Search

Prior work has demonstrated the presence of hysteresis effects in the control of affordance-guided behavior, in that behavioral transitions around a critical action boundary vary with directions of change in said action boundary. To date, research on this topic has overlooked the influence of the global context on these phenomena. We employ an affordance-based reaching task, whereby participants were asked to move a target to a goal by passing through one of two apertures (size variable or size constant). It was found that the direction of change in the size of the variable aperture influenced the point of behavioral transitions, and this effect interacted with the location of a given goal. In addition, we considered fluctuations in the entropy of participants' reach trajectories as a window into the nature of the behavioral phase transitions. Differences in the structure of entropy were found depending on the direction of change in the size variable aperture. These results are discussed in light of a dynamical systems approach, and recommendations for future work are made. [ABSTRACT FROM AUTHOR]

The temperature dependence of spectra can reveal important insights into the structural and dynamical behavior of the system being probed. In the case of linear spectra, this has been exploited to investigate the thermodynamic driving forces governing the spectral response. Indeed, the temperature derivative of a spectrum can be used to obtain effective energetic and entropic profiles as a function of the measured frequency. The former can further be used to predict the temperature-dependent spectrum via a van't Hoff relation. However, these approaches are not directly applicable to nonlinear, complex-valued spectra, such as vibrational sum-frequency generation (SFG) or two-dimensional infrared (2D-IR) photon echo spectra. Here, we show how the energetic and entropic driving forces governing such nonlinear spectra can be determined and used within a generalized van't Hoff relation to predict their temperature dependence. The central idea is to allow the underlying energetic profiles to themselves be complex-valued. We illustrate this approach for 2D-IR spectra of water and SFG spectra of the air–water interface and demonstrate the accuracy of the generalized van't Hoff relationship and its implications for the origin of temperature-dependent spectral changes. [ABSTRACT FROM AUTHOR]

The authors previously developed a divide-and-conquer (DC)-based non-local excited-state calculation method for large systems using dynamical polarizability [Nakai and Yoshikawa, J. Chem. Phys. 146, 124123 (2017)]. This method evaluates the excitation energies and oscillator strengths using information on the dynamical polarizability poles. This article proposes a novel analysis of the previously developed method to obtain further configuration information on excited states, including excitation and de-excitation coefficients of each excitation configuration. Numerical applications to simple molecules, such as ethylene, hydrogen molecule, ammonia, and pyridazine, confirmed that the proposed analysis could accurately reproduce the excitation and de-excitation coefficients. The combination with the DC scheme enables both the local and non-local excited states of large systems with an excited nature to be treated. [ABSTRACT FROM AUTHOR]

Liquid–liquid phase separation in biology has recently been shown to play a major role in the spatial control of biomolecular components within the cell. However, as they are phase transitions, these processes also display nontrivial dynamics. A model phase-separating system of DNA nanostars provides unique access to nucleation physics in a biomolecular context, as phase separation is driven near room temperature by highly thermo-responsive DNA hybridization and at modest DNA concentrations. By measuring the delay time for phase-separated droplets to appear, we demonstrate that the dynamics of DNA nanostar phase separation reflect that of a metastable binary mixture of patchy particles. For sufficiently deep temperature quenches, droplets undergo spinodal decomposition and grow spontaneously, driven by Brownian motion and coalescence of phase-separated droplets, as confirmed by comparing experimental measurements to particle-based simulations. Near the coexistence boundary, droplet growth slows substantially, indicative of a nucleation process. The temperature dependence of droplet appearance times can be predicted by a classical nucleation picture with mean field exponents and demonstrates that a theory previously used to predict equilibrium phase diagrams can also distinguish spinodal and nucleation dynamical regimes. These dynamical principles are relevant to behaviors associated with liquid–liquid phase separating systems, such as their spatial patterning, reaction coupling, and biological function. [ABSTRACT FROM AUTHOR]

We present comprehensive numerical results from a study of model H, which describes phase separation kinetics in binary fluid mixtures. We study the pattern dynamics of both density and velocity fields in d = 2, 3. The density length scales show three distinct regimes, in accordance with analytical arguments. The velocity length scale shows a diffusive behavior. We also study the scaling behavior of the morphologies for density and velocity fields and observe dynamical scaling in the relevant correlation functions and structure factors. Finally, we study the effect of quenched random field disorder on spinodal decomposition in model H. [ABSTRACT FROM AUTHOR]

Product service system (PSS) is an effective approach to achieve win–win situation between service providers and consumers, and the design of PSS is the first step of its application. However, on the one hand, PSS is relatively an abstract concept, and what exactly should be considered when designing a PSS still require further exploration; on the other hand, the fast development of information and web technologies bring both opportunities and challenges for PSS design. For example, how to efficiently design the smart & connected products that support remote monitoring and control of service operation, how to build the dynamic service activity flow model that can be intuitively and visually read by human engineers and also can be conveniently deployed on computers, and how to realise edge–cloud collaboration between service providers and consumers. In this regard, a service requirement-oriented four-step generic PSS design method is established, including service mode selection and structured service order generation → smart & connected service product configuration → dynamic event-state knowledge graph-based service activity flow and service resource network configuration → industrial internet-based edge–cloud collaborative service delivering. Finally, a real design case of carbon block grinding and polishing PSS is used for verification. ABBREVIATIONS: AS: assembly service; C: controlling targets or objectives; I: input; M: enabling methods or mechanisms; MRO: maintenance, repair, and operation; MS: maintenance service; O: output; OEE: overall equipment effectiveness; PSS: product service system; PV: processing volume; RxEy-Sz: the relation from Event y to the State z of Service activity x; RxSz-Ey: the relation from the State z of Service activity x to Event y; StateAiCurrent: the current state of Service activity i; StateAiFuturek: the kth possible future state of Service activity i; StateAiHistoryj: the jth historical state of service activity i; StateEy: the state of Event y; Tq: the qth time point in the time line. [ABSTRACT FROM AUTHOR]

The time-dependent relaxation of a dynamical system may exhibit a power-law behavior that is superimposed by log-periodic oscillations. D. Sornette [Phys. Rep. 297, 239 (1998)] showed that this behavior can be explained by a discrete scale invariance of the system, which is associated with discrete and equidistant timescales on a logarithmic scale. Examples include such diverse fields as financial crashes, random diffusion, and quantum topological materials. Recent time-resolved experiments and molecular dynamics simulations suggest that discrete scale invariance may also apply to hierarchical dynamics in proteins, where several fast local conformational changes are a prerequisite for a slow global transition to occur. Employing entropy-based timescale analysis and Markov state modeling to a simple one-dimensional hierarchical model and biomolecular simulation data, it is found that hierarchical systems quite generally give rise to logarithmically spaced discrete timescales. By introducing a one-dimensional reaction coordinate that collectively accounts for the hierarchically coupled degrees of freedom, the free energy landscape exhibits a characteristic staircase shape with two metastable end states, which causes the log-periodic time evolution of the system. The period of the log-oscillations reflects the effective roughness of the energy landscape and can, in simple cases, be interpreted in terms of the barriers of the staircase landscape. [ABSTRACT FROM AUTHOR]

To conceptualise biological and mental processes, often a dynamical systems perspective is suggested. In addition to dynamics, the structure of the contextual makeup or world configuration (of an organism or brain) plays a crucial role too, as well as adaptivity of the processes. This paper provides a conceptual perspective where the structure, dynamics, and adaptivity of these processes are distinguished and related to each other via adaptive dynamical systems. Moreover, it is shown how networks can be used to represent this conceptual perspective. Here an adaptive dynamical system of any order of adaptivity can be covered where any level can exert control over the level below. The approach is illustrated by case studies for higher-order adaptive evolutionary processes. One of these case studies shows a fifth-order adaptive dynamical system that models how due to bad environmental influences at a young age, epigenetic effects can lead to a lifelong mental disorder.

Cognitive processes underlie economic relations. In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic and adaptive responses that define cognitive microeconomics.

This review explores the complex relationship between Fear ofFalling (FoF) and Fear of Heights (FoH), and their impact onhuman postural control. FoF encompasses a spectrum of psychologicaland physiological responses that dynamically influencepostural control, while FoH involves perceptual distortionsand heightened physiological arousal in response to elevatedenvironments. Through a comprehensive literature review,we examine the research methods and findings of studieson FoF and FoH. We further propose that Optimal FeedbackControl (OFC) theory is a suitable framework to understandthe computational aspects of how these fears modify posturalcontrol. We aim to provide a nuanced understanding ofFoF and FoH, not only as psychological phenomena but ascomplex, dynamic interactions of cognitive, physiological, andmotor processes influencing an individual's interaction withtheir environment.

As a foundation for social interaction, interpersonal coordination is facilitated by positive social qualities (e.g., cooperation), but undermined in negative contexts (e.g., conflict). Exactly how social factors shape coordination is less clear. Previous literature notes that the way people attend to others impacts how interactions unfold. It is possible therefore, that patterns of social attention also govern coordination. We examined this proposition by using virtual reality to investigate how attentional focus (self vs. other) and partner gaze (direct vs. averted) influence the spontaneous emergence of coordination. The results indicated that: (i) coordination was enhanced in the other (cf. self) focus condition; (ii) coordination was diminished in the averted (cf. direct) gaze condition. These findings suggest that changes in social attention impact interpersonal coordination. More broadly, this work provides further evidence that the emergence of interpersonal coordination fluctuates as a function of social context.

Reservoir computing (RC) offers distinct advantages in extracting spatiotemporal information with low training costs by separating recurrent neural networks into a fixed network with recurrent connections. The quality of the fixed network, known as the reservoir, plays a pivotal role in the performance of the RC system. Our work aims to provide a unified synaptic development framework for RC, constructing a more biologically plausible reservoir to model and understand the neural networks development within the human brain. In this paper, we propose an Autonomous Synaptic Development Reservoir Computing model (ASD-RC) based on an adaptive network of phase oscillators. The reservoir autonomously adjusts the distribution of connection weights in response to external stimuli, forming a task-specific structure. Through experiments and theoretical analyses, we demonstrate that ASD-RC can emulate various synaptic development rules of biological neural networks in \textit{vivo}, including the Hebbian rule and STDP. Furthermore, experiments reveal that combining different development rules can enhance performance on prediction tasks compared to using a single development rule, showcasing the emergence and effects of synergistic development that improve information processing capacity.

Music-making relies on precise temporal control and mutual coordination among performers, particularly to maintain tempo. We evaluate the impact of human-machine interaction and rhythmic subdivisions on tempo change in musical trios. A synchronization-continuation task was performed by trios of human participants interacting with confederates or with algorithmic (i.e. machine) models. Sounded tone onsets were produced by a linear error-correction model, delay-coupled model, and Kuramoto model that replaced a human participant. Inter-onset intervals were examined from participants who performed rhythms in both in-phase and anti-phase conditions while a third group member was either a human or algorithmic model. Trios drifted toward faster tempi more when they contained a human than an algorithmic model. Tempo drift also increased for the aligned rhythms (in-phase) compared to rhythms with rhythmic subdivisions (anti-phase). Finally, the tested algorithmic models replicated the confederate's tempo drift without the use of any period correction mechanisms. This research advances our understanding of unintentional tempo drift, offering insights into ensemble dynamics and models of temporal coordination in groups. Implications for musical coordination and avenues for future research are discussed.

Dyadic social interaction is a complex coordination task involving a large number of interconnected variables. Previous research has shown that metastability -- persistence for an extended, but impermanent, period of time in a non-stable state of a system -- can be a useful lens for understanding what makes an interaction successful. However, this framework has thus far only been applied to para-conversational signals like heart rate and prosody -- not to the semantic content of a conversation. Here, we present pink noise analysis of semantic trajectories as a metric for conversational success and apply this technique to a large open conversation dataset. Our results demonstrate that pink noise in a conversation predicts a host of variables representing participants' perception of conversation quality. These results have implications for optimizing a whole host of difficult dyadic conversations -- like those between political partisans -- and human-computer interactions, with applications for improving large language models' adaptability.

We propose a neural dynamic architecture that models negation processing. The architecture receives a visual scene and a relational phrase like ``The blue object is not to the right of the yellow object'' or ``The blue object is to the right of the green object'' as input, and autonomously determines whether the phrase correctly describes the visual scene. The model is built out of empirically founded components for perceptually grounded cognition and constrained by neural principles. We demonstrate that the model can explain two commonly found reaction time effects: the negation effect in which reaction times are higher for negated than for affirmative phrases, and the polarity-by-truth-value interaction effect in which reaction times for false negated phrases are faster than those for true negated phrases whereas the opposite is true for affirmative phrases. The model is consistent with some aspects of the two-step simulation theory.

Many challenging problems in linguistic analysis concern structures that have a hybrid character---they show evidence of belonging to two, independently motivated types. Proposals often assign them to one or the other class, requiring complication of the theory to handle their exceptionality. We suggest that there is no satisfactory answer to such conundrums under standard, type-based representational theories, for those theories are founded on discrete topologies. As an alternative, we propose “Metric Grammars”--grammatical systems founded on connected topologies. A metric grammar, a recurrent map that has a neural network at its core, changes its grammatical system slightly with each instance of language experience. Focusing on a grammaticalization episode from the history of English---the development of “sort of" and “kind of" from Noun-Preposition structures into adverbs---we provide evidence that metric grammars exhibit statistical anticipation of categorical change, a phenomenon that is difficult to account for with discrete-topology models.

Team coordination is essential for effective performance during critical, stressful events. To better understand processes and states involved at multiple levels of team coordination, we assessed the correspondence between low- and high-level coordination in teams participating in simulation-based medical team training. We computed a measure of low-level team coordination with Multidimensional Recurrence Quantification Analysis, applied to arm movement, heart rate, and skin conductance data. High-level team coordination was captured by annotating video recordings for explicit and implicit, information and action coordination. Three linear mixed-effects model were run, each predicting a type of low-level coordination, based on high-level coordination annotations, accounting for multiple observations per team. Our findings showed that, compared to periods without annotated coordination, explicit- and implicit- information coordination corresponded to significantly different low-level team coordination across each of the studied modalities. Further research is required to assess additional factors related to the temporal variability observed in low-level coordination.

Interpersonal synchrony is a foundation of social interaction. However, as a form of coordination, synchrony is limited to regular, rhythmic actions. As such, research regarding the relationship between synchrony and social factors may not generalise to other forms of interpersonal behaviour. Here, we explored whether factors known to influence synchrony, also impact a complimentary form of coordination, complexity matching. When people interact, complex patterns of variability inherent to their individual behaviour can become more similar (i.e., more coordinated). In pairs, participants completed four walking trials that manipulated social interdependence while their gait patterns were captured. We also measured subclinical levels of social anxiety. Although data collection is ongoing, the results point to social anxiety having a detrimental effect on individual behavioural variability, and in turn, complexity matching. Effects of the interdependence manipulation were also evident, but await further data. These results are discussed with respect to theories of interpersonal dynamics.

Over the last two decades, an increasing number of cognitive scientists have turned to mechanistic explanatory frameworks in their efforts to describe and explain cognitive phenomena. Most mechanistic frameworks conceive of cognitive systems as composed of functionally-individuated components whose functions are narrowly defined by their ranges of possible inputs and outputs, as well as their relations to other components within the phenomenon-producing mechanism. In this paper, I argue that this modular view of cognitive mechanisms as linear systems is not applicable to biological cognitive systems, and offer an alternative characterization using the methodology of Dynamical Systems Theory.

The research focus on adaptivity in complex systems has propelled an exploration of diverse interactions characterized by state transition processes. However, the investigation of functional variances among processes, rooted in fundamental operands, remains insufficient. Recognizing this gap is crucial for unveiling the constituents of state transitions and their functional boundaries during ongoing adaptivity. To address this, our central focus is on quantifying the functional variance in the interactions of fundamental operands. This approach enables a systematic study of complex adaptive networks grounded in the dynamics of cognitive abilities, where elements adapt and evolve based on cognitive processes. To underscore this point, we emphasize translating ontologically irreducible networks into functionally representable ones at the meso-level, which is essential for assessing their effectiveness. Our active investigation during state transitions explores external interventions, aiming to shed light on mutual influences.

We present a novel theory and neural process model of visual selective attention to answer long-standing questions in the field of visual attention. We show that the model with fixed parameter values can explain the unexpected efficiency of triple conjunction search (Nordfang & Wolfe, 2014), the influence of a task-irrelevant size singleton on search (Proulx, 2007), and how a third correlated but task-irrelevant feature improves search efficiency (Found, 1998). It also accounts for critical findings in the attention capture literature without the need to introduce different modes (Bacon & Egeth, 1994), signal-suppression (Gaspelin, Leonard, & Luck, 2015; Gaspelin & Luck, 2018; Lien, Ruthruff, & Hauck, 2021) or an attentional window (Theeuwes, 1992, 2023), shedding new light on recent debates.

Goal-directed behavior requires a dynamic balance between cognitive stability and flexibility. This balance can be modulated by performance-contingent reward. Converging evidence suggests that such rewards promote stability by increasing cue maintenance for response preparation in tasks like the AX continuous performance task. However, task switching studies showed oppositional effects of performance-contingent reward depending on the immediate reward history: Only remaining high reward prospect increases stability, whereas increasing reward prospect increases flexibility. The present study tests whether the flexibility-enhancing effect of increasing reward prospect generalizes beyond task switching scenarios. In a novel cued global-local task, the cue-validity effect served to indicate cognitive flexibility versus stability. Evidence from two experiments shows that increasing reward prospect reduces the cue-validity effects but only in error rates. This suggests more flexibility in terms of increased reactive control compared to remaining high reward prospect, which could be functionally adaptive to prevent extreme stability.

Cognitive development has been hypothesised to be stagelike between the ages of 5-8 years (e.g., Piaget). Yet, cognition varies from moment to moment, in every task, for every child. Studies have demonstrated that cognitive variability is non-trivial, non-random, and meaningful, but attempts for systematic and large-scale longitudinal measurements of cognitive variability have scarcely been undertaken.This project's goal is to create a more detailed empirical record and dynamical account of intra-individual variability in cognitive development of children. We aim to do this with a 3-year longitudinal and multimodal data collection starting at 5 years of age. Half-yearly measurements will be complemented with periods of daily measurements. Our ultimate aim is to build a variability corpus in which we can study variability patterns and developmental transitions, and to connect our findings to “Piaget's new theory”. Our poster will present our methodology and findings from a pilot study.

Self-other integration plays a vital role in efficient synchronization with other humans. Previous research has shown that in simple rhythmic joint action tasks (e.g., tapping), self-other integration can be described using mathematical models of coupled oscillators, representing within- and between-person action-perception links. The present study focuses on investigating self-other behavioral and inter-brain dynamics (dual-EEG) when synchronization is either the goal of the task itself or rather an emergent phenomenon in complex continuous interactions. More specifically, participants produce improvised movements in a ‘mirror-game' paradigm while being explicitly asked to synchronize with the partner (synchronized condition) or produce independent movements with visual feedback of each other (spontaneous condition). Mathematical models of coupled oscillators will be used to reveal emergent dynamics of self-other integration on behavioral and neural level. Moreover, we hypothesize that stronger interpersonal synchronization in the spontaneous condition will lead to stronger sensorimotor alpha and beta desynchronization and higher inter-brain synchronization.

When children solve cognitive problems together, they coordinate their speech, hand movements and head movements. Previous studies with adults have shown that such multimodal coordination is related to better collaboration. We do not know whether this is true for children, however. In this study, dyads of children (6-10 years) discussed and solved balance scale problems together. To investigate children's multimodal coordination, we measured their speech, hand movements and head movements throughout their bouts of discussion, and applied multidimensional Recurrence Quantification Analysis (MdRQA) on these timeseries. We coded the type of collaboration the children engaged in during these bouts of discussion. We measured performance regarding predicting to which side the balance scale would tilt. We will analyse how children's multimodal coordination is related to the type of collaboration and to their performance on the balance scale problems. Our results will show how successful collaboration between children emerges from their multimodal coordination.

Even when early implanted, children with cochlear implants show heterogeneous language skills and often struggle with pragmatic communication aspects. Research aimed at elucidating specific weaknesses at the interactional level has yielded inconsistent findings. We analyse dyadic interactions involving nine hearing-impaired children and fourteen normal-hearing children engaging with an adult during a referential (treasure-hunting) task, periodically alternated with role-reversal sub-tasks (e.g., child-led referential-tasks, child-storytelling). Our investigation employs a multi-level analysis approach, encompassing acoustic features (F0, intensity, speech duration, speech rate), turn-taking dynamics (duration, gaps, overlaps), laughter responsiveness and pragmatic functions, convergence of these features, dialogue acts, contingency, and task success. We compare interactional patterns across groups and conditions. The adoption of a multi-level characterization is grounded in the hypothesis that alignment at "lower levels" serves a functional role and concurrently offers insights into alignment at a conceptual level, thereby facilitating mutual understanding and conversational success, giving insights on underpinning neuro-psychological processes.

How do children bootstrap language through noisy supervision? Most prior works focused on tracking co-occurrences between individual words and referents. We model cross-situational learning (CSL) at sentence level with few (1000) training examples. We compare reservoir computing (RC) and LSTMs on three datasets including complex robotic commands. For most experiments, reservoirs yield superior performance over LSTMs. Surprisingly, reservoirs demonstrate robust generalization when increasing vocabulary size: the error grows slowly. On the contrary, LSTMs are not robust: the number of hidden units needs to be dramatically increased to follow up vocabulary size increase, which is questionable from a biological or cognitive perspective. This suggests that that random projections used in RC helps to bootstrap generalization quickly. To our knowledge, this is a new result in developmental learning modelling. We analyse the evolution of internal representations during training of both recurrent networks and suggest why reservoir generalization seems more efficient.

This study introduces the nonlinear inertial amplifier resilient friction bearing isolators (NIARFBI). Another friction base isolator, namely the nonlinear inertial amplifier friction pendulum system (NIAFPS), is derived by eliminating the static damping from the governing equation of motion of the NIARFBI. The damping for NIAFPS is generated through the motion of the friction element of the isolator during the movement of the base layer. These novel isolators are placed at the base of the multiple degrees of freedom (MDOF) systems to reduce their dynamic responses. Every element of the highly nonlinear governing equations of motion of the dynamic systems isolated by each nonlinear isolator is linearized using the stochastic linearization approach. The specific mathematical formulation for the optimal design parameters for each nonlinear isolator is derived using the H2 optimization approach. In order to determine the dynamic responses analytically, the transfer function is established. The dynamic responses of the novel isolators are compared with the dynamic responses of the traditional isolators. Accordingly, the displacement reduction capacities of optimum NIARFBI and NIAFPS are significantly 93.60% and 70.82%, superior to TRFBI and TFPS. The acceleration reduction capacities of optimum NIARFBI and NIAFPS are 88.27% and 58.92% superior to TRFBI and TFPS. The recently developed expressions for this study are all mathematically accurate and applicable for practical applications. [ABSTRACT FROM AUTHOR]

This paper considers C 2 random dynamical systems in a Banach space, and proves that under some mild conditions, SRB measures are characterized by invariant measures satisfying Pesin's entropy formula, in which entropy is equal to the sum of positive Lyapunov exponents of the system. This can be regarded as a random version of the main result in Blumenthal and Young's paper [8]. [ABSTRACT FROM AUTHOR]

Increasing data transmission volumes are causing more frequent and more severe network congestion. In order to handle spikes in network traffic, a substantially bigger buffer has been included into the system. Bufferbloat, which happens when a bigger buffer is implemented, exacerbates network congestion. Using the transfer control protocol (TCP) congestion management strategy with active queue management (AQM) can fix this issue. As congestion increases, it becomes increasingly difficult to forecast and fine‐tune dynamic AQM/TCP systems in order to achieve acceptable performance. To shed new light on the AQM system, we plan to use deep reinforcement learning (DRL) techniques. It is possible that AQM can learn about the appropriate drop policy the same way people do when using a model‐free technique like DRL‐AQM. After training in a simple network scenario, DRL‐AQM is able to recognize complex patterns in the data traffic model and apply them to improve performance in a wide variety of scenarios. Offline training precedes deployment in our approach. In many cases, the model does not require any further parameter tweaks after training. Even in the most complicated networks, AQM algorithms have proven to be effective, regardless of the network's complexity. Minimizing buffer capacity use is an important goal of DRL‐AQM. It automatically and continually adjusts to changes in network connectivity. [ABSTRACT FROM AUTHOR]

The surge of graph neural networks has catalyzed significant advancements in recommendation systems by enabling more effective modeling of user-item interactions within undirected bipartite graphs. However, the proliferation of graph neural network architectures, coupled with the absence of standardized benchmarking frameworks, presents challenges in systematically evaluating and comparing different dynamic recommendation models. In response, we propose a comprehensive benchmarking study of bipartite graph neural network operators for recommendation systems using the PyTorch geometric library. Our contributions include the development of a flexible benchmarking framework encompassing data preprocessing, model training, and evaluation protocols, facilitating fair comparison across diverse dynamic recommendation scenarios. We rigorously assess the performance of various graph neural network models, ranging from traditional methods to state-of-the-art architectures, on the MovieLens100k dataset. Through insightful analysis of experimental results, we elucidate the strengths and weaknesses of different graph neural network operators and offer practical suggestions for model selection and configuration. Our work aims to foster transparency, reproducibility, and innovation in graph neural network-based dynamic recommendation systems, providing a valuable resource for researchers and practitioners in the field. [ABSTRACT FROM AUTHOR]

When a finite group freely acts on a topological space, we can define its index and coindex. They roughly measure the size of the given action. We explore the interaction between this index theory and topological dynamics. Given a fixed-point free dynamical system, the set of p -periodic points admits a natural free action of ℤ / p ℤ for each prime number p. We are interested in the growth of its index and coindex as p → ∞. Our main result shows that there exists a fixed-point free dynamical system having the divergent coindex sequence. This solves a problem posed by M. Tsukamoto, M. Tsutaya and M. Yoshinaga, G -index, topological dynamics and marker property, preprint (2020), arXiv:2012.15372. [ABSTRACT FROM AUTHOR]

A nonlinear dynamic model of a train–bridge-longitudinal track coupling system considering the dynamic contact of the bridge–track interface (BTI) is established. Based on the previously established BTI contact characterization model and train–bridge coupling joint simulation model, the proposed model is verified from both static and dynamic perspectives. Accordingly, the dynamic displacement variation law of the bridge–track structure before, during and after the train passing through the side pier settlement area, the additional stress dynamic variation law and the interlayer connection dynamic variation law are analyzed. Moreover, the amplification effect of the wheel repeated "beating effect" on the dynamic contact force of "disengagement-closure-redisengagement" of the BTI void area is also analyzed. The results show that the accuracy of the proposed model can be verified from both static and dynamic perspectives. During the train passes through the settlement area, the dynamic deformation of track is the largest. Before and after the train passes through the settlement area, the dynamic deformation of track is basically the same. During the train passes through the settlement area, the BTI completes a dynamic contact process of "disengagement-closure-redisengagement". The static effect of the side pier settlement on the dynamic bonding force of sliding layer is much larger than the dynamic effect of the train load. The dynamic effect of the train load is much larger than the static effect of the side pier settlement on the dynamic bonding force of the CA mortar and fasteners. The side pier settlement has a dynamic amplification effect on the train wheelset. Along the mileage direction, there are interface voids and contact areas in the dynamic bonding force of sliding layer and CA mortar, while there are no voids and contact areas in the dynamic bonding force of fasteners. [ABSTRACT FROM AUTHOR]

The high-damping viscoelastic (VE) materials are widely used to reduce unwanted vibrations in civil structures. However, the significant frequency and temperature dependences from high-damping VE materials cause a challenge in efficiently analyzing the structural dynamic behaviors. The aim of this study is to propose a generalized dynamic analysis method for the linear structural system with VE elements. The implementation steps and advantages of the dynamic analysis method are presented. In this method, the frequency-dependent matrices and non-proportional damping induced by VE elements are fully considered in modal coordinates, and then the state-space representation converted from the modal-based transfer function is adopted to implement the time-domain simulation. Any popular VE constitutive models which accurately describe frequency-dependent matrices can be applied to the method. The number of the state-space representation can be adjusted according to the interested frequency range. Numerical applications performed on a viscoelastically damped structure, a VE base-isolated structure, and a structure with a VE tuned mass damper (TMD) demonstrate the numerical accuracy, efficiency, and generality of the proposed dynamic analysis method. [ABSTRACT FROM AUTHOR]

Given integers n , k , ℓ such that 0 < k < n , 1 < ℓ and an integer vector x = (x 1 , ... , x n) , denote by m = m (x) the number of entries of x that are multiple of ℓ. Choose n − k entries of x as follows: if m (x) ≥ n − k , take n − k smallest entries of x that are multiple of ℓ ; if n − k > m (x) , take all m such entries, if any, and add n − k − m largest entries of x. In one step, the chosen n − k entries (bears) keep their values, while the remaining k (bulls) are reduced by 1. Repeat such steps getting the sequence S = S (n , k , ℓ , x 0) = (x 0 → x 1 → ... → x j → ...). We prove that it is "quasi-periodic". More precisely, there exists a number N = N (n , k , ℓ , x 0) such that for all j ≥ N we have m (x j) ≥ n − k and r a n g e (x j) ≤ ℓ , where r a n g e (x) = max (x i ∣ i ∈ [ n ]) − min (x i ∣ i ∈ [ n ]) with [ n ] = { 1 , ... , n }. Furthermore, after N steps, the system "moves like a screw". Assuming that x 1 ≤ ⋯ ≤ x n , introduce the cyclical order on [ n ] considering 1 and n as neighbors. Then, bears and bulls partition [ n ] into two intervals that are shifted by k with every ℓ steps. Then, after every p = ℓ n / G C D (n , k) = ℓ L C M (n , k) / k steps all entries of x are reduced by the same value δ = p k / n , that is, x i j − x i j + p = δ for all i ∈ [ n ] and j ≥ N. We prove that N (n , k , ℓ , x 0) ≤ (ℓ + r a n g e (x 0)) ⌈ n / k ⌉ and give an algorithm computing N in time polynomial in n , k , ℓ , and log (1 + r a n g e (x 0)). Furthermore, x j , for any j ≥ 0 , can be computed in time polynomial in n , k , ℓ , log (1 + r a n g e (x 0)) , and log (1 + j). In case k = n − 1 and ℓ = 2 such screw dynamical system are applicable to impartial games; see Gurvich, Martynov, Maximchuk, and Vyalyi, "On Remoteness Functions of Exact Slow k -NIM with k + 1 Piles", https://arxiv.org/abs/2304.06498 (2023). [ABSTRACT FROM AUTHOR]

Stochastic collocation (SC) is a well‐known non‐intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full‐field uncertainty propagation that characterizes the distributions of the high‐dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter‐to‐solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. This work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data‐driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC‐over‐dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full‐field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics. [ABSTRACT FROM AUTHOR]

The emergence of localized radial patterns from a Turing instability has been well studied in two- and three-dimensional settings and predicted for higher spatial dimensions. We prove the existence of localized (n+1) -dimensional radial patterns in general two-component reaction–diffusion systems near a Turing instability, where n>0 is taken to be a continuous parameter. We determine the explicit dependence of each pattern's radial profile on the dimension n through the introduction of (n+1) -dimensional Bessel functions, revealing a deep connection between the formation of localized radial patterns in different spatial dimensions. [ABSTRACT FROM AUTHOR]

Using the Darboux transformation method, the general Lax equation is solved and a collection of new exact solutions together with one-soliton solutions, singular one-soliton solutions, periodic solutions, singular periodic solution, two-soliton solutions, singular two-soliton solutions, two-periodic solutions and singular two-periodic solutions is obtained. Using traveling wave transformation, the Lax equation is transfigured to a conservative dynamical system (CDS) of dimension four with three equilibrium points involving two parameters γ and v. The CDS has various quasi-periodic motions for fixed values of the parameters γ and v at different initial conditions. Furthermore, effects of the parameters γ and v are shown on the quasiperiodic motions of the CDS by means of phase sections and time series plots. This approach can be applied to a heterogeneity of nonlinear model equations or partial differential equations for describing their inherent nonlinear phenomena. [ABSTRACT FROM AUTHOR]

Summary: Complex temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. Temporal motifs are important patterns in temporal complex networks that have many applications in solving problems related to this type of networks. In this paper, a novel method for controlling temporal complex networks using temporal motifs is proposed. First, the most important effective temporal motifs in the controllability processes of complex networks have been identified and it has been shown that the network can be fully controlled using these temporal motifs. Then, an algorithm for extracting temporal motifs is proposed. This algorithm has been proposed to identify effective temporal motifs in network controllability to optimally identify control nodes. To increase the efficiency of extracting temporal motifs, a method for predicting the temporal motif‐based link has been proposed, which predicts temporal motifs. The results of the simulation of the proposed method based on temporal motifs and its implementation on real‐world temporal complex networks demonstrates that its performance was better than the conventional controllability methods. [ABSTRACT FROM AUTHOR]

In many biomass conversion processes, the separation of cellulosic sugars from residual, lignin-rich solids is a critical step, and achieving high recovery yields of sugars by conventional tangential crossflow and vacuum filtration is challenged by the presence of biomass solids, which rapidly foul filters, resulting in decreased throughput. Considering the performance limitations of traditional filtration methods, dynamic filtration, which generates high shear at the membrane surface to decrease fouling, is emerging as a viable alternative for demanding solid–liquid separations. For high solids separations, there is little available information regarding the performance, limitations, and energy consumption of dynamic filtration. To that end, here we characterized the performance of a dynamic filtration module, specifically a rotating ceramic disc (RCD) filter, for the aseptic recovery of cellulosic sugars from biomass solids following pretreatment and enzymatic hydrolysis. We show how RCD rotational velocity and percent biomass solids impact the filter throughput. Additionally, we used computational fluid dynamics (CFD) simulations to estimate the shear rate at the membrane surface and to visualize hydrodynamic profiles within the module. With the combined CFD simulations and experimental results, we estimated the energy demand and operating expenses for a viable dynamic filtration system operating with a lignocellulosic feed slurry. Our results indicate that an RCD filter can achieve ≥95% recovery of sugars and produce a retentate slurry containing 12 wt% insoluble solids with low energy consumption (a 2.2-fold improvement over cross-flow filtration) and low operating costs ($0.06 per kg sugars). These results show a viable path towards operationally reliable, energy efficient, and cost-effective separations of sterilized cellulosic sugars from biomass solids and highlight the potential of dynamic filtration systems for challenging solid–liquid separations. [ABSTRACT FROM AUTHOR]

In random sequential adsorption (RSA), objects are deposited on a substrate randomly, irreversibly, and sequentially. Attempts of deposition that lead to an overlap with previously deposited objects are discarded. The process continues until the system reaches a jammed state when no further additions are possible. We analyze a class of RSA models on a two-row square ladder graph in which landing on an empty site in a graph is allowed when at least b neighboring sites in the graph are unoccupied ( b ∈ N ). In this paper we complement this typical way of studying RSA models by analyzing also the structure of the set of all jammed states in a static way, disregarding the dynamics that led to a particular jammed state. In both considered settings (dynamic and static) we provide explicit expressions for key statistics that describe the average proportion of the substrate covered by deposited objects, and then we comment on significant differences between the two settings. We illustrate all of our findings through a toy model for ensembles of trapped Rydberg atoms with blockade range b. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Materials and Methods Results Conclusions This study aimed to evaluate the efficiency of and discrepancies between planned and final implant positions using dynamic computer‐assisted implant surgery (d‐CAIS) and autonomous robotic computer‐assisted implant surgery (r‐CAIS) in clinical practice.The study included 83 patients, who received 135 implants between December 2022 and March 2024 (r‐CAIS group: 43 patients with 71 implants; d‐CAIS group: 40 patients with 64 implants). Cone‐beam computed tomography scans taken before and after surgery assessed linear and angular deviations between the groups in both 2D and 3D spaces. The duration of surgery was also analysed.The angular deviation between d‐CAIS and r‐CAIS was 3.61° ± 1.65° versus 1.62° ± 0.93° (p < 0.001), the platform deviation was 1.12 ± 0.51 mm versus 0.50 ± 0.19 mm (p < 0.001) and the apex deviation was 1.36 ± 0.57 mm versus 0.58 ± 0.21 mm (p < 0.001). The d‐CAIS group experienced significantly longer drilling and implant placement times compared to the r‐CAIS group (10.6 ± 3.8 vs. 8.3 ± 3.4 min, p < 0.01), while preparation time showed no statistical difference between the groups (7.2 ± 3.3 vs. 6.2 ± 2.7 min, p > 0.05).The robotic system demonstrated higher accuracy and efficiency of implant placement than the dynamic navigation system in partially edentulous patients. [ABSTRACT FROM AUTHOR]

The Sommerfeld effect is a destructive phenomenon in rotating systems with a non-ideal electrical source, which causes instability in the system by applying a dynamic jump around critical speeds. In this article, the Sommerfeld effect has been investigated for the first time using the Timoshenko beam theory for an eccentric continuous shaft with internal and external damping. After deriving the governing equations and finding displacement functions using the semi-analytical method, the Sommerfeld effect near the critical speeds is detected using the instantaneous power balance method. As confirmation of the correctness of the derived relations, it has been shown that for thin shafts, there is a good consistency between the results obtained from the Euler–Bernoulli and Timoshenko theories in the early modes. However, it was observed that at higher critical speeds, the jump amplitude decreases, and the unstable speed range increases significantly, so the probability of entering the vicinity of the instability range in the next mode is not unexpected. Since no effect has been ignored in this study, the dynamic analysis of the Sommerfeld jump in thick shafts is also possible. Despite the common belief that Timoshenko beam theory is only considered suitable for studying thick shafts, it has been shown that the effects of shear deformation are significant in high-speed systems, even for non-thick shafts, and regardless of them in higher modes, it causes a calculation error in determining the point of occurrence of the Sommerfeld phenomenon. [ABSTRACT FROM AUTHOR]

The Sommerfeld Effect pertains to the non-linear jump phenomenon near the resonance frequency of an excited structure powered by a non-ideal drive that is incapable to supply the required power to the oscillating sub-system. Often this situation is dealt with by increasing the viscous damping present in the system at the expense of considerable power loss. However, the use of non-viscous damping for the attenuation of the Sommerfeld Effect is considered in very few articles. In this paper, the dynamics of a non-ideal DC motor-driven vibrating system with frictional slip as the dissipation element is presented. Use of the method of multiple time scales is employed to find the response of the system semi-analytically. The non-linear dynamical characteristics of the response as the motor speed crosses the resonance condition is analyzed using the linear stability analysis and subsequently, the obtained response is verified using numerical results. The transition of the operating condition between stable and unstable zones and the post-resonance dynamic behavior of such systems indicated that under proper parametric condition, it is possible to achieve a smooth variation of operating speed via a combination of stick and slip motion. Furthermore, the rate of voltage increment is also found to play a pivotal role in surpassing the critical speed of the system. [ABSTRACT FROM AUTHOR]

This paper presents the band gaps and dynamics of locally resonant meta-plate with stiffness micro-adjustable high static and low dynamic resonator, in which a Euler-buckled beam provides negative stiffness. The resonator is periodically installed on a thin plate through a threaded linkage. By changing the axial displacement of the buckled beam, the total stiffness of the resonator is adjusted. Through the static analysis, the critical axial compression required for the buckled beam to provide negative stiffness is discussed. With the help of classical thin plate theory, the dynamic equation of the system is obtained. The band gaps of the locally resonant meta-plate are calculated by employing the plane wave expansion method. Considering the simply supported boundary condition, dynamic responses of the finite size locally resonant plate are shown with the aid of numerical simulation. The effects of the axial compression of the buckled beam, the mass of resonator, the damping of oscillators and plates, and spring stiffness of the system on the band gap are analyzed in detail. [ABSTRACT FROM AUTHOR]

Ferroelastic textures called star patterns are transformable into grilles whose rigid bars replace domain boundaries and are connected with hinges at their junctions. The grilles show several levels of self‐similarity. Exact formulae for the scaling factors as functions of the shape and number N of the arms are provided. The minimal dynamic model of such systems involves force constants at only one level (generation) of self‐similarity. The minimal set of force constants ensures stability of the stars with N = 2 and N = 3. At N = 4 the minimal model exhibits exactly one mode of zero frequency. The reaction of the four‐arm star to a tensile stress applied at the level of the force constants depends on the self‐similarity scaling factor and on the separation between the applied stress and the generation where the observation is made. In some regions of these parameters the reaction is analogous to auxetics but it also shows negative tensile characteristics, that is, a shrinking in the direction of the stress axis. With increasing distance from the external perturbation toward outer generations of self‐similarity the system shows a surprisingly fast restoration of the symmetry broken by the stress. [ABSTRACT FROM AUTHOR]

The article presents two distributed motion planning methods for dynamically coordinating multiple automated guide vehicles (AGVs) in an industrial setting, aiming to enhance their flexibility, robustness and scalability. A predictive motion model is utilized to describe the transport process mathematically as a dynamical system. Subsequently, alternating direction method of multipliers (ADMM)-based decomposition techniques, both serial and parallel, are developed to coordinate the AGVs and mitigate the computational burden. The efficacy of the distributed methods is verified through testing in industrial scenarios, demonstrating that they can significantly improve multi-AGV system transport productivity with minimal computational effort. Notably, the parallel scheme exhibits superior performance in coordinating the AGVs compared to the serial scheme. [ABSTRACT FROM AUTHOR]