM AUTHOR]

Abstract In complex systems, the system’s self-regulation processes can transition between states of equilibrium and disequilibrium, leading to changes in the distribution of players within the playing space. Actions that are surprising, rare, or out of the ordinary tend to be valued for their potential to destabilize the opposing defensive structure, altering player dispersion, and creating fragile spaces for the attack. With advancements in understanding the influence of the environment on players’ affordances, the tactical consequences from individuals’ solutions to various scenarios and their impact on the game context becomes a rich area for investigation. This study compared defensive dispersion in the moments preceding and following original actions in small-sided games. The original actions were obtained using the Creative Behavior Assessment in Team Sports (CBATS) observational matrix. At the same time, defensive dispersion was derived from positional data collected via GPS and processed using dedicated routines in MATLAB to obtain variables such as Stretch Index, Surface Area, Team Width, Team Length, and Individual Player Area. The Kolmogorov-Smirnov test was employed for normality, and the General Linear Model with Repeated Measures was used to compare pre-and post-action moments across different action classifications (pass and shot) and game configurations (SSG4v4 − Small, SSG4v4 − Large, SSG5v5 − Small, and SSG5v5 − Large). Differences were found in the Stretch Index, Surface Area, and Team Width variables in SSG4v4 − Small and Surface Area in SSG5v5 − Small (p

Background: The Industrial Internet of Things (IIoT) has revolutionized operations for businesses, and fog computing is a valuable resource management and job scheduling tool that has facilitated the transformation. In this regard, efficient resource usage will be more useful for the performance and energy cost-saving limit of IIoT system. Objective: The article proposed a new energy-aware metaheuristic approach to enhancing the performance and efficiency of IIoT systems in fog computing environments. The study aims to come up with a methodology that strikes a balance between efficiency in compute requirements and energy consumption. Methodology: A meta-heuristic method inspired by natural processes such as genetic algorithms and simulated annealing is applied to optimize the selection of which jobs should be scheduled. This approach takes into account several parameters like when the job is needed, availability of resources, and usage patterns to efficiently schedule jobs across the network. Results: The results show that the proposed approach drastically improves energy efficiency and system performance. In this paper, the fog orchestration master intends to divide the workload between fog and cloud in an excellent manner and resolve specific Issues of fog computing in IIoT environment. This manages to keep energy usage low and the operating efficiency high. Conclusions: Metaheuristic optimization techniques integrate into fog computing environments for IIoT job schedule complexity. This methodology enhances the sustainability of IIoT operations, and their ability to meet robust performance requirements over time. Our findings provide the crucial insight needed to enable industries that need seamless IIoT integration and plan further research in the field of energy-efficient fog computing.

monitoring, and control rely on precise dynamic models that can capture the inherent nonlinearities of chemical systems. However, rigorous modeling of complex industrial processes can be computationally demanding. Meta modeling using machine learning methodologies offers a viable approach to generate computationally efficient surrogate representations. Specifically, Echo State Networks (ESNs) are a promising neural network approach for meta-modeling nonlinear dynamical systems. ESNs simplify training through fixed input weights while they focus learning on output weights. This study explores the development of ESN-based digital twins for a nonlinear dynamic process. An ESN is employed to construct a meta-model of a simulated continuously stirred tank reactor with biochemical kinetic. The network was trained on input-output data obtained from the simulation of an ordinary differential equation system, and the performance was evaluated both in-sample and out-of-sample. The results indicate that the ESN meta-model can successfully approximate the underlying dynamics, accurately capturing temporal evolution. A closed-loop digital twin deployment using the ESN surrogate also showed reliable behavior. This work presents initial steps toward developing digital twins of chemical processes using ESN-driven meta-modeling. The findings suggest ESNs can effectively generate computationally efficient surrogate representations of nonlinear dynamical systems. Such digital twins hold promise for online process monitoring and optimized control of industrial plants.

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]

Sustainability is an inherently complex problem. Often, a reductionist mindset underpins corporate sustainability practices. Understanding the collective impact of systems thinking and paradoxical cognition on sustainability practices could foster new cognitive strategies for sustainability efforts. Informed by stakeholder theory, this study investigates the impacts of systems thinking and paradoxical cognition on sustainability practices in small and medium‐sized enterprises (SMEs). Primary data was collected through a survey of SME managers in the United Arab Emirates (n = 554), and the hypothesized model was analyzed using structural equation modelling. The findings imply that systems thinking, paradoxical thinking, and the ability to recognize paradoxical tensions positively influenced sustainability practices. The study's findings offer novel insights advocating for the integration of cognitive frameworks and sustainability practices in the context SMEs, highlighting the need for a shift from traditional linear management approaches to more adaptive and integrative strategies. [ABSTRACT FROM AUTHOR]

A model for determining the oil market transferability turmoil on the financial markets of the Iranian economy using the dynamic systems approach. At first, data related to oil, gold, stock exchange and foreign exchange were extracted from statistics related to the World Bank, Central Bank and Statistics Center of Iran and were analyzed with statistical analysis and simulation software. Then the research model was constructed using simulation methods and system analysis and the results were analyzed. The oil market in supply and demand for price determination is based on global systemic behavior. this simulation has used the factors affecting oil supply, oil demand, the expectations that shape this supply and demand, as well as macro factors such as macroeconomic indicators of the US economy, sanctions on the oil sector in Iran, the rate of world industry development and the available knowledge on oil substitution. Hidden mechanisms are the main reason for some oil price behaviors. The results of the research have led to the forecast of oil prices in the baseline scenario until 2025. The presence of political problems due to the interconnectedness of parallel markets in Iran causes widespread fluctuations in the currency and gold sectors in the Iranian economy. [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]

Controlling the dissemination of fake news is paramount for societal well-being. This paper explores how to govern fake news on social media, considering the reputation of both government and social media platform, reward and punishment mechanisms, as well as the associated costs and benefits from the perspective of evolutionary game theory. Within the game, the government can opt for supervision or no supervision, the social media platform can decide whether to respond to government strategies actively or not, while individuals can choose to forward or abstrain from forwarding fake news. The results show that the system will stabilize in three states contingent upon initial parameters: (not forward, not active, supervise), (forward, not active, supervise), and (forward, active, supervise). Key factors influencing the governance of fake news are also identified. Individually, reducing the benefits of forwarding fake news and increasing penalties from both the government and social media can discourage individuals from sharing fake news. On the social media level, minimizing the cost of their responses to government regulation, decreasing the revenue they gain from individual retweeting of fake news, and increasing the benefits of their responses to government policies can incentivize proactive social media responses. At the government level, enhancing rewards for social media compliance with government policies and enforcing penalties for non-compliance can motivate social media to align with regulatory strategies. The findings in this paper provide valuable insights into governing fake news. [ABSTRACT FROM AUTHOR]

There has been significant interest and progress in understanding the role of caregiver unpredictability on brain maturation, cognitive and socioemotional development, and psychopathology. Theoretical consensus has emerged about the unique influence of unpredictability in shaping children's experience, distinct from other adverse exposures or features of stress exposure. Nonetheless, the field still lacks theoretical and empirical common ground due to difficulties in accurately conceptualizing and measuring unpredictability in the caregiver–child relationship. In this paper, we first provide an overview of the role of unpredictability in theories of caregiving and childhood adversity and present four issues that are currently under-discussed but are crucial to the field. Focusing on how moment-to-moment and day-to-day dynamics are at the heart of caregiver unpredictability, we review three approaches aiming to address some of these nuances: Environmental statistics, entropy, and dynamic systems. Lastly, we conclude with a broad summary and suggest future research directions. Systematic progress in this field can inform interventions and policies aiming to increase stability in the lives of children. [ABSTRACT FROM AUTHOR]

Soil fertility depletion presents a negative feedback mechanism that could have impacted early adopters of agriculture. We consider whether such feedback can lead to population cycles among early agriculturalists, such as the boom-and-bust patterns suggested by an increasing amount of evidence for Neolithic Europe. Using general mathematical arguments, we show that this is unlikely, due to the interplay of two factors. First, there is an important mathematical difference between biotic (i.e., logistic) and abiotic resource replenishment; soil nutrients are better modeled by the abiotic case, which leads to more stable dynamics. Second, under realistic conditions, the resource replenishment process operates on fast time scales compared to attainable population growth rates, reinforcing the tendency towards stable dynamics. Both these factors are relevant for early agricultural societies and imply that nutrient depletion is likely not the main contributing factor to boom-and-bust cycles observed in the archaeological record. [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]

Summary: This research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others. [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]

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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.)

Spread of novel coronavirus and other flu-like illnesses, periodically causing increased death and morbidity rates, places pressures on national health systems. In order to provide a reliable long-term forecast of the new coronavirus infection rate, this research employs novel Gaidai bio-system reliability technique, especially suitable for multi-regional biological, environmental and public health systems. The goal of this study was to directly apply state of art statistical techniques to unprocessed raw clinical data, utilizing a multicenter, population-based biostatistical methodology. Epidemiological risks have been accurately forecasted, specifically for European Union member states. Based on their clinical survey data, suggested spatiotemporal methodology may be applied in a variety of public biological and health applications.

Modal truncation, an advanced algorithm for model reduction in dynamic systems, efficiently simplifies complex models by selectively discarding less influential eigenmodes, maintaining a balance between computational efficiency and model accuracy. This paper explores the algorithm's application to a 48th order building model. Proceed to reduce this model to lower orders, then analyze errors in time and frequency domains. Modal truncation algorithm systematically reduces model dimensions while preserving critical dynamic attributes. Numerical simulations reveal a favorable reduction order range (from order 6th to order 25th) for optimal balance, with sensitivity observed at order 25th. From the results obtained, depending on specific requirements, users can use a lower-order model corresponding to the allowed error to replace the original system. Recommendations include iterative refinement for adaptive reduction orders and in-depth analysis around critical points. This algorithm becomes an effective method for researchers dealing with high-dimensional dynamic systems, offering simpler yet accurate model representations. As technology develops, continued refinements and applications of modal truncation are expected, solidifying its role in the realm of model reduction.

Abstract Background Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems. Methods In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state. Results The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called “dark genes” that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis. Conclusions The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

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]

This study examined the reciprocal association between counseling trainees' trait and state anxiety and their clients' symptom distress and the mediating effects of working alliance. Data set included 6,888 sessions conducted by 211 master's level beginning therapists with their 1,216 clients in China. Trainees completed a measure of trait anxiety at the beginning of practicum. Before each session, trainees completed the state anxiety measure and clients reported their symptom distress; and after every session, both the trainees and clients rated their perceived working alliance. Multilevel modeling showed that (a) Therapist trainees' level of trait anxiety did not predict overall client outcome in terms of symptom reduction. (b) At the session-to-session level, higher therapist state anxiety before one session significantly predicted higher client distress before the next session, and higher client distress before one session significantly predicted higher therapist state anxiety before the subsequent session. (c) Therapist and client perceptions of working alliance were both significant mediators of the reciprocal relationship between therapist state anxiety and client distress. Findings suggested that it was trainees' state anxiety, instead of trait anxiety, that reciprocally related to client symptom outcome at the session-to-session level. Implications for clinical training were discussed. PRACTICAL IMPLICATIONS A generally more anxious therapist trainee does not seem to have worse overall clinical effectiveness compared with a generally less anxious trainee. From session to session, trainees who experience less state anxiety tend to establish a stronger working alliance with their clients, which leads to more client symptom relief. Evidence suggests against selecting trainees based on their trait anxiety level; but it is recommended that supervisors help trainees manage their state anxiety before a session. [ABSTRACT FROM AUTHOR]

The ranking of nodes in a network according to their centrality or ''importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The COVID-19 pandemic has recently rejuvenated the interest in this problem, as the ranking may be used to decide who should be tested, or vaccinated, first, in a population of asymptomatic individuals. In this paper, we review classic methods for node ranking and compare their performance in a benchmark network that considers the community-based structure of society. The outcome of the ranking procedure is then used to decide which individuals should be tested, and possibly quarantined, first. Finally, we also review the extension of these ranking methods to weighted graphs and explore the importance of weights in a contact network by providing a toy model and comparing node rankings for this case in the context of disease spread. [ABSTRACT FROM AUTHOR]

The problem of designing interval parity relations to solve the problem of fault detection in stationary systems described by linear and nonlinear dynamic models under external disturbances is studied. The solution is based on a model of the original system that has the minimal dimension, estimates some linear function of the system's output vector, and is insensitive or minimally sensitive to disturbances. The results obtained allow designing interval parity relations based on which the problem of diagnosing faults is solved. The theoretical results are illustrated by an example. [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]

In the present study, we employed a multi-method approach to study the process by which mothers of preschool-aged children actively socialize complex emotions. Within the context of a dynamic systems framework, we examined the associations between maternal meta-emotion philosophies, via an interview-based assessment (the Meta-Emotion Interview, MEI), and the use of emotion words mapped onto a state-space grid during a narrative storytelling task. Mothers were asked to read an emotionally evocative, wordless picture-story book in a comfortable setting in their homes to their preschool-aged child. All interactions were audio recorded, transcribed verbatim, and categories of negative and positive emotion words, and their behavioural expressions were coded from a rubric by independent raters with a 96% agreement rate. We found that higher levels of maternal awareness of emotion and emotion coaching strategies on the MEI were correlated with a wide array of positive and negative emotion word usage and behavioral expressions of emotion during the storytelling task, as well as with more flexible, fluid, and diverse state-space grid profiles. These results are consistent with meta-emotion theory and suggest that meta-emotion philosophies have an implicit influence on the ways in which caregivers socialize emotions in natural settings. We argue that an SSG methodology adds an innovative dimension to the study of meta-emotion and emotion socialization. Highlights: Examined emotion socialization with the use of a state space grid (SSG) methodology. Emotion awareness and coaching strategies correlated with more diverse SSGs. These results were most pronounced when exploring negative emotions. SSGs add a novel approach for integrating meta-emotion and emotion socialization. [ABSTRACT FROM AUTHOR]

Background: Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems. Methods: In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state. Results: The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis. Conclusions: The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis. [ABSTRACT FROM AUTHOR]

The present work discusses the application of damping for friction-influenced dynamic systems to inhibit stick-slip vibrations. The stick-slip can be eliminated from the system with an appropriate selection of system parameters like lower friction coefficient, lower value of the ratio of static to Coulomb friction, and higher driving velocity. Sometimes due to functional constraints, these parameters cannot be selected as per the will. The alternate approach is to embed the system with proper damping to ensure dynamics without stick-slip vibrations. Present work encapsulates this aspect viz. numerical simulation of a 1 DOF system by incorporating damping to obtain the responses in terms of displacement and velocity versus time plots. The study presents a noble approach to determine the value of damping critical in ensuring the stick-slip free vibrations. [ABSTRACT FROM AUTHOR]

Many complicated dynamical events may be broken down into simpler pieces and efficiently described by a system that shifts among a variety of conditionally dynamical modes. Building on switching linear dynamical systems, we develop a new model that extends the switching linear dynamical systems for better discovering these dynamical modes. In the proposed model, the linear dynamics of latent variables can be described by a higher-order vector autoregressive process, which makes it feasible to evaluate the higher-order dependency relationships in the dynamics. In addition, the transition of switching states is determined by a stick-breaking logistic regression, overcoming the limitation of a restricted geometric state duration and recovering the symmetric dependency between the switching states and the latent variables from asymmetric relationships. Furthermore, logistic regression evidence potentials can appear as conditionally Gaussian potentials by utilizing the Pólya-gamma augmentation strategy. Filtering and smoothing algorithms and Bayesian inference for parameter learning in the proposed model are presented. The utility and versatility of the proposed model are demonstrated on synthetic data and public functional magnetic resonance imaging data. Our model improves the current methods for learning the switching linear dynamical modes, which will facilitate the identification and assessment of the dynamics of complex systems. [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]

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This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

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.

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, r2) 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.

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]